From 9688653870d5055da51d113e76d92c1cfc33691e Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 30 Nov 2024 21:50:23 -0800 Subject: [PATCH 001/509] [mojo-lang] Rename `inout` -> `mut` and `borrowed` -> `read` Per extensive discussion over on this public thread: https://github.com/modularml/mojo/issues/3623 We're moving to rename the `inout` argument convention to be called simply `mut`, and renames `borrowed` to `read` which can still be generally elided. This reduces the need to understand references for the basic conventions that many people work with, while providing a more strictly-correct and consistent model. These words are now "soft" keywords instead of "hard" keywords as well. This still maintains support for the `inout` and `borrowed` keywords, though they will eventually be removed. MODULAR_ORIG_COMMIT_REV_ID: e2b41cfb4cb8bb0b2e67ade93d32d7ef8989428e --- docs/changelog-released.md | 4 +- docs/changelog.md | 6 + examples/life/gridv1.mojo | 2 +- examples/life/gridv2.mojo | 2 +- examples/matmul.mojo | 18 +-- examples/nbody.mojo | 6 +- examples/notebooks/RayTracing.ipynb | 8 +- examples/operators/my_complex.mojo | 20 +-- .../algorithm/bench_elementwise.mojo | 2 +- stdlib/benchmarks/builtin/bench_int.mojo | 2 +- stdlib/benchmarks/builtin/bench_sort.mojo | 36 ++--- stdlib/benchmarks/collections/bench_dict.mojo | 6 +- .../benchmarks/collections/bench_string.mojo | 16 +- stdlib/benchmarks/hashlib/bench_hash.mojo | 8 +- stdlib/benchmarks/math/bench_math.mojo | 6 +- stdlib/benchmarks/utils/bench_formatter.mojo | 4 +- stdlib/benchmarks/utils/bench_memmem.mojo | 4 +- stdlib/src/base64/_b64encode.mojo | 2 +- stdlib/src/base64/base64.mojo | 2 +- stdlib/src/builtin/_format_float.mojo | 12 +- stdlib/src/builtin/_location.mojo | 2 +- stdlib/src/builtin/_pybind.mojo | 4 +- stdlib/src/builtin/_stubs.mojo | 4 +- stdlib/src/builtin/bool.mojo | 10 +- stdlib/src/builtin/builtin_list.mojo | 22 +-- stdlib/src/builtin/builtin_slice.mojo | 4 +- stdlib/src/builtin/debug_assert.mojo | 2 +- stdlib/src/builtin/dtype.mojo | 4 +- stdlib/src/builtin/error.mojo | 2 +- stdlib/src/builtin/file.mojo | 6 +- stdlib/src/builtin/file_descriptor.mojo | 4 +- stdlib/src/builtin/float_literal.mojo | 8 +- stdlib/src/builtin/format_int.mojo | 4 +- stdlib/src/builtin/int.mojo | 32 ++-- stdlib/src/builtin/int_literal.mojo | 18 +-- stdlib/src/builtin/none.mojo | 2 +- stdlib/src/builtin/object.mojo | 48 +++--- stdlib/src/builtin/range.mojo | 20 +-- stdlib/src/builtin/simd.mojo | 36 ++--- stdlib/src/builtin/string_literal.mojo | 6 +- stdlib/src/builtin/swap.mojo | 2 +- stdlib/src/builtin/tuple.mojo | 2 +- stdlib/src/builtin/uint.mojo | 28 ++-- stdlib/src/builtin/value.mojo | 2 +- stdlib/src/collections/counter.mojo | 24 +-- stdlib/src/collections/deque.mojo | 39 +++-- stdlib/src/collections/dict.mojo | 48 +++--- stdlib/src/collections/inline_array.mojo | 4 +- stdlib/src/collections/inline_list.mojo | 4 +- stdlib/src/collections/list.mojo | 40 +++-- stdlib/src/collections/optional.mojo | 6 +- stdlib/src/collections/set.mojo | 28 ++-- stdlib/src/collections/string.mojo | 20 +-- stdlib/src/collections/vector.mojo | 10 +- stdlib/src/hashlib/_ahash.mojo | 10 +- stdlib/src/hashlib/_hasher.mojo | 8 +- stdlib/src/math/math.mojo | 4 +- stdlib/src/memory/arc.mojo | 4 +- stdlib/src/memory/maybe_uninitialized.mojo | 16 +- stdlib/src/memory/owned_pointer.mojo | 8 +- stdlib/src/memory/pointer.mojo | 2 +- stdlib/src/memory/unsafe_pointer.mojo | 6 +- stdlib/src/os/atomic.mojo | 18 +-- stdlib/src/os/fstat.mojo | 4 +- stdlib/src/pathlib/path.mojo | 6 +- stdlib/src/pwd/pwd.mojo | 2 +- stdlib/src/python/_bindings.mojo | 18 +-- stdlib/src/python/_cpython.mojo | 150 +++++++++--------- stdlib/src/python/python.mojo | 20 +-- stdlib/src/python/python_object.mojo | 48 +++--- stdlib/src/random/random.mojo | 2 +- stdlib/src/sys/ffi.mojo | 2 +- stdlib/src/tempfile/tempfile.mojo | 10 +- stdlib/src/testing/testing.mojo | 2 +- stdlib/src/utils/format.mojo | 26 ++- stdlib/src/utils/index.mojo | 8 +- stdlib/src/utils/inline_string.mojo | 20 +-- stdlib/src/utils/lock.mojo | 14 +- stdlib/src/utils/span.mojo | 4 +- stdlib/src/utils/static_tuple.mojo | 4 +- stdlib/src/utils/string_slice.mojo | 10 +- stdlib/src/utils/stringref.mojo | 4 +- stdlib/src/utils/variant.mojo | 16 +- stdlib/src/utils/write.mojo | 32 ++-- stdlib/test/builtin/test_debug_assert.mojo | 2 +- stdlib/test/builtin/test_object.mojo | 2 +- stdlib/test/builtin/test_print.mojo | 4 +- stdlib/test/builtin/test_range.mojo | 2 +- stdlib/test/builtin/test_sort.mojo | 6 +- stdlib/test/hashlib/test_hasher.mojo | 18 +-- stdlib/test/python/test_ownership.mojo | 12 +- stdlib/test/python/test_python_info.mojo | 2 +- stdlib/test/python/test_python_interop.mojo | 8 +- stdlib/test/python/test_python_object.mojo | 2 +- stdlib/test/python/test_python_to_mojo.mojo | 2 +- stdlib/test/tempfile/test_tempfile.mojo | 8 +- stdlib/test/utils/test_format.mojo | 2 +- stdlib/test/utils/test_format_to_stdout.mojo | 2 +- 98 files changed, 602 insertions(+), 609 deletions(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 1bb87df302..5b3c84bdcf 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -6088,7 +6088,7 @@ busy this week. - 📢 The `__clone__` method for copying a value is now named `__copy__` to better follow Python term of art. -- 📢 The `__copy__` method now takes its self argument as a "borrowed" value, +- 📢 The `__copy__` method now takes its self argument as a "read" value, instead of taking it by reference. This makes it easier to write, works for `@register_passable` types, and exposes more optimization opportunities to the early optimizer and dataflow analysis passes. @@ -6153,7 +6153,7 @@ busy this week. Note that `@register_passable` types must use the later style. - 📢 The default argument convention is now the `borrowed` convention. A - "borrowed" argument is passed like a C++ `const&` so it doesn't need to + "read" argument is passed like a C++ `const&` so it doesn't need to invoke the copy constructor (aka the `__clone__` method) when passing a value to the function. There are two differences from C++ `const&`: diff --git a/docs/changelog.md b/docs/changelog.md index 601e9f60d5..640824629c 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -294,6 +294,12 @@ what we publish. ### 🦋 Changed +- The `inout` and `borrowed` argument conventions have been renamed to the `mut` + and `read` argument conventions (respectively). These verbs reflect + declaratively what the callee can do to the argument value passed into the + caller, without tying in the requirement for the programmer to know about + advanced features like references. + - The argument convention for `__init__` methods has been changed from `inout` to `out`, reflecting that an `__init__` method initializes its `self` without reading from it. This also enables spelling the type of an initializer diff --git a/examples/life/gridv1.mojo b/examples/life/gridv1.mojo index 9b7ec2e386..b06cb51315 100644 --- a/examples/life/gridv1.mojo +++ b/examples/life/gridv1.mojo @@ -32,7 +32,7 @@ struct Grid(StringableRaising): def __getitem__(self, row: Int, col: Int) -> Int: return self.data[row][col] - def __setitem__(inout self, row: Int, col: Int, value: Int) -> None: + def __setitem__(mut self, row: Int, col: Int, value: Int) -> None: self.data[row][col] = value # ===-------------------------------------------------------------------===# diff --git a/examples/life/gridv2.mojo b/examples/life/gridv2.mojo index 574d41e314..3e43b5988a 100644 --- a/examples/life/gridv2.mojo +++ b/examples/life/gridv2.mojo @@ -71,7 +71,7 @@ struct Grid[rows: Int, cols: Int](StringableRaising): def __getitem__(self, row: Int, col: Int) -> Int8: return (self.data + row * cols + col)[] - def __setitem__(inout self, row: Int, col: Int, value: Int8) -> None: + def __setitem__(mut self, row: Int, col: Int, value: Int8) -> None: (self.data + row * cols + col)[] = value # ===-------------------------------------------------------------------===# diff --git a/examples/matmul.mojo b/examples/matmul.mojo index 1d58d68bb3..ad81185519 100644 --- a/examples/matmul.mojo +++ b/examples/matmul.mojo @@ -70,7 +70,7 @@ struct Matrix[rows: Int, cols: Int]: fn __getitem__(self, y: Int, x: Int) -> Scalar[type]: return self.load(y, x) - fn __setitem__(inout self, y: Int, x: Int, val: Scalar[type]): + fn __setitem__(mut self, y: Int, x: Int, val: Scalar[type]): self.store(y, x, val) fn load[nelts: Int = 1](self, y: Int, x: Int) -> SIMD[type, nelts]: @@ -100,7 +100,7 @@ def run_matmul_numpy() -> Float64: return gflops -fn matmul_naive(inout C: Matrix, A: Matrix, B: Matrix): +fn matmul_naive(mut C: Matrix, A: Matrix, B: Matrix): for m in range(C.rows): for k in range(A.cols): for n in range(C.cols): @@ -108,7 +108,7 @@ fn matmul_naive(inout C: Matrix, A: Matrix, B: Matrix): # Using stdlib vectorize function -fn matmul_vectorized(inout C: Matrix, A: Matrix, B: Matrix): +fn matmul_vectorized(mut C: Matrix, A: Matrix, B: Matrix): for m in range(C.rows): for k in range(A.cols): @@ -123,7 +123,7 @@ fn matmul_vectorized(inout C: Matrix, A: Matrix, B: Matrix): # Parallelize the code by using the builtin parallelize function # num_workers is the number of worker threads to use in parallalize -fn matmul_parallelized(inout C: Matrix, A: Matrix, B: Matrix): +fn matmul_parallelized(mut C: Matrix, A: Matrix, B: Matrix): var num_workers = C.rows @parameter @@ -150,7 +150,7 @@ fn tile[tiled_fn: Tile2DFunc, tile_x: Int, tile_y: Int](end_x: Int, end_y: Int): # Use the above tile function to perform tiled matmul # Also parallelize with num_workers threads -fn matmul_tiled(inout C: Matrix, A: Matrix, B: Matrix): +fn matmul_tiled(mut C: Matrix, A: Matrix, B: Matrix): var num_workers = C.rows @parameter @@ -177,7 +177,7 @@ fn matmul_tiled(inout C: Matrix, A: Matrix, B: Matrix): # Unroll the vectorized loop by a constant factor # Also parallelize with num_workers threads -fn matmul_unrolled[mode: Int](inout C: Matrix, A: Matrix, B: Matrix): +fn matmul_unrolled[mode: Int](mut C: Matrix, A: Matrix, B: Matrix): var num_workers: Int if mode == 1: num_workers = info.num_physical_cores() @@ -232,7 +232,7 @@ fn tile_parallel[ # a global memory location, which can thrash the cache. # Also partially unroll the loop over the reduction dimension (K) # and reorder the reduction inner loop with the row iteration inner loop -fn matmul_reordered(inout C: Matrix, A: Matrix, B: Matrix): +fn matmul_reordered(mut C: Matrix, A: Matrix, B: Matrix): alias tile_m = 32 alias tile_n = 32 alias tile_k = max(4, K // 256) @@ -282,7 +282,7 @@ fn matmul_reordered(inout C: Matrix, A: Matrix, B: Matrix): @always_inline fn bench[ - func: fn (inout Matrix, Matrix, Matrix) -> None, name: StringLiteral + func: fn (mut Matrix, Matrix, Matrix) -> None, name: StringLiteral ](base_gflops: Float64, np_gflops: Float64) raises: var A = Matrix[M, K].rand() var B = Matrix[K, N].rand() @@ -313,7 +313,7 @@ fn bench[ @always_inline fn test_matrix_equal[ - func: fn (inout Matrix, Matrix, Matrix) -> None + func: fn (mut Matrix, Matrix, Matrix) -> None ](C: Matrix, A: Matrix, B: Matrix) raises -> Bool: """Runs a matmul function on A and B and tests the result for equality with C on every element. diff --git a/examples/nbody.mojo b/examples/nbody.mojo index 694f81be68..eedb4150fb 100644 --- a/examples/nbody.mojo +++ b/examples/nbody.mojo @@ -35,7 +35,7 @@ struct Planet: var mass: Float64 fn __init__( - inout self, + mut self, pos: SIMD[DType.float64, 4], velocity: SIMD[DType.float64, 4], mass: Float64, @@ -119,7 +119,7 @@ alias INITIAL_SYSTEM = List[Planet](Sun, Jupiter, Saturn, Uranus, Neptune) @always_inline -fn offset_momentum(inout bodies: List[Planet]): +fn offset_momentum(mut bodies: List[Planet]): var p = SIMD[DType.float64, 4]() for body in bodies: @@ -132,7 +132,7 @@ fn offset_momentum(inout bodies: List[Planet]): @always_inline -fn advance(inout bodies: List[Planet], dt: Float64): +fn advance(mut bodies: List[Planet], dt: Float64): for i in range(len(INITIAL_SYSTEM)): for j in range(len(INITIAL_SYSTEM) - i - 1): var body_i = bodies[i] diff --git a/examples/notebooks/RayTracing.ipynb b/examples/notebooks/RayTracing.ipynb index 1ce972b6b4..66a768da6e 100644 --- a/examples/notebooks/RayTracing.ipynb +++ b/examples/notebooks/RayTracing.ipynb @@ -422,7 +422,7 @@ " var material: Material\n", "\n", " @always_inline\n", - " fn intersects(self, orig: Vec3f, dir: Vec3f, inout dist: Float32) -> Bool:\n", + " fn intersects(self, orig: Vec3f, dir: Vec3f, mut dist: Float32) -> Bool:\n", " \"\"\"This method returns True if a given ray intersects this sphere.\n", " And if it does, it writes in the `dist` parameter the distance to the\n", " origin of the ray.\n", @@ -698,9 +698,9 @@ " orig: Vec3f,\n", " dir: Vec3f,\n", " spheres: List[Sphere],\n", - " inout material: Material,\n", - " inout hit: Vec3f,\n", - " inout N: Vec3f,\n", + " mut material: Material,\n", + " mut hit: Vec3f,\n", + " mut N: Vec3f,\n", ") -> Bool:\n", " var spheres_dist = inf[DType.float32]()\n", "\n", diff --git a/examples/operators/my_complex.mojo b/examples/operators/my_complex.mojo index f13462dc26..bd4d03f275 100644 --- a/examples/operators/my_complex.mojo +++ b/examples/operators/my_complex.mojo @@ -55,7 +55,7 @@ struct Complex( fn __str__(self) -> String: return String.write(self) - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write("(", self.re) if self.im < 0: writer.write(" - ", -self.im) @@ -78,7 +78,7 @@ struct Complex( else: raise "index out of bounds" - fn __setitem__(inout self, idx: Int, value: Float64) raises: + fn __setitem__(mut self, idx: Int, value: Float64) raises: if idx == 0: self.re = value elif idx == 1: @@ -109,11 +109,11 @@ struct Complex( def __radd__(self, lhs: Float64) -> Self: return Self(self.re + lhs, self.im) - def __iadd__(inout self, rhs: Self): + def __iadd__(mut self, rhs: Self): self.re += rhs.re self.im += rhs.im - def __iadd__(inout self, rhs: Float64): + def __iadd__(mut self, rhs: Float64): self.re += rhs def __sub__(self, rhs: Self) -> Self: @@ -125,11 +125,11 @@ struct Complex( def __rsub__(self, lhs: Float64) -> Self: return Self(lhs - self.re, -self.im) - def __isub__(inout self, rhs: Self): + def __isub__(mut self, rhs: Self): self.re -= rhs.re self.im -= rhs.im - def __isub__(inout self, rhs: Float64): + def __isub__(mut self, rhs: Float64): self.re -= rhs def __mul__(self, rhs: Self) -> Self: @@ -144,13 +144,13 @@ struct Complex( def __rmul__(self, lhs: Float64) -> Self: return Self(lhs * self.re, lhs * self.im) - def __imul__(inout self, rhs: Self): + def __imul__(mut self, rhs: Self): new_re = self.re * rhs.re - self.im * rhs.im new_im = self.re * rhs.im + self.im * rhs.re self.re = new_re self.im = new_im - def __imul__(inout self, rhs: Float64): + def __imul__(mut self, rhs: Float64): self.re *= rhs self.im *= rhs @@ -168,14 +168,14 @@ struct Complex( denom = self.squared_norm() return Self((lhs * self.re) / denom, (-lhs * self.im) / denom) - def __itruediv__(inout self, rhs: Self): + def __itruediv__(mut self, rhs: Self): denom = rhs.squared_norm() new_re = (self.re * rhs.re + self.im * rhs.im) / denom new_im = (self.im * rhs.re - self.re * rhs.im) / denom self.re = new_re self.im = new_im - def __itruediv__(inout self, rhs: Float64): + def __itruediv__(mut self, rhs: Float64): self.re /= rhs self.im /= rhs diff --git a/stdlib/benchmarks/algorithm/bench_elementwise.mojo b/stdlib/benchmarks/algorithm/bench_elementwise.mojo index 34130a2c37..322b66ad9c 100644 --- a/stdlib/benchmarks/algorithm/bench_elementwise.mojo +++ b/stdlib/benchmarks/algorithm/bench_elementwise.mojo @@ -27,7 +27,7 @@ from utils.index import Index, IndexList # Benchmark elementwise # ===----------------------------------------------------------------------===# @parameter -fn bench_elementwise[n: Int](inout b: Bencher) raises: +fn bench_elementwise[n: Int](mut b: Bencher) raises: var vector = Buffer[DType.index, n].stack_allocation() for i in range(len(vector)): diff --git a/stdlib/benchmarks/builtin/bench_int.mojo b/stdlib/benchmarks/builtin/bench_int.mojo index 5a469936f8..b65cd5abaf 100644 --- a/stdlib/benchmarks/builtin/bench_int.mojo +++ b/stdlib/benchmarks/builtin/bench_int.mojo @@ -21,7 +21,7 @@ from benchmark import Bench, BenchConfig, Bencher, BenchId # Benchmarks # ===----------------------------------------------------------------------===# @parameter -fn bench_stringify_small_integers(inout b: Bencher) raises: +fn bench_stringify_small_integers(mut b: Bencher) raises: @always_inline @parameter fn call_fn(): diff --git a/stdlib/benchmarks/builtin/bench_sort.mojo b/stdlib/benchmarks/builtin/bench_sort.mojo index 0d482a72cf..0872012f78 100644 --- a/stdlib/benchmarks/builtin/bench_sort.mojo +++ b/stdlib/benchmarks/builtin/bench_sort.mojo @@ -34,7 +34,7 @@ from stdlib.builtin.sort import ( @always_inline fn randomize_list[ dt: DType -](inout list: List[Scalar[dt]], size: Int, max: Scalar[dt] = Scalar[dt].MAX): +](mut list: List[Scalar[dt]], size: Int, max: Scalar[dt] = Scalar[dt].MAX): @parameter if dt.is_integral(): randint(list.data, size, 0, int(max)) @@ -46,7 +46,7 @@ fn randomize_list[ @always_inline -fn insertion_sort[type: DType](inout list: List[Scalar[type]]): +fn insertion_sort[type: DType](mut list: List[Scalar[type]]): @parameter fn _less_than( lhs: _SortWrapper[Scalar[type]], rhs: _SortWrapper[Scalar[type]] @@ -57,7 +57,7 @@ fn insertion_sort[type: DType](inout list: List[Scalar[type]]): @always_inline -fn small_sort[size: Int, type: DType](inout list: List[Scalar[type]]): +fn small_sort[size: Int, type: DType](mut list: List[Scalar[type]]): @parameter fn _less_than( lhs: _SortWrapper[Scalar[type]], rhs: _SortWrapper[Scalar[type]] @@ -68,7 +68,7 @@ fn small_sort[size: Int, type: DType](inout list: List[Scalar[type]]): @always_inline -fn heap_sort[type: DType](inout list: List[Scalar[type]]): +fn heap_sort[type: DType](mut list: List[Scalar[type]]): @parameter fn _less_than( lhs: _SortWrapper[Scalar[type]], rhs: _SortWrapper[Scalar[type]] @@ -83,14 +83,14 @@ fn heap_sort[type: DType](inout list: List[Scalar[type]]): # ===----------------------------------------------------------------------===# -fn bench_tiny_list_sort[type: DType](inout m: Bench) raises: +fn bench_tiny_list_sort[type: DType](mut m: Bench) raises: alias small_list_size = 5 @parameter for count in range(2, small_list_size + 1): @parameter - fn bench_sort_list(inout b: Bencher) raises: + fn bench_sort_list(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[Scalar[type]].alloc(count) var list = List[Scalar[type]](ptr=ptr, length=count, capacity=count) @@ -109,7 +109,7 @@ fn bench_tiny_list_sort[type: DType](inout m: Bench) raises: _ = list^ @parameter - fn bench_small_sort(inout b: Bencher) raises: + fn bench_small_sort(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[Scalar[type]].alloc(count) var list = List[Scalar[type]](ptr=ptr, length=count, capacity=count) @@ -128,7 +128,7 @@ fn bench_tiny_list_sort[type: DType](inout m: Bench) raises: _ = list^ @parameter - fn bench_insertion_sort(inout b: Bencher) raises: + fn bench_insertion_sort(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[Scalar[type]].alloc(count) var list = List[Scalar[type]](ptr=ptr, length=count, capacity=count) @@ -162,9 +162,9 @@ fn bench_tiny_list_sort[type: DType](inout m: Bench) raises: # ===----------------------------------------------------------------------===# -fn bench_small_list_sort[type: DType](inout m: Bench, count: Int) raises: +fn bench_small_list_sort[type: DType](mut m: Bench, count: Int) raises: @parameter - fn bench_sort_list(inout b: Bencher) raises: + fn bench_sort_list(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[Scalar[type]].alloc(count) var list = List[Scalar[type]](ptr=ptr, length=count, capacity=count) @@ -183,7 +183,7 @@ fn bench_small_list_sort[type: DType](inout m: Bench, count: Int) raises: _ = list^ @parameter - fn bench_insertion_sort(inout b: Bencher) raises: + fn bench_insertion_sort(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[Scalar[type]].alloc(count) var list = List[Scalar[type]](ptr=ptr, length=count, capacity=count) @@ -214,9 +214,9 @@ fn bench_small_list_sort[type: DType](inout m: Bench, count: Int) raises: # ===----------------------------------------------------------------------===# -fn bench_large_list_sort[type: DType](inout m: Bench, count: Int) raises: +fn bench_large_list_sort[type: DType](mut m: Bench, count: Int) raises: @parameter - fn bench_sort_list(inout b: Bencher) raises: + fn bench_sort_list(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[Scalar[type]].alloc(count) var list = List[Scalar[type]](ptr=ptr, length=count, capacity=count) @@ -235,7 +235,7 @@ fn bench_large_list_sort[type: DType](inout m: Bench, count: Int) raises: _ = list^ @parameter - fn bench_heap_sort(inout b: Bencher) raises: + fn bench_heap_sort(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[Scalar[type]].alloc(count) var list = List[Scalar[type]](ptr=ptr, length=count, capacity=count) @@ -267,11 +267,9 @@ fn bench_large_list_sort[type: DType](inout m: Bench, count: Int) raises: # ===----------------------------------------------------------------------===# -fn bench_low_cardinality_list_sort( - inout m: Bench, count: Int, delta: Int -) raises: +fn bench_low_cardinality_list_sort(mut m: Bench, count: Int, delta: Int) raises: @parameter - fn bench_sort_list(inout b: Bencher) raises: + fn bench_sort_list(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[UInt8].alloc(count) var list = List[UInt8](ptr=ptr, length=count, capacity=count) @@ -290,7 +288,7 @@ fn bench_low_cardinality_list_sort( _ = list^ @parameter - fn bench_heap_sort(inout b: Bencher) raises: + fn bench_heap_sort(mut b: Bencher) raises: seed(1) var ptr = UnsafePointer[UInt8].alloc(count) var list = List[UInt8](ptr=ptr, length=count, capacity=count) diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index c88c633579..b50d97a98d 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -38,7 +38,7 @@ fn make_dict[size: Int]() -> Dict[Int, Int]: # Benchmark Dict init # ===----------------------------------------------------------------------===# @parameter -fn bench_dict_init(inout b: Bencher) raises: +fn bench_dict_init(mut b: Bencher) raises: @always_inline @parameter fn call_fn(): @@ -54,7 +54,7 @@ fn bench_dict_init(inout b: Bencher) raises: # Benchmark Dict Insert # ===----------------------------------------------------------------------===# @parameter -fn bench_dict_insert[size: Int](inout b: Bencher) raises: +fn bench_dict_insert[size: Int](mut b: Bencher) raises: """Insert 100 new items.""" var items = make_dict[size]() @@ -72,7 +72,7 @@ fn bench_dict_insert[size: Int](inout b: Bencher) raises: # Benchmark Dict Lookup # ===----------------------------------------------------------------------===# @parameter -fn bench_dict_lookup[size: Int](inout b: Bencher) raises: +fn bench_dict_lookup[size: Int](mut b: Bencher) raises: """Lookup 100 items.""" var items = make_dict[size]() var closest_divisor = ceil(100 / size) diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index 7a46242714..85ee5050ee 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -65,7 +65,7 @@ fn make_string[ # Benchmark string init # ===----------------------------------------------------------------------===# @parameter -fn bench_string_init(inout b: Bencher) raises: +fn bench_string_init(mut b: Bencher) raises: @always_inline @parameter fn call_fn(): @@ -84,7 +84,7 @@ fn bench_string_count[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN", sequence: StringLiteral = "a", -](inout b: Bencher) raises: +](mut b: Bencher) raises: var items = make_string[length](filename + ".txt") @always_inline @@ -105,7 +105,7 @@ fn bench_string_split[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN", sequence: Optional[StringLiteral] = None, -](inout b: Bencher) raises: +](mut b: Bencher) raises: var items = make_string[length](filename + ".txt") @always_inline @@ -130,7 +130,7 @@ fn bench_string_split[ @parameter fn bench_string_splitlines[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" -](inout b: Bencher) raises: +](mut b: Bencher) raises: var items = make_string[length](filename + ".txt") @always_inline @@ -149,7 +149,7 @@ fn bench_string_splitlines[ @parameter fn bench_string_lower[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" -](inout b: Bencher) raises: +](mut b: Bencher) raises: var items = make_string[length](filename + ".txt") @always_inline @@ -168,7 +168,7 @@ fn bench_string_lower[ @parameter fn bench_string_upper[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" -](inout b: Bencher) raises: +](mut b: Bencher) raises: var items = make_string[length](filename + ".txt") @always_inline @@ -190,7 +190,7 @@ fn bench_string_replace[ filename: StringLiteral = "UN_charter_EN", old: StringLiteral = "a", new: StringLiteral = "A", -](inout b: Bencher) raises: +](mut b: Bencher) raises: var items = make_string[length](filename + ".txt") @always_inline @@ -209,7 +209,7 @@ fn bench_string_replace[ @parameter fn bench_string_is_valid_utf8[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" -](inout b: Bencher) raises: +](mut b: Bencher) raises: var items = make_string[length](filename + ".html") @always_inline diff --git a/stdlib/benchmarks/hashlib/bench_hash.mojo b/stdlib/benchmarks/hashlib/bench_hash.mojo index 6ac5e62013..0c6873f109 100644 --- a/stdlib/benchmarks/hashlib/bench_hash.mojo +++ b/stdlib/benchmarks/hashlib/bench_hash.mojo @@ -599,7 +599,7 @@ fn gen_word_pairs[words: String = words_en]() -> List[String]: # Benchmarks # ===----------------------------------------------------------------------===# @parameter -fn bench_small_keys[s: String](inout b: Bencher) raises: +fn bench_small_keys[s: String](mut b: Bencher) raises: var words = gen_word_pairs[s]() @always_inline @@ -613,7 +613,7 @@ fn bench_small_keys[s: String](inout b: Bencher) raises: @parameter -fn bench_small_keys_new_hash_function[s: String](inout b: Bencher) raises: +fn bench_small_keys_new_hash_function[s: String](mut b: Bencher) raises: var words = gen_word_pairs[s]() @always_inline @@ -627,7 +627,7 @@ fn bench_small_keys_new_hash_function[s: String](inout b: Bencher) raises: @parameter -fn bench_long_key[s: String](inout b: Bencher) raises: +fn bench_long_key[s: String](mut b: Bencher) raises: @always_inline @parameter fn call_fn(): @@ -638,7 +638,7 @@ fn bench_long_key[s: String](inout b: Bencher) raises: @parameter -fn bench_long_key_new_hash_function[s: String](inout b: Bencher) raises: +fn bench_long_key_new_hash_function[s: String](mut b: Bencher) raises: @always_inline @parameter fn call_fn(): diff --git a/stdlib/benchmarks/math/bench_math.mojo b/stdlib/benchmarks/math/bench_math.mojo index 1ba4175a74..b7d4c1ae43 100644 --- a/stdlib/benchmarks/math/bench_math.mojo +++ b/stdlib/benchmarks/math/bench_math.mojo @@ -62,7 +62,7 @@ var int_inputs = make_int_inputs(0, 10_000_000, 1_000_000) @parameter fn bench_math[ math_f1p: fn[type: DType, size: Int] (SIMD[type, size]) -> SIMD[type, size] -](inout b: Bencher) raises: +](mut b: Bencher) raises: @always_inline @parameter fn call_fn() raises: @@ -81,7 +81,7 @@ fn bench_math3[ math_f3p: fn[type: DType, size: Int] ( SIMD[type, size], SIMD[type, size], SIMD[type, size] ) -> SIMD[type, size] -](inout b: Bencher) raises: +](mut b: Bencher) raises: @always_inline @parameter fn call_fn() raises: @@ -96,7 +96,7 @@ fn bench_math3[ # Benchmark lcm/gcd # ===----------------------------------------------------------------------===# @parameter -fn bench_math2[math_f2p: fn (Int, Int, /) -> Int](inout b: Bencher) raises: +fn bench_math2[math_f2p: fn (Int, Int, /) -> Int](mut b: Bencher) raises: @always_inline @parameter fn call_fn() raises: diff --git a/stdlib/benchmarks/utils/bench_formatter.mojo b/stdlib/benchmarks/utils/bench_formatter.mojo index 3d22aa92a8..818a0a2cff 100644 --- a/stdlib/benchmarks/utils/bench_formatter.mojo +++ b/stdlib/benchmarks/utils/bench_formatter.mojo @@ -31,7 +31,7 @@ from utils.stringref import _align_down, _memchr, _memmem # Benchmarks # ===----------------------------------------------------------------------===# @parameter -fn bench_writer_int[n: Int](inout b: Bencher) raises: +fn bench_writer_int[n: Int](mut b: Bencher) raises: @always_inline @parameter fn call_fn(): @@ -43,7 +43,7 @@ fn bench_writer_int[n: Int](inout b: Bencher) raises: @parameter -fn bench_writer_simd[n: Int](inout b: Bencher) raises: +fn bench_writer_simd[n: Int](mut b: Bencher) raises: @always_inline @parameter fn call_fn(): diff --git a/stdlib/benchmarks/utils/bench_memmem.mojo b/stdlib/benchmarks/utils/bench_memmem.mojo index 9cca51fc83..97925b2884 100644 --- a/stdlib/benchmarks/utils/bench_memmem.mojo +++ b/stdlib/benchmarks/utils/bench_memmem.mojo @@ -189,7 +189,7 @@ fn _memmem_baseline[ # Benchmarks # ===----------------------------------------------------------------------===# @parameter -fn bench_find_baseline(inout b: Bencher) raises: +fn bench_find_baseline(mut b: Bencher) raises: @always_inline @parameter fn call_fn(): @@ -204,7 +204,7 @@ fn bench_find_baseline(inout b: Bencher) raises: @parameter -fn bench_find_optimized(inout b: Bencher) raises: +fn bench_find_optimized(mut b: Bencher) raises: @always_inline @parameter fn call_fn(): diff --git a/stdlib/src/base64/_b64encode.mojo b/stdlib/src/base64/_b64encode.mojo index f2179b88d0..74b8c31501 100644 --- a/stdlib/src/base64/_b64encode.mojo +++ b/stdlib/src/base64/_b64encode.mojo @@ -213,7 +213,7 @@ fn store_incomplete_simd[ # TODO: Use Span instead of List as input when Span is easier to use @no_inline fn b64encode_with_buffers( - input_bytes: List[UInt8, _], inout result: List[UInt8, _] + input_bytes: List[UInt8, _], mut result: List[UInt8, _] ): alias simd_width = sys.simdbytewidth() alias input_simd_width = simd_width * 3 // 4 diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index 0787b01ff2..d9a3f7f71b 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -65,7 +65,7 @@ fn _ascii_to_value(char: String) -> Int: # TODO: Use Span instead of List as input when Span is easier to use -fn b64encode(input_bytes: List[UInt8, _], inout result: List[UInt8, _]): +fn b64encode(input_bytes: List[UInt8, _], mut result: List[UInt8, _]): """Performs base64 encoding on the input string. Args: diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index 7101144eca..b46e9ce306 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -40,7 +40,7 @@ struct _UInt128: var high: UInt64 var low: UInt64 - fn __iadd__(inout self, n: UInt64): + fn __iadd__(mut self, n: UInt64): var sum = (self.low + n) & UInt64.MAX self.high += 1 if sum < self.low else 0 self.low = sum @@ -89,9 +89,7 @@ struct FP[type: DType, CarrierDType: DType = FPUtils[type].uint_type]: alias small_divisor = pow(10, Self.kappa) -fn _write_float[ - W: Writer, type: DType, // -](inout writer: W, value: Scalar[type]): +fn _write_float[W: Writer, type: DType, //](mut writer: W, value: Scalar[type]): """Write a SIMD float type into a Writer, using the dragonbox algorithm for perfect roundtrip, shortest representable format, and high performance. Paper: https://github.com/jk-jeon/dragonbox/blob/master/other_files/Dragonbox.pdf @@ -228,7 +226,7 @@ fn _write_float[ fn _to_decimal[ CarrierDType: DType, //, type: DType -](inout sig: Scalar[CarrierDType], inout exp: Int): +](mut sig: Scalar[CarrierDType], mut exp: Int): """Transform the raw binary significand to decimal significand, and biased binary exponent into a decimal power of 10 exponent. """ @@ -500,7 +498,7 @@ fn _umul128[ fn _remove_trailing_zeros[ CarrierDType: DType -](inout sig: Scalar[CarrierDType], inout exp: Int): +](mut sig: Scalar[CarrierDType], mut exp: Int): """Fastest alg for removing trailing zeroes: https://github.com/jk-jeon/rtz_benchmark. """ @@ -629,7 +627,7 @@ fn _check_divisibility_and_divide_by_pow10[ CarrierDType: DType, //, carrier_bits: Int, divide_magic_number: StaticTuple[UInt32, 2], -](inout n: Scalar[CarrierDType], N: Int) -> Bool: +](mut n: Scalar[CarrierDType], N: Int) -> Bool: # Make sure the computation for max_n does not overflow. debug_assert(N + 1 <= _floor_log10_pow2(carrier_bits)) diff --git a/stdlib/src/builtin/_location.mojo b/stdlib/src/builtin/_location.mojo index 2816204b62..463cba7b08 100644 --- a/stdlib/src/builtin/_location.mojo +++ b/stdlib/src/builtin/_location.mojo @@ -42,7 +42,7 @@ struct _SourceLocation(Writable, Stringable): """ return "At " + str(self) + ": " + str(msg) - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats the source location to the provided Writer. diff --git a/stdlib/src/builtin/_pybind.mojo b/stdlib/src/builtin/_pybind.mojo index fd2613961f..24b0fdc619 100644 --- a/stdlib/src/builtin/_pybind.mojo +++ b/stdlib/src/builtin/_pybind.mojo @@ -76,7 +76,7 @@ fn pointer_bitcast[ fn gen_pytype_wrapper[ T: Pythonable, name: StringLiteral, -](inout module: PythonObject) raises: +](mut module: PythonObject) raises: # TODO(MOCO-1301): Add support for member method generation. # TODO(MOCO-1302): Add support for generating member field as computed properties. # TODO(MOCO-1307): Add support for constructor generation. @@ -99,7 +99,7 @@ fn add_wrapper_to_module[ PythonObject, TypedPythonObject["Tuple"] ) raises -> PythonObject, func_name: StringLiteral, -](inout module_obj: PythonObject) raises: +](mut module_obj: PythonObject) raises: var module = TypedPythonObject["Module"](unsafe_unchecked_from=module_obj) Python.add_functions( module, diff --git a/stdlib/src/builtin/_stubs.mojo b/stdlib/src/builtin/_stubs.mojo index 92e661dc00..c116d52408 100644 --- a/stdlib/src/builtin/_stubs.mojo +++ b/stdlib/src/builtin/_stubs.mojo @@ -29,12 +29,12 @@ struct __MLIRType[T: AnyTrivialRegType](Movable, Copyable): trait _IntNext(Copyable): - fn __next__(inout self) -> Int: + fn __next__(mut self) -> Int: ... trait _UIntNext(Copyable): - fn __next__(inout self) -> UInt: + fn __next__(mut self) -> UInt: ... diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index a877d384ac..e48f1c9f5a 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -155,7 +155,7 @@ struct Bool( @always_inline("nodebug") @implicit - fn __init__[T: ImplicitlyBoolable, //](inout self, value: T): + fn __init__[T: ImplicitlyBoolable, //](mut self, value: T): """Convert an ImplicitlyBoolable value to a Bool. Parameters: @@ -226,7 +226,7 @@ struct Bool( return String.write(self) @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this boolean to the provided Writer. @@ -404,7 +404,7 @@ struct Bool( return __mlir_op.`pop.and`(self.value, rhs.value) @always_inline("nodebug") - fn __iand__(inout self, rhs: Bool): + fn __iand__(mut self, rhs: Bool): """Computes `self & rhs` and store the result in `self`. Args: @@ -440,7 +440,7 @@ struct Bool( return __mlir_op.`pop.or`(self.value, rhs.value) @always_inline("nodebug") - fn __ior__(inout self, rhs: Bool): + fn __ior__(mut self, rhs: Bool): """Computes `self | rhs` and store the result in `self`. Args: @@ -476,7 +476,7 @@ struct Bool( return __mlir_op.`pop.xor`(self.value, rhs.value) @always_inline("nodebug") - fn __ixor__(inout self, rhs: Bool): + fn __ixor__(mut self, rhs: Bool): """Computes `self ^ rhs` and stores the result in `self`. Args: diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index c50a2006a5..0272fbc2d6 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -135,7 +135,7 @@ struct _VariadicListIter[type: AnyTrivialRegType]: var index: Int var src: VariadicList[type] - fn __next__(inout self) -> type: + fn __next__(mut self) -> type: self.index += 1 return self.src[self.index - 1] @@ -239,12 +239,12 @@ struct _VariadicListMemIter[ var src: Pointer[Self.variadic_list_type, list_origin] fn __init__( - inout self, index: Int, ref [list_origin]list: Self.variadic_list_type + mut self, index: Int, ref [list_origin]list: Self.variadic_list_type ): self.index = index self.src = Pointer.address_of(list) - fn __next__(inout self) -> Self.variadic_list_type.reference_type: + fn __next__(mut self) -> Self.variadic_list_type.reference_type: self.index += 1 # TODO: Need to make this return a dereferenced reference, not a # reference that must be deref'd by the user. @@ -303,7 +303,7 @@ struct VariadicListMem[ Parameters: elt_is_mutable: True if the elements of the list are mutable for an - inout or owned argument. + mut or owned argument. element_type: The type of the elements in the list. origin: The reference origin of the underlying elements. """ @@ -326,7 +326,7 @@ struct VariadicListMem[ # Life cycle methods # ===-------------------------------------------------------------------===# - # Provide support for borrowed variadic arguments. + # Provide support for read-only variadic arguments. @doc_private @always_inline @implicit @@ -339,9 +339,9 @@ struct VariadicListMem[ self.value = value self._is_owned = False - # Provide support for variadics of *inout* arguments. The reference will + # Provide support for variadics of *mut* arguments. The reference will # automatically be inferred to be mutable, and the !kgen.variadic will have - # convention=inout. + # convention=mut. alias _inout_variadic_type = __mlir_type[ `!kgen.variadic<`, Self._mlir_ref_type, `, inout>` ] @@ -489,7 +489,7 @@ struct VariadicPack[ Parameters: elt_is_mutable: True if the elements of the list are mutable for an - inout or owned argument pack. + mut or owned argument pack. origin: The reference origin of the underlying elements. element_trait: The trait that each element of the pack conforms to. element_types: The list of types held by the argument pack. @@ -519,7 +519,7 @@ struct VariadicPack[ Args: value: The argument to construct the pack with. - is_owned: Whether this is an 'owned' pack or 'inout'/'borrowed'. + is_owned: Whether this is an 'owned' pack or 'mut'/'read-only'. """ self._value = value self._is_owned = is_owned @@ -602,7 +602,7 @@ struct VariadicPack[ """Apply a function to each element of the pack in order. This applies the specified function (which must be parametric on the element type) to each element of the pack, from the first element to the last, passing - in each element as a borrowed argument. + in each element as a read-only argument. Parameters: func: The function to apply to each element. @@ -619,7 +619,7 @@ struct VariadicPack[ """Apply a function to each element of the pack in order. This applies the specified function (which must be parametric on the element type) to each element of the pack, from the first element to the last, passing - in each element as a borrowed argument. + in each element as a read-only argument. Parameters: func: The function to apply to each element. diff --git a/stdlib/src/builtin/builtin_slice.mojo b/stdlib/src/builtin/builtin_slice.mojo index cc5a6d2f1a..0d0fe96750 100644 --- a/stdlib/src/builtin/builtin_slice.mojo +++ b/stdlib/src/builtin/builtin_slice.mojo @@ -66,7 +66,7 @@ struct Slice( @always_inline fn __init__( - inout self, + mut self, start: Optional[Int], end: Optional[Int], step: Optional[Int], @@ -115,7 +115,7 @@ struct Slice( return self.__str__() @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Write Slice string representation to a `Writer`. Parameters: diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index 25d3d8b4e5..4d0f1bb23a 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -308,7 +308,7 @@ struct _ThreadContext(Writable): self.thread_y = _get_id["thread", "y"]() self.thread_z = _get_id["thread", "z"]() - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write( "block: [", self.block_x, diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index acdd3eba30..1d1c37d16f 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -176,7 +176,7 @@ struct DType( return String.write(self) @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this dtype to the provided Writer. @@ -327,7 +327,7 @@ struct DType( """ return hash(UInt8(self._as_i8())) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with this `DType` value. Parameters: diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index c319e708ea..1d19b5d201 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -162,7 +162,7 @@ struct Error( return String.write(self) @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this error to the provided Writer. diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index 3185adc6b9..823cbf9348 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -112,7 +112,7 @@ struct FileHandle: except: pass - fn close(inout self) raises: + fn close(mut self) raises: """Closes the file handle.""" if not self.handle: return @@ -405,7 +405,7 @@ struct FileHandle: return pos @always_inline - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): """ Write a span of bytes to the file. @@ -423,7 +423,7 @@ struct FileHandle: if err_msg: abort(err_msg^.consume_as_error()) - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): """Write a sequence of Writable arguments to the provided Writer. Parameters: diff --git a/stdlib/src/builtin/file_descriptor.mojo b/stdlib/src/builtin/file_descriptor.mojo index 2843c9dcbf..8f7709d027 100644 --- a/stdlib/src/builtin/file_descriptor.mojo +++ b/stdlib/src/builtin/file_descriptor.mojo @@ -59,7 +59,7 @@ struct FileDescriptor(Writer): self.value = f._get_raw_fd() @always_inline - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): """ Write a span of bytes to the file. @@ -83,7 +83,7 @@ struct FileDescriptor(Writer): written, ) - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): """Write a sequence of Writable arguments to the provided Writer. Parameters: diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 81308401b5..906e8d40dc 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -466,7 +466,7 @@ struct FloatLiteral( # ===------------------------------------------------------------------===# @always_inline("nodebug") - fn __iadd__(inout self, rhs: FloatLiteral): + fn __iadd__(mut self, rhs: FloatLiteral): """In-place addition operator. Args: @@ -475,7 +475,7 @@ struct FloatLiteral( self = self + rhs @always_inline("nodebug") - fn __isub__(inout self, rhs: FloatLiteral): + fn __isub__(mut self, rhs: FloatLiteral): """In-place subtraction operator. Args: @@ -484,7 +484,7 @@ struct FloatLiteral( self = self - rhs @always_inline("nodebug") - fn __imul__(inout self, rhs: FloatLiteral): + fn __imul__(mut self, rhs: FloatLiteral): """In-place multiplication operator. Args: @@ -493,7 +493,7 @@ struct FloatLiteral( self = self * rhs @always_inline("nodebug") - fn __itruediv__(inout self, rhs: FloatLiteral): + fn __itruediv__(mut self, rhs: FloatLiteral): """In-place division. Args: diff --git a/stdlib/src/builtin/format_int.mojo b/stdlib/src/builtin/format_int.mojo index efdc0015c5..0239369fbe 100644 --- a/stdlib/src/builtin/format_int.mojo +++ b/stdlib/src/builtin/format_int.mojo @@ -249,7 +249,7 @@ fn _write_int[ type: DType, W: Writer, ]( - inout writer: W, + mut writer: W, value: Scalar[type], /, radix: Int = 10, @@ -268,7 +268,7 @@ fn _try_write_int[ type: DType, W: Writer, ]( - inout writer: W, + mut writer: W, value: Scalar[type], /, radix: Int = 10, diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 4d7e18dbac..61a74df2dc 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -418,7 +418,7 @@ struct Int( @always_inline("nodebug") @implicit - fn __init__[IndexerTy: Indexer](inout self, value: IndexerTy): + fn __init__[IndexerTy: Indexer](mut self, value: IndexerTy): """Construct Int from the given Indexer value. Parameters: @@ -765,7 +765,7 @@ struct Int( # ===----------------------------------------------------------------------===# @always_inline("nodebug") - fn __iadd__(inout self, rhs: Int): + fn __iadd__(mut self, rhs: Int): """Compute `self + rhs` and save the result in self. Args: @@ -774,7 +774,7 @@ struct Int( self = self + rhs @always_inline("nodebug") - fn __isub__(inout self, rhs: Int): + fn __isub__(mut self, rhs: Int): """Compute `self - rhs` and save the result in self. Args: @@ -783,7 +783,7 @@ struct Int( self = self - rhs @always_inline("nodebug") - fn __imul__(inout self, rhs: Int): + fn __imul__(mut self, rhs: Int): """Compute self*rhs and save the result in self. Args: @@ -791,7 +791,7 @@ struct Int( """ self = self * rhs - fn __itruediv__(inout self, rhs: Int): + fn __itruediv__(mut self, rhs: Int): """Compute `self / rhs`, convert to int, and save the result in self. Since `floor(self / rhs)` is equivalent to `self // rhs`, this yields @@ -803,7 +803,7 @@ struct Int( self = self // rhs @always_inline("nodebug") - fn __ifloordiv__(inout self, rhs: Int): + fn __ifloordiv__(mut self, rhs: Int): """Compute `self // rhs` and save the result in self. Args: @@ -811,7 +811,7 @@ struct Int( """ self = self // rhs - fn __imod__(inout self, rhs: Int): + fn __imod__(mut self, rhs: Int): """Compute `self % rhs` and save the result in self. Args: @@ -820,7 +820,7 @@ struct Int( self = self % rhs @always_inline("nodebug") - fn __ipow__(inout self, rhs: Int): + fn __ipow__(mut self, rhs: Int): """Compute `pow(self, rhs)` and save the result in self. Args: @@ -829,7 +829,7 @@ struct Int( self = self**rhs @always_inline("nodebug") - fn __ilshift__(inout self, rhs: Int): + fn __ilshift__(mut self, rhs: Int): """Compute `self << rhs` and save the result in self. Args: @@ -838,7 +838,7 @@ struct Int( self = self << rhs @always_inline("nodebug") - fn __irshift__(inout self, rhs: Int): + fn __irshift__(mut self, rhs: Int): """Compute `self >> rhs` and save the result in self. Args: @@ -847,7 +847,7 @@ struct Int( self = self >> rhs @always_inline("nodebug") - fn __iand__(inout self, rhs: Int): + fn __iand__(mut self, rhs: Int): """Compute `self & rhs` and save the result in self. Args: @@ -856,7 +856,7 @@ struct Int( self = self & rhs @always_inline("nodebug") - fn __ixor__(inout self, rhs: Int): + fn __ixor__(mut self, rhs: Int): """Compute `self ^ rhs` and save the result in self. Args: @@ -865,7 +865,7 @@ struct Int( self = self ^ rhs @always_inline("nodebug") - fn __ior__(inout self, rhs: Int): + fn __ior__(mut self, rhs: Int): """Compute self|rhs and save the result in self. Args: @@ -1141,7 +1141,7 @@ struct Int( # TODO(MOCO-636): switch to DType.index return _hash_simd(Scalar[DType.int64](self)) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with this int value. Parameters: @@ -1180,7 +1180,7 @@ struct Int( # Methods # ===-------------------------------------------------------------------===# - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this integer to the provided Writer. @@ -1193,7 +1193,7 @@ struct Int( writer.write(Int64(self)) - fn write_padded[W: Writer](self, inout writer: W, width: Int): + fn write_padded[W: Writer](self, mut writer: W, width: Int): """Write the int right-aligned to a set padding. Parameters: diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 0432c933d3..360ed0e9a6 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -378,7 +378,7 @@ struct IntLiteral( # ===----------------------------------------------------------------------===# @always_inline("nodebug") - fn __iadd__(inout self, rhs: Self): + fn __iadd__(mut self, rhs: Self): """Compute `self + rhs` and save the result in self. Args: @@ -387,7 +387,7 @@ struct IntLiteral( self = self + rhs @always_inline("nodebug") - fn __isub__(inout self, rhs: Self): + fn __isub__(mut self, rhs: Self): """Compute `self - rhs` and save the result in self. Args: @@ -396,7 +396,7 @@ struct IntLiteral( self = self - rhs @always_inline("nodebug") - fn __imul__(inout self, rhs: Self): + fn __imul__(mut self, rhs: Self): """Compute self*rhs and save the result in self. Args: @@ -405,7 +405,7 @@ struct IntLiteral( self = self * rhs @always_inline("nodebug") - fn __ifloordiv__(inout self, rhs: Self): + fn __ifloordiv__(mut self, rhs: Self): """Compute self//rhs and save the result in self. Args: @@ -414,7 +414,7 @@ struct IntLiteral( self = self // rhs @always_inline("nodebug") - fn __ilshift__(inout self, rhs: Self): + fn __ilshift__(mut self, rhs: Self): """Compute `self << rhs` and save the result in self. Args: @@ -423,7 +423,7 @@ struct IntLiteral( self = self << rhs @always_inline("nodebug") - fn __irshift__(inout self, rhs: Self): + fn __irshift__(mut self, rhs: Self): """Compute `self >> rhs` and save the result in self. Args: @@ -432,7 +432,7 @@ struct IntLiteral( self = self >> rhs @always_inline("nodebug") - fn __iand__(inout self, rhs: Self): + fn __iand__(mut self, rhs: Self): """Compute `self & rhs` and save the result in self. Args: @@ -441,7 +441,7 @@ struct IntLiteral( self = self & rhs @always_inline("nodebug") - fn __ixor__(inout self, rhs: Self): + fn __ixor__(mut self, rhs: Self): """Compute `self ^ rhs` and save the result in self. Args: @@ -450,7 +450,7 @@ struct IntLiteral( self = self ^ rhs @always_inline("nodebug") - fn __ior__(inout self, rhs: Self): + fn __ior__(mut self, rhs: Self): """Compute self|rhs and save the result in self. Args: diff --git a/stdlib/src/builtin/none.mojo b/stdlib/src/builtin/none.mojo index 3cfc856b32..2e5dc6ef35 100644 --- a/stdlib/src/builtin/none.mojo +++ b/stdlib/src/builtin/none.mojo @@ -75,7 +75,7 @@ struct NoneType( return "None" @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Write `None` to a writer stream. Parameters: diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index 1eb57a4acb..21b345b89d 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -124,7 +124,7 @@ struct _RefCountedAttrsDict: ) @always_inline - fn set(inout self, key: StringLiteral, value: _ObjectImpl) raises: + fn set(mut self, key: StringLiteral, value: _ObjectImpl) raises: if key in self.impl[]: self.impl[][key].destroy() self.impl[][key] = value @@ -210,7 +210,7 @@ struct _Function(CollectionElement, CollectionElementNew): """The function pointer.""" @always_inline - fn __init__[FnT: AnyTrivialRegType](inout self, value: FnT): + fn __init__[FnT: AnyTrivialRegType](mut self, value: FnT): # FIXME: No "pointer bitcast" for signature function pointers. var f = UnsafePointer[Int16]() UnsafePointer.address_of(f).bitcast[FnT]()[] = value @@ -320,7 +320,7 @@ struct _ObjectImpl( self.value = Self.type(value) @always_inline - fn __init__[dt: DType](inout self, value: SIMD[dt, 1]): + fn __init__[dt: DType](mut self, value: SIMD[dt, 1]): @parameter if dt.is_integral(): self.value = Self.type(value) @@ -517,7 +517,7 @@ struct _ObjectImpl( return self.get_as_int().cast[DType.float64]() @staticmethod - fn coerce_comparison_type(inout lhs: _ObjectImpl, inout rhs: _ObjectImpl): + fn coerce_comparison_type(mut lhs: _ObjectImpl, mut rhs: _ObjectImpl): """Coerces two values of arithmetic type to the appropriate lowest-common denominator type for performing comparisons, in order of increasing priority: bool, int, and then float. @@ -528,7 +528,7 @@ struct _ObjectImpl( return @parameter - fn convert(inout value: _ObjectImpl, id: Int, to: Int): + fn convert(mut value: _ObjectImpl, id: Int, to: Int): if to == Self.int: value = value.convert_bool_to_int() else: @@ -543,7 +543,7 @@ struct _ObjectImpl( convert(lhs, lhsId, rhsId) @staticmethod - fn coerce_arithmetic_type(inout lhs: _ObjectImpl, inout rhs: _ObjectImpl): + fn coerce_arithmetic_type(mut lhs: _ObjectImpl, mut rhs: _ObjectImpl): """Coerces two values of arithmetic type to the appropriate lowest-common denominator type for performing arithmetic operations. Bools are always converted to integers, to match Python's behavior. @@ -560,7 +560,7 @@ struct _ObjectImpl( lhs = lhs.convert_int_to_float() @staticmethod - fn coerce_integral_type(inout lhs: _ObjectImpl, inout rhs: _ObjectImpl): + fn coerce_integral_type(mut lhs: _ObjectImpl, mut rhs: _ObjectImpl): """Coerces two values of integral type to the appropriate lowest-common denominator type for performing bitwise operations. """ @@ -571,7 +571,7 @@ struct _ObjectImpl( else: lhs = lhs.convert_bool_to_int() - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Performs conversion to string according to Python semantics. """ @@ -785,7 +785,7 @@ struct object( self._value = value @always_inline - fn __init__[dt: DType](inout self, value: SIMD[dt, 1]): + fn __init__[dt: DType](mut self, value: SIMD[dt, 1]): """Initializes the object with a generic scalar value. If the scalar value type is bool, it is converted to a boolean. Otherwise, it is converted to the appropriate integer or floating point type. @@ -842,7 +842,7 @@ struct object( self._value = impl @always_inline - fn __init__[*Ts: CollectionElement](inout self, value: ListLiteral[*Ts]): + fn __init__[*Ts: CollectionElement](mut self, value: ListLiteral[*Ts]): """Initializes the object from a list literal. Parameters: @@ -1003,7 +1003,7 @@ struct object( """ return self.__bool__() - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Performs conversion to string according to Python semantics. @@ -1537,7 +1537,7 @@ struct object( # ===------------------------------------------------------------------=== # @always_inline - fn __iadd__(inout self, rhs: object) raises: + fn __iadd__(mut self, rhs: object) raises: """In-place addition or concatenation operator. Args: @@ -1546,7 +1546,7 @@ struct object( self = self + rhs @always_inline - fn __isub__(inout self, rhs: object) raises: + fn __isub__(mut self, rhs: object) raises: """In-place subtraction operator. Args: @@ -1555,7 +1555,7 @@ struct object( self = self - rhs @always_inline - fn __imul__(inout self, rhs: object) raises: + fn __imul__(mut self, rhs: object) raises: """In-place multiplication operator. Args: @@ -1564,7 +1564,7 @@ struct object( self = self * rhs @always_inline - fn __ipow__(inout self, rhs: object) raises: + fn __ipow__(mut self, rhs: object) raises: """In-place exponentiation operator. Args: @@ -1573,7 +1573,7 @@ struct object( self = self**rhs @always_inline - fn __imod__(inout self, rhs: object) raises: + fn __imod__(mut self, rhs: object) raises: """In-place modulo operator. Args: @@ -1582,7 +1582,7 @@ struct object( self = self % rhs @always_inline - fn __itruediv__(inout self, rhs: object) raises: + fn __itruediv__(mut self, rhs: object) raises: """In-place true division operator. Args: @@ -1591,7 +1591,7 @@ struct object( self = self / rhs @always_inline - fn __ifloordiv__(inout self, rhs: object) raises: + fn __ifloordiv__(mut self, rhs: object) raises: """In-place floor division operator. Args: @@ -1600,7 +1600,7 @@ struct object( self = self // rhs @always_inline - fn __ilshift__(inout self, rhs: object) raises: + fn __ilshift__(mut self, rhs: object) raises: """In-place left shift operator. Args: @@ -1609,7 +1609,7 @@ struct object( self = self << rhs @always_inline - fn __irshift__(inout self, rhs: object) raises: + fn __irshift__(mut self, rhs: object) raises: """In-place right shift operator. Args: @@ -1618,7 +1618,7 @@ struct object( self = self >> rhs @always_inline - fn __iand__(inout self, rhs: object) raises: + fn __iand__(mut self, rhs: object) raises: """In-place AND operator. Args: @@ -1627,7 +1627,7 @@ struct object( self = self & rhs @always_inline - fn __ior__(inout self, rhs: object) raises: + fn __ior__(mut self, rhs: object) raises: """In-place OR operator. Args: @@ -1636,7 +1636,7 @@ struct object( self = self | rhs @always_inline - fn __ixor__(inout self, rhs: object) raises: + fn __ixor__(mut self, rhs: object) raises: """In-place XOR operator. Args: @@ -1936,7 +1936,7 @@ struct object( return self._value.get_obj_attr(key) @always_inline - fn __setattr__(inout self, key: StringLiteral, value: object) raises: + fn __setattr__(mut self, key: StringLiteral, value: object) raises: """Sets the named attribute. Args: diff --git a/stdlib/src/builtin/range.mojo b/stdlib/src/builtin/range.mojo index 587b859e16..2af6fb942d 100644 --- a/stdlib/src/builtin/range.mojo +++ b/stdlib/src/builtin/range.mojo @@ -60,7 +60,7 @@ struct _ZeroStartingRange(Sized, ReversibleRange, _IntIterable): return self @always_inline - fn __next__(inout self) -> Int: + fn __next__(mut self) -> Int: var curr = self.curr self.curr -= 1 return self.end - curr @@ -94,7 +94,7 @@ struct _SequentialRange(Sized, ReversibleRange, _IntIterable): return self @always_inline - fn __next__(inout self) -> Int: + fn __next__(mut self) -> Int: var start = self.start self.start += 1 return start @@ -134,7 +134,7 @@ struct _StridedRangeIterator(Sized): return 0 @always_inline - fn __next__(inout self) -> Int: + fn __next__(mut self) -> Int: var result = self.start self.start += self.step return result @@ -162,7 +162,7 @@ struct _StridedRange(Sized, ReversibleRange, _StridedIterable): return _StridedRangeIterator(self.start, self.end, self.step) @always_inline - fn __next__(inout self) -> Int: + fn __next__(mut self) -> Int: var result = self.start self.start += self.step return result @@ -337,7 +337,7 @@ struct _UIntZeroStartingRange(UIntSized): return self @always_inline - fn __next__(inout self) -> UInt: + fn __next__(mut self) -> UInt: var curr = self.curr self.curr -= 1 return self.end - curr @@ -368,7 +368,7 @@ struct _UIntStridedRangeIterator(UIntSized): return select(self.start < self.end, self.end - self.start, 0) @always_inline - fn __next__(inout self) -> UInt: + fn __next__(mut self) -> UInt: var result = self.start self.start += self.step return result @@ -406,7 +406,7 @@ struct _UIntStridedRange(UIntSized, _UIntStridedIterable): return _UIntStridedRangeIterator(self.start, self.end, self.step) @always_inline - fn __next__(inout self) -> UInt: + fn __next__(mut self) -> UInt: if self.start >= self.end: return self.end var result = self.start @@ -478,7 +478,7 @@ struct _ZeroStartingScalarRange[type: DType]: return self @always_inline - fn __next__(inout self) -> Scalar[type]: + fn __next__(mut self) -> Scalar[type]: var curr = self.curr self.curr -= 1 return self.end - curr @@ -515,7 +515,7 @@ struct _SequentialScalarRange[type: DType]: return self @always_inline - fn __next__(inout self) -> Scalar[type]: + fn __next__(mut self) -> Scalar[type]: var start = self.start self.start += 1 return start @@ -557,7 +557,7 @@ struct _StridedScalarRangeIterator[type: DType]: return self.end < self.start @always_inline - fn __next__(inout self) -> Scalar[type]: + fn __next__(mut self) -> Scalar[type]: var result = self.start self.start += self.step return result diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 332220f11f..a70d660217 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -354,7 +354,7 @@ struct SIMD[type: DType, size: Int]( @always_inline("nodebug") @implicit fn __init__( - inout self, + mut self, value: __mlir_type[`!pop.simd<`, size.value, `, `, type.value, `>`], ): """Initializes the SIMD vector with the underlying mlir value. @@ -588,7 +588,7 @@ struct SIMD[type: DType, size: Int]( fn __init__[ int_type: DType, // - ](inout self, *, from_bits: SIMD[int_type, size]): + ](mut self, *, from_bits: SIMD[int_type, size]): """Initializes the SIMD vector from the bits of an integral SIMD vector. Parameters: @@ -619,7 +619,7 @@ struct SIMD[type: DType, size: Int]( ](self.value, index(idx).value) @always_inline("nodebug") - fn __setitem__(inout self, idx: Int, val: Scalar[type]): + fn __setitem__(mut self, idx: Int, val: Scalar[type]): """Sets an element in the vector. Args: @@ -1076,7 +1076,7 @@ struct SIMD[type: DType, size: Int]( # ===------------------------------------------------------------------=== # @always_inline("nodebug") - fn __iadd__(inout self, rhs: Self): + fn __iadd__(mut self, rhs: Self): """Performs in-place addition. The vector is mutated where each element at position `i` is computed as @@ -1089,7 +1089,7 @@ struct SIMD[type: DType, size: Int]( self = self + rhs @always_inline("nodebug") - fn __isub__(inout self, rhs: Self): + fn __isub__(mut self, rhs: Self): """Performs in-place subtraction. The vector is mutated where each element at position `i` is computed as @@ -1102,7 +1102,7 @@ struct SIMD[type: DType, size: Int]( self = self - rhs @always_inline("nodebug") - fn __imul__(inout self, rhs: Self): + fn __imul__(mut self, rhs: Self): """Performs in-place multiplication. The vector is mutated where each element at position `i` is computed as @@ -1115,7 +1115,7 @@ struct SIMD[type: DType, size: Int]( self = self * rhs @always_inline("nodebug") - fn __itruediv__(inout self, rhs: Self): + fn __itruediv__(mut self, rhs: Self): """In-place true divide operator. The vector is mutated where each element at position `i` is computed as @@ -1128,7 +1128,7 @@ struct SIMD[type: DType, size: Int]( self = self / rhs @always_inline("nodebug") - fn __ifloordiv__(inout self, rhs: Self): + fn __ifloordiv__(mut self, rhs: Self): """In-place flood div operator. The vector is mutated where each element at position `i` is computed as @@ -1141,7 +1141,7 @@ struct SIMD[type: DType, size: Int]( self = self // rhs @always_inline("nodebug") - fn __imod__(inout self, rhs: Self): + fn __imod__(mut self, rhs: Self): """In-place mod operator. The vector is mutated where each element at position `i` is computed as @@ -1154,7 +1154,7 @@ struct SIMD[type: DType, size: Int]( self = self.__mod__(rhs) @always_inline("nodebug") - fn __ipow__(inout self, rhs: Int): + fn __ipow__(mut self, rhs: Int): """In-place pow operator. The vector is mutated where each element at position `i` is computed as @@ -1167,7 +1167,7 @@ struct SIMD[type: DType, size: Int]( self = self.__pow__(rhs) @always_inline("nodebug") - fn __iand__(inout self, rhs: Self): + fn __iand__(mut self, rhs: Self): """Computes `self & rhs` and save the result in `self`. Constraints: @@ -1183,7 +1183,7 @@ struct SIMD[type: DType, size: Int]( self = self & rhs @always_inline("nodebug") - fn __ixor__(inout self, rhs: Self): + fn __ixor__(mut self, rhs: Self): """Computes `self ^ rhs` and save the result in `self`. Constraints: @@ -1199,7 +1199,7 @@ struct SIMD[type: DType, size: Int]( self = self ^ rhs @always_inline("nodebug") - fn __ior__(inout self, rhs: Self): + fn __ior__(mut self, rhs: Self): """Computes `self | rhs` and save the result in `self`. Constraints: @@ -1215,7 +1215,7 @@ struct SIMD[type: DType, size: Int]( self = self | rhs @always_inline("nodebug") - fn __ilshift__(inout self, rhs: Self): + fn __ilshift__(mut self, rhs: Self): """Computes `self << rhs` and save the result in `self`. Constraints: @@ -1228,7 +1228,7 @@ struct SIMD[type: DType, size: Int]( self = self << rhs @always_inline("nodebug") - fn __irshift__(inout self, rhs: Self): + fn __irshift__(mut self, rhs: Self): """Computes `self >> rhs` and save the result in `self`. Constraints: @@ -1643,7 +1643,7 @@ struct SIMD[type: DType, size: Int]( """ return _hash_simd(self) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with this SIMD value. Parameters: @@ -1782,7 +1782,7 @@ struct SIMD[type: DType, size: Int]( ](self.value) @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this SIMD value to the provided Writer. @@ -3422,7 +3422,7 @@ fn _floor(x: SIMD) -> __type_of(x): fn _write_scalar[ dtype: DType, W: Writer, //, -](inout writer: W, value: Scalar[dtype]): +](mut writer: W, value: Scalar[dtype]): @parameter if dtype == DType.bool: if value: diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 367047a85c..554ddeddfd 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -123,7 +123,7 @@ struct StringLiteral( return __mlir_op.`pop.string.concat`(self.value, rhs.value) @always_inline("nodebug") - fn __iadd__(inout self, rhs: StringLiteral): + fn __iadd__(mut self, rhs: StringLiteral): """Concatenate a string literal to an existing one. Can only be evaluated at compile time using the `alias` keyword, which will write the result into the binary. @@ -388,7 +388,7 @@ struct StringLiteral( """ return hash(self.unsafe_ptr(), len(self)) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with the underlying bytes. Parameters: @@ -551,7 +551,7 @@ struct StringLiteral( """ return _FormatCurlyEntry.format(self, args) - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this string literal to the provided Writer. diff --git a/stdlib/src/builtin/swap.mojo b/stdlib/src/builtin/swap.mojo index fed7502fd1..549e71e28b 100644 --- a/stdlib/src/builtin/swap.mojo +++ b/stdlib/src/builtin/swap.mojo @@ -17,7 +17,7 @@ These are Mojo built-ins, so you don't need to import them. @always_inline -fn swap[T: Movable](inout lhs: T, inout rhs: T): +fn swap[T: Movable](mut lhs: T, mut rhs: T): """Swaps the two given arguments. Parameters: diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index 6d7f45a029..50fdf9f708 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -59,7 +59,7 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): @always_inline("nodebug") fn __init__( - inout self, + mut self, *, owned storage: VariadicPack[_, CollectionElement, *element_types], ): diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index cb3de1bcb5..dad8ed6167 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -130,7 +130,7 @@ struct UInt(IntLike, _HashableWithHasher): return String.write(self) @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Formats this integer to the provided Writer. Parameters: @@ -168,7 +168,7 @@ struct UInt(IntLike, _HashableWithHasher): # TODO(MOCO-636): switch to DType.index return _hash_simd(Scalar[DType.uint64](self)) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with this uint value. Parameters: @@ -403,7 +403,7 @@ struct UInt(IntLike, _HashableWithHasher): # ===----------------------------------------------------------------------===# @always_inline("nodebug") - fn __iadd__(inout self, rhs: UInt): + fn __iadd__(mut self, rhs: UInt): """Compute `self + rhs` and save the result in self. Args: @@ -412,7 +412,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self + rhs @always_inline("nodebug") - fn __isub__(inout self, rhs: UInt): + fn __isub__(mut self, rhs: UInt): """Compute `self - rhs` and save the result in self. Args: @@ -421,7 +421,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self - rhs @always_inline("nodebug") - fn __imul__(inout self, rhs: UInt): + fn __imul__(mut self, rhs: UInt): """Compute self*rhs and save the result in self. Args: @@ -429,7 +429,7 @@ struct UInt(IntLike, _HashableWithHasher): """ self = self * rhs - fn __itruediv__(inout self, rhs: UInt): + fn __itruediv__(mut self, rhs: UInt): """Compute `self / rhs`, convert to int, and save the result in self. Since `floor(self / rhs)` is equivalent to `self // rhs`, this yields @@ -441,7 +441,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self // rhs @always_inline("nodebug") - fn __ifloordiv__(inout self, rhs: UInt): + fn __ifloordiv__(mut self, rhs: UInt): """Compute `self // rhs` and save the result in self. Args: @@ -449,7 +449,7 @@ struct UInt(IntLike, _HashableWithHasher): """ self = self // rhs - fn __imod__(inout self, rhs: UInt): + fn __imod__(mut self, rhs: UInt): """Compute `self % rhs` and save the result in self. Args: @@ -458,7 +458,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self % rhs @always_inline("nodebug") - fn __ipow__(inout self, rhs: UInt): + fn __ipow__(mut self, rhs: UInt): """Compute `pow(self, rhs)` and save the result in self. Args: @@ -467,7 +467,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self**rhs @always_inline("nodebug") - fn __ilshift__(inout self, rhs: UInt): + fn __ilshift__(mut self, rhs: UInt): """Compute `self << rhs` and save the result in self. Args: @@ -476,7 +476,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self << rhs @always_inline("nodebug") - fn __irshift__(inout self, rhs: UInt): + fn __irshift__(mut self, rhs: UInt): """Compute `self >> rhs` and save the result in self. Args: @@ -485,7 +485,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self >> rhs @always_inline("nodebug") - fn __iand__(inout self, rhs: UInt): + fn __iand__(mut self, rhs: UInt): """Compute `self & rhs` and save the result in self. Args: @@ -494,7 +494,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self & rhs @always_inline("nodebug") - fn __ixor__(inout self, rhs: UInt): + fn __ixor__(mut self, rhs: UInt): """Compute `self ^ rhs` and save the result in self. Args: @@ -503,7 +503,7 @@ struct UInt(IntLike, _HashableWithHasher): self = self ^ rhs @always_inline("nodebug") - fn __ior__(inout self, rhs: UInt): + fn __ior__(mut self, rhs: UInt): """Compute self|rhs and save the result in self. Args: diff --git a/stdlib/src/builtin/value.mojo b/stdlib/src/builtin/value.mojo index fab16a7b44..3dc291c522 100644 --- a/stdlib/src/builtin/value.mojo +++ b/stdlib/src/builtin/value.mojo @@ -109,7 +109,7 @@ trait ExplicitlyCopyable: initializer is called intentionally by the programmer. An explicit copy initializer is just a normal `__init__` method that takes - a `borrowed` argument of `Self`. + a `read-only` argument of `Self`. Example implementing the `ExplicitlyCopyable` trait on `Foo` which requires the `__init__(.., Self)` method: diff --git a/stdlib/src/collections/counter.mojo b/stdlib/src/collections/counter.mojo index a4a2ac1df3..55baedf606 100644 --- a/stdlib/src/collections/counter.mojo +++ b/stdlib/src/collections/counter.mojo @@ -114,7 +114,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): """ return self.get(key, 0) - fn __setitem__(inout self, value: V, count: Int): + fn __setitem__(mut self, value: V, count: Int): """Set a value in the keyword Counter by key. Args: @@ -276,7 +276,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): return +result^ # Remove zero and negative counts - fn __iadd__(inout self, other: Self): + fn __iadd__(mut self, other: Self): """Add counts from another Counter to this Counter. Args: @@ -301,7 +301,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): return +result^ # Remove zero and negative counts - fn __isub__(inout self, other: Self): + fn __isub__(mut self, other: Self): """Subtract counts from another Counter from this Counter. Args: @@ -329,7 +329,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): return result^ - fn __iand__(inout self, other: Self): + fn __iand__(mut self, other: Self): """Intersection: keep common elements with the minimum count. Args: @@ -370,7 +370,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): return result^ - fn __ior__(inout self, other: Self): + fn __ior__(mut self, other: Self): """Union: keep all elements with the maximum count. Args: @@ -382,7 +382,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): if newcount > 0: self[key] = newcount - fn _keep_positive(inout self): + fn _keep_positive(mut self): """Remove zero and negative counts from the Counter.""" for key_ref in self.keys(): var key = key_ref[] @@ -451,7 +451,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): """ return self._data.get(value, default) - fn pop(inout self, value: V) raises -> Int: + fn pop(mut self, value: V) raises -> Int: """Remove a value from the Counter by value. Args: @@ -465,7 +465,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): """ return self._data.pop(value) - fn pop(inout self, value: V, owned default: Int) raises -> Int: + fn pop(mut self, value: V, owned default: Int) raises -> Int: """Remove a value from the Counter by value. Args: @@ -507,11 +507,11 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): """ return self._data.items() - fn clear(inout self): + fn clear(mut self): """Remove all elements from the Counter.""" self._data.clear() - fn popitem(inout self) raises -> CountTuple[V]: + fn popitem(mut self) raises -> CountTuple[V]: """Remove and return an arbitrary (key, value) pair from the Counter. Returns: @@ -573,7 +573,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): elements.append(item.key) return elements - fn update(inout self, other: Self): + fn update(mut self, other: Self): """Update the Counter, like `dict.update()` but add counts instead of replacing them. @@ -584,7 +584,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): var item = item_ref[] self._data[item.key] = self._data.get(item.key, 0) + item.value - fn subtract(inout self, other: Self): + fn subtract(mut self, other: Self): """Subtract count. Both inputs and outputs may be zero or negative. Args: diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index 37ec9d97bf..cdaf52332d 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -142,7 +142,7 @@ struct Deque[ElementType: CollectionElement]( self = Self(variadic_list=values^) fn __init__( - inout self, *, owned variadic_list: VariadicListMem[ElementType, _] + mut self, *, owned variadic_list: VariadicListMem[ElementType, _] ): """Constructs a deque from the given values. @@ -226,7 +226,7 @@ struct Deque[ElementType: CollectionElement]( new.append(element[]) return new^ - fn __iadd__(inout self, other: Self): + fn __iadd__(mut self, other: Self): """Appends the elements of other deque into self. Args: @@ -257,7 +257,7 @@ struct Deque[ElementType: CollectionElement]( new.append(element[]) return new^ - fn __imul__(inout self, n: Int): + fn __imul__(mut self, n: Int): """Concatenates self `n` times in place. Args: @@ -410,7 +410,7 @@ struct Deque[ElementType: CollectionElement]( fn write_to[ RepresentableElementType: RepresentableCollectionElement, WriterType: Writer, //, - ](self: Deque[RepresentableElementType], inout writer: WriterType): + ](self: Deque[RepresentableElementType], mut writer: WriterType): """Writes `my_deque.__str__()` to a `Writer`. Parameters: @@ -491,7 +491,7 @@ struct Deque[ElementType: CollectionElement]( # Methods # ===-------------------------------------------------------------------===# - fn append(inout self, owned value: ElementType): + fn append(mut self, owned value: ElementType): """Appends a value to the right side of the deque. Args: @@ -508,7 +508,7 @@ struct Deque[ElementType: CollectionElement]( if self._head == self._tail: self._realloc(self._capacity << 1) - fn appendleft(inout self, owned value: ElementType): + fn appendleft(mut self, owned value: ElementType): """Appends a value to the left side of the deque. Args: @@ -525,7 +525,7 @@ struct Deque[ElementType: CollectionElement]( if self._head == self._tail: self._realloc(self._capacity << 1) - fn clear(inout self): + fn clear(mut self): """Removes all elements from the deque leaving it with length 0. Resets the underlying storage capacity to `_min_capacity`. @@ -561,7 +561,7 @@ struct Deque[ElementType: CollectionElement]( count += 1 return count - fn extend(inout self, owned values: List[ElementType]): + fn extend(mut self, owned values: List[ElementType]): """Extends the right side of the deque by consuming elements of the list argument. Args: @@ -593,7 +593,7 @@ struct Deque[ElementType: CollectionElement]( (src + i).move_pointee_into(self._data + self._tail) self._tail = self._physical_index(self._tail + 1) - fn extendleft(inout self, owned values: List[ElementType]): + fn extendleft(mut self, owned values: List[ElementType]): """Extends the left side of the deque by consuming elements from the list argument. Acts as series of left appends resulting in reversed order of elements in the list argument. @@ -676,7 +676,7 @@ struct Deque[ElementType: CollectionElement]( return idx raise "ValueError: Given element is not in deque" - fn insert(inout self, idx: Int, owned value: ElementType) raises: + fn insert(mut self, idx: Int, owned value: ElementType) raises: """Inserts the `value` into the deque at position `idx`. Args: @@ -723,10 +723,7 @@ struct Deque[ElementType: CollectionElement]( fn remove[ EqualityElementType: EqualityComparableCollectionElement, // - ]( - inout self: Deque[EqualityElementType], - value: EqualityElementType, - ) raises: + ](mut self: Deque[EqualityElementType], value: EqualityElementType,) raises: """Removes the first occurrence of the `value`. Parameters: @@ -797,7 +794,7 @@ struct Deque[ElementType: CollectionElement]( return (self._data + self._head)[] - fn pop(inout self) raises -> ElementType as element: + fn pop(mut self) raises -> ElementType as element: """Removes and returns the element from the right side of the deque. Returns: @@ -821,7 +818,7 @@ struct Deque[ElementType: CollectionElement]( return - fn popleft(inout self) raises -> ElementType as element: + fn popleft(mut self) raises -> ElementType as element: """Removes and returns the element from the left side of the deque. Returns: @@ -845,7 +842,7 @@ struct Deque[ElementType: CollectionElement]( return - fn reverse(inout self): + fn reverse(mut self): """Reverses the elements of the deque in-place.""" last = self._head + len(self) - 1 for i in range(len(self) // 2): @@ -855,7 +852,7 @@ struct Deque[ElementType: CollectionElement]( (self._data + src).move_pointee_into(self._data + dst) (self._data + src).init_pointee_move(tmp^) - fn rotate(inout self, n: Int = 1): + fn rotate(mut self, n: Int = 1): """Rotates the deque by `n` steps. If `n` is positive, rotates to the right. @@ -932,7 +929,7 @@ struct Deque[ElementType: CollectionElement]( """ return logical_index & (self._capacity - 1) - fn _prepare_for_new_elements(inout self, n_total: Int, n_retain: Int): + fn _prepare_for_new_elements(mut self, n_total: Int, n_retain: Int): """Prepares the deque’s internal buffer for adding new elements by reallocating memory and retaining the specified number of existing elements. @@ -958,7 +955,7 @@ struct Deque[ElementType: CollectionElement]( self._head = 0 self._tail = n_retain - fn _realloc(inout self, new_capacity: Int): + fn _realloc(mut self, new_capacity: Int): """Relocates data to a new storage buffer. Args: @@ -1018,7 +1015,7 @@ struct _DequeIter[ fn __iter__(self) -> Self: return self - fn __next__(inout self) -> Pointer[ElementType, deque_lifetime]: + fn __next__(mut self) -> Pointer[ElementType, deque_lifetime]: @parameter if forward: self.index += 1 diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 0e545e8a87..d35229a17e 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -79,7 +79,7 @@ struct _DictEntryIter[ var src: Pointer[Dict[K, V], dict_origin] fn __init__( - inout self, index: Int, seen: Int, ref [dict_origin]dict: Dict[K, V] + mut self, index: Int, seen: Int, ref [dict_origin]dict: Dict[K, V] ): self.index = index self.seen = seen @@ -90,7 +90,7 @@ struct _DictEntryIter[ @always_inline fn __next__( - inout self, + mut self, ) -> Pointer[DictEntry[K, V], __origin_of(self.src[]._entries[0].value())]: while True: var opt_entry_ref = Pointer.address_of( @@ -141,7 +141,7 @@ struct _DictKeyIter[ return self fn __next__( - inout self, + mut self, ) -> Pointer[K, __origin_of(self.iter.__next__()[].key)]: return Pointer.address_of(self.iter.__next__()[].key) @@ -187,7 +187,7 @@ struct _DictValueIter[ ) ) - fn __next__(inout self) -> Self.ref_type: + fn __next__(mut self) -> Self.ref_type: var entry_ref = self.iter.__next__() # Cast through a pointer to grant additional mutability because # _DictEntryIter.next erases it. @@ -331,7 +331,7 @@ struct _DictIndex: var data = self.data.bitcast[Int64]() return int(data.load(slot & (reserved - 1))) - fn set_index(inout self, reserved: Int, slot: Int, value: Int): + fn set_index(mut self, reserved: Int, slot: Int, value: Int): if reserved <= 128: var data = self.data.bitcast[Int8]() return data.store(slot & (reserved - 1), value) @@ -599,7 +599,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( """ return self._find_ref(key) - fn __setitem__(inout self, owned key: K, owned value: V): + fn __setitem__(mut self, owned key: K, owned value: V): """Set a value in the dictionary by key. Args: @@ -650,7 +650,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( result.update(other) return result^ - fn __ior__(inout self, other: Self): + fn __ior__(mut self, other: Self): """Merge self with other in place. Args: @@ -805,7 +805,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( """ return self.find(key).or_else(default) - fn pop(inout self, key: K, owned default: V) -> V: + fn pop(mut self, key: K, owned default: V) -> V: """Remove a value from the dictionary by key. Args: @@ -822,7 +822,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( except: return default - fn pop(inout self, key: K) raises -> V: + fn pop(mut self, key: K) raises -> V: """Remove a value from the dictionary by key. Args: @@ -850,7 +850,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( return entry_value^.reap_value() raise "KeyError" - fn popitem(inout self) raises -> DictEntry[K, V]: + fn popitem(mut self) raises -> DictEntry[K, V]: """Remove and return a (key, value) pair from the dictionary. Pairs are returned in LIFO order. popitem() is useful to destructively iterate over a dictionary, as often used in set algorithms. If the dictionary is empty, calling popitem() raises a KeyError. @@ -916,7 +916,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( """ return _DictEntryIter(0, 0, self) - fn update(inout self, other: Self, /): + fn update(mut self, other: Self, /): """Update the dictionary with the key/value pairs from other, overwriting existing keys. The argument must be positional only. @@ -926,7 +926,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( for entry in other.items(): self[entry[].key] = entry[].value - fn clear(inout self): + fn clear(mut self): """Remove all elements from the dictionary.""" self.size = 0 self._n_entries = 0 @@ -934,7 +934,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( self._index = _DictIndex(self._reserved()) fn setdefault( - inout self, key: K, owned default: V + mut self, key: K, owned default: V ) raises -> ref [self._find_ref(key)] V: """Get a value from the dictionary by key, or set it to a default if it doesn't exist. @@ -960,12 +960,12 @@ struct Dict[K: KeyElement, V: CollectionElement]( entries.append(None) return entries - fn _insert(inout self, owned key: K, owned value: V): + fn _insert(mut self, owned key: K, owned value: V): self._insert(DictEntry[K, V](key^, value^)) fn _insert[ safe_context: Bool = False - ](inout self, owned entry: DictEntry[K, V]): + ](mut self, owned entry: DictEntry[K, V]): @parameter if not safe_context: self._maybe_resize() @@ -983,10 +983,10 @@ struct Dict[K: KeyElement, V: CollectionElement]( fn _get_index(self, slot: Int) -> Int: return self._index.get_index(self._reserved(), slot) - fn _set_index(inout self, slot: Int, index: Int): + fn _set_index(mut self, slot: Int, index: Int): return self._index.set_index(self._reserved(), slot, index) - fn _next_index_slot(self, inout slot: Int, inout perturb: UInt64): + fn _next_index_slot(self, mut slot: Int, mut perturb: UInt64): alias PERTURB_SHIFT = 5 perturb >>= PERTURB_SHIFT slot = ((5 * slot) + int(perturb + 1)) & (self._reserved() - 1) @@ -1023,7 +1023,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( fn _over_compact_factor(self) -> Bool: return 4 * self._n_entries > 3 * self._reserved() - fn _maybe_resize(inout self): + fn _maybe_resize(mut self): if not self._over_load_factor(): if self._over_compact_factor(): self._compact() @@ -1040,7 +1040,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( if entry: self._insert[safe_context=True](entry.unsafe_take()) - fn _compact(inout self): + fn _compact(mut self): self._index = _DictIndex(self._reserved()) var right = 0 for left in range(self.size): @@ -1129,7 +1129,7 @@ struct OwnedKwargsDict[V: CollectionElement]( return self._dict[key] @always_inline - fn __setitem__(inout self, key: Self.key_type, value: V): + fn __setitem__(mut self, key: Self.key_type, value: V): """Set a value in the keyword dictionary by key. Args: @@ -1182,7 +1182,7 @@ struct OwnedKwargsDict[V: CollectionElement]( return self._dict.find(key) @always_inline - fn pop(inout self, key: self.key_type, owned default: V) -> V: + fn pop(mut self, key: self.key_type, owned default: V) -> V: """Remove a value from the dictionary by key. Args: @@ -1197,7 +1197,7 @@ struct OwnedKwargsDict[V: CollectionElement]( return self._dict.pop(key, default^) @always_inline - fn pop(inout self, key: self.key_type) raises -> V: + fn pop(mut self, key: self.key_type) raises -> V: """Remove a value from the dictionary by key. Args: @@ -1270,9 +1270,9 @@ struct OwnedKwargsDict[V: CollectionElement]( return _DictEntryIter(0, 0, self._dict) @always_inline - fn _insert(inout self, owned key: Self.key_type, owned value: V): + fn _insert(mut self, owned key: Self.key_type, owned value: V): self._dict._insert(key^, value^) @always_inline - fn _insert(inout self, key: StringLiteral, owned value: V): + fn _insert(mut self, key: StringLiteral, owned value: V): self._insert(String(key), value^) diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 9112e51818..bb8809612d 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -112,7 +112,7 @@ struct InlineArray[ ]() fn __init__( - inout self, + mut self, *, owned unsafe_assume_initialized: InlineArray[ UnsafeMaybeUninitialized[Self.ElementType], Self.size @@ -171,7 +171,7 @@ struct InlineArray[ @always_inline fn __init__( - inout self, + mut self, *, owned storage: VariadicListMem[Self.ElementType, _], ): diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index 01486e07ed..516be41287 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -54,7 +54,7 @@ struct _InlineListIter[ return self fn __next__( - inout self, + mut self, ) -> Pointer[T, __origin_of(self.src[][0])]: @parameter if forward: @@ -243,7 +243,7 @@ struct InlineList[ElementType: CollectionElementNew, capacity: Int = 16](Sized): count += int(rebind[C](e[]) == value) return count - fn append(inout self, owned value: ElementType): + fn append(mut self, owned value: ElementType): """Appends a value to the list. Args: diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index b3442b1228..19b477a47e 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -63,7 +63,7 @@ struct _ListIter[ return self fn __next__( - inout self, + mut self, ) -> Pointer[T, list_origin]: @parameter if forward: @@ -178,9 +178,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( for value in span: self.append(value[]) - fn __init__( - inout self, *, ptr: UnsafePointer[T], length: Int, capacity: Int - ): + fn __init__(mut self, *, ptr: UnsafePointer[T], length: Int, capacity: Int): """Constructs a list from a pointer, its length, and its capacity. Args: @@ -320,7 +318,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( result.__mul(x) return result^ - fn __imul__(inout self, x: Int): + fn __imul__(mut self, x: Int): """Multiplies the list by x in place. Args: @@ -341,7 +339,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( result.extend(other^) return result^ - fn __iadd__(inout self, owned other: Self): + fn __iadd__(mut self, owned other: Self): """Appends the elements of other into self. Args: @@ -420,7 +418,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( @no_inline fn write_to[ W: Writer, U: RepresentableCollectionElement, // - ](self: List[U, *_], inout writer: W): + ](self: List[U, *_], mut writer: W): """Write `my_list.__str__()` to a `Writer`. Parameters: @@ -477,7 +475,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( """ return len(self) * sizeof[T]() - fn _realloc(inout self, new_capacity: Int): + fn _realloc(mut self, new_capacity: Int): var new_data = UnsafePointer[T].alloc(new_capacity) _move_pointee_into_many_elements[hint_trivial_type]( @@ -491,7 +489,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( self.data = new_data self.capacity = new_capacity - fn append(inout self, owned value: T): + fn append(mut self, owned value: T): """Appends a value to this list. Args: @@ -502,7 +500,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( (self.data + self.size).init_pointee_move(value^) self.size += 1 - fn insert(inout self, i: Int, owned value: T): + fn insert(mut self, i: Int, owned value: T): """Inserts a value to the list at the given index. `a.insert(len(a), value)` is equivalent to `a.append(value)`. @@ -531,7 +529,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( earlier_idx -= 1 later_idx -= 1 - fn __mul(inout self, x: Int): + fn __mul(mut self, x: Int): """Appends the original elements of this list x-1 times. ```mojo @@ -550,7 +548,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( for i in range(x - 1): self.extend(orig) - fn extend(inout self, owned other: List[T, *_]): + fn extend(mut self, owned other: List[T, *_]): """Extends this list by consuming the elements of `other`. Args: @@ -590,7 +588,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # list. self.size = final_size - fn pop(inout self, i: Int = -1) -> T: + fn pop(mut self, i: Int = -1) -> T: """Pops a value from the list at the given index. Args: @@ -614,7 +612,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( self._realloc(self.capacity // 2) return ret_val^ - fn reserve(inout self, new_capacity: Int): + fn reserve(mut self, new_capacity: Int): """Reserves the requested capacity. If the current capacity is greater or equal, this is a no-op. @@ -627,7 +625,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( return self._realloc(new_capacity) - fn resize(inout self, new_size: Int, value: T): + fn resize(mut self, new_size: Int, value: T): """Resizes the list to the given new size. If the new size is smaller than the current one, elements at the end @@ -646,7 +644,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( (self.data + i).init_pointee_copy(value) self.size = new_size - fn resize(inout self, new_size: Int): + fn resize(mut self, new_size: Int): """Resizes the list to the given new size. With no new value provided, the new size must be smaller than or equal @@ -667,7 +665,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( self.size = new_size self.reserve(new_size) - fn reverse(inout self): + fn reverse(mut self): """Reverses the elements of the list.""" var earlier_idx = 0 @@ -744,13 +742,13 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( return i raise "ValueError: Given element is not in list" - fn clear(inout self): + fn clear(mut self): """Clears the elements in the list.""" for i in range(self.size): (self.data + i).destroy_pointee() self.size = 0 - fn steal_data(inout self) -> UnsafePointer[T]: + fn steal_data(mut self) -> UnsafePointer[T]: """Take ownership of the underlying pointer from the list. Returns: @@ -840,7 +838,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( return (self.data + idx)[] @always_inline - fn unsafe_set(inout self, idx: Int, owned value: T): + fn unsafe_set(mut self, idx: Int, owned value: T): """Write a value to a given location without checking index bounds. Users should consider using `my_list[idx] = value` instead of this method as it @@ -897,7 +895,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( count += 1 return count - fn swap_elements(inout self, elt_idx_1: Int, elt_idx_2: Int): + fn swap_elements(mut self, elt_idx_1: Int, elt_idx_2: Int): """Swaps elements at the specified indexes if they are different. ```mojo diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index eb019dd463..95c78a9706 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -281,7 +281,7 @@ struct Optional[T: CollectionElement]( fn write_to[ W: Writer, U: RepresentableCollectionElement, // - ](self: Optional[U], inout writer: W): + ](self: Optional[U], mut writer: W): """Write Optional string representation to a `Writer`. Parameters: @@ -333,7 +333,7 @@ struct Optional[T: CollectionElement]( debug_assert(self.__bool__(), ".value() on empty Optional") return self._value.unsafe_get[T]() - fn take(inout self) -> T: + fn take(mut self) -> T: """Move the value out of the Optional. The caller takes ownership over the new value, which is moved @@ -351,7 +351,7 @@ struct Optional[T: CollectionElement]( abort(".take() on empty Optional") return self.unsafe_take() - fn unsafe_take(inout self) -> T: + fn unsafe_take(mut self) -> T: """Unsafely move the value out of the Optional. The caller takes ownership over the new value, which is moved diff --git a/stdlib/src/collections/set.mojo b/stdlib/src/collections/set.mojo index 500e4c73b2..9c017dbfe8 100644 --- a/stdlib/src/collections/set.mojo +++ b/stdlib/src/collections/set.mojo @@ -149,7 +149,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): """ return self.intersection(other) - fn __iand__(inout self, other: Self): + fn __iand__(mut self, other: Self): """In-place set intersection. Updates the set to contain only the elements which are already in @@ -172,7 +172,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): """ return self.union(other) - fn __ior__(inout self, other: Self): + fn __ior__(mut self, other: Self): """In-place set union. Updates the set to contain all elements in the `other` set @@ -195,7 +195,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): """ return self.difference(other) - fn __isub__(inout self, other: Self): + fn __isub__(mut self, other: Self): """In-place set subtraction. Updates the set to remove any elements from the `other` set. @@ -260,7 +260,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): """ return self.symmetric_difference(other) - fn __ixor__(inout self, other: Self): + fn __ixor__(mut self, other: Self): """Overloads the ^= operator. Works like as `symmetric_difference_update` method. Updates the set with the symmetric difference of itself and another set. @@ -334,7 +334,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): fn write_to[ W: Writer, U: RepresentableKeyElement, - ](self: Set[U], inout writer: W): + ](self: Set[U], mut writer: W): """Write Set string representation to a `Writer`. Parameters: @@ -366,7 +366,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): # here we rely on Set being a trivial wrapper of a Dict return _DictKeyIter(_DictEntryIter(0, 0, self._data)) - fn add(inout self, t: T): + fn add(mut self, t: T): """Add an element to the set. Args: @@ -374,7 +374,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): """ self._data[t] = None - fn remove(inout self, t: T) raises: + fn remove(mut self, t: T) raises: """Remove an element from the set. Args: @@ -385,7 +385,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): """ self._data.pop(t) - fn pop(inout self) raises -> T: + fn pop(mut self) raises -> T: """Remove any one item from the set, and return it. As an implementation detail this will remove the first item @@ -454,7 +454,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): result.add(e[]) return result^ - fn update(inout self, other: Self): + fn update(mut self, other: Self): """In-place set update. Updates the set to contain all elements in the `other` set @@ -466,7 +466,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): for e in other: self.add(e[]) - fn intersection_update(inout self, other: Self): + fn intersection_update(mut self, other: Self): """In-place set intersection update. Updates the set by retaining only elements found in both this set and the `other` set, @@ -479,7 +479,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): # careful about concurrent iteration + mutation self.difference_update(self - other) - fn difference_update(inout self, other: Self): + fn difference_update(mut self, other: Self): """In-place set subtraction. Updates the set by removing all elements found in the `other` set, @@ -566,7 +566,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): return result^ - fn symmetric_difference_update(inout self, other: Self): + fn symmetric_difference_update(mut self, other: Self): """Updates the set with the symmetric difference of itself and another set. Args: @@ -574,7 +574,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): """ self = self.symmetric_difference(other) - fn discard(inout self, value: T): + fn discard(mut self, value: T): """Remove a value from the set if it exists. Pass otherwise. Args: @@ -585,7 +585,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): except: pass - fn clear(inout self): + fn clear(mut self): """Removes all elements from the set. This method modifies the set in-place, removing all of its elements. diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 1b38236b10..d75d2098cd 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -904,7 +904,7 @@ struct String( # Factory dunders # ===------------------------------------------------------------------=== # - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): """Write a byte span to this String. Args: @@ -913,7 +913,7 @@ struct String( """ self._iadd[False](bytes) - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): """Write a sequence of Writable arguments to the provided Writer. Parameters: @@ -1267,7 +1267,7 @@ struct String( """ return Self._add[True](other.as_bytes(), self.as_bytes()) - fn _iadd[has_null: Bool](inout self, other: Span[Byte]): + fn _iadd[has_null: Bool](mut self, other: Span[Byte]): var s_len = self.byte_length() var o_len = len(other) var o_ptr = other.unsafe_ptr() @@ -1288,7 +1288,7 @@ struct String( s_ptr[sum_len] = 0 @always_inline - fn __iadd__(inout self, other: String): + fn __iadd__(mut self, other: String): """Appends another string to this string. Args: @@ -1297,7 +1297,7 @@ struct String( self._iadd[True](other.as_bytes()) @always_inline - fn __iadd__(inout self, other: StringLiteral): + fn __iadd__(mut self, other: StringLiteral): """Appends another string literal to this string. Args: @@ -1306,7 +1306,7 @@ struct String( self._iadd[False](other.as_bytes()) @always_inline - fn __iadd__(inout self, other: StringSlice): + fn __iadd__(mut self, other: StringSlice): """Appends another string slice to this string. Args: @@ -1423,7 +1423,7 @@ struct String( # Methods # ===------------------------------------------------------------------=== # - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this string to the provided Writer. @@ -1622,7 +1622,7 @@ struct String( var length = len(self._buffer) return length - int(length > 0) - fn _steal_ptr(inout self) -> UnsafePointer[UInt8]: + fn _steal_ptr(mut self) -> UnsafePointer[UInt8]: """Transfer ownership of pointer to the underlying memory. The caller is responsible for freeing up the memory. @@ -2014,7 +2014,7 @@ struct String( """ return hash(self.as_string_slice()) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with the underlying bytes. Parameters: @@ -2309,7 +2309,7 @@ struct String( var result = String(buffer) return result^ - fn reserve(inout self, new_capacity: Int): + fn reserve(mut self, new_capacity: Int): """Reserves the requested capacity. Args: diff --git a/stdlib/src/collections/vector.mojo b/stdlib/src/collections/vector.mojo index 1dc60fcc76..1c9385c528 100644 --- a/stdlib/src/collections/vector.mojo +++ b/stdlib/src/collections/vector.mojo @@ -42,7 +42,7 @@ struct _VecIter[ var size: Int var vec: UnsafePointer[vec_type] - fn __next__(inout self) -> type: + fn __next__(mut self) -> type: self.i += 1 return deref(self.vec, self.i - 1) @@ -175,7 +175,7 @@ struct InlinedFixedVector[ self.dynamic_data = UnsafePointer[type]() @always_inline - fn append(inout self, value: type): + fn append(mut self, value: type): """Appends a value to this vector. Args: @@ -225,7 +225,7 @@ struct InlinedFixedVector[ return self.dynamic_data[normalized_idx - Self.static_size] @always_inline - fn __setitem__(inout self, idx: Int, value: type): + fn __setitem__(mut self, idx: Int, value: type): """Sets a vector element at the given index. Args: @@ -245,7 +245,7 @@ struct InlinedFixedVector[ else: self.dynamic_data[normalized_idx - Self.static_size] = value - fn clear(inout self): + fn clear(mut self): """Clears the elements in the vector.""" self.current_size = 0 @@ -255,7 +255,7 @@ struct InlinedFixedVector[ alias _iterator = _VecIter[type, Self, Self._deref_iter_impl] - fn __iter__(inout self) -> Self._iterator: + fn __iter__(mut self) -> Self._iterator: """Iterate over the vector. Returns: diff --git a/stdlib/src/hashlib/_ahash.mojo b/stdlib/src/hashlib/_ahash.mojo index 9adb7d08e0..56488c9a8d 100644 --- a/stdlib/src/hashlib/_ahash.mojo +++ b/stdlib/src/hashlib/_ahash.mojo @@ -110,7 +110,7 @@ struct AHasher[key: U256](_Hasher): self.extra_keys = U128(pi_key[2], pi_key[3]) @always_inline - fn _update(inout self, new_data: UInt64): + fn _update(mut self, new_data: UInt64): """Update the buffer value with new data. Args: @@ -119,7 +119,7 @@ struct AHasher[key: U256](_Hasher): self.buffer = _folded_multiply(new_data ^ self.buffer, MULTIPLE) @always_inline - fn _large_update(inout self, new_data: U128): + fn _large_update(mut self, new_data: U128): """Update the buffer value with new data. Args: @@ -129,7 +129,7 @@ struct AHasher[key: U256](_Hasher): var combined = _folded_multiply(xored[0], xored[1]) self.buffer = rotate_bits_left[ROT]((self.buffer + self.pad) ^ combined) - fn _update_with_bytes(inout self, data: UnsafePointer[UInt8], length: Int): + fn _update_with_bytes(mut self, data: UnsafePointer[UInt8], length: Int): """Consume provided data to update the internal buffer. Args: @@ -160,7 +160,7 @@ struct AHasher[key: U256](_Hasher): var value = _read_small(data, length) self._large_update(value) - fn _update_with_simd(inout self, new_data: SIMD[_, _]): + fn _update_with_simd(mut self, new_data: SIMD[_, _]): """Update the buffer value with new data. Args: @@ -182,7 +182,7 @@ struct AHasher[key: U256](_Hasher): for i in range(0, v64.size, 2): self._large_update(U128(v64[i], v64[i + 1])) - fn update[T: _HashableWithHasher](inout self, value: T): + fn update[T: _HashableWithHasher](mut self, value: T): """Update the buffer value with new hashable value. Args: diff --git a/stdlib/src/hashlib/_hasher.mojo b/stdlib/src/hashlib/_hasher.mojo index e29257a9e0..f30e4de21a 100644 --- a/stdlib/src/hashlib/_hasher.mojo +++ b/stdlib/src/hashlib/_hasher.mojo @@ -17,7 +17,7 @@ from ._ahash import AHasher trait _HashableWithHasher: - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): ... @@ -25,13 +25,13 @@ trait _Hasher: fn __init__(out self): ... - fn _update_with_bytes(inout self, data: UnsafePointer[UInt8], length: Int): + fn _update_with_bytes(mut self, data: UnsafePointer[UInt8], length: Int): ... - fn _update_with_simd(inout self, value: SIMD[_, _]): + fn _update_with_simd(mut self, value: SIMD[_, _]): ... - fn update[T: _HashableWithHasher](inout self, value: T): + fn update[T: _HashableWithHasher](mut self, value: T): ... fn finish(owned self) -> UInt64: diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 98ff2cca07..0c06a6b00a 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -1108,7 +1108,7 @@ fn iota[ buff.store(i, i + offset) -fn iota[type: DType, //](inout v: List[Scalar[type], *_], offset: Int = 0): +fn iota[type: DType, //](mut v: List[Scalar[type], *_], offset: Int = 0): """Fill a list with consecutive numbers starting from the specified offset. Parameters: @@ -1121,7 +1121,7 @@ fn iota[type: DType, //](inout v: List[Scalar[type], *_], offset: Int = 0): iota(v.data, len(v), offset) -fn iota(inout v: List[Int, *_], offset: Int = 0): +fn iota(mut v: List[Int, *_], offset: Int = 0): """Fill a list with consecutive numbers starting from the specified offset. Args: diff --git a/stdlib/src/memory/arc.mojo b/stdlib/src/memory/arc.mojo index 37c03786f7..d7b236af72 100644 --- a/stdlib/src/memory/arc.mojo +++ b/stdlib/src/memory/arc.mojo @@ -60,11 +60,11 @@ struct _ArcPointerInner[T: Movable]: self.refcount = Scalar[DType.uint64](1) self.payload = value^ - fn add_ref(inout self): + fn add_ref(mut self): """Atomically increment the refcount.""" _ = self.refcount.fetch_add(1) - fn drop_ref(inout self) -> Bool: + fn drop_ref(mut self) -> Bool: """Atomically decrement the refcount and return true if the result hits zero.""" return self.refcount.fetch_sub(1) == 1 diff --git a/stdlib/src/memory/maybe_uninitialized.mojo b/stdlib/src/memory/maybe_uninitialized.mojo index 20901a6fff..8d224bdd88 100644 --- a/stdlib/src/memory/maybe_uninitialized.mojo +++ b/stdlib/src/memory/maybe_uninitialized.mojo @@ -62,7 +62,7 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): fn __init__[ MovableType: Movable ]( - inout self: UnsafeMaybeUninitialized[MovableType], + mut self: UnsafeMaybeUninitialized[MovableType], owned value: MovableType, ): """The memory is now considered initialized. @@ -98,7 +98,7 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): fn copy_from[ CopyableType: ExplicitlyCopyable ]( - inout self: UnsafeMaybeUninitialized[CopyableType], + mut self: UnsafeMaybeUninitialized[CopyableType], other: UnsafeMaybeUninitialized[CopyableType], ): """Copy another object. @@ -117,7 +117,7 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): @always_inline fn copy_from[ CopyableType: ExplicitlyCopyable - ](inout self: UnsafeMaybeUninitialized[CopyableType], other: CopyableType): + ](mut self: UnsafeMaybeUninitialized[CopyableType], other: CopyableType): """Copy another object. This function assumes that the current memory is uninitialized. @@ -152,8 +152,8 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): fn move_from[ MovableType: Movable ]( - inout self: UnsafeMaybeUninitialized[MovableType], - inout other: UnsafeMaybeUninitialized[MovableType], + mut self: UnsafeMaybeUninitialized[MovableType], + mut other: UnsafeMaybeUninitialized[MovableType], ): """Move another object. @@ -174,7 +174,7 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): fn move_from[ MovableType: Movable ]( - inout self: UnsafeMaybeUninitialized[MovableType], + mut self: UnsafeMaybeUninitialized[MovableType], other: UnsafePointer[MovableType], ): """Move another object. @@ -196,7 +196,7 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): fn write[ MovableType: Movable ]( - inout self: UnsafeMaybeUninitialized[MovableType], + mut self: UnsafeMaybeUninitialized[MovableType], owned value: MovableType, ): """Write a value into an uninitialized memory location. @@ -235,7 +235,7 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): return UnsafePointer.address_of(self._array).bitcast[Self.ElementType]() @always_inline - fn assume_initialized_destroy(inout self): + fn assume_initialized_destroy(mut self): """Runs the destructor of the internal value. Calling this method assumes that the memory is initialized. diff --git a/stdlib/src/memory/owned_pointer.mojo b/stdlib/src/memory/owned_pointer.mojo index 4b22f14066..3974daa99f 100644 --- a/stdlib/src/memory/owned_pointer.mojo +++ b/stdlib/src/memory/owned_pointer.mojo @@ -31,7 +31,7 @@ struct OwnedPointer[T: AnyType]: # Life cycle methods # ===-------------------------------------------------------------------===# - fn __init__[T: Movable](inout self: OwnedPointer[T], owned value: T): + fn __init__[T: Movable](mut self: OwnedPointer[T], owned value: T): """Construct a new OwnedPointer[] by moving the passed value into a new backing allocation. Parameters: @@ -45,7 +45,7 @@ struct OwnedPointer[T: AnyType]: fn __init__[ T: ExplicitlyCopyable - ](inout self: OwnedPointer[T], *, copy_value: T): + ](mut self: OwnedPointer[T], *, copy_value: T): """Construct a new OwnedPointer[] by explicitly copying the passed value into a new backing allocation. Parameters: @@ -59,7 +59,7 @@ struct OwnedPointer[T: AnyType]: fn __init__[ T: Copyable, U: NoneType = None - ](inout self: OwnedPointer[T], value: T): + ](mut self: OwnedPointer[T], value: T): """Construct a new OwnedPointer[] by copying the passed value into a new backing allocation. Parameters: @@ -74,7 +74,7 @@ struct OwnedPointer[T: AnyType]: fn __init__[ T: ExplicitlyCopyable - ](inout self: OwnedPointer[T], *, other: OwnedPointer[T],): + ](mut self: OwnedPointer[T], *, other: OwnedPointer[T],): """Construct a new OwnedPointer[] by explicitly copying the value from another OwnedPointer[]. Parameters: diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index d95a8c1141..4fa37a6e61 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -274,7 +274,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): return String.write(self) @always_inline("nodebug") - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats the address space to the provided Writer. diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 9c1a6eac92..3f8db596af 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -272,7 +272,7 @@ struct UnsafePointer[ return self + (-1 * Int(offset.__mlir_index__())) @always_inline - fn __iadd__[T: IntLike, //](inout self, offset: T): + fn __iadd__[T: IntLike, //](mut self, offset: T): """Add an offset to this pointer. Parameters: @@ -284,7 +284,7 @@ struct UnsafePointer[ self = self + offset @always_inline - fn __isub__[T: IntLike, //](inout self, offset: T): + fn __isub__[T: IntLike, //](mut self, offset: T): """Subtract an offset from this pointer. Parameters: @@ -420,7 +420,7 @@ struct UnsafePointer[ return hex(int(self)) @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this pointer address to the provided Writer. diff --git a/stdlib/src/os/atomic.mojo b/stdlib/src/os/atomic.mojo index 28980d33e9..e2b8f1c65e 100644 --- a/stdlib/src/os/atomic.mojo +++ b/stdlib/src/os/atomic.mojo @@ -53,7 +53,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: self.value = value @always_inline - fn load(inout self) -> Scalar[type]: + fn load(mut self) -> Scalar[type]: """Loads the current value from the atomic. Returns: @@ -92,7 +92,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: ) @always_inline - fn fetch_add(inout self, rhs: Scalar[type]) -> Scalar[type]: + fn fetch_add(mut self, rhs: Scalar[type]) -> Scalar[type]: """Performs atomic in-place add. Atomically replaces the current value with the result of arithmetic @@ -111,7 +111,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: return Self._fetch_add(value_addr, rhs) @always_inline - fn __iadd__(inout self, rhs: Scalar[type]): + fn __iadd__(mut self, rhs: Scalar[type]): """Performs atomic in-place add. Atomically replaces the current value with the result of arithmetic @@ -126,7 +126,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: _ = self.fetch_add(rhs) @always_inline - fn fetch_sub(inout self, rhs: Scalar[type]) -> Scalar[type]: + fn fetch_sub(mut self, rhs: Scalar[type]) -> Scalar[type]: """Performs atomic in-place sub. Atomically replaces the current value with the result of arithmetic @@ -150,7 +150,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: ](value_addr.address, rhs.value) @always_inline - fn __isub__(inout self, rhs: Scalar[type]): + fn __isub__(mut self, rhs: Scalar[type]): """Performs atomic in-place sub. Atomically replaces the current value with the result of arithmetic @@ -166,7 +166,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: @always_inline fn compare_exchange_weak( - inout self, inout expected: Scalar[type], desired: Scalar[type] + mut self, mut expected: Scalar[type], desired: Scalar[type] ) -> Bool: """Atomically compares the self value with that of the expected value. If the values are equal, then the self value is replaced with the @@ -224,7 +224,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: _max_impl[scope=scope](ptr, rhs) @always_inline - fn max(inout self, rhs: Scalar[type]): + fn max(mut self, rhs: Scalar[type]): """Performs atomic in-place max. Atomically replaces the current value with the result of max of the @@ -264,7 +264,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: _min_impl[scope=scope](ptr, rhs) @always_inline - fn min(inout self, rhs: Scalar[type]): + fn min(mut self, rhs: Scalar[type]): """Performs atomic in-place min. Atomically replaces the current value with the result of min of the @@ -294,7 +294,7 @@ fn _compare_exchange_weak_integral_impl[ type: DType, //, *, scope: StringLiteral ]( value_addr: UnsafePointer[Scalar[type], **_], - inout expected: Scalar[type], + mut expected: Scalar[type], desired: Scalar[type], ) -> Bool: constrained[type.is_integral(), "the input type must be integral"]() diff --git a/stdlib/src/os/fstat.mojo b/stdlib/src/os/fstat.mojo index ba298916d4..83e493bc7a 100644 --- a/stdlib/src/os/fstat.mojo +++ b/stdlib/src/os/fstat.mojo @@ -96,7 +96,7 @@ struct stat_result(Stringable, Writable): """User defined flags for file.""" fn __init__( - inout self, + mut self, /, *, st_mode: Int, @@ -151,7 +151,7 @@ struct stat_result(Stringable, Writable): self.st_flags = st_flags @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this path to the provided Writer. diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index 5e47d0d846..c6e9016b5e 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -124,7 +124,7 @@ struct Path( res /= suffix return res - fn __itruediv__(inout self, suffix: String): + fn __itruediv__(mut self, suffix: String): """Joins two paths using the system-defined path separator. Args: @@ -153,7 +153,7 @@ struct Path( """ return self.path.byte_length() > 0 - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this path to the provided Writer. @@ -225,7 +225,7 @@ struct Path( return hash(self.path) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with the path string value. Parameters: diff --git a/stdlib/src/pwd/pwd.mojo b/stdlib/src/pwd/pwd.mojo index a6342d42b4..105b26e7f2 100644 --- a/stdlib/src/pwd/pwd.mojo +++ b/stdlib/src/pwd/pwd.mojo @@ -40,7 +40,7 @@ struct Passwd(Stringable): var pw_shell: String """Shell program.""" - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Formats this string to the provided Writer. Parameters: diff --git a/stdlib/src/python/_bindings.mojo b/stdlib/src/python/_bindings.mojo index 2921ebb3bd..f5e14cd329 100644 --- a/stdlib/src/python/_bindings.mojo +++ b/stdlib/src/python/_bindings.mojo @@ -33,13 +33,13 @@ from python.python import _get_global_python_itf trait ConvertibleFromPython(CollectionElement): - """Denotes a type that can attempt construction from a borrowed Python + """Denotes a type that can attempt construction from a read-only Python object. """ @staticmethod fn try_from_python(obj: PythonObject) raises -> Self: - """Attempt to construct an instance of this object from a borrowed + """Attempt to construct an instance of this object from a read-only Python value. Args: @@ -113,7 +113,7 @@ fn python_type_object[ basicsize: sizeof[PyMojoObject[T]](), itemsize: 0, flags: Py_TPFLAGS_DEFAULT, - # Note: This pointer is only "borrowed" by PyType_FromSpec. + # Note: This pointer is only "read-only" by PyType_FromSpec. slots: slots.unsafe_ptr(), } @@ -138,7 +138,7 @@ fn python_type_object[ # # The latter is the C function signature that the CPython API expects a # PyObject initializer function to have. The former is an unsafe form of the -# `fn(inout self)` signature that Mojo types with default constructors provide. +# `fn(mut self)` signature that Mojo types with default constructors provide. # # To support CPython calling a Mojo types default constructor, we need to # provide a wrapper function (around the Mojo constructor) that has the @@ -221,22 +221,22 @@ fn py_c_function_wrapper[ # > When a C function is called from Python, it borrows references to its # > arguments from the caller. The caller owns a reference to the object, - # > so the borrowed reference’s lifetime is guaranteed until the function - # > returns. Only when such a borrowed reference must be stored or passed + # > so the read-only reference’s lifetime is guaranteed until the function + # > returns. Only when such a read-only reference must be stored or passed # > on, it must be turned into an owned reference by calling Py_INCREF(). # > # > -- https://docs.python.org/3/extending/extending.html#ownership-rules # SAFETY: # Here we illegally (but carefully) construct _owned_ `PythonObject` - # values from the borrowed object reference arguments. We are careful + # values from the read-only object reference arguments. We are careful # down below to prevent the destructor for these objects from running # so that we do not illegally decrement the reference count of these # objects we do not own. # - # This is valid to do, because these are passed using the `borrowed` + # This is valid to do, because these are passed using the `read-only` # argument convention to `user_func`, so logically they are treated - # as Python borrowed references. + # as Python read-only references. var py_self = PythonObject(py_self_ptr) var args = TypedPythonObject["Tuple"]( unsafe_unchecked_from=PythonObject(args_ptr) diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index defbd9d4b9..4a28b2d117 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -492,7 +492,7 @@ struct PyObject(Stringable, Representable, Writable): # Methods # ===-------------------------------------------------------------------===# - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Formats to the provided Writer. Parameters: @@ -580,7 +580,7 @@ struct PyModuleDef_Base(Stringable, Representable, Writable): # Methods # ===-------------------------------------------------------------------===# - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Formats to the provided Writer. Parameters: @@ -700,7 +700,7 @@ struct PyModuleDef(Stringable, Representable, Writable): # Methods # ===-------------------------------------------------------------------===# - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Formats to the provided Writer. Parameters: @@ -797,7 +797,7 @@ struct CPython: pass @staticmethod - fn destroy(inout existing: CPython): + fn destroy(mut existing: CPython): if existing.logging_enabled: print("CPython destroy") var remaining_refs = existing.total_ref_count.take_pointee() @@ -861,15 +861,15 @@ struct CPython: # Reference count management # ===-------------------------------------------------------------------===# - fn _inc_total_rc(inout self): + fn _inc_total_rc(mut self): var v = self.total_ref_count.take_pointee() self.total_ref_count.init_pointee_move(v + 1) - fn _dec_total_rc(inout self): + fn _dec_total_rc(mut self): var v = self.total_ref_count.take_pointee() self.total_ref_count.init_pointee_move(v - 1) - fn Py_IncRef(inout self, ptr: PyObjectPtr): + fn Py_IncRef(mut self, ptr: PyObjectPtr): """[Reference]( https://docs.python.org/3/c-api/refcounting.html#c.Py_IncRef). """ @@ -879,7 +879,7 @@ struct CPython: self.lib.call["Py_IncRef"](ptr) self._inc_total_rc() - fn Py_DecRef(inout self, ptr: PyObjectPtr): + fn Py_DecRef(mut self, ptr: PyObjectPtr): """[Reference]( https://docs.python.org/3/c-api/refcounting.html#c.Py_DecRef). """ @@ -893,7 +893,7 @@ struct CPython: # have to always be the case - but often it is and it's convenient for # debugging. We shouldn't rely on this function anywhere - its only purpose # is debugging. - fn _Py_REFCNT(inout self, ptr: PyObjectPtr) -> Int: + fn _Py_REFCNT(mut self, ptr: PyObjectPtr) -> Int: if ptr._get_ptr_as_int() == 0: return -1 # NOTE: @@ -915,19 +915,19 @@ struct CPython: # Python GIL and threading # ===-------------------------------------------------------------------===# - fn PyGILState_Ensure(inout self) -> PyGILState_STATE: + fn PyGILState_Ensure(mut self) -> PyGILState_STATE: """[Reference]( https://docs.python.org/3/c-api/init.html#c.PyGILState_Ensure). """ return self.lib.call["PyGILState_Ensure", PyGILState_STATE]() - fn PyGILState_Release(inout self, state: PyGILState_STATE): + fn PyGILState_Release(mut self, state: PyGILState_STATE): """[Reference]( https://docs.python.org/3/c-api/init.html#c.PyGILState_Release). """ self.lib.call["PyGILState_Release"](state) - fn PyEval_SaveThread(inout self) -> UnsafePointer[PyThreadState]: + fn PyEval_SaveThread(mut self) -> UnsafePointer[PyThreadState]: """[Reference]( https://docs.python.org/3/c-api/init.html#c.PyEval_SaveThread). """ @@ -936,7 +936,7 @@ struct CPython: "PyEval_SaveThread", UnsafePointer[PyThreadState] ]() - fn PyEval_RestoreThread(inout self, state: UnsafePointer[PyThreadState]): + fn PyEval_RestoreThread(mut self, state: UnsafePointer[PyThreadState]): """[Reference]( https://docs.python.org/3/c-api/init.html#c.PyEval_RestoreThread). """ @@ -946,7 +946,7 @@ struct CPython: # Python Dict operations # ===-------------------------------------------------------------------===# - fn PyDict_New(inout self) -> PyObjectPtr: + fn PyDict_New(mut self) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/dict.html#c.PyDict_New). """ @@ -964,7 +964,7 @@ struct CPython: # int PyDict_SetItem(PyObject *p, PyObject *key, PyObject *val) fn PyDict_SetItem( - inout self, dict_obj: PyObjectPtr, key: PyObjectPtr, value: PyObjectPtr + mut self, dict_obj: PyObjectPtr, key: PyObjectPtr, value: PyObjectPtr ) -> c_int: """[Reference]( https://docs.python.org/3/c-api/dict.html#c.PyDict_SetItem). @@ -982,7 +982,7 @@ struct CPython: return r fn PyDict_GetItemWithError( - inout self, dict_obj: PyObjectPtr, key: PyObjectPtr + mut self, dict_obj: PyObjectPtr, key: PyObjectPtr ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/dict.html#c.PyDict_GetItemWithError). @@ -994,7 +994,7 @@ struct CPython: self.log("PyDict_GetItemWithError, key: ", key._get_ptr_as_int()) return r - fn PyDict_Check(inout self, maybe_dict: PyObjectPtr) -> Bool: + fn PyDict_Check(mut self, maybe_dict: PyObjectPtr) -> Bool: """[Reference]( https://docs.python.org/3/c-api/dict.html#c.PyDict_Check). """ @@ -1006,7 +1006,7 @@ struct CPython: self.Py_DecRef(my_type) return result - fn PyDict_Type(inout self) -> PyObjectPtr: + fn PyDict_Type(mut self) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/dict.html#c.PyDict_Type). """ @@ -1016,7 +1016,7 @@ struct CPython: # int PyDict_Next(PyObject *p, Py_ssize_t *ppos, PyObject **pkey, PyObject **pvalue) fn PyDict_Next( - inout self, dictionary: PyObjectPtr, p: Int + mut self, dictionary: PyObjectPtr, p: Int ) -> PyKeysValuePair: """[Reference]( https://docs.python.org/3/c-api/dict.html#c.PyDict_Next). @@ -1061,7 +1061,7 @@ struct CPython: # ===-------------------------------------------------------------------===# fn PyImport_ImportModule( - inout self, + mut self, name: StringRef, ) -> PyObjectPtr: """[Reference]( @@ -1081,7 +1081,7 @@ struct CPython: self._inc_total_rc() return r - fn PyImport_AddModule(inout self, name: StringRef) -> PyObjectPtr: + fn PyImport_AddModule(mut self, name: StringRef) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/import.html#c.PyImport_AddModule). """ @@ -1090,7 +1090,7 @@ struct CPython: ) fn PyModule_Create( - inout self, + mut self, name: String, ) -> PyObjectPtr: """[Reference]( @@ -1118,7 +1118,7 @@ struct CPython: ) fn PyModule_AddFunctions( - inout self, + mut self, mod: PyObjectPtr, functions: UnsafePointer[PyMethodDef], ) -> c_int: @@ -1128,7 +1128,7 @@ struct CPython: return self.lib.call["PyModule_AddFunctions", c_int](mod, functions) fn PyModule_AddObjectRef( - inout self, + mut self, module: PyObjectPtr, name: UnsafePointer[c_char], value: PyObjectPtr, @@ -1141,7 +1141,7 @@ struct CPython: module, name, value ) - fn PyModule_GetDict(inout self, name: PyObjectPtr) -> PyObjectPtr: + fn PyModule_GetDict(mut self, name: PyObjectPtr) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/module.html#c.PyModule_GetDict). """ @@ -1151,7 +1151,7 @@ struct CPython: # Python Type operations # ===-------------------------------------------------------------------===# - fn Py_TYPE(inout self, ob_raw: PyObjectPtr) -> UnsafePointer[PyTypeObject]: + fn Py_TYPE(mut self, ob_raw: PyObjectPtr) -> UnsafePointer[PyTypeObject]: """Get the PyTypeObject field of a Python object.""" # Note: @@ -1165,12 +1165,12 @@ struct CPython: return ob_raw.unsized_obj_ptr[].object_type fn PyType_GetName( - inout self, type: UnsafePointer[PyTypeObject] + mut self, type: UnsafePointer[PyTypeObject] ) -> PyObjectPtr: return self.lib.call["PyType_GetName", PyObjectPtr](type) fn PyType_FromSpec( - inout self, spec: UnsafePointer[PyType_Spec] + mut self, spec: UnsafePointer[PyType_Spec] ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/type.html#c.PyType_FromSpec). @@ -1178,7 +1178,7 @@ struct CPython: return self.lib.call["PyType_FromSpec", PyObjectPtr](spec) fn PyType_GenericAlloc( - inout self, + mut self, type: UnsafePointer[PyTypeObject], nitems: Py_ssize_t, ) -> PyObjectPtr: @@ -1188,7 +1188,7 @@ struct CPython: # Python Evaluation # ===-------------------------------------------------------------------===# - fn PyRun_SimpleString(inout self, strref: StringRef) -> Bool: + fn PyRun_SimpleString(mut self, strref: StringRef) -> Bool: """Executes the given Python code. Args: @@ -1207,7 +1207,7 @@ struct CPython: ) fn PyRun_String( - inout self, + mut self, strref: StringRef, globals: PyObjectPtr, locals: PyObjectPtr, @@ -1234,7 +1234,7 @@ struct CPython: return result fn PyEval_EvalCode( - inout self, + mut self, co: PyObjectPtr, globals: PyObjectPtr, locals: PyObjectPtr, @@ -1248,14 +1248,14 @@ struct CPython: self._inc_total_rc() return result - fn PyEval_GetBuiltins(inout self) -> PyObjectPtr: + fn PyEval_GetBuiltins(mut self) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/reflection.html#c.PyEval_GetBuiltins). """ return self.lib.call["PyEval_GetBuiltins", PyObjectPtr]() fn Py_CompileString( - inout self, + mut self, strref: StringRef, filename: StringRef, compile_mode: Int, @@ -1275,7 +1275,7 @@ struct CPython: # ===-------------------------------------------------------------------===# fn Py_Is( - inout self, + mut self, rhs: PyObjectPtr, lhs: PyObjectPtr, ) -> Bool: @@ -1289,7 +1289,7 @@ struct CPython: else: return rhs == lhs - fn PyObject_Type(inout self, obj: PyObjectPtr) -> PyObjectPtr: + fn PyObject_Type(mut self, obj: PyObjectPtr) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_Type). """ @@ -1298,7 +1298,7 @@ struct CPython: self._inc_total_rc() return p - fn PyObject_Str(inout self, obj: PyObjectPtr) -> PyObjectPtr: + fn PyObject_Str(mut self, obj: PyObjectPtr) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_Str). """ @@ -1308,7 +1308,7 @@ struct CPython: return p fn PyObject_GetItem( - inout self, obj: PyObjectPtr, key: PyObjectPtr + mut self, obj: PyObjectPtr, key: PyObjectPtr ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_GetItem). @@ -1330,7 +1330,7 @@ struct CPython: return r fn PyObject_SetItem( - inout self, obj: PyObjectPtr, key: PyObjectPtr, value: PyObjectPtr + mut self, obj: PyObjectPtr, key: PyObjectPtr, value: PyObjectPtr ) -> c_int: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_SetItem). @@ -1352,7 +1352,7 @@ struct CPython: return r fn PyObject_GetAttrString( - inout self, + mut self, obj: PyObjectPtr, name: StringRef, ) -> PyObjectPtr: @@ -1378,7 +1378,7 @@ struct CPython: return r fn PyObject_SetAttrString( - inout self, obj: PyObjectPtr, name: StringRef, new_value: PyObjectPtr + mut self, obj: PyObjectPtr, name: StringRef, new_value: PyObjectPtr ) -> c_int: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_SetAttrString). @@ -1402,7 +1402,7 @@ struct CPython: return r fn PyObject_CallObject( - inout self, + mut self, callable_obj: PyObjectPtr, args: PyObjectPtr, ) -> PyObjectPtr: @@ -1426,7 +1426,7 @@ struct CPython: return r fn PyObject_Call( - inout self, + mut self, callable_obj: PyObjectPtr, args: PyObjectPtr, kwargs: PyObjectPtr, @@ -1450,26 +1450,26 @@ struct CPython: self._inc_total_rc() return r - fn PyObject_IsTrue(inout self, obj: PyObjectPtr) -> c_int: + fn PyObject_IsTrue(mut self, obj: PyObjectPtr) -> c_int: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_IsTrue). """ return self.lib.call["PyObject_IsTrue", c_int](obj) - fn PyObject_Length(inout self, obj: PyObjectPtr) -> Int: + fn PyObject_Length(mut self, obj: PyObjectPtr) -> Int: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_Length). """ return int(self.lib.call["PyObject_Length", Int](obj)) - fn PyObject_Hash(inout self, obj: PyObjectPtr) -> Int: + fn PyObject_Hash(mut self, obj: PyObjectPtr) -> Int: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_Hash). """ return int(self.lib.call["PyObject_Hash", Int](obj)) fn PyObject_GetIter( - inout self, traversablePyObject: PyObjectPtr + mut self, traversablePyObject: PyObjectPtr ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_GetIter). @@ -1496,7 +1496,7 @@ struct CPython: # Python Tuple operations # ===-------------------------------------------------------------------===# - fn PyTuple_New(inout self, count: Int) -> PyObjectPtr: + fn PyTuple_New(mut self, count: Int) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/tuple.html#c.PyTuple_New). """ @@ -1515,7 +1515,7 @@ struct CPython: return r fn PyTuple_GetItem( - inout self, tuple: PyObjectPtr, pos: Py_ssize_t + mut self, tuple: PyObjectPtr, pos: Py_ssize_t ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/tuple.html#c.PyTuple_GetItem). @@ -1523,7 +1523,7 @@ struct CPython: return self.lib.call["PyTuple_GetItem", PyObjectPtr](tuple, pos) fn PyTuple_SetItem( - inout self, tuple_obj: PyObjectPtr, index: Int, element: PyObjectPtr + mut self, tuple_obj: PyObjectPtr, index: Int, element: PyObjectPtr ) -> c_int: """[Reference]( https://docs.python.org/3/c-api/tuple.html#c.PyTuple_SetItem). @@ -1540,7 +1540,7 @@ struct CPython: # Python List operations # ===-------------------------------------------------------------------===# - fn PyList_New(inout self, length: Int) -> PyObjectPtr: + fn PyList_New(mut self, length: Int) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/list.html#c.PyList_New). """ @@ -1559,7 +1559,7 @@ struct CPython: return r fn PyList_SetItem( - inout self, list_obj: PyObjectPtr, index: Int, value: PyObjectPtr + mut self, list_obj: PyObjectPtr, index: Int, value: PyObjectPtr ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/list.html#c.PyList_SetItem). @@ -1573,7 +1573,7 @@ struct CPython: ) fn PyList_GetItem( - inout self, list_obj: PyObjectPtr, index: Int + mut self, list_obj: PyObjectPtr, index: Int ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/list.html#c.PyList_GetItem). @@ -1585,7 +1585,7 @@ struct CPython: # ref: https://docs.python.org/3/c-api/concrete.html # ===-------------------------------------------------------------------===# - fn Py_None(inout self) -> PyObjectPtr: + fn Py_None(mut self) -> PyObjectPtr: """Get a None value, of type NoneType. [Reference]( https://docs.python.org/3/c-api/none.html#c.Py_None).""" @@ -1607,7 +1607,7 @@ struct CPython: # Boolean Objects # ===-------------------------------------------------------------------===# - fn PyBool_FromLong(inout self, value: c_long) -> PyObjectPtr: + fn PyBool_FromLong(mut self, value: c_long) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/bool.html#c.PyBool_FromLong). """ @@ -1629,7 +1629,7 @@ struct CPython: # Integer Objects # ===-------------------------------------------------------------------===# - fn PyLong_FromSsize_t(inout self, value: c_ssize_t) -> PyObjectPtr: + fn PyLong_FromSsize_t(mut self, value: c_ssize_t) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/long.html#c.PyLong_FromSsize_t). """ @@ -1647,7 +1647,7 @@ struct CPython: self._inc_total_rc() return r - fn PyLong_FromSize_t(inout self, value: c_size_t) -> PyObjectPtr: + fn PyLong_FromSize_t(mut self, value: c_size_t) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/long.html#c.PyLong_FromSize_t). """ @@ -1665,7 +1665,7 @@ struct CPython: self._inc_total_rc() return r - fn PyLong_AsSsize_t(inout self, py_object: PyObjectPtr) -> c_ssize_t: + fn PyLong_AsSsize_t(mut self, py_object: PyObjectPtr) -> c_ssize_t: """[Reference]( https://docs.python.org/3/c-api/long.html#c.PyLong_AsSsize_t). """ @@ -1675,7 +1675,7 @@ struct CPython: # Floating-Point Objects # ===-------------------------------------------------------------------===# - fn PyFloat_FromDouble(inout self, value: Float64) -> PyObjectPtr: + fn PyFloat_FromDouble(mut self, value: Float64) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/float.html#c.PyFloat_FromDouble). """ @@ -1693,7 +1693,7 @@ struct CPython: self._inc_total_rc() return r - fn PyFloat_AsDouble(inout self, py_object: PyObjectPtr) -> Float64: + fn PyFloat_AsDouble(mut self, py_object: PyObjectPtr) -> Float64: """[Reference]( https://docs.python.org/3/c-api/float.html#c.PyFloat_AsDouble). """ @@ -1703,7 +1703,7 @@ struct CPython: # Unicode Objects # ===-------------------------------------------------------------------===# - fn PyUnicode_DecodeUTF8(inout self, strref: StringRef) -> PyObjectPtr: + fn PyUnicode_DecodeUTF8(mut self, strref: StringRef) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/unicode.html#c.PyUnicode_DecodeUTF8). """ @@ -1725,7 +1725,7 @@ struct CPython: self._inc_total_rc() return r - fn PyUnicode_DecodeUTF8(inout self, strslice: StringSlice) -> PyObjectPtr: + fn PyUnicode_DecodeUTF8(mut self, strslice: StringSlice) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/unicode.html#c.PyUnicode_DecodeUTF8). """ @@ -1746,7 +1746,7 @@ struct CPython: self._inc_total_rc() return r - fn PySlice_FromSlice(inout self, slice: Slice) -> PyObjectPtr: + fn PySlice_FromSlice(mut self, slice: Slice) -> PyObjectPtr: # Convert Mojo Slice to Python slice parameters # Note: Deliberately avoid using `span.indices()` here and instead pass # the Slice parameters directly to Python. Python's C implementation @@ -1773,7 +1773,7 @@ struct CPython: return py_slice - fn PyUnicode_AsUTF8AndSize(inout self, py_object: PyObjectPtr) -> StringRef: + fn PyUnicode_AsUTF8AndSize(mut self, py_object: PyObjectPtr) -> StringRef: """[Reference]( https://docs.python.org/3/c-api/unicode.html#c.PyUnicode_AsUTF8AndSize). """ @@ -1788,19 +1788,19 @@ struct CPython: # Python Error operations # ===-------------------------------------------------------------------===# - fn PyErr_Clear(inout self): + fn PyErr_Clear(mut self): """[Reference]( https://docs.python.org/3/c-api/exceptions.html#c.PyErr_Clear). """ self.lib.call["PyErr_Clear"]() - fn PyErr_Occurred(inout self) -> Bool: + fn PyErr_Occurred(mut self) -> Bool: """[Reference]( https://docs.python.org/3/c-api/exceptions.html#c.PyErr_Occurred). """ return not self.lib.call["PyErr_Occurred", PyObjectPtr]().is_null() - fn PyErr_Fetch(inout self) -> PyObjectPtr: + fn PyErr_Fetch(mut self) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/exceptions.html#c.PyErr_Fetch). """ @@ -1827,14 +1827,14 @@ struct CPython: _ = traceback return r - fn PyErr_SetNone(inout self, type: PyObjectPtr): + fn PyErr_SetNone(mut self, type: PyObjectPtr): """[Reference]( https://docs.python.org/3/c-api/exceptions.html#c.PyErr_SetNone). """ self.lib.call["PyErr_SetNone"](type) fn PyErr_SetString( - inout self, + mut self, type: PyObjectPtr, message: UnsafePointer[c_char], ): @@ -1848,10 +1848,10 @@ struct CPython: # ===-------------------------------------------------------------------===# fn get_error_global( - inout self, + mut self, global_name: StringLiteral, ) -> PyObjectPtr: - """Get a Python borrowed reference to the specified global exception + """Get a Python read-only reference to the specified global exception object. """ @@ -1872,7 +1872,7 @@ struct CPython: # Python Iterator operations # ===-------------------------------------------------------------------===# - fn PyIter_Next(inout self, iterator: PyObjectPtr) -> PyObjectPtr: + fn PyIter_Next(mut self, iterator: PyObjectPtr) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/iter.html#c.PyIter_Next). """ @@ -1893,13 +1893,13 @@ struct CPython: self._inc_total_rc() return next_obj - fn PyIter_Check(inout self, obj: PyObjectPtr) -> Bool: + fn PyIter_Check(mut self, obj: PyObjectPtr) -> Bool: """[Reference]( https://docs.python.org/3/c-api/iter.html#c.PyIter_Check). """ return self.lib.call["PyIter_Check", c_int](obj) != 0 - fn PySequence_Check(inout self, obj: PyObjectPtr) -> Bool: + fn PySequence_Check(mut self, obj: PyObjectPtr) -> Bool: """[Reference]( https://docs.python.org/3/c-api/sequence.html#c.PySequence_Check). """ @@ -1910,7 +1910,7 @@ struct CPython: # ===-------------------------------------------------------------------===# fn PySlice_New( - inout self, start: PyObjectPtr, stop: PyObjectPtr, step: PyObjectPtr + mut self, start: PyObjectPtr, stop: PyObjectPtr, step: PyObjectPtr ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/slice.html#c.PySlice_New). diff --git a/stdlib/src/python/python.mojo b/stdlib/src/python/python.mojo index 80437fc9e2..8771e54c6f 100644 --- a/stdlib/src/python/python.mojo +++ b/stdlib/src/python/python.mojo @@ -47,10 +47,10 @@ fn _init_python_global() -> _PythonGlobal: struct _PythonGlobal: var cpython: CPython - fn __moveinit__(inout self, owned other: Self): + fn __moveinit__(mut self, owned other: Self): self.cpython = other.cpython^ - fn __init__(inout self): + fn __init__(mut self): self.cpython = CPython() fn __del__(owned self): @@ -99,7 +99,7 @@ struct Python: """ self.impl = existing.impl - fn eval(inout self, code: StringRef) -> Bool: + fn eval(mut self, code: StringRef) -> Bool: """Executes the given Python code. Args: @@ -127,7 +127,7 @@ struct Python: `PythonObject` containing the result of the evaluation. """ var cpython = _get_global_python_itf().cpython() - # PyImport_AddModule returns a borrowed reference. + # PyImport_AddModule returns a read-only reference. var module = PythonObject.from_borrowed_ptr( cpython.PyImport_AddModule(name) ) @@ -265,7 +265,7 @@ struct Python: @staticmethod fn add_functions( - inout module: TypedPythonObject["Module"], + mut module: TypedPythonObject["Module"], owned functions: List[PyMethodDef], ) raises: """Adds functions to a PyModule object. @@ -287,7 +287,7 @@ struct Python: @staticmethod fn unsafe_add_methods( - inout module: TypedPythonObject["Module"], + mut module: TypedPythonObject["Module"], functions: UnsafePointer[PyMethodDef], ) raises: """Adds methods to a PyModule object. @@ -314,7 +314,7 @@ struct Python: @staticmethod fn add_object( - inout module: TypedPythonObject["Module"], + mut module: TypedPythonObject["Module"], name: StringLiteral, value: PythonObject, ) raises: @@ -366,7 +366,7 @@ struct Python: return PythonObject([]) @no_inline - fn __str__(inout self, str_obj: PythonObject) -> StringRef: + fn __str__(mut self, str_obj: PythonObject) -> StringRef: """Return a string representing the given Python object. Args: @@ -379,7 +379,7 @@ struct Python: return cpython.PyUnicode_AsUTF8AndSize(str_obj.py_object) @staticmethod - fn throw_python_exception_if_error_state(inout cpython: CPython) raises: + fn throw_python_exception_if_error_state(mut cpython: CPython) raises: """Raise an exception if CPython interpreter is in an error state. Args: @@ -389,7 +389,7 @@ struct Python: raise Python.unsafe_get_python_exception(cpython) @staticmethod - fn unsafe_get_python_exception(inout cpython: CPython) -> Error: + fn unsafe_get_python_exception(mut cpython: CPython) -> Error: """Get the `Error` object corresponding to the current CPython interpreter error state. diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index a579696179..4566b11501 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -87,7 +87,7 @@ struct _PyIter(Sized): # Trait implementations # ===-------------------------------------------------------------------===# - fn __next__(inout self: _PyIter) -> PythonObject: + fn __next__(mut self: _PyIter) -> PythonObject: """Return the next item and update to point to subsequent item. Returns: @@ -222,7 +222,7 @@ struct TypedPythonObject[type_hint: StringLiteral]( raise Python.unsafe_get_python_exception(cpython) # TODO(MSTDL-911): Avoid unnecessary owned reference counts when - # returning borrowed PythonObject values. + # returning read-only PythonObject values. return PythonObject.from_borrowed_ptr(item) @@ -276,7 +276,7 @@ struct PythonObject( @staticmethod fn from_borrowed_ptr(borrowed_ptr: PyObjectPtr) -> Self: - """Initialize this object from a borrowed reference-counted Python + """Initialize this object from a read-only reference-counted Python object pointer. The reference count of the pointee object will be incremented, and @@ -294,7 +294,7 @@ struct PythonObject( pointer returned by 'Borrowed reference'-type objects. Args: - borrowed_ptr: A borrowed reference counted pointer to a Python + borrowed_ptr: A read-only reference counted pointer to a Python object. Returns: @@ -303,7 +303,7 @@ struct PythonObject( var cpython = _get_global_python_itf().cpython() # SAFETY: - # We were passed a Python 'borrowed reference', so for it to be + # We were passed a Python 'read-only reference', so for it to be # safe to store this reference, we must increment the reference # count to convert this to a 'strong reference'. cpython.Py_IncRef(borrowed_ptr) @@ -374,7 +374,7 @@ struct PythonObject( self.py_object = cpython.PyLong_FromSsize_t(integer) @implicit - fn __init__[dt: DType](inout self, value: SIMD[dt, 1]): + fn __init__[dt: DType](mut self, value: SIMD[dt, 1]): """Initialize the object with a generic scalar value. If the scalar value type is bool, it is converted to a boolean. Otherwise, it is converted to the appropriate integer or floating point type. @@ -427,7 +427,7 @@ struct PythonObject( self.py_object = cpython.PyUnicode_DecodeUTF8(string.as_string_slice()) @implicit - fn __init__[*Ts: CollectionElement](inout self, value: ListLiteral[*Ts]): + fn __init__[*Ts: CollectionElement](mut self, value: ListLiteral[*Ts]): """Initialize the object from a list literal. Parameters: @@ -470,7 +470,7 @@ struct PythonObject( _ = cpython.PyList_SetItem(self.py_object, i, obj.py_object) @implicit - fn __init__[*Ts: CollectionElement](inout self, value: Tuple[*Ts]): + fn __init__[*Ts: CollectionElement](mut self, value: Tuple[*Ts]): """Initialize the object from a tuple literal. Parameters: @@ -715,7 +715,7 @@ struct PythonObject( Python.throw_python_exception_if_error_state(cpython) return PythonObject(result) - fn __setitem__(inout self, *args: PythonObject, value: PythonObject) raises: + fn __setitem__(mut self, *args: PythonObject, value: PythonObject) raises: """Set the value with the given key or keys. Args: @@ -782,7 +782,7 @@ struct PythonObject( return PythonObject(result_obj) fn _call_single_arg_inplace_method( - inout self, method_name: StringRef, rhs: PythonObject + mut self, method_name: StringRef, rhs: PythonObject ) raises: var cpython = _get_global_python_itf().cpython() var tuple_obj = cpython.PyTuple_New(1) @@ -831,7 +831,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rmul__", lhs) - fn __imul__(inout self, rhs: PythonObject) raises: + fn __imul__(mut self, rhs: PythonObject) raises: """In-place multiplication. Calls the underlying object's `__imul__` method. @@ -868,7 +868,7 @@ struct PythonObject( """ return self._call_single_arg_method("__radd__", lhs) - fn __iadd__(inout self, rhs: PythonObject) raises: + fn __iadd__(mut self, rhs: PythonObject) raises: """Immediate addition and concatenation. Args: @@ -902,7 +902,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rsub__", lhs) - fn __isub__(inout self, rhs: PythonObject) raises: + fn __isub__(mut self, rhs: PythonObject) raises: """Immediate subtraction. Args: @@ -939,7 +939,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rfloordiv__", lhs) - fn __ifloordiv__(inout self, rhs: PythonObject) raises: + fn __ifloordiv__(mut self, rhs: PythonObject) raises: """Immediate floor division. Args: @@ -973,7 +973,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rtruediv__", lhs) - fn __itruediv__(inout self, rhs: PythonObject) raises: + fn __itruediv__(mut self, rhs: PythonObject) raises: """Immediate division. Args: @@ -1007,7 +1007,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rmod__", lhs) - fn __imod__(inout self, rhs: PythonObject) raises: + fn __imod__(mut self, rhs: PythonObject) raises: """Immediate modulo. Args: @@ -1039,7 +1039,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rxor__", lhs) - fn __ixor__(inout self, rhs: PythonObject) raises: + fn __ixor__(mut self, rhs: PythonObject) raises: """Immediate exclusive OR. Args: @@ -1072,7 +1072,7 @@ struct PythonObject( """ return self._call_single_arg_method("__ror__", lhs) - fn __ior__(inout self, rhs: PythonObject) raises: + fn __ior__(mut self, rhs: PythonObject) raises: """Immediate bitwise OR. Args: @@ -1105,7 +1105,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rand__", lhs) - fn __iand__(inout self, rhs: PythonObject) raises: + fn __iand__(mut self, rhs: PythonObject) raises: """Immediate bitwise AND. Args: @@ -1138,7 +1138,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rrshift__", lhs) - fn __irshift__(inout self, rhs: PythonObject) raises: + fn __irshift__(mut self, rhs: PythonObject) raises: """Immediate bitwise right shift. Args: @@ -1171,7 +1171,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rlshift__", lhs) - fn __ilshift__(inout self, rhs: PythonObject) raises: + fn __ilshift__(mut self, rhs: PythonObject) raises: """Immediate bitwise left shift. Args: @@ -1202,7 +1202,7 @@ struct PythonObject( """ return self._call_single_arg_method("__rpow__", lhs) - fn __ipow__(inout self, rhs: PythonObject) raises: + fn __ipow__(mut self, rhs: PythonObject) raises: """Immediate power of. Args: @@ -1412,7 +1412,7 @@ struct PythonObject( debug_assert(result != -1, "object is not hashable") return result - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with this python object hash value. Parameters: @@ -1480,7 +1480,7 @@ struct PythonObject( _ = python_str return mojo_str - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this Python object to the provided Writer. diff --git a/stdlib/src/random/random.mojo b/stdlib/src/random/random.mojo index e536c6f533..8ac75d5a8f 100644 --- a/stdlib/src/random/random.mojo +++ b/stdlib/src/random/random.mojo @@ -224,7 +224,7 @@ fn randn[ return -fn shuffle[T: CollectionElement, //](inout list: List[T]): +fn shuffle[T: CollectionElement, //](mut list: List[T]): """Shuffles the elements of the list randomly. Performs an in-place Fisher-Yates shuffle on the provided list. diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index fea193e75b..6d65ab7447 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -176,7 +176,7 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): # TODO(#15590): Implement support for windows and remove the always_inline. @always_inline - fn close(inout self): + fn close(mut self): """Delete the DLHandle object unloading the associated dynamic library. """ diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index 55c3ca42d2..509c234ac9 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -212,7 +212,7 @@ struct TemporaryDirectory: """Whether to ignore cleanup errors.""" fn __init__( - inout self, + mut self, suffix: String = "", prefix: String = "tmp", dir: Optional[String] = None, @@ -290,7 +290,7 @@ struct NamedTemporaryFile: """Name of the file.""" fn __init__( - inout self, + mut self, mode: String = "w", name: Optional[String] = None, suffix: String = "", @@ -345,7 +345,7 @@ struct NamedTemporaryFile: except: pass - fn close(inout self) raises: + fn close(mut self) raises: """Closes the file handle.""" self._file_handle.close() if self._delete: @@ -403,7 +403,7 @@ struct NamedTemporaryFile: """ return self._file_handle.seek(offset, whence) - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): """Write a sequence of Writable arguments to the provided Writer. Parameters: @@ -416,7 +416,7 @@ struct NamedTemporaryFile: write_buffered[buffer_size=4096](file, args) @always_inline - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): """ Write a span of bytes to the file. diff --git a/stdlib/src/testing/testing.mojo b/stdlib/src/testing/testing.mojo index 38423c9aa4..20173be736 100644 --- a/stdlib/src/testing/testing.mojo +++ b/stdlib/src/testing/testing.mojo @@ -536,7 +536,7 @@ struct assert_raises: @always_inline fn __init__( - inout self, + mut self, *, contains: String, location: Optional[_SourceLocation] = None, diff --git a/stdlib/src/utils/format.mojo b/stdlib/src/utils/format.mojo index eab8549b19..af4bbc8d7c 100644 --- a/stdlib/src/utils/format.mojo +++ b/stdlib/src/utils/format.mojo @@ -80,7 +80,7 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): self.format_spec = other.format_spec fn __init__( - inout self, + mut self, first_curly: Int, last_curly: Int, field: Self._FieldVariantType, @@ -267,17 +267,17 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): fn _handle_field_and_break[ T: Stringlike ]( - inout self, + mut self, fmt_src: T, len_pos_args: Int, i: Int, start_value: Int, - inout automatic_indexing_count: Int, - inout raised_automatic_index: Optional[Int], - inout manual_indexing_count: Int, - inout raised_manual_index: Optional[Int], - inout raised_kwarg_field: Optional[String], - inout total_estimated_entry_byte_width: Int, + mut automatic_indexing_count: Int, + mut raised_automatic_index: Optional[Int], + mut manual_indexing_count: Int, + mut raised_manual_index: Optional[Int], + mut raised_kwarg_field: Optional[String], + mut total_estimated_entry_byte_width: Int, ) raises -> Bool: alias S = StringSlice[StaticConstantOrigin] @@ -350,7 +350,7 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): fn _format_entry[ len_pos_args: Int - ](self, inout res: String, args: Self._args_t, inout auto_idx: Int) raises: + ](self, mut res: String, args: Self._args_t, mut auto_idx: Int) raises: # TODO(#3403 and/or #3252): this function should be able to use # Writer syntax when the type implements it, since it will give great # performance benefits. This also needs to be able to check if the given @@ -603,7 +603,7 @@ struct _FormatSpec: """ fn __init__( - inout self, + mut self, fill: UInt8 = ord(" "), align: UInt8 = 0, sign: UInt8 = ord("-"), @@ -680,7 +680,7 @@ struct _FormatSpec: return None # TODO: this should be in StringSlice.__format__(self, spec: FormatSpec, *, writer: Writer): - fn format(self, inout res: String, item: StringSlice) raises: + fn format(self, mut res: String, item: StringSlice) raises: """Transform a String according to its format specification. Args: @@ -691,9 +691,7 @@ struct _FormatSpec: # TODO: align, fill, etc. res += item - fn format[ - T: _CurlyEntryFormattable - ](self, inout res: String, item: T) raises: + fn format[T: _CurlyEntryFormattable](self, mut res: String, item: T) raises: """Stringify a type according to its format specification. Args: diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 174cde61a9..0ea645ee88 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -404,7 +404,7 @@ struct IndexList[ return int(self.data[idx]) @always_inline("nodebug") - fn __setitem__[index: Int](inout self, val: Int): + fn __setitem__[index: Int](mut self, val: Int): """Sets an element in the tuple at the given static index. Parameters: @@ -416,7 +416,7 @@ struct IndexList[ self.data.__setitem__[index](val) @always_inline("nodebug") - fn __setitem__[index: Int](inout self, val: Self._int_type): + fn __setitem__[index: Int](mut self, val: Self._int_type): """Sets an element in the tuple at the given static index. Parameters: @@ -428,7 +428,7 @@ struct IndexList[ self.data.__setitem__[index](val) @always_inline("nodebug") - fn __setitem__(inout self, idx: Int, val: Int): + fn __setitem__(mut self, idx: Int, val: Int): """Sets an element in the tuple at the given index. Args: @@ -755,7 +755,7 @@ struct IndexList[ return buf^ @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this int tuple to the provided Writer. diff --git a/stdlib/src/utils/inline_string.mojo b/stdlib/src/utils/inline_string.mojo index 055ad748a8..cf8caafc31 100644 --- a/stdlib/src/utils/inline_string.mojo +++ b/stdlib/src/utils/inline_string.mojo @@ -103,7 +103,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): # Operator dunders # ===------------------------------------------------------------------=== # - fn __iadd__(inout self, literal: StringLiteral): + fn __iadd__(mut self, literal: StringLiteral): """Appends another string to this string. Args: @@ -111,7 +111,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): """ self.__iadd__(StringRef(literal)) - fn __iadd__(inout self, string: String): + fn __iadd__(mut self, string: String): """Appends another string to this string. Args: @@ -119,7 +119,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): """ self.__iadd__(string.as_string_slice()) - fn __iadd__(inout self, str_slice: StringSlice[_]): + fn __iadd__(mut self, str_slice: StringSlice[_]): """Appends another string to this string. Args: @@ -395,7 +395,7 @@ struct _FixedString[CAP: Int]( # Operator dunders # ===------------------------------------------------------------------=== # - fn __iadd__(inout self, literal: StringLiteral) raises: + fn __iadd__(mut self, literal: StringLiteral) raises: """Appends another string to this string. Args: @@ -403,7 +403,7 @@ struct _FixedString[CAP: Int]( """ self.__iadd__(literal.as_string_slice()) - fn __iadd__(inout self, string: String) raises: + fn __iadd__(mut self, string: String) raises: """Appends another string to this string. Args: @@ -412,7 +412,7 @@ struct _FixedString[CAP: Int]( self.__iadd__(string.as_string_slice()) @always_inline - fn __iadd__(inout self, str_slice: StringSlice[_]) raises: + fn __iadd__(mut self, str_slice: StringSlice[_]) raises: """Appends another string to this string. Args: @@ -438,7 +438,7 @@ struct _FixedString[CAP: Int]( # ===------------------------------------------------------------------=== # fn _iadd_non_raising( - inout self, + mut self, bytes: Span[Byte, _], ) -> Optional[Error]: var total_len = len(self) + len(bytes) @@ -467,11 +467,11 @@ struct _FixedString[CAP: Int]( return None - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write_bytes(self.as_bytes()) @always_inline - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): """ Write a byte span to this String. @@ -481,7 +481,7 @@ struct _FixedString[CAP: Int]( """ _ = self._iadd_non_raising(bytes) - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): """Write a sequence of Writable arguments to the provided Writer. Parameters: diff --git a/stdlib/src/utils/lock.mojo b/stdlib/src/utils/lock.mojo index dbae8c0d63..3916cd92cc 100644 --- a/stdlib/src/utils/lock.mojo +++ b/stdlib/src/utils/lock.mojo @@ -65,7 +65,7 @@ struct BlockingSpinLock: self.counter = Atomic[DType.int64](Self.UNLOCKED) - fn lock(inout self, owner: Int): + fn lock(mut self, owner: Int): """Acquires the lock. Args: @@ -79,7 +79,7 @@ struct BlockingSpinLock: waiter.wait() expected = Self.UNLOCKED - fn unlock(inout self, owner: Int) -> Bool: + fn unlock(mut self, owner: Int) -> Bool: """Releases the lock. Args: @@ -108,7 +108,7 @@ struct BlockingScopedLock: """The underlying lock instance.""" fn __init__( - inout self, + mut self, lock: UnsafePointer[Self.LockType], ): """Primary constructor. @@ -120,8 +120,8 @@ struct BlockingScopedLock: self.lock = lock fn __init__( - inout self, - inout lock: Self.LockType, + mut self, + mut lock: Self.LockType, ): """Secondary constructor. @@ -132,14 +132,14 @@ struct BlockingScopedLock: self.lock = UnsafePointer.address_of(lock) @no_inline - fn __enter__(inout self): + fn __enter__(mut self): """Acquire the lock on entry. This is done by setting the owner of the lock to own address.""" var address = UnsafePointer[Self].address_of(self) self.lock[].lock(int(address)) @no_inline - fn __exit__(inout self): + fn __exit__(mut self): """Release the lock on exit. Reset the address on the underlying lock.""" var address = UnsafePointer[Self].address_of(self) diff --git a/stdlib/src/utils/span.mojo b/stdlib/src/utils/span.mojo index 5a9588d107..3ec2b3030c 100644 --- a/stdlib/src/utils/span.mojo +++ b/stdlib/src/utils/span.mojo @@ -69,7 +69,7 @@ struct _SpanIter[ @always_inline fn __next__( - inout self, + mut self, ) -> Pointer[T, origin]: @parameter if forward: @@ -150,7 +150,7 @@ struct Span[ @always_inline fn __init__[ size: Int, // - ](inout self, ref [origin]array: InlineArray[T, size]): + ](mut self, ref [origin]array: InlineArray[T, size]): """Construct a Span from an InlineArray. Parameters: diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index 9a14c36e24..7a907b7ed0 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -231,7 +231,7 @@ struct StaticTuple[element_type: AnyTrivialRegType, size: Int](Sized): @always_inline("nodebug") fn __setitem__[ IntLike: IntLike, // - ](inout self, idx: IntLike, val: Self.element_type): + ](mut self, idx: IntLike, val: Self.element_type): """Stores a single value into the tuple at the specified dynamic index. Parameters: @@ -252,7 +252,7 @@ struct StaticTuple[element_type: AnyTrivialRegType, size: Int](Sized): self = tmp @always_inline("nodebug") - fn __setitem__[index: Int](inout self, val: Self.element_type): + fn __setitem__[index: Int](mut self, val: Self.element_type): """Stores a single value into the tuple at the specified index. Parameters: diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 96f6aaeada..48be446549 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -190,9 +190,7 @@ struct _StringSliceIter[ var ptr: UnsafePointer[UInt8] var length: Int - fn __init__( - inout self, *, unsafe_pointer: UnsafePointer[UInt8], length: Int - ): + fn __init__(mut self, *, unsafe_pointer: UnsafePointer[UInt8], length: Int): self.index = 0 if forward else length self.ptr = unsafe_pointer self.length = length @@ -203,7 +201,7 @@ struct _StringSliceIter[ fn __iter__(self) -> Self: return self - fn __next__(inout self) -> StringSlice[origin]: + fn __next__(mut self) -> StringSlice[origin]: @parameter if forward: var byte_len = 1 @@ -356,7 +354,7 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable].type,]( @implicit fn __init__[ O: ImmutableOrigin, // - ](inout self: StringSlice[O], ref [O]value: String): + ](mut self: StringSlice[O], ref [O]value: String): """Construct an immutable StringSlice. Parameters: @@ -395,7 +393,7 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable].type,]( var s = S(ptr=self.unsafe_ptr(), length=b_len) return b_len - _count_utf8_continuation_bytes(s) - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """Formats this string slice to the provided `Writer`. Parameters: diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 1a19e5788b..e2a0303100 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -357,7 +357,7 @@ struct StringRef( """ return hash(self.data, self.length) - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): """Updates hasher with the underlying bytes. Parameters: @@ -411,7 +411,7 @@ struct StringRef( return String.write("StringRef(", repr(str(self)), ")") @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats this StringRef to the provided Writer. diff --git a/stdlib/src/utils/variant.mojo b/stdlib/src/utils/variant.mojo index 07dc22da63..6936a3a003 100644 --- a/stdlib/src/utils/variant.mojo +++ b/stdlib/src/utils/variant.mojo @@ -18,7 +18,7 @@ You can use this type to implement variant/sum types. For example: from utils import Variant alias IntOrString = Variant[Int, String] -fn to_string(inout x: IntOrString) -> String: +fn to_string(mut x: IntOrString) -> String: if x.isa[String](): return x[String] # x.isa[Int]() @@ -81,7 +81,7 @@ struct Variant[*Ts: CollectionElement]( ```mojo from utils import Variant alias IntOrString = Variant[Int, String] - fn to_string(inout x: IntOrString) -> String: + fn to_string(mut x: IntOrString) -> String: if x.isa[String](): return x[String] # x.isa[Int]() @@ -124,7 +124,7 @@ struct Variant[*Ts: CollectionElement]( self._impl = __mlir_attr[`#kgen.unknown : `, Self._mlir_type] @implicit - fn __init__[T: CollectionElement](inout self, owned value: T): + fn __init__[T: CollectionElement](mut self, owned value: T): """Create a variant with one of the types. Parameters: @@ -239,7 +239,7 @@ struct Variant[*Ts: CollectionElement]( return UnsafePointer(discr_ptr).bitcast[UInt8]()[] @always_inline - fn take[T: CollectionElement](inout self) -> T: + fn take[T: CollectionElement](mut self) -> T: """Take the current value of the variant with the provided type. The caller takes ownership of the underlying value. @@ -260,7 +260,7 @@ struct Variant[*Ts: CollectionElement]( return self.unsafe_take[T]() @always_inline - fn unsafe_take[T: CollectionElement](inout self) -> T: + fn unsafe_take[T: CollectionElement](mut self) -> T: """Unsafely take the current value of the variant with the provided type. The caller takes ownership of the underlying value. @@ -284,7 +284,7 @@ struct Variant[*Ts: CollectionElement]( @always_inline fn replace[ Tin: CollectionElement, Tout: CollectionElement - ](inout self, owned value: Tin) -> Tout: + ](mut self, owned value: Tin) -> Tout: """Replace the current value of the variant with the provided type. The caller takes ownership of the underlying value. @@ -311,7 +311,7 @@ struct Variant[*Ts: CollectionElement]( @always_inline fn unsafe_replace[ Tin: CollectionElement, Tout: CollectionElement - ](inout self, owned value: Tin) -> Tout: + ](mut self, owned value: Tin) -> Tout: """Unsafely replace the current value of the variant with the provided type. The caller takes ownership of the underlying value. @@ -337,7 +337,7 @@ struct Variant[*Ts: CollectionElement]( self.set[Tin](value^) return x^ - fn set[T: CollectionElement](inout self, owned value: T): + fn set[T: CollectionElement](mut self, owned value: T): """Set the variant value. This will call the destructor on the old value, and update the variant's diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index bbb574670b..2d9351036f 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -43,11 +43,11 @@ trait Writer: var s: String # Writer requirement to write a Span of Bytes - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): self.s._iadd[False](bytes) # Writer requirement to take multiple args - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): @parameter fn write_arg[T: Writable](arg: T): arg.write_to(self) @@ -55,7 +55,7 @@ trait Writer: args.each[write_arg]() # Also make it Writable to allow `print` to write the inner String - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write(self.s) @@ -66,7 +66,7 @@ trait Writer: # Pass multiple args to the Writer. The Int and StringLiteral types # call `writer.write_bytes` in their own `write_to` implementations. - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write("Point(", self.x, ", ", self.y, ")") # Enable conversion to a String using `str(point)` @@ -90,7 +90,7 @@ trait Writer: """ @always_inline - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): """ Write a `Span[Byte]` to this `Writer`. @@ -100,7 +100,7 @@ trait Writer: """ ... - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): """Write a sequence of Writable arguments to the provided Writer. Parameters: @@ -135,7 +135,7 @@ trait Writable: var x: Float64 var y: Float64 - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): var string = "Point" # Write a single `Span[Byte]`: writer.write_bytes(string.as_bytes()) @@ -144,7 +144,7 @@ trait Writable: ``` """ - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): """ Formats the string representation of this type to the provided Writer. @@ -165,7 +165,7 @@ trait Writable: fn write_args[ W: Writer, *Ts: Writable ]( - inout writer: W, + mut writer: W, args: VariadicPack[_, Writable, *Ts], *, sep: StaticString = "", @@ -238,7 +238,7 @@ struct _WriteBufferHeap(Writer): self.data.free() @always_inline - fn write_bytes(inout self, bytes: Span[UInt8, _]): + fn write_bytes(mut self, bytes: Span[UInt8, _]): len_bytes = len(bytes) # If empty then return if len_bytes == 0: @@ -248,7 +248,7 @@ struct _WriteBufferHeap(Writer): self.data[i + self.pos] = ptr[i] self.pos += len_bytes - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): @parameter fn write_arg[T: Writable](arg: T): arg.write_to(self) @@ -262,10 +262,10 @@ struct _ArgBytes(Writer): fn __init__(out self): self.size = 0 - fn write_bytes(inout self, bytes: Span[UInt8, _]): + fn write_bytes(mut self, bytes: Span[UInt8, _]): self.size += len(bytes) - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): @parameter fn write_arg[T: Writable](arg: T): arg.write_to(self) @@ -284,7 +284,7 @@ struct _WriteBufferStack[W: MovableWriter, //, capacity: Int](Writer): self.pos = 0 self.writer = writer^ - fn flush(inout self): + fn flush(mut self): self.writer.write_bytes( Span[Byte, ImmutableAnyOrigin]( ptr=self.data.unsafe_ptr(), length=self.pos @@ -292,7 +292,7 @@ struct _WriteBufferStack[W: MovableWriter, //, capacity: Int](Writer): ) self.pos = 0 - fn write_bytes(inout self, bytes: Span[Byte, _]): + fn write_bytes(mut self, bytes: Span[Byte, _]): len_bytes = len(bytes) # If empty then return if len_bytes == 0: @@ -309,7 +309,7 @@ struct _WriteBufferStack[W: MovableWriter, //, capacity: Int](Writer): memcpy(self.data.unsafe_ptr() + self.pos, bytes.unsafe_ptr(), len_bytes) self.pos += len_bytes - fn write[*Ts: Writable](inout self, *args: *Ts): + fn write[*Ts: Writable](mut self, *args: *Ts): @parameter fn write_arg[T: Writable](arg: T): arg.write_to(self) diff --git a/stdlib/test/builtin/test_debug_assert.mojo b/stdlib/test/builtin/test_debug_assert.mojo index 9df1b1e1f3..6d8ceb57f3 100644 --- a/stdlib/test/builtin/test_debug_assert.mojo +++ b/stdlib/test/builtin/test_debug_assert.mojo @@ -52,5 +52,5 @@ def test_debug_assert_writable(): struct WritableOnly: var message: String - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write(self.message) diff --git a/stdlib/test/builtin/test_object.mojo b/stdlib/test/builtin/test_object.mojo index 65c118665a..899c75da17 100644 --- a/stdlib/test/builtin/test_object.mojo +++ b/stdlib/test/builtin/test_object.mojo @@ -183,7 +183,7 @@ def test_function(lhs, rhs) -> object: return lhs + rhs -# These are all marked borrowed because 'object' doesn't support function +# These are all marked read-only because 'object' doesn't support function # types with owned arguments. def test_function_raises(a) -> object: raise Error("Error from function type") diff --git a/stdlib/test/builtin/test_print.mojo b/stdlib/test/builtin/test_print.mojo index a18b7366ff..4ace3a30c1 100644 --- a/stdlib/test/builtin/test_print.mojo +++ b/stdlib/test/builtin/test_print.mojo @@ -63,7 +63,7 @@ struct PrintChecker: fn stream(self) -> FileDescriptor: return self.tmp._file_handle._get_raw_fd() - fn check_line(inout self, expected: String, msg: String = "") raises: + fn check_line(mut self, expected: String, msg: String = "") raises: print(end="", file=self.stream(), flush=True) _ = self.tmp.seek(self.cursor) var result = self.tmp.read()[:-1] @@ -72,7 +72,7 @@ struct PrintChecker: self.cursor += len(result) + 1 fn check_line_starts_with( - inout self, prefix: String, msg: String = "" + mut self, prefix: String, msg: String = "" ) raises: print(end="", file=self.stream(), flush=True) _ = self.tmp.seek(self.cursor) diff --git a/stdlib/test/builtin/test_range.mojo b/stdlib/test/builtin/test_range.mojo index b3a1f62be6..051656d27d 100644 --- a/stdlib/test/builtin/test_range.mojo +++ b/stdlib/test/builtin/test_range.mojo @@ -193,7 +193,7 @@ def test_scalar_range(): assert_equal(r.end, 16) assert_equal(r.step, 4) - fn append_many(inout list: List, *values: list.T): + fn append_many(mut list: List, *values: list.T): for value in values: list.append(value[]) diff --git a/stdlib/test/builtin/test_sort.mojo b/stdlib/test/builtin/test_sort.mojo index 7ff2834d46..3c98be083f 100644 --- a/stdlib/test/builtin/test_sort.mojo +++ b/stdlib/test/builtin/test_sort.mojo @@ -45,7 +45,7 @@ fn random_numbers[ return result -# fn assert_sorted[dtype: DType](inout list: List[Scalar[dtype]]) raises: +# fn assert_sorted[dtype: DType](mut list: List[Scalar[dtype]]) raises: # sort[dtype](list) # for i in range(1, len(list)): # assert_true( @@ -53,7 +53,7 @@ fn random_numbers[ # ) -fn assert_sorted_string(inout list: List[String]) raises: +fn assert_sorted_string(mut list: List[String]) raises: for i in range(1, len(list)): assert_true( list[i] >= list[i - 1], str(list[i - 1]) + " > " + str(list[i]) @@ -62,7 +62,7 @@ fn assert_sorted_string(inout list: List[String]) raises: fn assert_sorted[ type: ComparableCollectionElement -](inout list: List[type]) raises: +](mut list: List[type]) raises: for i in range(1, len(list)): assert_true(list[i] >= list[i - 1], "error at index: " + str(i)) diff --git a/stdlib/test/hashlib/test_hasher.mojo b/stdlib/test/hashlib/test_hasher.mojo index 809d494c7e..4c10ac50a5 100644 --- a/stdlib/test/hashlib/test_hasher.mojo +++ b/stdlib/test/hashlib/test_hasher.mojo @@ -30,14 +30,14 @@ struct DummyHasher(_Hasher): fn __init__(out self): self._dummy_value = 0 - fn _update_with_bytes(inout self, data: UnsafePointer[UInt8], length: Int): + fn _update_with_bytes(mut self, data: UnsafePointer[UInt8], length: Int): for i in range(length): self._dummy_value += data[i].cast[DType.uint64]() - fn _update_with_simd(inout self, value: SIMD[_, _]): + fn _update_with_simd(mut self, value: SIMD[_, _]): self._dummy_value += value.cast[DType.uint64]().reduce_add() - fn update[T: _HashableWithHasher](inout self, value: T): + fn update[T: _HashableWithHasher](mut self, value: T): value.__hash__(self) fn finish(owned self) -> UInt64: @@ -48,7 +48,7 @@ struct DummyHasher(_Hasher): struct SomeHashableStruct(_HashableWithHasher): var _value: Int64 - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): hasher._update_with_simd(self._value) @@ -69,7 +69,7 @@ struct ComplexeHashableStruct(_HashableWithHasher): var _value1: SomeHashableStruct var _value2: SomeHashableStruct - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): hasher.update(self._value1) hasher.update(self._value2) @@ -96,10 +96,10 @@ struct ComplexHashableStructWithList(_HashableWithHasher): var _value2: SomeHashableStruct var _value3: List[UInt8] - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): hasher.update(self._value1) hasher.update(self._value2) - # This is okay because self is passed as borrowed so the pointer will + # This is okay because self is passed as read-only so the pointer will # be valid until at least the end of the function hasher._update_with_bytes( data=self._value3.unsafe_ptr(), @@ -115,10 +115,10 @@ struct ComplexHashableStructWithListAndWideSIMD(_HashableWithHasher): var _value3: List[UInt8] var _value4: SIMD[DType.uint32, 4] - fn __hash__[H: _Hasher](self, inout hasher: H): + fn __hash__[H: _Hasher](self, mut hasher: H): hasher.update(self._value1) hasher.update(self._value2) - # This is okay because self is passed as borrowed so the pointer will + # This is okay because self is passed as read-only so the pointer will # be valid until at least the end of the function hasher._update_with_bytes( data=self._value3.unsafe_ptr(), diff --git a/stdlib/test/python/test_ownership.mojo b/stdlib/test/python/test_ownership.mojo index b2f1203332..ec7a71d89e 100644 --- a/stdlib/test/python/test_ownership.mojo +++ b/stdlib/test/python/test_ownership.mojo @@ -17,42 +17,42 @@ from python import Python, PythonObject from testing import assert_equal -fn test_import(inout python: Python) raises: +fn test_import(mut python: Python) raises: var my_module: PythonObject = Python.import_module("my_module") var py_string = my_module.my_function("Hello") var str = String(python.__str__(py_string)) assert_equal(str, "Formatting the string from Lit with Python: Hello") -fn test_list(inout python: Python) raises: +fn test_list(mut python: Python) raises: var b: PythonObject = Python.import_module("builtins") var my_list = PythonObject([1, 2.34, "False"]) var py_string = str(my_list) assert_equal(py_string, "[1, 2.34, 'False']") -fn test_tuple(inout python: Python) raises: +fn test_tuple(mut python: Python) raises: var b: PythonObject = Python.import_module("builtins") var my_tuple = PythonObject((1, 2.34, "False")) var py_string = str(my_tuple) assert_equal(py_string, "(1, 2.34, 'False')") -fn test_call_ownership(inout python: Python) raises: +fn test_call_ownership(mut python: Python) raises: var obj: PythonObject = [1, "5"] var py_string = str(obj) var string = python.__str__(py_string) assert_equal(string, "[1, '5']") -fn test_getitem_ownership(inout python: Python) raises: +fn test_getitem_ownership(mut python: Python) raises: var obj: PythonObject = [1, "5"] var py_string = str(obj[1]) var string = python.__str__(py_string) assert_equal(string, "5") -fn test_getattr_ownership(inout python: Python) raises: +fn test_getattr_ownership(mut python: Python) raises: var my_module: PythonObject = Python.import_module("my_module") var obj = my_module.Foo(4) var py_string = str(obj.bar) diff --git a/stdlib/test/python/test_python_info.mojo b/stdlib/test/python/test_python_info.mojo index 1728a10c24..b1b7eb29e7 100644 --- a/stdlib/test/python/test_python_info.mojo +++ b/stdlib/test/python/test_python_info.mojo @@ -19,7 +19,7 @@ from python._cpython import PythonVersion from testing import assert_equal -fn test_python_version(inout python: Python) raises: +fn test_python_version(mut python: Python) raises: var version = "3.10.8 (main, Nov 24 2022, 08:08:27) [Clang 14.0.6 ]" var pythonVersion = PythonVersion(version) assert_equal(pythonVersion.major, 3) diff --git a/stdlib/test/python/test_python_interop.mojo b/stdlib/test/python/test_python_interop.mojo index be71cfd2dc..3e1a465628 100644 --- a/stdlib/test/python/test_python_interop.mojo +++ b/stdlib/test/python/test_python_interop.mojo @@ -17,7 +17,7 @@ from python.python import Python, PythonObject, _get_global_python_itf from testing import assert_equal -fn test_execute_python_string(inout python: Python) -> String: +fn test_execute_python_string(mut python: Python) -> String: try: _ = Python.evaluate("print('evaluated by PyRunString')") return str(Python.evaluate("'a' + 'b'")) @@ -25,7 +25,7 @@ fn test_execute_python_string(inout python: Python) -> String: return str(e) -fn test_local_import(inout python: Python) -> String: +fn test_local_import(mut python: Python) -> String: try: var my_module: PythonObject = Python.import_module("my_module") if my_module: @@ -37,7 +37,7 @@ fn test_local_import(inout python: Python) -> String: return str(e) -fn test_dynamic_import(inout python: Python, times: Int = 1) -> String: +fn test_dynamic_import(mut python: Python, times: Int = 1) -> String: alias INLINE_MODULE = """ called_already = False def hello(name): @@ -56,7 +56,7 @@ def hello(name): return str(e) -fn test_call(inout python: Python) -> String: +fn test_call(mut python: Python) -> String: try: var my_module: PythonObject = Python.import_module("my_module") return str( diff --git a/stdlib/test/python/test_python_object.mojo b/stdlib/test/python/test_python_object.mojo index eed042c564..92180fb8d0 100644 --- a/stdlib/test/python/test_python_object.mojo +++ b/stdlib/test/python/test_python_object.mojo @@ -21,7 +21,7 @@ from testing import assert_equal, assert_false, assert_raises, assert_true from utils import StringRef -def test_dunder_methods(inout python: Python): +def test_dunder_methods(mut python: Python): var a = PythonObject(34) var b = PythonObject(10) diff --git a/stdlib/test/python/test_python_to_mojo.mojo b/stdlib/test/python/test_python_to_mojo.mojo index 0fbf502b7a..d621943f9c 100644 --- a/stdlib/test/python/test_python_to_mojo.mojo +++ b/stdlib/test/python/test_python_to_mojo.mojo @@ -17,7 +17,7 @@ from python import Python, PythonObject from testing import assert_equal, assert_false, assert_true -fn test_string_to_python_to_mojo(inout python: Python) raises: +fn test_string_to_python_to_mojo(mut python: Python) raises: var py_string = PythonObject("mojo") var py_string_capitalized = py_string.capitalize() diff --git a/stdlib/test/tempfile/test_tempfile.mojo b/stdlib/test/tempfile/test_tempfile.mojo index c186776e6e..d84ac25554 100644 --- a/stdlib/test/tempfile/test_tempfile.mojo +++ b/stdlib/test/tempfile/test_tempfile.mojo @@ -55,7 +55,7 @@ struct TempEnvWithCleanup: """Function called after the context manager exits if an error occurs.""" fn __init__( - inout self, + mut self, vars_to_set: Dict[String, String], clean_up_function: fn () raises -> None, ): @@ -63,20 +63,20 @@ struct TempEnvWithCleanup: self._vars_back = Dict[String, String]() self.clean_up_function = clean_up_function - def __enter__(inout self): + def __enter__(mut self): for key_value in self.vars_to_set.items(): var key = key_value[].key var value = key_value[].value self._vars_back[key] = os.getenv(key) _ = os.setenv(key, value, overwrite=True) - fn __exit__(inout self): + fn __exit__(mut self): for key_value in self.vars_to_set.items(): var key = key_value[].key var value = key_value[].value _ = os.setenv(key, value, overwrite=True) - def __exit__(inout self, error: Error) -> Bool: + def __exit__(mut self, error: Error) -> Bool: self.__exit__() self.clean_up_function() return False diff --git a/stdlib/test/utils/test_format.mojo b/stdlib/test/utils/test_format.mojo index e15d744a88..975d26464b 100644 --- a/stdlib/test/utils/test_format.mojo +++ b/stdlib/test/utils/test_format.mojo @@ -34,7 +34,7 @@ struct Point(Writable, Stringable): var y: Int @no_inline - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write("Point(", self.x, ", ", self.y, ")") @no_inline diff --git a/stdlib/test/utils/test_format_to_stdout.mojo b/stdlib/test/utils/test_format_to_stdout.mojo index c487ea14db..fed40ec33c 100644 --- a/stdlib/test/utils/test_format_to_stdout.mojo +++ b/stdlib/test/utils/test_format_to_stdout.mojo @@ -26,7 +26,7 @@ struct Point(Writable): var x: Int var y: Int - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write("Point(", self.x, ", ", self.y, ")") From 8c671cb9f6a17bac7b88732a185829043456cada Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sun, 1 Dec 2024 07:46:06 -0800 Subject: [PATCH 002/509] [******][GPU Enhance FPUtils to support fnuz float types MODULAR_ORIG_COMMIT_REV_ID: 8a2976ab0b99ec2d63ac2c0136bd89b40782b5ac --- stdlib/src/utils/numerics.mojo | 97 ++++++++++++++++++++++++++-------- 1 file changed, 76 insertions(+), 21 deletions(-) diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index f9a52a9972..f7d99244fe 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -67,9 +67,9 @@ struct FPUtils[ """ @parameter - if type is DType.float8e4m3: + if type in (DType.float8e4m3, DType.float8e4m3fnuz): return 3 - elif type is DType.float8e5m2: + elif type in (DType.float8e5m2, DType.float8e5m2fnuz): return 2 elif type is DType.float16: return 10 @@ -94,8 +94,12 @@ struct FPUtils[ @parameter if type is DType.float8e4m3: return 7 + elif type is DType.float8e4m3fnuz: + return 8 elif type is DType.float8e5m2: return 15 + elif type is DType.float8e5m2fnuz: + return 16 elif type is DType.float16: return 15 elif type is DType.float32 or type is DType.bfloat16: @@ -114,18 +118,7 @@ struct FPUtils[ The min exponent. """ - @parameter - if type is DType.float8e4m3: - return -6 - elif type is DType.float8e5m2: - return -14 - elif type is DType.float16: - return -14 - elif type is DType.float32 or type is DType.bfloat16: - return -126 - else: - constrained[type is DType.float64, "unsupported float type"]() - return -1022 + return -Self.max_exponent() + 1 @staticmethod @always_inline("nodebug") @@ -137,9 +130,9 @@ struct FPUtils[ """ @parameter - if type is DType.float8e4m3: + if type in (DType.float8e4m3, DType.float8e4m3fnuz): return 4 - elif type is DType.float8e5m2: + elif type in (DType.float8e5m2, DType.float8e5m2fnuz): return 5 elif type is DType.float16: return 5 @@ -537,6 +530,15 @@ fn nan[type: DType]() -> Scalar[type]: value = __mlir_attr[`#pop.simd<"nan"> : !pop.scalar`], ]() ) + elif type is DType.float8e5m2fnuz: + return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( + __mlir_op.`kgen.param.constant`[ + _type = __mlir_type[`!pop.scalar`], + value = __mlir_attr[ + `#pop.simd<"nan"> : !pop.scalar` + ], + ]() + ) elif type is DType.float8e4m3: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ @@ -544,6 +546,15 @@ fn nan[type: DType]() -> Scalar[type]: value = __mlir_attr[`#pop.simd<"nan"> : !pop.scalar`], ]() ) + elif type is DType.float8e4m3fnuz: + return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( + __mlir_op.`kgen.param.constant`[ + _type = __mlir_type[`!pop.scalar`], + value = __mlir_attr[ + `#pop.simd<"nan"> : !pop.scalar` + ], + ]() + ) elif type is DType.float16: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ @@ -600,7 +611,10 @@ fn isnan[ """ @parameter - if not type.is_floating_point(): + if not type.is_floating_point() or type in ( + DType.float8e4m3fnuz, + DType.float8e5m2fnuz, + ): return False alias int_dtype = _integral_type_of[type]() @@ -654,6 +668,15 @@ fn inf[type: DType]() -> Scalar[type]: value = __mlir_attr[`#pop.simd<"inf"> : !pop.scalar`], ]() ) + elif type is DType.float8e5m2fnuz: + return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( + __mlir_op.`kgen.param.constant`[ + _type = __mlir_type[`!pop.scalar`], + value = __mlir_attr[ + `#pop.simd<"inf"> : !pop.scalar` + ], + ]() + ) elif type is DType.float8e4m3: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ @@ -661,6 +684,15 @@ fn inf[type: DType]() -> Scalar[type]: value = __mlir_attr[`#pop.simd<"inf"> : !pop.scalar`], ]() ) + elif type is DType.float8e4m3fnuz: + return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( + __mlir_op.`kgen.param.constant`[ + _type = __mlir_type[`!pop.scalar`], + value = __mlir_attr[ + `#pop.simd<"inf"> : !pop.scalar` + ], + ]() + ) elif type is DType.float16: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ @@ -721,6 +753,15 @@ fn neg_inf[type: DType]() -> Scalar[type]: value = __mlir_attr[`#pop.simd<"-inf"> : !pop.scalar`], ]() ) + elif type is DType.float8e5m2fnuz: + return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( + __mlir_op.`kgen.param.constant`[ + _type = __mlir_type[`!pop.scalar`], + value = __mlir_attr[ + `#pop.simd<"-inf"> : !pop.scalar` + ], + ]() + ) elif type is DType.float8e4m3: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ @@ -728,6 +769,15 @@ fn neg_inf[type: DType]() -> Scalar[type]: value = __mlir_attr[`#pop.simd<"-inf"> : !pop.scalar`], ]() ) + elif type is DType.float8e4m3fnuz: + return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( + __mlir_op.`kgen.param.constant`[ + _type = __mlir_type[`!pop.scalar`], + value = __mlir_attr[ + `#pop.simd<"-inf"> : !pop.scalar` + ], + ]() + ) elif type is DType.float16: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ @@ -801,7 +851,9 @@ fn max_finite[type: DType]() -> Scalar[type]: return 18446744073709551615 elif type is DType.float8e4m3: return 448 - elif type is DType.float8e5m2: + elif type is DType.float8e4m3fnuz: + return 240 + elif type in (DType.float8e5m2, DType.float8e5m2fnuz): return 57344 elif type is DType.float16: return 65504 @@ -932,9 +984,11 @@ fn isinf[ """ @parameter - if not type.is_floating_point(): + if not type.is_floating_point() or type in ( + DType.float8e4m3fnuz, + DType.float8e5m2fnuz, + ): return False - elif type is DType.float8e5m2: # For the float8e5m2 both 7C and FC are infinity. alias int_dtype = _integral_type_of[type]() @@ -1087,10 +1141,11 @@ fn ulp[ constrained[type.is_floating_point(), "the type must be floating point"]() + alias inf_val = SIMD[type, simd_width](inf[type]()) + var nan_mask = isnan(x) var xabs = abs(x) var inf_mask = isinf(xabs) - alias inf_val = SIMD[type, simd_width](inf[type]()) var x2 = nextafter(xabs, inf_val) var x2_inf_mask = isinf(x2) From 782a277e6b94fcc8006ceb949198fdd705dc1377 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sun, 1 Dec 2024 08:16:34 -0800 Subject: [PATCH 003/509] [Stdlib] Simplify the code for powf function, NFC MODULAR_ORIG_COMMIT_REV_ID: 60447a21c51b70796fbc62aa27c2760259703809 --- stdlib/src/builtin/simd.mojo | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index a70d660217..bacee6e4bf 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -802,7 +802,7 @@ struct SIMD[type: DType, size: Int]( specified exponent value. """ constrained[type.is_numeric(), "the SIMD type must be numeric"]() - return _pow[type, size, DType.index](self, exp) + return _pow(self, SIMD[DType.index, size](exp)) # TODO(#22771): remove this overload. @always_inline("nodebug") @@ -2952,17 +2952,13 @@ fn _tbl1( @always_inline fn _pow[ - BaseTy: DType, simd_width: Int, ExpTy: DType -](base: SIMD[BaseTy, simd_width], exp: SIMD[ExpTy, simd_width]) -> __type_of( - base -): + simd_width: Int +](base: SIMD[_, simd_width], exp: SIMD[_, simd_width]) -> __type_of(base): """Computes the power of the elements of a SIMD vector raised to the corresponding elements of another SIMD vector. Parameters: - BaseTy: The `dtype` of the `base` SIMD vector. simd_width: The width of the input and output SIMD vectors. - ExpTy: The `dtype` of the `exp` SIMD vector. Args: base: Base of the power operation. @@ -2973,9 +2969,9 @@ fn _pow[ """ @parameter - if ExpTy.is_floating_point() and BaseTy == ExpTy: + if exp.type.is_floating_point() and base.type is exp.type: return _powf(base, exp) - elif ExpTy.is_integral(): + elif exp.type.is_integral(): # Common cases if all(exp == 2): return base * base @@ -3030,12 +3026,15 @@ fn _powf[ @always_inline -fn _powi[type: DType](base: Scalar[type], exp: Int32) -> __type_of(base): +fn _powi(base: Scalar, exp: Int32) -> __type_of(base): + alias type = base.type + if type.is_integral() and exp < 0: # Not defined for Integers, this should raise an # exception. debug_assert(False, "exponent < 0 is undefined for integers") return 0 + var a = base var b = abs(exp) if type.is_floating_point() else exp var res: Scalar[type] = 1 From d84ee3223f1a753672f82722502f623bbcce5904 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 1 Dec 2024 09:22:56 -0800 Subject: [PATCH 004/509] [mojo-docs] Update the documentation to new keywords This does a big migration of the documentation over to the new argument convention spellings, and also catches some uses of `inout` in inits that moved to `out`. MODULAR_ORIG_COMMIT_REV_ID: b5562aa2bbcd3260a83e354ee97964c3d58920b0 --- docs/manual/basics.mdx | 2 +- docs/manual/errors.mdx | 14 +++--- docs/manual/functions.mdx | 14 +++--- docs/manual/lifecycle/death.mdx | 2 +- docs/manual/lifecycle/life.mdx | 14 +++--- docs/manual/operators.mdx | 28 +++++------ docs/manual/packages.md | 2 +- docs/manual/parameters/index.mdx | 4 +- docs/manual/pointers.mdx | 2 +- docs/manual/structs.mdx | 6 +-- docs/manual/traits.mdx | 2 +- docs/manual/values/lifetimes.mdx | 6 +-- docs/manual/values/ownership.mdx | 64 +++++++++++++------------- docs/manual/values/value-semantics.mdx | 4 +- stdlib/docs/bencher/Bench.md | 18 ++++---- stdlib/docs/bencher/BenchConfig.md | 2 +- stdlib/docs/bencher/BenchId.md | 4 +- stdlib/docs/bencher/Bencher.md | 10 ++-- stdlib/docs/bencher/BenchmarkInfo.md | 2 +- stdlib/docs/style-guide.md | 2 +- 20 files changed, 101 insertions(+), 101 deletions(-) diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index 2253f8c7c9..938701d322 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -95,7 +95,7 @@ passed by reference, the short answer is: `def` functions receive arguments The longer short answer is that Mojo allows you to specify for each argument whether it should be passed by value (as `owned`), or whether it should be -passed by reference (as `borrowed` for an immutable reference, or as `inout` +passed by reference (as `read` for an immutable reference, or as `mut` for a mutable reference). This feature is entwined with Mojo's value ownership model, which protects you diff --git a/docs/manual/errors.mdx b/docs/manual/errors.mdx index c87fab7777..47e1a23711 100644 --- a/docs/manual/errors.mdx +++ b/docs/manual/errors.mdx @@ -298,14 +298,14 @@ import time struct Timer: var start_time: Int - fn __init__(inout self): + fn __init__(out self): self.start_time = 0 - fn __enter__(inout self) -> Self: + fn __enter__(mut self) -> Self: self.start_time = time.perf_counter_ns() return self - fn __exit__(inout self): + fn __exit__(mut self): end_time = time.perf_counter_ns() elapsed_time_ms = round(((end_time - self.start_time) / 1e6), 3) print("Elapsed time:", elapsed_time_ms, "milliseconds") @@ -371,19 +371,19 @@ import time struct ConditionalTimer: var start_time: Int - fn __init__(inout self): + fn __init__(out self): self.start_time = 0 - fn __enter__(inout self) -> Self: + fn __enter__(mut self) -> Self: self.start_time = time.perf_counter_ns() return self - fn __exit__(inout self): + fn __exit__(mut self): end_time = time.perf_counter_ns() elapsed_time_ms = round(((end_time - self.start_time) / 1e6), 3) print("Elapsed time:", elapsed_time_ms, "milliseconds") - fn __exit__(inout self, e: Error) raises -> Bool: + fn __exit__(mut self, e: Error) raises -> Bool: if str(e) == "just a warning": print("Suppressing error:", e) self.__exit__() diff --git a/docs/manual/functions.mdx b/docs/manual/functions.mdx index d35c3cad30..19d4c2e067 100644 --- a/docs/manual/functions.mdx +++ b/docs/manual/functions.mdx @@ -51,7 +51,7 @@ Here's everything to know about `fn`: value). * By default, arguments are received as an immutable reference (values are - read-only, using the `borrowed` [argument + read-only, using the `read` [argument convention](/mojo/manual/values/ownership#argument-conventions)). This prevents accidental mutations, and permits the use of non-copyable types @@ -59,7 +59,7 @@ Here's everything to know about `fn`: If you want a local copy, you can simply assign the value to a local variable. Or, you can get a mutable reference to the value by declaring the - `inout` [argument + `mut` [argument convention](/mojo/manual/values/ownership#argument-conventions)). * If the function raises an exception, it must be explicitly declared with the @@ -106,7 +106,7 @@ Here's everything to know about `def`: `def` function doesn't declare a return type of `None`, it's considered to return an `object` by default.) -* Arguments are mutable. Arguments default to using the `borrowed` +* Arguments are mutable. Arguments default to using the `read` [argument convention](/mojo/manual/values/ownership#argument-conventions) like an `fn` function, with a special addition: if the function mutates the argument, it makes a mutable copy. @@ -179,7 +179,7 @@ fn use_defaults(): ``` However, you cannot define a default value for an argument that's declared as -[`inout`](/mojo/manual/values/ownership#mutable-arguments-inout). +[`mut`](/mojo/manual/values/ownership#mutable-arguments-mut). Any optional arguments must appear after any required arguments. [Keyword-only arguments](#positional-only-and-keyword-only-arguments), discussed later, can @@ -280,7 +280,7 @@ of the value itself. You must add an empty subscript operator `[]` to dereference the reference and retrieve the value: ```mojo -def make_worldly(inout *strs: String): +def make_worldly(mut *strs: String): # Requires extra [] to dereference the reference for now. for i in strs: i[] += " world" @@ -291,7 +291,7 @@ Alternately, subscripting into a `VariadicListMem` returns the argument value, and doesn't require any dereferencing: ```mojo -fn make_worldly(inout *strs: String): +fn make_worldly(mut *strs: String): # This "just works" as you'd expect! for i in range(len(strs)): strs[i] += " world" @@ -415,7 +415,7 @@ There are currently a few limitations: ```mojo # Not supported yet. - fn borrowed_var_kwargs(borrowed **kwargs: Int): ... + fn read_var_kwargs(read **kwargs: Int): ... ``` * All the variadic keyword arguments must have the same type, and this diff --git a/docs/manual/lifecycle/death.mdx b/docs/manual/lifecycle/death.mdx index 4494e8b446..2f7ee808f7 100644 --- a/docs/manual/lifecycle/death.mdx +++ b/docs/manual/lifecycle/death.mdx @@ -353,7 +353,7 @@ struct TwoStrings: # self.str1 has been transferred so it is also destroyed now; # `self.__del__()` is not called (avoiding an infinite loop). - fn dump(inout self): + fn dump(mut self): print('str1:', self.str1) print('str2:', self.str2) diff --git a/docs/manual/lifecycle/life.mdx b/docs/manual/lifecycle/life.mdx index 800a81163d..91646caa5f 100644 --- a/docs/manual/lifecycle/life.mdx +++ b/docs/manual/lifecycle/life.mdx @@ -71,7 +71,7 @@ var mine = MyPet("Loki", 4) ``` An instance of `MyPet` can also be -[borrowed](/mojo/manual/values/ownership#borrowed-arguments-borrowed) +[read](/mojo/manual/values/ownership#read-arguments-read) and destroyed, but it currently can't be copied or moved. We believe this is a good default starting point, because there are no built-in @@ -99,8 +99,8 @@ example, you might want a default constructor that sets some default values and takes no arguments, and then additional constructors that accept more arguments. Just be aware that, in order to modify any fields, each constructor must -declare the `self` argument with the [`inout` -convention](/mojo/manual/values/ownership#mutable-arguments-inout). If you +declare the `self` argument with the [`out` +convention](/mojo/manual/values/ownership#mutable-arguments-mut). If you want to call one constructor from another, you simply call upon that constructor as you would externally (you don't need to pass `self`). @@ -287,7 +287,7 @@ mistakes when referring to the current struct name. Also, notice that the `existing` argument in `__copyinit__()` is immutable because the default [argument convention](/mojo/manual/values/ownership#argument-conventions) is -`borrowed`—this is a good thing because this function should not modify the +`read`—this is a good thing because this function should not modify the contents of the value being copied. ::: @@ -337,7 +337,7 @@ struct HeapArray: (self.data + i).destroy_pointee() self.data.free() - fn append(inout self, val: Int): + fn append(mut self, val: Int): # Update the array for demo purposes if self.size < self.cap: (self.data + self.size).init_pointee_copy(val) @@ -478,7 +478,7 @@ struct HeapArray: (self.data + i).destroy_pointee() self.data.free() - fn append(inout self, val: Int): + fn append(mut self, val: Int): # Update the array for demo purposes if self.size < self.cap: (self.data + self.size).init_pointee_copy(val) @@ -529,7 +529,7 @@ values that are expensive to copy. To go further and ensure your type can never be copied, you can make it "move-only" by implementing `__moveinit__()` and *excluding* `__copyinit__()`. A move-only type can be passed to other variables and passed into functions -with any argument convention (`borrowed`, `inout`, and `owned`)—the only catch +with any argument convention (`read`, `mut`, and `owned`)—the only catch is that you must use the `^` transfer sigil to end the lifetime of a move-only type when assigning it to a new variable or when passing it as an `owned` argument. diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index e8a72e4a8c..a11cbd2cd8 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -849,10 +849,10 @@ struct MyInt: def __radd__(self, lhs: Int) -> Self: return MyInt(self.value + lhs) - def __iadd__(inout self, rhs: MyInt) -> None: + def __iadd__(mut self, rhs: MyInt) -> None: self.value += rhs.value - def __iadd__(inout self, rhs: Int) -> None: + def __iadd__(mut self, rhs: Int) -> None: self.value += rhs ``` @@ -953,7 +953,7 @@ struct MySeq[type: CollectionElement]: fn __getitem__(self, idx: Int) -> type: # Return element at the given index ... - fn __setitem__(inout self, idx: Int, value: type): + fn __setitem__(mut self, idx: Int, value: type): # Assign the element at the given index the provided value ``` @@ -1116,7 +1116,7 @@ struct Complex( fn __str__(self) -> String: return String.write(self) - fn write_to[W: Writer](self, inout writer: W): + fn write_to[W: Writer](self, mut writer: W): writer.write("(", self.re) if self.im < 0: writer.write(" - ", -self.im) @@ -1169,7 +1169,7 @@ this example. else: raise "index out of bounds" - def __setitem__(inout self, idx: Int, value: Float64) -> None: + def __setitem__(mut self, idx: Int, value: Float64) -> None: if idx == 0: self.re = value elif idx == 1: @@ -1372,44 +1372,44 @@ f1 / c1 = (-0.068665598535133904 - 0.37193865873197529i) Now let's implement support for the in-place assignment operators: `+=`, `-=`, `*=`, and `/=`. These modify the original value, so we need to mark `self` as -being an `inout` argument and update the `re` and `im` fields instead of +being an `mut` argument and update the `re` and `im` fields instead of returning a new `Complex` instance. And once again, we'll overload the definitions to support both a `Complex` and a `Float64` operand. ```mojo # ... - def __iadd__(inout self, rhs: Self) -> None: + def __iadd__(mut self, rhs: Self) -> None: self.re += rhs.re self.im += rhs.im - def __iadd__(inout self, rhs: Float64) -> None: + def __iadd__(mut self, rhs: Float64) -> None: self.re += rhs - def __isub__(inout self, rhs: Self) -> None: + def __isub__(mut self, rhs: Self) -> None: self.re -= rhs.re self.im -= rhs.im - def __isub__(inout self, rhs: Float64) -> None: + def __isub__(mut self, rhs: Float64) -> None: self.re -= rhs - def __imul__(inout self, rhs: Self) -> None: + def __imul__(mut self, rhs: Self) -> None: new_re = self.re * rhs.re - self.im * rhs.im new_im = self.re * rhs.im + self.im * rhs.re self.re = new_re self.im = new_im - def __imul__(inout self, rhs: Float64) -> None: + def __imul__(mut self, rhs: Float64) -> None: self.re *= rhs self.im *= rhs - def __itruediv__(inout self, rhs: Self) -> None: + def __itruediv__(mut self, rhs: Self) -> None: denom = rhs.squared_norm() new_re = (self.re * rhs.re + self.im * rhs.im) / denom new_im = (self.im * rhs.re - self.re * rhs.im) / denom self.re = new_re self.im = new_im - def __itruediv__(inout self, rhs: Float64) -> None: + def __itruediv__(mut self, rhs: Float64) -> None: self.re /= rhs self.im /= rhs ``` diff --git a/docs/manual/packages.md b/docs/manual/packages.md index ec7f1b55ea..1b1b6927e0 100644 --- a/docs/manual/packages.md +++ b/docs/manual/packages.md @@ -23,7 +23,7 @@ struct MyPair: var first: Int var second: Int - fn __init__(inout self, first: Int, second: Int): + fn __init__(out self, first: Int, second: Int): self.first = first self.second = second diff --git a/docs/manual/parameters/index.mdx b/docs/manual/parameters/index.mdx index 788c87977a..be9066c974 100644 --- a/docs/manual/parameters/index.mdx +++ b/docs/manual/parameters/index.mdx @@ -402,7 +402,7 @@ struct MyStruct: fn __init__(out self): pass - fn foo(inout self): + fn foo(mut self): print("calling instance method") @staticmethod @@ -411,7 +411,7 @@ struct MyStruct: fn test_static_overload(): var a = MyStruct() - # `foo(inout self)` takes precedence over a static method. + # `foo(mut self)` takes precedence over a static method. a.foo() ``` diff --git a/docs/manual/pointers.mdx b/docs/manual/pointers.mdx index 37f0a84977..57482a3781 100644 --- a/docs/manual/pointers.mdx +++ b/docs/manual/pointers.mdx @@ -525,6 +525,6 @@ differences from `UnsafePointer` which make it safer: while the pointer exists. The `Pointer` type shouldn't be confused with the immutable and mutable -references used with the `borrowed` and `inout` argument conventions. Those +references used with the `read` and `mut` argument conventions. Those references do not require explicit dereferencing, unlike a `Pointer` or `UnsafePointer`. diff --git a/docs/manual/structs.mdx b/docs/manual/structs.mdx index 59acf95f66..92f0fdc1c0 100644 --- a/docs/manual/structs.mdx +++ b/docs/manual/structs.mdx @@ -50,8 +50,8 @@ struct MyPair: self.second = second ``` -Notice that the first argument in the `__init__()` method is `inout self`. For -now, ignore `inout` (it's an [argument +Notice that the first argument in the `__init__()` method is `out self`. For +now, ignore `out` (it's an [argument convention](/mojo/manual/values/ownership#argument-conventions) that declares `self` as a mutable reference); all you need to know right now is that `self` must be the first argument. It references the current struct instance @@ -310,7 +310,7 @@ When you add the `@value` decorator, Mojo synthesizes each special method above only if it doesn't exist already. That is, you can still implement a custom version of each method. -In addition to the `inout` argument convention you already saw with +In addition to the `out` argument convention you already saw with `__init__()`, this code also introduces `owned`, which is another argument convention that ensures the argument has unique ownership of the value. For more detail, see the section about [value diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index 4e1ae7b321..99da582c4b 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -441,7 +441,7 @@ struct Dog(Stringable, Representable, Writable): fn __str__(self) -> String: return String.write(self) - fn write_to[W: Writer](self, inout writer: W) -> None: + fn write_to[W: Writer](self, mut writer: W) -> None: writer.write("Dog(", self.name, ", ", self.age, ")") var dog = Dog("Rex", 5) diff --git a/docs/manual/values/lifetimes.mdx b/docs/manual/values/lifetimes.mdx index 24ff2b7982..39c2402f43 100644 --- a/docs/manual/values/lifetimes.mdx +++ b/docs/manual/values/lifetimes.mdx @@ -205,8 +205,8 @@ You can use the `ref` keyword with arguments and return values to specify a reference with parametric mutability. That is, they can be either mutable or immutable. -From inside the called function, a `ref` argument looks like a `borrowed` or -`inout` argument. +From inside the called function, a `ref` argument looks like a `read` or +`mut` argument. A `ref` return value looks like any other return value to the calling function, but it is a *reference* to an existing value, not a copy. @@ -282,7 +282,7 @@ Mutable: Goodbye ### `ref` return values Like `ref` arguments, `ref` return values allow a function to return a mutable -or immutable reference to a value. Like a `borrowed` or `inout` argument, these +or immutable reference to a value. Like a `read` or `mut` argument, these references don't need to be dereferenced. `ref` return values can be an efficient way to handle updating items in a diff --git a/docs/manual/values/ownership.mdx b/docs/manual/values/ownership.mdx index 5748afe073..d45ecc7c70 100644 --- a/docs/manual/values/ownership.mdx +++ b/docs/manual/values/ownership.mdx @@ -65,11 +65,11 @@ An argument convention specifies whether an argument is mutable or immutable, and whether the function owns the value. Each convention is defined by a keyword at the beginning of an argument declaration: -* `borrowed`: The function receives an **immutable reference**. This means the +* `read`: The function receives an **immutable reference**. This means the function can read the original value (it is *not* a copy), but it cannot mutate (modify) it. `def` functions treat this differently, as described below. -* `inout`: The function receives a **mutable reference**. This means the +* `mut`: The function receives a **mutable reference**. This means the function can read and mutate the original value (it is *not* a copy). * `owned`: The function takes **ownership** of a value. This means the function @@ -80,7 +80,7 @@ keyword at the beginning of an argument declaration: * `ref`: The function gets a reference with an parametric mutability: that is, the reference can be either mutable or immutable. You can think of `ref` - arguments as a generalization of the `borrowed` and `inout` conventions. + arguments as a generalization of the `read` and `mut` conventions. `ref` arguments are an advanced topic, and they're described in more detail in [Lifetimes and references](/mojo/manual/values/lifetimes). @@ -88,7 +88,7 @@ For example, this function has one argument that's a mutable reference and one that's immutable: ```mojo -fn add(inout x: Int, borrowed y: Int): +fn add(mut x: Int, read y: Int): x += y fn main(): @@ -99,14 +99,14 @@ fn main(): ``` You've probably already seen some function arguments that don't declare a -convention. by default, all arguments are `borrowed`. +convention. by default, all arguments are `read`. In the following sections, we'll explain each of these argument conventions in more detail. -## Borrowed arguments (`borrowed`) +## Borrowed arguments (`read`) -The `borrowed` convention is the default for all arguments. But `def` and `fn` -functions treat `borrowed` arguments somewhat differently: +The `read` convention is the default for all arguments. But `def` and `fn` +functions treat `read` arguments somewhat differently: * In a [`def` function](/mojo/manual/functions#def-functions), if you mutate the value in the body of the function, the function receives a mutable copy of @@ -119,7 +119,7 @@ functions treat `borrowed` arguments somewhat differently: assign it to a local variable: ```mojo - var my_copy = borrowed_arg + var my_copy = read_arg ``` In both cases, the original value on the caller side can't be changed by the @@ -142,7 +142,7 @@ print_list(list) [1, 2, 3, 4] ``` -Here the `list` argument to `print_list()` is borrowed and not mutated, so the +Here the `list` argument to `print_list()` is read and not mutated, so the `print_list()` function gets an immutable reference to the original `List`, and doesn't do any copying. @@ -152,9 +152,9 @@ and destructor are not invoked for a borrow. ### Compared to C++ and Rust -Mojo's borrowed argument convention is similar in some ways to passing an +Mojo's read argument convention is similar in some ways to passing an argument by `const&` in C++, which also avoids a copy of the value and disables -mutability in the callee. However, the borrowed convention differs from +mutability in the callee. However, the read convention differs from `const&` in C++ in two important ways: * The Mojo compiler implements a lifetime checker that ensures that values are @@ -174,10 +174,10 @@ passing small values, and Rust defaults to moving values instead of passing them around by borrow. These policy and syntax decisions allow Mojo to provide an easier-to-use programming model. -## Mutable arguments (`inout`) +## Mutable arguments (`mut`) -If you'd like your function to receive a **mutable reference**, add the `inout` -keyword in front of the argument name. You can think of `inout` like this: it +If you'd like your function to receive a **mutable reference**, add the `mut` +keyword in front of the argument name. You can think of `mut` like this: it means any changes to the value *in*side the function are visible *out*side the function. @@ -186,7 +186,7 @@ For example, this `mutate()` function updates the original `list` value: ```mojo from collections import List -def mutate(inout l: List[Int]): +def mutate(mut l: List[Int]): l.append(5) var list = List(1, 2, 3, 4) @@ -217,18 +217,18 @@ print_list(list) [1, 2, 3, 4, 5] ``` -Although the code using `inout` isn't that much shorter, it's more memory +Although the code using `mut` isn't that much shorter, it's more memory efficient because it does not make a copy of the value. -However, remember that the values passed as `inout` must already be mutable. -For example, if you try to take a `borrowed` value and pass it to another -function as `inout`, you'll get a compiler error because Mojo can't form a +However, remember that the values passed as `mut` must already be mutable. +For example, if you try to take a `read` value and pass it to another +function as `mut`, you'll get a compiler error because Mojo can't form a mutable reference from an immutable reference. :::note You cannot define [default -values](/mojo/manual/functions#optional-arguments) for `inout` +values](/mojo/manual/functions#optional-arguments) for `mut` arguments. ::: @@ -236,14 +236,14 @@ arguments. ### Argument exclusivity Mojo enforces *argument exclusivity* for mutable references. This means that if -a function receives a mutable reference to a value (such as an `inout` argument), +a function receives a mutable reference to a value (such as an `mut` argument), it can't receive any other references to the same value—mutable or immutable. That is, a mutable reference can't have any other references that *alias* it. For example, consider the following code example: ```mojo -fn append_twice(inout s: String, other: String): +fn append_twice(mut s: String, other: String): # Mojo knows 's' and 'other' cannot be the same string. s += other s += other @@ -251,8 +251,8 @@ fn append_twice(inout s: String, other: String): fn invalid_access(): var my_string = str("o") - # error: passing `my_string` inout is invalid since it is also passed - # borrowed. + # error: passing `my_string` mut is invalid since it is also passed + # read. append_twice(my_string, my_string) print(my_string) ``` @@ -374,7 +374,7 @@ destroyed at the end of the function. ```mojo from collections import List -def add_to_list(owned name: String, inout list: List[String]): +def add_to_list(owned name: String, mut list: List[String]): list.append(name^) # name is uninitialized, nothing to destroy @@ -429,16 +429,16 @@ function is essentially just sugaring for the `fn` function: [`object`](/mojo/stdlib/builtin/object/object) type (whereas as `fn` requires all types be explicitly declared). -* A `def` function can treat a `borrowed` argument as mutable (in which case it +* A `def` function can treat a `read` argument as mutable (in which case it receives a mutable copy). An `fn` function must make this copy explicitly. For example, these two functions have the exact same behavior. ```mojo -def def_example(a: Int, inout b: Int, owned c): +def def_example(a: Int, mut b: Int, owned c): pass -fn fn_example(a_in: Int, inout b: Int, owned c: object): +fn fn_example(a_in: Int, mut b: Int, owned c: object): var a = a_in pass ``` @@ -450,12 +450,12 @@ copying large numbers of strings.) ### Borrowed versus owned in `def` functions -The difference between `borrowed` and `owned` in a `def` function may be a +The difference between `read` and `owned` in a `def` function may be a little subtle. In both cases, you can end up with a uniquely-owned value that's a copy of the original value. -* The `borrowed` argument always gets an immutable reference or a local copy. - You can't transfer a value into a `borrowed` argument. +* The `read` argument always gets an immutable reference or a local copy. + You can't transfer a value into a `read` argument. * The `owned` argument always gets a uniquely owned value, which may have been copied or transferred from the callee. Using `owned` arguments without the diff --git a/docs/manual/values/value-semantics.mdx b/docs/manual/values/value-semantics.mdx index 403db993b6..58b1896f2c 100644 --- a/docs/manual/values/value-semantics.mdx +++ b/docs/manual/values/value-semantics.mdx @@ -120,9 +120,9 @@ and only make a copy when it's explicitly requested. The arguments above are mutable because a [`def` function](/mojo/manual/functions#def-functions) has special treatment for the default -[`borrowed` argument convention](/mojo/manual/values/ownership#argument-conventions). +[`read` argument convention](/mojo/manual/values/ownership#argument-conventions). -Whereas, `fn` functions always receive `borrowed` arguments as immutable +Whereas, `fn` functions always receive `read` arguments as immutable references. This is a memory optimization to avoid making unnecessary copies. diff --git a/stdlib/docs/bencher/Bench.md b/stdlib/docs/bencher/Bench.md index 1e71a29c53..19133b572f 100644 --- a/stdlib/docs/bencher/Bench.md +++ b/stdlib/docs/bencher/Bench.md @@ -33,7 +33,7 @@ Defines the main Benchmark struct which executes a Benchmark and print result.
```mojo -__init__(inout self: Self, config: Optional[BenchConfig] = #kgen.none, mode: Mode = 0) +__init__(out self: Self, config: Optional[BenchConfig] = #kgen.none, mode: Mode = 0) ```
@@ -56,7 +56,7 @@ Constructs a Benchmark object based on specific configuration and mode.
```mojo -bench_with_input[T: AnyType, bench_fn: fn(inout Bencher, $0) capturing -> None](inout self: Self, bench_id: BenchId, input: T, throughput_elems: Optional[Int] = #kgen.none) +bench_with_input[T: AnyType, bench_fn: fn(mut Bencher, $0) capturing -> None](mut self: Self, bench_id: BenchId, input: T, throughput_elems: Optional[Int] = #kgen.none) ```
@@ -66,7 +66,7 @@ Benchmarks an input function with input args of type AnyType. **Parameters:** - ​T (`AnyType`): Benchmark function input type. -- ​bench_fn (`fn(inout Bencher, $0) capturing -> None`): The function to +- ​bench_fn (`fn(mut Bencher, $0) capturing -> None`): The function to be benchmarked. **Args:** @@ -83,7 +83,7 @@ Benchmarks an input function with input args of type AnyType.
```mojo -bench_with_input[T: AnyTrivialRegType, bench_fn: fn(inout Bencher, $0) capturing -> None](inout self: Self, bench_id: BenchId, input: T, throughput_elems: Optional[Int] = #kgen.none) +bench_with_input[T: AnyTrivialRegType, bench_fn: fn(mut Bencher, $0) capturing -> None](mut self: Self, bench_id: BenchId, input: T, throughput_elems: Optional[Int] = #kgen.none) ```
@@ -93,7 +93,7 @@ Benchmarks an input function with input args of type AnyTrivialRegType. **Parameters:** - ​T (`AnyTrivialRegType`): Benchmark function input type. -- ​bench_fn (`fn(inout Bencher, $0) capturing -> None`): The function to +- ​bench_fn (`fn(mut Bencher, $0) capturing -> None`): The function to be benchmarked. **Args:** @@ -112,7 +112,7 @@ Benchmarks an input function with input args of type AnyTrivialRegType.
```mojo -bench_function[bench_fn: fn(inout Bencher) capturing -> None](inout self: Self, bench_id: BenchId, throughput_elems: Optional[Int] = #kgen.none) +bench_function[bench_fn: fn(mut Bencher) capturing -> None](mut self: Self, bench_id: BenchId, throughput_elems: Optional[Int] = #kgen.none) ```
@@ -121,7 +121,7 @@ Benchmarks or Tests an input function. **Parameters:** -- ​bench_fn (`fn(inout Bencher) capturing -> None`): The function to be +- ​bench_fn (`fn(mut Bencher) capturing -> None`): The function to be benchmarked. **Args:** @@ -137,7 +137,7 @@ Benchmarks or Tests an input function.
```mojo -bench_function[bench_fn: fn(inout Bencher) raises capturing -> None](inout self: Self, bench_id: BenchId, throughput_elems: Optional[Int] = #kgen.none) +bench_function[bench_fn: fn(mut Bencher) raises capturing -> None](mut self: Self, bench_id: BenchId, throughput_elems: Optional[Int] = #kgen.none) ```
@@ -146,7 +146,7 @@ Benchmarks or Tests an input function. **Parameters:** -- ​bench_fn (`fn(inout Bencher) raises capturing -> None`): The function +- ​bench_fn (`fn(mut Bencher) raises capturing -> None`): The function to be benchmarked. **Args:** diff --git a/stdlib/docs/bencher/BenchConfig.md b/stdlib/docs/bencher/BenchConfig.md index cebb46f13f..8a83984d5e 100644 --- a/stdlib/docs/bencher/BenchConfig.md +++ b/stdlib/docs/bencher/BenchConfig.md @@ -48,7 +48,7 @@ frequency.
```mojo -__init__(inout self: out_file: Optional[Path] = None, min_runtime_secs: SIMD[float64, 1] = 1.0, max_runtime_secs: SIMD[float64, 1] = 2.0, min_warmuptime_secs: SIMD[float64, 1] = 1.0, max_batch_size: Int = 0, max_iters: Int = 1000000000, num_repetitions: Int = 1, flush_denormals: Bool = True) +__init__(out self: out_file: Optional[Path] = None, min_runtime_secs: SIMD[float64, 1] = 1.0, max_runtime_secs: SIMD[float64, 1] = 2.0, min_warmuptime_secs: SIMD[float64, 1] = 1.0, max_batch_size: Int = 0, max_iters: Int = 1000000000, num_repetitions: Int = 1, flush_denormals: Bool = True) ```
diff --git a/stdlib/docs/bencher/BenchId.md b/stdlib/docs/bencher/BenchId.md index e305f890fe..0d215bd972 100644 --- a/stdlib/docs/bencher/BenchId.md +++ b/stdlib/docs/bencher/BenchId.md @@ -30,7 +30,7 @@ execution.
-`__init__(inout self: Self, func_name: String, input_id: String)` +`__init__(out self: Self, func_name: String, input_id: String)`
@@ -47,7 +47,7 @@ Constructs a Benchmark Id object from input function name and Id phrase.
-`__init__(inout self: Self, func_name: String)` +`__init__(out self: Self, func_name: String)`
diff --git a/stdlib/docs/bencher/Bencher.md b/stdlib/docs/bencher/Bencher.md index 4270a7a714..91d68e80d8 100644 --- a/stdlib/docs/bencher/Bencher.md +++ b/stdlib/docs/bencher/Bencher.md @@ -30,7 +30,7 @@ Defines a Bencher struct which facilitates the timing of a target function.
-`__init__(inout self: Self, num_iters: Int)` +`__init__(out self: Self, num_iters: Int)`
@@ -48,7 +48,7 @@ Constructs a Bencher object to run and time a function.
-`iter[iter_fn: fn() capturing -> None](inout self: Self)` +`iter[iter_fn: fn() capturing -> None](mut self: Self)`
@@ -65,7 +65,7 @@ of times.
-`iter[iter_fn: fn() raises capturing -> None](inout self: Self)` +`iter[iter_fn: fn() raises capturing -> None](mut self: Self)`
@@ -85,7 +85,7 @@ of times.
-`iter_custom[iter_fn: fn(Int) capturing -> Int](inout self: Self)` +`iter_custom[iter_fn: fn(Int) capturing -> Int](mut self: Self)`
@@ -101,7 +101,7 @@ Times a target function with custom number of iterations.
-`iter_custom[iter_fn: fn(Int) raises capturing -> Int](inout self: Self)` +`iter_custom[iter_fn: fn(Int) raises capturing -> Int](mut self: Self)`
diff --git a/stdlib/docs/bencher/BenchmarkInfo.md b/stdlib/docs/bencher/BenchmarkInfo.md index 88eafec487..52b2922fa0 100644 --- a/stdlib/docs/bencher/BenchmarkInfo.md +++ b/stdlib/docs/bencher/BenchmarkInfo.md @@ -33,7 +33,7 @@ Defines a Benchmark Info struct to record execution Statistics.
-`__init__(inout self: Self, name: String, result: Report, elems: Optional[Int])` +`__init__(out self: Self, name: String, result: Report, elems: Optional[Int])`
diff --git a/stdlib/docs/style-guide.md b/stdlib/docs/style-guide.md index f1c135cf4f..dc07d62088 100644 --- a/stdlib/docs/style-guide.md +++ b/stdlib/docs/style-guide.md @@ -283,7 +283,7 @@ the same type: ```mojo struct MyStruct: # Invoked as `MyStruct(other)` - fn __init__(inout self, other: Self): + fn __init__(out self, other: Self): # do a deep copy of MyStruct ``` From 1341e18488fe0b49a2dc8dca031fe501d0f38393 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 1 Dec 2024 09:29:25 -0800 Subject: [PATCH 005/509] [mojo-lang][KGEN] Rename argument convention enums This renames the ArgConvention enums over to follow the new naming in the Mojo source. This also makes a variety of changes to improve consistency in the codebase, cleaning up old remnants of 'inout' etc. MODULAR_ORIG_COMMIT_REV_ID: 117874f7161ed1df3aa1db967cd42678c6e358a2 --- proposals/improved-hash-module.md | 44 ++++++++++++------------- proposals/opt-in-implicit-conversion.md | 2 +- proposals/remove-let-decls.md | 2 +- stdlib/src/builtin/builtin_list.mojo | 4 +-- 4 files changed, 26 insertions(+), 26 deletions(-) diff --git a/proposals/improved-hash-module.md b/proposals/improved-hash-module.md index c2f9c460fb..ee2bd138f5 100644 --- a/proposals/improved-hash-module.md +++ b/proposals/improved-hash-module.md @@ -62,16 +62,16 @@ of the `Hashable` trait. ```mojo trait Hasher: """Trait which every hash function implementer needs to implement.""" - fn __init__(inout self): + fn __init__(out self): """Expects a no argument instantiation.""" ... - fn _update_with_bytes(inout self, bytes: DTypePointer[DType.uint8], n: Int): + fn _update_with_bytes(mut self, bytes: DTypePointer[DType.uint8], n: Int): """Conribute to the hash value based on a sequence of bytes. Use only for complex types which are not just a composition of Hashable types.""" ... - fn _update_with_simd[dt: DType, size: Int](inout self, value: SIMD[dt, size]): + fn _update_with_simd[dt: DType, size: Int](mut self, value: SIMD[dt, size]): """Contribute to the hash value with a compile time know fix size value. Used inside of std lib to avoid runtime branching.""" ... - fn update[T: Hashable](inout self, value: T): + fn update[T: Hashable](mut self, value: T): """Contribute to the hash value with a Hashable value. Should be used by implementors of Hashable types which are a composition of Hashable types.""" ... fn _finish[dt: DType = DType.uint64](owned self) -> Scalar[dt]: @@ -93,13 +93,13 @@ Below you can see a dummy implementation of a `DefaultHasher` struct DefaultHasher(Hasher): var hash: UInt64 - fn __init__(inout self): + fn __init__(out self): self.hash = 42 - fn _update_with_bytes(inout self, bytes: DTypePointer[DType.uint8], n: Int): + fn _update_with_bytes(mut self, bytes: DTypePointer[DType.uint8], n: Int): ... - fn _update_with_simd[dt: DType, size: Int](inout self, value: SIMD[dt, size]): + fn _update_with_simd[dt: DType, size: Int](mut self, value: SIMD[dt, size]): ... - fn update[T: Hashable](inout self, value: T): + fn update[T: Hashable](mut self, value: T): ... fn _finish[dt: DType = DType.uint64](owned self) -> Scalar[dt]: return self.hash.cast[dt]() @@ -112,7 +112,7 @@ data flow paradigm instead of call return. ```mojo trait Hashable: - fn hash_with[H: Hasher](self, inout hasher: H): + fn hash_with[H: Hasher](self, mut hasher: H): ... ``` @@ -128,7 +128,7 @@ struct Person(Hashable): var name: String var age: Int - fn __hash__[H: Hasher](self, inout hasher: H): + fn __hash__[H: Hasher](self, mut hasher: H): hasher.update(self.name) hasher.update(self.age) ``` @@ -155,21 +155,21 @@ from random import random_si64 trait Hashable: """Trait which every hashable type needs to implement.""" - fn __hash__[H: Hasher](self, inout hasher: H): + fn __hash__[H: Hasher](self, mut hasher: H): ... trait Hasher: """Trait which every hash function implementer needs to implement.""" - fn __init__(inout self): + fn __init__(out self): """Expects a no argument instantiation.""" ... - fn _update_with_bytes(inout self, bytes: DTypePointer[DType.uint8], n: Int): + fn _update_with_bytes(mut self, bytes: DTypePointer[DType.uint8], n: Int): """Conribute to the hash value based on a sequence of bytes. Use only for complex types which are not just a composition of Hashable types.""" ... - fn _update_with_simd[dt: DType, size: Int](inout self, value: SIMD[dt, size]): + fn _update_with_simd[dt: DType, size: Int](mut self, value: SIMD[dt, size]): """Contribute to the hash value with a compile time know fix size value. Used inside of std lib to avoid runtime branching.""" ... - fn update[T: Hashable](inout self, value: T): + fn update[T: Hashable](mut self, value: T): """Contribute to the hash value with a Hashable value. Should be used by implementors of Hashable types which are a composition of Hashable types.""" ... fn _finish[dt: DType = DType.uint64](owned self) -> Scalar[dt]: @@ -184,7 +184,7 @@ struct MyInt(Hashable): var value: Int @always_inline - fn __hash__[H: Hasher](self, inout hasher: H): + fn __hash__[H: Hasher](self, mut hasher: H): hasher._update_with_simd(Int64(self.value)) @value @@ -193,7 +193,7 @@ struct MyString(Hashable): var value: StringLiteral @always_inline - fn __hash__[H: Hasher](self, inout hasher: H): + fn __hash__[H: Hasher](self, mut hasher: H): hasher.update(MyInt(len(self.value))) hasher._update_with_bytes(self.value.data().bitcast[DType.uint8](), len(self.value)) @@ -203,7 +203,7 @@ struct Person(Hashable): var name: MyString var age: MyInt - fn __hash__[H: Hasher](self, inout hasher: H): + fn __hash__[H: Hasher](self, mut hasher: H): hasher.update(self.name) hasher.update(self.age) @@ -235,7 +235,7 @@ struct DJBX33A_Hasher[custom_secret: UInt64 = 0](Hasher): var secret: UInt64 @always_inline - fn __init__(inout self): + fn __init__(out self): self.hash_data = 5361 @parameter if custom_secret != 0: @@ -244,13 +244,13 @@ struct DJBX33A_Hasher[custom_secret: UInt64 = 0](Hasher): self.secret = _DJBX33A_SECRET() @always_inline - fn _update_with_bytes(inout self, bytes: DTypePointer[DType.uint8], n: Int): + fn _update_with_bytes(mut self, bytes: DTypePointer[DType.uint8], n: Int): """The algorithm is not optimal.""" for i in range(n): self.hash_data = self.hash_data * 33 + bytes.load(i).cast[DType.uint64]() @always_inline - fn _update_with_simd[dt: DType, size: Int](inout self, value: SIMD[dt, size]): + fn _update_with_simd[dt: DType, size: Int](mut self, value: SIMD[dt, size]): """The algorithm is not optimal.""" alias size_in_bytes = size * dt.sizeof() var bytes = bitcast[DType.uint8, size_in_bytes](value) @@ -259,7 +259,7 @@ struct DJBX33A_Hasher[custom_secret: UInt64 = 0](Hasher): self.hash_data = self.hash_data * 33 + bytes[i].cast[DType.uint64]() @always_inline - fn update[T: Hashable](inout self, value: T): + fn update[T: Hashable](mut self, value: T): value.__hash__(self) @always_inline diff --git a/proposals/opt-in-implicit-conversion.md b/proposals/opt-in-implicit-conversion.md index ea3858da06..e2d7dc7e81 100644 --- a/proposals/opt-in-implicit-conversion.md +++ b/proposals/opt-in-implicit-conversion.md @@ -69,7 +69,7 @@ let foo: Foo = 10 ```python struct Foo: @implicit_conversion - fn __init__(inout self, i: Int): + fn __init__(out self, i: Int): pass var foo: Foo = 10 diff --git a/proposals/remove-let-decls.md b/proposals/remove-let-decls.md index ec09b19359..34f8eb3b7c 100644 --- a/proposals/remove-let-decls.md +++ b/proposals/remove-let-decls.md @@ -68,7 +68,7 @@ a great way to define defaulted field values, e.g.: struct Thing: # This is not actually supported right now, but imagine it were. let field = 42 - fn __init__(inout self): + fn __init__(out self): self.field = 17 # shouldn't be able to overwrite field? ``` diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 0272fbc2d6..be5ddaaa05 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -311,7 +311,7 @@ struct VariadicListMem[ alias reference_type = Pointer[element_type, origin] alias _mlir_ref_type = Self.reference_type._mlir_type alias _mlir_type = __mlir_type[ - `!kgen.variadic<`, Self._mlir_ref_type, `, borrow_in_mem>` + `!kgen.variadic<`, Self._mlir_ref_type, `, read_mem>` ] var value: Self._mlir_type @@ -343,7 +343,7 @@ struct VariadicListMem[ # automatically be inferred to be mutable, and the !kgen.variadic will have # convention=mut. alias _inout_variadic_type = __mlir_type[ - `!kgen.variadic<`, Self._mlir_ref_type, `, inout>` + `!kgen.variadic<`, Self._mlir_ref_type, `, mut>` ] @always_inline From 86bc06d463c0724007a79d8c3cead51c5da00e96 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sun, 1 Dec 2024 15:05:58 -0800 Subject: [PATCH 006/509] [******][GPU] Introduce a new GridIdx operation MODULAR_ORIG_COMMIT_REV_ID: 5ffe1df491c0c3f092ed645ffbe0756ebceeec04 --- stdlib/src/math/math.mojo | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 0c06a6b00a..2c7ee78a5d 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -1154,6 +1154,23 @@ fn fma(a: Int, b: Int, c: Int) -> Int: return a * b + c +@always_inline +fn fma(a: UInt, b: UInt, c: UInt) -> UInt: + """Performs `fma` (fused multiply-add) on the inputs. + + The result is `(a * b) + c`. + + Args: + a: The first input. + b: The second input. + c: The third input. + + Returns: + `(a * b) + c`. + """ + return a * b + c + + @always_inline("nodebug") fn fma[ type: DType, simd_width: Int From d02bb01500d7d022610e28bbcd65e7c61224daec Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Mon, 2 Dec 2024 13:00:47 -0700 Subject: [PATCH 007/509] [stdlib] Simplify `String.split` with isspace Now that `StringSlice.isspace()` exists, use it in `String.split` to avoid the `str(...)` calls. MODULAR_ORIG_COMMIT_REV_ID: 9cdd0d76aa059456be6bcdf79301e09bcf2730ec --- stdlib/src/collections/string.mojo | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index d75d2098cd..a6051bbb81 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -1816,7 +1816,7 @@ struct String( # Python adds all "whitespace chars" as one separator # if no separator was specified for s in self[lhs:]: - if not str(s).isspace(): # TODO: with StringSlice.isspace() + if not s.isspace(): break lhs += s.byte_length() # if it went until the end of the String, then @@ -1830,7 +1830,7 @@ struct String( break rhs = lhs + num_bytes(self.unsafe_ptr()[lhs]) for s in self[lhs + num_bytes(self.unsafe_ptr()[lhs]) :]: - if str(s).isspace(): # TODO: with StringSlice.isspace() + if s.isspace(): break rhs += s.byte_length() From 57f4962e2eb730b6b3a59b18c061fad0e7c1a698 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Mon, 2 Dec 2024 12:39:52 -0800 Subject: [PATCH 008/509] [External] [stdlib] Optimize `_StringSliceIter` to not have branching in forward iteration (#51441) [External] [stdlib] Optimize `_StringSliceIter` to not have branching in forward iteration Optimize `_StringSliceIter` to not have branching in forward iteration. This also fixes an error in the iterator's `__len__` logic which assumed it would be called only at the beginning of the iteration Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3546 MODULAR_ORIG_COMMIT_REV_ID: 0919af33312b082a4bac7d1547a193fba378e56f --- stdlib/src/utils/string_slice.mojo | 58 ++++++++++-------------- stdlib/test/collections/test_string.mojo | 30 ++++++------ 2 files changed, 42 insertions(+), 46 deletions(-) diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 48be446549..666a25c45c 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -80,13 +80,9 @@ fn _utf8_first_byte_sequence_length(b: Byte) -> Int: debug_assert( (b & 0b1100_0000) != 0b1000_0000, - ( - "Function `_utf8_first_byte_sequence_length()` does not work" - " correctly if given a continuation byte." - ), + "Function does not work correctly if given a continuation byte.", ) - var flipped = ~b - return int(count_leading_zeros(flipped) + (flipped >> 7)) + return int(count_leading_zeros(~b)) + int(b < 0b1000_0000) fn _shift_unicode_to_utf8(ptr: UnsafePointer[UInt8], c: Int, num_bytes: Int): @@ -186,17 +182,13 @@ struct _StringSliceIter[ """ var index: Int - var continuation_bytes: Int - var ptr: UnsafePointer[UInt8] + var ptr: UnsafePointer[Byte] var length: Int - fn __init__(mut self, *, unsafe_pointer: UnsafePointer[UInt8], length: Int): + fn __init__(mut self, *, unsafe_pointer: UnsafePointer[Byte], length: Int): self.index = 0 if forward else length self.ptr = unsafe_pointer self.length = length - alias S = Span[Byte, StaticConstantOrigin] - var s = S(ptr=self.ptr, length=self.length) - self.continuation_bytes = _count_utf8_continuation_bytes(s) fn __iter__(self) -> Self: return self @@ -204,26 +196,14 @@ struct _StringSliceIter[ fn __next__(mut self) -> StringSlice[origin]: @parameter if forward: - var byte_len = 1 - if self.continuation_bytes > 0: - var byte_type = _utf8_byte_type(self.ptr[self.index]) - if byte_type != 0: - byte_len = int(byte_type) - self.continuation_bytes -= byte_len - 1 + byte_len = _utf8_first_byte_sequence_length(self.ptr[self.index]) + i = self.index self.index += byte_len - return StringSlice[origin]( - ptr=self.ptr + (self.index - byte_len), length=byte_len - ) + return StringSlice[origin](ptr=self.ptr + i, length=byte_len) else: - var byte_len = 1 - if self.continuation_bytes > 0: - var byte_type = _utf8_byte_type(self.ptr[self.index - 1]) - if byte_type != 0: - while byte_type == 1: - byte_len += 1 - var b = self.ptr[self.index - byte_len] - byte_type = _utf8_byte_type(b) - self.continuation_bytes -= byte_len - 1 + byte_len = 1 + while _utf8_byte_type(self.ptr[self.index - byte_len]) == 1: + byte_len += 1 self.index -= byte_len return StringSlice[origin]( ptr=self.ptr + self.index, length=byte_len @@ -231,14 +211,26 @@ struct _StringSliceIter[ @always_inline fn __has_next__(self) -> Bool: - return self.__len__() > 0 + @parameter + if forward: + return self.index < self.length + else: + return self.index > 0 fn __len__(self) -> Int: @parameter if forward: - return self.length - self.index - self.continuation_bytes + remaining = self.length - self.index + cont = _count_utf8_continuation_bytes( + Span[Byte, ImmutableAnyOrigin]( + ptr=self.ptr + self.index, length=remaining + ) + ) + return remaining - cont else: - return self.index - self.continuation_bytes + return self.index - _count_utf8_continuation_bytes( + Span[Byte, ImmutableAnyOrigin](ptr=self.ptr, length=self.index) + ) @value diff --git a/stdlib/test/collections/test_string.mojo b/stdlib/test/collections/test_string.mojo index c70ff3a50c..da23f5d9c7 100644 --- a/stdlib/test/collections/test_string.mojo +++ b/stdlib/test/collections/test_string.mojo @@ -1273,19 +1273,23 @@ def test_string_iter(): var idx = -1 vs = String("mojo🔥") - for item in vs: - idx += 1 - if idx == 0: - assert_equal("m", item) - elif idx == 1: - assert_equal("o", item) - elif idx == 2: - assert_equal("j", item) - elif idx == 3: - assert_equal("o", item) - elif idx == 4: - assert_equal("🔥", item) - assert_equal(4, idx) + var iterator = vs.__iter__() + assert_equal(5, len(iterator)) + var item = iterator.__next__() + assert_equal("m", item) + assert_equal(4, len(iterator)) + item = iterator.__next__() + assert_equal("o", item) + assert_equal(3, len(iterator)) + item = iterator.__next__() + assert_equal("j", item) + assert_equal(2, len(iterator)) + item = iterator.__next__() + assert_equal("o", item) + assert_equal(1, len(iterator)) + item = iterator.__next__() + assert_equal("🔥", item) + assert_equal(0, len(iterator)) var items = List[String]( "mojo🔥", From 4a01dbb17438c851a8d2cc9887a72e611768d28e Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 3 Dec 2024 02:01:11 +0000 Subject: [PATCH 009/509] Update lockfiles to point to latest nightly version: 24.6.0.dev2024120221 --- examples/life/magic.lock | 427 ++++++++++++++------------- examples/magic.lock | 427 ++++++++++++++------------- examples/notebooks/magic.lock | 528 +++++++++++++++++----------------- examples/operators/magic.lock | 427 ++++++++++++++------------- magic.lock | 427 ++++++++++++++------------- 5 files changed, 1151 insertions(+), 1085 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 57221ff76d..8325a74676 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -8,8 +8,8 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.11.8-py312h178313f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.11.9-py312h178313f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.13-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda @@ -26,10 +26,10 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.1-hab05fe4_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.2-hdeadb07_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h7bd072d_8.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.2-h3a84f74_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.3-h3a84f74_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.1-hf42f96a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-hf42f96a_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.6-h0e61686_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.6-hdec8b0f_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.449-h5558e3c_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.14.0-h5cfcd09_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda @@ -84,7 +84,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/httptools-0.6.4-py312h66e93f0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.26.2-pyh0610db2_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.26.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda @@ -173,12 +173,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_0.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-24.6.0.dev2024120105-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-24.6.0.dev2024120105-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-24.6.0.dev2024120105-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-24.6.0.dev2024120105-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-24.6.0.dev2024120221-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-24.6.0.dev2024120221-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-24.6.0.dev2024120221-3.12release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-24.6.0.dev2024120221-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_0.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-24.6.0.dev2024120105-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-24.6.0.dev2024120221-release.conda - 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kind: conda name: typing_extensions version: 4.12.2 - build: pyha770c72_0 + build: pyha770c72_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_0.conda - sha256: 0fce54f8ec3e59f5ef3bb7641863be4e1bf1279623e5af3d3fa726e8f7628ddb - md5: ebe6952715e1d5eb567eeebf25250fa7 + url: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_1.conda + sha256: 337be7af5af8b2817f115b3b68870208b30c31d3439bec07bfb2d8f4823e3568 + md5: d17f13df8b65464ca316cbc000a3cb64 depends: - - python >=3.8 + - python >=3.9 license: PSF-2.0 license_family: PSF - size: 39888 - timestamp: 1717802653893 + size: 39637 + timestamp: 1733188758212 - kind: conda name: tzdata version: 2024b From 30fa4c3a3dce868617a3f74bff1f81909563338f Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 2 Dec 2024 14:15:19 -0800 Subject: [PATCH 010/509] [******][GPU] Disable print functionality on AMD GPUs MODULAR_ORIG_COMMIT_REV_ID: c167530a4bb665c5176661e38257dcd419203882 --- stdlib/src/builtin/io.mojo | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index 259f065987..c0b55f993e 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -266,6 +266,12 @@ fn print[ flush: If set to true, then the stream is forcibly flushed. file: The output stream. """ + + # TODO(MSTDL-1027): Print on AMD GPUs is not implemented yet. + @parameter + if is_amd_gpu(): + return + write_buffered[buffer_size=4096](file, values, sep=sep, end=end) @parameter From a4f67f16b8f334d7d2a9cd6722f2655a5d9d85e8 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 2 Dec 2024 18:33:08 -0800 Subject: [PATCH 011/509] [mojo-stdlib] Minor cleanups for `Tuple`, NFC. These are just minor improvements to `Tuple` from a walkthrough with @VerdagonModular. MODULAR_ORIG_COMMIT_REV_ID: 352c9d3bc6494f4696a405bf3d992cfced5dce89 --- stdlib/src/builtin/tuple.mojo | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index 50fdf9f708..00bede563f 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -81,8 +81,10 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): UnsafePointer.address_of(self[i]) ) - # Mark the elements as destroyed. - storage._is_owned = False + # Do not destroy the elements when 'storage' goes away. + __mlir_op.`lit.ownership.mark_destroyed`( + __get_mvalue_as_litref(storage) + ) fn __del__(owned self): """Destructor that destroys all of the elements.""" @@ -126,6 +128,7 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): UnsafePointer.address_of(existing[i]).move_pointee_into( UnsafePointer.address_of(self[i]) ) + # Note: The destructor on `existing` is auto-disabled 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build: pyhd8ed1ab_2 + build_number: 2 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhff2d567_1.conda - sha256: 74843f871e5cd8a1baf5ed8c406c571139c287141efe532f8ffbdafa3664d244 - md5: 8508b703977f4c4ada34d657d051972c + url: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda + sha256: da157b19bcd398b9804c5c52fc000fcb8ab0525bdb9c70f95beaa0bb42f85af1 + md5: 3bfed7e6228ebf2f7b9eaa47f1b4e2aa depends: - python >=3.8 license: Apache-2.0 license_family: APACHE - size: 60380 - timestamp: 1731802602808 + size: 60164 + timestamp: 1733203368787 - kind: conda name: pandas version: 2.2.3 @@ -7170,18 +7175,20 @@ packages: - kind: conda name: pycparser version: '2.22' - build: pyhd8ed1ab_0 + build: pyh29332c3_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda - sha256: 406001ebf017688b1a1554b49127ca3a4ac4626ec0fd51dc75ffa4415b720b64 - md5: 844d9eb3b43095b031874477f7d70088 + url: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda + sha256: 79db7928d13fab2d892592223d7570f5061c192f27b9febd1a418427b719acc6 + md5: 12c566707c80111f9799308d9e265aef depends: - - python >=3.8 + - python >=3.9 + - python license: BSD-3-Clause license_family: BSD - size: 105098 - timestamp: 1711811634025 + size: 110100 + timestamp: 1733195786147 - kind: conda name: pydantic version: 2.10.2 @@ -7451,19 +7458,19 @@ packages: - kind: conda name: python-dateutil version: 2.9.0.post0 - build: pyhff2d567_0 + build: pyhff2d567_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_0.conda - sha256: 3888012c5916efaef45d503e3e544bbcc571b84426c1bb9577799ada9efefb54 - md5: b6dfd90a2141e573e4b6a81630b56df5 + url: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda + sha256: a50052536f1ef8516ed11a844f9413661829aa083304dc624c5925298d078d79 + md5: 5ba79d7c71f03c678c8ead841f347d6e depends: - python >=3.9 - six >=1.5 license: Apache-2.0 - license_family: APACHE - size: 221925 - timestamp: 1731919374686 + size: 222505 + timestamp: 1733215763718 - kind: conda name: python-dotenv version: 1.0.1 @@ -7905,24 +7912,24 @@ packages: - kind: conda name: requests version: 2.32.3 - build: pyhd8ed1ab_0 + build: pyhd8ed1ab_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda - sha256: 5845ffe82a6fa4d437a2eae1e32a1ad308d7ad349f61e337c0a890fe04c513cc - md5: 5ede4753180c7a550a443c430dc8ab52 + url: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda + sha256: d701ca1136197aa121bbbe0e8c18db6b5c94acbd041c2b43c70e5ae104e1d8ad + md5: a9b9368f3701a417eac9edbcae7cb737 depends: - certifi >=2017.4.17 - charset-normalizer >=2,<4 - idna >=2.5,<4 - - python >=3.8 + - python >=3.9 - urllib3 >=1.21.1,<3 constrains: - chardet >=3.0.2,<6 license: Apache-2.0 - license_family: APACHE - size: 58810 - timestamp: 1717057174842 + size: 58723 + timestamp: 1733217126197 - kind: conda name: rich version: 13.9.4 @@ -8063,18 +8070,19 @@ packages: - kind: conda name: six version: 1.16.0 - build: pyh6c4a22f_0 + build: pyhd8ed1ab_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2 - sha256: a85c38227b446f42c5b90d9b642f2c0567880c15d72492d8da074a59c8f91dd6 - md5: e5f25f8dbc060e9a8d912e432202afc2 + url: https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyhd8ed1ab_1.conda + sha256: 761b96fe7bea080b9c35dd2f87f788eb94b61d462645bd16f361e80ae0ff017c + md5: a516da6cfe1ec0a55e49737f017b4965 depends: - - python + - python >=3.9 license: MIT license_family: MIT - size: 14259 - timestamp: 1620240338595 + size: 16385 + timestamp: 1733216901349 - kind: conda name: snappy version: 1.2.1 @@ -8488,22 +8496,23 @@ packages: - kind: conda name: urllib3 version: 2.2.3 - build: pyhd8ed1ab_0 + build: pyhd8ed1ab_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_0.conda - sha256: b6bb34ce41cd93956ad6eeee275ed52390fb3788d6c75e753172ea7ac60b66e5 - md5: 6b55867f385dd762ed99ea687af32a69 + url: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.3-pyhd8ed1ab_1.conda + sha256: 416e30a1c3262275f01a3e22e783118d9e9d2872a739a9ed860d06fa9c7593d5 + md5: 4a2d8ef7c37b8808c5b9b750501fffce depends: - brotli-python >=1.0.9 - h2 >=4,<5 - pysocks >=1.5.6,<2.0,!=1.5.7 - - python >=3.8 + - python >=3.9 - zstandard >=0.18.0 license: MIT license_family: MIT - size: 98076 - timestamp: 1726496531769 + size: 98077 + timestamp: 1733206968917 - kind: conda name: uvicorn version: 0.32.1 From afa3873ace056f5cb9324f6d4973e99e19ca9d72 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 2 Dec 2024 23:15:51 -0800 Subject: [PATCH 013/509] [******][GPU] Simplify definition of sleep function, NFC MODULAR_ORIG_COMMIT_REV_ID: 9f1a40a5d1c09bcaf5a9dade8491f28052905d42 --- stdlib/src/time/time.mojo | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 7b68624fdf..71eb1e80c2 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -25,6 +25,7 @@ from sys import ( external_call, is_amd_gpu, is_nvidia_gpu, + is_gpu, llvm_intrinsic, os_is_linux, os_is_windows, @@ -383,7 +384,7 @@ fn sleep(sec: Int): """ @parameter - if is_nvidia_gpu() or is_amd_gpu(): + if is_gpu(): return sleep(Float64(sec)) @parameter From 74607e6cc2b290faacab6878c28f58599e5c1151 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Tue, 3 Dec 2024 01:16:17 -0800 Subject: [PATCH 014/509] [******] Adjust the section comments MODULAR_ORIG_COMMIT_REV_ID: d1ddd74230122ada93e1237fa6e6e5a495964b16 --- .../algorithm/bench_elementwise.mojo | 4 +- stdlib/benchmarks/builtin/bench_int.mojo | 8 +- stdlib/benchmarks/builtin/bench_sort.mojo | 24 +- stdlib/benchmarks/collections/bench_dict.mojo | 24 +- .../benchmarks/collections/bench_string.mojo | 40 +- stdlib/benchmarks/hashlib/bench_hash.mojo | 600 +++++++++--------- stdlib/benchmarks/math/bench_math.mojo | 20 +- stdlib/benchmarks/utils/bench_formatter.mojo | 12 +- stdlib/benchmarks/utils/bench_memmem.mojo | 16 +- stdlib/docs/style-guide.md | 4 +- stdlib/src/base64/base64.mojo | 20 +- stdlib/src/bit/bit.mojo | 48 +- stdlib/src/builtin/_stubs.mojo | 8 +- stdlib/src/builtin/builtin_list.mojo | 12 +- stdlib/src/builtin/dtype.mojo | 12 +- stdlib/src/builtin/error.mojo | 4 +- stdlib/src/builtin/float_literal.mojo | 4 +- stdlib/src/builtin/format_int.mojo | 16 +- stdlib/src/builtin/int.mojo | 8 +- stdlib/src/builtin/int_literal.mojo | 12 +- stdlib/src/builtin/sort.mojo | 20 +- stdlib/src/builtin/string_literal.mojo | 4 +- stdlib/src/builtin/tuple.mojo | 4 +- stdlib/src/builtin/uint.mojo | 8 +- stdlib/src/collections/deque.mojo | 4 +- stdlib/src/collections/inline_array.mojo | 4 +- stdlib/src/collections/inline_list.mojo | 4 +- stdlib/src/collections/list.mojo | 4 +- stdlib/src/collections/optional.mojo | 8 +- stdlib/src/collections/vector.mojo | 8 +- stdlib/src/math/polynomial.mojo | 8 +- stdlib/src/memory/memory.mojo | 28 +- stdlib/src/memory/pointer.mojo | 8 +- stdlib/src/memory/unsafe.mojo | 4 +- stdlib/src/os/atomic.mojo | 4 +- stdlib/src/sys/_assembly.mojo | 4 +- stdlib/src/sys/_libc.mojo | 16 +- stdlib/src/sys/ffi.mojo | 12 +- stdlib/src/sys/info.mojo | 4 +- stdlib/src/sys/intrinsics.mojo | 36 +- stdlib/src/time/time.mojo | 28 +- stdlib/src/utils/format.mojo | 12 +- stdlib/src/utils/index.mojo | 20 +- stdlib/src/utils/inline_string.mojo | 8 +- stdlib/src/utils/lock.mojo | 4 +- stdlib/src/utils/loop.mojo | 20 +- stdlib/src/utils/static_tuple.mojo | 8 +- stdlib/src/utils/string_slice.mojo | 4 +- stdlib/src/utils/stringref.mojo | 8 +- stdlib/src/utils/write.mojo | 10 +- stdlib/test/collections/test_deque.mojo | 8 +- 51 files changed, 609 insertions(+), 609 deletions(-) diff --git a/stdlib/benchmarks/algorithm/bench_elementwise.mojo b/stdlib/benchmarks/algorithm/bench_elementwise.mojo index 322b66ad9c..2a302ec80f 100644 --- a/stdlib/benchmarks/algorithm/bench_elementwise.mojo +++ b/stdlib/benchmarks/algorithm/bench_elementwise.mojo @@ -23,9 +23,9 @@ from buffer import Buffer from utils.index import Index, IndexList -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark elementwise -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_elementwise[n: Int](mut b: Bencher) raises: var vector = Buffer[DType.index, n].stack_allocation() diff --git a/stdlib/benchmarks/builtin/bench_int.mojo b/stdlib/benchmarks/builtin/bench_int.mojo index b65cd5abaf..2d65c690c3 100644 --- a/stdlib/benchmarks/builtin/bench_int.mojo +++ b/stdlib/benchmarks/builtin/bench_int.mojo @@ -17,9 +17,9 @@ from benchmark import Bench, BenchConfig, Bencher, BenchId -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmarks -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_stringify_small_integers(mut b: Bencher) raises: @always_inline @@ -32,9 +32,9 @@ fn bench_stringify_small_integers(mut b: Bencher) raises: b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): var m = Bench(BenchConfig(num_repetitions=1)) m.bench_function[bench_stringify_small_integers]( diff --git a/stdlib/benchmarks/builtin/bench_sort.mojo b/stdlib/benchmarks/builtin/bench_sort.mojo index 0872012f78..a44fc3a150 100644 --- a/stdlib/benchmarks/builtin/bench_sort.mojo +++ b/stdlib/benchmarks/builtin/bench_sort.mojo @@ -26,9 +26,9 @@ from stdlib.builtin.sort import ( sort, ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Utils -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -78,9 +78,9 @@ fn heap_sort[type: DType](mut list: List[Scalar[type]]): _heap_sort[_less_than](list) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark sort functions with a tiny list size -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn bench_tiny_list_sort[type: DType](mut m: Bench) raises: @@ -157,9 +157,9 @@ fn bench_tiny_list_sort[type: DType](mut m: Bench) raises: ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark sort functions with a small list size -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn bench_small_list_sort[type: DType](mut m: Bench, count: Int) raises: @@ -209,9 +209,9 @@ fn bench_small_list_sort[type: DType](mut m: Bench, count: Int) raises: ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark sort functions with a large list size -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn bench_large_list_sort[type: DType](mut m: Bench, count: Int) raises: @@ -262,9 +262,9 @@ fn bench_large_list_sort[type: DType](mut m: Bench, count: Int) raises: ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark sort functions with low delta lists -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn bench_low_cardinality_list_sort(mut m: Bench, count: Int, delta: Int) raises: @@ -314,9 +314,9 @@ fn bench_low_cardinality_list_sort(mut m: Bench, count: Int, delta: Int) raises: ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index b50d97a98d..e93406f837 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -24,9 +24,9 @@ from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run from bit import bit_ceil -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Data -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn make_dict[size: Int]() -> Dict[Int, Int]: var d = Dict[Int, Int]() for i in range(0, size): @@ -34,9 +34,9 @@ fn make_dict[size: Int]() -> Dict[Int, Int]: return d -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Dict init -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_dict_init(mut b: Bencher) raises: @always_inline @@ -50,9 +50,9 @@ fn bench_dict_init(mut b: Bencher) raises: b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Dict Insert -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_dict_insert[size: Int](mut b: Bencher) raises: """Insert 100 new items.""" @@ -68,9 +68,9 @@ fn bench_dict_insert[size: Int](mut b: Bencher) raises: keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Dict Lookup -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_dict_lookup[size: Int](mut b: Bencher) raises: """Lookup 100 items.""" @@ -96,9 +96,9 @@ fn bench_dict_lookup[size: Int](mut b: Bencher) raises: keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Dict Memory Footprint -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn total_bytes_used(items: Dict[Int, Int]) -> Int: @@ -121,9 +121,9 @@ fn total_bytes_used(items: Dict[Int, Int]) -> Int: return amnt_bytes -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): seed() var m = Bench(BenchConfig(num_repetitions=1)) diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index 85ee5050ee..3cee895b73 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -25,9 +25,9 @@ from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run from utils._utf8_validation import _is_valid_utf8 -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Data -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn make_string[ length: UInt = 0 ](filename: StringLiteral = "UN_charter_EN.txt") -> String: @@ -61,9 +61,9 @@ fn make_string[ return abort[String]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string init -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_init(mut b: Bencher) raises: @always_inline @@ -76,9 +76,9 @@ fn bench_string_init(mut b: Bencher) raises: b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string count -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_count[ length: UInt = 0, @@ -97,9 +97,9 @@ fn bench_string_count[ keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string split -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_split[ length: UInt = 0, @@ -124,9 +124,9 @@ fn bench_string_split[ keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string splitlines -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_splitlines[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" @@ -143,9 +143,9 @@ fn bench_string_splitlines[ keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string lower -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_lower[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" @@ -162,9 +162,9 @@ fn bench_string_lower[ keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string upper -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_upper[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" @@ -181,9 +181,9 @@ fn bench_string_upper[ keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string replace -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_replace[ length: UInt = 0, @@ -203,9 +203,9 @@ fn bench_string_replace[ keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark string _is_valid_utf8 -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_string_is_valid_utf8[ length: UInt = 0, filename: StringLiteral = "UN_charter_EN" @@ -222,9 +222,9 @@ fn bench_string_is_valid_utf8[ keep(bool(items)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): seed() var m = Bench(BenchConfig(num_repetitions=5)) diff --git a/stdlib/benchmarks/hashlib/bench_hash.mojo b/stdlib/benchmarks/hashlib/bench_hash.mojo index 0c6873f109..291701710d 100644 --- a/stdlib/benchmarks/hashlib/bench_hash.mojo +++ b/stdlib/benchmarks/hashlib/bench_hash.mojo @@ -49,7 +49,7 @@ alias words_ar = """ تحت, الأشياء, معه, يريد, أننا, أنظر, لما, اعرف, إلي, ثلاثة, انتظر, الرجال, الذين, حصلت, أني, سعيد, لابد, عزيزتي, الشيء, فكرة, انهم, الله, الباب, سيدى, دائما, رأيت, مشكلة, استطيع, تكن, تذهب, ليلة, شيئ, أظن, طوال, - جميل, وهو, الشرطة, او, دولار, السيارة, وهذا, كبير, مني, بسرعة, النار, الأمور, سمعت, أشعر, يعرف, + جميل, وهو, الشرطة, او, دولار, السيارة, وهذا, كبير, مني, بسرعة, النار, الأمور, سمعت, أشعر, يعرف, أعني, لدى, بهذه, أحب, سنوات, بأس, الأفضل, بالنسبة, أنتم, عظيم, يقول, جميلة, جون, جاك, بسبب, الوحيد, أمر, بل, بالفعل, الشخص, الي, دعني, خارج, اجل, الخير, ــ, حالك, للغاية, فحسب, كانوا, أردت, فتاة, بشأن, يعني, كبيرة, ترى, آسفة, دقيقة, أنهم, يستطيع, احد, بأنك, تعمل, @@ -80,7 +80,7 @@ alias words_ar = """ غدا, ظننت, ولن, المرأة, لهذه, تحرك, يهم, تبقى, الطبيب, اسم, انظري, تبا, أتذكر, فترة, ساعات, تفكر, تحصل, بأي, النقود, لعبة, زوجتي, الكلام, ستفعل, أسف, فهو, الملك, مدينة, بكم, الوحيدة, أمام, عدد, اخرج, بول, سأعود, جئت, لأني, تحدث, السلامة, الماضية, أمك, اعتقدت, مره, مساء, بطريقة, الرب, ابدا, أهذا, وفي, وكل, أتيت, منكم, - انتهى, بوب, بعيدا, ضع, وجود, تعود, زلت, اللعينة, نقوم, كلنا, أحصل, يريدون, تأخذ, المحتمل, الشمس, بدأ, + انتهى, بوب, بعيدا, ضع, وجود, تعود, زلت, اللعينة, نقوم, كلنا, أحصل, يريدون, تأخذ, المحتمل, الشمس, بدأ, ارجوك, المسيح, جاء, كهذا, سنذهب, تعالى, إثنان, فعلا, حتي, سيحدث, الجيد, وشك, القادم, معرفة, صورة, أعود, اسمي, طلب, آنسة, الثانية, فقدت, حفلة, تنظر, مثير, اننى, وصلت, أنتظر, السماء, يقولون, الهراء, معهم, ابي, وعندما, مجموعة, العاهرة, ماري, حسن, الزواج, نحو, دعيني, الجديدة, مهم, أمس, اتصل, ابتعد, هراء, ستة, @@ -91,7 +91,7 @@ alias words_ar = """ الدخول, جين, امرأة, متأكدة, هيه, تخبرني, مدى, إلهى, احب, عما, نرى, بيننا, تعيش, قتلت, الأحمق, تشارلي, بيل, عليكم, سؤال, طلبت, الهواء, وهذه, صوت, انتم, ميلاد, ماكس, - تعتقدين, الحديث, الجانب, صديقك, ذا, خطر, أطلق, الشارع, عملية, ببعض, تتكلم, مختلف, تحمل, مساعدة, + تعتقدين, الحديث, الجانب, صديقك, ذا, خطر, أطلق, الشارع, عملية, ببعض, تتكلم, مختلف, تحمل, مساعدة, بضعة, المناسب, المنطقة, قم, بالداخل, البداية, لأجل, زوجتك, مقابل, يحب, هاري, ممتاز, قريبا, سنكون, فعلته, بتلك, التفكير, أسفل, للعمل, العجوز, امي, الكلب, انتظري, مازال, إننا, اشعر, الجيش, شرطة """ @@ -256,13 +256,13 @@ alias words_he = """ לגרום, המשחק, שרה, לעצמך, במיוחד, המשטרה, צוות, אחזור, שאמרתי, גברים, קורא, בראש, רחוק, למקום, לשלם, להפסיק, מיוחד, הז, שמו, שמחה, כיף, אגיד, למי, ניתן, מאחורי, תמשיך, כיצד, להוציא, מתים, כולכם, אצל, חבל, האישה, לעצמי, גברתי, תוכלי, רואים, דוד, להציל, שצריך, - בעלי, דוקטור, חג, לעבודה, בוודאי, תעשי, הוד, מילה, ברצינות, הארץ, עשינו, לאנשים, רצה, + בעלי, דוקטור, חג, לעבודה, בוודאי, תעשי, הוד, מילה, ברצינות, הארץ, עשינו, לאנשים, רצה, עזוב, יצא, נתן, שניות, בעיר, סי, חשבת, שאלות, אלינו, ידע, תנו, לשים, שאולי, בכך, יכולת, אן, היד, שאוכל, מין, דקה, לדאוג, שמה, תרצה, ראה, הצילו, נוסף, החרא, אופן, כשהוא, צעיר, הפה, עולה, עובדת, שמך, לתפוס, נמצאת, כלבה, האקדח, עדיף, הטלפון, טום, פול, חכו, קר, תלך, במקרה, יעשה, שניכם, הארי, זוז, יקירתי, בהצלחה, לשבת, אנא, דין, מכיוון, יד, הקטנה, לבן, בנו, בעצמי, יין, תוריד, - למישהו, מייק, מול, נזוז, ככל, הלוואי, בעצמך, לרגע, קשור, בשקט, האל, ישנה, מעמד, כזאת, + למישהו, מייק, מול, נזוז, ככל, הלוואי, בעצמך, לרגע, קשור, בשקט, האל, ישנה, מעמד, כזאת, רד, אחורה, איכפת, איתם, ממנה, חם, מבקש, שש, מידע, השנה, אכן, אהבתי, בשעה, בסוף, שקרה, לכו, אליה, לבחור, תחשוב, ספק, המים, הפנים, לכולם, תדאגי, קחי, שתוק, לברוח, מתוק, ארלי, התיק, שים, מישהי, לקרות, לטפל, לחפש, הידיים, ח, במצב, ואל @@ -270,313 +270,313 @@ alias words_he = """ # Source: https://www.101languages.net/latvian/most-common-latvian-words/ alias words_lv = """ - ir, es, un, tu, tas, ka, man, to, vai, ko, ar, kas, par, tā, kā, viņš, uz, no, tev, - mēs, nav, jūs, bet, labi, jā, lai, nē, mani, ja, bija, viņa, esmu, viņu, tevi, esi, - mums, tad, tikai, ne, viņi, kad, jums, arī, viss, nu, kur, pie, jau, tik, tur, te, vēl, - būs, visu, šeit, tagad, kaut, ļoti, pēc, viņam, taču, savu, gan, paldies, būtu, mūsu, - šo, lūdzu, mans, kāpēc, kungs, kāds, varbūt, tās, jūsu, cik, ak, daudz, jo, esam, - zinu, mana, zini, visi, būt, tam, šī, var, līdz, viens, pa, pat, esat, nekad, domāju, - nezinu, vairs, tiešām, tie, vien, kurš, varētu, dievs, neesmu, prom, tieši, kādu, aiziet, - šis, manu, protams, vajag, neko, vienkārši, tāpēc, gribu, varu, nāc, atpakaļ, mūs, - kārtībā, iet, kopā, viņiem, pats, pirms, domā, vienmēr, gribi, nekas, bez, tava, - vienu, ej, viņai, vairāk, notiek, nevaru, pret, tavs, teica, tavu, biju, dēļ, viņas, - laiku, neviens, kādēļ, vari, labāk, patīk, dari, mājās, nebija, cilvēki, ārā, viņus, - ejam, kāda, piedod, laikam, atkal, šķiet, trīs, sevi, ser, laiks, laika, nekā, manis, - iekšā, labs, tāds, darīt, harij, nevar, viena, lieliski, kuru, šīs, sauc, šurp, teicu, - laikā, tos, pagaidi, neesi, tevis, draugs, pārāk, tēvs, šodien, teikt, dienu, visiem, - tātad, notika, hei, zināt, bijis, sveiks, atvainojiet, tika, naudu, varam, savas, citu, - tādu, manas, redzi, šajā, kam, tajā, jābūt, vecīt, tiem, runā, cilvēku, taisnība, saka, - visus, mīlu, lietas, grib, tēt, izskatās, tiek, noteikti, nozīmē, kamēr, divi, it, tāpat, - tāda, ilgi, katru, dēls, noticis, jauki, redzēt, pareizi, lūk, kundze, aiz, iespējams, - pateikt, nebūtu, gandrīz, vīrs, cilvēks, ātri, žēl, pasaules, rokas, liekas, palīdzēt, - līdzi, visas, saki, negribu, vietā, gadus, starp, skaties, tomēr, tūlīt, džek, nevajag, - sev, vajadzētu, būšu, dzīvi, droši, gadu, priekšu, skaidrs, gribēju, nāk, paskaties, mazliet, - tikko, nebūs, augšā, ceru, joprojām, nevis, ātrāk, ļauj, gribētu, liels, zina, vārdu, reizi, - pasaulē, savā, sveiki, dienas, miris, dod, priekšā, galā, klau, cilvēkiem, tavas, patiesībā, - visa, vārds, gatavs, durvis, velns, nedaudz, naudas, redzēju, velna, manā, drīz, pāri, dzīve, - vēlies, nemaz, priekš, bērni, vieta, pāris, darbu, vajadzīgs, tālāk, rīt, roku, klāt, grūti, - beidz, laba, klausies, dara, varat, sveika, biji, vismaz, kopš, redzu, saproti, kura, draugi, - zemes, šovakar, patiešām, kaa, vietu, dieva, vajadzēja, mašīnu, lejā, saku, ceļu, gada, tādēļ, - cauri, runāt, ņem, oh, divas, lieta, tikt, šie, teici, vēlāk, vaļā, nogalināt, redzējis, jāiet, - nespēju, savus, atceries, ūdens, šejienes, labu, diena, mīļā, atvaino, doties, atrast, saprotu, - abi, reiz, jādara, nesaprotu, meitene, darbs, nevari, tai, nedomāju, pilnīgi, nakti, nekādu, - pati, gadiem, vēlos, taa, kādas, cits, ejiet, pirmais, a, būsi, mamma, lietu, slikti, pašu, - acis, diezgan, pasaki, gadā, puiši, asv, sava, nost, cilvēkus, džeks, manuprāt, mājas, o, - bērns, leo, otru, nopietni, vecais, laukā, caur, dzīves, izdarīt, sieviete, vienalga, - nogalināja, dzīvo, kādreiz, čau, sirds, paliec, gribat, vēlreiz, kuras, mazais, vietas, - piedodiet, laipni, palikt, brauc, ei, the, paliek, apkārt, sievietes, tālu, garām, pirmo, - dzīvot, nāciet, runāju, kuri, tiks, jüs, ceļā, nauda, nevienam, māja, vienīgais, īsti, - sapratu, gluži, svarīgi, atvainojos, i, sen, iespēja, tavā, pavisam, nāves, māte, citi, - viegli, zem, notiks, darba, nepatīk, daži, galvu, dienā, hallo, bērnu, neesam, kungi, beidzot, - nedrīkst, vajadzēs, māju, sieva, kādam, puika, kļūst, prieks, esot, iesim, daļa, pasaule, - pietiek, visā, saviem, rīta, pagaidiet, tētis, mājā, mieru, vīru, palīdzību, dzirdēju, - tādas, dzīvs, strādā, tām, vēlas, nakts, īpaši, jūtos, nolādēts, meitenes, pusi, mammu, mees, - aizveries, vispār, dzīvību, kurā, kādā, vārdā, mašīna, būsim, vispirms, vinji, nevienu, šos, - tiksimies, džeik, vinjsh, vaina, turpini, kādi, jaunu, tuvu, atradu, vēlu, varēja, citādi, šim, - satikt, neuztraucies, pārliecināts, liec, diez, liela, doktor, nevaram, palīdzi, uzmanīgi, dažas, - šiem, atgriezies, gribēja, priecājos, parasti, valsts, asinis, tēti, you, mierā, piemēram, - jautājums, atā, bijām, zemē, pasauli, spēlē, blakus, izskaties, pirmā, nomira, paši, šobrīd, - daru, gaida, tādi, iešu, labākais, jauks, maz, pieder, jauns, nezināju, uzmanību, skaista, - prātā, brālis, patiesību, mierīgi, šai, dr, patiesi, jēzus, mārtij, zināju, suns, juus, sievu, - dzirdi, tepat, mamm, tēvu, tēva, frodo, sasodīts, desmit, stundas, tavi, mazā, džon, cita, - vajadzīga, forši, minūtes, mīlestība, nebiju, saprast, izbeidz, šoreiz, labā, dāmas, kurienes, - problēma, šādi, spēj, gadījumā, tiesa, kuģi, pēdējā, tici, esiet, atceros, katrs, nee, palīgā, - mister, liek, likās, domāt, vīri, pēdējo, traks, reizes, vienīgā, tiesības, skolā, turies, beigas, - karš, pīter, uguni, pietiks, vienam, vienā, pakaļ, jauna, zemi, puisis, ziniet, negribi, labrīt, - ap, cilvēka, draugu, atver, nezini, sāra, vēlaties, gadi, dažreiz, rokās, dabūt, nomierinies, - istabā, agrāk, ieroci, savām, meiteni, paņem, meklē, pār, seju, ziņu, dzirdējis, zinām, gatavi, - braukt, sāka, sāk, dievam, neesat, dzirdēt, spēle, bērniem, izdarīja, muļķības, doma, pēdējais, - dīvaini, atdod, ziņas, bankas, darāt, vakar, ceļš, neviena, brāli, otrā, atgriezties, galvas, - pietiekami, gulēt, uzreiz, iespēju, bijusi, karalis, bobij, šrek, tikpat, palīdziet, durvīm, - vecāki, atrodas, smieklīgi, kuģa, bail, godīgi, pēkšņi, nedēļas, māsa, skrien, ceļa, džeims, gars, - lielu, mašīnā, bojā, kurieni, ļaudis, dārgais, vecs, ūdeni, kūper, eju, mašīnas, ideja, kājas, - spēles, galvenais, citiem, jātiek, skaisti, nāvi, vinju, problēmas, vērts, drīkstu, domājat, visur, - bieži, manai, citas, apsolu, zelta, strādāju, dzimšanas, jūtu, naktī, dārgā, atbildi, noticēt, - klājas, izdevās, dok, redzat, gana, divus, ģimene, runa, stāsts, braucam, brīnišķīgi, ģimenes, - kuģis, čārlij, hey, kä, sheit, ved, atrada, mirusi, meita, paklau, nevēlos, bērnus, boss, kaptein, - nekāda, roze, nespēj, vīrietis, brīdi, īsts, dzīvē, tādā, manī, jūras, jaunkundz, iemesls, sakot, - manam, daudzi, varēsi, pateicos, jaunais, policija, pilnībā, nekur, jauka, nedari, kurus, zināms, - jautājumu, seko, re, padomā, pusē, visām, mīļais, dolāru, gadžet, katram, izdarīji, šīm, vienīgi, - mirt, apmēram, spēku, jauno, mr, celies, iepriekš, prātu, vēlētos, četri, lietām, redzēji, nevajadzētu, - donna, jaa, ticu, minūtēm, sievieti, nāve, jūties, nezina, parādi, malā, redz, uh, gredzenu, uzmanies, - kara, drošībā, sapnis, bijāt, grāmatu, slepkava, vinja, paga, pieci, pilsētā, drošs, pateikšu, gāja, - spēli, beigās, hanna, princese, jebkad, dakter, veids, palīdzība, stāstu, izmantot, spēlēt, gaisā, - darīšu, došos, dodas, kreisi, negribēju, mazāk, pastāsti, tak, devās, sirdi, misis, vis, patiesība, - veidā, harijs, cenšos, tuvāk, kurp, klausieties, sāp, ļaujiet, neticami, kungu, sīkais, iedomāties, - daļu, mazs, iedod, mazo, meklēju, parunāt, jādodas, sevis, pārējie, veicas, otra, mīlestību, zēns, - dodies, galam, sem, bīstami, zvēru, iespējas, maza, ellē, virs, nekādas, maniem, skatieties, šonakt, - svēto, kapteinis, iepazīties, pazīstu, turp, gredzens, nepareizi, lieliska, īstais, pagaidām, kājām, - mirklīti, pašlaik, d, poter, saprati, aprunāties, paša, šejieni, interesanti, nevarētu, pašā, paskat, - bailes, skolas, vārdus, aizmirsti, gaismas, kāp, zēni, darīsim, pašam, beidzies, sauca, māti, akmens, + ir, es, un, tu, tas, ka, man, to, vai, ko, ar, kas, par, tā, kā, viņš, uz, no, tev, + mēs, nav, jūs, bet, labi, jā, lai, nē, mani, ja, bija, viņa, esmu, viņu, tevi, esi, + mums, tad, tikai, ne, viņi, kad, jums, arī, viss, nu, kur, pie, jau, tik, tur, te, vēl, + būs, visu, šeit, tagad, kaut, ļoti, pēc, viņam, taču, savu, gan, paldies, būtu, mūsu, + šo, lūdzu, mans, kāpēc, kungs, kāds, varbūt, tās, jūsu, cik, ak, daudz, jo, esam, + zinu, mana, zini, visi, būt, tam, šī, var, līdz, viens, pa, pat, esat, nekad, domāju, + nezinu, vairs, tiešām, tie, vien, kurš, varētu, dievs, neesmu, prom, tieši, kādu, aiziet, + šis, manu, protams, vajag, neko, vienkārši, tāpēc, gribu, varu, nāc, atpakaļ, mūs, + kārtībā, iet, kopā, viņiem, pats, pirms, domā, vienmēr, gribi, nekas, bez, tava, + vienu, ej, viņai, vairāk, notiek, nevaru, pret, tavs, teica, tavu, biju, dēļ, viņas, + laiku, neviens, kādēļ, vari, labāk, patīk, dari, mājās, nebija, cilvēki, ārā, viņus, + ejam, kāda, piedod, laikam, atkal, šķiet, trīs, sevi, ser, laiks, laika, nekā, manis, + iekšā, labs, tāds, darīt, harij, nevar, viena, lieliski, kuru, šīs, sauc, šurp, teicu, + laikā, tos, pagaidi, neesi, tevis, draugs, pārāk, tēvs, šodien, teikt, dienu, visiem, + tātad, notika, hei, zināt, bijis, sveiks, atvainojiet, tika, naudu, varam, savas, citu, + tādu, manas, redzi, šajā, kam, tajā, jābūt, vecīt, tiem, runā, cilvēku, taisnība, saka, + visus, mīlu, lietas, grib, tēt, izskatās, tiek, noteikti, nozīmē, kamēr, divi, it, tāpat, + tāda, ilgi, katru, dēls, noticis, jauki, redzēt, pareizi, lūk, kundze, aiz, iespējams, + pateikt, nebūtu, gandrīz, vīrs, cilvēks, ātri, žēl, pasaules, rokas, liekas, palīdzēt, + līdzi, visas, saki, negribu, vietā, gadus, starp, skaties, tomēr, tūlīt, džek, nevajag, + sev, vajadzētu, būšu, dzīvi, droši, gadu, priekšu, skaidrs, gribēju, nāk, paskaties, mazliet, + tikko, nebūs, augšā, ceru, joprojām, nevis, ātrāk, ļauj, gribētu, liels, zina, vārdu, reizi, + pasaulē, savā, sveiki, dienas, miris, dod, priekšā, galā, klau, cilvēkiem, tavas, patiesībā, + visa, vārds, gatavs, durvis, velns, nedaudz, naudas, redzēju, velna, manā, drīz, pāri, dzīve, + vēlies, nemaz, priekš, bērni, vieta, pāris, darbu, vajadzīgs, tālāk, rīt, roku, klāt, grūti, + beidz, laba, klausies, dara, varat, sveika, biji, vismaz, kopš, redzu, saproti, kura, draugi, + zemes, šovakar, patiešām, kaa, vietu, dieva, vajadzēja, mašīnu, lejā, saku, ceļu, gada, tādēļ, + cauri, runāt, ņem, oh, divas, lieta, tikt, šie, teici, vēlāk, vaļā, nogalināt, redzējis, jāiet, + nespēju, savus, atceries, ūdens, šejienes, labu, diena, mīļā, atvaino, doties, atrast, saprotu, + abi, reiz, jādara, nesaprotu, meitene, darbs, nevari, tai, nedomāju, pilnīgi, nakti, nekādu, + pati, gadiem, vēlos, taa, kādas, cits, ejiet, pirmais, a, būsi, mamma, lietu, slikti, pašu, + acis, diezgan, pasaki, gadā, puiši, asv, sava, nost, cilvēkus, džeks, manuprāt, mājas, o, + bērns, leo, otru, nopietni, vecais, laukā, caur, dzīves, izdarīt, sieviete, vienalga, + nogalināja, dzīvo, kādreiz, čau, sirds, paliec, gribat, vēlreiz, kuras, mazais, vietas, + piedodiet, laipni, palikt, brauc, ei, the, paliek, apkārt, sievietes, tālu, garām, pirmo, + dzīvot, nāciet, runāju, kuri, tiks, jüs, ceļā, nauda, nevienam, māja, vienīgais, īsti, + sapratu, gluži, svarīgi, atvainojos, i, sen, iespēja, tavā, pavisam, nāves, māte, citi, + viegli, zem, notiks, darba, nepatīk, daži, galvu, dienā, hallo, bērnu, neesam, kungi, beidzot, + nedrīkst, vajadzēs, māju, sieva, kādam, puika, kļūst, prieks, esot, iesim, daļa, pasaule, + pietiek, visā, saviem, rīta, pagaidiet, tētis, mājā, mieru, vīru, palīdzību, dzirdēju, + tādas, dzīvs, strādā, tām, vēlas, nakts, īpaši, jūtos, nolādēts, meitenes, pusi, mammu, mees, + aizveries, vispār, dzīvību, kurā, kādā, vārdā, mašīna, būsim, vispirms, vinji, nevienu, šos, + tiksimies, džeik, vinjsh, vaina, turpini, kādi, jaunu, tuvu, atradu, vēlu, varēja, citādi, šim, + satikt, neuztraucies, pārliecināts, liec, diez, liela, doktor, nevaram, palīdzi, uzmanīgi, dažas, + šiem, atgriezies, gribēja, priecājos, parasti, valsts, asinis, tēti, you, mierā, piemēram, + jautājums, atā, bijām, zemē, pasauli, spēlē, blakus, izskaties, pirmā, nomira, paši, šobrīd, + daru, gaida, tādi, iešu, labākais, jauks, maz, pieder, jauns, nezināju, uzmanību, skaista, + prātā, brālis, patiesību, mierīgi, šai, dr, patiesi, jēzus, mārtij, zināju, suns, juus, sievu, + dzirdi, tepat, mamm, tēvu, tēva, frodo, sasodīts, desmit, stundas, tavi, mazā, džon, cita, + vajadzīga, forši, minūtes, mīlestība, nebiju, saprast, izbeidz, šoreiz, labā, dāmas, kurienes, + problēma, šādi, spēj, gadījumā, tiesa, kuģi, pēdējā, tici, esiet, atceros, katrs, nee, palīgā, + mister, liek, likās, domāt, vīri, pēdējo, traks, reizes, vienīgā, tiesības, skolā, turies, beigas, + karš, pīter, uguni, pietiks, vienam, vienā, pakaļ, jauna, zemi, puisis, ziniet, negribi, labrīt, + ap, cilvēka, draugu, atver, nezini, sāra, vēlaties, gadi, dažreiz, rokās, dabūt, nomierinies, + istabā, agrāk, ieroci, savām, meiteni, paņem, meklē, pār, seju, ziņu, dzirdējis, zinām, gatavi, + braukt, sāka, sāk, dievam, neesat, dzirdēt, spēle, bērniem, izdarīja, muļķības, doma, pēdējais, + dīvaini, atdod, ziņas, bankas, darāt, vakar, ceļš, neviena, brāli, otrā, atgriezties, galvas, + pietiekami, gulēt, uzreiz, iespēju, bijusi, karalis, bobij, šrek, tikpat, palīdziet, durvīm, + vecāki, atrodas, smieklīgi, kuģa, bail, godīgi, pēkšņi, nedēļas, māsa, skrien, ceļa, džeims, gars, + lielu, mašīnā, bojā, kurieni, ļaudis, dārgais, vecs, ūdeni, kūper, eju, mašīnas, ideja, kājas, + spēles, galvenais, citiem, jātiek, skaisti, nāvi, vinju, problēmas, vērts, drīkstu, domājat, visur, + bieži, manai, citas, apsolu, zelta, strādāju, dzimšanas, jūtu, naktī, dārgā, atbildi, noticēt, + klājas, izdevās, dok, redzat, gana, divus, ģimene, runa, stāsts, braucam, brīnišķīgi, ģimenes, + kuģis, čārlij, hey, kä, sheit, ved, atrada, mirusi, meita, paklau, nevēlos, bērnus, boss, kaptein, + nekāda, roze, nespēj, vīrietis, brīdi, īsts, dzīvē, tādā, manī, jūras, jaunkundz, iemesls, sakot, + manam, daudzi, varēsi, pateicos, jaunais, policija, pilnībā, nekur, jauka, nedari, kurus, zināms, + jautājumu, seko, re, padomā, pusē, visām, mīļais, dolāru, gadžet, katram, izdarīji, šīm, vienīgi, + mirt, apmēram, spēku, jauno, mr, celies, iepriekš, prātu, vēlētos, četri, lietām, redzēji, nevajadzētu, + donna, jaa, ticu, minūtēm, sievieti, nāve, jūties, nezina, parādi, malā, redz, uh, gredzenu, uzmanies, + kara, drošībā, sapnis, bijāt, grāmatu, slepkava, vinja, paga, pieci, pilsētā, drošs, pateikšu, gāja, + spēli, beigās, hanna, princese, jebkad, dakter, veids, palīdzība, stāstu, izmantot, spēlēt, gaisā, + darīšu, došos, dodas, kreisi, negribēju, mazāk, pastāsti, tak, devās, sirdi, misis, vis, patiesība, + veidā, harijs, cenšos, tuvāk, kurp, klausieties, sāp, ļaujiet, neticami, kungu, sīkais, iedomāties, + daļu, mazs, iedod, mazo, meklēju, parunāt, jādodas, sevis, pārējie, veicas, otra, mīlestību, zēns, + dodies, galam, sem, bīstami, zvēru, iespējas, maza, ellē, virs, nekādas, maniem, skatieties, šonakt, + svēto, kapteinis, iepazīties, pazīstu, turp, gredzens, nepareizi, lieliska, īstais, pagaidām, kājām, + mirklīti, pašlaik, d, poter, saprati, aprunāties, paša, šejieni, interesanti, nevarētu, pašā, paskat, + bailes, skolas, vārdus, aizmirsti, gaismas, kāp, zēni, darīsim, pašam, beidzies, sauca, māti, akmens, grāmatas, diemžēl, tevī, kļūt, endij, patika, nabaga, tuvojas, tēvoci, dienām, plāns """ # Source: https://www.101languages.net/polish/most-common-polish-words/ alias words_pl = """ -nie, to, się, w, na, i, z, co, jest, że, do, tak, jak, o, mnie, a, ale, mi, za, ja, ci, tu, ty, czy, -tym, go, tego, tylko, jestem, po, cię, ma, już, mam, jesteś, może, pan, dla, coś, dobrze, wiem, jeśli, -teraz, proszę, od, wszystko, tam, więc, masz, nic, on, być, gdzie, będzie, są, ten, mogę, ciebie, -bardzo, sobie, kiedy, ze, wiesz, no, jej, jeszcze, pani, był, mój, chcę, było, dlaczego, by, przez, -nas, tutaj, chcesz, jego, ją, ich, nigdy, żeby, też, kto, naprawdę, przepraszam, bo, mamy, porządku, -możesz, dobra, mu, dziękuję, ona, domu, panie, muszę, nawet, chyba, hej, właśnie, prawda, zrobić, te, -zawsze, będę, moja, gdy, je, trochę, nam, moje, cześć, bez, nim, była, tej, jesteśmy, dalej, pana, -dzięki, wszyscy, musisz, twój, lat, tobą, więcej, ktoś, czas, ta, który, chce, powiedzieć, chodź, dobry, -mną, niech, sam, razem, chodzi, czego, boże, stało, musimy, raz, albo, prostu, będziesz, dzień, możemy, -was, myślę, czym, daj, lepiej, czemu, ludzie, ok, przed, życie, ludzi, robisz, my, niż, tych, kim, rzeczy, -myślisz, powiedz, przy, twoja, oni, oczywiście, nikt, siebie, stąd, niego, twoje, miał, jeden, mówi, -powiedział, moim, czasu, u, dziś, im, które, musi, wtedy, taki, aby, pod, dwa, temu, pewnie, takie, cóż, -wszystkie, mojego, dużo, cholera, kurwa, wie, znaczy, wygląda, dzieje, mieć, ile, iść, potem, będziemy, -dzieci, dlatego, cały, byłem, moją, skąd, szybko, jako, kochanie, stary, trzeba, miejsce, myśli, można, -sie, jasne, mojej, wam, swoje, zaraz, wiele, nią, rozumiem, nich, wszystkich, jakieś, jakiś, kocham, idź, -tę, mają, mówię, mówisz, dzisiaj, nad, pomóc, takiego, przestań, tobie, jutro, robić, jaki, mamo, kilka, -przykro, wiedzieć, ojciec, widzisz, zbyt, zobaczyć, która, ani, tyle, trzy, tą, sposób, miałem, tato, niej, -później, pieniądze, robi, kogoś, kiedyś, zanim, widzę, pracy, świetnie, pewno, myślałem, będą, bardziej, -życia, długo, och, sir, ponieważ, aż, dni, nocy, każdy, dnia, znowu, oh, chciałem, taka, swoją, twoim, -widziałem, stanie, powiem, imię, wy, żebyś, nadzieję, twojej, panu, spokój, słuchaj, rację, spójrz, razie, -znam, pierwszy, koniec, chciałbym, we, nami, jakie, posłuchaj, problem, przecież, dobre, nasz, dziecko, drzwi, -nasze, miło, czuję, mógł, żyje, jeżeli, człowiek, powiedziałem, gdyby, roku, dom, sama, potrzebuję, -wszystkim, zostać, wciąż, dokładnie, mama, którzy, mówić, zamknij, mów, twoją, chwilę, zrobił, samo, idziemy, -nadal, jesteście, zabić, były, sobą, kogo, lub, lubię, the, podoba, minut, bym, chciał, bądź, czegoś, gdzieś, -mówiłem, chodźmy, znaleźć, poza, spokojnie, wcześniej, został, rozumiesz, mogą, prawie, wydaje, miała, mały, -byłeś, facet, zrobię, macie, żadnych, razy, noc, ciągle, broń, moich, twojego, końcu, pomocy, czekaj, znasz, -oczy, weź, idę, halo, dość, innego, pomysł, jakby, trzymaj, jedno, ojca, porozmawiać, pamiętasz, lata, -powinieneś, którą, powodu, takim, niczego, powinniśmy, oto, napisy, jednak, świat, pokoju, żebym, sprawy, -dwie, samochód, swój, wystarczy, pewien, źle, pozwól, numer, jedną, miejscu, you, drogi, byłam, dokąd, miłość, -panowie, pieniędzy, którego, matka, rano, dwóch, całe, patrz, rzecz, nowy, idzie, wyglądasz, bóg, byś, życiu, -nimi, nikogo, całą, swojego, świecie, sprawa, dziewczyna, prawo, byli, zostaw, wiedziałem, jedna, widzieć, -swoim, kobiety, uważaj, najpierw, właściwie, dam, również, diabła, chcą, którym, zrób, da, jednego, dać, -musiał, ręce, powinienem, których, znów, powiedziała, wczoraj, czujesz, zaczekaj, sądzę, śmierć, mówił, -podczas, której, całkiem, pracę, żona, pójdę, pamiętam, powiedziałeś, mówią, wiemy, jezu, witam, cholery, -swoich, telefon, wielu, także, poważnie, skoro, miejsca, robię, śmierci, słyszałem, wina, zrobiłem, dobranoc, -parę, prawdę, swojej, serce, inaczej, dziewczyny, kobieta, powiesz, martw, rób, pytanie, pięć, innych, one, -gra, natychmiast, wrócić, szybciej, jednym, cokolwiek, wierzę, wcale, wieczór, ważne, człowieka, wielki, nowa, -dopiero, ziemi, gdybym, tata, poznać, stać, jack, myślałam, witaj, słowa, zrobiłeś, gówno, john, dolarów, -sprawę, inne, idziesz, miałam, wiecie, chciałam, zobaczenia, widziałeś, żyć, każdym, nasza, panią, wspaniale, -chwili, każdego, nowego, nieźle, takich, między, dostać, powinien, dawaj, dopóki, naszych, naszej, świata, -chłopaki, chcemy, poczekaj, jaką, człowieku, czasem, żadnego, inny, przynajmniej, nazywa, super, naszego, -szczęście, potrzebuje, godziny, zabrać, powrotem, syn, lecz, słucham, twoich, udało, boga, pokój, działa, -ogóle, naszym, szkoły, możliwe, wiedział, wyjść, wszystkiego, byłoby, daleko, wieczorem, skarbie, jaka, -mógłbym, ostatni, możecie, cztery, doktorze, zrobimy, mąż, przeciwko, zgadza, zrobisz, czasie, czasami, -brzmi, raczej, ciało, należy, miasta, miałeś, taką, brat, cieszę, rozmawiać, cała, czymś, wybacz, twarz, -mała, chcecie, dr, pojęcia, lubisz, głowę, najbardziej, dziwne, głowy, wody, pół, wiadomość, policja, -strony, l, pl, mogłem, mieli, widzenia, pewna, ruszaj, wracaj, ode, popatrz, końca, plan, kiedykolwiek, -wejść, została, rok, syna, uda, wrócę, zewnątrz, droga, uwierzyć, późno, zostało, zostanie, zły, kapitanie, -potrzebujemy, byliśmy, zobaczymy, gotowy, obchodzi, jechać, rodziny, widziałam, drodze, czeka, środku, film, -spać, człowiekiem, zupełnie, taa, pomóż, mieliśmy, pomoc, słowo, innym, ostatnio, and, zna, mogła, pójść, -chłopcy, wziąć, mógłbyś, tłumaczenie, potrzebujesz, słyszysz, blisko, godzin, miłości, góry, zabił, piękna, -napisów, pokaż, moi, lubi, robota, prawa, ciężko, kimś, dół, rękę, nazywam, wielkie, część, wkrótce, naszą, -jedziemy, zapomnij, prosto, radę, robimy, powinnaś, gdybyś, chociaż, zależy, stronie, wypadek, tydzień, byłaś, -nowe, małe, praca, drogę, chłopak, zrobi, widział, mieście, synu, oznacza, krew, mógłby, krwi, górę, joe, wasza, -robią, tędy, wszędzie, temat, pierwsze, zobacz, ponad, kraju, mało, racja, tymi, cicho, chciała, powiedziałam, -leci, powinno, mówiąc, serca, chciałabym, miasto, george, spotkać, mniej, e, przyjaciel, mówiłeś, kłopoty, -miesięcy, jakąś, żaden, zostań, roboty, zatrzymać, frank, nieważne, głupi, pa, koleś, sprawie, spotkanie, ojcze, -pewnego, spróbuj, drugi, znalazłem, pracować, całym, zostały, złe, niemożliwe, jakoś, zdjęcia, stronę, wiedzą, it, -dziewczynę, zaczyna, mogli, samego, sądzisz, rodzina, razu, trudno, samochodu, okay, boję, szkoda, wami, charlie, -dał, środka, ojcem, piękne, dawno, choć, panem, przykład, nagle, bracie, żadnej, drugiej, przyjaciół, otwórz, -myśleć, doktor, chwileczkę, pracuje, najlepszy, brata, czyż, często, http, powinnam, odejść, trzech, chodźcie, -nazwisko, szansę, ciała, policji, szkole, prawdopodobnie, serio, matki, org, wolno, sami, muszą, zabierz, -słyszałeś, siostra, uspokój, wystarczająco, początku, faceta, problemy, 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nowego, nieźle, takich, między, dostać, powinien, dawaj, dopóki, naszych, naszej, świata, +chłopaki, chcemy, poczekaj, jaką, człowieku, czasem, żadnego, inny, przynajmniej, nazywa, super, naszego, +szczęście, potrzebuje, godziny, zabrać, powrotem, syn, lecz, słucham, twoich, udało, boga, pokój, działa, +ogóle, naszym, szkoły, możliwe, wiedział, wyjść, wszystkiego, byłoby, daleko, wieczorem, skarbie, jaka, +mógłbym, ostatni, możecie, cztery, doktorze, zrobimy, mąż, przeciwko, zgadza, zrobisz, czasie, czasami, +brzmi, raczej, ciało, należy, miasta, miałeś, taką, brat, cieszę, rozmawiać, cała, czymś, wybacz, twarz, +mała, chcecie, dr, pojęcia, lubisz, głowę, najbardziej, dziwne, głowy, wody, pół, wiadomość, policja, +strony, l, pl, mogłem, mieli, widzenia, pewna, ruszaj, wracaj, ode, popatrz, końca, plan, kiedykolwiek, +wejść, została, rok, syna, uda, wrócę, zewnątrz, droga, uwierzyć, późno, zostało, zostanie, zły, kapitanie, +potrzebujemy, byliśmy, zobaczymy, gotowy, obchodzi, jechać, rodziny, widziałam, drodze, czeka, środku, film, +spać, człowiekiem, zupełnie, taa, pomóż, mieliśmy, pomoc, słowo, innym, ostatnio, and, zna, mogła, pójść, +chłopcy, wziąć, mógłbyś, tłumaczenie, potrzebujesz, słyszysz, blisko, godzin, miłości, góry, zabił, piękna, +napisów, pokaż, moi, lubi, robota, prawa, ciężko, kimś, dół, rękę, nazywam, wielkie, część, wkrótce, naszą, +jedziemy, zapomnij, prosto, radę, robimy, powinnaś, gdybyś, chociaż, zależy, stronie, wypadek, tydzień, byłaś, +nowe, małe, praca, drogę, chłopak, zrobi, widział, mieście, synu, oznacza, krew, mógłby, krwi, górę, joe, wasza, +robią, tędy, wszędzie, temat, pierwsze, zobacz, ponad, kraju, mało, racja, tymi, cicho, chciała, powiedziałam, +leci, powinno, mówiąc, serca, chciałabym, miasto, george, spotkać, mniej, e, przyjaciel, mówiłeś, kłopoty, +miesięcy, jakąś, żaden, zostań, roboty, zatrzymać, frank, nieważne, głupi, pa, koleś, sprawie, spotkanie, ojcze, +pewnego, spróbuj, drugi, znalazłem, pracować, całym, zostały, złe, niemożliwe, jakoś, zdjęcia, stronę, wiedzą, it, +dziewczynę, zaczyna, mogli, samego, sądzisz, rodzina, razu, trudno, samochodu, okay, boję, szkoda, wami, charlie, +dał, środka, ojcem, piękne, dawno, choć, panem, przykład, nagle, bracie, żadnej, drugiej, przyjaciół, otwórz, +myśleć, doktor, chwileczkę, pracuje, najlepszy, brata, czyż, często, http, powinnam, odejść, trzech, chodźcie, +nazwisko, szansę, ciała, policji, szkole, prawdopodobnie, serio, matki, org, wolno, sami, muszą, zabierz, +słyszałeś, siostra, uspokój, wystarczająco, początku, faceta, problemy, szefie, broni, me, zostawić, czuje, +będziecie, przyszedł, wiedziałam, kilku, inni, b, głowie, historia, według, www, wezmę, nowym, czekać, stój, +mężczyzna, mówiłam, pokazać, około, wracam, wieku, jakaś, pierwsza, niczym, zabiję, zdjęcie, zabawne, rodzice, +musiałem, całkowicie, sprawdzić, mike, przyjdzie, sześć, kupić, dobrym, żonę, dasz, pomoże, nogi, obok, ruszać, +trzymać, zadzwonić, panno, godzinę, boli, oraz, spokoju, walczyć, wróci, tom, wspólnego, zmienić, ostatnie, uwagę, +znać, jednej, dłużej, powie, pogadać, łatwo, większość, nikomu, michael, córka, niedługo, powodzenia, tygodniu, +włosy, niestety, górze, kochasz, prawdziwy, historii, ulicy, musicie, gotowi, chwila, samym, grać, zadzwonię, +strasznie, mieszka, kocha, rady, tyłu, jakim, obiecuję, tysięcy, pomyślałem, pracuję, jedynie, pozwolić, uwaga, proste, zacząć, myśl, wstawaj, rany, prawdziwe, takiej, jakiegoś, umrzeć, złego, okazji """ # Source: https://www.101languages.net/greek/most-common-greek-words/ alias words_el = """ - να, το, δεν, θα, είναι, και, μου, με, ο, για, την, σου, τα, τον, η, τι, σε, που, του, αυτό, στο, ότι, - από, τη, της, ναι, σας, ένα, εδώ, τους, αν, όχι, μια, μας, είσαι, αλλά, κι, οι, πρέπει, είμαι, ήταν, - πολύ, στην, δε, γιατί, εγώ, τώρα, πως, εντάξει, τις, κάτι, ξέρω, μην, έχει, έχω, εσύ, θέλω, καλά, - έτσι, στη, στον, αυτή, ξέρεις, κάνεις, εκεί, σαν, μόνο, μπορώ, όταν, έχεις, μαζί, πώς, τίποτα, - ευχαριστώ, όλα, κάνω, πάμε, ή, ποτέ, τόσο, πού, αυτά, έλα, στα, μέσα, κάνει, των, μπορεί, κύριε, πιο, - σπίτι, παρακαλώ, λοιπόν, μπορείς, αυτός, υπάρχει, ακόμα, πίσω, λίγο, πάντα, είμαστε, γεια, τότε, - ειναι, μετά, πω, έχουμε, μη, ένας, ποιος, νομίζω, πριν, απλά, δω, δουλειά, παιδιά, οχι, αλήθεια, - όλοι, ίσως, λες, όπως, ας, θέλεις, μα, άλλο, είπε, ζωή, πάω, δύο, ωραία, έναν, καλό, απο, κάνουμε, - έξω, κοίτα, είχε, στις, πάνω, είπα, πες, χρόνια, ούτε, κάτω, είστε, ώρα, θες, σένα, έχουν, γυναίκα, - μένα, μέρα, καλή, φορά, όμως, κανείς, κάθε, ε, οτι, αρέσει, ήμουν, μέχρι, δυο, είχα, μαμά, χωρίς, - καλύτερα, πας, πράγματα, πάει, σήμερα, κάποιος, ήθελα, θέλει, θεέ, έπρεπε, λέει, μία, σωστά, αυτόν, - μπορούμε, συμβαίνει, ακριβώς, έγινε, πόσο, επειδή, λεφτά, πολλά, μόλις, εμένα, λένε, πεις, συγγνώμη, - γρήγορα, ω, έκανε, λυπάμαι, γίνει, παιδί, περίμενε, έκανα, φίλε, βλέπω, μέρος, στιγμή, φαίνεται, + να, το, δεν, θα, είναι, και, μου, με, ο, για, την, σου, τα, τον, η, τι, σε, που, του, αυτό, στο, ότι, + από, τη, της, ναι, σας, ένα, εδώ, τους, αν, όχι, μια, μας, είσαι, αλλά, κι, οι, πρέπει, είμαι, ήταν, + πολύ, στην, δε, γιατί, εγώ, τώρα, πως, εντάξει, τις, κάτι, ξέρω, μην, έχει, έχω, εσύ, θέλω, καλά, + έτσι, στη, στον, αυτή, ξέρεις, κάνεις, εκεί, σαν, μόνο, μπορώ, όταν, έχεις, μαζί, πώς, τίποτα, + ευχαριστώ, όλα, κάνω, πάμε, ή, ποτέ, τόσο, πού, αυτά, έλα, στα, μέσα, κάνει, των, μπορεί, κύριε, πιο, + σπίτι, παρακαλώ, λοιπόν, μπορείς, αυτός, υπάρχει, ακόμα, πίσω, λίγο, πάντα, είμαστε, γεια, τότε, + ειναι, μετά, πω, έχουμε, μη, ένας, ποιος, νομίζω, πριν, απλά, δω, δουλειά, παιδιά, οχι, αλήθεια, + όλοι, ίσως, λες, όπως, ας, θέλεις, μα, άλλο, είπε, ζωή, πάω, δύο, ωραία, έναν, καλό, απο, κάνουμε, + έξω, κοίτα, είχε, στις, πάνω, είπα, πες, χρόνια, ούτε, κάτω, είστε, ώρα, θες, σένα, έχουν, γυναίκα, + μένα, μέρα, καλή, φορά, όμως, κανείς, κάθε, ε, οτι, αρέσει, ήμουν, μέχρι, δυο, είχα, μαμά, χωρίς, + καλύτερα, πας, πράγματα, πάει, σήμερα, κάποιος, ήθελα, θέλει, θεέ, έπρεπε, λέει, μία, σωστά, αυτόν, + μπορούμε, συμβαίνει, ακριβώς, έγινε, πόσο, επειδή, λεφτά, πολλά, μόλις, εμένα, λένε, πεις, συγγνώμη, + γρήγορα, ω, έκανε, λυπάμαι, γίνει, παιδί, περίμενε, έκανα, φίλε, βλέπω, μέρος, στιγμή, φαίνεται, πρόβλημα, άλλη, είπες, φυσικά, κάποιον, όσο, πήγαινε, πάλι, λάθος, ως, έχετε, εσένα, πράγμα, κυρία, - χρόνο, στους, πάρω, μπαμπά, δικό, απ, γίνεται, εσείς, λέω, συγνώμη, όλο, μητέρα, έκανες, πιστεύω, - ήσουν, κάποια, σίγουρα, υπάρχουν, όλη, ενα, αυτο, ξέρει, μωρό, ιδέα, δει, μάλλον, ίδιο, πάρε, είδα, - αύριο, βλέπεις, νέα, κόσμο, νομίζεις, τί, εμείς, σταμάτα, πάρει, αγάπη, πατέρας, όλους, αρκετά, - χρειάζεται, καιρό, φορές, κάνουν, ακόμη, α, πατέρα, προς, αμέσως, πια, ηταν, χαρά, απόψε, όνομα, - μάλιστα, μόνος, μεγάλη, κανένα, ελα, πραγματικά, αυτοί, πει, πότε, εχω, βράδυ, αυτές, θέλετε, κάνετε, - σημαίνει, πρώτη, ποιο, πόλη, μπορούσα, ποια, γαμώτο, ήδη, τελευταία, άνθρωποι, τέλος, απλώς, νόμιζα, - ξέρετε, μέρες, δεις, θέση, αυτούς, καταλαβαίνω, φύγε, χέρια, εκτός, ήξερα, οπότε, λεπτά, μακριά, - κάνε, αμάξι, δική, λεπτό, μεγάλο, μήπως, κορίτσι, μάτια, ελάτε, πρόκειται, πόρτα, δίκιο, βοήθεια, - ήρθε, μιλήσω, δρόμο, εαυτό, καθόλου, ορίστε, βρω, πειράζει, μπορείτε, καλός, πέρα, κοντά, εννοώ, - τέτοιο, μπροστά, έρθει, χρειάζομαι, χέρι, ελπίζω, δώσε, διάολο, φύγω, ιστορία, όπλο, αφού, πρωί, - νύχτα, ωραίο, τύπος, ξανά, θυμάσαι, δούμε, κατά, εννοείς, αγαπώ, κακό, θέμα, εδω, αυτήν, τρόπο, - κεφάλι, είχες, μερικές, μιλάς, φίλος, άνθρωπος, φύγουμε, όλες, σκατά, ανθρώπους, βέβαια, άντρας, - κάποιο, πάνε, αστυνομία, αλλιώς, συνέβη, χαίρομαι, άλλα, περισσότερο, καλύτερο, εκείνη, πάρεις, τo, - νερό, ώρες, σίγουρος, vα, τρεις, εχεις, πρώτα, μπορούσε, σ, οταν, δρ, πιστεύεις, μόνη, ποιός, καμιά, - κανέναν, πέθανε, εχει, ετσι, αγόρι, ανησυχείς, άντρες, δωμάτιο, ομάδα, ίδια, εμπρός, βρούμε, βοηθήσω, - τέτοια, πήρε, τρία, λόγο, μικρό, αντίο, o, πέντε, πήγε, καν, ευκαιρία, είδες, έρχεται, δηλαδή, - αργότερα, ήθελε, πούμε, λέμε, όπου, αλλα, κόρη, κόσμος, γυναίκες, τηλέφωνο, εάν, δώσω, καρδιά, βρήκα, - γραφείο, επίσης, νιώθω, σχέση, θέλουν, ισως, τέλεια, είχαμε, κάπου, μυαλό, ώστε, καλημέρα, σχολείο, - θεός, μικρή, τρέχει, ψέματα, ξέρουμε, οικογένεια, εισαι, θυμάμαι, κ, ενός, φίλοι, πρόσεχε, - καταλαβαίνεις, αργά, ντε, θέλουμε, σύντομα, πήρα, σχεδόν, παιχνίδι, κύριοι, γειά, μήνες, μπαμπάς, - σοβαρά, δολάρια, τουλάχιστον, χρήματα, πείτε, πόδια, αίμα, κοπέλα, φαγητό, ειμαι, ποιον, μερικά, - δύσκολο, μπορούν, βρεις, όμορφη, φύγεις, τύχη, πλάκα, έρθεις, άντρα, κορίτσια, μείνε, αστείο, καμία, - είχαν, χάρη, άλλος, πρεπει, σημασία, φυλακή, νεκρός, συγχωρείτε, φοβάμαι, μπράβο, γύρω, κανένας, μεταξύ, - τ, χθες, πολλές, όνομά, τζακ, ρε, καληνύχτα, πολυ, φύγει, αφήσω, ήθελες, tι, ήρθες, ακούς, πρώτο, γιατι, - ηρέμησε, γι, πάρουμε, πάρα, άλλους, κατάλαβα, έρθω, συνέχεια, έλεγα, γλυκιά, νοιάζει, χριστέ, βιβλίο, - κύριος, μ, χώρα, αρχή, ήρθα, πεθάνει, γη, έτοιμος, εγω, άσχημα, συμβεί, αυτοκίνητο, ζωής, τελικά, φέρω, - τρόπος, κατάσταση, www, περιμένω, σημαντικό, όσα, σκέφτηκα, μιλήσουμε, αφήστε, τωρα, ακούω, γιος, σκοτώσω, - δύναμη, κα, κε, εκείνο, γονείς, μιλάω, σκοτώσει, ολα, μείνει, μείνω, αρέσουν, δεv, υπόθεση, φίλους, όπλα, - υποθέτω, εμάς, ενώ, έξι, σχέδιο, άρεσε, καφέ, σκότωσε, χρειαζόμαστε, φίλο, σωστό, προσπαθώ, κάναμε, - κοιτάξτε, μoυ, κου, ποτό, εσάς, έι, έφυγε, ταινία, μοιάζει, κρεβάτι, εχουμε, περιμένει, νέο, μπορούσες, - μάθω, αφήσεις, περιμένετε, χρειάζεσαι, υπήρχε, μισό, δέκα, αφεντικό, περίπου, άλλοι, λόγος, ξέρουν, κάποτε, - βρήκες, καλύτερη, υπέροχο, τζον, δίπλα, σκάσε, θεού, άκουσα, φύγετε, λέξη, παρά, επόμενη, λέτε, περάσει, - πόσα, γίνεις, σώμα, ν, πήρες, τελείωσε, γιο, ρούχα, σκέφτομαι, εσυ, άλλες, γυρίσω, βάλω, μουσική, ραντεβού, - φωτιά, έδωσε, πάτε, φοβάσαι, βρει, δείξω, γίνω, βοηθήσει, τύπο, σειρά, αξίζει, μείνεις, είπαν, άλλον, - κυρίες, λίγη, πέρασε, κάτσε, πήγα, δείτε, μιας, βδομάδα, έρχομαι, προσοχή, εύκολο, ερώτηση, υπέροχα, - σίγουρη, νοσοκομείο, τρελός, ενας, βάλε, πόλεμο, φέρε, δικά, τιμή, κατάλαβες, ταξίδι, οποίο, δουλεύει, θεό, - μικρέ, μάθεις, βρίσκεται, πολλοί, δες, πάρτε, παντού, πρόσωπο, μήνυμα, αδερφή, μιλάει, παλιά, πουθενά, - κράτα, περίπτωση, φως, επάνω, έλεγε, συμφωνία, οπως, ολοι, πρώτος, δεσποινίς, γιατρός, γνωρίζω, σαμ, - σκέφτεσαι, ει, φίλη, σεξ, έκαναν, προβλήματα, κάπως, ό, τελευταίο, ακούσει, τζο, καλώς, επιλογή, - σταματήστε, τόσα, οτιδήποτε, περισσότερα, άδεια, πάρτι, περίμενα, ακούγεται, gmteam, ήξερες, καιρός, - μαλλιά, καλύτερος, κανεις, φρανκ, μέση, συνέχισε, τίποτε, φωτογραφία, κατι, μεγάλος, περιοχή, άσε, καθώς, - είδε, λόγια, μήνα, μαλακίες, όμορφο, δώρο, στόμα, χάλια, εντελώς, μακάρι, τελειώσει, γνώμη, γιατρέ, ξερω, - πλευρά, μέλλον, θάνατο, νιώθεις, έτοιμοι, κομμάτι, μάθει, μιλάμε, ψηλά, αέρα, ερωτήσεις, αυτού, δώσει, - φεύγω, σημείο, τηλεόραση, κυριε, πραγματικότητα, ανάγκη, βοηθήσεις, προσπάθησε, γύρνα, άφησε, λίγα, κάντε, - είvαι, βλέπετε, αυτη, δείπνο, επιτέλους, κέντρο, περίεργο, ακούστε, πλοίο, κάποιες, δικός, σoυ, οικογένειά, - μιλήσει, πλέον, υπόσχομαι, περιμένεις, ήξερε, σκοτώσεις, ενταξει, δώσεις, εκει, ήμασταν, έρχονται, κώλο, - ρωτήσω, παίρνει, σιγά, σήκω, στοιχεία, αδελφή, βασικά, μένει, άκρη, πηγαίνετε, παίρνεις, tο, περιμένουμε, - συγχωρείς, μικρός, πόδι, δίνει, εκατομμύρια, ξενοδοχείο, αποστολή, ενδιαφέρον, χάρηκα, αεροπλάνο, γάμο, - χιλιάδες, υόρκη, οκ, ευχαριστούμε, καλα, κοιτάς, σα, π, χρόνος, ησυχία, ασφάλεια, εκείνος, a, βρήκε, - τέσσερα, βγάλω, μπες, συχνά, ημέρα, μάνα, εν, αγαπάς, άνθρωπο, γραμμή, φωτογραφίες, προσέχεις, ύπνο, - μυστικό, σχετικά, είδους, σκέψου, χριστούγεννα, κόσμου, τομ, μισώ, σύστημα, δουλειές, τελείως, πεθάνω, - αλλάξει, δεξιά, συνήθως, δουλεύω, μάικλ, εβδομάδα, νούμερο, λείπει, έτοιμη, τμήμα, βγει, ψυχή, έπεσε, - κάθαρμα, ματιά, οποία, πληροφορίες, μονο, κρίμα, τραγούδι, μαγαζί, δουλεύεις, μαζι, τέλειο, κύριο, - λέγεται, τσάρλι, πεθάνεις, σκεφτόμουν, καλησπέρα, συγχαρητήρια, φωνή, εκ, άτομο, παίζεις, σκάφος, - φαίνεσαι, ξαφνικά, παραπάνω, ατύχημα, θελω, ξέχνα, ήρθατε, εναντίον, τραπέζι, γράμμα, μείνετε, αμερική, - βασιλιάς, υπό, μπάνιο, ποτε, ίδιος, προφανώς, μαλάκα, αδερφός, άνδρες, nαι, χρονών, ναί, κλειδί, δις, - γιαγιά, παράξενο, πτώμα, βρήκαμε, μιλήσεις, υποτίθεται, ορκίζομαι, δυνατά, ποιό, θάλασσα, παίρνω, άκουσες, - παρέα, αριστερά, έμαθα, μάχη, μηχανή, σάρα, ζωντανός, όνειρο, παλιό, μπορούσαμε, πάντως, ανάμεσα, έχασα, - νωρίς, κάποιοι, άκου, παίζει, φτάνει, δίνω, βγες, υπέροχη, νόημα, έλεγχο, μέτρα, ξερεις, ζει, δείχνει, + χρόνο, στους, πάρω, μπαμπά, δικό, απ, γίνεται, εσείς, λέω, συγνώμη, όλο, μητέρα, έκανες, πιστεύω, + ήσουν, κάποια, σίγουρα, υπάρχουν, όλη, ενα, αυτο, ξέρει, μωρό, ιδέα, δει, μάλλον, ίδιο, πάρε, είδα, + αύριο, βλέπεις, νέα, κόσμο, νομίζεις, τί, εμείς, σταμάτα, πάρει, αγάπη, πατέρας, όλους, αρκετά, + χρειάζεται, καιρό, φορές, κάνουν, ακόμη, α, πατέρα, προς, αμέσως, πια, ηταν, χαρά, απόψε, όνομα, + μάλιστα, μόνος, μεγάλη, κανένα, ελα, πραγματικά, αυτοί, πει, πότε, εχω, βράδυ, αυτές, θέλετε, κάνετε, + σημαίνει, πρώτη, ποιο, πόλη, μπορούσα, ποια, γαμώτο, ήδη, τελευταία, άνθρωποι, τέλος, απλώς, νόμιζα, + ξέρετε, μέρες, δεις, θέση, αυτούς, καταλαβαίνω, φύγε, χέρια, εκτός, ήξερα, οπότε, λεπτά, μακριά, + κάνε, αμάξι, δική, λεπτό, μεγάλο, μήπως, κορίτσι, μάτια, ελάτε, πρόκειται, πόρτα, δίκιο, βοήθεια, + ήρθε, μιλήσω, δρόμο, εαυτό, καθόλου, ορίστε, βρω, πειράζει, μπορείτε, καλός, πέρα, κοντά, εννοώ, + τέτοιο, μπροστά, έρθει, χρειάζομαι, χέρι, ελπίζω, δώσε, διάολο, φύγω, ιστορία, όπλο, αφού, πρωί, + νύχτα, ωραίο, τύπος, ξανά, θυμάσαι, δούμε, κατά, εννοείς, αγαπώ, κακό, θέμα, εδω, αυτήν, τρόπο, + κεφάλι, είχες, μερικές, μιλάς, φίλος, άνθρωπος, φύγουμε, όλες, σκατά, ανθρώπους, βέβαια, άντρας, + κάποιο, πάνε, αστυνομία, αλλιώς, συνέβη, χαίρομαι, άλλα, περισσότερο, καλύτερο, εκείνη, πάρεις, τo, + νερό, ώρες, σίγουρος, vα, τρεις, εχεις, πρώτα, μπορούσε, σ, οταν, δρ, πιστεύεις, μόνη, ποιός, καμιά, + κανέναν, πέθανε, εχει, ετσι, αγόρι, ανησυχείς, άντρες, δωμάτιο, ομάδα, ίδια, εμπρός, βρούμε, βοηθήσω, + τέτοια, πήρε, τρία, λόγο, μικρό, αντίο, o, πέντε, πήγε, καν, ευκαιρία, είδες, έρχεται, δηλαδή, + αργότερα, ήθελε, πούμε, λέμε, όπου, αλλα, κόρη, κόσμος, γυναίκες, τηλέφωνο, εάν, δώσω, καρδιά, βρήκα, + γραφείο, επίσης, νιώθω, σχέση, θέλουν, ισως, τέλεια, είχαμε, κάπου, μυαλό, ώστε, καλημέρα, σχολείο, + θεός, μικρή, τρέχει, ψέματα, ξέρουμε, οικογένεια, εισαι, θυμάμαι, κ, ενός, φίλοι, πρόσεχε, + καταλαβαίνεις, αργά, ντε, θέλουμε, σύντομα, πήρα, σχεδόν, παιχνίδι, κύριοι, γειά, μήνες, μπαμπάς, + σοβαρά, δολάρια, τουλάχιστον, χρήματα, πείτε, πόδια, αίμα, κοπέλα, φαγητό, ειμαι, ποιον, μερικά, + δύσκολο, μπορούν, βρεις, όμορφη, φύγεις, τύχη, πλάκα, έρθεις, άντρα, κορίτσια, μείνε, αστείο, καμία, + είχαν, χάρη, άλλος, πρεπει, σημασία, φυλακή, νεκρός, συγχωρείτε, φοβάμαι, μπράβο, γύρω, κανένας, μεταξύ, + τ, χθες, πολλές, όνομά, τζακ, ρε, καληνύχτα, πολυ, φύγει, αφήσω, ήθελες, tι, ήρθες, ακούς, πρώτο, γιατι, + ηρέμησε, γι, πάρουμε, πάρα, άλλους, κατάλαβα, έρθω, συνέχεια, έλεγα, γλυκιά, νοιάζει, χριστέ, βιβλίο, + κύριος, μ, χώρα, αρχή, ήρθα, πεθάνει, γη, έτοιμος, εγω, άσχημα, συμβεί, αυτοκίνητο, ζωής, τελικά, φέρω, + τρόπος, κατάσταση, www, περιμένω, σημαντικό, όσα, σκέφτηκα, μιλήσουμε, αφήστε, τωρα, ακούω, γιος, σκοτώσω, + δύναμη, κα, κε, εκείνο, γονείς, μιλάω, σκοτώσει, ολα, μείνει, μείνω, αρέσουν, δεv, υπόθεση, φίλους, όπλα, + υποθέτω, εμάς, ενώ, έξι, σχέδιο, άρεσε, καφέ, σκότωσε, χρειαζόμαστε, φίλο, σωστό, προσπαθώ, κάναμε, + κοιτάξτε, μoυ, κου, ποτό, εσάς, έι, έφυγε, ταινία, μοιάζει, κρεβάτι, εχουμε, περιμένει, νέο, μπορούσες, + μάθω, αφήσεις, περιμένετε, χρειάζεσαι, υπήρχε, μισό, δέκα, αφεντικό, περίπου, άλλοι, λόγος, ξέρουν, κάποτε, + βρήκες, καλύτερη, υπέροχο, τζον, δίπλα, σκάσε, θεού, άκουσα, φύγετε, λέξη, παρά, επόμενη, λέτε, περάσει, + πόσα, γίνεις, σώμα, ν, πήρες, τελείωσε, γιο, ρούχα, σκέφτομαι, εσυ, άλλες, γυρίσω, βάλω, μουσική, ραντεβού, + φωτιά, έδωσε, πάτε, φοβάσαι, βρει, δείξω, γίνω, βοηθήσει, τύπο, σειρά, αξίζει, μείνεις, είπαν, άλλον, + κυρίες, λίγη, πέρασε, κάτσε, πήγα, δείτε, μιας, βδομάδα, έρχομαι, προσοχή, εύκολο, ερώτηση, υπέροχα, + σίγουρη, νοσοκομείο, τρελός, ενας, βάλε, πόλεμο, φέρε, δικά, τιμή, κατάλαβες, ταξίδι, οποίο, δουλεύει, θεό, + μικρέ, μάθεις, βρίσκεται, πολλοί, δες, πάρτε, παντού, πρόσωπο, μήνυμα, αδερφή, μιλάει, παλιά, πουθενά, + κράτα, περίπτωση, φως, επάνω, έλεγε, συμφωνία, οπως, ολοι, πρώτος, δεσποινίς, γιατρός, γνωρίζω, σαμ, + σκέφτεσαι, ει, φίλη, σεξ, έκαναν, προβλήματα, κάπως, ό, τελευταίο, ακούσει, τζο, καλώς, επιλογή, + σταματήστε, τόσα, οτιδήποτε, περισσότερα, άδεια, πάρτι, περίμενα, ακούγεται, gmteam, ήξερες, καιρός, + μαλλιά, καλύτερος, κανεις, φρανκ, μέση, συνέχισε, τίποτε, φωτογραφία, κατι, μεγάλος, περιοχή, άσε, καθώς, + είδε, λόγια, μήνα, μαλακίες, όμορφο, δώρο, στόμα, χάλια, εντελώς, μακάρι, τελειώσει, γνώμη, γιατρέ, ξερω, + πλευρά, μέλλον, θάνατο, νιώθεις, έτοιμοι, κομμάτι, μάθει, μιλάμε, ψηλά, αέρα, ερωτήσεις, αυτού, δώσει, + φεύγω, σημείο, τηλεόραση, κυριε, πραγματικότητα, ανάγκη, βοηθήσεις, προσπάθησε, γύρνα, άφησε, λίγα, κάντε, + είvαι, βλέπετε, αυτη, δείπνο, επιτέλους, κέντρο, περίεργο, ακούστε, πλοίο, κάποιες, δικός, σoυ, οικογένειά, + μιλήσει, πλέον, υπόσχομαι, περιμένεις, ήξερε, σκοτώσεις, ενταξει, δώσεις, εκει, ήμασταν, έρχονται, κώλο, + ρωτήσω, παίρνει, σιγά, σήκω, στοιχεία, αδελφή, βασικά, μένει, άκρη, πηγαίνετε, παίρνεις, tο, περιμένουμε, + συγχωρείς, μικρός, πόδι, δίνει, εκατομμύρια, ξενοδοχείο, αποστολή, ενδιαφέρον, χάρηκα, αεροπλάνο, γάμο, + χιλιάδες, υόρκη, οκ, ευχαριστούμε, καλα, κοιτάς, σα, π, χρόνος, ησυχία, ασφάλεια, εκείνος, a, βρήκε, + τέσσερα, βγάλω, μπες, συχνά, ημέρα, μάνα, εν, αγαπάς, άνθρωπο, γραμμή, φωτογραφίες, προσέχεις, ύπνο, + μυστικό, σχετικά, είδους, σκέψου, χριστούγεννα, κόσμου, τομ, μισώ, σύστημα, δουλειές, τελείως, πεθάνω, + αλλάξει, δεξιά, συνήθως, δουλεύω, μάικλ, εβδομάδα, νούμερο, λείπει, έτοιμη, τμήμα, βγει, ψυχή, έπεσε, + κάθαρμα, ματιά, οποία, πληροφορίες, μονο, κρίμα, τραγούδι, μαγαζί, δουλεύεις, μαζι, τέλειο, κύριο, + λέγεται, τσάρλι, πεθάνεις, σκεφτόμουν, καλησπέρα, συγχαρητήρια, φωνή, εκ, άτομο, παίζεις, σκάφος, + φαίνεσαι, ξαφνικά, παραπάνω, ατύχημα, θελω, ξέχνα, ήρθατε, εναντίον, τραπέζι, γράμμα, μείνετε, αμερική, + βασιλιάς, υπό, μπάνιο, ποτε, ίδιος, προφανώς, μαλάκα, αδερφός, άνδρες, nαι, χρονών, ναί, κλειδί, δις, + γιαγιά, παράξενο, πτώμα, βρήκαμε, μιλήσεις, υποτίθεται, ορκίζομαι, δυνατά, ποιό, θάλασσα, παίρνω, άκουσες, + παρέα, αριστερά, έμαθα, μάχη, μηχανή, σάρα, ζωντανός, όνειρο, παλιό, μπορούσαμε, πάντως, ανάμεσα, έχασα, + νωρίς, κάποιοι, άκου, παίζει, φτάνει, δίνω, βγες, υπέροχη, νόημα, έλεγχο, μέτρα, ξερεις, ζει, δείχνει, βρες, τού """ # Source: https://www.101languages.net/russian/most-common-russian-words/ alias words_ru = """ -я, не, что, в, и, ты, это, на, с, он, вы, как, мы, да, а, мне, меня, у, нет, так, но, то, все, тебя, его, -за, о, она, тебе, если, они, бы, же, ну, здесь, к, из, есть, чтобы, для, хорошо, когда, вас, только, по, -вот, просто, был, знаю, нас, всё, было, от, может, кто, вам, очень, их, там, будет, уже, почему, еще, -быть, где, спасибо, ничего, сейчас, или, могу, хочу, нам, чем, мой, до, надо, этого, ее, теперь, давай, -знаешь, нужно, больше, этом, нибудь, раз, со, была, этот, ему, ладно, эй, время, тоже, даже, хочешь, -сказал, ли, себя, думаю, пока, должен, потому, никогда, ни, тут, ещё, её, пожалуйста, сюда, привет, -тогда, конечно, моя, него, сегодня, один, тобой, правда, лучше, об, были, того, можно, мной, всегда, -сказать, день, сэр, без, можешь, чего, эти, дело, значит, лет, много, во, делать, буду, порядке, должны, -такой, ведь, ним, всего, сделать, хотел, твой, жизнь, ей, мистер, потом, через, себе, них, всех, такое, -им, куда, том, мама, после, человек, люди, слишком, иди, зачем, этим, немного, сколько, этой, знаете, -боже, ней, эту, который, отец, свою, деньги, два, под, твоя, мои, никто, моей, думаешь, друг, жизни, -эта, назад, видел, кажется, точно, вместе, люблю, мог, случилось, сам, нравится, черт, какой, людей, -папа, домой, тот, скажи, которые, должна, три, всем, сделал, возможно, прошу, будем, дома, парень, -снова, говорит, место, отсюда, можем, будешь, пошли, делаешь, совсем, говорил, понимаю, завтра, хочет, -простите, разве, давайте, хотите, отлично, сказала, туда, прямо, времени, вами, лишь, своей, хватит, -думал, можете, дом, дела, знать, дай, понял, помочь, говорить, слушай, свои, поэтому, прости, знает, -именно, знал, тем, кого, смотри, каждый, ваш, похоже, найти, моего, наш, мать, одна, имя, про, говорю, -будут, оно, свой, нельзя, извините, стоит, действительно, зовут, поговорить, доктор, перед, несколько, -нужен, происходит, ко, господи, возьми, мою, тех, нами, вижу, должно, наверное, откуда, понимаешь, верно, -скоро, уж, деле, твои, пусть, всю, хотела, при, более, ребята, нее, быстро, подожди, идти, надеюсь, чём, -работу, видеть, такая, этих, уверен, нужна, года, раньше, такие, руки, видишь, какая, посмотри, сын, -самом, ваша, послушай, равно, наши, другой, ага, мир, извини, минут, против, твоей, пор, жить, ж, жаль, -вообще, могли, хотя, человека, пора, ради, говорят, почти, твою, могут, над, весь, первый, чёрт, слышал, -собой, брат, вещи, дня, скажу, говоришь, нормально, своего, мое, ваше, итак, будь, ночь, хоть, ясно, -плохо, дверь, вопрос, господин, давно, денег, ваши, ка, мисс, одну, глаза, пять, будто, между, пойду, -опять, работа, самое, иногда, детей, этому, рад, здорово, бог, одного, ночи, готов, номер, которая, -машину, любовь, дорогая, виду, одно, прекрасно, вон, своих, быстрее, отца, женщина, достаточно, рядом, -убить, таким, пойдем, смерти, дети, такого, правильно, месте, никаких, сказали, здравствуйте, пару, две, -видела, долго, хороший, ах, кроме, алло, нашей, прав, вчера, вечером, жена, миссис, чтоб, друга, нужны, -кем, какие, те, увидеть, утро, смогу, неё, сама, моему, большой, сразу, работать, сердце, стал, своим, -сначала, могла, вроде, ними, говори, голову, дальше, помнишь, либо, ума, одной, вечер, случае, взять, -проблемы, помощь, добрый, год, думала, делает, скорее, слова, капитан, последний, важно, дней, помню, -ночью, утром, моих, произошло, которую, боюсь, также, вашей, ой, стой, твоего, никого, дорогой, убил, -насчет, друзья, самый, проблема, видели, вперед, дерьмо, понятно, чувствую, наша, будете, тому, имею, -вернуться, придется, пришел, спать, стать, столько, говорила, пойти, иначе, работает, девушка, час, -момент, моим, умер, думаете, доброе, слово, новый, часов, мире, знаем, твое, мальчик, однажды, интересно, -конец, играть, a, заткнись, сделали, посмотреть, идет, узнать, свое, права, хорошая, город, джон, -долларов, парни, идем, говорите, уйти, понять, знала, поздно, нашли, работы, скажите, сделаю, увидимся, -какого, другие, идея, пошел, доме, дочь, имеет, приятно, лицо, наших, обо, понимаете, руку, часть, -смотрите, вся, собираюсь, четыре, прежде, хотят, скажешь, чувак, дайте, сделала, кофе, джек, верю, -ждать, затем, большое, сами, неужели, моё, любит, мужчина, дать, господа, таких, осталось, которой, -далеко, вернусь, сильно, ох, сможешь, кому, вашего, посмотрим, машина, подождите, свет, чуть, серьезно, -пришли, оружие, решил, смысле, видите, тихо, нашел, свидания, путь, той, совершенно, следующий, которого, -места, парня, вдруг, пути, мадам, какое, шанс, сестра, нашего, ужасно, минуту, вокруг, другом, иду, -других, хотели, нем, смерть, подумал, фильм, оставь, делаете, уверена, кровь, говорили, внимание, -помогите, идите, держи, получить, оба, взял, спокойно, обычно, мало, забыл, странно, смотреть, поехали, -дал, часа, прекрати, посмотрите, готовы, вернулся, поверить, позже, милая, женщины, любишь, довольно, -обратно, остаться, думать, та, стороны, полиция, тело, тысяч, делал, машины, угодно, муж, году, неплохо, -бога, некоторые, конце, милый, the, рождения, трудно, добро, любви, больно, невозможно, спокойной, -слышишь, типа, получил, которое, приятель, хуже, никому, честь, успокойся, вашу, маленький, выглядит, -чарли, сына, неделю, i, девочка, делаю, шесть, ноги, история, рассказать, послушайте, часто, кстати, -двух, забудь, которых, следует, знают, пришла, семья, станет, матери, ребенок, план, проблем, например, -сделай, воды, немедленно, мира, сэм, телефон, перестань, правду, второй, прощения, ту, наше, уходи, твоих, -помоги, пол, внутри, нему, смог, десять, нашу, около, бывает, самого, большая, леди, сможем, вниз, легко, -делай, единственный, рада, меньше, волнуйся, хотим, полагаю, мам, иметь, своими, мере, наконец, начала, -минутку, работе, пожаловать, другого, двое, никакого, честно, школе, лучший, умереть, дам, насколько, -всей, малыш, оставить, безопасности, ненавижу, школу, осторожно, сынок, джо, таки, пытался, другое, б, -клянусь, машине, недели, стало, истории, пришлось, выглядишь, чему, сможет, купить, слышала, знали, -настоящий, сих, выйти, людям, замечательно, полиции, огонь, пойдём, спросить, дядя, детка, среди, особенно, -твоим, комнате, шоу, выпить, постоянно, делают, позвольте, родители, письмо, городе, случай, месяцев, мужик, -благодарю, o, ребенка, смешно, ответ, города, образом, любой, полностью, увидел, еду, имени, вместо, -абсолютно, обязательно, улице, твоё, убили, ваших, ехать, крови, решение, вина, поможет, своё, секунду, -обещаю, начать, голос, вещь, друзей, показать, нечего, э, месяц, подарок, приехал, самая, молодец, сделаем, -крайней, женщин, собираешься, конца, страшно, новости, идиот, потерял, спасти, вернуть, узнал, слушайте, -хотелось, сон, поняла, прошло, комнату, семь, погоди, главное, рано, корабль, пытаюсь, игра, умерла, -повезло, всему, возьму, таком, моем, глаз, настолько, идём, удачи, готова, семьи, садись, гарри, держись, -звучит, мило, война, человеком, право, такую, вопросы, представить, работаю, имеешь, красивая, идёт, никакой, -профессор, думает, войны, стала, стали, оттуда, известно, слышу, начал, подумать, позвонить, старый, придётся, -историю, вести, твоему, последнее, хочется, миллионов, нашла, способ, отношения, земле, фрэнк, получится, -говоря, связи, многие, пошёл, пистолет, убью, руках, получилось, президент, остановить, тьi, оставил, одним, -you, утра, боль, хорошие, пришёл, открой, брось, вставай, находится, поговорим, кино, людьми, полицию, покажу, +я, не, что, в, и, ты, это, на, с, он, вы, как, мы, да, а, мне, меня, у, нет, так, но, то, все, тебя, его, +за, о, она, тебе, если, они, бы, же, ну, здесь, к, из, есть, чтобы, для, хорошо, когда, вас, только, по, +вот, просто, был, знаю, нас, всё, было, от, может, кто, вам, очень, их, там, будет, уже, почему, еще, +быть, где, спасибо, ничего, сейчас, или, могу, хочу, нам, чем, мой, до, надо, этого, ее, теперь, давай, +знаешь, нужно, больше, этом, нибудь, раз, со, была, этот, ему, ладно, эй, время, тоже, даже, хочешь, +сказал, ли, себя, думаю, пока, должен, потому, никогда, ни, тут, ещё, её, пожалуйста, сюда, привет, +тогда, конечно, моя, него, сегодня, один, тобой, правда, лучше, об, были, того, можно, мной, всегда, +сказать, день, сэр, без, можешь, чего, эти, дело, значит, лет, много, во, делать, буду, порядке, должны, +такой, ведь, ним, всего, сделать, хотел, твой, жизнь, ей, мистер, потом, через, себе, них, всех, такое, +им, куда, том, мама, после, человек, люди, слишком, иди, зачем, этим, немного, сколько, этой, знаете, +боже, ней, эту, который, отец, свою, деньги, два, под, твоя, мои, никто, моей, думаешь, друг, жизни, +эта, назад, видел, кажется, точно, вместе, люблю, мог, случилось, сам, нравится, черт, какой, людей, +папа, домой, тот, скажи, которые, должна, три, всем, сделал, возможно, прошу, будем, дома, парень, +снова, говорит, место, отсюда, можем, будешь, пошли, делаешь, совсем, говорил, понимаю, завтра, хочет, +простите, разве, давайте, хотите, отлично, сказала, туда, прямо, времени, вами, лишь, своей, хватит, +думал, можете, дом, дела, знать, дай, понял, помочь, говорить, слушай, свои, поэтому, прости, знает, +именно, знал, тем, кого, смотри, каждый, ваш, похоже, найти, моего, наш, мать, одна, имя, про, говорю, +будут, оно, свой, нельзя, извините, стоит, действительно, зовут, поговорить, доктор, перед, несколько, +нужен, происходит, ко, господи, возьми, мою, тех, нами, вижу, должно, наверное, откуда, понимаешь, верно, +скоро, уж, деле, твои, пусть, всю, хотела, при, более, ребята, нее, быстро, подожди, идти, надеюсь, чём, +работу, видеть, такая, этих, уверен, нужна, года, раньше, такие, руки, видишь, какая, посмотри, сын, +самом, ваша, послушай, равно, наши, другой, ага, мир, извини, минут, против, твоей, пор, жить, ж, жаль, +вообще, могли, хотя, человека, пора, ради, говорят, почти, твою, могут, над, весь, первый, чёрт, слышал, +собой, брат, вещи, дня, скажу, говоришь, нормально, своего, мое, ваше, итак, будь, ночь, хоть, ясно, +плохо, дверь, вопрос, господин, давно, денег, ваши, ка, мисс, одну, глаза, пять, будто, между, пойду, +опять, работа, самое, иногда, детей, этому, рад, здорово, бог, одного, ночи, готов, номер, которая, +машину, любовь, дорогая, виду, одно, прекрасно, вон, своих, быстрее, отца, женщина, достаточно, рядом, +убить, таким, пойдем, смерти, дети, такого, правильно, месте, никаких, сказали, здравствуйте, пару, две, +видела, долго, хороший, ах, кроме, алло, нашей, прав, вчера, вечером, жена, миссис, чтоб, друга, нужны, +кем, какие, те, увидеть, утро, смогу, неё, сама, моему, большой, сразу, работать, сердце, стал, своим, +сначала, могла, вроде, ними, говори, голову, дальше, помнишь, либо, ума, одной, вечер, случае, взять, +проблемы, помощь, добрый, год, думала, делает, скорее, слова, капитан, последний, важно, дней, помню, +ночью, утром, моих, произошло, которую, боюсь, также, вашей, ой, стой, твоего, никого, дорогой, убил, +насчет, друзья, самый, проблема, видели, вперед, дерьмо, понятно, чувствую, наша, будете, тому, имею, +вернуться, придется, пришел, спать, стать, столько, говорила, пойти, иначе, работает, девушка, час, +момент, моим, умер, думаете, доброе, слово, новый, часов, мире, знаем, твое, мальчик, однажды, интересно, +конец, играть, a, заткнись, сделали, посмотреть, идет, узнать, свое, права, хорошая, город, джон, +долларов, парни, идем, говорите, уйти, понять, знала, поздно, нашли, работы, скажите, сделаю, увидимся, +какого, другие, идея, пошел, доме, дочь, имеет, приятно, лицо, наших, обо, понимаете, руку, часть, +смотрите, вся, собираюсь, четыре, прежде, хотят, скажешь, чувак, дайте, сделала, кофе, джек, верю, +ждать, затем, большое, сами, неужели, моё, любит, мужчина, дать, господа, таких, осталось, которой, +далеко, вернусь, сильно, ох, сможешь, кому, вашего, посмотрим, машина, подождите, свет, чуть, серьезно, +пришли, оружие, решил, смысле, видите, тихо, нашел, свидания, путь, той, совершенно, следующий, которого, +места, парня, вдруг, пути, мадам, какое, шанс, сестра, нашего, ужасно, минуту, вокруг, другом, иду, +других, хотели, нем, смерть, подумал, фильм, оставь, делаете, уверена, кровь, говорили, внимание, +помогите, идите, держи, получить, оба, взял, спокойно, обычно, мало, забыл, странно, смотреть, поехали, +дал, часа, прекрати, посмотрите, готовы, вернулся, поверить, позже, милая, женщины, любишь, довольно, +обратно, остаться, думать, та, стороны, полиция, тело, тысяч, делал, машины, угодно, муж, году, неплохо, +бога, некоторые, конце, милый, the, рождения, трудно, добро, любви, больно, невозможно, спокойной, +слышишь, типа, получил, которое, приятель, хуже, никому, честь, успокойся, вашу, маленький, выглядит, +чарли, сына, неделю, i, девочка, делаю, шесть, ноги, история, рассказать, послушайте, часто, кстати, +двух, забудь, которых, следует, знают, пришла, семья, станет, матери, ребенок, план, проблем, например, +сделай, воды, немедленно, мира, сэм, телефон, перестань, правду, второй, прощения, ту, наше, уходи, твоих, +помоги, пол, внутри, нему, смог, десять, нашу, около, бывает, самого, большая, леди, сможем, вниз, легко, +делай, единственный, рада, меньше, волнуйся, хотим, полагаю, мам, иметь, своими, мере, наконец, начала, +минутку, работе, пожаловать, другого, двое, никакого, честно, школе, лучший, умереть, дам, насколько, +всей, малыш, оставить, безопасности, ненавижу, школу, осторожно, сынок, джо, таки, пытался, другое, б, +клянусь, машине, недели, стало, истории, пришлось, выглядишь, чему, сможет, купить, слышала, знали, +настоящий, сих, выйти, людям, замечательно, полиции, огонь, пойдём, спросить, дядя, детка, среди, особенно, +твоим, комнате, шоу, выпить, постоянно, делают, позвольте, родители, письмо, городе, случай, месяцев, мужик, +благодарю, o, ребенка, смешно, ответ, города, образом, любой, полностью, увидел, еду, имени, вместо, +абсолютно, обязательно, улице, твоё, убили, ваших, ехать, крови, решение, вина, поможет, своё, секунду, +обещаю, начать, голос, вещь, друзей, показать, нечего, э, месяц, подарок, приехал, самая, молодец, сделаем, +крайней, женщин, собираешься, конца, страшно, новости, идиот, потерял, спасти, вернуть, узнал, слушайте, +хотелось, сон, поняла, прошло, комнату, семь, погоди, главное, рано, корабль, пытаюсь, игра, умерла, +повезло, всему, возьму, таком, моем, глаз, настолько, идём, удачи, готова, семьи, садись, гарри, держись, +звучит, мило, война, человеком, право, такую, вопросы, представить, работаю, имеешь, красивая, идёт, никакой, +профессор, думает, войны, стала, стали, оттуда, известно, слышу, начал, подумать, позвонить, старый, придётся, +историю, вести, твоему, последнее, хочется, миллионов, нашла, способ, отношения, земле, фрэнк, получится, +говоря, связи, многие, пошёл, пистолет, убью, руках, получилось, президент, остановить, тьi, оставил, одним, +you, утра, боль, хорошие, пришёл, открой, брось, вставай, находится, поговорим, кино, людьми, полицию, покажу, волосы, последние, брата, месяца """ @@ -595,9 +595,9 @@ fn gen_word_pairs[words: String = words_en]() -> List[String]: return result -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmarks -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_small_keys[s: String](mut b: Bencher) raises: var words = gen_word_pairs[s]() @@ -648,9 +648,9 @@ fn bench_long_key_new_hash_function[s: String](mut b: Bencher) raises: b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): var m = Bench(BenchConfig(num_repetitions=1)) m.bench_function[bench_small_keys[words_ar]](BenchId("bench_small_keys_ar")) diff --git a/stdlib/benchmarks/math/bench_math.mojo b/stdlib/benchmarks/math/bench_math.mojo index b7d4c1ae43..3e17eaf282 100644 --- a/stdlib/benchmarks/math/bench_math.mojo +++ b/stdlib/benchmarks/math/bench_math.mojo @@ -19,9 +19,9 @@ from random import * from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Data -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# alias input_type = Float32 @@ -54,9 +54,9 @@ fn make_int_inputs(begin: Int, end: Int, num: Int) -> List[Int]: var inputs = make_inputs(0, 10_000, 1_000_000) var int_inputs = make_int_inputs(0, 10_000_000, 1_000_000) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark math_func -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter @@ -73,9 +73,9 @@ fn bench_math[ b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark fma -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_math3[ math_f3p: fn[type: DType, size: Int] ( @@ -92,9 +92,9 @@ fn bench_math3[ b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark lcm/gcd -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_math2[math_f2p: fn (Int, Int, /) -> Int](mut b: Bencher) raises: @always_inline @@ -107,9 +107,9 @@ fn bench_math2[math_f2p: fn (Int, Int, /) -> Int](mut b: Bencher) raises: b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): seed() var m = Bench(BenchConfig(num_repetitions=1)) diff --git a/stdlib/benchmarks/utils/bench_formatter.mojo b/stdlib/benchmarks/utils/bench_formatter.mojo index 818a0a2cff..12896f3c91 100644 --- a/stdlib/benchmarks/utils/bench_formatter.mojo +++ b/stdlib/benchmarks/utils/bench_formatter.mojo @@ -22,14 +22,14 @@ from builtin.dtype import _uint_type_of_width from utils.stringref import _align_down, _memchr, _memmem -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Data -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmarks -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_writer_int[n: Int](mut b: Bencher) raises: @always_inline @@ -54,9 +54,9 @@ fn bench_writer_simd[n: Int](mut b: Bencher) raises: b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): var m = Bench(BenchConfig(num_repetitions=1)) m.bench_function[bench_writer_int[42]](BenchId("bench_writer_int_42")) diff --git a/stdlib/benchmarks/utils/bench_memmem.mojo b/stdlib/benchmarks/utils/bench_memmem.mojo index 97925b2884..777557784e 100644 --- a/stdlib/benchmarks/utils/bench_memmem.mojo +++ b/stdlib/benchmarks/utils/bench_memmem.mojo @@ -23,9 +23,9 @@ from memory import UnsafePointer, bitcast, memcmp, pack_bits from utils.stringref import _align_down, _memchr, _memmem -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Data -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# var haystack = """Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer sed dictum est, et finibus ipsum. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Nam tincidunt vel lacus vitae pulvinar. Donec ac ligula elementum, mollis purus a, lacinia quam. Maecenas vulputate mauris quis sem euismod sollicitudin. Proin accumsan nulla vel nisl congue varius. Morbi a erat dui. Aliquam maximus interdum orci, vitae pretium lorem bibendum non. Vestibulum eu lacus ullamcorper, egestas dui vel, pharetra ipsum. Pellentesque sagittis, urna a tincidunt sodales, leo sem placerat eros, vitae molestie felis diam at dolor. Donec viverra sem sit amet facilisis laoreet. Morbi semper convallis nisi, vitae congue velit tincidunt vel. Fusce ultrices, libero vel venenatis placerat, justo tellus porttitor massa, at volutpat tortor nunc id dui. Morbi eu ex quis odio porttitor ultricies vel eget massa. Aenean quis luctus nulla. Fusce sit amet leo at quam hendrerit mattis. Morbi sed quam nisl. Quisque purus enim, iaculis sed laoreet vel, pellentesque ut orci. Vivamus risus orci, varius eu pharetra quis, tincidunt non enim. Suspendisse bibendum lacus ex, quis blandit lectus malesuada a. Maecenas iaculis porta lacus, sit amet tristique ante scelerisque non. Proin auctor elit in lacus dictum egestas. Pellentesque tincidunt justo sed vehicula blandit. Pellentesque vehicula facilisis tellus in viverra. @@ -143,9 +143,9 @@ Curabitur auctor volutpat diam vitae vehicula. Vivamus est arcu, efficitur nec i var needle = "school" # a word intentionally not in the test data -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Baseline `_memmem` implementation -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline fn _memmem_baseline[ type: DType @@ -185,9 +185,9 @@ fn _memmem_baseline[ return UnsafePointer[Scalar[type]]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmarks -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @parameter fn bench_find_baseline(mut b: Bencher) raises: @always_inline @@ -218,9 +218,9 @@ fn bench_find_optimized(mut b: Bencher) raises: b.iter[call_fn]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Benchmark Main -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# def main(): var m = Bench(BenchConfig(num_repetitions=1)) m.bench_function[bench_find_baseline](BenchId("find_baseline")) diff --git a/stdlib/docs/style-guide.md b/stdlib/docs/style-guide.md index dc07d62088..82002c152c 100644 --- a/stdlib/docs/style-guide.md +++ b/stdlib/docs/style-guide.md @@ -111,9 +111,9 @@ structure of header comments separating the various kinds of methods that can be defined on structs. ```mojo -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # MyStruct -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# struct MyStruct(Sized, Stringable): diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index d9a3f7f71b..6a9d585fb5 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -26,9 +26,9 @@ import bit from ._b64encode import b64encode_with_buffers as _b64encode_with_buffers -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utilities -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -59,9 +59,9 @@ fn _ascii_to_value(char: String) -> Int: return -1 -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # b64encode -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # TODO: Use Span instead of List as input when Span is easier to use @@ -106,9 +106,9 @@ fn b64encode(input_bytes: List[UInt8, _]) -> String: return String(result^) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # b64decode -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -153,9 +153,9 @@ fn b64decode(str: String) -> String: return p -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # b16encode -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn b16encode(str: String) -> String: @@ -190,9 +190,9 @@ fn b16encode(str: String) -> String: return String(out^) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # b16decode -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/bit/bit.mojo b/stdlib/src/bit/bit.mojo index 5405411ab1..74a428d3df 100644 --- a/stdlib/src/bit/bit.mojo +++ b/stdlib/src/bit/bit.mojo @@ -22,9 +22,9 @@ from bit import count_leading_zeros from sys import llvm_intrinsic, sizeof from sys.info import bitwidthof -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # count_leading_zeros -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -66,9 +66,9 @@ fn count_leading_zeros[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # count_trailing_zeros -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -110,9 +110,9 @@ fn count_trailing_zeros[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # bit_reverse -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -154,9 +154,9 @@ fn bit_reverse[ ](val) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # byte_swap -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -212,9 +212,9 @@ fn byte_swap[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # pop_count -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -256,9 +256,9 @@ fn pop_count[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # bit_not -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -286,9 +286,9 @@ fn bit_not[ return __mlir_op.`pop.simd.xor`(val.value, neg_one.value) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # bit_width -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -343,9 +343,9 @@ fn bit_width[ return bitwidth - leading_zero -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # is_power_of_two -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # reference: https://en.cppreference.com/w/cpp/numeric/has_single_bit @@ -391,9 +391,9 @@ fn is_power_of_two[ return (val > 0) & (val & (val - 1) == 0) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # bit_ceil -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # reference: https://en.cppreference.com/w/cpp/numeric/bit_ceil @@ -447,9 +447,9 @@ fn bit_ceil[ return (val > 1).select(1 << bit_width(val - ones), ones) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # bit_floor -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # reference: https://en.cppreference.com/w/cpp/numeric/bit_floor @@ -500,9 +500,9 @@ fn bit_floor[ return (val > 0).select(1 << (bit_width(val) - 1), zeros) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # rotate_bits_left -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -578,9 +578,9 @@ fn rotate_bits_left[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # rotate_bits_right -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/builtin/_stubs.mojo b/stdlib/src/builtin/_stubs.mojo index c116d52408..3b5a12fdac 100644 --- a/stdlib/src/builtin/_stubs.mojo +++ b/stdlib/src/builtin/_stubs.mojo @@ -13,9 +13,9 @@ from builtin.range import _StridedRangeIterator, _UIntStridedRangeIterator -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # __MLIRType -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @register_passable("trivial") @@ -23,9 +23,9 @@ struct __MLIRType[T: AnyTrivialRegType](Movable, Copyable): var value: T -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # parameter_for -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# trait _IntNext(Copyable): diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index be5ddaaa05..1e44efe5b4 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -17,9 +17,9 @@ These are Mojo built-ins, so you don't need to import them. from memory import Pointer, UnsafePointer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # ListLiteral -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# struct ListLiteral[*Ts: CollectionElement](Sized, CollectionElement): @@ -119,9 +119,9 @@ struct ListLiteral[*Ts: CollectionElement](Sized, CollectionElement): return value in self.storage -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # VariadicList / VariadicListMem -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value @@ -469,9 +469,9 @@ struct VariadicListMem[ ](0, self) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # VariadicPack -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# alias _AnyTypeMetaType = __mlir_type[`!lit.anytrait<`, AnyType, `>`] diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index 1d1c37d16f..4ac807acd6 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -511,9 +511,9 @@ struct DType( """ return 8 * self.sizeof() - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # dispatch_integral - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline fn dispatch_integral[ @@ -548,9 +548,9 @@ struct DType( else: raise Error("only integral types are supported") - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # dispatch_floating - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline fn dispatch_floating[ @@ -626,9 +626,9 @@ struct DType( "dispatch_custom: dynamic_type does not match any dtype parameters" ) - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # dispatch_arithmetic - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline fn dispatch_arithmetic[ diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index 1d19b5d201..e282d56ba0 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -23,9 +23,9 @@ from memory.memory import _free from utils import StringRef -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Error -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @register_passable diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 906e8d40dc..6cee98bafa 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -17,9 +17,9 @@ These are Mojo built-ins, so you don't need to import them. from math import Ceilable, CeilDivable, Floorable, Truncable -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # FloatLiteral -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value diff --git a/stdlib/src/builtin/format_int.mojo b/stdlib/src/builtin/format_int.mojo index 0239369fbe..0598bb816a 100644 --- a/stdlib/src/builtin/format_int.mojo +++ b/stdlib/src/builtin/format_int.mojo @@ -24,9 +24,9 @@ from utils import StaticString, StringSlice alias _DEFAULT_DIGIT_CHARS = "0123456789abcdefghijklmnopqrstuvwxyz" -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # bin -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn bin(num: Scalar, /, *, prefix: StaticString = "0b") -> String: @@ -82,9 +82,9 @@ fn bin[T: Indexer, //](num: T, /, *, prefix: StaticString = "0b") -> String: return bin(Scalar[DType.index](index(num)), prefix=prefix) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # hex -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn hex(value: Scalar, /, *, prefix: StaticString = "0x") -> String: @@ -144,9 +144,9 @@ fn hex(value: Scalar[DType.bool], /, *, prefix: StaticString = "0x") -> String: return hex(value.cast[DType.int8](), prefix=prefix) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # oct -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn oct(value: Scalar, /, *, prefix: StaticString = "0o") -> String: @@ -206,9 +206,9 @@ fn oct(value: Scalar[DType.bool], /, *, prefix: StaticString = "0o") -> String: return oct(value.cast[DType.int8](), prefix=prefix) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Integer formatting utilities -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn _try_format_int( diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 61a74df2dc..2c9a13234b 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -760,9 +760,9 @@ struct Int( """ return __mlir_op.`index.or`(self.value, rhs.value) - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # In place operations. - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline("nodebug") fn __iadd__(mut self, rhs: Int): @@ -873,9 +873,9 @@ struct Int( """ self = self | rhs - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # Reversed operations - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline("nodebug") fn __radd__(self, value: Int) -> Int: diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 360ed0e9a6..3d50918458 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -373,9 +373,9 @@ struct IntLiteral( ](self.value, rhs.value) ) - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # In place operations. - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline("nodebug") fn __iadd__(mut self, rhs: Self): @@ -458,9 +458,9 @@ struct IntLiteral( """ self = self | rhs - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # Reversed operations - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline("nodebug") fn __radd__(self, value: Self) -> Self: @@ -717,9 +717,9 @@ struct IntLiteral( """ return -(self // -denominator) - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # Methods - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline("nodebug") fn _bit_width(self) -> IntLiteral: diff --git a/stdlib/src/builtin/sort.mojo b/stdlib/src/builtin/sort.mojo index 093cc41914..78447a82fb 100644 --- a/stdlib/src/builtin/sort.mojo +++ b/stdlib/src/builtin/sort.mojo @@ -24,9 +24,9 @@ from memory import UnsafePointer from utils import Span -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # sort -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# alias insertion_sort_threshold = 32 @@ -260,9 +260,9 @@ fn _quicksort[ stack.append(ImmSpan(ptr=ptr, length=pivot)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # stable sort -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn _merge[ @@ -366,9 +366,9 @@ fn _stable_sort[ temp_buff.free() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # partition -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -501,9 +501,9 @@ fn partition[ _partition[_cmp_fn](span, k) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # sort -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Junction from public to private API @@ -686,9 +686,9 @@ fn sort[ sort[_cmp_fn, stable=stable](span) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # sort networks -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 554ddeddfd..3e889e760a 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -26,9 +26,9 @@ from utils._visualizers import lldb_formatter_wrapping_type from utils.format import _CurlyEntryFormattable, _FormatCurlyEntry from utils.string_slice import _StringSliceIter, _to_string_list -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # StringLiteral -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @lldb_formatter_wrapping_type diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index 00bede563f..b18221e3b3 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -21,9 +21,9 @@ from memory import UnsafePointer from utils._visualizers import lldb_formatter_wrapping_type -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Tuple -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @lldb_formatter_wrapping_type diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index dad8ed6167..9c3feb155f 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -398,9 +398,9 @@ struct UInt(IntLike, _HashableWithHasher): """ return __mlir_op.`index.ceildivu`(self.value, denominator.value) - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # In place operations. - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline("nodebug") fn __iadd__(mut self, rhs: UInt): @@ -511,9 +511,9 @@ struct UInt(IntLike, _HashableWithHasher): """ self = self | rhs - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# # Reversed operations - # ===----------------------------------------------------------------------===# + # ===-------------------------------------------------------------------===# @always_inline("nodebug") fn __radd__(self, value: Self) -> Self: diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index cdaf52332d..c79efe4a30 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -26,9 +26,9 @@ from collections import Optional from bit import bit_ceil from memory import UnsafePointer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Deque -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# struct Deque[ElementType: CollectionElement]( diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index bb8809612d..93064e0220 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -25,9 +25,9 @@ from sys.intrinsics import _type_is_eq from memory import UnsafePointer from memory.maybe_uninitialized import UnsafeMaybeUninitialized -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Array -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn _inline_array_construction_checks[size: Int](): diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index 516be41287..4e2e90349f 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -24,9 +24,9 @@ from sys.intrinsics import _type_is_eq from memory.maybe_uninitialized import UnsafeMaybeUninitialized -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # InlineList -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value struct _InlineListIter[ list_mutability: Bool, //, diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 19b477a47e..b470023659 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -30,9 +30,9 @@ from utils import Span from .optional import Optional -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # List -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index 95c78a9706..73518b55a5 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -43,9 +43,9 @@ struct _NoneType(CollectionElement, CollectionElementNew): pass -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Optional -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value @@ -383,9 +383,9 @@ struct Optional[T: CollectionElement]( return default -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # OptionalReg -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @register_passable("trivial") diff --git a/stdlib/src/collections/vector.mojo b/stdlib/src/collections/vector.mojo index 1c9385c528..62196e6a15 100644 --- a/stdlib/src/collections/vector.mojo +++ b/stdlib/src/collections/vector.mojo @@ -25,9 +25,9 @@ from memory import Pointer, UnsafePointer, memcpy from utils import StaticTuple -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # _VecIter -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value @@ -54,9 +54,9 @@ struct _VecIter[ return self.size - self.i -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # InlinedFixedVector -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/math/polynomial.mojo b/stdlib/src/math/polynomial.mojo index 8397218380..35384d33c1 100644 --- a/stdlib/src/math/polynomial.mojo +++ b/stdlib/src/math/polynomial.mojo @@ -21,9 +21,9 @@ from math.polynomial import polynomial_evaluate from collections import List -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # polynomial_evaluate -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -49,9 +49,9 @@ fn polynomial_evaluate[ return _horner_evaluate[coefficients](x) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Horner Method -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 1cbf5c43d3..8d3a525cb4 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -44,9 +44,9 @@ fn _align_down(value: Int, alignment: Int) -> Int: return value._positive_div(alignment) * alignment -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # memcmp -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -146,9 +146,9 @@ fn memcmp[ return _memcmp_impl(s1.bitcast[Byte](), s2.bitcast[Byte](), byte_count) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # memcpy -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -259,9 +259,9 @@ fn memcpy[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # memset -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -304,9 +304,9 @@ fn memset[ _memset_impl(ptr.bitcast[Byte](), value, count * sizeof[type]()) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # memset_zero -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -356,9 +356,9 @@ fn memset_zero[ ptr.store(i, 0) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # stack_allocation -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -447,9 +447,9 @@ fn stack_allocation[ ]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # malloc -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -475,9 +475,9 @@ fn _malloc[ ](alignment.value, size.value) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # aligned_free -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 4fa37a6e61..db7b78bc18 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -20,9 +20,9 @@ from memory import Pointer """ -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # AddressSpace -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value @@ -290,9 +290,9 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): writer.write("AddressSpace(", self.value(), ")") -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Pointer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value diff --git a/stdlib/src/memory/unsafe.mojo b/stdlib/src/memory/unsafe.mojo index 82e995de53..450c18d199 100644 --- a/stdlib/src/memory/unsafe.mojo +++ b/stdlib/src/memory/unsafe.mojo @@ -21,9 +21,9 @@ from memory import bitcast from sys import bitwidthof -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # bitcast -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") diff --git a/stdlib/src/os/atomic.mojo b/stdlib/src/os/atomic.mojo index e2b8f1c65e..5eaac49644 100644 --- a/stdlib/src/os/atomic.mojo +++ b/stdlib/src/os/atomic.mojo @@ -284,9 +284,9 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: Self.min(UnsafePointer.address_of(self.value), rhs) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utilities -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/sys/_assembly.mojo b/stdlib/src/sys/_assembly.mojo index 6cb2123393..2074afde29 100644 --- a/stdlib/src/sys/_assembly.mojo +++ b/stdlib/src/sys/_assembly.mojo @@ -14,9 +14,9 @@ from sys.intrinsics import _mlirtype_is_eq -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # 0-arg -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") diff --git a/stdlib/src/sys/_libc.mojo b/stdlib/src/sys/_libc.mojo index 6751a60e62..ca0104d19c 100644 --- a/stdlib/src/sys/_libc.mojo +++ b/stdlib/src/sys/_libc.mojo @@ -22,9 +22,9 @@ from sys.ffi import OpaquePointer, c_char, c_int from memory import UnsafePointer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # stdlib.h — core C standard library operations -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -37,9 +37,9 @@ fn exit(status: c_int): external_call["exit", NoneType](status) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # stdio.h — input/output operations -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# alias FILE_ptr = OpaquePointer @@ -74,9 +74,9 @@ fn pclose(stream: FILE_ptr) -> c_int: return external_call["pclose", c_int](stream) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # unistd.h -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -86,9 +86,9 @@ fn dup(oldfd: c_int) -> c_int: return external_call[name, c_int](oldfd) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # dlfcn.h — dynamic library operations -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 6d65ab7447..2d6926568d 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -404,9 +404,9 @@ fn _get_dylib_function[ return new_func -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Globals -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# struct _Global[ @@ -462,9 +462,9 @@ fn _get_global_or_null[name: StringLiteral]() -> OpaquePointer: ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # external_call -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -502,9 +502,9 @@ fn external_call[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # _external_call_const -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index 2fe91f3b99..ccd39c7ee2 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -864,9 +864,9 @@ fn _macos_version() raises -> Tuple[Int, Int, Int]: return (major, minor, patch) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Detect GPU on host side -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index dbc833dcc8..3eb81a505a 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -26,9 +26,9 @@ from memory import AddressSpace, UnsafePointer from ._assembly import inlined_assembly from .info import is_nvidia_gpu, sizeof -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # llvm_intrinsic -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -90,9 +90,9 @@ fn llvm_intrinsic[ ](loaded_pack) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # _gather -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # NOTE: Converting from a scalar to a pointer is unsafe! The resulting pointer @@ -176,9 +176,9 @@ fn gather[ return result -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # _scatter -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -254,9 +254,9 @@ fn scatter[ _ = base -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # prefetch -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @register_passable("trivial") @@ -500,9 +500,9 @@ fn prefetch[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # masked load -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -546,9 +546,9 @@ fn masked_load[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # masked store -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -588,9 +588,9 @@ fn masked_store[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # compressed store -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -628,9 +628,9 @@ fn compressed_store[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # strided load -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") @@ -668,9 +668,9 @@ fn strided_load[ return gather(offset, mask, passthrough) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # strided store -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline("nodebug") diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 71eb1e80c2..853c6d2d56 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -34,9 +34,9 @@ from sys._assembly import inlined_assembly from memory import UnsafePointer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utilities -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Enums used in time.h 's glibc alias _CLOCK_REALTIME = 0 @@ -169,9 +169,9 @@ fn _thread_cputime_nanoseconds() -> Int: return _gettime_as_nsec_unix(_CLOCK_THREAD_CPUTIME_ID) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # perf_counter -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -188,9 +188,9 @@ fn perf_counter() -> Float64: return Float64(_monotonic_nanoseconds()) / _NSEC_PER_SEC -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # perf_counter_ns -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -215,9 +215,9 @@ fn perf_counter_ns() -> Int: return _monotonic_nanoseconds() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # now -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -235,9 +235,9 @@ fn now() -> Int: return perf_counter_ns() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # monotonic -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -254,9 +254,9 @@ fn monotonic() -> Int: return perf_counter_ns() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # time_function -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -336,9 +336,9 @@ fn time_function[func: fn () capturing [_] -> None]() -> Int: return abort[Int](err) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # sleep -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn sleep(sec: Float64): diff --git a/stdlib/src/utils/format.mojo b/stdlib/src/utils/format.mojo index af4bbc8d7c..5c1dc2cf8b 100644 --- a/stdlib/src/utils/format.mojo +++ b/stdlib/src/utils/format.mojo @@ -22,9 +22,9 @@ from utils.string_slice import Stringlike # people who want to write their own templating engines. This is not yet done # because the implementation is incomplete and we are missing crucial features. -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Formatter -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # NOTE(#3765): an interesting idea would be to allow custom start and end @@ -417,9 +417,9 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): auto_idx += 1 -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Format Specification -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# trait _CurlyEntryFormattable(Stringable, Representable): @@ -714,6 +714,6 @@ struct _FormatSpec: res += str(item) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utils -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 0ea645ee88..b526aa84ff 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -29,9 +29,9 @@ from builtin.io import _get_dtype_printf_format, _snprintf from . import unroll from .static_tuple import StaticTuple -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utilities -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -48,10 +48,10 @@ fn _reduce_and_fn(a: Bool, b: Bool) -> Bool: return a and b -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Integer and Bool Tuple Utilities: # Utilities to operate on tuples of integers or tuples of bools. -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -147,9 +147,9 @@ fn _bool_tuple_reduce[ return c -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # IndexList: -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn _type_of_width[bitwidth: Int, unsigned: Bool]() -> DType: @@ -828,9 +828,9 @@ struct IndexList[ ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Factory functions for creating index. -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline fn Index[ T0: Intable, //, @@ -1029,9 +1029,9 @@ fn Index[ return __type_of(result)(int(x), int(y), int(z), int(w), int(v)) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utils -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/utils/inline_string.mojo b/stdlib/src/utils/inline_string.mojo index cf8caafc31..add0e6c7cc 100644 --- a/stdlib/src/utils/inline_string.mojo +++ b/stdlib/src/utils/inline_string.mojo @@ -23,9 +23,9 @@ from memory import UnsafePointer, memcpy from utils import StringSlice, Variant -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # InlineString -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value @@ -296,9 +296,9 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # __FixedString -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value diff --git a/stdlib/src/utils/lock.mojo b/stdlib/src/utils/lock.mojo index 3916cd92cc..6459952b4e 100644 --- a/stdlib/src/utils/lock.mojo +++ b/stdlib/src/utils/lock.mojo @@ -18,9 +18,9 @@ from time import sleep from memory import UnsafePointer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # SpinWaiter -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# struct SpinWaiter: diff --git a/stdlib/src/utils/loop.mojo b/stdlib/src/utils/loop.mojo index c0a6aa3f5e..1d61112379 100644 --- a/stdlib/src/utils/loop.mojo +++ b/stdlib/src/utils/loop.mojo @@ -23,9 +23,9 @@ from utils import unroll """ -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # unroll -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -51,9 +51,9 @@ fn unroll[ func[i, j]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # unroll -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -84,9 +84,9 @@ fn unroll[ func[i, j, k]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # unroll _ZeroStartingRange -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -125,9 +125,9 @@ fn unroll[ func[i]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # unroll _SequentialRange -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline fn unroll[ func: fn[idx: Int] () capturing [_] -> None, @@ -164,9 +164,9 @@ fn unroll[ func[i]() -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # unroll _StridedRange -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline fn unroll[ func: fn[idx: Int] () capturing [_] -> None, diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index 7a907b7ed0..6904888e65 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -21,9 +21,9 @@ from utils import StaticTuple from memory import UnsafePointer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utilities -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline @@ -93,9 +93,9 @@ fn _create_array[ return array -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # StaticTuple -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn _static_tuple_construction_checks[size: Int](): diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 666a25c45c..34a33eb3e6 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -1010,9 +1010,9 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable].type,]( return output^ -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utils -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# trait Stringlike: diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index e2a0303100..eaf6a285e3 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -35,9 +35,9 @@ fn _align_down(value: Int, alignment: Int) -> Int: return value._positive_div(alignment) * alignment -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # StringRef -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @value @@ -691,9 +691,9 @@ struct StringRef( ) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utilities -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# @always_inline diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index 2d9351036f..4d53f23afd 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -20,7 +20,7 @@ from memory import UnsafePointer, memcpy from utils import Span, StaticString -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# trait Writer: @@ -119,9 +119,9 @@ trait Writer: # To only have to implement `write_bytes` to make a type a valid Writer -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Writable -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# trait Writable: @@ -157,9 +157,9 @@ trait Writable: ... -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Utils -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn write_args[ diff --git a/stdlib/test/collections/test_deque.mojo b/stdlib/test/collections/test_deque.mojo index 84b759eb91..fd2db54440 100644 --- a/stdlib/test/collections/test_deque.mojo +++ b/stdlib/test/collections/test_deque.mojo @@ -16,9 +16,9 @@ from collections import Deque from testing import assert_equal, assert_false, assert_raises, assert_true -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # Implementation tests -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn test_impl_init_default() raises: @@ -666,9 +666,9 @@ fn test_impl_imul() raises: assert_equal((q._data + 0)[], 3) -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# # API Interface tests -# ===----------------------------------------------------------------------===# +# ===-----------------------------------------------------------------------===# fn test_init_variadic_list() raises: From fcb15eac9353bd0e7ce8cea78f8a8302a5a6348c Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Tue, 3 Dec 2024 02:00:57 -0800 Subject: [PATCH 015/509] [Stdlib] Extend StringLiteral import support to work on Stringable MODULAR_ORIG_COMMIT_REV_ID: b4843f75d8d57376b45716189e89a2f0c41b1315 --- docs/changelog.md | 4 +-- stdlib/src/builtin/constrained.mojo | 2 +- stdlib/src/builtin/string_literal.mojo | 27 +++++++++++--------- stdlib/test/builtin/test_string_literal.mojo | 10 ++++++++ stdlib/test/utils/test_string_slice.mojo | 2 +- 5 files changed, 29 insertions(+), 16 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 640824629c..664fe17057 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -288,9 +288,9 @@ what we publish. memory allocation and performance. These options allow for optimized memory usage and reduced buffer reallocations, providing flexibility based on application requirements. -- A new `StringLiteral.from_string[someString]()` method is available. It +- A new `StringLiteral.get[some_stringable]()` method is available. It allows forming a runtime-constant StringLiteral from a compile-time-dynamic - `String` value. + `Stringable` value. ### 🦋 Changed diff --git a/stdlib/src/builtin/constrained.mojo b/stdlib/src/builtin/constrained.mojo index 09d934ff1c..b50ca11e71 100644 --- a/stdlib/src/builtin/constrained.mojo +++ b/stdlib/src/builtin/constrained.mojo @@ -88,4 +88,4 @@ fn constrained[cond: Bool, msg: String](): "at least two cores are required" ]() """ - constrained[cond, StringLiteral.from_string[msg]()]() + constrained[cond, StringLiteral.get[msg]()]() diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 3e889e760a..dc7d05d49a 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -89,7 +89,7 @@ struct StringLiteral( # for now. @always_inline("nodebug") @staticmethod - fn from_string[value: String]() -> StringLiteral: + fn _from_string[value: String]() -> StringLiteral: """Form a string literal from an arbitrary compile-time String value. Parameters: @@ -106,6 +106,20 @@ struct StringLiteral( `> : !kgen.string`, ] + @always_inline("nodebug") + @staticmethod + fn get[type: Stringable, //, value: type]() -> StringLiteral: + """Form a string literal from an arbitrary compile-time stringable value. + + Parameters: + type: The type of the value. + value: The value to serialize. + + Returns: + The string value as a StringLiteral. + """ + return Self._from_string[str(value)]() + # ===-------------------------------------------------------------------===# # Operator dunders # ===-------------------------------------------------------------------===# @@ -920,14 +934,3 @@ struct StringLiteral( A copy of the string with no leading whitespaces. """ return str(self).lstrip() - - -fn _to_string_literal[val: Int]() -> StringLiteral: - alias s = StringLiteral.from_string[str(val)]() - return s - - -fn _to_string_literal[val: SIMD]() -> StringLiteral: - constrained[val.type.is_integral(), "input type must be integral"]() - alias s = StringLiteral.from_string[str(val)]() - return s diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index 2aaa26e063..28ca91a1af 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -474,6 +474,15 @@ def test_float_conversion(): _ = ("not a float").__float__() +def test_string_literal_from_stringable(): + assert_equal(StringLiteral.get["hello"](), "hello") + assert_equal(StringLiteral.get[String("hello")](), "hello") + assert_equal(StringLiteral.get[42](), "42") + assert_equal( + StringLiteral.get[SIMD[DType.int64, 4](1, 2, 3, 4)](), "[1, 2, 3, 4]" + ) + + def main(): test_add() test_iadd() @@ -506,3 +515,4 @@ def main(): test_split() test_splitlines() test_float_conversion() + test_string_literal_from_stringable() diff --git a/stdlib/test/utils/test_string_slice.mojo b/stdlib/test/utils/test_string_slice.mojo index 191d98a7d6..057601c270 100644 --- a/stdlib/test/utils/test_string_slice.mojo +++ b/stdlib/test/utils/test_string_slice.mojo @@ -487,7 +487,7 @@ def test_splitlines(): _assert_equal(s.splitlines(keepends=True), items) -fn main() raises: +def main(): test_string_literal_byte_span() test_string_byte_span() test_heap_string_from_string_slice() From 312784ca3c229069248f4a581c0e244fead2779d Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 3 Dec 2024 17:07:28 -0600 Subject: [PATCH 016/509] [DRAFT] feat: Use `Origin[..]` instead of `Origin[..].type` in `Span` and `StringSlice` * Change `ImmutableOrigin` and `MutableOrigin` to be aliases for `Origin[False]` and `Origin[True]`, respectively, instead of raw `!lit.origin` MLIR types. MODULAR_ORIG_COMMIT_REV_ID: 62c5102b8ded23f6a5507021f2863d1c1080a383 --- stdlib/src/builtin/builtin_list.mojo | 5 ++++- stdlib/src/builtin/type_aliases.mojo | 4 ++-- stdlib/src/memory/arc.mojo | 2 +- stdlib/src/utils/span.mojo | 16 +++++++++------- stdlib/src/utils/string_slice.mojo | 4 ++-- 5 files changed, 18 insertions(+), 13 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 1e44efe5b4..80047f0343 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -236,7 +236,10 @@ struct _VariadicListMemIter[ alias variadic_list_type = VariadicListMem[elt_type, elt_origin] var index: Int - var src: Pointer[Self.variadic_list_type, list_origin] + var src: Pointer[ + Self.variadic_list_type, + list_origin._mlir_origin, + ] fn __init__( mut self, index: Int, ref [list_origin]list: Self.variadic_list_type diff --git a/stdlib/src/builtin/type_aliases.mojo b/stdlib/src/builtin/type_aliases.mojo index 5bbee8d763..7c7c6ff25b 100644 --- a/stdlib/src/builtin/type_aliases.mojo +++ b/stdlib/src/builtin/type_aliases.mojo @@ -18,10 +18,10 @@ These are Mojo built-ins, so you don't need to import them. alias AnyTrivialRegType = __mlir_type.`!kgen.type` """Represents any register passable Mojo data type.""" -alias ImmutableOrigin = __mlir_type.`!lit.origin<0>` +alias ImmutableOrigin = Origin[False] """Immutable origin reference type.""" -alias MutableOrigin = __mlir_type.`!lit.origin<1>` +alias MutableOrigin = Origin[True] """Mutable origin reference type.""" alias ImmutableAnyOrigin = __mlir_attr.`#lit.any.origin : !lit.origin<0>` diff --git a/stdlib/src/memory/arc.mojo b/stdlib/src/memory/arc.mojo index d7b236af72..ecabe6c9d4 100644 --- a/stdlib/src/memory/arc.mojo +++ b/stdlib/src/memory/arc.mojo @@ -146,7 +146,7 @@ struct ArcPointer[T: Movable]( ]( ref [self_life]self, ) -> ref [ - _lit_mut_cast[self_life, result_mutable=True].result + _lit_mut_cast[self_life._mlir_origin, result_mutable=True].result ] T: """Returns a mutable reference to the managed value. diff --git a/stdlib/src/utils/span.mojo b/stdlib/src/utils/span.mojo index 3ec2b3030c..396744aa4e 100644 --- a/stdlib/src/utils/span.mojo +++ b/stdlib/src/utils/span.mojo @@ -48,7 +48,7 @@ trait AsBytes: struct _SpanIter[ is_mutable: Bool, //, T: CollectionElement, - origin: Origin[is_mutable].type, + origin: Origin[is_mutable], forward: Bool = True, ]: """Iterator for Span. @@ -70,7 +70,7 @@ struct _SpanIter[ @always_inline fn __next__( mut self, - ) -> Pointer[T, origin]: + ) -> Pointer[T, origin._mlir_origin]: @parameter if forward: self.index += 1 @@ -97,7 +97,7 @@ struct _SpanIter[ struct Span[ is_mutable: Bool, //, T: CollectionElement, - origin: Origin[is_mutable].type, + origin: Origin[is_mutable], ](CollectionElementNew): """A non owning view of contiguous data. @@ -259,7 +259,7 @@ struct Span[ return self._data - fn as_ref(self) -> Pointer[T, origin]: + fn as_ref(self) -> Pointer[T, origin._mlir_origin]: """ Gets a Pointer to the first element of this slice. @@ -267,7 +267,7 @@ struct Span[ A Pointer pointing at the first element of this slice. """ - return Pointer[T, origin].address_of(self._data[0]) + return Pointer[T, origin._mlir_origin].address_of(self._data[0]) @always_inline fn copy_from[ @@ -358,13 +358,15 @@ struct Span[ for element in self: element[] = value - fn get_immutable(self) -> Span[T, _lit_mut_cast[origin, False].result]: + fn get_immutable( + self, + ) -> Span[T, _lit_mut_cast[origin._mlir_origin, False].result]: """ Return an immutable version of this span. Returns: A span covering the same elements, but without mutability. """ - return Span[T, _lit_mut_cast[origin, False].result]( + return Span[T, _lit_mut_cast[origin._mlir_origin, False].result]( ptr=self._data, length=self._len ) diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 34a33eb3e6..394dccf89c 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -170,7 +170,7 @@ fn _memrmem[ @value struct _StringSliceIter[ is_mutable: Bool, //, - origin: Origin[is_mutable].type, + origin: Origin[is_mutable], forward: Bool = True, ]: """Iterator for `StringSlice` over unicode characters. @@ -235,7 +235,7 @@ struct _StringSliceIter[ @value @register_passable("trivial") -struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable].type,]( +struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( Stringable, Sized, Writable, From ecb608453e302b4b5e8a579edad8cf824437a949 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 3 Dec 2024 17:39:50 -0600 Subject: [PATCH 017/509] [stdlib] feat: Use `Origin[..]` in `UnsafePointer` parameter MODULAR_ORIG_COMMIT_REV_ID: 229c6c76108284ab19aaa2e3fef02c288a1706ec --- stdlib/src/memory/unsafe_pointer.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 3f8db596af..cc760f1c01 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -53,7 +53,7 @@ struct UnsafePointer[ *, address_space: AddressSpace = AddressSpace.GENERIC, alignment: Int = _default_alignment[type](), - origin: Origin[True].type = MutableAnyOrigin, + origin: Origin[True] = MutableAnyOrigin, ]( ImplicitlyBoolable, CollectionElement, @@ -197,7 +197,7 @@ struct UnsafePointer[ """ # We're unsafe, so we can have unsafe things. - alias _ref_type = Pointer[type, origin, address_space] + alias _ref_type = Pointer[type, origin._mlir_origin, address_space] return __get_litref_as_mvalue( __mlir_op.`lit.ref.from_pointer`[_type = _ref_type._mlir_type]( UnsafePointer[ @@ -961,7 +961,7 @@ struct UnsafePointer[ /, address_space: AddressSpace = Self.address_space, alignment: Int = Self.alignment, - origin: Origin[True].type = Self.origin, + origin: Origin[True] = Self.origin, ](self) -> UnsafePointer[ T, address_space=address_space, alignment=alignment, origin=origin ]: From 58891453d6f93c786e2647b297a8e5cad8f62b0a Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 3 Dec 2024 18:13:10 -0600 Subject: [PATCH 018/509] [stdlib] feat: Use `Origin[..]` in `Pointer` and iterator type parameter (Part 3/X) Also changes `_lit_mut_cast[]` to take an `Origin` as well. MODULAR_ORIG_COMMIT_REV_ID: 1b9b88f28acc46ed2c02b368892c1cddbdf98b7e --- stdlib/src/builtin/builtin_list.mojo | 6 +++--- stdlib/src/collections/deque.mojo | 2 +- stdlib/src/collections/inline_list.mojo | 2 +- stdlib/src/collections/list.mojo | 2 +- stdlib/src/memory/arc.mojo | 2 +- stdlib/src/memory/pointer.mojo | 4 ++-- stdlib/src/memory/unsafe_pointer.mojo | 2 +- stdlib/src/utils/span.mojo | 10 +++++----- 8 files changed, 15 insertions(+), 15 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 80047f0343..fa24d3c55c 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -238,7 +238,7 @@ struct _VariadicListMemIter[ var index: Int var src: Pointer[ Self.variadic_list_type, - list_origin._mlir_origin, + list_origin, ] fn __init__( @@ -283,12 +283,12 @@ struct _lit_origin_union[ struct _lit_mut_cast[ is_mutable: Bool, //, - operand: Origin[is_mutable].type, + operand: Origin[is_mutable], result_mutable: Bool, ]: alias result = __mlir_attr[ `#lit.origin.mutcast<`, - operand, + operand._mlir_origin, `> : !lit.origin<`, +result_mutable.value, `>`, diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index c79efe4a30..6d45725350 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -995,7 +995,7 @@ struct Deque[ElementType: CollectionElement]( struct _DequeIter[ deque_mutability: Bool, //, ElementType: CollectionElement, - deque_lifetime: Origin[deque_mutability].type, + deque_lifetime: Origin[deque_mutability], forward: Bool = True, ]: """Iterator for Deque. diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index 4e2e90349f..f98e307940 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -32,7 +32,7 @@ struct _InlineListIter[ list_mutability: Bool, //, T: CollectionElementNew, capacity: Int, - list_origin: Origin[list_mutability].type, + list_origin: Origin[list_mutability], forward: Bool = True, ]: """Iterator for InlineList. diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index b470023659..d4791dd79e 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -40,7 +40,7 @@ struct _ListIter[ list_mutability: Bool, //, T: CollectionElement, hint_trivial_type: Bool, - list_origin: Origin[list_mutability].type, + list_origin: Origin[list_mutability], forward: Bool = True, ]: """Iterator for List. diff --git a/stdlib/src/memory/arc.mojo b/stdlib/src/memory/arc.mojo index ecabe6c9d4..d7b236af72 100644 --- a/stdlib/src/memory/arc.mojo +++ b/stdlib/src/memory/arc.mojo @@ -146,7 +146,7 @@ struct ArcPointer[T: Movable]( ]( ref [self_life]self, ) -> ref [ - _lit_mut_cast[self_life._mlir_origin, result_mutable=True].result + _lit_mut_cast[self_life, result_mutable=True].result ] T: """Returns a mutable reference to the managed value. diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index db7b78bc18..925707a0ea 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -300,7 +300,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): struct Pointer[ is_mutable: Bool, //, type: AnyType, - origin: Origin[is_mutable].type, + origin: Origin[is_mutable], address_space: AddressSpace = AddressSpace.GENERIC, ](CollectionElementNew, Stringable): """Defines a non-nullable safe pointer. @@ -316,7 +316,7 @@ struct Pointer[ `!lit.ref<`, type, `, `, - origin, + origin._mlir_origin, `, `, address_space._value.value, `>`, diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index cc760f1c01..0d12a44a78 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -197,7 +197,7 @@ struct UnsafePointer[ """ # We're unsafe, so we can have unsafe things. - alias _ref_type = Pointer[type, origin._mlir_origin, address_space] + alias _ref_type = Pointer[type, origin, address_space] return __get_litref_as_mvalue( __mlir_op.`lit.ref.from_pointer`[_type = _ref_type._mlir_type]( UnsafePointer[ diff --git a/stdlib/src/utils/span.mojo b/stdlib/src/utils/span.mojo index 396744aa4e..38bb86cbe5 100644 --- a/stdlib/src/utils/span.mojo +++ b/stdlib/src/utils/span.mojo @@ -70,7 +70,7 @@ struct _SpanIter[ @always_inline fn __next__( mut self, - ) -> Pointer[T, origin._mlir_origin]: + ) -> Pointer[T, origin]: @parameter if forward: self.index += 1 @@ -259,7 +259,7 @@ struct Span[ return self._data - fn as_ref(self) -> Pointer[T, origin._mlir_origin]: + fn as_ref(self) -> Pointer[T, origin]: """ Gets a Pointer to the first element of this slice. @@ -267,7 +267,7 @@ struct Span[ A Pointer pointing at the first element of this slice. """ - return Pointer[T, origin._mlir_origin].address_of(self._data[0]) + return Pointer[T, origin].address_of(self._data[0]) @always_inline fn copy_from[ @@ -360,13 +360,13 @@ struct Span[ fn get_immutable( self, - ) -> Span[T, _lit_mut_cast[origin._mlir_origin, False].result]: + ) -> Span[T, _lit_mut_cast[origin, False].result]: """ Return an immutable version of this span. Returns: A span covering the same elements, but without mutability. """ - return Span[T, _lit_mut_cast[origin._mlir_origin, False].result]( + return Span[T, _lit_mut_cast[origin, False].result]( ptr=self._data, length=self._len ) From 40c6a2339d4ad36d2801155e6d84bf77ad6bc075 Mon Sep 17 00:00:00 2001 From: Owen Hilyard Date: Tue, 3 Dec 2024 16:21:57 -0800 Subject: [PATCH 019/509] [External] [docs] Stdlib insider documentation (#51673) [External] [docs] Stdlib insider documentation People working on the standard library need to have some more information about the API contracts and behavior of the runtime and compiler builtins in order to be able to write correct and performant code in the stdlib. This is not intended to be high effort documentation or acknowledgement that a feature is stable, but simply communication of enough information for those of us without access to the compiler source and autogenerated mlir documentation to work with them correctly. Co-authored-by: Owen Hilyard Closes modularml/mojo#3793 MODULAR_ORIG_COMMIT_REV_ID: 9f09d235fd6ec0dae76897a38621e779e460fc23 --- proposals/stdlib-insider-docs.md | 136 +++++++++++++++++++++++++++++++ stdlib/docs/internal/README.md | 16 ++++ stdlib/docs/internal/mlir.md | 10 +++ stdlib/docs/internal/runtime.md | 10 +++ 4 files changed, 172 insertions(+) create mode 100644 proposals/stdlib-insider-docs.md create mode 100644 stdlib/docs/internal/README.md create mode 100644 stdlib/docs/internal/mlir.md create mode 100644 stdlib/docs/internal/runtime.md diff --git a/proposals/stdlib-insider-docs.md b/proposals/stdlib-insider-docs.md new file mode 100644 index 0000000000..1bf0abfef2 --- /dev/null +++ b/proposals/stdlib-insider-docs.md @@ -0,0 +1,136 @@ +# Stdlib Insider Docs + +Owen Hilyard, Created November 17, 2024 + +**Status**: Initial Proposal + +## Motivation + +For most languages, people who work on the standard library have the ability +to look inside of the compiler to clarify questions they have about the +semantics of particular operations or compiler builtins. For Mojo, that is +not the case for many people working on the standard library. As a result, +the exact semantics of some important compiler builtins, such as +`lit.ownership.mark_destroyed`, are not fully known to a large number of +people working on the standard library. For example, the fact that +`lit.ownership.mark_destroyed` still runs the destructors of fields was a +surprise to many at the stdlib meeting. This creates issues where Modular +employees have to catch misuses. These language-internal dialects are, like +Mojo itself, subject to enhancements, breaking changes, and even complete +removal. This presents a problem since the stdlib is correctness-critical code, +and when people who don't understand the API contract of a construct use it +in correctness-critical code, issues are bound to happen. + +## Proposal + +In order to help address this, I propose the creation of a "stdlib insider" +document, which contains information on MLIR operations/types, WIP features and +other parts of the language which are either intended to only be used in the +standard library/MAX drivers or language features which are still subject to +change and evolution. This can be substantially less polished than the Mojo +manual, including "X is like Y in C++ but ...", links to academic +papers, links to LLVM docs, pseudocode, and other ways one might explain a +concept to a colleague. This document, likely maintained as a markdown file in +/docs, is intended to be internally facing to core Mojo developers. This means +that a large "everything in here is subject to being totally rewritten in a +bugfix release or security update, do not use outside of the standard library +or MAX" warning, which will hopefully dissuade people from using what is +documented there in ways that will get them stuck on old Mojo versions. + +For MLIR operations, I'd like the following information documented +for each operation used in the standard library; Operation name, +arguments (potentially as Mojo function syntax), a description of the +operation, pre-conditions, post-conditions, and clear hazards. Clear hazards +would ways in which the operation can cause UB (ex: is what happens with a +null pointer well defined?), conditions under which the operation will force +the program to abort or other like "`lit.ownership.mark_destroyed` +still runs the destructors of fields" which may be surprising behavior. + +For MLIR types, information about what a type is intended to do, what +parameters the type has (and their types), the size of the instantiated type +(for alignment), and any non-trivialities in the type (is it ok to copy/move +it?). MLIR attributes should have similar information. + +For features, a short description of what the feature is intended for, and +then syntax examples that show the capabilities of the feature. Ideally, some +differentiation between the design and the current implementation should be +present, slowly moving parts of the documentation over as they are available +on nightly. Documenting known sharp edges or limitations is also helpful, for +instance if trait objects could only represent a single trait (ex: no Movable +\+ Copyable + Formattable trait) or if some part of the implementation has a +high time or space complexity (ex: O(N^2) compile time overhead in the number +of traits in a trait object). + +## Current State + +At present, the majority of MLIR operations are things which I think +are reasonable to explain with a link to LLVM or C++ docs. For example, `pop.max` +is mostly self-explanatory, so unless there are extra semantics I don't +think it really needs more of an explanation than "see C++ std::max". + +What I consider the important things to document: + +### `pop.external_call` + +Lots of people want to call into OpenSSL to get basic HTTPS working, and that +needs to be done correctly. There's also a lot of ABI issues around this, +for instance whether Mojo structs are C layout (for now) or whether we need +a mechanism to force that behavior. + +### `co.*` + +This area is WIP, but some community discussion around the direction would be +helpful. Information about the API of each of the types would also be nice +since it looks like we would need to use MLIR to implement future combinators +like Rust's `FuturesUnordered`. Documentation around synchronization +requirements is also important for correctness as people move towards async io. + +### `#kgen.param.expr LLVM type conversions, +Documenting limitations is also helpful, for instance, can is `@call` ok to +use? Can I directly write phi nodes? I had a lot of difficulty interacting +with anything that returns a `!llvm.struct` or `!llvm.vec`. + +### `pop.inline_asm` + +A lot of CPU functionality doesn't have LLVM intrinsics and we'll need to use +assembly (CPU time stamp counters, poking MSRs for runtime capability +discovery, etc). I personally ran into difficulties doing multiple return +(ex: x86 `cpuid` returns results in 4 registers). Information on the asm +dialect, how to create clobbers and scratch registers, and rules for control +flow (ex: can I implement computed gotos, jump into the middle of a function or +return from the current function?). + +### `!lit.origin.set` + +This is used in Coroutines (presumably to store capture origins), but it looks +like it may be useful for storing collections of references. + +### The backing allocator(s) for `pop.aligned_alloc` and `pop.global_alloc` + +Time spent in GDB has led me to believe this is tcmalloc, but knowing for +sure means we have information like the minimum alignment the allocator will +provide (storing information in the lower bits), what kind of caching it does, +information about how it handles memory overcommit being off (db servers), and +what kind of instrumentation we might have access to. diff --git a/stdlib/docs/internal/README.md b/stdlib/docs/internal/README.md new file mode 100644 index 0000000000..b80fd03dca --- /dev/null +++ b/stdlib/docs/internal/README.md @@ -0,0 +1,16 @@ +## WARNING + +Everything in this file/directory is subject to revision on any bugfix or security +update. We (the stdlib team and contributors), reserve the right to remove, +change the API contracts of, rename, or cause to instantly crash the program, +any operation described in here. These are **PRIVATE** APIs and implementation +details for the Mojo stdlib and for MAX to use. **WE WILL CHANGE IT WHENEVER +WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. + +## Purpose + +This directory contains internal documentation for the implementation details +of the Mojo compiler, runtime, and stdlib. You must always reference the +current version of this documentation on the branch nightly before using the +operations or behavior documented within. Any new files should contain the +above warning. diff --git a/stdlib/docs/internal/mlir.md b/stdlib/docs/internal/mlir.md new file mode 100644 index 0000000000..ca0c174c4b --- /dev/null +++ b/stdlib/docs/internal/mlir.md @@ -0,0 +1,10 @@ +## WARNING + +Everything in this file is subject to revision on any bugfix or security +update. We (the stdlib team and contributors), reserve the right to remove, +change the API contracts of, rename, or cause to instantly crash the program, +any operation described in here. These are **PRIVATE** APIs and implementation +details for the Mojo stdlib and for MAX to use. **WE WILL CHANGE IT WHENEVER +WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. + +## MLIR Documentation diff --git a/stdlib/docs/internal/runtime.md b/stdlib/docs/internal/runtime.md new file mode 100644 index 0000000000..6d4921138b --- /dev/null +++ b/stdlib/docs/internal/runtime.md @@ -0,0 +1,10 @@ +## WARNING + +Everything in this file is subject to revision on any bugfix or security +update. We (the stdlib team and contributors), reserve the right to remove, +change the API contracts of, rename, or cause to instantly crash the program, +any operation described in here. These are **PRIVATE** APIs and implementation +details for the Mojo stdlib and for MAX to use. **WE WILL CHANGE IT WHENEVER +WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. + +## Runtime Documentation From 792299899de4867e5fe2e0bfb45090c6298aae0e Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 3 Dec 2024 18:48:05 -0600 Subject: [PATCH 020/509] [stdlib] feat: Rename `Origin.type` to `Origin._mlir_type` + migrate ~remaining `.type uses (Part 4/X) * Use Origin in dict.mojo and builtin_list.mojo types This does *not* update `VariadicPack` and `VariadicListMem` yet, which will require more improvements to parameter inference. MODULAR_ORIG_COMMIT_REV_ID: c4d20f6842ce361d124ee2e246431af0b3efcd6a --- stdlib/src/builtin/builtin_list.mojo | 18 ++++++++++-------- stdlib/src/builtin/reversed.mojo | 4 ++-- stdlib/src/builtin/type_aliases.mojo | 6 +++--- stdlib/src/collections/dict.mojo | 6 +++--- 4 files changed, 18 insertions(+), 16 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index fa24d3c55c..9e6b5972ea 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -221,7 +221,7 @@ struct VariadicList[type: AnyTrivialRegType](Sized): struct _VariadicListMemIter[ elt_is_mutable: Bool, //, elt_type: AnyType, - elt_origin: Origin[elt_is_mutable].type, + elt_origin: Origin[elt_is_mutable], list_origin: ImmutableOrigin, ]: """Iterator for VariadicListMem. @@ -233,7 +233,9 @@ struct _VariadicListMemIter[ list_origin: The origin of the VariadicListMem. """ - alias variadic_list_type = VariadicListMem[elt_type, elt_origin] + alias variadic_list_type = VariadicListMem[ + elt_type, elt_origin._mlir_origin + ] var index: Int var src: Pointer[ @@ -267,14 +269,14 @@ struct _VariadicListMemIter[ # TODO: parametric aliases would be nice. struct _lit_origin_union[ is_mutable: Bool, //, - a: Origin[is_mutable].type, - b: Origin[is_mutable].type, + a: Origin[is_mutable], + b: Origin[is_mutable], ]: alias result = __mlir_attr[ `#lit.origin.union<`, - a, + a._mlir_origin, `,`, - b, + b._mlir_origin, `> : !lit.origin<`, is_mutable.value, `>`, @@ -298,7 +300,7 @@ struct _lit_mut_cast[ struct VariadicListMem[ elt_is_mutable: Bool, //, element_type: AnyType, - origin: Origin[elt_is_mutable].type, + origin: Origin[elt_is_mutable]._mlir_type, ](Sized): """A utility class to access variadic function arguments of memory-only types that may have ownership. It exposes references to the elements in a @@ -483,7 +485,7 @@ alias _AnyTypeMetaType = __mlir_type[`!lit.anytrait<`, AnyType, `>`] @register_passable struct VariadicPack[ elt_is_mutable: Bool, //, - origin: Origin[elt_is_mutable].type, + origin: Origin[elt_is_mutable]._mlir_type, element_trait: _AnyTypeMetaType, *element_types: element_trait, ](Sized): diff --git a/stdlib/src/builtin/reversed.mojo b/stdlib/src/builtin/reversed.mojo index 352f6d10b4..a1c984e81f 100644 --- a/stdlib/src/builtin/reversed.mojo +++ b/stdlib/src/builtin/reversed.mojo @@ -142,7 +142,7 @@ fn reversed[ K: KeyElement, V: CollectionElement, dict_mutability: Bool, - dict_origin: Origin[dict_mutability].type, + dict_origin: Origin[dict_mutability], ](ref value: _DictValueIter[K, V, dict_origin]) -> _DictValueIter[ K, V, dict_origin, False ]: @@ -169,7 +169,7 @@ fn reversed[ K: KeyElement, V: CollectionElement, dict_mutability: Bool, - dict_origin: Origin[dict_mutability].type, + dict_origin: Origin[dict_mutability], ](ref value: _DictEntryIter[K, V, dict_origin]) -> _DictEntryIter[ K, V, dict_origin, False ]: diff --git a/stdlib/src/builtin/type_aliases.mojo b/stdlib/src/builtin/type_aliases.mojo index 7c7c6ff25b..4139618270 100644 --- a/stdlib/src/builtin/type_aliases.mojo +++ b/stdlib/src/builtin/type_aliases.mojo @@ -51,7 +51,7 @@ struct Origin[is_mutable: Bool]: is_mutable: Whether the origin is mutable. """ - alias type = __mlir_type[ + alias _mlir_type = __mlir_type[ `!lit.origin<`, is_mutable.value, `>`, @@ -61,7 +61,7 @@ struct Origin[is_mutable: Bool]: # Fields # ===-------------------------------------------------------------------===# - var _mlir_origin: Self.type + var _mlir_origin: Self._mlir_type # ===-------------------------------------------------------------------===# # Life cycle methods @@ -73,7 +73,7 @@ struct Origin[is_mutable: Bool]: # Span[Byte, __origin_of(self)] @implicit @always_inline("nodebug") - fn __init__(out self, mlir_origin: Self.type): + fn __init__(out self, mlir_origin: Self._mlir_type): """Initialize an Origin from a raw MLIR `!lit.origin` value. Args: diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index d35229a17e..99aa18c45f 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -61,7 +61,7 @@ struct _DictEntryIter[ dict_mutability: Bool, //, K: KeyElement, V: CollectionElement, - dict_origin: Origin[dict_mutability].type, + dict_origin: Origin[dict_mutability], forward: Bool = True, ]: """Iterator over immutable DictEntry references. @@ -120,7 +120,7 @@ struct _DictKeyIter[ dict_mutability: Bool, //, K: KeyElement, V: CollectionElement, - dict_origin: Origin[dict_mutability].type, + dict_origin: Origin[dict_mutability], forward: Bool = True, ]: """Iterator over immutable Dict key references. @@ -158,7 +158,7 @@ struct _DictValueIter[ dict_mutability: Bool, //, K: KeyElement, V: CollectionElement, - dict_origin: Origin[dict_mutability].type, + dict_origin: Origin[dict_mutability], forward: Bool = True, ]: """Iterator over Dict value references. These are mutable if the dict From 5d690ca5ae8bf436495b092a2736fd50786c5105 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 3 Dec 2024 15:48:15 -1000 Subject: [PATCH 021/509] [stdlib] Align max_exponent with C/Python and clarify terms - Move back to setting the FPUtils.max_exponent() to return the max value disregarding `-inf` and `inf` bitpatterns. The actual max exponent is -1, but this is for consistency with pythons `sys.float_info.max_exp` and C++ constants such as FLT_MAX_EXP. `frexp` returns the real exponent + 1 and normalizes from [1..2] to [0.5..1] so these values align with that function that is shared between C/Python/Mojo, put clarification in the docstring. - Remove FPUtils.min_exponent(), this could mean various things i.e. `min_subnormal_exponent`, `min_normal_exponent`, does it follow `max_exponent` by adding 1 to align with `frexp`? This was only used in _format_float. - Put the correct exponent_bias in for the fnuz types. - Clarify some variable names to say what they actually are. MODULAR_ORIG_COMMIT_REV_ID: 1c72346acc7e5290dc5d76f4d6efa5e2a42faf1a --- stdlib/src/builtin/_format_float.mojo | 11 +++--- stdlib/src/math/math.mojo | 7 +++- stdlib/src/utils/numerics.mojo | 54 +++++++++++---------------- 3 files changed, 31 insertions(+), 41 deletions(-) diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index b46e9ce306..4c5fc6797e 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -71,9 +71,8 @@ struct FP[type: DType, CarrierDType: DType = FPUtils[type].uint_type]: alias carrier_bits = sizeof[Self.CarrierDType]() * 8 alias sig_bits = FPUtils[type].mantissa_width() alias exp_bits = FPUtils[type].exponent_width() - alias min_exponent = FPUtils[type].min_exponent() - alias max_exponent = FPUtils[type].max_exponent() - alias exp_bias = -Self.max_exponent + alias neg_exp_bias = -FPUtils[type].exponent_bias() + alias min_normal_exp = Self.neg_exp_bias + 1 alias cache_bits = 64 if Self.CarrierDType == DType.uint32 else 128 alias min_k = -31 if Self.CarrierDType == DType.uint32 else -292 alias max_k = 46 if Self.CarrierDType == DType.uint32 else 326 @@ -129,7 +128,7 @@ fn _write_float[W: Writer, type: DType, //](mut writer: W, value: Scalar[type]): # - The significand (sig) is the raw binary fraction # - The exponent (exp) is still in biased form var sig = FPUtils.get_mantissa_uint(casted) - var exp = FPUtils.get_exponent_without_bias(casted) + var exp = FPUtils.get_exponent_biased(casted) var sign = FPUtils.get_sign(casted) if isinf(value): @@ -235,7 +234,7 @@ fn _to_decimal[ # For normal numbers if binary_exp != 0: - binary_exp += FP[type].exp_bias - FP[type].sig_bits + binary_exp += FP[type].neg_exp_bias - FP[type].sig_bits if two_fc == 0: var minus_k = (binary_exp * 631305 - 261663) >> 21 var beta = binary_exp + _floor_log2_pow10(-minus_k) @@ -293,7 +292,7 @@ fn _to_decimal[ two_fc |= Scalar[CarrierDType](1) << (FP[type].sig_bits + 1) else: # For subnormal numbers - binary_exp = FP[type].min_exponent - FP[type].sig_bits + binary_exp = FP[type].min_normal_exp - FP[type].sig_bits ########################################## # Step 1: Schubfach multiplier calculation diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 2c7ee78a5d..7b998c7220 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -645,6 +645,8 @@ fn frexp[ type: DType, simd_width: Int, // ](x: SIMD[type, simd_width]) -> StaticTuple[SIMD[type, simd_width], 2]: """Breaks floating point values into a fractional part and an exponent part. + This follows C and Python in increasing the exponent by 1 and normalizing the + fraction from 0.5 to 1.0 instead of 1.0 to 2.0. Constraints: The input must be a floating-point type. @@ -664,14 +666,15 @@ fn frexp[ constrained[type.is_floating_point(), "must be a floating point value"]() alias T = SIMD[type, simd_width] alias zero = T(0) - alias max_exponent = FPUtils[type].max_exponent() - 1 + # Add one to the resulting exponent up by subtracting 1 from the bias + alias exponent_bias = FPUtils[type].exponent_bias() - 1 alias mantissa_width = FPUtils[type].mantissa_width() var mask1 = _frexp_mask1[simd_width, type]() var mask2 = _frexp_mask2[simd_width, type]() var x_int = x.to_bits() var selector = x != zero var exp = selector.select( - (((mask1 & x_int) >> mantissa_width) - max_exponent).cast[type](), + (((mask1 & x_int) >> mantissa_width) - exponent_bias).cast[type](), zero, ) var frac = selector.select(T(from_bits=x_int & ~mask1 | mask2), zero) diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index f7d99244fe..e1ec498345 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -84,41 +84,25 @@ struct FPUtils[ @staticmethod @always_inline("nodebug") fn max_exponent() -> IntLiteral: - """Returns the max exponent of a floating point type, taking into - account special reserved cases such infinity and nan. + """Returns the max exponent of a floating point type without accounting + for inf representations. This is not + the maximum representable exponent, which is generally equal to + the exponent_bias. Returns: The max exponent. """ @parameter - if type is DType.float8e4m3: - return 7 - elif type is DType.float8e4m3fnuz: + if type in (DType.float8e4m3, DType.float8e4m3fnuz): return 8 - elif type is DType.float8e5m2: - return 15 - elif type is DType.float8e5m2fnuz: + elif type in (DType.float8e5m2, DType.float8e5m2fnuz, DType.float16): return 16 - elif type is DType.float16: - return 15 - elif type is DType.float32 or type is DType.bfloat16: - return 127 + elif type in (DType.bfloat16, DType.float32): + return 128 else: constrained[type is DType.float64, "unsupported float type"]() - return 1023 - - @staticmethod - @always_inline("nodebug") - fn min_exponent() -> IntLiteral: - """Returns the min exponent of a floating point type, taking into - account special reserved cases such as infinity and nan. - - Returns: - The min exponent. - """ - - return -Self.max_exponent() + 1 + return 1024 @staticmethod @always_inline("nodebug") @@ -132,11 +116,9 @@ struct FPUtils[ @parameter if type in (DType.float8e4m3, DType.float8e4m3fnuz): return 4 - elif type in (DType.float8e5m2, DType.float8e5m2fnuz): + elif type in (DType.float8e5m2, DType.float8e5m2fnuz, DType.float16): return 5 - elif type is DType.float16: - return 5 - elif type is DType.float32 or type is DType.bfloat16: + elif type in (DType.float32, DType.bfloat16): return 8 else: constrained[type is DType.float64, "unsupported float type"]() @@ -160,7 +142,12 @@ struct FPUtils[ Returns: The exponent bias. """ - return Self.max_exponent() + + @parameter + if type in (DType.float8e4m3fnuz, DType.float8e5m2fnuz): + return Self.max_exponent() + else: + return Self.max_exponent() - 1 @staticmethod @always_inline @@ -306,14 +293,15 @@ struct FPUtils[ @staticmethod @always_inline - fn get_exponent_without_bias(value: Scalar[type]) -> Int: - """Returns the exponent bits of the floating-point value. + fn get_exponent_biased(value: Scalar[type]) -> Int: + """Returns the biased exponent of the floating-point value as an Int, + this is how the value is stored before subtracting the exponent bias. Args: value: The floating-point value. Returns: - Returns the exponent bits. + The biased exponent as an Int. """ return int( Self.bitcast_to_uint(value) >> Self.mantissa_width() From 6f26bd84abc73a293d0f86221befde07e615e85c Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 3 Dec 2024 18:39:38 -1000 Subject: [PATCH 022/509] [stdlib] Clarify the differences for the float8 types These types have confusing differences, clarify the naming and exactly how they're different in the doc strings. MODULAR_ORIG_COMMIT_REV_ID: 80525ac525b1538664a33ae6d053addc0f95fcac --- stdlib/src/builtin/dtype.mojo | 58 ++++++++++++++++++++++++++++++----- stdlib/src/builtin/simd.mojo | 58 ++++++++++++++++++++++++++++++----- 2 files changed, 100 insertions(+), 16 deletions(-) diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index 4ac807acd6..1ed5dd09c6 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -66,23 +66,65 @@ struct DType( alias float8e5m2 = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) - """Represents a FP8E5M2 floating point format whose bitwidth is 8.""" + """Represents a FP8E5M2 floating point format from the [OFP8 + standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). + + The 8 bits are encoded as `seeeeemm`: + - (s)ign: 1 bit + - (e)xponent: 5 bits + - (m)antissa: 2 bits + - exponent bias: 15 + - nan: {0,1}11111{01,10,11} + - inf: 01111100 + - -inf: 11111100 + - -0: 10000000 + """ alias float8e5m2fnuz = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) - """Represents a FP8E5M2FNUZ floating point format for AMD GPU whose bitwdith is 8. - This dtype only supports finite and NaN values. NaN is when sign bit is - set and all other exponent and mantissa bits are 0.""" + """Represents a FP8E5M2FNUZ floating point format. + + The 8 bits are encoded as `seeeeemm`: + - (s)ign: 1 bit + - (e)xponent: 5 bits + - (m)antissa: 2 bits + - exponent bias: 16 + - nan: 10000000 + - fn: finite (no inf or -inf encodings) + - uz: unsigned zero (no -0 encoding) + """ alias float8e4m3 = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) - """Represents a FP8E4M3 floating point format whose bitwidth is 8.""" + """Represents a FP8E4M3 floating point format from the [OFP8 + standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). + + This type is named `float8_e4m3fn` (the "fn" stands for "finite") in some + frameworks, as it does not encode -inf or inf. + + The 8 bits are encoded as `seeeemmm`: + - (s)ign: 1 bit + - (e)xponent: 4 bits + - (m)antissa: 3 bits + - exponent bias: 7 + - nan: 01111111, 11111111 + - -0: 10000000 + - fn: finite (no inf or -inf encodings) + """ alias float8e4m3fnuz = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) - """Represents a FP8E4M3FNUZ floating point format for AMD GPU whose bitwdith is 8. - This dtype only supports finite and NaN values. NaN is when sign bit is - set and all other exponent and mantissa bits are 0.""" + """Represents a FP8E4M3FNUZ floating point format. + + The 8 bits are encoded as `seeeemmm`: + - (s)ign: 1 bit + - (e)xponent: 4 bits + - (m)antissa: 3 bits + - exponent bias: 8 + - nan: 10000000 + - fn: finite (no inf or -inf encodings) + - uz: unsigned zero (no -0 encoding) + """ alias bfloat16 = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index bacee6e4bf..350eda13d0 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -94,17 +94,59 @@ alias UInt64 = Scalar[DType.uint64] """Represents a 64-bit unsigned scalar integer.""" alias Float8e5m2 = Scalar[DType.float8e5m2] -"""Represents a FP8E5M2 floating point format whose bitwidth is 8.""" +"""Represents a FP8E5M2 floating point format from the [OFP8 +standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). + +The 8 bits are encoded as `seeeeemm`: +- (s)ign: 1 bit +- (e)xponent: 5 bits +- (m)antissa: 2 bits +- exponent bias: 15 +- nan: {0,1}11111{01,10,11} +- inf: 01111100 +- -inf: 11111100 +- -0: 10000000 +""" alias Float8e5m2fnuz = Scalar[DType.float8e5m2fnuz] -"""Represents a FP8E5M2FNUZ floating point format for AMD GPU whose bitwdith is 8. - This dtype only supports finite and NaN values. NaN is when sign bit is set and - all other exponent and mantissa bits are 0.""" +"""Represents a FP8E5M2FNUZ floating point format. + +The 8 bits are encoded as `seeeeemm`: +- (s)ign: 1 bit +- (e)xponent: 5 bits +- (m)antissa: 2 bits +- exponent bias: 16 +- nan: 10000000 +- fn: finite (no inf or -inf encodings) +- uz: unsigned zero (no -0 encoding) +""" alias Float8e4m3 = Scalar[DType.float8e4m3] -"""Represents a FP8E4M3 floating point format whose bitwidth is 8.""" +"""Represents a FP8E4M3 floating point format from the [OFP8 +standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). + +This type is named `float8_e4m3fn` (the "fn" stands for "finite") in some +frameworks, as it does not encode -inf or inf. + +The 8 bits are encoded as `seeeemmm`: +- (s)ign: 1 bit +- (e)xponent: 4 bits +- (m)antissa: 3 bits +- exponent bias: 7 +- nan: 01111111, 11111111 +- -0: 10000000 +- fn: finite (no inf or -inf encodings) +""" alias Float8e4m3fnuz = Scalar[DType.float8e4m3fnuz] -"""Represents a FP8E4M3FNUZ floating point format for AMD GPU whose bitwdith is 8. - This dtype only supports finite and NaN values. NaN is when sign bit is set and - all other exponent and mantissa bits are 0.""" +"""Represents a FP8E4M3FNUZ floating point format. + +The 8 bits are encoded as `seeeemmm`: +- (s)ign: 1 bit +- (e)xponent: 4 bits +- (m)antissa: 3 bits +- exponent bias: 8 +- nan: 10000000 +- fn: finite (no inf or -inf encodings) +- uz: unsigned zero (no -0 encoding) +""" alias BFloat16 = Scalar[DType.bfloat16] """Represents a 16-bit brain floating point value.""" alias Float16 = Scalar[DType.float16] From 43a2506183a91ef657faeeb0b1273af255d55032 Mon Sep 17 00:00:00 2001 From: Gustas <37534529+PunkPun@users.noreply.github.com> Date: Tue, 3 Dec 2024 21:00:08 -0800 Subject: [PATCH 023/509] [External] [Docs] Update the notebook tutorial (#52029) [External] [Docs] Update the notebook tutorial And add more clarification to end users not familiar with conda .yml files #3803 --------- Co-authored-by: Gustas <37534529+PunkPun@users.noreply.github.com> Co-authored-by: Arthur Evans Closes modularml/mojo#3834 MODULAR_ORIG_COMMIT_REV_ID: db24078163bb662924f5bf0ae744c1c41cd008c0 --- examples/notebooks/README.md | 33 +++++++++++++++++++++++++++++---- 1 file changed, 29 insertions(+), 4 deletions(-) diff --git a/examples/notebooks/README.md b/examples/notebooks/README.md index 4ee3854595..d94bb7bf4b 100644 --- a/examples/notebooks/README.md +++ b/examples/notebooks/README.md @@ -4,9 +4,7 @@ Mojo supports programming in [Jupyter notebooks](https://jupyter.org/), just like Python. This page explains how to get started with Mojo notebooks, and this repo -directory contains notebooks that demonstrate some of Mojo's features -(most of which we originally published on the [Mojo -Playground](https://playground.modular.com/)). +directory contains notebooks that demonstrate some of Mojo's features. If you're not familiar with Jupyter notebooks, they're files that allow you to create documents with live code, equations, visualizations, and explanatory @@ -67,6 +65,7 @@ If you have [`magic`](https://docs.modular.com/magic) you can run the following command to launch JupyterLab from this directory: ```sh +# Run from an active conda or magic shell environment magic run jupyter lab ``` @@ -74,7 +73,20 @@ After a moment, it will open a browser window with JupterLab running. #### Using conda -Create a Conda environment, activate that enviroment, and install JupyterLab. +Conda allows you to export environments in `.yml` format. (For more +information, see +[Managing environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) +in the Conda documentation.) + +If you already have a working Jupyter environment on a different computer and +server you can export it using the following command. + +```sh +# Example of exporting environment.yml from a conda environment named `your-env` +conda env export --name your_env > environment.yml +``` + +To create a Conda environment, activate that environment, and install JupyterLab. ``` sh # Create a Conda environment if you don't have one @@ -87,6 +99,19 @@ conda run -n mojo-repo jupyter lab After a moment, it will open a browser window with JupterLab running. +#### Using more magic + +Magic allows you to create an environment from a Conda `environment.yml`. + +```sh +# Create a magic environment if you don't have one +magic init mojo-repo --import environment.yml +# Activate the environment +cd mojo-repo && magic shell +# run JupyterLab +magic run jupyter lab +``` + ### 2. Run the .ipynb notebooks The left nav bar should show all the notebooks in this directory. From ec3dad7a2768396a509482d04c566f505be8deef Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 4 Dec 2024 19:15:26 +0000 Subject: [PATCH 024/509] Update lockfiles to point to latest nightly version: 24.6.0.dev2024120416 --- examples/life/magic.lock | 1627 +++++++++++++------------- examples/magic.lock | 1403 ++++++++++++---------- examples/notebooks/magic.lock | 2079 ++++++++++++++++++--------------- examples/operators/magic.lock | 1403 ++++++++++++---------- magic.lock | 1424 +++++++++++----------- 5 files changed, 4220 insertions(+), 3716 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 9c78b39352..c962798beb 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -8,14 +8,13 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.11.9-py312h178313f_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.13-hb9d3cd8_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.6.2.post1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/asgiref-3.8.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.2.0-pyh71513ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.0-hb88c0a9_10.conda @@ -26,10 +25,10 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.1-hab05fe4_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.2-hdeadb07_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h7bd072d_8.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.3-h3a84f74_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.4-h3a84f74_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.1-hf42f96a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-hf42f96a_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.6-hdec8b0f_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.6-h159bff8_2.conda - 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(Maybe they should be infer-mostly parameters?). - Document automatically parameterized parameter lists. MODULAR_ORIG_COMMIT_REV_ID: 23ab820cebbc7895413a4962acb661849866a6c6 --- docs/manual/parameters/index.mdx | 139 +++++++++++++++++++++++-------- 1 file changed, 102 insertions(+), 37 deletions(-) diff --git a/docs/manual/parameters/index.mdx b/docs/manual/parameters/index.mdx index be9066c974..9b3ba3250f 100644 --- a/docs/manual/parameters/index.mdx +++ b/docs/manual/parameters/index.mdx @@ -3,11 +3,24 @@ title: "Parameterization: compile-time metaprogramming" description: An introduction to parameters and compile-time metaprogramming. --- -Many languages have facilities for *metaprogramming*: that is, for writing code that generates or modifies code. Python has facilities for dynamic metaprogramming: features like decorators, metaclasses, and many more. These features make Python very flexible and productive, but since they're dynamic, they come with runtime overhead. Other languages have static or compile-time metaprogramming features, like C preprocessor macros and C++ templates. These can be limiting and hard to use. - -To support Modular's work in AI, Mojo aims to provide powerful, easy-to-use metaprogramming with zero runtime cost. This compile-time metaprogramming uses the same language as runtime programs, so you don't have to learn a new language—just a few new features. - -The main new feature is *parameters*. You can think of a parameter as a compile-time variable that becomes a runtime constant. This usage of "parameter" is probably different from what you're used to from other languages, where "parameter" and "argument" are often used interchangeably. In Mojo, "parameter" and "parameter expression" refer to compile-time values, and "argument" and "expression" refer to runtime values. +Many languages have facilities for *metaprogramming*: that is, for writing code that +generates or modifies code. Python has facilities for dynamic metaprogramming: features +like decorators, metaclasses, and many more. These features make Python very flexible +and productive, but since they're dynamic, they come with runtime overhead. Other +languages have static or compile-time metaprogramming features, like C preprocessor +macros and C++ templates. These can be limiting and hard to use. + +To support Modular's work in AI, Mojo aims to provide powerful, easy-to-use +metaprogramming with zero runtime cost. This compile-time metaprogramming uses the same +language as runtime programs, so you don't have to learn a new language—just a few new +features. + +The main new feature is *parameters*. You can think of a parameter as a +compile-time variable that becomes a runtime constant. This usage of "parameter" +is probably different from what you're used to from other languages, where +"parameter" and "argument" are often used interchangeably. In Mojo, "parameter" +and "parameter expression" refer to compile-time values, and "argument" and +"expression" refer to runtime values. In Mojo, you can add parameters to a struct or function. You can also define named parameter expressions—aliases—that you can use as runtime constants. @@ -104,9 +117,9 @@ repeat[count=2](42) This updated function takes any `Stringable` type, so you can pass it an `Int`, `String`, or `Bool` value. -You can't pass the `count` as a positional keyword without also specifying `MsgType`. -You can put `//` after `MsgType` to specify that it's always inferred by the argument. Now -you can pass the following parameter `count` positionally: +You can't pass the `count` as a positional keyword without also specifying +`MsgType`. You can put `//` after `MsgType` to specify that it's always inferred +by the argument. Now you can pass the following parameter `count` positionally: ```mojo fn repeat[MsgType: Stringable, //, count: Int](msg: MsgType): @@ -173,8 +186,8 @@ use a parameterized struct. In this case, when you create an instance of passing in this case is a *type*. That's OK: a Mojo type is a valid compile-time value.) -You'll see that `ElementType` is used throughout the struct where you'd usually see a -type name. For example, as the formal type for the `elements` in the +You'll see that `ElementType` is used throughout the struct where you'd usually +see a type name. For example, as the formal type for the `elements` in the constructor, and the return type of the `__getitem__()` method. Here's an example of using `GenericArray`: @@ -216,10 +229,10 @@ The method returns an instance of `GenericArray[Float64]`. When creating a generic struct, you might want to define some methods that require extra features. For example, consider a collection like `GenericArray` -that holds instances of `CollectionElement`. The `CollectionElement` trait -only requires that the stored data type be copyable and movable. This -imposes a lot of limitations: you can't implement a `sort()` method because -you can't guarantee that the stored type supports the comparison operators; you can't +that holds instances of `CollectionElement`. The `CollectionElement` trait only +requires that the stored data type be copyable and movable. This imposes a lot +of limitations: you can't implement a `sort()` method because you can't +guarantee that the stored type supports the comparison operators; you can't write a useful `__str__()` or `__repr__()` dunder method because you can't guarantee that the stored type supports conversion to a string. @@ -293,9 +306,9 @@ floating point numbers, so it's not practical to define all of the possible SIMD variations. Mojo's [`SIMD`](/mojo/stdlib/builtin/simd/SIMD) type (defined as a struct) -exposes the common SIMD operations through its methods, and makes the SIMD data type -and size values parametric. This allows you to directly map your data to the -SIMD vectors on any hardware. +exposes the common SIMD operations through its methods, and makes the SIMD data +type and size values parametric. This allows you to directly map your data to +the SIMD vectors on any hardware. Here's a cut-down (non-functional) version of Mojo's `SIMD` type definition: @@ -590,8 +603,8 @@ fn use_defaults() raises: ``` Recall that when a parametric function is called, Mojo can infer the parameter values. -That is, it can use the parameter values attached to an argument value (see the `sqrt[]()` -example above). If the parametric function also has a default value defined, +That is, it can use the parameter values attached to an argument value (see the +`sqrt[]()` example above). If the parametric function also has a default value defined, then the inferred parameter type takes precedence. For example, in the following code, we update the parametric `speak[]()` function @@ -661,9 +674,9 @@ you can't leave it out of the parameter list and let Mojo infer it from `value`: dependent_type[Float64(2.2)]() # Error! ``` -Infer-only parameters are a special class of parameters that are **always** -inferred from context. Infer-only parameters are placed at the **beginning** of -the parameter list, set off from other parameters by the `//` sigil: +Infer-only parameters are a special class of parameters that are **always** either +inferred from context or specified by keyword. Infer-only parameters are placed at the +**beginning** of the parameter list, set off from other parameters by the `//` sigil: ```mojo fn example[type: CollectionElement, //, list: List[type]]() @@ -688,8 +701,25 @@ Because infer-only parameters are declared at the beginning of the parameter list, other parameters can depend on them, and the compiler will always attempt to infer the infer-only values from bound parameters or arguments. -If the compiler can't infer the value of an infer-only parameter, compilation -fails. +There are sometimes cases where it's useful to specify an infer-only parameter by +keyword. For example, the [`StringSlice`](/mojo/stdlib/utils/string_slice/StringSlice) +type is parametric on [origin](/mojo/manual/values/lifetimes): + +```mojo +struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]: ... +``` + +Here, the `StringSlice` `is_mutable` parameter is infer-only. The value is usually +inferred when you create an instance of `StringSlice`. Binding the `is_mutable` +parameter by keyword lets you define a new type that's constrained to an +immutable origin: + +```mojo +alias ImmutableStringSlice = StringSlice[is_mutable=False] +``` + +If the compiler can't infer the value of an infer-only parameter, and it's not +specified by keyword, compilation fails. ## Variadic parameters @@ -757,7 +787,8 @@ print('result type:', x.element_type, 'length:', len(x)) result type: float32 length: 4 ``` -Note that the resulting length is the sum of the input vector lengths, and this is expressed with a simple `+` operation. +Note that the resulting length is the sum of the input vector lengths, and this is +expressed with a simple `+` operation. ### Powerful compile-time programming @@ -797,11 +828,11 @@ print("Elements sum:", reduce_add(x)) Elements sum: 10 ``` -This makes use of the [`@parameter`](/mojo/manual/decorators/parameter) decorator to create a parametric if condition, which is an `if` statement that -runs at compile-time. It requires that its condition be a valid parameter -expression, and ensures that only the live branch of the `if` statement is -compiled into the program. (This is similar to use of the `@parameter` decorator -with a `for` loop shown earlier.) +This makes use of the [`@parameter`](/mojo/manual/decorators/parameter) decorator to +create a parametric if condition, which is an `if` statement that runs at compile-time. +It requires that its condition be a valid parameter expression, and ensures that only +the live branch of the `if` statement is compiled into the program. (This is similar to +use of the `@parameter` decorator with a `for` loop shown earlier.) ## Mojo types are just parameter expressions @@ -960,9 +991,27 @@ MyType["Hello", *_] MyType["Hello", _, _, _] ``` -When a parameter is explicitly unbound with the `_` or `*_` expression, you -**must** specify a value for that parameter to use the type. Any default value -from the original type declaration is ignored. +The `*_` expression specifically matches any parameters that can be specified by +position (positional-only or positional-or-keyword). To unbind keyword-only parameters, +use the double-star-underscore expression, `**_`, which matches any parameters that can +be specified by keyword (positional-or-keyword or keyword-only). + +```mojo +@value +struct KeyWordStruct[pos_or_kw: Int, *, kw_only: Int = 10]: + pass + +# Unbind both pos_or_kw and kw_only parameters +fn use_kw_struct(k: KeyWordStruct[**_]): + pass + +def main(): + use_kw_struct(KeyWordStruct[10, kw_only=11]()) +``` + +When a parameter is explicitly unbound with the `_`, `*_`, or `**_` expressions, you +**must** specify a value for that parameter to use the type. Any default value from the +original type declaration is ignored. Partially-bound and unbound parametric types can be used in some contexts where the missing (unbound) parameters will be supplied later—such as in @@ -1025,11 +1074,11 @@ parameterized. The `vec` argument takes an argument of type `SIMD[*_]`. This is an [unbound parameterized type](#fully-bound-partially-bound-and-unbound-types)—that is, it doesn't specify any parameter values for the type. Mojo treats the unbound parameters -on `vec` as implicit parameters on the function. This is roughly equivalent to -the following code, which includes *explicit* input parameters: +on `vec` as infer-only parameters on the function. This is roughly equivalent to +the following codes: ```mojo -fn print_params[t: DType, s: Int](vec: SIMD[t, s]): +fn print_params[t: DType, s: Int, //](vec: SIMD[t, s]): print(vec.type) print(vec.size) ``` @@ -1084,7 +1133,23 @@ print(c) [1, 0, 2, 0, 3, 0, 4, 0] ``` -As shown in the example, you can use the magic `__type_of(x)` call if you just want to match the type of an argument. In this case, it's more convenient and compact that writing the equivalent `SIMD[v1.type, v1.size]`. +As shown in the example, you can use the magic `__type_of(x)` call if you just want to +match the type of an argument. In this case, it's more convenient and compact that +writing the equivalent `SIMD[v1.type, v1.size]`. + +### Automatic parameterization of parameters + +You can also take advantage of automatic parameterization in a function's +parameter list. For example: + +```mojo +fn foo[value: SIMD](): + pass + +# Equivalent to: +fn foo[type: DType, size: Int, //, value: SIMD[type, size]](): + pass +``` ### Automatic parameterization with partially-bound types From bac9f13523e8b8d6eff800f18a9debe5425664a3 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 4 Dec 2024 13:05:03 -0600 Subject: [PATCH 026/509] [stdlib] polish: Expose `_lit_mut_cast` as `Origin.cast_from` alias This obviates the need to import the obscure `_lit_mut_cast` parametric alias hack, and instead more closely associates this operation as a "method" on Origin. MODULAR_ORIG_COMMIT_REV_ID: 6d07f58832f984f3ef68bc1f0e19f6877ca3e91a --- stdlib/src/builtin/builtin_list.mojo | 16 +--------------- stdlib/src/builtin/type_aliases.mojo | 26 ++++++++++++++++++++++++++ stdlib/src/memory/arc.mojo | 3 +-- stdlib/src/utils/span.mojo | 5 ++--- 4 files changed, 30 insertions(+), 20 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 9e6b5972ea..d736c55aa6 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -283,20 +283,6 @@ struct _lit_origin_union[ ] -struct _lit_mut_cast[ - is_mutable: Bool, //, - operand: Origin[is_mutable], - result_mutable: Bool, -]: - alias result = __mlir_attr[ - `#lit.origin.mutcast<`, - operand._mlir_origin, - `> : !lit.origin<`, - +result_mutable.value, - `>`, - ] - - struct VariadicListMem[ elt_is_mutable: Bool, //, element_type: AnyType, @@ -443,7 +429,7 @@ struct VariadicListMem[ # cast mutability of self to match the mutability of the element, # since that is what we want to use in the ultimate reference and # the union overall doesn't matter. - _lit_mut_cast[__origin_of(self), elt_is_mutable].result, + Origin[elt_is_mutable].cast_from[__origin_of(self)].result, ].result ] element_type: """Gets a single element on the variadic list. diff --git a/stdlib/src/builtin/type_aliases.mojo b/stdlib/src/builtin/type_aliases.mojo index 4139618270..b3358188b5 100644 --- a/stdlib/src/builtin/type_aliases.mojo +++ b/stdlib/src/builtin/type_aliases.mojo @@ -57,6 +57,18 @@ struct Origin[is_mutable: Bool]: `>`, ] + alias cast_from = _lit_mut_cast[result_mutable=is_mutable] + """Cast an existing Origin to be of the specified mutability. + + This is a low-level way to coerce Origin mutability. This should be used + rarely, typically when building low-level fundamental abstractions. Strongly + consider alternatives before reaching for this "escape hatch". + + Safety: + This is an UNSAFE operation if used to cast an immutable origin to + a mutable origin. + """ + # ===-------------------------------------------------------------------===# # Fields # ===-------------------------------------------------------------------===# @@ -79,3 +91,17 @@ struct Origin[is_mutable: Bool]: Args: mlir_origin: The raw MLIR origin value.""" self._mlir_origin = mlir_origin + + +struct _lit_mut_cast[ + is_mutable: Bool, //, + result_mutable: Bool, + operand: Origin[is_mutable], +]: + alias result = __mlir_attr[ + `#lit.origin.mutcast<`, + operand._mlir_origin, + `> : !lit.origin<`, + result_mutable.value, + `>`, + ] diff --git a/stdlib/src/memory/arc.mojo b/stdlib/src/memory/arc.mojo index d7b236af72..feb4c42711 100644 --- a/stdlib/src/memory/arc.mojo +++ b/stdlib/src/memory/arc.mojo @@ -46,7 +46,6 @@ print(ArcPointer(String("ok"))[]) from os.atomic import Atomic -from builtin.builtin_list import _lit_mut_cast from memory import UnsafePointer, stack_allocation @@ -146,7 +145,7 @@ struct ArcPointer[T: Movable]( ]( ref [self_life]self, ) -> ref [ - _lit_mut_cast[self_life, result_mutable=True].result + MutableOrigin.cast_from[self_life].result ] T: """Returns a mutable reference to the managed value. diff --git a/stdlib/src/utils/span.mojo b/stdlib/src/utils/span.mojo index 38bb86cbe5..282a3bc229 100644 --- a/stdlib/src/utils/span.mojo +++ b/stdlib/src/utils/span.mojo @@ -22,7 +22,6 @@ from utils import Span from collections import InlineArray -from builtin.builtin_list import _lit_mut_cast from memory import Pointer, UnsafePointer @@ -360,13 +359,13 @@ struct Span[ fn get_immutable( self, - ) -> Span[T, _lit_mut_cast[origin, False].result]: + ) -> Span[T, ImmutableOrigin.cast_from[origin].result]: """ Return an immutable version of this span. Returns: A span covering the same elements, but without mutability. """ - return Span[T, _lit_mut_cast[origin, False].result]( + return Span[T, ImmutableOrigin.cast_from[origin].result]( ptr=self._data, length=self._len ) From dfc8f49c472d79b6141e9baf0431953ede97a05e Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Wed, 4 Dec 2024 11:19:04 -0800 Subject: [PATCH 027/509] [Docs] Add Intro to pointers doc. Also includes some improvements to the existing API docs. MODULAR_ORIG_COMMIT_REV_ID: de875f1b71f56ec072111cde4936a45b0ed2968f --- docs/changelog-released.md | 6 +- .../images/owned-pointer-diagram-dark.png | Bin 0 -> 26898 bytes docs/manual/images/owned-pointer-diagram.png | Bin 0 -> 26431 bytes docs/manual/index.md | 3 +- docs/manual/pointers/index.mdx | 272 ++++++++++++++++++ .../unsafe-pointers.mdx} | 86 ++---- examples/notebooks/environment.yml | 14 + stdlib/src/memory/arc.mojo | 78 ++--- stdlib/src/memory/owned_pointer.mojo | 3 + stdlib/src/memory/pointer.mojo | 3 + stdlib/src/memory/unsafe_pointer.mojo | 18 +- 11 files changed, 383 insertions(+), 100 deletions(-) create mode 100644 docs/manual/images/owned-pointer-diagram-dark.png create mode 100644 docs/manual/images/owned-pointer-diagram.png create mode 100644 docs/manual/pointers/index.mdx rename docs/manual/{pointers.mdx => pointers/unsafe-pointers.mdx} (85%) create mode 100644 examples/notebooks/environment.yml diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 5b3c84bdcf..79b64934eb 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -60,7 +60,7 @@ detailed information in the following sections: [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) instead. Functions that previously took a `DTypePointer` now take an equivalent `UnsafePointer`. For more information on using pointers, see [Unsafe - pointers](/mojo/manual/pointers) in the Mojo Manual. + pointers](/mojo/manual/pointers/unsafe-pointers) in the Mojo Manual. - There are many new standard library APIs, with new features for strings, collections, and interacting with the filesystem and environment. Changes are @@ -518,7 +518,7 @@ detailed information in the following sections: - `DTypePointer`, `LegacyPointer`, and `Pointer` have been removed. Use [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) instead. For more information on using pointers, see [Unsafe - pointers](/mojo/manual/pointers) in the Mojo Manual. + pointers](/mojo/manual/pointers/unsafe-pointers) in the Mojo Manual. Functions that previously took a `DTypePointer` now take an equivalent `UnsafePointer`. A quick rule for conversion from `DTypePointer` to @@ -1013,7 +1013,7 @@ Big themes for this release: - New Mojo Manual pages on [Control flow](/mojo/manual/control-flow), [Testing](/mojo/tools/testing) and using - [unsafe pointers](/mojo/manual/pointers). + [unsafe pointers](/mojo/manual/pointers/unsafe-pointers). ### Language changes diff --git a/docs/manual/images/owned-pointer-diagram-dark.png b/docs/manual/images/owned-pointer-diagram-dark.png new file mode 100644 index 0000000000000000000000000000000000000000..db8049790a34506ee083088c53719e484b639666 GIT binary patch literal 26898 zcmZ5|by(Cv*Y>h5($d}1%_7|(5-YiMcPI_gAt6emu#_w%E{F<)^`s!L(`c z2ge_&p$e%SX4-&2*dSVJDn`LJ+xY}&CgZxxy|#%OCsH|8vY5wO&6_5!>5EAe%KB7l zIL^M^%tkcF=jHIl$Ao|M$?uS}ZW_|4_~Nm$sgw{usl}gBLzFF3vpgcy&7A-c!E&c(PcSj}WlFLABNWEyy1r@bV zx5gajPVcy-`D*Z$%q!oUU*26!E8`wv96>_*J*eK0xOnt*jjAf9xI!(T1A6Or6s%v| zr}v5I@X#35uy|)F_w)29a(=Kr7{#8>J=INd@>eoob`l!0?W(tqb-lJ_V@o&l3Lpun 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zn#eGh5crnZF*U4=f9hOCUN}x55O(f!1XZkk-rZU~aIF_^?Mu+V4msqJgoFe&s8W?Q zJveSnht1Bk`Nk4ixtt=9H{Q9WGe|n>6wgz}65YDUW~7Y{zY$nvw%w+ zhL|Ymh$AZTXY`IY7ksC=%MP5?ki^_fv3Qjw)P6ua@4^g8cU3 ig+IBV?f;`&Iv`GWS-w@ksAhtKe`=SsaYf3OA^#77jjuoe literal 0 HcmV?d00001 diff --git a/docs/manual/index.md b/docs/manual/index.md index ba52bcd6c4..7d152a1b2b 100644 --- a/docs/manual/index.md +++ b/docs/manual/index.md @@ -56,7 +56,8 @@ feedback](https://www.modular.com/community). - **Pointers** - - [Unsafe pointers](/mojo/manual/pointers) + - [Intro to pointers](/mojo/manual/pointers/) + - [Unsafe pointers](/mojo/manual/pointers/unsafe-pointers) - **Python** diff --git a/docs/manual/pointers/index.mdx b/docs/manual/pointers/index.mdx new file mode 100644 index 0000000000..075fa33ea5 --- /dev/null +++ b/docs/manual/pointers/index.mdx @@ -0,0 +1,272 @@ +--- +title: Intro to pointers +sidebar_position: 1 +description: An overview of accessing memory using Mojo's pointer types. +--- + +A pointer is an indirect reference to one or more values stored in memory. The +pointer is a value that holds an address to memory, and provides APIs to store +and retrieve values to that memory. The value pointed to by a pointer is also +known as a _pointee_. + +The Mojo standard library includes several types of pointers, which provide +different sets of features. All of these pointer types are _generic_—they can +point to any type of value, and the value type is specified as a parameter. For +example, the following code creates an `OwnedPointer` that points to an `Int` +value: + +```mojo +var ptr: OwnedPointer[Int] +ptr = OwnedPointer(100) +``` + +The `ptr` variable has a value of type `OwnedPointer[Int]`. The pointer *points +to* a value of type `Int`, as shown in Figure 1. + + +

+ +![](../images/owned-pointer-diagram.png#light) +![](../images/owned-pointer-diagram-dark.png#dark) + +
Figure 1. Pointer and pointee
+
+ +Accessing the memory—to retrieve or update a value—is called +_dereferencing_ the pointer. You can dereference a pointer by following the +variable name with an empty pair of square brackets: + +```mojo +# Update an initialized value +ptr[] += 10 +# Access an initialized value +print(ptr[]) +``` + +## Pointer terminology + +Before we jump into the pointer types, here are a few terms you’ll run across. Some +of them may already be familiar to you. + +- **Safe pointers**: are designed to prevent memory errors. Unless you use one + of the APIs that are specially designated as unsafe, you can use these + pointers without worrying about memory issues like double-free or + use-after-free. + +- **Nullable pointers**: can point to an invalid memory location (typically 0, +or a “null pointer”). Safe pointers aren’t nullable. + +- **Smart pointers**: own their pointees, which means that the value they point + to may be deallocated when the pointer itself is destroyed. Non-owning + pointers may point to values owned elsewhere, or may require some manual + management of the value lifecycle. + +- **Memory allocation**: some pointer types can allocate memory to store their + pointees, while other pointers can only point to pre-existing values. Memory + allocation can either be implicit (that is, performed automatically when + initializing a pointer with a value) or explicit. + +- **Uninitialized memory**: refers to memory locations that haven’t been + initialized with a value, which may therefore contain random data. + Newly-allocated memory is uninitialized. The safe pointer types don’t allow + users to access memory that’s uninitialized. Unsafe pointers can allocate a + block of uninitialized memory locations and then initialize them one at a time. + Being able to access uninitialized memory is unsafe by definition. + +- **Copyable types**: can be copied implicitly (for example, by assigning a + value to a variable). Also called *implicitly copyable types*. + + ```mojo + copied_ptr = ptr + ``` + + *Explicitly copyable* types require the user to request a copy, using a + constructor with a keyword argument: + + ```mojo + copied_owned_ptr = OwnedPointer(other=owned_ptr) + ``` + +## Pointer types + +The Mojo standard library includes several pointer types with different +characteristics: + +- [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) is a safe pointer that points + to a single value that it doesn’t own. + +- [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) is a smart + pointer that points to a single value, and maintains exclusive ownership of + that value. + +- [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer) is a reference-counted + smart pointer that points to an owned value with ownership potentially shared + with other instances of `ArcPointer`. + +- [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) points to + one or more consecutive memory locations, and can refer to uninitialized + memory. + +Table 1 summarizes the different types of pointers: + +
+ +| | `Pointer` | `OwnedPointer` | `ArcPointer` | `UnsafePointer` | +| --- | --- | --- | --- | --- | +| Safe | Yes | Yes | Yes | No | +| Allocates memory | No | Implicitly 1 | Implicitly 1 | Explicitly | +| Owns pointee(s) | No | Yes | Yes | No 2 | +| Copyable | Yes | No 3 | Yes | Yes | +| Nullable | No | No | No | Yes | +| Can point to uninitialized memory | No | No | No | Yes | +| Can point to multiple values (array-like access) | No | No | No | Yes | + +
Table 1. Pointer types
+
+ +1 `OwnedPointer` and `ArcPointer` implicitly allocate memory when you +initialize the pointer with a value. + +2 `UnsafePointer` provides unsafe methods for initializing and +destroying instances of the stored type. The user is responsible for managing +the lifecycle of stored values. + +3 `OwnedPointer` is explicitly copyable, but explicitly copying an +`OwnedPointer` copies the *stored value* into a new `OwnedPointer`. + +The following sections provide more details on each pointer type. + +## `Pointer` + +The [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) type is a safe pointer that +points to a initialized value that it doesn’t own. Some example use cases for a +`Pointer` include: + +- Storing a reference to a related type. For example, a list’s iterator object +might hold a `Pointer` back to the original list. + +- Passing the memory location for a single value to external code via +`external_call()`. + +- Where you need an API to return a long-lived reference to a value. (Currently +the iterators for standard library collection types like `List` return +pointers.) + +You can construct a `Pointer` using the `address_of()` static method: + +```python +ptr = Pointer.address_of(some_value) +``` + +You can also create a `Pointer` by copying an existing `Pointer`. + +A `Pointer` carries an [`origin`](/mojo/manual/values/lifetimes) for the stored +value, so Mojo can track the lifetime of the referenced value. + +## `OwnedPointer` + +The [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) type is a +smart pointer designed for cases where there is single ownership of the +underlying data. An `OwnedPointer` points to a single item, which is passed in +when you initialize the `OwnedPointer`. The `OwnedPointer` allocates memory and +moves or copies the value into the reserved memory. + +```python +o_ptr = OwnedPointer(some_big_struct) +``` + +An owned pointer can hold almost any type of item, but the stored item must be +either `Movable`, `Copyable`, or `ExplicitlyCopyable`. + +Since an `OwnedPointer` is designed to enforce single ownership, the pointer +itself can be moved, but not copied. + +Note: Currently, you can’t create an `Optional[OwnedPointer[T]]` because the +`Optional` type only works with types that are both movable and copyable. This +restricts some use cases that would otherwise be a natural fit +for`OwnedPointer`, including self-referential data structures like linked lists +and trees. (Until this use case is supported for `OwnedPointer`, it’s possible +to use`ArcPointer` where you need a smart pointer that can be `Optional`.) + +## `ArcPointer` + +An [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer) is a reference-counted +smart pointer, ideal for shared resources where the last owner for a given value +may not be clear. Like an `OwnedPointer`, it points to a single value, and it +allocates memory when you initialize the `ArcPointer` with a value: + +```python +attributesDict = Dict[String, String]() +attributes = ArcPointer(attributesDict) +``` + +Unlike an `OwnedPointer`, an `ArcPointer` can be freely copied. All instances +of a given `ArcPointer` share a reference count, which is incremented whenever +the `ArcPointer` is copied and decremented whenever an instance is destroyed. +When the reference count reaches zero, the stored value is destroyed and the +allocated memory is freed. + +You can use `ArcPointer` to implement safe reference-semantic types. For +example, in the following code snippet `SharedDict` uses an `ArcPointer` to +store a dictionary. Copying an instance of `SharedDict` only copies the +`ArcPointer`, not the dictionary, which is shared between all of the copies. + +```python +from memory import ArcPointer +from collections import Dict + +struct SharedDict: + var attributes: ArcPointer[Dict[String, String]] + + fn __init__(out self): + attributesDict = Dict[String, String]() + self.attributes = ArcPointer(attributesDict) + + fn __copyinit__(out self, other: Self): + self.attributes = other.attributes + + def __setitem__(inout self, key: String, value: String): + self.attributes[][key] = value + + def __getitem__(self, key: String) -> String: + return self.attributes[].get(key, default="") + +def main(): + thing1 = SharedDict() + thing2 = thing1 + thing1["Flip"] = "Flop" + print(thing2["Flip"]) +``` + +Note: The reference count is stored using an +[`Atomic`]([/mojo/stdlib/os/atomic/Atomic](https://docs.modular-staging.com/mojo/stdlib/os/atomic/Atomic)) +value to ensure that updates to the reference count are thread-safe. However, +Mojo doesn’t currently enforce exclusive access across thread boundaries, so +it’s possible to form race conditions. + +## UnsafePointer + +[`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) is a +low-level pointer that can access a block of contiguous memory locations, which +might be uninitialized. It’s analogous to a raw pointer in the C and C++ +programming languages. `UnsafePointer` provides unsafe methods for initializing +and destroying stored values, as well as for accessing the values once they’re +initialized. + +As the name suggests, `UnsafePointer` doesn’t provide any memory safety +guarantees, so you should reserve it for cases when none of the other pointer +types will do the job. Here are some use cases where you might want to use an +`UnsafePointer`: + +- Building a high-performance array-like structure, such as `List` or `Tensor`. + A single `UnsafePointer` can access many values, and gives you a lot of + control over how you allocate, use, and deallocate memory. Being able to + access uninitialized memory means that you can preallocate a block of memory, + and initialize values incrementally as they are added to the collection. + +- Interacting with external libraries including C++ and Python. You can + use`UnsafePointer` to pass a buffer full of data to or from an external + library. + +For more information, see [Unsafe +pointers](/mojo/manual/pointers/unsafe-pointers). diff --git a/docs/manual/pointers.mdx b/docs/manual/pointers/unsafe-pointers.mdx similarity index 85% rename from docs/manual/pointers.mdx rename to docs/manual/pointers/unsafe-pointers.mdx index 57482a3781..a2a5f04acd 100644 --- a/docs/manual/pointers.mdx +++ b/docs/manual/pointers/unsafe-pointers.mdx @@ -3,26 +3,24 @@ title: Unsafe pointers description: Using unsafe pointers to access dynamically-allocated memory. --- -The [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) type -creates an indirect reference to a location in memory. +The [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) type is +one of several pointer types available in the standard library to indirectly +reference locations in memory. + You can use an `UnsafePointer` to dynamically allocate and free memory, or to point to memory allocated by some other piece of code. You can use these pointers to write code that interacts with low-level interfaces, to interface -with other programming languages, or to build certain kinds of data structures. +with other programming languages, or to build array-like data structures. But as the name suggests, they're inherently *unsafe*. For example, when using unsafe pointers, you're responsible for ensuring that memory gets allocated and freed correctly. -:::note - -In addition to unsafe pointers, Mojo supports a safe -[`Pointer`](/mojo/stdlib/memory/pointer/Pointer) type. See -[`UnsafePointer` and `Pointer`](#unsafepointer-and-pointer) for a brief -comparison of the types. +In general, you should prefer safe pointer types when possible, reserving +`UnsafePointer` for those use cases where no other pointer type works. +For a comparison of standard library pointer types, see [Intro to +pointers](/mojo/manual/pointers/). -::: - -## What is a pointer? +## Unsafe pointer basics An `UnsafePointer` is a type that holds an address to memory. You can store and retrieve values in that memory. The `UnsafePointer` type is *generic*—it can @@ -40,8 +38,8 @@ ptr.init_pointee_copy(100)
-![](./images/pointer-diagram.png#light) -![](./images/pointer-diagram-dark.png#dark) +![](../images/pointer-diagram.png#light) +![](../images/pointer-diagram-dark.png#dark)
Figure 1. Pointer and pointee
@@ -69,20 +67,20 @@ structures. For details, see At any given time, a pointer can be in one of several states: -* Uninitialized. Just like any variable, a variable of type `UnsafePointer` can +- Uninitialized. Just like any variable, a variable of type `UnsafePointer` can be declared but uninitialized. ```mojo var ptr: UnsafePointer[Int] ``` -* Null. A null pointer has an address of 0, indicating an invalid pointer. +- Null. A null pointer has an address of 0, indicating an invalid pointer. ```mojo ptr = UnsafePointer[Int]() ``` -* Pointing to allocated, uninitialized memory. The `alloc()` static method +- Pointing to allocated, uninitialized memory. The `alloc()` static method returns a pointer to a newly-allocated block of memory with space for the specified number of elements of the pointee's type. @@ -93,7 +91,7 @@ At any given time, a pointer can be in one of several states: Trying to dereference a pointer to uninitialized memory results in undefined behavior. -* Pointing to initialized memory. You can initialize an allocated, uninitialized +- Pointing to initialized memory. You can initialize an allocated, uninitialized pointer by moving or copying an existing value into the memory. Or you can use the `address_of()` static method to get a pointer to an existing value. @@ -113,7 +111,7 @@ At any given time, a pointer can be in one of several states: ptr[] = newValue ``` -* Dangling. When you free the pointer's allocated memory, you're left with a +- Dangling. When you free the pointer's allocated memory, you're left with a *dangling pointer*. The address still points to its previous location, but the memory is no longer allocated to this pointer. Trying to dereference the pointer, or calling any method that would access the memory location results @@ -127,8 +125,8 @@ The following diagram shows the lifecycle of an `UnsafePointer`:
-![](./images/pointer-lifecycle.png#light) -![](./images/pointer-lifecycle-dark.png#dark) +![](../images/pointer-lifecycle.png#light) +![](../images/pointer-lifecycle-dark.png#dark)
Figure 2. Lifecycle of an UnsafePointer
@@ -293,8 +291,8 @@ ptr += 1
-![](./images/pointer-offset.png#light) -![](./images/pointer-offset-dark.png#dark) +![](../images/pointer-offset.png#light) +![](../images/pointer-offset-dark.png#dark)
Figure 3. Pointer arithmetic
@@ -353,7 +351,7 @@ from memory import UnsafePointer def share_array(): np = Python.import_module("numpy") arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) - ptr = arr.__array_interface__["data"][0].unsafe_get_as_pointer[DType.int64]() + ptr = arr.ctypes.data.unsafe_get_as_pointer[DType.int64]() for i in range(9): print(ptr[i], end=", ") @@ -364,11 +362,10 @@ share_array() 1, 2, 3, 4, 5, 6, 7, 8, 9, ``` -NumPy arrays implement the -[array interface protocol](https://numpy.org/doc/stable/reference/arrays.interface.html), -which defines the `__array_interface__` object used in the example, where -`__array_interface__["data"][0]` is a Python integer holding the address of the -underlying data. The `unsafe_get_as_pointer()` method constructs an +This example uses the NumPy +[`ndarray.ctype`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.ctypes.html#numpy.ndarray.ctypes) +attribute to access the raw pointer to the underlying storage +(`ndarray.ctype.data`). The `unsafe_get_as_pointer()` method constructs an `UnsafePointer` to this address. ### Working with C/C++ pointers @@ -459,8 +456,8 @@ you want to access just the red values, you can use a strided load or store.
-![](./images/strided-load-storage.png#light) -![](./images/strided-load-storage-dark.png#dark) +![](../images/strided-load-storage.png#light) +![](../images/strided-load-storage-dark.png#dark)
Figure 4. Strided load
@@ -496,35 +493,14 @@ from (pointer address) + offset[n]. Unsafe pointers are unsafe for several reasons: -* Memory management is up to the user. You need to manually allocate +- Memory management is up to the user. You need to manually allocate and free memory, and be aware of when other APIs are allocating or freeing memory for you. -* `UnsafePointer` values are *nullable*—that is, the pointer +- `UnsafePointer` values are *nullable*—that is, the pointer is not guaranteed to point to anything. And even when a pointer points to allocated memory, that memory may not be *initialized*. -* Mojo doesn't track lifetimes for the data pointed to by an `UnsafePointer`. +- Mojo doesn't track lifetimes for the data pointed to by an `UnsafePointer`. When you use an `UnsafePointer`, managing memory and knowing when to destroy objects is your responsibility. - -## `UnsafePointer` and `Pointer` - -The [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) type is essentially a -safe pointer type. Like a pointer, you can derference a `Pointer` using the -dereference operator, `[]`. However, the `Pointer` type has several -differences from `UnsafePointer` which make it safer: - -* A `Pointer` is *non-nullable*: it always points to something. -* You can't allocate or free memory using a `Pointer`—only point to an - existing value. -* A `Pointer` only refers to a single value. You can't do pointer arithmetic - with a `Pointer`. -* A `Pointer` has an associated *origin*, which connects it back to an - original, owned value. The origin ensures that the value won't be destroyed - while the pointer exists. - -The `Pointer` type shouldn't be confused with the immutable and mutable -references used with the `read` and `mut` argument conventions. Those -references do not require explicit dereferencing, unlike a `Pointer` or -`UnsafePointer`. diff --git a/examples/notebooks/environment.yml b/examples/notebooks/environment.yml new file mode 100644 index 0000000000..45a48799ef --- /dev/null +++ b/examples/notebooks/environment.yml @@ -0,0 +1,14 @@ +name: mojo-notebooks +channels: + - pytorch + - https://conda.modular.com/max/ + - conda-forge + - defaults +dependencies: + - python>=3.11,<3.12 + - max>=24.4.0dev6 + - max = "*" +pip = ">=24.0,<25" +jupyterlab = ">=4.2.5,<5" +matplotlib = ">=3.9.2,<4" +numpy = ">=1.26.4,<2" diff --git a/stdlib/src/memory/arc.mojo b/stdlib/src/memory/arc.mojo index feb4c42711..8680d06560 100644 --- a/stdlib/src/memory/arc.mojo +++ b/stdlib/src/memory/arc.mojo @@ -10,38 +10,13 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -"""Pointer-counted smart pointers. +"""Reference-counted smart pointers. -Example usage: +You can import these APIs from the `memory` package. For example: ```mojo from memory import ArcPointer -var p = ArcPointer(4) -var p2 = p -p2[]=3 -print(3 == p[]) ``` - -Subscripting(`[]`) is done by `Pointer`, -in order to ensure that the underlying `ArcPointer` outlive the operation. - -It is highly DISCOURAGED to manipulate an `ArcPointer` through `UnsafePointer`. -Mojo's ASAP deletion policy ensure values are destroyed at last use. -Do not unsafely dereference the `ArcPointer` inner `UnsafePointer` field. -See [Lifecycle](https://docs.modular.com/mojo/manual/lifecycle/). - -```mojo -# Illustration of what NOT to do, in order to understand: -print(ArcPointer(String("ok"))._inner[].payload) -#........................^ASAP ^already freed -``` - -Always use `Pointer` subscripting (`[]`): - -```mojo -print(ArcPointer(String("ok"))[]) -``` - """ from os.atomic import Atomic @@ -79,9 +54,34 @@ struct ArcPointer[T: Movable]( This pointer is copyable, including across threads, maintaining a reference count to the underlying data. + When you initialize an `ArcPointer` with a value, it allocates memory and + moves the value into the allocated memory. Copying an instance of an + `ArcPointer` increments the reference count. Destroying an instance + decrements the reference count. When the reference count reaches zero, + `ArcPointer` destroys the value and frees its memory. + This pointer itself is thread-safe using atomic accesses to reference count the underlying data, but references returned to the underlying data are not - thread safe. + thread-safe. + + Subscripting an `ArcPointer` (`ptr[]`) returns a mutable reference to the + stored value. This is the only safe way to access the stored value. Other + methods, such as using the `unsafe_ptr()` method to retrieve an unsafe + pointer to the stored value, or accessing the private fields of an + `ArcPointer`, are unsafe and may result in memory errors. + + For a comparison with other pointer types, see [Intro to + pointers](/mojo/manual/pointers/) in the Mojo Manual. + + Examples: + + ```mojo + from memory import ArcPointer + var p = ArcPointer(4) + var p2 = p + p2[]=3 + print(3 == p[]) + ``` Parameters: T: The type of the stored value. @@ -126,9 +126,9 @@ struct ArcPointer[T: Movable]( @no_inline fn __del__(owned self): - """Delete the smart pointer reference. + """Delete the smart pointer. - Decrement the ref count for the reference. If there are no more + Decrement the reference count for the stored value. If there are no more references, delete the object and free its memory.""" if self._inner[].drop_ref(): # Call inner destructor, then free the memory. @@ -161,7 +161,7 @@ struct ArcPointer[T: Movable]( """Retrieves a pointer to the underlying memory. Returns: - The UnsafePointer to the underlying memory. + The `UnsafePointer` to the pointee. """ # TODO: consider removing this method. return UnsafePointer.address_of(self._inner[].payload) @@ -175,23 +175,27 @@ struct ArcPointer[T: Movable]( return self._inner[].refcount.load() fn __is__(self, rhs: Self) -> Bool: - """Returns True if the two ArcPointers point at the same object. + """Returns True if the two `ArcPointer` instances point at the same + object. Args: - rhs: The other ArcPointer. + rhs: The other `ArcPointer`. Returns: - True if the two ArcPointers point at the same object and False otherwise. + True if the two `ArcPointers` instances point at the same object and + False otherwise. """ return self._inner == rhs._inner fn __isnot__(self, rhs: Self) -> Bool: - """Returns True if the two ArcPointers point at different objects. + """Returns True if the two `ArcPointer` instances point at different + objects. Args: - rhs: The other ArcPointer. + rhs: The other `ArcPointer`. Returns: - True if the two ArcPointers point at different objects and False otherwise. + True if the two `ArcPointer` instances point at different objects + and False otherwise. """ return self._inner != rhs._inner diff --git a/stdlib/src/memory/owned_pointer.mojo b/stdlib/src/memory/owned_pointer.mojo index 3974daa99f..5d1ddb367d 100644 --- a/stdlib/src/memory/owned_pointer.mojo +++ b/stdlib/src/memory/owned_pointer.mojo @@ -21,6 +21,9 @@ struct OwnedPointer[T: AnyType]: system such that no more than one mutable alias for the underlying data may exist. + For a comparison with other pointer types, see [Intro to + pointers](/mojo/manual/pointers/) in the Mojo Manual. + Parameters: T: The type to be stored in the OwnedPointer[]. """ diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 925707a0ea..16a4eadd29 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -305,6 +305,9 @@ struct Pointer[ ](CollectionElementNew, Stringable): """Defines a non-nullable safe pointer. + For a comparison with other pointer types, see [Intro to + pointers](/mojo/manual/pointers/) in the Mojo Manual. + Parameters: is_mutable: Whether the pointee data may be mutated through this. type: Type of the underlying data. diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 0d12a44a78..f4971c68af 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -63,7 +63,17 @@ struct UnsafePointer[ Intable, Comparable, ): - """This is a pointer type that can point to any generic value that is movable. + """UnsafePointer[T] represents an indirect reference to one or more values of + type T consecutively in memory, and can refer to uninitialized memory. + + Because it supports referring to uninitialized memory, it provides unsafe + methods for initializing and destroying instances of T, as well as methods + for accessing the values once they are initialized. + + For more information see [Unsafe + pointers](/mojo/manual/pointers/unsafe-pointers) in the Mojo Manual. For a + comparison with other pointer types, see [Intro to + pointers](/mojo/manual/pointers/). Parameters: type: The type the pointer points to. @@ -84,12 +94,12 @@ struct UnsafePointer[ address_space._value.value, `>`, ] + """The underlying pointer type.""" # ===-------------------------------------------------------------------===# # Fields # ===-------------------------------------------------------------------===# - """The underlying pointer type.""" var address: Self._mlir_type """The underlying pointer.""" @@ -106,7 +116,7 @@ struct UnsafePointer[ @always_inline @implicit fn __init__(out self, value: Self._mlir_type): - """Create a pointer with the input value. + """Create a pointer from a low-level pointer primitive. Args: value: The MLIR value of the pointer to construct with. @@ -129,7 +139,7 @@ struct UnsafePointer[ @always_inline fn __init__(out self, *, other: Self): - """Copy the object. + """Copy an existing pointer. Args: other: The value to copy. From c0c2161c8037006663715773fc45ece9d5fa44cc Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Wed, 4 Dec 2024 12:24:01 -0700 Subject: [PATCH 028/509] [stdlib] Remove copies in `StaticTuple.getitem` Remove the copies of the internal array before getting an element in `StaticTuple`. There is no need to create a copy before calling `pop.array.gep` as seen by the use in `InlineArray`. We'll look into the `setitem` copies in a follow-up as they are causing some errors still that need investigation. MODULAR_ORIG_COMMIT_REV_ID: 214bff889d1299f766dae591ef5ce86fbb2f1999 --- stdlib/src/utils/static_tuple.mojo | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index 6904888e65..ff745bedec 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -218,15 +218,10 @@ struct StaticTuple[element_type: AnyTrivialRegType, size: Int](Sized): debug_assert( int(idx.__mlir_index__()) < size, "index must be within bounds" ) - # Copy the array so we can get its address, because we can't take the - # address of 'self' in a non-mutating method. - var arrayCopy = self.array var ptr = __mlir_op.`pop.array.gep`( - UnsafePointer.address_of(arrayCopy).address, idx.__mlir_index__() + UnsafePointer.address_of(self.array).address, idx.__mlir_index__() ) - var result = UnsafePointer(ptr)[] - _ = arrayCopy - return result + return UnsafePointer(ptr)[] @always_inline("nodebug") fn __setitem__[ From 996fc97e74aec49ae7d9e8e97a5661d9c82410d5 Mon Sep 17 00:00:00 2001 From: rd4com <144297616+rd4com@users.noreply.github.com> Date: Wed, 4 Dec 2024 11:41:12 -0800 Subject: [PATCH 029/509] [External] [Stdlib] Add `PythonObject.__contains__` (#51430) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [External] [Stdlib] Add `PythonObject.__contains__` Simple and small implementation, Improving the `🐍 Python` experience ! Example usage: ```mojo x = PythonObject([1,2,3]) if 1 in x: print("1 in x") ``` That method should be replaced by a `c-python` function if possible. Co-authored-by: rd4com <144297616+rd4com@users.noreply.github.com> Closes modularml/mojo#3101 MODULAR_ORIG_COMMIT_REV_ID: d08c1f856feda307b8da58ec0cf3442553b3acc7 --- docs/changelog.md | 10 +++ stdlib/src/python/_cpython.mojo | 9 +++ stdlib/src/python/python_object.mojo | 21 ++++++ ..._for_test_python_object_dunder_contains.py | 40 +++++++++++ stdlib/test/python/test_python_cpython.mojo | 40 +++++++++++ stdlib/test/python/test_python_object.mojo | 26 +++++++ .../test_python_object_dunder_contains.mojo | 70 +++++++++++++++++++ 7 files changed, 216 insertions(+) create mode 100644 stdlib/test/python/module_for_test_python_object_dunder_contains.py create mode 100644 stdlib/test/python/test_python_cpython.mojo create mode 100644 stdlib/test/python/test_python_object_dunder_contains.mojo diff --git a/docs/changelog.md b/docs/changelog.md index 664fe17057..b101a22608 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -566,6 +566,16 @@ what we publish. fn take_imm_slice(a: ImmStringSlice): ... ``` +- Added `PythonObject.__contains__`. + ([PR #3101](https://github.com/modularml/mojo/pull/3101) by [@rd4com](https://github.com/rd4com)) + + Example usage: + + ```mojo + x = PythonObject([1,2,3]) + if 1 in x: + print("1 in x") + ### ❌ Removed - The `UnsafePointer.bitcast` overload for `DType` has been removed. Wrap your diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index 4a28b2d117..05426aa8eb 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -1348,7 +1348,16 @@ struct CPython: ", parent obj:", obj._get_ptr_as_int(), ) + return r + fn PyObject_HasAttrString( + mut self, + obj: PyObjectPtr, + name: StringRef, + ) -> Int: + var r = self.lib.get_function[ + fn (PyObjectPtr, UnsafePointer[UInt8]) -> Int + ]("PyObject_HasAttrString")(obj, name.data) return r fn PyObject_GetAttrString( diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index 4566b11501..a6b3a8a45f 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -1325,6 +1325,27 @@ struct PythonObject( """ return self._call_zero_arg_method("__invert__") + fn __contains__(self, rhs: PythonObject) raises -> Bool: + """Contains dunder. + + Calls the underlying object's `__contains__` method. + + Args: + rhs: Right hand value. + + Returns: + True if rhs is in self. + """ + # TODO: replace/optimize with c-python function. + # TODO: implement __getitem__ step for cpython membership test operator. + var cpython = _get_global_python_itf().cpython() + if cpython.PyObject_HasAttrString(self.py_object, "__contains__"): + return self._call_single_arg_method("__contains__", rhs).__bool__() + for v in self: + if v == rhs: + return True + return False + # see https://github.com/python/cpython/blob/main/Objects/call.c # for decrement rules fn __call__( diff --git a/stdlib/test/python/module_for_test_python_object_dunder_contains.py b/stdlib/test/python/module_for_test_python_object_dunder_contains.py new file mode 100644 index 0000000000..40ec598dee --- /dev/null +++ b/stdlib/test/python/module_for_test_python_object_dunder_contains.py @@ -0,0 +1,40 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +class Class_no_iterable_no_contains: + x = 1 + + +class Class_no_iterable_but_contains: + x = 123 + + def __contains__(self, rhs): + return rhs == self.x + + +class Class_iterable_no_contains: + def __init__(self): + self.data = [123, 456] + + def __iter__(self): + self.i = 0 + return self + + def __next__(self): + if self.i >= len(self.data): + raise StopIteration + else: + tmp = self.data[self.i] + self.i += 1 + return tmp diff --git a/stdlib/test/python/test_python_cpython.mojo b/stdlib/test/python/test_python_cpython.mojo new file mode 100644 index 0000000000..77d4bfdb59 --- /dev/null +++ b/stdlib/test/python/test_python_cpython.mojo @@ -0,0 +1,40 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# XFAIL: asan && !system-darwin +# RUN: %mojo %s + +from python import Python, PythonObject +from testing import assert_equal, assert_false, assert_raises, assert_true + + +def test_PyObject_HasAttrString(mut python: Python): + var Cpython_env = python.impl._cpython + + var the_object = PythonObject(0) + var result = Cpython_env[].PyObject_HasAttrString( + the_object.py_object, "__contains__" + ) + assert_equal(0, result) + + the_object = PythonObject([1, 2, 3]) + result = Cpython_env[].PyObject_HasAttrString( + the_object.py_object, "__contains__" + ) + assert_equal(1, result) + _ = the_object + + +def main(): + # initializing Python instance calls init_python + var python = Python() + test_PyObject_HasAttrString(python) diff --git a/stdlib/test/python/test_python_object.mojo b/stdlib/test/python/test_python_object.mojo index 92180fb8d0..b1c6799157 100644 --- a/stdlib/test/python/test_python_object.mojo +++ b/stdlib/test/python/test_python_object.mojo @@ -576,6 +576,31 @@ fn test_py_slice() raises: _ = with_2d[0:1][4] +def test_contains_dunder(): + with assert_raises(contains="'int' object is not iterable"): + var z = PythonObject(0) + _ = 5 in z + + var x = PythonObject([1.1, 2.2]) + assert_true(1.1 in x) + assert_false(3.3 in x) + + x = PythonObject(["Hello", "World"]) + assert_true("World" in x) + + x = PythonObject((1.5, 2)) + assert_true(1.5 in x) + assert_false(3.5 in x) + + var y = Dict[PythonObject, PythonObject]() + y["A"] = "A" + y["B"] = 5 + x = PythonObject(y) + assert_true("A" in x) + assert_false("C" in x) + assert_true("B" in x) + + def main(): # initializing Python instance calls init_python var python = Python() @@ -593,3 +618,4 @@ def main(): test_getitem_raises() test_setitem_raises() test_py_slice() + test_contains_dunder() diff --git a/stdlib/test/python/test_python_object_dunder_contains.mojo b/stdlib/test/python/test_python_object_dunder_contains.mojo new file mode 100644 index 0000000000..5d4c722278 --- /dev/null +++ b/stdlib/test/python/test_python_object_dunder_contains.mojo @@ -0,0 +1,70 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# XFAIL: asan && !system-darwin +# RUN: %mojo %s + +from python import Python, PythonObject +from testing import assert_equal, assert_false, assert_raises, assert_true +from collections import Dict + + +def test_contains_dunder(mut python: Python): + with assert_raises(contains="'int' object is not iterable"): + var z = PythonObject(0) + _ = 5 in z + + var x = PythonObject([1.1, 2.2]) + assert_true(1.1 in x) + assert_false(3.3 in x) + + x = PythonObject(["Hello", "World"]) + assert_true("World" in x) + + x = PythonObject((1.5, 2)) + assert_true(1.5 in x) + assert_false(3.5 in x) + + var y = Dict[PythonObject, PythonObject]() + y["A"] = "A" + y["B"] = 5 + x = PythonObject(y) + assert_true("A" in x) + assert_false("C" in x) + assert_true("B" in x) + + # tests with python modules: + module = python.import_module( + "module_for_test_python_object_dunder_contains" + ) + + x = module.Class_no_iterable_but_contains() + assert_true(123 in x) + + x = module.Class_no_iterable_no_contains() + with assert_raises( + contains="'Class_no_iterable_no_contains' object is not iterable" + ): + _ = 123 in x + + x = module.Class_iterable_no_contains() + assert_true(123 in x) + assert_true(456 in x) + assert_false(234 in x) + x.data.append(234) + assert_true(234 in x) + + +def main(): + # initializing Python instance calls init_python + var python = Python() + test_contains_dunder(python) From 31daaaaf5743191bd40dcfdc0e25d132c1214bf9 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Wed, 4 Dec 2024 15:22:00 -0500 Subject: [PATCH 030/509] [stdlib] Add `__reversed__` to `Span` All the machinery is already in place, so this just wires up `reversed()` for `Span`. MODULAR_ORIG_COMMIT_REV_ID: 91b588f3e0d91f27d9bc643c1e081cc27b52600e --- docs/changelog.md | 4 ++++ stdlib/src/builtin/reversed.mojo | 21 +++++++++++++++++++++ stdlib/src/utils/span.mojo | 13 +++++++++++-- stdlib/test/utils/test_span.mojo | 11 +++++++++++ 4 files changed, 47 insertions(+), 2 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index b101a22608..177c2bbd9f 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -292,6 +292,10 @@ what we publish. allows forming a runtime-constant StringLiteral from a compile-time-dynamic `Stringable` value. +- `Span` now implements `__reversed__`. This means that one can get a + reverse iterator over a `Span` using `reversed(my_span)`. Users should + currently prefer this method over `my_span[::-1]`. + ### 🦋 Changed - The `inout` and `borrowed` argument conventions have been renamed to the `mut` diff --git a/stdlib/src/builtin/reversed.mojo b/stdlib/src/builtin/reversed.mojo index a1c984e81f..e01947a3fb 100644 --- a/stdlib/src/builtin/reversed.mojo +++ b/stdlib/src/builtin/reversed.mojo @@ -19,6 +19,7 @@ from collections import Deque, Dict from collections.deque import _DequeIter from collections.dict import _DictEntryIter, _DictKeyIter, _DictValueIter from collections.list import _ListIter +from utils.span import Span, _SpanIter from .range import _StridedRange @@ -193,3 +194,23 @@ fn reversed[ return _DictEntryIter[K, V, dict_origin, False]( src[]._reserved() - 1, 0, src ) + + +@always_inline +fn reversed[ + T: CollectionElement +](value: Span[T]) -> _SpanIter[T, value.origin, forward=False]: + """Get a reversed iterator of the input Span. + + **Note**: iterators are currently non-raising. + + Parameters: + T: The type of the elements in the Span. + + Args: + value: The Span to get the reversed iterator of. + + Returns: + The reversed iterator of the Span. + """ + return value.__reversed__() diff --git a/stdlib/src/utils/span.mojo b/stdlib/src/utils/span.mojo index 282a3bc229..5aa2fb4cf7 100644 --- a/stdlib/src/utils/span.mojo +++ b/stdlib/src/utils/span.mojo @@ -224,13 +224,22 @@ struct Span[ @always_inline fn __iter__(self) -> _SpanIter[T, origin]: - """Get an iterator over the elements of the span. + """Get an iterator over the elements of the Span. Returns: - An iterator over the elements of the span. + An iterator over the elements of the Span. """ return _SpanIter(0, self) + @always_inline + fn __reversed__(self) -> _SpanIter[T, origin, forward=False]: + """Iterate backwards over the Span. + + Returns: + A reversed iterator of the Span elements. + """ + return _SpanIter[forward=False](len(self), self) + # ===------------------------------------------------------------------===# # Trait implementations # ===------------------------------------------------------------------===# diff --git a/stdlib/test/utils/test_span.mojo b/stdlib/test/utils/test_span.mojo index ebf04f8157..a666788354 100644 --- a/stdlib/test/utils/test_span.mojo +++ b/stdlib/test/utils/test_span.mojo @@ -204,6 +204,16 @@ def test_ref(): assert_true(s.as_ref() == Pointer.address_of(l.unsafe_ptr()[])) +def test_reversed(): + var forward = InlineArray[Int, 3](1, 2, 3) + var backward = InlineArray[Int, 3](3, 2, 1) + var s = Span[Int](forward) + var i = 0 + for num in reversed(s): + assert_equal(num[], backward[i]) + i += 1 + + def main(): test_span_list_int() test_span_list_str() @@ -215,3 +225,4 @@ def main(): test_bool() test_fill() test_ref() + test_reversed() From ba8c566c8e7c586ae5ad302efd5e343b85ea39df Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Wed, 4 Dec 2024 15:54:54 -0700 Subject: [PATCH 031/509] [stdlib] Clean up setting `_is_owned` for destroying packs Use `lit.ownership.mark_destroyed` instead of setting the private member of `_is_owned` of `VariadicListMem` when used in downstream collections. MODULAR_ORIG_COMMIT_REV_ID: a5503dce64c34fe17044834d9808a888417a944d --- stdlib/src/collections/deque.mojo | 18 +++++++++--------- stdlib/src/collections/inline_array.mojo | 6 ++++-- stdlib/src/collections/list.mojo | 16 +++++++++------- 3 files changed, 22 insertions(+), 18 deletions(-) diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index 6d45725350..a0bb3cd262 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -139,17 +139,15 @@ struct Deque[ElementType: CollectionElement]( Args: values: The values to populate the deque with. """ - self = Self(variadic_list=values^) + self = Self(elements=values^) - fn __init__( - mut self, *, owned variadic_list: VariadicListMem[ElementType, _] - ): + fn __init__(mut self, *, owned elements: VariadicListMem[ElementType, _]): """Constructs a deque from the given values. Args: - variadic_list: The values to populate the deque with. + elements: The values to populate the deque with. """ - args_length = len(variadic_list) + args_length = len(elements) if args_length < self.default_capacity: capacity = self.default_capacity @@ -159,12 +157,14 @@ struct Deque[ElementType: CollectionElement]( self = Self(capacity=capacity) for i in range(args_length): - src = UnsafePointer.address_of(variadic_list[i]) + src = UnsafePointer.address_of(elements[i]) dst = self._data + i src.move_pointee_into(dst) - # Mark the elements as unowned to avoid del'ing uninitialized objects. - variadic_list._is_owned = False + # Do not destroy the elements when their backing storage goes away. + __mlir_op.`lit.ownership.mark_destroyed`( + __get_mvalue_as_litref(elements) + ) self._tail = args_length diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 93064e0220..6901ed1327 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -194,8 +194,10 @@ struct InlineArray[ var eltptr = UnsafePointer.address_of(self.unsafe_get(i)) UnsafePointer.address_of(storage[i]).move_pointee_into(eltptr) - # Mark the elements as already destroyed. - storage._is_owned = False + # Do not destroy the elements when their backing storage goes away. + __mlir_op.`lit.ownership.mark_destroyed`( + __get_mvalue_as_litref(storage) + ) fn __init__(out self, *, other: Self): """Explicitly copy the provided value. diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index d4791dd79e..033ceeb464 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -144,26 +144,28 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Args: values: The values to populate the list with. """ - self = Self(variadic_list=values^) + self = Self(elements=values^) - fn __init__(out self, *, owned variadic_list: VariadicListMem[T, _]): + fn __init__(out self, *, owned elements: VariadicListMem[T, _]): """Constructs a list from the given values. Args: - variadic_list: The values to populate the list with. + elements: The values to populate the list with. """ - var length = len(variadic_list) + var length = len(elements) self = Self(capacity=length) for i in range(length): - var src = UnsafePointer.address_of(variadic_list[i]) + var src = UnsafePointer.address_of(elements[i]) var dest = self.data + i src.move_pointee_into(dest) - # Mark the elements as unowned to avoid del'ing uninitialized objects. - variadic_list._is_owned = False + # Do not destroy the elements when their backing storage goes away. + __mlir_op.`lit.ownership.mark_destroyed`( + __get_mvalue_as_litref(elements) + ) self.size = length From c49ccbd6698d857de2618ccd006a8473ac73909e Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Wed, 4 Dec 2024 16:14:26 -0700 Subject: [PATCH 032/509] [stdlib] Add tests for `StaticTuple.setitem` Move the existing `getitem` tests from `test_tuple.mojo` and start a separate test file for `StaticTuple`. Add tests for `setitem` as we are can use it to figure out why we do the copying in `setitem` and `getitem` before the gep. MODULAR_ORIG_COMMIT_REV_ID: c0ab1928aa8da0045a96f27cbe808d287f02c2a7 --- stdlib/test/utils/test_static_tuple.mojo | 52 ++++++++++++++++++++++++ stdlib/test/utils/test_tuple.mojo | 18 -------- 2 files changed, 52 insertions(+), 18 deletions(-) create mode 100644 stdlib/test/utils/test_static_tuple.mojo diff --git a/stdlib/test/utils/test_static_tuple.mojo b/stdlib/test/utils/test_static_tuple.mojo new file mode 100644 index 0000000000..67f3fbc0e8 --- /dev/null +++ b/stdlib/test/utils/test_static_tuple.mojo @@ -0,0 +1,52 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %mojo %s + +from testing import assert_equal +from utils import StaticTuple + + +def test_getitem(): + # Should be constructible from a single element + # as well as a variadic list of elements. + var tup1 = StaticTuple[Int, 1](1) + assert_equal(tup1[0], 1) + + var tup2 = StaticTuple[Int, 2](1, 1) + assert_equal(tup2[0], 1) + assert_equal(tup2[1], 1) + + var tup3 = StaticTuple[Int, 3](1, 2, 3) + assert_equal(tup3[0], 1) + assert_equal(tup3[1], 2) + assert_equal(tup3[2], 3) + + assert_equal(tup1[Int(0)], 1) + + +def test_setitem(): + var t = StaticTuple[Int, 3](1, 2, 3) + + t[0] = 100 + assert_equal(t[0], 100) + + t[1] = 200 + assert_equal(t[1], 200) + + t[2] = 300 + assert_equal(t[2], 300) + + +def main(): + test_getitem() + test_setitem() diff --git a/stdlib/test/utils/test_tuple.mojo b/stdlib/test/utils/test_tuple.mojo index f6ccb2ce3a..0748552be0 100644 --- a/stdlib/test/utils/test_tuple.mojo +++ b/stdlib/test/utils/test_tuple.mojo @@ -19,23 +19,6 @@ from testing import assert_equal, assert_false, assert_true from utils import IndexList, StaticTuple -def test_static_tuple(): - var tup1 = StaticTuple[Int, 1](1) - assert_equal(tup1[0], 1) - - var tup2 = StaticTuple[Int, 2](1, 1) - assert_equal(tup2[0], 1) - assert_equal(tup2[1], 1) - - var tup3 = StaticTuple[Int, 3](1, 2, 3) - assert_equal(tup3[0], 1) - assert_equal(tup3[1], 2) - assert_equal(tup3[2], 3) - - assert_equal(tup3[0], 1) - assert_equal(tup3[Int(0)], 1) - - def test_static_int_tuple(): assert_equal(str(IndexList[1](1)), "(1,)") @@ -72,6 +55,5 @@ def test_tuple_literal(): def main(): - test_static_tuple() test_static_int_tuple() test_tuple_literal() From aeb7a1fe7231461551739f65a7596a847e3c347b Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Wed, 4 Dec 2024 17:48:23 -0800 Subject: [PATCH 033/509] [mojo-lang] Fix a bad error message. This fixes https://github.com/modularml/mojo/issues/3829 MODULAR_ORIG_COMMIT_REV_ID: ce65257270bfe6443b2f91dae96590fe4a1c2528 --- docs/changelog.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 177c2bbd9f..e781cf6279 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -620,6 +620,9 @@ what we publish. - [Issue #3815](https://github.com/modularml/mojo/issues/3815) - [BUG] Mutability not preserved when taking the union of two origins. +- [Issue #3829](https://github.com/modularml/mojo/issues/3829) - Poor error + message when invoking a function pointer upon an argument of the wrong origin + - The VS Code extension now auto-updates its private copy of the MAX SDK. - The variadic initializer for `SIMD` now works in parameter expressions. From 8942c27059d659c26c6f46ec1523cd6778007143 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 4 Dec 2024 20:16:52 -0800 Subject: [PATCH 034/509] [******][GPU] Extend the time operations to be GPU generic MODULAR_ORIG_COMMIT_REV_ID: 9acc0eef450d97eccca0550b5f6f1eabda7e0023 --- stdlib/src/time/time.mojo | 33 +++++++++++++-------------------- 1 file changed, 13 insertions(+), 20 deletions(-) diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 853c6d2d56..936ca89459 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -126,6 +126,13 @@ fn _gettime_as_nsec_unix(clockid: Int) -> Int: ) +@always_inline +fn _gpu_clock() -> Int: + """Returns a 64-bit unsigned cycle counter.""" + alias asm = "llvm.nvvm.read.ptx.sreg.clock64" if is_nvidia_gpu() else "llvm.amdgcn.s.memtime" + return int(llvm_intrinsic[asm, Int64]()) + + @always_inline fn _realtime_nanoseconds() -> Int: """Returns the current realtime time in nanoseconds""" @@ -137,7 +144,9 @@ fn _monotonic_nanoseconds() -> Int: """Returns the current monotonic time in nanoseconds""" @parameter - if os_is_windows(): + if is_gpu(): + return _gpu_clock() + elif os_is_windows(): var ft = _FILETIME() external_call["GetSystemTimePreciseAsFileTime", NoneType]( Pointer.address_of(ft) @@ -151,7 +160,6 @@ fn _monotonic_nanoseconds() -> Int: @always_inline fn _monotonic_raw_nanoseconds() -> Int: """Returns the current monotonic time in nanoseconds""" - return _gettime_as_nsec_unix(_CLOCK_MONOTONIC_RAW) @@ -204,14 +212,6 @@ fn perf_counter_ns() -> Int: Returns: The current time in ns. """ - - @parameter - if is_nvidia_gpu(): - return int( - inlined_assembly[ - "mov.u64 $0, %globaltimer;", UInt64, constraints="=l" - ]() - ) return _monotonic_nanoseconds() @@ -349,17 +349,10 @@ fn sleep(sec: Float64): """ @parameter - if is_nvidia_gpu(): - var nsec = sec * 1.0e9 - llvm_intrinsic["llvm.nvvm.nanosleep", NoneType]( - nsec.cast[DType.int32]() - ) - return - elif is_amd_gpu(): + if is_gpu(): var nsec = sec * 1.0e9 - llvm_intrinsic["llvm.amdgcn.s.sleep", NoneType]( - nsec.cast[DType.int32]() - ) + alias intrinsic = "llvm.nvvm.nanosleep" if is_nvidia_gpu() else "llvm.amdgcn.s.sleep" + llvm_intrinsic[intrinsic, NoneType](nsec.cast[DType.int32]()) return alias NANOSECONDS_IN_SECOND = 1_000_000_000 From b0094d3d9dfc6e38154393862098c22a0c1dec89 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Wed, 4 Dec 2024 23:45:56 -0500 Subject: [PATCH 035/509] [stdlib] Move `strip`,`lstrip`,`rstrip` onto StringSlice (#52118) MODULAR_ORIG_COMMIT_REV_ID: 72291fc94933188ec12d2dae10c011023b026a75 --- docs/changelog.md | 2 + stdlib/benchmarks/hashlib/bench_hash.mojo | 2 +- stdlib/src/collections/string.mojo | 48 ++------ stdlib/src/os/path/path.mojo | 16 ++- stdlib/src/utils/string_slice.mojo | 136 +++++++++++++++++++--- stdlib/test/hashlib/test_ahash.mojo | 2 +- stdlib/test/utils/test_string_slice.mojo | 99 ++++++++++++++++ 7 files changed, 243 insertions(+), 62 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index e781cf6279..788d1e9c95 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -296,6 +296,8 @@ what we publish. reverse iterator over a `Span` using `reversed(my_span)`. Users should currently prefer this method over `my_span[::-1]`. +- `StringSlice` now implements `strip`, `rstrip`, and `lstrip`. + ### 🦋 Changed - The `inout` and `borrowed` argument conventions have been renamed to the `mut` diff --git a/stdlib/benchmarks/hashlib/bench_hash.mojo b/stdlib/benchmarks/hashlib/bench_hash.mojo index 291701710d..6d2ba54044 100644 --- a/stdlib/benchmarks/hashlib/bench_hash.mojo +++ b/stdlib/benchmarks/hashlib/bench_hash.mojo @@ -586,7 +586,7 @@ fn gen_word_pairs[words: String = words_en]() -> List[String]: try: var list = words.split(",") for w in list: - var w1 = w[].strip() + var w1 = str(w[].strip()) for w in list: var w2 = w[].strip() result.append(w1 + " " + w2) diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index a6051bbb81..47f06707c2 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -1916,7 +1916,7 @@ struct String( res.append(0) return String(res^) - fn strip(self, chars: String) -> String: + fn strip(self, chars: StringSlice) -> StringSlice[__origin_of(self)]: """Return a copy of the string with leading and trailing characters removed. @@ -1929,7 +1929,7 @@ struct String( return self.lstrip(chars).rstrip(chars) - fn strip(self) -> String: + fn strip(self) -> StringSlice[__origin_of(self)]: """Return a copy of the string with leading and trailing whitespaces removed. @@ -1938,7 +1938,7 @@ struct String( """ return self.lstrip().rstrip() - fn rstrip(self, chars: String) -> String: + fn rstrip(self, chars: StringSlice) -> StringSlice[__origin_of(self)]: """Return a copy of the string with trailing characters removed. Args: @@ -1948,29 +1948,17 @@ struct String( A copy of the string with no trailing characters. """ - var r_idx = self.byte_length() - while r_idx > 0 and self[r_idx - 1] in chars: - r_idx -= 1 + return self.as_string_slice().rstrip(chars) - return self[:r_idx] - - fn rstrip(self) -> String: + fn rstrip(self) -> StringSlice[__origin_of(self)]: """Return a copy of the string with trailing whitespaces removed. Returns: A copy of the string with no trailing whitespaces. """ - var r_idx = self.byte_length() - # TODO (#933): should use this once llvm intrinsics can be used at comp time - # for s in self.__reversed__(): - # if not s.isspace(): - # break - # r_idx -= 1 - while r_idx > 0 and _isspace(self._buffer.unsafe_get(r_idx - 1)): - r_idx -= 1 - return self[:r_idx] - - fn lstrip(self, chars: String) -> String: + return self.as_string_slice().rstrip() + + fn lstrip(self, chars: StringSlice) -> StringSlice[__origin_of(self)]: """Return a copy of the string with leading characters removed. Args: @@ -1980,29 +1968,15 @@ struct String( A copy of the string with no leading characters. """ - var l_idx = 0 - while l_idx < self.byte_length() and self[l_idx] in chars: - l_idx += 1 + return self.as_string_slice().lstrip(chars) - return self[l_idx:] - - fn lstrip(self) -> String: + fn lstrip(self) -> StringSlice[__origin_of(self)]: """Return a copy of the string with leading whitespaces removed. Returns: A copy of the string with no leading whitespaces. """ - var l_idx = 0 - # TODO (#933): should use this once llvm intrinsics can be used at comp time - # for s in self: - # if not s.isspace(): - # break - # l_idx += 1 - while l_idx < self.byte_length() and _isspace( - self._buffer.unsafe_get(l_idx) - ): - l_idx += 1 - return self[l_idx:] + return self.as_string_slice().lstrip() fn __hash__(self) -> UInt: """Hash the underlying buffer using builtin hash. diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 3840a97fac..64edffce5e 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -229,11 +229,10 @@ fn dirname[PathLike: os.PathLike, //](path: PathLike) -> String: The directory component of a pathname. """ var fspath = path.__fspath__() - alias sep = str(os.sep) - var i = fspath.rfind(sep) + 1 + var i = fspath.rfind(os.sep) + 1 var head = fspath[:i] - if head and head != sep * len(head): - return head.rstrip(sep) + if head and head != os.sep * len(head): + return head.rstrip(os.sep) return head @@ -365,7 +364,7 @@ def split[PathLike: os.PathLike, //](path: PathLike) -> (String, String): i = fspath.rfind(os.sep) + 1 head, tail = fspath[:i], fspath[i:] if head and head != str(os.sep) * len(head): - head = head.rstrip(sep) + head = str(head.rstrip(sep)) return head, tail @@ -386,11 +385,10 @@ fn basename[PathLike: os.PathLike, //](path: PathLike) -> String: The basename from the path. """ var fspath = path.__fspath__() - alias sep = str(os.sep) - var i = fspath.rfind(sep) + 1 + var i = fspath.rfind(os.sep) + 1 var head = fspath[i:] - if head and head != sep * len(head): - return head.rstrip(sep) + if head and head != os.sep * len(head): + return head.rstrip(os.sep) return head diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 394dccf89c..b6f9daa45a 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -616,13 +616,33 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( # ===------------------------------------------------------------------===# @always_inline - fn strip(self) -> StringSlice[origin]: - """Gets a StringRef with leading and trailing whitespaces removed. - This only takes ASCII whitespace into account: - `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. + fn strip(self, chars: StringSlice) -> Self: + """Return a copy of the string with leading and trailing characters + removed. + + Args: + chars: A set of characters to be removed. Defaults to whitespace. Returns: - A StringRef with leading and trailing whitespaces removed. + A copy of the string with no leading or trailing characters. + + Examples: + + ```mojo + print("himojohi".strip("hi")) # "mojo" + ``` + . + """ + + return self.lstrip(chars).rstrip(chars) + + @always_inline + fn strip(self) -> Self: + """Return a copy of the string with leading and trailing whitespaces + removed. + + Returns: + A copy of the string with no leading or trailing whitespaces. Examples: @@ -631,15 +651,103 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( ``` . """ - # FIXME: this can already do full isspace support with iterator - var start: Int = 0 - var end: Int = len(self) - var ptr = self.unsafe_ptr() - while start < end and _isspace(ptr[start]): - start += 1 - while end > start and _isspace(ptr[end - 1]): - end -= 1 - return StringSlice[origin](ptr=ptr + start, length=end - start) + return self.lstrip().rstrip() + + @always_inline + fn rstrip(self, chars: StringSlice) -> Self: + """Return a copy of the string with trailing characters removed. + + Args: + chars: A set of characters to be removed. Defaults to whitespace. + + Returns: + A copy of the string with no trailing characters. + + Examples: + + ```mojo + print("mojohi".strip("hi")) # "mojo" + ``` + . + """ + + var r_idx = self.byte_length() + while r_idx > 0 and self[r_idx - 1] in chars: + r_idx -= 1 + + return Self(unsafe_from_utf8=self.as_bytes()[:r_idx]) + + @always_inline + fn rstrip(self) -> Self: + """Return a copy of the string with trailing whitespaces removed. + + Returns: + A copy of the string with no trailing whitespaces. + + Examples: + + ```mojo + print("mojo ".strip()) # "mojo" + ``` + . + """ + var r_idx = self.byte_length() + # TODO (#933): should use this once llvm intrinsics can be used at comp time + # for s in self.__reversed__(): + # if not s.isspace(): + # break + # r_idx -= 1 + while r_idx > 0 and _isspace(self.as_bytes()[r_idx - 1]): + r_idx -= 1 + return Self(unsafe_from_utf8=self.as_bytes()[:r_idx]) + + @always_inline + fn lstrip(self, chars: StringSlice) -> Self: + """Return a copy of the string with leading characters removed. + + Args: + chars: A set of characters to be removed. Defaults to whitespace. + + Returns: + A copy of the string with no leading characters. + + Examples: + + ```mojo + print("himojo".strip("hi")) # "mojo" + ``` + . + """ + + var l_idx = 0 + while l_idx < self.byte_length() and self[l_idx] in chars: + l_idx += 1 + + return Self(unsafe_from_utf8=self.as_bytes()[l_idx:]) + + @always_inline + fn lstrip(self) -> Self: + """Return a copy of the string with leading whitespaces removed. + + Returns: + A copy of the string with no leading whitespaces. + + Examples: + + ```mojo + print(" mojo".strip()) # "mojo" + ``` + . + """ + var l_idx = 0 + # TODO (#933): should use this once llvm intrinsics can be used at comp time + # for s in self: + # if not s.isspace(): + # break + # l_idx += 1 + while l_idx < self.byte_length() and _isspace(self.as_bytes()[l_idx]): + l_idx += 1 + return Self(unsafe_from_utf8=self.as_bytes()[l_idx:]) @always_inline fn as_bytes(self) -> Span[Byte, origin]: diff --git a/stdlib/test/hashlib/test_ahash.mojo b/stdlib/test/hashlib/test_ahash.mojo index f1fdc69a62..e696b32c76 100644 --- a/stdlib/test/hashlib/test_ahash.mojo +++ b/stdlib/test/hashlib/test_ahash.mojo @@ -580,7 +580,7 @@ fn gen_word_pairs[words: String = words_en]() -> List[String]: try: var list = words.split(", ") for w in list: - var w1 = w[].strip() + var w1 = str(w[].strip()) for w in list: var w2 = w[].strip() result.append(w1 + " " + w2) diff --git a/stdlib/test/utils/test_string_slice.mojo b/stdlib/test/utils/test_string_slice.mojo index 057601c270..7ef357692b 100644 --- a/stdlib/test/utils/test_string_slice.mojo +++ b/stdlib/test/utils/test_string_slice.mojo @@ -487,6 +487,102 @@ def test_splitlines(): _assert_equal(s.splitlines(keepends=True), items) +def test_rstrip(): + # with default rstrip chars + var empty_string = "".as_string_slice() + assert_true(empty_string.rstrip() == "") + + var space_string = " \t\n\r\v\f ".as_string_slice() + assert_true(space_string.rstrip() == "") + + var str0 = " n ".as_string_slice() + assert_true(str0.rstrip() == " n") + + var str1 = "string".as_string_slice() + assert_true(str1.rstrip() == "string") + + var str2 = "something \t\n\t\v\f".as_string_slice() + assert_true(str2.rstrip() == "something") + + # with custom chars for rstrip + var str3 = "mississippi".as_string_slice() + assert_true(str3.rstrip("sip") == "m") + + var str4 = "mississippimississippi \n ".as_string_slice() + assert_true(str4.rstrip("sip ") == "mississippimississippi \n") + assert_true(str4.rstrip("sip \n") == "mississippim") + + +def test_lstrip(): + # with default lstrip chars + var empty_string = "".as_string_slice() + assert_true(empty_string.lstrip() == "") + + var space_string = " \t\n\r\v\f ".as_string_slice() + assert_true(space_string.lstrip() == "") + + var str0 = " n ".as_string_slice() + assert_true(str0.lstrip() == "n ") + + var str1 = "string".as_string_slice() + assert_true(str1.lstrip() == "string") + + var str2 = " \t\n\t\v\fsomething".as_string_slice() + assert_true(str2.lstrip() == "something") + + # with custom chars for lstrip + var str3 = "mississippi".as_string_slice() + assert_true(str3.lstrip("mis") == "ppi") + + var str4 = " \n mississippimississippi".as_string_slice() + assert_true(str4.lstrip("mis ") == "\n mississippimississippi") + assert_true(str4.lstrip("mis \n") == "ppimississippi") + + +def test_strip(): + # with default strip chars + var empty_string = "".as_string_slice() + assert_true(empty_string.strip() == "") + alias comp_empty_string_stripped = "".as_string_slice().strip() + assert_true(comp_empty_string_stripped == "") + + var space_string = " \t\n\r\v\f ".as_string_slice() + assert_true(space_string.strip() == "") + alias comp_space_string_stripped = " \t\n\r\v\f ".as_string_slice().strip() + assert_true(comp_space_string_stripped == "") + + var str0 = " n ".as_string_slice() + assert_true(str0.strip() == "n") + alias comp_str0_stripped = " n ".as_string_slice().strip() + assert_true(comp_str0_stripped == "n") + + var str1 = "string".as_string_slice() + assert_true(str1.strip() == "string") + alias comp_str1_stripped = ("string").strip() + assert_true(comp_str1_stripped == "string") + + var str2 = " \t\n\t\v\fsomething \t\n\t\v\f".as_string_slice() + alias comp_str2_stripped = (" \t\n\t\v\fsomething \t\n\t\v\f").strip() + assert_true(str2.strip() == "something") + assert_true(comp_str2_stripped == "something") + + # with custom strip chars + var str3 = "mississippi".as_string_slice() + assert_true(str3.strip("mips") == "") + assert_true(str3.strip("mip") == "ssiss") + alias comp_str3_stripped = "mississippi".as_string_slice().strip("mips") + assert_true(comp_str3_stripped == "") + + var str4 = " \n mississippimississippi \n ".as_string_slice() + assert_true(str4.strip(" ") == "\n mississippimississippi \n") + assert_true(str4.strip("\nmip ") == "ssissippimississ") + + alias comp_str4_stripped = ( + " \n mississippimississippi \n ".as_string_slice().strip(" ") + ) + assert_true(comp_str4_stripped == "\n mississippimississippi \n") + + def main(): test_string_literal_byte_span() test_string_byte_span() @@ -505,3 +601,6 @@ def main(): test_combination_10_good_10_bad_utf8_sequences() test_count_utf8_continuation_bytes() test_splitlines() + test_rstrip() + test_lstrip() + test_strip() From 4598da1d9f8d6bcb1c5753be181f88f3addf773f Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 4 Dec 2024 20:52:56 -0800 Subject: [PATCH 036/509] [Stdlib] Make time functions return the value as UInt MODULAR_ORIG_COMMIT_REV_ID: 55cac07ab34491dca8faf99109c5acaa8e8fbef5 --- stdlib/src/time/time.mojo | 36 ++++++++++++++++----------------- stdlib/test/time/test_time.mojo | 4 ++-- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 936ca89459..7364d85324 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -47,7 +47,7 @@ alias _CLOCK_MONOTONIC_RAW = 4 # Constants alias _NSEC_PER_USEC = 1000 -alias _NSEC_PER_MSEC = 1000000 +alias _NSEC_PER_MSEC = 1_000_000 alias _USEC_PER_MSEC = 1000 alias _MSEC_PER_SEC = 1000 alias _NSEC_PER_SEC = _NSEC_PER_USEC * _USEC_PER_MSEC * _MSEC_PER_SEC @@ -69,7 +69,7 @@ struct _CTimeSpec(Stringable): self.tv_sec = 0 self.tv_subsec = 0 - fn as_nanoseconds(self) -> Int: + fn as_nanoseconds(self) -> UInt: @parameter if os_is_linux(): return self.tv_sec * _NSEC_PER_SEC + self.tv_subsec @@ -91,7 +91,7 @@ struct _FILETIME: self.dwLowDateTime = 0 self.dwHighDateTime = 0 - fn as_nanoseconds(self) -> Int: + fn as_nanoseconds(self) -> UInt: # AFTER subtracting windows offset the return value fits in a signed int64 # BEFORE subtracting windows offset the return value does not fit in a signed int64 # Taken from https://github.com/microsoft/STL/blob/c8d1efb6d504f6392acf8f6d01fd703f7c8826c0/stl/src/xtime.cpp#L50 @@ -116,7 +116,7 @@ fn _clock_gettime(clockid: Int) -> _CTimeSpec: @always_inline -fn _gettime_as_nsec_unix(clockid: Int) -> Int: +fn _gettime_as_nsec_unix(clockid: Int) -> UInt: if os_is_linux(): var ts = _clock_gettime(clockid) return ts.as_nanoseconds() @@ -127,20 +127,20 @@ fn _gettime_as_nsec_unix(clockid: Int) -> Int: @always_inline -fn _gpu_clock() -> Int: +fn _gpu_clock() -> UInt: """Returns a 64-bit unsigned cycle counter.""" alias asm = "llvm.nvvm.read.ptx.sreg.clock64" if is_nvidia_gpu() else "llvm.amdgcn.s.memtime" return int(llvm_intrinsic[asm, Int64]()) @always_inline -fn _realtime_nanoseconds() -> Int: +fn _realtime_nanoseconds() -> UInt: """Returns the current realtime time in nanoseconds""" return _gettime_as_nsec_unix(_CLOCK_REALTIME) @always_inline -fn _monotonic_nanoseconds() -> Int: +fn _monotonic_nanoseconds() -> UInt: """Returns the current monotonic time in nanoseconds""" @parameter @@ -158,20 +158,20 @@ fn _monotonic_nanoseconds() -> Int: @always_inline -fn _monotonic_raw_nanoseconds() -> Int: +fn _monotonic_raw_nanoseconds() -> UInt: """Returns the current monotonic time in nanoseconds""" return _gettime_as_nsec_unix(_CLOCK_MONOTONIC_RAW) @always_inline -fn _process_cputime_nanoseconds() -> Int: +fn _process_cputime_nanoseconds() -> UInt: """Returns the high-resolution per-process timer from the CPU""" return _gettime_as_nsec_unix(_CLOCK_PROCESS_CPUTIME_ID) @always_inline -fn _thread_cputime_nanoseconds() -> Int: +fn _thread_cputime_nanoseconds() -> UInt: """Returns the thread-specific CPU-time clock""" return _gettime_as_nsec_unix(_CLOCK_THREAD_CPUTIME_ID) @@ -202,7 +202,7 @@ fn perf_counter() -> Float64: @always_inline -fn perf_counter_ns() -> Int: +fn perf_counter_ns() -> UInt: """Return the value (in nanoseconds) of a performance counter, i.e. a clock with the highest available resolution to measure a short duration. It does include time elapsed during sleep and is system-wide. The reference @@ -221,7 +221,7 @@ fn perf_counter_ns() -> Int: @always_inline -fn now() -> Int: +fn now() -> UInt: """Deprecated: Please use time.perf_counter_ns instead. Returns the current monotonic time time in nanoseconds. This function @@ -241,7 +241,7 @@ fn now() -> Int: @always_inline -fn monotonic() -> Int: +fn monotonic() -> UInt: """ Returns the current monotonic time time in nanoseconds. This function queries the current platform's monotonic clock, making it useful for @@ -263,7 +263,7 @@ fn monotonic() -> Int: @parameter fn _time_function_windows[ func: fn () raises capturing [_] -> None -]() raises -> Int: +]() raises -> UInt: """Calculates elapsed time in Windows""" var ticks_per_sec: _WINDOWS_LARGE_INTEGER = 0 @@ -294,7 +294,7 @@ fn _time_function_windows[ @always_inline @parameter -fn time_function[func: fn () raises capturing [_] -> None]() raises -> Int: +fn time_function[func: fn () raises capturing [_] -> None]() raises -> UInt: """Measures the time spent in the function. Parameters: @@ -316,7 +316,7 @@ fn time_function[func: fn () raises capturing [_] -> None]() raises -> Int: @always_inline @parameter -fn time_function[func: fn () capturing [_] -> None]() -> Int: +fn time_function[func: fn () capturing [_] -> None]() -> UInt: """Measures the time spent in the function. Parameters: @@ -333,7 +333,7 @@ fn time_function[func: fn () capturing [_] -> None]() -> Int: try: return time_function[raising_func]() except err: - return abort[Int](err) + return abort[UInt](err) # ===-----------------------------------------------------------------------===# @@ -369,7 +369,7 @@ fn sleep(sec: Float64): _ = rem -fn sleep(sec: Int): +fn sleep(sec: UInt): """Suspends the current thread for the seconds specified. Args: diff --git a/stdlib/test/time/test_time.mojo b/stdlib/test/time/test_time.mojo index 985a0ec9f6..342e453774 100644 --- a/stdlib/test/time/test_time.mojo +++ b/stdlib/test/time/test_time.mojo @@ -28,7 +28,7 @@ from testing import assert_true @always_inline @parameter fn time_me(): - sleep(1) + sleep(1.0) @always_inline @@ -50,7 +50,7 @@ fn time_templated_function[ fn time_capturing_function(iters: Int) -> Int: @parameter fn time_fn(): - sleep(1) + sleep(1.0) return time_function[time_fn]() From 1f1151c33b47d5565f52e635563d78d8c451c558 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 4 Dec 2024 23:43:34 -0800 Subject: [PATCH 037/509] [Stdlib] Add string overload for StringLiteral.get, NFC MODULAR_ORIG_COMMIT_REV_ID: 2aa197a5b091954efe63da52515e1aade752ed2a --- stdlib/src/builtin/string_literal.mojo | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index dc7d05d49a..7b1598b481 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -106,6 +106,19 @@ struct StringLiteral( `> : !kgen.string`, ] + @always_inline("nodebug") + @staticmethod + fn get[value: String]() -> StringLiteral: + """Form a string literal from an arbitrary compile-time String value. + + Parameters: + value: The value to convert to StringLiteral. + + Returns: + The string value as a StringLiteral. + """ + return Self._from_string[value]() + @always_inline("nodebug") @staticmethod fn get[type: Stringable, //, value: type]() -> StringLiteral: From b0458f998d70ead300c055aa56515df917bd0fc9 Mon Sep 17 00:00:00 2001 From: Austin Doolittle Date: Thu, 5 Dec 2024 08:22:01 -0800 Subject: [PATCH 038/509] [******] Extend math.min to support boolean Extends our min function to support boolean values MODULAR_ORIG_COMMIT_REV_ID: 141e7e80532f1ad91a113d001ee2a185855de2b1 --- stdlib/src/builtin/math.mojo | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index eb1b088242..7293e16db2 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -236,7 +236,7 @@ fn min(x: SIMD, y: __type_of(x), /) -> __type_of(x): corresponding elements in x and y. Constraints: - The type of the inputs must be numeric. + The type of the inputs must be numeric or boolean. Args: x: First SIMD vector. @@ -245,8 +245,16 @@ fn min(x: SIMD, y: __type_of(x), /) -> __type_of(x): Returns: A SIMD vector containing the elementwise minimum of x and y. """ - constrained[x.type.is_numeric(), "the SIMD type must be numeric"]() - return __mlir_op.`pop.min`(x.value, y.value) + + @parameter + if x.type is DType.bool: + return min(x.cast[DType.uint8](), y.cast[DType.uint8]()).cast[x.type]() + else: + constrained[ + x.type.is_numeric(), "the SIMD type must be numeric or boolean" + ]() + + return __mlir_op.`pop.min`(x.value, y.value) # ===----------------------------------------------------------------------=== # From d3fb32dc0c3836ddd7c74a10eb7a23eae7ac488a Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Thu, 5 Dec 2024 09:46:11 -0700 Subject: [PATCH 039/509] [stdlib] Move `Span` from `utils` to `memory` `Span` is no longer a util, but is a common vocabulary type used throughout stdlib APIs. As such, promote it to `memory` module. MODULAR_ORIG_COMMIT_REV_ID: 33bae4c7dcc8191118d669985405f31599de386c --- docs/changelog.md | 4 +++- stdlib/src/builtin/_format_float.mojo | 4 ++-- stdlib/src/builtin/debug_assert.mojo | 3 +-- stdlib/src/builtin/file.mojo | 4 ++-- stdlib/src/builtin/file_descriptor.mojo | 4 +--- stdlib/src/builtin/io.mojo | 1 - stdlib/src/builtin/reversed.mojo | 2 +- stdlib/src/builtin/simd.mojo | 4 ++-- stdlib/src/builtin/sort.mojo | 4 +--- stdlib/src/builtin/string_literal.mojo | 4 ++-- stdlib/src/collections/list.mojo | 4 +--- stdlib/src/collections/string.mojo | 3 +-- stdlib/src/math/math.mojo | 3 +-- stdlib/src/memory/__init__.mojo | 1 + stdlib/src/{utils => memory}/span.mojo | 4 ++-- stdlib/src/os/path/path.mojo | 3 ++- stdlib/src/prelude/__init__.mojo | 3 ++- stdlib/src/tempfile/tempfile.mojo | 3 ++- stdlib/src/utils/__init__.mojo | 1 - stdlib/src/utils/_utf8_validation.mojo | 2 +- stdlib/src/utils/inline_string.mojo | 2 +- stdlib/src/utils/string_slice.mojo | 3 +-- stdlib/src/utils/stringref.mojo | 2 +- stdlib/src/utils/write.mojo | 7 +++---- stdlib/test/builtin/test_sort_issue_1018.mojo | 4 +--- stdlib/test/collections/test_list.mojo | 4 +--- stdlib/test/hashlib/test_ahash.mojo | 4 +--- stdlib/test/{utils => memory}/test_span.mojo | 4 +--- stdlib/test/utils/test_string_slice.mojo | 3 ++- 29 files changed, 40 insertions(+), 54 deletions(-) rename stdlib/src/{utils => memory}/span.mojo (99%) rename stdlib/test/{utils => memory}/test_span.mojo (98%) diff --git a/docs/changelog.md b/docs/changelog.md index 788d1e9c95..d5ab2b45a1 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -363,7 +363,7 @@ what we publish. `String.write`. Here's an example of using all the changes: ```mojo - from utils import Span + from memory import Span @value struct NewString(Writer, Writable): @@ -582,6 +582,8 @@ what we publish. if 1 in x: print("1 in x") +- `Span` has moved from the `utils` module to the `memory` module. + ### ❌ Removed - The `UnsafePointer.bitcast` overload for `DType` has been removed. Wrap your diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index 4c5fc6797e..95d7e8a30e 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -28,9 +28,9 @@ from math import log2 from sys.info import sizeof from builtin.io import _printf -from memory import bitcast +from memory import bitcast, Span -from utils import Span, StaticTuple +from utils import StaticTuple from utils.numerics import FPUtils, isinf, isnan diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index 4d0f1bb23a..e66c968189 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -23,9 +23,8 @@ from sys.ffi import c_char, c_size_t, c_uint, external_call from sys.param_env import env_get_string from builtin._location import __call_location, _SourceLocation -from memory import UnsafePointer +from memory import UnsafePointer, Span -from utils import Span from utils.write import ( _ArgBytes, _WriteBufferHeap, diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index 823cbf9348..f5f4e6e5f6 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -35,9 +35,9 @@ from os import PathLike, abort from sys import external_call, sizeof from sys.ffi import OpaquePointer -from memory import AddressSpace, UnsafePointer +from memory import AddressSpace, UnsafePointer, Span -from utils import Span, StringRef, StringSlice, write_buffered +from utils import StringRef, StringSlice, write_buffered @register_passable diff --git a/stdlib/src/builtin/file_descriptor.mojo b/stdlib/src/builtin/file_descriptor.mojo index 8f7709d027..fef115e471 100644 --- a/stdlib/src/builtin/file_descriptor.mojo +++ b/stdlib/src/builtin/file_descriptor.mojo @@ -27,9 +27,7 @@ from sys.ffi import external_call from sys.info import is_gpu from builtin.io import _printf -from memory import UnsafePointer - -from utils import Span +from memory import UnsafePointer, Span @value diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index c0b55f993e..bb15718f20 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -33,7 +33,6 @@ from builtin.file_descriptor import FileDescriptor from memory import UnsafePointer, memcpy from utils import ( - Span, StaticString, StringRef, StringSlice, diff --git a/stdlib/src/builtin/reversed.mojo b/stdlib/src/builtin/reversed.mojo index e01947a3fb..9e1d778299 100644 --- a/stdlib/src/builtin/reversed.mojo +++ b/stdlib/src/builtin/reversed.mojo @@ -19,7 +19,7 @@ from collections import Deque, Dict from collections.deque import _DequeIter from collections.dict import _DictEntryIter, _DictKeyIter, _DictValueIter from collections.list import _ListIter -from utils.span import Span, _SpanIter +from memory.span import Span, _SpanIter from .range import _StridedRange diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 350eda13d0..2471fbf58a 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -50,9 +50,9 @@ from builtin.dtype import _uint_type_of_width from builtin.format_int import _try_write_int from builtin.io import _snprintf from documentation import doc_private -from memory import UnsafePointer, bitcast +from memory import UnsafePointer, bitcast, Span -from utils import IndexList, Span, StaticTuple, StringSlice +from utils import IndexList, StaticTuple, StringSlice from utils._visualizers import lldb_formatter_wrapping_type from utils.numerics import FPUtils from utils.numerics import isnan as _isnan diff --git a/stdlib/src/builtin/sort.mojo b/stdlib/src/builtin/sort.mojo index 78447a82fb..23429d9b4f 100644 --- a/stdlib/src/builtin/sort.mojo +++ b/stdlib/src/builtin/sort.mojo @@ -20,9 +20,7 @@ from math import ceil from sys import bitwidthof from bit import count_leading_zeros -from memory import UnsafePointer - -from utils import Span +from memory import UnsafePointer, Span # ===-----------------------------------------------------------------------===# # sort diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 7b1598b481..377173470f 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -19,9 +19,9 @@ from collections import List from hashlib._hasher import _HashableWithHasher, _Hasher from sys.ffi import c_char -from memory import UnsafePointer, memcpy +from memory import UnsafePointer, memcpy, Span -from utils import Span, StaticString, StringRef, StringSlice, Writable, Writer +from utils import StaticString, StringRef, StringSlice, Writable, Writer from utils._visualizers import lldb_formatter_wrapping_type from utils.format import _CurlyEntryFormattable, _FormatCurlyEntry from utils.string_slice import _StringSliceIter, _to_string_list diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 033ceeb464..13ee261638 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -24,9 +24,7 @@ from os import abort from sys import sizeof from sys.intrinsics import _type_is_eq -from memory import Pointer, UnsafePointer, memcpy - -from utils import Span +from memory import Pointer, UnsafePointer, memcpy, Span from .optional import Optional diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 47f06707c2..90ed9b5cd1 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -23,12 +23,11 @@ from sys.ffi import c_char from sys.intrinsics import _type_is_eq from bit import count_leading_zeros -from memory import UnsafePointer, memcmp, memcpy +from memory import UnsafePointer, memcmp, memcpy, Span from python import PythonObject from utils import ( IndexList, - Span, StaticString, StringRef, StringSlice, diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 7b998c7220..9d5f53e0e4 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -36,9 +36,8 @@ from sys.info import _current_arch from bit import count_trailing_zeros from builtin.dtype import _integral_type_of from builtin.simd import _modf, _simd_apply -from memory import UnsafePointer +from memory import UnsafePointer, Span -from utils import Span from utils.index import IndexList from utils.numerics import FPUtils, isnan, nan from utils.static_tuple import StaticTuple diff --git a/stdlib/src/memory/__init__.mojo b/stdlib/src/memory/__init__.mojo index b2faf4890a..2717a5127a 100644 --- a/stdlib/src/memory/__init__.mojo +++ b/stdlib/src/memory/__init__.mojo @@ -16,5 +16,6 @@ from .arc import ArcPointer from .memory import memcmp, memcpy, memset, memset_zero, stack_allocation from .owned_pointer import OwnedPointer from .pointer import AddressSpace, Pointer +from .span import AsBytes, Span from .unsafe import bitcast, pack_bits from .unsafe_pointer import UnsafePointer diff --git a/stdlib/src/utils/span.mojo b/stdlib/src/memory/span.mojo similarity index 99% rename from stdlib/src/utils/span.mojo rename to stdlib/src/memory/span.mojo index 5aa2fb4cf7..cc5ebbb298 100644 --- a/stdlib/src/utils/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -13,10 +13,10 @@ """Implements the Span type. -You can import these APIs from the `utils.span` module. For example: +You can import these APIs from the `memory` module. For example: ```mojo -from utils import Span +from memory import Span ``` """ diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 64edffce5e..4e65dfbf34 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -24,7 +24,8 @@ from pwd import getpwuid from stat import S_ISDIR, S_ISLNK, S_ISREG from sys import has_neon, os_is_linux, os_is_macos, os_is_windows -from utils import Span, StringSlice +from memory import Span +from utils import StringSlice from .. import PathLike from .._linux_aarch64 import _lstat as _lstat_linux_arm diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 5551b826ee..3cb066f0d4 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -136,4 +136,5 @@ from builtin.value import ( from documentation import doc_private from memory import AddressSpace, Pointer -from utils import AsBytes, Writable, Writer +from memory.span import AsBytes +from utils import Writable, Writer diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index 509c234ac9..48ccb1ddcd 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -24,7 +24,8 @@ import sys from collections import List, Optional from pathlib import Path -from utils import Span, write_buffered +from memory import Span +from utils import write_buffered alias TMP_MAX = 10_000 diff --git a/stdlib/src/utils/__init__.mojo b/stdlib/src/utils/__init__.mojo index 0f3b95ced0..fa6129cc1c 100644 --- a/stdlib/src/utils/__init__.mojo +++ b/stdlib/src/utils/__init__.mojo @@ -16,7 +16,6 @@ from .index import Index, IndexList, product from .inline_string import InlineString from .lock import BlockingScopedLock, BlockingSpinLock, SpinWaiter from .loop import unroll -from .span import AsBytes, Span from .static_tuple import StaticTuple from .string_slice import StaticString, StringSlice from .stringref import StringRef diff --git a/stdlib/src/utils/_utf8_validation.mojo b/stdlib/src/utils/_utf8_validation.mojo index e7ca5a5e3b..eb514733ca 100644 --- a/stdlib/src/utils/_utf8_validation.mojo +++ b/stdlib/src/utils/_utf8_validation.mojo @@ -28,7 +28,7 @@ https://github.com/simdutf/SimdUnicode/blob/main/src/UTF8.cs from base64._b64encode import _sub_with_saturation from sys.intrinsics import llvm_intrinsic -from memory import UnsafePointer +from memory import UnsafePointer, Span alias TOO_SHORT: UInt8 = 1 << 0 alias TOO_LONG: UInt8 = 1 << 1 diff --git a/stdlib/src/utils/inline_string.mojo b/stdlib/src/utils/inline_string.mojo index add0e6c7cc..8c6cfb3166 100644 --- a/stdlib/src/utils/inline_string.mojo +++ b/stdlib/src/utils/inline_string.mojo @@ -19,7 +19,7 @@ from collections import InlineArray, Optional from os import abort from sys import sizeof -from memory import UnsafePointer, memcpy +from memory import UnsafePointer, memcpy, Span from utils import StringSlice, Variant diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index b6f9daa45a..504b058954 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -27,10 +27,9 @@ from sys import bitwidthof, simdwidthof from sys.intrinsics import unlikely from bit import count_leading_zeros -from memory import UnsafePointer, memcmp, memcpy +from memory import UnsafePointer, memcmp, memcpy, Span from memory.memory import _memcmp_impl_unconstrained -from utils import Span from utils.format import _CurlyEntryFormattable, _FormatCurlyEntry from ._utf8_validation import _is_valid_utf8 diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index eaf6a285e3..75e864b405 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -20,7 +20,7 @@ from sys.ffi import c_char from bit import count_trailing_zeros from builtin.dtype import _uint_type_of_width -from memory import UnsafePointer, memcmp, pack_bits +from memory import UnsafePointer, memcmp, pack_bits, Span from memory.memory import _memcmp_impl_unconstrained from utils import StringSlice diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index 4d53f23afd..6d68951b9b 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -15,10 +15,9 @@ from collections import InlineArray from sys.info import is_gpu -from builtin.io import _printf -from memory import UnsafePointer, memcpy +from memory import UnsafePointer, memcpy, Span -from utils import Span, StaticString +from utils import StaticString # ===-----------------------------------------------------------------------===# @@ -36,7 +35,7 @@ trait Writer: Example: ```mojo - from utils import Span + from memory import Span @value struct NewString(Writer, Writable): diff --git a/stdlib/test/builtin/test_sort_issue_1018.mojo b/stdlib/test/builtin/test_sort_issue_1018.mojo index e8cfb4e1f0..af50f5b896 100644 --- a/stdlib/test/builtin/test_sort_issue_1018.mojo +++ b/stdlib/test/builtin/test_sort_issue_1018.mojo @@ -15,9 +15,7 @@ from random import rand -from memory import UnsafePointer - -from utils import Span +from memory import UnsafePointer, Span fn sort_test[D: DType, name: StringLiteral](size: Int, max: Int) raises: diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index cd173fc66c..1def6849ea 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -15,12 +15,10 @@ from collections import List from sys.info import sizeof -from memory import UnsafePointer +from memory import UnsafePointer, Span from test_utils import CopyCounter, MoveCounter from testing import assert_equal, assert_false, assert_raises, assert_true -from utils import Span - def test_mojo_issue_698(): var list = List[Float64]() diff --git a/stdlib/test/hashlib/test_ahash.mojo b/stdlib/test/hashlib/test_ahash.mojo index e696b32c76..4545e947cb 100644 --- a/stdlib/test/hashlib/test_ahash.mojo +++ b/stdlib/test/hashlib/test_ahash.mojo @@ -19,11 +19,9 @@ from time import now from bit import pop_count from builtin._location import __call_location -from memory import memset_zero, stack_allocation +from memory import memset_zero, stack_allocation, Span from testing import assert_equal, assert_not_equal, assert_true -from utils import Span - # Source: https://www.101languages.net/arabic/most-common-arabic-words/ alias words_ar = """ لا, من, هذا, أن, في, أنا, على, ما, هل, diff --git a/stdlib/test/utils/test_span.mojo b/stdlib/test/memory/test_span.mojo similarity index 98% rename from stdlib/test/utils/test_span.mojo rename to stdlib/test/memory/test_span.mojo index a666788354..b8bde201e9 100644 --- a/stdlib/test/utils/test_span.mojo +++ b/stdlib/test/memory/test_span.mojo @@ -14,11 +14,9 @@ from collections import InlineArray, List -from memory import UnsafePointer +from memory import UnsafePointer, Span from testing import assert_equal, assert_true -from utils import Span - def test_span_list_int(): var l = List[Int](1, 2, 3, 4, 5, 6, 7) diff --git a/stdlib/test/utils/test_string_slice.mojo b/stdlib/test/utils/test_string_slice.mojo index 7ef357692b..dfeb37b12f 100644 --- a/stdlib/test/utils/test_string_slice.mojo +++ b/stdlib/test/utils/test_string_slice.mojo @@ -14,7 +14,8 @@ from testing import assert_equal, assert_false, assert_true -from utils import Span, StringSlice +from memory import Span +from utils import StringSlice from utils._utf8_validation import _is_valid_utf8 from utils.string_slice import _count_utf8_continuation_bytes From daef3ce726ea0ad8ac022c3ee900f486b45afcd4 Mon Sep 17 00:00:00 2001 From: Austin Doolittle Date: Thu, 5 Dec 2024 08:52:40 -0800 Subject: [PATCH 040/509] [******] Extend math.max to support boolean Extends our max function to support boolean values MODULAR_ORIG_COMMIT_REV_ID: 51703fa8024d127eadd538055fee920053aafe11 --- stdlib/src/builtin/math.mojo | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 7293e16db2..428407f857 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -182,7 +182,7 @@ fn max(x: SIMD, y: __type_of(x), /) -> __type_of(x): corresponding elements in x and y. Constraints: - The type of the inputs must be numeric. + The type of the inputs must be numeric or boolean. Args: x: First SIMD vector. @@ -191,8 +191,16 @@ fn max(x: SIMD, y: __type_of(x), /) -> __type_of(x): Returns: A SIMD vector containing the elementwise maximum of x and y. """ - constrained[x.type.is_numeric(), "the SIMD type must be numeric"]() - return __mlir_op.`pop.max`(x.value, y.value) + + @parameter + if x.type is DType.bool: + return max(x.cast[DType.uint8](), y.cast[DType.uint8]()).cast[x.type]() + else: + constrained[ + x.type.is_numeric(), "the SIMD type must be numeric or boolean" + ]() + + return __mlir_op.`pop.max`(x.value, y.value) # ===----------------------------------------------------------------------=== # From 71b098fdd818724b0a175b9479dd11d8e5d96154 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 5 Dec 2024 13:47:55 -0600 Subject: [PATCH 041/509] [stdlib] cleanup: Remove `_lit_origin_union`; use `__origin_of(a, b)` instead This is a low-level operation that was only used in one place in practice. With the recent change to support using `__origin_of(a, b)` as a way to form origin unions, `_lit_origin_union` is no longer strictly necessary, so let's remove it. MODULAR_ORIG_COMMIT_REV_ID: 3128b8776cea534b0b58525368f2865805b57237 --- stdlib/src/builtin/builtin_list.mojo | 31 ++++++---------------------- 1 file changed, 6 insertions(+), 25 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index d736c55aa6..e74b93cad3 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -265,24 +265,6 @@ struct _VariadicListMemIter[ return len(self.src[]) - self.index -# Helper to compute the union of two origins: -# TODO: parametric aliases would be nice. -struct _lit_origin_union[ - is_mutable: Bool, //, - a: Origin[is_mutable], - b: Origin[is_mutable], -]: - alias result = __mlir_attr[ - `#lit.origin.union<`, - a._mlir_origin, - `,`, - b._mlir_origin, - `> : !lit.origin<`, - is_mutable.value, - `>`, - ] - - struct VariadicListMem[ elt_is_mutable: Bool, //, element_type: AnyType, @@ -424,13 +406,12 @@ struct VariadicListMem[ fn __getitem__( self, idx: Int ) -> ref [ - _lit_origin_union[ - origin, - # cast mutability of self to match the mutability of the element, - # since that is what we want to use in the ultimate reference and - # the union overall doesn't matter. - Origin[elt_is_mutable].cast_from[__origin_of(self)].result, - ].result + # cast mutability of self to match the mutability of the element, + # since that is what we want to use in the ultimate reference and + # the union overall doesn't matter. + Origin[elt_is_mutable] + .cast_from[__origin_of(origin, self)] + .result ] element_type: """Gets a single element on the variadic list. From 760b991898e4efaf75366a9986d17486d886f021 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Thu, 5 Dec 2024 17:42:27 -0500 Subject: [PATCH 042/509] [stdlib] Revert negative step slicing on Span. (#52196) MODULAR_ORIG_COMMIT_REV_ID: 0a0c51d90464c4cceeda1896f20f16e757cfd2bf --- stdlib/src/memory/span.mojo | 13 +++---------- stdlib/test/memory/test_span.mojo | 13 ------------- 2 files changed, 3 insertions(+), 23 deletions(-) diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index cc5ebbb298..497a084c88 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -205,16 +205,9 @@ struct Span[ var step: Int start, end, step = slc.indices(len(self)) - if step < 0: - step = -step - var new_len = (start - end + step - 1) // step - var buff = UnsafePointer[T].alloc(new_len) - i = 0 - while start > end: - buff[i] = self._data[start] - start -= step - i += 1 - return Span[T, origin](ptr=buff, length=new_len) + debug_assert( + step == 1, "Slice must be within bounds and step must be 1" + ) var res = Self( ptr=(self._data + start), length=len(range(start, end, step)) diff --git a/stdlib/test/memory/test_span.mojo b/stdlib/test/memory/test_span.mojo index b8bde201e9..92c49210c6 100644 --- a/stdlib/test/memory/test_span.mojo +++ b/stdlib/test/memory/test_span.mojo @@ -136,19 +136,6 @@ def test_span_slice(): assert_equal(res[0], 2) assert_equal(res[1], 3) assert_equal(res[2], 4) - # Test slicing with negative step - res = s[1::-1] - assert_equal(res[0], 2) - assert_equal(res[1], 1) - res.unsafe_ptr().free() - res = s[2:1:-1] - assert_equal(res[0], 3) - assert_equal(len(res), 1) - res.unsafe_ptr().free() - res = s[5:1:-2] - assert_equal(res[0], 5) - assert_equal(res[1], 3) - res.unsafe_ptr().free() def test_copy_from(): From 6acac62142cd570cbda3e9ed667acc74ff5f34d7 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 5 Dec 2024 17:26:09 -0600 Subject: [PATCH 043/509] [stdlib] cleanup: Remove `Stringlike` in favor of using `StringSlice` The requirements of `Stringlike` are largely the same as what `StringSlice` provides. So it's simpler and avoids extra monomorphizations if we make this code concrete instead of generic on `Stringlike`. `StringSlice` is already the common denominator of all the string types. MODULAR_ORIG_COMMIT_REV_ID: cf12cfa3455f00b1e0f06431856086a0e3a4f6bc --- stdlib/src/utils/format.mojo | 18 +++++++----------- stdlib/src/utils/string_slice.mojo | 24 ------------------------ 2 files changed, 7 insertions(+), 35 deletions(-) diff --git a/stdlib/src/utils/format.mojo b/stdlib/src/utils/format.mojo index 5c1dc2cf8b..752a34898d 100644 --- a/stdlib/src/utils/format.mojo +++ b/stdlib/src/utils/format.mojo @@ -16,8 +16,6 @@ from collections import Optional from memory import UnsafePointer -from utils.string_slice import Stringlike - # TODO: _FormatCurlyEntry and _FormatSpec should be public in the future for # people who want to write their own templating engines. This is not yet done # because the implementation is incomplete and we are missing crucial features. @@ -36,7 +34,7 @@ from utils.string_slice import Stringlike # a trait that all format specifications conform to) @value struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): - """The struct that handles `Stringlike` formatting by curly braces entries. + """The struct that handles string formatting by curly braces entries. This is internal for the types: `String`, `StringLiteral` and `StringSlice`. """ @@ -141,7 +139,7 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): return self.field.isa[Int]() @staticmethod - fn format[T: Stringlike](fmt_src: T, args: Self._args_t) raises -> String: + fn format(fmt_src: StringSlice, args: Self._args_t) raises -> String: """Format the entries. Args: @@ -177,9 +175,9 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): return res^ @staticmethod - fn _create_entries[ - T: Stringlike - ](fmt_src: T, len_pos_args: Int) raises -> (List[Self], Int): + fn _create_entries( + fmt_src: StringSlice, len_pos_args: Int + ) raises -> (List[Self], Int): """Returns a list of entries and its total estimated entry byte width. """ var manual_indexing_count = 0 @@ -264,11 +262,9 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): raise Error(l_err) return entries^, total_estimated_entry_byte_width - fn _handle_field_and_break[ - T: Stringlike - ]( + fn _handle_field_and_break( mut self, - fmt_src: T, + fmt_src: StringSlice, len_pos_args: Int, i: Int, start_value: Int, diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 504b058954..e572747d4e 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -1122,30 +1122,6 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( # ===-----------------------------------------------------------------------===# -trait Stringlike: - """Trait intended to be used only with `String`, `StringLiteral` and - `StringSlice`.""" - - fn byte_length(self) -> Int: - """Get the string length in bytes. - - Returns: - The length of this string in bytes. - - Notes: - This does not include the trailing null terminator in the count. - """ - ... - - fn unsafe_ptr(self) -> UnsafePointer[UInt8]: - """Get raw pointer to the underlying data. - - Returns: - The raw pointer to the data. - """ - ... - - fn _to_string_list[ T: CollectionElement, # TODO(MOCO-1446): Make `T` parameter inferred len_fn: fn (T) -> Int, From ccd2e8116545da11771b2bf0f65ac8a315e0ef9f Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Fri, 6 Dec 2024 05:51:58 -0800 Subject: [PATCH 044/509] [******][GPU] Rename has_nvidia/amd to include accelerator MODULAR_ORIG_COMMIT_REV_ID: 32397c88a4cd6f805e69ec1c5383564c9c76deb3 --- stdlib/src/sys/__init__.mojo | 4 ++-- stdlib/src/sys/info.mojo | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/stdlib/src/sys/__init__.mojo b/stdlib/src/sys/__init__.mojo index baf2af6fa7..e469d501b5 100644 --- a/stdlib/src/sys/__init__.mojo +++ b/stdlib/src/sys/__init__.mojo @@ -19,7 +19,7 @@ from .ffi import DEFAULT_RTLD, RTLD, DLHandle, external_call from .info import ( alignof, bitwidthof, - has_amd_gpu, + has_amd_gpu_accelerator, has_avx, has_avx2, has_avx512f, @@ -28,7 +28,7 @@ from .info import ( has_neon, has_neon_int8_dotprod, has_neon_int8_matmul, - has_nvidia_gpu, + has_nvidia_gpu_accelerator, has_sse4, has_vnni, is_amd_gpu, diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index ccd39c7ee2..3cb4b2787a 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -870,7 +870,7 @@ fn _macos_version() raises -> Tuple[Int, Int, Int]: @always_inline("nodebug") -fn has_amd_gpu() -> Bool: +fn has_amd_gpu_accelerator() -> Bool: """Returns True if the host system has an AMD GPU and False otherwise. Returns: @@ -880,7 +880,7 @@ fn has_amd_gpu() -> Bool: @always_inline("nodebug") -fn has_nvidia_gpu() -> Bool: +fn has_nvidia_gpu_accelerator() -> Bool: """Returns True if the host system has an NVIDIA GPU and False otherwise. Returns: From 1bebfaf99d20d4e201baa76006d08746417f02b7 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Fri, 6 Dec 2024 06:02:03 -0800 Subject: [PATCH 045/509] [Stdlib] Remove the time.now function MODULAR_ORIG_COMMIT_REV_ID: 189e758f6f3a147fad07afd382c09146a470a168 --- examples/reduce.mojo | 1 - stdlib/src/time/__init__.mojo | 1 - stdlib/src/time/time.mojo | 22 +--------------------- stdlib/test/hashlib/test_ahash.mojo | 1 - stdlib/test/time/test_time.mojo | 2 -- 5 files changed, 1 insertion(+), 26 deletions(-) diff --git a/examples/reduce.mojo b/examples/reduce.mojo index 8e213c6b86..9babfd91a0 100644 --- a/examples/reduce.mojo +++ b/examples/reduce.mojo @@ -17,7 +17,6 @@ # Reductions and scans are common algorithm patterns in parallel computing. from random import rand -from time import now from algorithm import sum from benchmark import Unit, benchmark, keep diff --git a/stdlib/src/time/__init__.mojo b/stdlib/src/time/__init__.mojo index 5d01e75fdf..6e5097653a 100644 --- a/stdlib/src/time/__init__.mojo +++ b/stdlib/src/time/__init__.mojo @@ -14,7 +14,6 @@ from .time import ( monotonic, - now, perf_counter, perf_counter_ns, sleep, diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 7364d85324..0167c26548 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -15,7 +15,7 @@ You can import these APIs from the `time` package. For example: ```mojo -from time import now +from time import perf_counter_ns ``` """ @@ -215,26 +215,6 @@ fn perf_counter_ns() -> UInt: return _monotonic_nanoseconds() -# ===-----------------------------------------------------------------------===# -# now -# ===-----------------------------------------------------------------------===# - - -@always_inline -fn now() -> UInt: - """Deprecated: Please use time.perf_counter_ns instead. - - Returns the current monotonic time time in nanoseconds. This function - queries the current platform's monotonic clock, making it useful for - measuring time differences, but the significance of the returned value - varies depending on the underlying implementation. - - Returns: - The current time in ns. - """ - return perf_counter_ns() - - # ===-----------------------------------------------------------------------===# # monotonic # ===-----------------------------------------------------------------------===# diff --git a/stdlib/test/hashlib/test_ahash.mojo b/stdlib/test/hashlib/test_ahash.mojo index 4545e947cb..c9f01c3a14 100644 --- a/stdlib/test/hashlib/test_ahash.mojo +++ b/stdlib/test/hashlib/test_ahash.mojo @@ -15,7 +15,6 @@ from hashlib._ahash import AHasher from hashlib._hasher import _hash_with_hasher as hash from hashlib.hash import hash as old_hash -from time import now from bit import pop_count from builtin._location import __call_location diff --git a/stdlib/test/time/test_time.mojo b/stdlib/test/time/test_time.mojo index 342e453774..0148bfeeef 100644 --- a/stdlib/test/time/test_time.mojo +++ b/stdlib/test/time/test_time.mojo @@ -15,7 +15,6 @@ from sys import os_is_windows from time import ( monotonic, - now, perf_counter, perf_counter_ns, sleep, @@ -60,7 +59,6 @@ fn test_time() raises: assert_true(perf_counter() > 0) assert_true(perf_counter_ns() > 0) - assert_true(now() > 0) assert_true(monotonic() > 0) var t1 = time_function[time_me]() From ce50e8af79b2959711c0c76d29eb149db55502ed Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Fri, 6 Dec 2024 06:25:21 -0800 Subject: [PATCH 046/509] [Stdlib] Add new has_accelerator helper function, NFC MODULAR_ORIG_COMMIT_REV_ID: dc9c60cda00ff29c9b6dc11d8fbabdccd146953e --- stdlib/src/sys/__init__.mojo | 1 + stdlib/src/sys/info.mojo | 10 ++++++++++ 2 files changed, 11 insertions(+) diff --git a/stdlib/src/sys/__init__.mojo b/stdlib/src/sys/__init__.mojo index e469d501b5..b0777f5c6e 100644 --- a/stdlib/src/sys/__init__.mojo +++ b/stdlib/src/sys/__init__.mojo @@ -19,6 +19,7 @@ from .ffi import DEFAULT_RTLD, RTLD, DLHandle, external_call from .info import ( alignof, bitwidthof, + has_accelerator, has_amd_gpu_accelerator, has_avx, has_avx2, diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index 3cb4b2787a..ee149ca72a 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -869,6 +869,16 @@ fn _macos_version() raises -> Tuple[Int, Int, Int]: # ===-----------------------------------------------------------------------===# +@always_inline("nodebug") +fn has_accelerator() -> Bool: + """Returns True if the host system has an accelerator and False otherwise. + + Returns: + True if the host system has an accelerator. + """ + return _accelerator_arch() != "" + + @always_inline("nodebug") fn has_amd_gpu_accelerator() -> Bool: """Returns True if the host system has an AMD GPU and False otherwise. From fd5cd6e79d483025b2f7342572c4cbdfb5d79561 Mon Sep 17 00:00:00 2001 From: modularbot Date: Fri, 6 Dec 2024 17:36:42 +0000 Subject: [PATCH 047/509] Update lockfiles to point to latest nightly version: 24.6.0.dev2024120616 --- examples/life/magic.lock | 1513 +++++++++++++------------- examples/magic.lock | 1490 ++++++++++++-------------- examples/notebooks/magic.lock | 1886 +++++++++++++++------------------ examples/operators/magic.lock | 1501 ++++++++++++-------------- magic.lock | 1501 ++++++++++++-------------- 5 files changed, 3556 insertions(+), 4335 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index c962798beb..8a7706a5c8 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -29,7 +29,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.1-hf42f96a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-hf42f96a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.6-h159bff8_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.449-h5558e3c_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.458-h5558e3c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.14.0-h5cfcd09_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda @@ -56,7 +56,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/email_validator-2.2.0-hd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.5-pyhff2d567_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.6-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-cli-0.0.5-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.16.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/fluidsynth-2.3.7-hd992666_0.conda @@ -69,7 +69,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.5.0-py312h66e93f0_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.10.0-pyhff2d567_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.10.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-he02047a_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-he02047a_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda @@ -82,7 +82,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyhd8ed1ab_1.conda - 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We can't quite yet remove the copy in the runtime index case of `setitem` which is still being investigated. While tracking this down, add an explicit test case for `StaticTuple.setitem` for compile-time index to make sure we didn't break anything. MODULAR_ORIG_COMMIT_REV_ID: 95818ecfb9845f7fcd410481c64894c4ab00f631 --- stdlib/src/utils/static_tuple.mojo | 4 +--- stdlib/test/utils/test_static_tuple.mojo | 4 ++++ 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index ff745bedec..0788b54b43 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -257,6 +257,4 @@ struct StaticTuple[element_type: AnyTrivialRegType, size: Int](Sized): val: The value to store. """ constrained[index < size]() - var tmp = self - _set_array_elem[index, size, Self.element_type](val, tmp.array) - self = tmp + _set_array_elem[index, size, Self.element_type](val, self.array) diff --git a/stdlib/test/utils/test_static_tuple.mojo b/stdlib/test/utils/test_static_tuple.mojo index 67f3fbc0e8..b2f46d55af 100644 --- a/stdlib/test/utils/test_static_tuple.mojo +++ b/stdlib/test/utils/test_static_tuple.mojo @@ -46,6 +46,10 @@ def test_setitem(): t[2] = 300 assert_equal(t[2], 300) + alias idx: Int = 0 + t.__setitem__[idx](400) + assert_equal(t[0], 400) + def main(): test_getitem() From 04cdc70bdfee882e0cb8f351d93e727b195ca5b3 Mon Sep 17 00:00:00 2001 From: Evan Ovadia Date: Fri, 6 Dec 2024 12:51:23 -0500 Subject: [PATCH 049/509] [mojo-lang] Adds __disable_del keyword, to destroy linear types. Adds __disable_del keyword, to allow the user to destroy `@explicit_destroy` types. All these names are temporary, we'll all figure those out soon. MODULAR_ORIG_COMMIT_REV_ID: 5005b11c4c3bdba25d096c5ffc300debebfd5523 --- docs/changelog.md | 4 ++++ stdlib/src/builtin/coroutine.mojo | 8 ++++---- stdlib/src/memory/owned_pointer.mojo | 4 ++-- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index d5ab2b45a1..02a1a13eef 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -298,6 +298,10 @@ what we publish. - `StringSlice` now implements `strip`, `rstrip`, and `lstrip`. +- Introduced the `@explicit_destroy` annotation, the `__disable_del` keyword, + the `UnknownDestructibility` trait, and the `ImplicitlyDestructible` keyword, + for the experimental explicitly destroyed types feature. + ### 🦋 Changed - The `inout` and `borrowed` argument conventions have been renamed to the `mut` diff --git a/stdlib/src/builtin/coroutine.mojo b/stdlib/src/builtin/coroutine.mojo index b1e7c4bd55..04688695c8 100644 --- a/stdlib/src/builtin/coroutine.mojo +++ b/stdlib/src/builtin/coroutine.mojo @@ -134,7 +134,7 @@ struct Coroutine[type: AnyType, origins: OriginSet]: fn force_destroy(owned self): """Destroy the coroutine object.""" __mlir_op.`co.destroy`(self._handle) - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(self)) + __disable_del self @always_inline fn __await__(owned self) -> type as out: @@ -147,7 +147,7 @@ struct Coroutine[type: AnyType, origins: OriginSet]: # Black magic! Internal implementation detail! # Don't you dare copy this code! 😤 var handle = self._handle - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(self)) + __disable_del self __mlir_op.`co.await`[_type=NoneType]( handle, __mlir_op.`lit.ref.to_pointer`(__get_mvalue_as_litref(out)), @@ -217,7 +217,7 @@ struct RaisingCoroutine[type: AnyType, origins: OriginSet]: fn force_destroy(owned self): """Destroy the coroutine object.""" __mlir_op.`co.destroy`(self._handle) - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(self)) + __disable_del self @always_inline fn __await__(owned self) raises -> type as out: @@ -230,7 +230,7 @@ struct RaisingCoroutine[type: AnyType, origins: OriginSet]: # Black magic! Internal implementation detail! # Don't you dare copy this code! 😤 var handle = self._handle - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(self)) + __disable_del self if __mlir_op.`co.await`[_type = __mlir_type.i1]( handle, __mlir_op.`lit.ref.to_pointer`(__get_mvalue_as_litref(out)), diff --git a/stdlib/src/memory/owned_pointer.mojo b/stdlib/src/memory/owned_pointer.mojo index 5d1ddb367d..4dd473023c 100644 --- a/stdlib/src/memory/owned_pointer.mojo +++ b/stdlib/src/memory/owned_pointer.mojo @@ -148,7 +148,7 @@ struct OwnedPointer[T: AnyType]: """ var r = self._inner.take_pointee() self._inner.free() - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(self)) + __disable_del self return r^ @@ -171,6 +171,6 @@ struct OwnedPointer[T: AnyType]: var ptr = self._inner # Prevent the destructor from running on `self` - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(self)) + __disable_del self return ptr From db30bf465cbbf412fa3c17bb33371c8a4c6faaea Mon Sep 17 00:00:00 2001 From: Evan Ovadia Date: Fri, 6 Dec 2024 15:11:58 -0500 Subject: [PATCH 050/509] Added associated types; we can now have aliases like `alias T: AnyType`, `alias N: Int`, etc. in a trait, and then specify them in structs that conform to that trait. MODULAR_ORIG_COMMIT_REV_ID: f1ca27c6c5a94c02424b841adf16ff1f3ea04dc9 --- docs/changelog.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 02a1a13eef..5398fd44c6 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -302,6 +302,10 @@ what we publish. the `UnknownDestructibility` trait, and the `ImplicitlyDestructible` keyword, for the experimental explicitly destroyed types feature. +- Added associated types; we can now have aliases like `alias T: AnyType`, + `alias N: Int`, etc. in a trait, and then specify them in structs that conform + to that trait. + ### 🦋 Changed - The `inout` and `borrowed` argument conventions have been renamed to the `mut` From f8c57ea939654b9c52c6007553a68fe5ea730378 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Fri, 6 Dec 2024 20:23:56 -0800 Subject: [PATCH 051/509] [Docs] Origin updates. Update Lifetimes, origins, and references page for origin improvements in in 24.6. MODULAR_ORIG_COMMIT_REV_ID: d40dc85d4c74ad922335489c0c569fcee95c15d1 --- docs/manual/values/lifetimes.mdx | 109 ++++++++++++++++++++++--------- 1 file changed, 78 insertions(+), 31 deletions(-) diff --git a/docs/manual/values/lifetimes.mdx b/docs/manual/values/lifetimes.mdx index 39c2402f43..5dc05d799a 100644 --- a/docs/manual/values/lifetimes.mdx +++ b/docs/manual/values/lifetimes.mdx @@ -80,23 +80,23 @@ struct to specify an origin with parametric mutability: struct ParametricRef[ is_mutable: Bool, //, - origin: Origin[is_mutable].type + origin: Origin[is_mutable] ]: pass ``` -Note that `Origin` *isn't an origin value*, it's a helper for specifying a +Note that `Origin` isn't an origin **value**, it's a helper for specifying a origin **type**. Origin types carry the mutability of a reference as a boolean parameter value, indicating whether the origin is mutable, immutable, or even with mutability depending on a parameter specified by the enclosing API. The `is_mutable` parameter here is an [infer-only -parameter](/mojo/manual/parameters/#infer-only-parameters). It's never -specified directly by the user, but always inferred from context. The -`origin` value is often inferred, as well. For example, the following code -creates a [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) to an existing -value, but doesn't need to specify an origin—the `origin` is inferred from -the variable passed in to the `address_of()` method. +parameter](/mojo/manual/parameters/#infer-only-parameters). It can't be passed +as a positional parameter—it's either inferred from context or specified by +keyword. The `origin` value is often inferred, as well. For example, the +following code creates a [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) to an +existing value, but doesn't need to specify an origin—the `origin` is inferred +from the variable passed in to the `address_of()` method. ```mojo from memory import Pointer @@ -114,15 +114,15 @@ A final type of origin value is an `OriginSet`. As the name suggests, an Most origin values are created by the compiler. As a developer, there are a few ways to specify origin values: -* Static origin. The `StaticConstantOrigin` - alias is an origin value representing immutable values that that last for the - duration of the program. String literal values have a `StaticConstantOrigin`. -* Derived origin. The `__origin_of()` magic function returns the origin associated - with the value (or values) passed in. -* Inferred origin. You can use inferred parameters to capture the origin - of a value passed in to a function. -* Wildcard origins. The `ImmutableAnyOrigin` and `MutableAnyOrigin` aliases - are special cases indicating a reference that might access any live value. +* Static origin. The `StaticConstantOrigin` alias is an origin value + representing immutable values that that last for the duration of the program. + String literal values have a `StaticConstantOrigin`. +* Derived origin. The `__origin_of()` magic function returns the origin + associated with the value (or values) passed in. +* Inferred origin. You can use inferred parameters to capture the origin of a + value passed in to a function. +* Wildcard origins. The `ImmutableAnyOrigin` and `MutableAnyOrigin` aliases are + special cases indicating a reference that might access any live value. #### Static origins @@ -138,8 +138,15 @@ static origin. #### Derived origins -Use the `__origin_of(value)` operator to obtain a value's origin. The -argument to `__origin_of()` can take an arbitrary expression: +Use the `__origin_of(value)` operator to obtain a value's origin. An argument +to `__origin_of()` can take an arbitrary expression that yields one of the +following: + +- An origin value. + +- A value with a memory location. + +For example: ```mojo __origin_of(self) @@ -148,7 +155,7 @@ __origin_of(foo()) ``` The `__origin_of()` operator is analyzed statically at compile time; -The expression passed to `__origin_of()` is never evaluated. (For example, +The expressions passed to `__origin_of()` are never evaluated. (For example, when the compiler analyzes `__origin_of(foo())`, it doesn't run the `foo()` function.) @@ -166,10 +173,19 @@ struct BoxedString: fn __init__(out self, value: String): self.o_ptr = OwnedPointer(value) - fn as_ptr(self) -> Pointer[String, __origin_of(self.o_ptr)]: + fn as_ptr(mut self) -> Pointer[String, __origin_of(self.o_ptr)]: return Pointer.address_of(self.o_ptr[]) ``` +Note that the `as_ptr()` method takes its `self` argument as `mut self`. If it +used the default `read` argument convention, it would be immutable, and the +derived origin (`__origin_of(self.o_ptr)`) would also be immutable. + +You can also pass multiple expressions to `__origin_of()` to express the union +of two or more origins: + +`__origin_of(a, b)` + #### Inferred origins The other common way to access an origin value is to *infer* it from the @@ -180,7 +196,7 @@ has an associated `origin`: struct Span[ is_mutable: Bool, //, T: CollectionElement, - origin: Origin[is_mutable].type, + origin: Origin[is_mutable], ](CollectionElementNew): """A non owning view of contiguous data. ``` @@ -229,9 +245,17 @@ to use a `ref` argument: The syntax for a `ref` argument is: -ref [origin_specifier] arg_name: arg_type +ref arg_name: arg_type + +Or: -The origin specifier passed inside the square brackets can be either: +ref [origin_specifier(s)] +arg_name: arg_type + +In the first form, the origin and mutability of the `ref` argument is inferred +from the value passed in. The second form includes an origin clause, consisting +of one or more origin specifiers inside square brackets. An origin +specifier can be either: * An origin value. @@ -244,22 +268,24 @@ The origin specifier passed inside the square brackets can be either: ref [self] ``` -* An underscore character (`_`) to indicate that the origin is *unbound*. You - can think of the underscore as a wildcard that will accept any origin: +* An [`AddressSpace`](/nightly/mojo/stdlib/memory/pointer/AddressSpace) value. + +* An underscore character (`_`) to indicate that the origin is *unbound*. This + is equivalent to omitting the origin specifier. ```mojo - def add_ref(ref a: Int, b: Int) -> Int: + def add_ref(ref a: Int, b: Int) -> Int: return a+b ``` You can also name the origin explicitly. This is useful if you want to specify -an `ImmutableOrigin` or `MutableLOrigin`, or if you want to bind to +an `ImmutableOrigin` or `MutableOrigin`, or if you want to bind to the `is_mutable` parameter. ```mojo def take_str_ref[ is_mutable: Bool, //, - origin: Origin[is_mutable].type + origin: Origin[is_mutable] ](ref [origin] s: String): @parameter if is_mutable: @@ -282,8 +308,14 @@ Mutable: Goodbye ### `ref` return values Like `ref` arguments, `ref` return values allow a function to return a mutable -or immutable reference to a value. Like a `read` or `mut` argument, these -references don't need to be dereferenced. +or immutable reference to a value. The syntax for a `ref` return value is: + +-> ref [origin_specifier(s)] + arg_type + +Note that you **must** specify an origin specifier for a `ref` return value. The +values allowed for origin specifiers are the same as the ones listed for +[`ref` arguments](#ref-arguments). `ref` return values can be an efficient way to handle updating items in a collection. The standard way to do this is by implementing the `__getitem__()` @@ -432,3 +464,18 @@ Diana Without parametric mutability, you'd need to write two versions of `__getitem__()`, one that accepts an immutable `self` and another that accepts a mutable `self`. + +#### Return values with union origins + +A `ref` return value can include multiple values in its origin specifier, which +yields the union of the origins. For example, the following `pick_one()` +function returns a reference to one of the two input strings, with an origin +that's a union of both origins. + +```mojo +def pick_one(cond: Bool, ref a: String, ref b: String) -> ref [a, b] String: + if cond: + return a + else: + return b +``` From cf307a230daeb93743834d8ff4e720d3b1d3404e Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 7 Dec 2024 06:29:22 +0000 Subject: [PATCH 052/509] Update lockfiles to point to latest nightly version: 24.6.0.dev2024120705 --- examples/life/magic.lock | 205 +++++++++++++++++----------------- examples/magic.lock | 205 +++++++++++++++++----------------- examples/notebooks/magic.lock | 203 ++++++++++++++++----------------- examples/operators/magic.lock | 205 +++++++++++++++++----------------- magic.lock | 205 +++++++++++++++++----------------- 5 files changed, 514 insertions(+), 509 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 8a7706a5c8..3326e0e536 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -13,10 +13,10 @@ environments: - 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md5: 1420c233cedc5e554c728d5f8b2b1657 + url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-24.6.0.dev2024120705-release.conda + sha256: ff854ead8fa7014dd8ed800998afb01ac9faeec55dfc84bcc2d6d21b38e5a8d1 + md5: ce9ee67733d1ab8c43031b67b475dba8 depends: - - max-core ==24.6.0.dev2024120616 release + - max-core ==24.6.0.dev2024120705 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 11371 - timestamp: 1733502206776 + size: 11370 + timestamp: 1733548613220 - kind: conda name: multidict version: 6.1.0 From 770ab7905f9d271cd2f3b6a4eaa65be0e6e6154c Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 6 Dec 2024 22:30:15 -0800 Subject: [PATCH 053/509] [mojo-lang] Fix bugs related to SRValue -> ref conversion. This fixes https://github.com/modularml/mojo/issues/3830, a collection of issues handling emission of SRValues to argument conventions that require a memory argument. In these cases, we need to emit the value to memory and re-infer. This was completely broken for indirect calls, and didn't handle the case of direct calls that failed to re-infer. MODULAR_ORIG_COMMIT_REV_ID: 172c9efe1ca9083e50f1f58629d0b990bb3090cd --- docs/changelog.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 5398fd44c6..3673d58fd7 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -635,6 +635,9 @@ what we publish. - [Issue #3829](https://github.com/modularml/mojo/issues/3829) - Poor error message when invoking a function pointer upon an argument of the wrong origin +- [Issue #3830](https://github.com/modularml/mojo/issues/3830) - Failures + emitting register RValues to ref arguments. + - The VS Code extension now auto-updates its private copy of the MAX SDK. - The variadic initializer for `SIMD` now works in parameter expressions. From 920ee3500507f026d6c70e1b287775779279c84b Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Sat, 7 Dec 2024 09:11:39 -1000 Subject: [PATCH 054/509] [mojo-stdlib] add implicit decorators where required Extra implicit decorators required for making the explicit constructor filtering more strict. MODULAR_ORIG_COMMIT_REV_ID: 82617a0e6e0de514abeda843bdadc48d42fdbbe3 --- stdlib/src/builtin/dtype.mojo | 10 +++++++++ stdlib/src/builtin/object.mojo | 5 ++++- stdlib/src/collections/string.mojo | 34 +++++++++++++++++++++++++++++- 3 files changed, 47 insertions(+), 2 deletions(-) diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index 1ed5dd09c6..b8a7dff5d6 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -155,6 +155,16 @@ struct DType( """ self = other + @always_inline + @implicit + fn __init__(out self, value: Self.type): + """Construct a DType from MLIR dtype. + + Args: + value: The MLIR dtype. + """ + self.value = value + @staticmethod fn _from_str(str: String) -> DType: """Construct a DType from a string. diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index 21b345b89d..fce05d2476 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -320,6 +320,7 @@ struct _ObjectImpl( self.value = Self.type(value) @always_inline + @implicit fn __init__[dt: DType](mut self, value: SIMD[dt, 1]): @parameter if dt.is_integral(): @@ -785,6 +786,7 @@ struct object( self._value = value @always_inline + @implicit fn __init__[dt: DType](mut self, value: SIMD[dt, 1]): """Initializes the object with a generic scalar value. If the scalar value type is bool, it is converted to a boolean. Otherwise, it is @@ -842,6 +844,7 @@ struct object( self._value = impl @always_inline + @implicit fn __init__[*Ts: CollectionElement](mut self, value: ListLiteral[*Ts]): """Initializes the object from a list literal. @@ -1861,7 +1864,7 @@ struct object( var impl = _ImmutableString(UnsafePointer[UInt8].alloc(1), 1) var char = self._value.get_as_string().data[index] impl.data.init_pointee_move(char) - return _ObjectImpl(impl) + return object(impl) return self._value.get_list_element(i._value.get_as_int().value) @always_inline diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 90ed9b5cd1..86449179a3 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -785,7 +785,8 @@ struct String( @always_inline @implicit fn __init__(out self, owned impl: List[UInt8, *_]): - """Construct a string from a buffer of bytes. + """Construct a string from a buffer of bytes without copying the + allocated data. The buffer must be terminated with a null byte: @@ -815,6 +816,37 @@ struct String( ptr=impl.steal_data(), length=size, capacity=capacity ) + @always_inline + @implicit + fn __init__(out self, impl: Self._buffer_type): + """Construct a string from a buffer of bytes, copying the allocated + data. Use the transfer operator ^ to avoid the copy. + + The buffer must be terminated with a null byte: + + ```mojo + var buf = List[UInt8]() + buf.append(ord('H')) + buf.append(ord('i')) + buf.append(0) + var hi = String(buf) + ``` + + Args: + impl: The buffer. + """ + debug_assert( + len(impl) > 0 and impl[-1] == 0, + "expected last element of String buffer to be null terminator", + ) + # We make a backup because steal_data() will clear size and capacity. + var size = impl.size + debug_assert( + impl[size - 1] == 0, + "expected last element of String buffer to be null terminator", + ) + self._buffer = impl + @always_inline fn __init__(out self): """Construct an uninitialized string.""" From 5efd510fc4320c3b9caec771844fa8f19ebe2a0f Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Sat, 7 Dec 2024 13:46:32 -1000 Subject: [PATCH 055/509] [mojo-lang] Diagnose more implicit conversion without `@implicit` decorator Now checks for @implicit decorator in all scenarios when callable is CallSyntax::kImplicitConvert. This previously wasn't happening in instances like var x: Foo = 42, self.foo = 42, and returning values. This fixes https://github.com/modularml/mojo/issues/3828 and Fixes MOCO-1485 MODULAR_ORIG_COMMIT_REV_ID: 3a6b1ba1b5d7d3ff2ac7ddc2e36dac70cc5455cf --- examples/notebooks/BoolMLIR.ipynb | 17 +- examples/notebooks/RayTracing.ipynb | 1994 ++++++++++++++------------- 2 files changed, 1009 insertions(+), 1002 deletions(-) diff --git a/examples/notebooks/BoolMLIR.ipynb b/examples/notebooks/BoolMLIR.ipynb index 355dc77909..514770e9e8 100644 --- a/examples/notebooks/BoolMLIR.ipynb +++ b/examples/notebooks/BoolMLIR.ipynb @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "tags": [] }, @@ -239,6 +239,7 @@ "\n", " # ...\n", "\n", + " @implicit\n", " fn __init__(out self, value: __mlir_type.i1):\n", " self.value = value" ] @@ -288,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "tags": [] }, @@ -306,6 +307,7 @@ " fn __init__(out self):\n", " self = OurFalse\n", "\n", + " @implicit\n", " fn __init__(out self, value: __mlir_type.i1):\n", " self.value = value" ] @@ -353,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "tags": [] }, @@ -369,6 +371,7 @@ "\n", " # ...\n", "\n", + " @implicit\n", " fn __init__(out self, value: __mlir_type.i1):\n", " self.value = value\n", "\n", @@ -429,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -441,6 +444,7 @@ "struct OurBool:\n", " var value: __mlir_type.i1\n", "\n", + " @implicit\n", " fn __init__(out self, value: __mlir_type.i1):\n", " self.value = value\n", "\n", @@ -495,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -507,6 +511,7 @@ "struct OurBool:\n", " var value: __mlir_type.i1\n", "\n", + " @implicit\n", " fn __init__(out self, value: __mlir_type.i1):\n", " self.value = value\n", "\n", diff --git a/examples/notebooks/RayTracing.ipynb b/examples/notebooks/RayTracing.ipynb index 66a768da6e..ea37cea474 100644 --- a/examples/notebooks/RayTracing.ipynb +++ b/examples/notebooks/RayTracing.ipynb @@ -1,1020 +1,1022 @@ { - "cells": [ - { - "cell_type": "raw", - "id": "7a924a43", - "metadata": {}, - "source": [ - "---\n", - "title: Ray tracing in Mojo\n", - "description: Learn how to draw 3D graphics with ray-traced lighting using Mojo.\n", - "website:\n", - " open-graph:\n", - " image: /static/images/mojo-social-card.png\n", - " twitter-card:\n", - " image: /static/images/mojo-social-card.png\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "e48ca293", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" - ] - }, - { - "cell_type": "markdown", - "id": "214584ac", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "# Ray tracing in Mojo" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c4edb2c9-5109-4e00-98c0-3aa92dbca7d1", - "metadata": {}, - "source": [ - "This tutorial about [ray\n", - "tracing](https://en.wikipedia.org/wiki/Ray_tracing_(graphics)) is based on the\n", - "popular tutorial [Understandable RayTracing in\n", - "C++](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing).\n", - "The mathematical explanations are well described in that tutorial, so we'll\n", - "just point you to the appropriate sections for reference as we implement a\n", - "basic ray tracer in Mojo." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "fbd4aa63-a178-41f2-ad66-b017c1c69d2a", - "metadata": {}, - "source": [ - "## Step 1: Basic definitions\n", - "\n", - "We'll start by defining a `Vec3f` struct, which will use to represent a vector\n", - "in 3D space as well as RGB pixels. We'll use a `SIMD` representation for our\n", - "vector to enable vectorized operations. The `SIMD` type is a fixed-size vector,\n", - "and its size must be a power of 2. So we'll use a size of 4 and always pad the\n", - "underlying storage with a 0." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "78a07829-05bc-4a67-8ccc-f2e1537c478c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from math import isqrt\n", - "\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct Vec3f:\n", - " var data: SIMD[DType.float32, 4]\n", - "\n", - " @always_inline\n", - " fn __init__(out self, x: Float32, y: Float32, z: Float32):\n", - " self.data = SIMD[DType.float32, 4](x, y, z, 0)\n", - "\n", - " @always_inline\n", - " fn __init__(out self, data: SIMD[DType.float32, 4]):\n", - " self.data = data\n", - "\n", - " @always_inline\n", - " @staticmethod\n", - " fn zero() -> Vec3f:\n", - " return Vec3f(0, 0, 0)\n", - "\n", - " @always_inline\n", - " fn __sub__(self, other: Vec3f) -> Vec3f:\n", - " return self.data - other.data\n", - "\n", - " @always_inline\n", - " fn __add__(self, other: Vec3f) -> Vec3f:\n", - " return self.data + other.data\n", - "\n", - " @always_inline\n", - " fn __matmul__(self, other: Vec3f) -> Float32:\n", - " return (self.data * other.data).reduce_add()\n", - "\n", - " @always_inline\n", - " fn __mul__(self, k: Float32) -> Vec3f:\n", - " return self.data * k\n", - "\n", - " @always_inline\n", - " fn __neg__(self) -> Vec3f:\n", - " return self.data * -1.0\n", - "\n", - " @always_inline\n", - " fn __getitem__(self, idx: Int) -> SIMD[DType.float32, 1]:\n", - " return self.data[idx]\n", - "\n", - " @always_inline\n", - " fn cross(self, other: Vec3f) -> Vec3f:\n", - " var self_zxy = self.data.shuffle[2, 0, 1, 3]()\n", - " var other_zxy = other.data.shuffle[2, 0, 1, 3]()\n", - " return (self_zxy * other.data - self.data * other_zxy).shuffle[\n", - " 2, 0, 1, 3\n", - " ]()\n", - "\n", - " @always_inline\n", - " fn normalize(self) -> Vec3f:\n", - " return self.data * isqrt(self @ self)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2619eb1b-103b-4453-9725-4480e388452e", - "metadata": {}, - "source": [ - "We now define our `Image` struct, which will store the RGB pixels of our\n", - "images. It also contains a method to convert this Mojo struct into a NumPy\n", - "image, which will be used for implementing a straightforward display\n", - "mechanism. We will also implement a function for loading PNG files from disk." - ] - }, - { - "cell_type": "markdown", - "id": "acd55d71", - "metadata": {}, - "source": [ - "First install the required libraries:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "53266408", - "metadata": {}, - "outputs": [], - "source": [ - "%%python\n", - "from importlib.util import find_spec\n", - "import shutil\n", - "import subprocess\n", - "\n", - "fix = \"\"\"\n", - "-------------------------------------------------------------------------\n", - "fix following the steps here:\n", - " https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719\n", - "-------------------------------------------------------------------------\n", - "\"\"\"\n", - "\n", - "def install_if_missing(name: str):\n", - " if find_spec(name):\n", - " return\n", - "\n", - " print(f\"{name} not found, installing...\")\n", - " try:\n", - " if shutil.which('python3'): python = \"python3\"\n", - " elif shutil.which('python'): python = \"python\"\n", - " else: raise (\"python not on path\" + fix)\n", - " subprocess.check_call([python, \"-m\", \"pip\", \"install\", name])\n", - " except:\n", - " raise ImportError(f\"{name} not found\" + fix)\n", - "\n", - "install_if_missing(\"numpy\")\n", - "install_if_missing(\"matplotlib\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ca218d22-0578-42af-a906-7f15a91c5bec", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from python import Python\n", - "from python import PythonObject\n", - "from memory import UnsafePointer\n", - "\n", - "struct Image:\n", - " # reference count used to make the object efficiently copyable\n", - " var rc: UnsafePointer[Int]\n", - " # the two dimensional image is represented as a flat array\n", - " var pixels: UnsafePointer[Vec3f]\n", - " var height: Int\n", - " var width: Int\n", - "\n", - " fn __init__(out self, height: Int, width: Int):\n", - " self.height = height\n", - " self.width = width\n", - " self.pixels = UnsafePointer[Vec3f].alloc(self.height * self.width)\n", - " self.rc = UnsafePointer[Int].alloc(1)\n", - " self.rc[] = 1\n", - "\n", - " fn __copyinit__(out self, other: Self):\n", - " other._inc_rc()\n", - " self.pixels = other.pixels\n", - " self.rc = other.rc\n", - " self.height = other.height\n", - " self.width = other.width\n", - "\n", - " fn __del__(owned self):\n", - " self._dec_rc()\n", - "\n", - " fn _dec_rc(self):\n", - " if self.rc[] > 1:\n", - " self.rc[] -= 1\n", - " return\n", - " self._free()\n", - "\n", - " fn _inc_rc(self):\n", - " self.rc[] += 1\n", - "\n", - " fn _free(self):\n", - " self.rc.free()\n", - " self.pixels.free()\n", - "\n", - " @always_inline\n", - " fn set(self, row: Int, col: Int, value: Vec3f) -> None:\n", - " self.pixels[self._pos_to_index(row, col)] = value\n", - "\n", - " @always_inline\n", - " fn _pos_to_index(self, row: Int, col: Int) -> Int:\n", - " # Convert a (rol, col) position into an index in the underlying linear storage\n", - " return row * self.width + col\n", - "\n", - " def to_numpy_image(self) -> PythonObject:\n", - " var np = Python.import_module(\"numpy\")\n", - " var plt = Python.import_module(\"matplotlib.pyplot\")\n", - "\n", - " var np_image = np.zeros((self.height, self.width, 3), np.float32)\n", - "\n", - " # We use raw pointers to efficiently copy the pixels to the numpy array\n", - " var out_pointer = np_image.__array_interface__[\"data\"][0].unsafe_get_as_pointer[DType.float32]()\n", - " var in_pointer = self.pixels.bitcast[Float32]()\n", - "\n", - " for row in range(self.height):\n", - " for col in range(self.width):\n", - " var index = self._pos_to_index(row, col)\n", - " for dim in range(3):\n", - " out_pointer[index * 3 + dim] = in_pointer[index * 4 + dim]\n", - "\n", - " return np_image\n", - "\n", - "\n", - "def load_image(fname: String) -> Image:\n", - " var np = Python.import_module(\"numpy\")\n", - " var plt = Python.import_module(\"matplotlib.pyplot\")\n", - "\n", - " var np_image = plt.imread(fname)\n", - " var rows = int(np_image.shape[0])\n", - " var cols = int(np_image.shape[1])\n", - " var image = Image(rows, cols)\n", - "\n", - " var in_pointer = np_image.__array_interface__[\"data\"][0].unsafe_get_as_pointer[DType.float32]()\n", - " var out_pointer = image.pixels.bitcast[Float32]()\n", - "\n", - " for row in range(rows):\n", - " for col in range(cols):\n", - " var index = image._pos_to_index(row, col)\n", - " for dim in range(3):\n", - " out_pointer[index * 4 + dim] = in_pointer[index * 3 + dim]\n", - " return image\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7a7dbe6f-f35f-42c5-ad37-b7400f6262f8", - "metadata": {}, - "source": [ - "We then add a function for quickly displaying an `Image` into the notebook. Our\n", - "Python interop comes in quite handy." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9a3a8e81-7500-439b-a8d2-8e71ceea3e8b", - "metadata": {}, - "outputs": [], - "source": [ - "def render(image: Image):\n", - " np = Python.import_module(\"numpy\")\n", - " plt = Python.import_module(\"matplotlib.pyplot\")\n", - " colors = Python.import_module(\"matplotlib.colors\")\n", - " dpi = 32\n", - " fig = plt.figure(1, [image.width // 10, image.height // 10], dpi)\n", - "\n", - " plt.imshow(image.to_numpy_image())\n", - " plt.axis(\"off\")\n", - " plt.show()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "5f42f174-d7d9-4f6c-9b4e-d0d586f8f5ec", - "metadata": {}, - "source": [ - "Finally, we test all our code so far with a simple image, which is the one\n", - "rendered in the [Step 1 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-1-write-an-image-to-the-disk)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "52005588-38e7-4ef0-affe-6d063b9bf9b7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "var image = Image(192, 256)\n", - "\n", - "for row in range(image.height):\n", - " for col in range(image.width):\n", - " image.set(\n", - " row,\n", - " col,\n", - " Vec3f(Float32(row) / image.height, Float32(col) / image.width, 0),\n", - " )\n", - "\n", - "render(image)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ef3206b4-0bbb-4108-a592-f748843e5d3d", - "metadata": {}, - "source": [ - "## Step 2: Ray tracing\n", - "\n", - "Now we'll perform ray tracing from a camera into a scene with a sphere. Before\n", - "reading the code below, we suggest you read more about how this works\n", - "conceptually from [Step 2 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-2-the-crucial-one-ray-tracing)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "778d8339-5b19-47ee-bdc6-a08e4ec5094c", - "metadata": {}, - "source": [ - "We first define the `Material` and `Sphere` structs, which are the new data\n", - "structures we'll need." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1c4a79de-0b65-48ac-97c9-187b74b96b9f", - "metadata": {}, - "outputs": [], - "source": [ - "from math import sqrt\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct Material:\n", - " var color: Vec3f\n", - " var albedo: Vec3f\n", - " var specular_component: Float32\n", - "\n", - " fn __init__(out self, color: Vec3f, albedo: Vec3f = Vec3f(0, 0, 0),\n", - " specular_component: Float32 = 0):\n", - " self.color = color\n", - " self.albedo = albedo\n", - " self.specular_component = specular_component\n", - "\n", - "alias W = 1024\n", - "alias H = 768\n", - "alias bg_color = Vec3f(0.02, 0.02, 0.02)\n", - "var shiny_yellow = Material(Vec3f(0.95, 0.95, 0.4), Vec3f(0.7, 0.6, 0), 30.0)\n", - "var green_rubber = Material(Vec3f( 0.3, 0.7, 0.3), Vec3f(0.9, 0.1, 0), 1.0)\n", - "\n", - "\n", - "@value\n", - "@register_passable(\"trivial\")\n", - "struct Sphere(CollectionElement):\n", - " var center: Vec3f\n", - " var radius: Float32\n", - " var material: Material\n", - "\n", - " @always_inline\n", - " fn intersects(self, orig: Vec3f, dir: Vec3f, mut dist: Float32) -> Bool:\n", - " \"\"\"This method returns True if a given ray intersects this sphere.\n", - " And if it does, it writes in the `dist` parameter the distance to the\n", - " origin of the ray.\n", - " \"\"\"\n", - " var L = orig - self.center\n", - " var a = dir @ dir\n", - " var b = 2 * (dir @ L)\n", - " var c = L @ L - self.radius * self.radius\n", - " var discriminant = b * b - 4 * a * c\n", - " if discriminant < 0:\n", - " return False\n", - " if discriminant == 0:\n", - " dist = -b / 2 * a\n", - " return True\n", - " var q = -0.5 * (b + sqrt(discriminant)) if b > 0 else -0.5 * (\n", - " b - sqrt(discriminant)\n", - " )\n", - " var t0 = q / a\n", - " var t1 = c / q\n", - " if t0 > t1:\n", - " t0 = t1\n", - " if t0 < 0:\n", - " t0 = t1\n", - " if t0 < 0:\n", - " return False\n", - "\n", - " dist = t0\n", - " return True\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e5ed8e76-efc1-4905-90ea-4cad6df0c553", - "metadata": {}, - "source": [ - "We then define a `cast_ray` method, which will be used to figure out the color\n", - "of a particular pixel in the image we'll produce. It basically works by\n", - "identifying whether this ray intersects the sphere or not." - ] - }, + "cells": [ + { + "cell_type": "raw", + "id": "7a924a43", + "metadata": {}, + "source": [ + "---\n", + "title: Ray tracing in Mojo\n", + "description: Learn how to draw 3D graphics with ray-traced lighting using Mojo.\n", + "website:\n", + " open-graph:\n", + " image: /static/images/mojo-social-card.png\n", + " twitter-card:\n", + " image: /static/images/mojo-social-card.png\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "e48ca293", + "metadata": {}, + "source": [ + "[//]: # REMOVE_FOR_WEBSITE\n", + "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" + ] + }, + { + "cell_type": "markdown", + "id": "214584ac", + "metadata": {}, + "source": [ + "[//]: # REMOVE_FOR_WEBSITE\n", + "# Ray tracing in Mojo" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c4edb2c9-5109-4e00-98c0-3aa92dbca7d1", + "metadata": {}, + "source": [ + "This tutorial about [ray\n", + "tracing](https://en.wikipedia.org/wiki/Ray_tracing_(graphics)) is based on the\n", + "popular tutorial [Understandable RayTracing in\n", + "C++](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing).\n", + "The mathematical explanations are well described in that tutorial, so we'll\n", + "just point you to the appropriate sections for reference as we implement a\n", + "basic ray tracer in Mojo." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "fbd4aa63-a178-41f2-ad66-b017c1c69d2a", + "metadata": {}, + "source": [ + "## Step 1: Basic definitions\n", + "\n", + "We'll start by defining a `Vec3f` struct, which will use to represent a vector\n", + "in 3D space as well as RGB pixels. We'll use a `SIMD` representation for our\n", + "vector to enable vectorized operations. The `SIMD` type is a fixed-size vector,\n", + "and its size must be a power of 2. So we'll use a size of 4 and always pad the\n", + "underlying storage with a 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78a07829-05bc-4a67-8ccc-f2e1537c478c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from math import isqrt\n", + "\n", + "\n", + "@register_passable(\"trivial\")\n", + "struct Vec3f:\n", + " var data: SIMD[DType.float32, 4]\n", + "\n", + " @always_inline\n", + " fn __init__(out self, x: Float32, y: Float32, z: Float32):\n", + " self.data = SIMD[DType.float32, 4](x, y, z, 0)\n", + "\n", + " @implicit\n", + " @always_inline\n", + " fn __init__(out self, data: SIMD[DType.float32, 4]):\n", + " self.data = data\n", + "\n", + " @always_inline\n", + " @staticmethod\n", + " fn zero() -> Vec3f:\n", + " return Vec3f(0, 0, 0)\n", + "\n", + " @always_inline\n", + " fn __sub__(self, other: Vec3f) -> Vec3f:\n", + " return self.data - other.data\n", + "\n", + " @always_inline\n", + " fn __add__(self, other: Vec3f) -> Vec3f:\n", + " return self.data + other.data\n", + "\n", + " @always_inline\n", + " fn __matmul__(self, other: Vec3f) -> Float32:\n", + " return (self.data * other.data).reduce_add()\n", + "\n", + " @always_inline\n", + " fn __mul__(self, k: Float32) -> Vec3f:\n", + " return self.data * k\n", + "\n", + " @always_inline\n", + " fn __neg__(self) -> Vec3f:\n", + " return self.data * -1.0\n", + "\n", + " @always_inline\n", + " fn __getitem__(self, idx: Int) -> SIMD[DType.float32, 1]:\n", + " return self.data[idx]\n", + "\n", + " @always_inline\n", + " fn cross(self, other: Vec3f) -> Vec3f:\n", + " var self_zxy = self.data.shuffle[2, 0, 1, 3]()\n", + " var other_zxy = other.data.shuffle[2, 0, 1, 3]()\n", + " return (self_zxy * other.data - self.data * other_zxy).shuffle[\n", + " 2, 0, 1, 3\n", + " ]()\n", + "\n", + " @always_inline\n", + " fn normalize(self) -> Vec3f:\n", + " return self.data * isqrt(self @ self)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2619eb1b-103b-4453-9725-4480e388452e", + "metadata": {}, + "source": [ + "We now define our `Image` struct, which will store the RGB pixels of our\n", + "images. It also contains a method to convert this Mojo struct into a NumPy\n", + "image, which will be used for implementing a straightforward display\n", + "mechanism. We will also implement a function for loading PNG files from disk." + ] + }, + { + "cell_type": "markdown", + "id": "acd55d71", + "metadata": {}, + "source": [ + "First install the required libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "53266408", + "metadata": {}, + "outputs": [], + "source": [ + "%%python\n", + "from importlib.util import find_spec\n", + "import shutil\n", + "import subprocess\n", + "\n", + "fix = \"\"\"\n", + "-------------------------------------------------------------------------\n", + "fix following the steps here:\n", + " https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719\n", + "-------------------------------------------------------------------------\n", + "\"\"\"\n", + "\n", + "def install_if_missing(name: str):\n", + " if find_spec(name):\n", + " return\n", + "\n", + " print(f\"{name} not found, installing...\")\n", + " try:\n", + " if shutil.which('python3'): python = \"python3\"\n", + " elif shutil.which('python'): python = \"python\"\n", + " else: raise (\"python not on path\" + fix)\n", + " subprocess.check_call([python, \"-m\", \"pip\", \"install\", name])\n", + " except:\n", + " raise ImportError(f\"{name} not found\" + fix)\n", + "\n", + "install_if_missing(\"numpy\")\n", + "install_if_missing(\"matplotlib\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ca218d22-0578-42af-a906-7f15a91c5bec", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from python import Python\n", + "from python import PythonObject\n", + "from memory import UnsafePointer\n", + "\n", + "struct Image:\n", + " # reference count used to make the object efficiently copyable\n", + " var rc: UnsafePointer[Int]\n", + " # the two dimensional image is represented as a flat array\n", + " var pixels: UnsafePointer[Vec3f]\n", + " var height: Int\n", + " var width: Int\n", + "\n", + " fn __init__(out self, height: Int, width: Int):\n", + " self.height = height\n", + " self.width = width\n", + " self.pixels = UnsafePointer[Vec3f].alloc(self.height * self.width)\n", + " self.rc = UnsafePointer[Int].alloc(1)\n", + " self.rc[] = 1\n", + "\n", + " fn __copyinit__(out self, other: Self):\n", + " other._inc_rc()\n", + " self.pixels = other.pixels\n", + " self.rc = other.rc\n", + " self.height = other.height\n", + " self.width = other.width\n", + "\n", + " fn __del__(owned self):\n", + " self._dec_rc()\n", + "\n", + " fn _dec_rc(self):\n", + " if self.rc[] > 1:\n", + " self.rc[] -= 1\n", + " return\n", + " self._free()\n", + "\n", + " fn _inc_rc(self):\n", + " self.rc[] += 1\n", + "\n", + " fn _free(self):\n", + " self.rc.free()\n", + " self.pixels.free()\n", + "\n", + " @always_inline\n", + " fn set(self, row: Int, col: Int, value: Vec3f) -> None:\n", + " self.pixels[self._pos_to_index(row, col)] = value\n", + "\n", + " @always_inline\n", + " fn _pos_to_index(self, row: Int, col: Int) -> Int:\n", + " # Convert a (rol, col) position into an index in the underlying linear storage\n", + " return row * self.width + col\n", + "\n", + " def to_numpy_image(self) -> PythonObject:\n", + " var np = Python.import_module(\"numpy\")\n", + " var plt = Python.import_module(\"matplotlib.pyplot\")\n", + "\n", + " var np_image = np.zeros((self.height, self.width, 3), np.float32)\n", + "\n", + " # We use raw pointers to efficiently copy the pixels to the numpy array\n", + " var out_pointer = np_image.__array_interface__[\"data\"][0].unsafe_get_as_pointer[DType.float32]()\n", + " var in_pointer = self.pixels.bitcast[Float32]()\n", + "\n", + " for row in range(self.height):\n", + " for col in range(self.width):\n", + " var index = self._pos_to_index(row, col)\n", + " for dim in range(3):\n", + " out_pointer[index * 3 + dim] = in_pointer[index * 4 + dim]\n", + "\n", + " return np_image\n", + "\n", + "\n", + "def load_image(fname: String) -> Image:\n", + " var np = Python.import_module(\"numpy\")\n", + " var plt = Python.import_module(\"matplotlib.pyplot\")\n", + "\n", + " var np_image = plt.imread(fname)\n", + " var rows = int(np_image.shape[0])\n", + " var cols = int(np_image.shape[1])\n", + " var image = Image(rows, cols)\n", + "\n", + " var in_pointer = np_image.__array_interface__[\"data\"][0].unsafe_get_as_pointer[DType.float32]()\n", + " var out_pointer = image.pixels.bitcast[Float32]()\n", + "\n", + " for row in range(rows):\n", + " for col in range(cols):\n", + " var index = image._pos_to_index(row, col)\n", + " for dim in range(3):\n", + " out_pointer[index * 4 + dim] = in_pointer[index * 3 + dim]\n", + " return image\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7a7dbe6f-f35f-42c5-ad37-b7400f6262f8", + "metadata": {}, + "source": [ + "We then add a function for quickly displaying an `Image` into the notebook. Our\n", + "Python interop comes in quite handy." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9a3a8e81-7500-439b-a8d2-8e71ceea3e8b", + "metadata": {}, + "outputs": [], + "source": [ + "def render(image: Image):\n", + " np = Python.import_module(\"numpy\")\n", + " plt = Python.import_module(\"matplotlib.pyplot\")\n", + " colors = Python.import_module(\"matplotlib.colors\")\n", + " dpi = 32\n", + " fig = plt.figure(1, [image.width // 10, image.height // 10], dpi)\n", + "\n", + " plt.imshow(image.to_numpy_image())\n", + " plt.axis(\"off\")\n", + " plt.show()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5f42f174-d7d9-4f6c-9b4e-d0d586f8f5ec", + "metadata": {}, + "source": [ + "Finally, we test all our code so far with a simple image, which is the one\n", + "rendered in the [Step 1 of the C++\n", + "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-1-write-an-image-to-the-disk)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "52005588-38e7-4ef0-affe-6d063b9bf9b7", + "metadata": { + "tags": [] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "id": "beeecde1-f365-4be5-a3b1-d8a145810be1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fn cast_ray(orig: Vec3f, dir: Vec3f, sphere: Sphere) -> Vec3f:\n", - " var dist: Float32 = 0\n", - " if not sphere.intersects(orig, dir, dist):\n", - " return bg_color\n", - "\n", - " return sphere.material.color\n" - ] + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "1baec786-664c-4fea-8be1-c2d5f25924e7", - "metadata": {}, - "source": [ - "Lastly, we parallelize the ray tracing for every pixel row-wise." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "var image = Image(192, 256)\n", + "\n", + "for row in range(image.height):\n", + " for col in range(image.width):\n", + " image.set(\n", + " row,\n", + " col,\n", + " Vec3f(Float32(row) / image.height, Float32(col) / image.width, 0),\n", + " )\n", + "\n", + "render(image)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ef3206b4-0bbb-4108-a592-f748843e5d3d", + "metadata": {}, + "source": [ + "## Step 2: Ray tracing\n", + "\n", + "Now we'll perform ray tracing from a camera into a scene with a sphere. Before\n", + "reading the code below, we suggest you read more about how this works\n", + "conceptually from [Step 2 of the C++\n", + "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-2-the-crucial-one-ray-tracing)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "778d8339-5b19-47ee-bdc6-a08e4ec5094c", + "metadata": {}, + "source": [ + "We first define the `Material` and `Sphere` structs, which are the new data\n", + "structures we'll need." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c4a79de-0b65-48ac-97c9-187b74b96b9f", + "metadata": {}, + "outputs": [], + "source": [ + "from math import sqrt\n", + "\n", + "@register_passable(\"trivial\")\n", + "struct Material:\n", + " var color: Vec3f\n", + " var albedo: Vec3f\n", + " var specular_component: Float32\n", + "\n", + " @implicit\n", + " fn __init__(out self, color: Vec3f, albedo: Vec3f = Vec3f(0, 0, 0),\n", + " specular_component: Float32 = 0):\n", + " self.color = color\n", + " self.albedo = albedo\n", + " self.specular_component = specular_component\n", + "\n", + "alias W = 1024\n", + "alias H = 768\n", + "alias bg_color = Vec3f(0.02, 0.02, 0.02)\n", + "var shiny_yellow = Material(Vec3f(0.95, 0.95, 0.4), Vec3f(0.7, 0.6, 0), 30.0)\n", + "var green_rubber = Material(Vec3f( 0.3, 0.7, 0.3), Vec3f(0.9, 0.1, 0), 1.0)\n", + "\n", + "\n", + "@value\n", + "@register_passable(\"trivial\")\n", + "struct Sphere(CollectionElement):\n", + " var center: Vec3f\n", + " var radius: Float32\n", + " var material: Material\n", + "\n", + " @always_inline\n", + " fn intersects(self, orig: Vec3f, dir: Vec3f, mut dist: Float32) -> Bool:\n", + " \"\"\"This method returns True if a given ray intersects this sphere.\n", + " And if it does, it writes in the `dist` parameter the distance to the\n", + " origin of the ray.\n", + " \"\"\"\n", + " var L = orig - self.center\n", + " var a = dir @ dir\n", + " var b = 2 * (dir @ L)\n", + " var c = L @ L - self.radius * self.radius\n", + " var discriminant = b * b - 4 * a * c\n", + " if discriminant < 0:\n", + " return False\n", + " if discriminant == 0:\n", + " dist = -b / 2 * a\n", + " return True\n", + " var q = -0.5 * (b + sqrt(discriminant)) if b > 0 else -0.5 * (\n", + " b - sqrt(discriminant)\n", + " )\n", + " var t0 = q / a\n", + " var t1 = c / q\n", + " if t0 > t1:\n", + " t0 = t1\n", + " if t0 < 0:\n", + " t0 = t1\n", + " if t0 < 0:\n", + " return False\n", + "\n", + " dist = t0\n", + " return True\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e5ed8e76-efc1-4905-90ea-4cad6df0c553", + "metadata": {}, + "source": [ + "We then define a `cast_ray` method, which will be used to figure out the color\n", + "of a particular pixel in the image we'll produce. It basically works by\n", + "identifying whether this ray intersects the sphere or not." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "beeecde1-f365-4be5-a3b1-d8a145810be1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fn cast_ray(orig: Vec3f, dir: Vec3f, sphere: Sphere) -> Vec3f:\n", + " var dist: Float32 = 0\n", + " if not sphere.intersects(orig, dir, dist):\n", + " return bg_color\n", + "\n", + " return sphere.material.color\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1baec786-664c-4fea-8be1-c2d5f25924e7", + "metadata": {}, + "source": [ + "Lastly, we parallelize the ray tracing for every pixel row-wise." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80c81118-acf9-4786-9a3a-694fe7f2d08a", + "metadata": { + "tags": [] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "id": "80c81118-acf9-4786-9a3a-694fe7f2d08a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - 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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from math import tan, acos\n", - "from algorithm import parallelize\n", - "\n", - "\n", - "fn create_image_with_sphere(sphere: Sphere, height: Int, width: Int) -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(row, col, cast_ray(Vec3f.zero(), dir, sphere))\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "\n", - "render(\n", - " create_image_with_sphere(Sphere(Vec3f(-3, 0, -16), 2, shiny_yellow), H, W)\n", - ")\n" - ] + "data": { + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "ee9bbf4c-91ad-4787-a332-b509fdc8d512", - "metadata": {}, - "source": [ - "## Step 3: More spheres\n", - "\n", - "This section corresponds to the [Step 3 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-3-add-more-spheres).\n", - "\n", - "We include here all the necessary changes:\n", - "\n", - "- We add 3 more spheres to the scene, 2 of them being of green rubber material.\n", - "- When we intersect the ray with the sphere, we render the color of the closest\n", - " sphere." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from math import tan, acos\n", + "from algorithm import parallelize\n", + "\n", + "\n", + "fn create_image_with_sphere(sphere: Sphere, height: Int, width: Int) -> Image:\n", + " var image = Image(height, width)\n", + "\n", + " @parameter\n", + " fn _process_row(row: Int):\n", + " var y = -((Float32(2.0) * row + 1) / height - 1)\n", + " for col in range(width):\n", + " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", + " var dir = Vec3f(x, y, -1).normalize()\n", + " image.set(row, col, cast_ray(Vec3f.zero(), dir, sphere))\n", + "\n", + " parallelize[_process_row](height)\n", + "\n", + " return image\n", + "\n", + "\n", + "render(\n", + " create_image_with_sphere(Sphere(Vec3f(-3, 0, -16), 2, shiny_yellow), H, W)\n", + ")\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ee9bbf4c-91ad-4787-a332-b509fdc8d512", + "metadata": {}, + "source": [ + "## Step 3: More spheres\n", + "\n", + "This section corresponds to the [Step 3 of the C++\n", + "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-3-add-more-spheres).\n", + "\n", + "We include here all the necessary changes:\n", + "\n", + "- We add 3 more spheres to the scene, 2 of them being of green rubber material.\n", + "- When we intersect the ray with the sphere, we render the color of the closest\n", + " sphere." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b4774ada-3cef-4ce3-a573-3fd721fe41b0", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "id": "b4774ada-3cef-4ce3-a573-3fd721fe41b0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from algorithm import parallelize\n", - "from utils.numerics import inf\n", - "from collections import List\n", - "\n", - "\n", - "fn scene_intersect(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " background: Material,\n", - ") -> Material:\n", - " var spheres_dist = inf[DType.float32]()\n", - " var material = background\n", - "\n", - " for i in range(spheres.size):\n", - " var dist = inf[DType.float32]()\n", - " if spheres[i].intersects(orig, dir, dist) and dist < spheres_dist:\n", - " spheres_dist = dist\n", - " material = spheres[i].material\n", - "\n", - " return material\n", - "\n", - "\n", - "fn cast_ray(\n", - " orig: Vec3f, dir: Vec3f, spheres: List[Sphere]\n", - ") -> Material:\n", - " var background = Material(Vec3f(0.02, 0.02, 0.02))\n", - " return scene_intersect(orig, dir, spheres, background)\n", - "\n", - "\n", - "fn create_image_with_spheres(\n", - " spheres: List[Sphere], height: Int, width: Int\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(row, col, cast_ray(Vec3f.zero(), dir, spheres).color)\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "var spheres = List[Sphere]()\n", - "spheres.append(Sphere(Vec3f(-3, 0, -16), 2, shiny_yellow))\n", - "spheres.append(Sphere(Vec3f(-1.0, -1.5, -12), 1.8, green_rubber))\n", - "spheres.append(Sphere(Vec3f( 1.5, -0.5, -18), 3, green_rubber))\n", - "spheres.append(Sphere(Vec3f( 7, 5, -18), 4, shiny_yellow))\n", - "\n", - "render(create_image_with_spheres(spheres, H, W))\n" - ] + 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l7W/fnM7OB08aFrnookU5/fT+ynKdCmNjU/nSl3bk8OHJR3z/Bz94ay6//J4ZTsWJWPWDqzJy7kgWbFqQzkF/y85Wh+46lCP3HkmSNMeb2fKxLRnd3j5TbH19HXnWs5blggsW5W1vOz+dnfWqIwGP4yMfuTWf/vTWXH31LlOxAEBlJiYeuPjbZCwAAHBcb29H6vVjk1+nn96fl71sdXrm4P6b3d0dueyy0x7lva184xu7c/XVO4/daiWjo1NpNttkZG+eKupF6t8rwopkwcYFWfq0pdWG4qQNrB7IwOqBJMnU0ancc8U9mbh/Yr3VbGV6fLrKeDlyZCqf+cy2HDw4mZ/8yY0ZGGikp6ceE7LQfiYmpjM2Np1vfnN3PvnJu6qOAwBwnMlYAAAgybEi9r/+16cfX9p3YKCRtWuHHrZU8Xywdevh7Nx5NEly8OBE3vGOr+SOOw5VnGp+W3zR4qz5t2uO3+5d1pvGQKPCRJxqrWYrR+45kumxYwXsoTsP5ca/uzHNyWbFyZL+/kbWrh3MS1+6Kr/8y5tTFPPv5yK0u3/+5zvy3vd+N9u2HTn+OxwAoComYwEAYJ7r729k4cLu77uvI+efvzDnnjtSUar2cfrp/ceXYt6/fzxr1gyleX8fNDY2lfvuc5K3bEW9SNeCrhT3T2r3nd6XobVDBhLnsKJWpP9BS6AXtSK9y3ozcWAiEwcfeX/nmXL48GSuvXZPTj+9P3feeSgLFnRleLir0kww301MTGfHjqPHV6646ab9+eY3d1ecCgDg4UzGAgDAPPRjP3ZWfuu3Ls6Dm62iSBYt6rYn4vdpNlvZvftoJieP/en05S/vyM///FWZbINpvbmsZ3FPNv/K5nTdX3jVu+tp9JuEnU+ak81MHJzIfV+9Lzf9/U1Vx0lybAWB4eGu/If/cG7e+tazq44D89rNN+/PW95yRQ4cOHaxxuHDk8ffBgComslYAACYZ3p66jn77JE0GrUkyXnnjWTlyr4YM3x8tVqRJUt6j99es2YwT3/6kuPl7F13Hcr27aNVxZtzilqR/lX96TutLz1LetI56KLh+arWqKV7YXf6VvZlwdkLkvsvJT9y75FMVFS4jI5OZXR0KjfcsDdf+cqOJEm9XsumTcMZGPBahZkwOTmd66/flxtu2JetWw/n0KHJqiMBADwmk7EAADAPrFs3lI9+9IVZtOjY0sQdHbV0dtaijD1x09PNjI9PH7/9O7/zzfz1X99QYaK5paOnIxe986IMrhlMvVH3EiWt6VaaU/dPoreS6//q+mz/1+2VZmo0ascvbunt7cgHP3hZLrlkSaWZYL7YvftoXvWqz+aWW/ZnbGz68Z8AAFABk7EAADBPdHbW8rznrcjGjQsyPNyV3l7LvJ6ser2W3t7a8dsXX7w4r3rVmiTHpnWuvHJ79u0bryrerDR41mD6TutLktS76ule2J265bK5X1EvUq8/8HpYcPaCtFqt7P3O3somZCcnm8eXKp+aauZzn9uW++47muc/f0X6+vychTJMT7fypS9tz623HsjOnaOKWABg1jAZCwAAc9jISFf+1/96Sc45Z0GKIjFmeOq1Wq1876+qQ4cm8qM/+plce+3uakPNMutftz5n/siZD9zhZcpjaSXT49P5xnu+kX037Ks6TZJje26fdlpf/uVfXppVqwaqjgNz0vj4dF772s/nC1+4Jw+czQQAaE8mYwEAYA677LKVueiiRUmSnp6OLFnSk6LQbpWlKIp87/B2d3fk9a9fnxe+cGWSZOvWI/nYx27P1PeWWOUhBtcMZvFFizO8cVgByxNXHNtPduXzV2bknJEkyfi+8dx75b1pTlbzvdZqJQcOTOR977shmzYtyKtetSadprvhpH3ta/fli188tiz51FQzd9xxUBELAMw6ylgAAJhDiiJ54QtPy0/91NlVR5mXurrqecMbNhy//eUv78i//MudythHMbR2KGtfvbbqGMxCRb3IyhesPH774JaD2fGVHZWVsUly8OBk/vIvb8hTnrI4L3/5GWk0alYkgCfpewv5ffWr9+X3fu9bFacBADg5likGAIBZ7vzzR/KWt2xKrXbshP/FFy/O+vXD1YYiSbJz59FceeW9+fKXd+S///dbqo7TNvpX9Wf1y1an//T+DHutcgpMHprM7mt3Z/8t+3P3p+5OKpycW7iwO8973oo8/elL88Y3brj/Z3MrStmT5RjOJ9/85q78wz/clOuv35frrttTdRwAgBNmmWIAAJgDiuLYJOaZZw7mx398bTo6alVH4vssWdKTV71qTYoi+djHbj8+6TMx0cz09DxcZ/H+5WV7l/ZmxXNXpOY1yynSGGhk+aXLU++u557L70lzsplWRd9je/aM5Z/+6fZMTjbz4z9+bLniRsNr/eQpYueyVquV8fHp40sQb9lyMP/jf2xJszkPf1cCAHOOyVgAAJilzjprML/zO5dk1aqBbNw4bF/YNrZz59HceuuBJK20Wsmf/Mm3c8UV91Yda8b1LOnJxp/cmJ6lPRlYNaBb4ZSbODiRI9uOZPtV27P101srzbJoUXfWrx/Kq161Jq9//YbHfwLMY7t3H82v//rXsmPH0STHLmq4+eb91YYCADgJJmMBAGAWq9WKLFzYldWrB/L0py/L0JCLKtvdkiU9WbKkJ8mx6Z///b/vyk037cveveOZmJjb+8k2Bhqp3T8V2LOkJws2LUhjoFFxKuaqzsHOdJ7dmcPbDqdrpCtTR6YyPT5dSZbdu8eye/dYzj57JNu3H7n/3iIjI13p6qpXkgnaTavVyt6949m69XC+/vWd2bbtyOM/CQBgljEZCwAAs8yiRd35q796TtavH8qyZX2p140Xzi6t7Nw5ll27juYXfuGqXHvt3N0Lr6gXOfutZ2fk7JEkx5Yo7hrpSlHzmqVck0cmM3lwMrf9j9uy/artlWYZHu7MggVdSY4tLf8nf/KsPPWpSyrNBO3i6NGp/MIvXJWrr96V7duPZGrKssQAwNxgMhYAAGahoji2NPHq1QNZu3YoK1f2Vx2JJ6XIkiU9GRxs5LzzFmZiopnbbjsw5yZkuxd1p2ukK/0r+9O7vLfqOMwzjb5GGn2N9K/uz+C9gxndPpqpo1OVZNm/fyL79x87EdPZWct3v7snvb0dWbt2MN3dTssw/+zYMZodO0aTJGNj07n11gPZuvVwxakAAMpjMhYAAGaJrq56/v7vn5dnPWtZ+voaqZkunNVarVZGR6eydevhvPrVn5tzJ6I3vGFDTvuB01LvqqcwvU1FmhPNTI1N5bo/vi57v7O36jhJkt7ejixf3puPfOQHsmbNUNVxYMb96Z9+O3/2Z99OkrRayejoVKanTcQCAHOLyVgAAJgl1q0bysaNw0mSRqOWVasGMjBwqi6ibCU5kZKs7MfPBw8ck6Io0tfXyJIlPXnhC0/LLbfsz1e/et+sX6Kxd3lvBlYPpH9Vfzp6/clJtWqdtTTqjYycO5J6Zz37bthX2YTs94yOTmX37rF8/vP35Prr9yVJli3ryVOessRFNo+prN8pfheWbc+esXztaw/8fvvOd/bk4MHJilMBAMwck7EAANDGfumXzs9v/MaFx2/XakWKYn6f1J1rWq1Wms1WvvnN3XnVqz6TQ4dm9wnq1S9bnQ1v2HDsdeqlSptoNVuZODCRa959TQ63yRT6g4vXF7/49Lz//c9PR0etwkRQjq98ZUde/erPZXT02IUQrVYrrdl93REAwOMyGQsAAG1u3bqhvOhFp+eZz1yaet3J+bmsKIrU60WWL+/NT//02bn++r359Ke3zroT1b3Le7PkkiVZcM6CFKb7aDNFrUhHT0dWvmBlDt99ONu/tD3NivdpbjYf+Ca//faDee97v5uLLlqcSy9dXmEqOHUOHBjPxz9+R66/fm/Gx6cf8poHAJhPTMYCAEAbeuUrz8z73vdcS1bOQ5/4xB15y1uunHUnrZc8dUku+JUL7A9L2zt89+F8/V1fz2QbLpP6Mz9zdt7znqd+372+p5hNHvjddccdh/Lyl38q9947WmEeAIBqPHgyVhkLAABtZPXq/vz0T5+djRsX5LnPXW5J4nnozjsP5oor7s3ll9+T//N/7q46zuPqXtSd1S9bnf7T+rPogkV6I9re5OHJ7Lx6Zw5uOZitn96aVhtd+HD22Qvy1KcuSZLU60Xe+MYNOfvskYpTwRPTbLbygQ/ckm9/e0+S5NChyXzqU3cfX54YAGA+sUwxAAC0oUajlhUr+vLa165Pf3+j6jhU5IwzBvPGNw7myJGpfPaz2zI11WrbKdmiXqRrQVdWvmBlGn1es8wOjf5GVj5/ZboWdOWeL9yT5kSzbQrZG27Ylxtu2Jck6ego8tznrsjatUNpNGouzqEtTU+3Mj3dPP725z+/bVZcSAQAMJNMxgIAQBtYtKg77373JVm7djAXXLAoHR32iZ3v7rrrUG677UDe974b87nPbas6zsM0+htZ//r16T+tP0NrhyxPzKwzcXAiB28/mF3X7Mrdn2q/8qgokvPOW5gzzxzIu971lKxePVB1JHiYz352a973vhuTJK1WK9/97t7s2jVWcSoAgOqZjAUAgDZRFMngYGdWrOjLpZcuy8qV/VVHok2sXj2Q1av7c+WV9+Zb39qdgwcnMjnZrDrWcbVGLSNnj6R3eW/VUeBJ6RzszKILFmV873g6BzszNTaV5kT7fI+1Wsm3v70nd999KFu3Hk5f37FTOI1GPYODDZOyVGJqqpmDByeOr9hw880Hcvnl91ScCgCgvZmMBQCACvX01PMnf/KsXHTRoqxa1Z9Go151JNpKKzt2HM3OnUfzq7/6lVx99a6qAx3XtaArT/3tpypjmfUmD09mfN947vjEHbn3C/dWHedh6vUip5/en+7uY78fLrlkSX7/95+ezk6/L5h5d9xxMD//81dl377xJMn+/ePZseNoxakAANqPyVgAAGgTtVqR1av7s2bNUNVRaEtFli3rzchIVzZtWpD9+ydy112HMlHl9F6R9CzuSc+SnhQdJvOY/Rr9jTT6Gxk4fSD9p/fn6O6jmT46XXWs46anW7nzzkPHb/f3N3LjjfuOl7FLlvRk4cLuquIxx7VarWzbdiSHD08mSe6441Buumn/8TIWAIDHZzIWAAAq1NfXkY997EV56lOXVh2FNtZqtXLo0GS2bx/Na1/7+dx++8HKstQatWx+++Ys2LQgHX0dKWoKWeaG6bHpTI1N5bvv/W52f3N31XEeVaNRy8BAI99bpfgd77ggP/VTZ1cbijlrcrKZn/3ZL+bKK49NjU9Pt3LgwEQeOJsIAMAjMRkLAABt4NxzR3LGGQMZGuqqOgptriiKDA52ptls5dJLl2Xx4u5885u7Z3wP2f5V/eld1puepT1pDDRm9HND2erd9dQ6axleP5zWVCsHbj2QqaNTVcd6mMnJZvbufWAq8dvf3pvPfW5bkqSjo8iFFy7O0JCL7Xny7r77UG655UCSY3vE3nHHoezZYxIWAODJMhkLAAAVKIrkve+9NK985Znp6KilZrqQJ6DVamVqqpXrr9+bH/uxz8z4MpHn/PtzsuJ5K1Kr1xIvWeao1nQrk4cnc81vX5NDdxx6/CdUrF4vjv8OGRho5KMffWEuvHBxxamYzf76r6/Pu951zfHbU1NNk7AAACfIZCwAAFTo4osX5YILFmX9+uHje/7BE1EURRqNIkuW9OQ1r1mXm2/en89/fluaJQ/IDq4ZzNC6ofSv6k+to1buJ4OKFfUi9e56lj1zWfpP68/Or+/M9Hj77CH7/aanW5mePtaUHTkylU984s588/uWWV67dijPec7yFIWrKHjA6OhU/r//764cODDxkPu/8pX7ZnzlBQCAucxkLAAAzLB3vvOivP3t599/y4lxnoxjf8Z9+tPb8vrXX56pqXJPmp/1Y2dl3U+sK/VzQDsa3T6ar/+nr2d8hqfQT7VXvWpN/uIvLrUKAw+xY8doXv7yT2XLlur2IQcAmKtMxgIAQAUuumhRfuRHzszTn74kSlhOzsy8fgbPHMzyS5dneMPwjHw+oBzXXbc773rX1Tk2GFvkla88MxdeuKjqWMygK6+8N5///LaH3HfkyFT27BmrKBEAwPyhjAUAgBmyadOC/NzPnWOZSE6Zoji2X+T0dErZz6/vtL6c8fIzXDvAvFbUimPfA7N4z8xbbjmQW245cPz2hg3DOf/8kYc9rl4v4ht+bmi1Wmk2H3jRXn31zrz3vddXmAgAYP5SxgIAAMxSmzcvzN/8zXPzmc9szQc/eGvVcWDO6VrQlbN/5uwc3HIwWz62Ja2pWdzIPsjf/d2N+cxntj7kvjPPHMiv/uqF6e11qmguuO22g/mDP7g24/fvd3zrrQce5xkAAJTFf2EDAEDJ6vUifX0d6enxn9+cWkuX9uaHfmh1du8ey7/8y10ZG5vKxMTJ7x9b1IrUu+upd9VPQUqYverd9Sy+aHE6ejpy9/+9O1OjU2megu+xql133Z5cd92eh9x3/vkjectbNmVysvGge4v09NTT2elnQTsaG5s6XrZ+v61bD+dTn7o7o6NTM5wKAIDvV7RaDyxm1dnZWWUWAACYky68cFHe856nZvny3qxa1R9LQHKq7dp1NPfccyR/8RfX55/+6faT/ngDqwey6S2b0r2wOz1Le05BQpjdpo5OZXT7aHZevTNbPrql6jil6O3tyJo1g/cvVfyAX/zF8/Pyl59RTSge09///Y350IceeVWEI0emsmXLwYcsVQwAwMyZmJg4/rZL8wEAoCSNRi3Ll/dm3bqhXHDBonSZMqQkixf33P+v+6Q+TlEv0j3Snb6VfRlcM2gyFu7X0dORwbMGc+jOQ1VHKc3o6FS+8529D7v/5pv35/bbDz7kvs7OY7/f6vXaTMWb1yYnp3PvvaOZnn5osXrjjftz7bV7HuVZAAC0C2UsAACUZMWK3rz//S/Iaaf1pbPTCWvaX9dwVzb/8ub0LO1J3bKkQJK/+Ivv5v3vv+kh961fP5y///vnZ8GCropSzS/33HMkb3zjF7Jr19GH3H/o0GRFiQAAOBHKWAAAKEm9XsuSJT0ZGTm5aUV4os48czDPetay3HjjvuzdO37Czy9qRTqHO9M5aAsbeCRdC7oycu5IRu8bzdiusarjzIiDBydz8OBDS7+urnq+8pUdGXyEnxVFkWzcuCALF/rddyLuvffIwyaQv+eee45k27Yj2bfvxH+uAwBQPXvGAgBASc46azCf/ORLs2xZb9VRmCcmJ5sZG5vKT//0lfnMZ7ad8PN7Fvfkkt++JD2L7RMLj6TVbKU51cyWj27JHf/rjqrjVKYoks7OeopH2AK9o6PIX//1c/OSl6ya+WCz2Pvff1Pe+c6vP+L7Wq1kfHx6hhMBAHAy7BkLAAAlajRqueyyldmwYTg9PZZ6ZeY0GrUURccJ7+NY1IssumBR+k7rS0e3PxPh0RS1IvXOeobWDWXF81dk3w37cvS+o4//xDnmscrBWq3IFVfc+7jT+Zs3L8w554yUEa9t3HDDvlx77e4n9NivfvW+jI0pXAEA5iJ/ZQMAwCnW29uRd7zjgmzevPARp4ag3dQatZz1b87K8IbhqqPArLD0aUuz5JIl+c6ff2delrGPpdls5W/+5sbHfdw733nRnC9jP/e5bXn3u6+pOgYAABVTxgIAQAmKIik0sVSgVivyYz92VjZuHM6HP3xr7nucomjZM5dlcM1guhfZ3xFOhJ/xJ+fKK+99UpOgHR21/Nt/uyZnnDFQQqqHu/vuQ/nHf9ySycnmCT/36qt3lpAIAIDZRhkLAACn0LESNkmcpKcatVqRV7zizDzveSvyuc9te+wytkiWPG1Jlj97+cwFhLmkuP9fq+ogs89VV+3IVVftOOHndXfX87SnLcmqVf0lpHq4rVsP58/+7Ns5etQSwgAAPDnKWAAAOEVqtSJvfeumXHLJzJ0kBqAiRbLqxauy8NyFuf2fb8/ovaNVJ5oXJiaa+dM//XY+/OFbZ+Tz7do1lomJE5+KBQCA71HGAgDAKVIUyTOesSw/9EOrq44CSZKeno709NQzNjad1vdN7hUdRWqNWmr1WjXhYA4Y3jicgTMGsu3ybcrYGdJstvLFL26vOgYAADxhylgAAIA5qL+/kd///afnllsO5Nd+7avZt2/8Ie9f+fyVWfm8leld0VtRQgAAAJj7lLEAAHAKDA11Zni4Mz09/hOb9tDRUct55y1MT09HGo2HT7/2LOnJ8MbhmQ8Gc02RdA13pWukKxP7J9Jq2kAWAAB4gDNFAABwCrz5zRvz2teuz+LFPVVHAWAG1Tvr2fSWTRm9bzTX/fF1Gd8z/vhPAgAA5g1lLAAAnAILFnRl9eqBqmPAw3R317N588Lceeeh3HbbgTQGOtOztCddC7qqjgZzQ5F0LehKa7qVWs0ezAAAwEMpYwEAAOawFSv68rd/+7xcddX2vOlNX8jCixZl05s3pdapNAIAAICy+esbAABgDqvVivT3N+7fz7hIUS/S0duRWoc/B+FUqnXVsvgpizNy3kiKelF1HAAAoE346xsAAADgJHUOdGbjmzdm3avXpdZwugUAADjGMsUAAHASzj9/YZ7//BW56KLFVUeBx9SzpCdn/MgZ6bG3MZSmKIrEUCwAAPAgylgAADgJl1yyOL/1WxfH2XfaXe+y3qz7iXWZbLaqjgJzWxG/EgAAgOOsmwMAADAPrOjtyE+uG8qlS3uqjgJzWu/S3mx686ac9gOnVR0FAABoA8pYAACAeWCkq57nLe/NxuGuqqPAnNY51JmVz1+ZkfNGTMgCAADKWAAAAAAAAIAyKGMBAOBJ6OysZfHi7gwMNKqOAkAbqnfW0zXclXp3veooAABAhZSxAADwJDzlKYvz8Y+/OG996zmxDiUA32/k3JFc8u5L7B0LAADzXEfVAQAAYDbq62tk3bqhNBomnphd+htFVvc3sn9iOgcmmlXHgTmro7cjHb0d6RzqrDoKAABQIZOxAAAA88gFI9155+aFeeaSnqqjAAAAwJxnMhYAAGAeadSKNGpFOmuW1wYAAICymYwFAAAAAAAAKIHJWAAAgDbXbLXyjT1j2TM2fcLP7euo5amLu9NVdy0uVGFozVBW/eCq7P3O3hzeerjqOAAAwAxTxgIAALS56VbyuXtG85194yf83OW9HTlvpCtd9RKCAY9r4eaFWbh5Yb77F99VxgIAwDykjAUAgBOwbFlP3vKWTdm4cUHqJg15Elpp5Uv3Hc3thyaf8HOareTe0akn9fkOTjTzP+84lK76Q/eIveXAxJP6eAAAAMATp4wFAIATMDLSnde/fkMWLuyuOgptqZXp1uM8opVct3c8X7rv6Iwk+v/bu/MYT+/Dvu+fmdmd3eXyECmSK9KkJCuS7MqSE1mu5dqx4xx1GxQNgsZA0ziFkQOF/yhcJ2laFKgTqEFg/ZGihqPYVlLVNpSoSaNETmzZoU2ROiJFoURS9JK7y13uvTOzu3PP776ep3+sNOJKFPfgfueZ4/UCBMz1036wAEnMvOf7fDvjKk8vdLfkzwIAAACuJ8YCAADcIYNJnX95rpXL3de/2/Vc++ZPxQIAAAA7lxgLAABwG0ZVncG3HIPtTaocWx2KrcC32XdwX/bfvT/j3jj1jY7QAwAAu4YYCwAAcBueW+7nt863r/tYVSdXerd3tyuwu739z709j/zYIzn+seNZP7Xe9BwAAGCLiLEAAACvo6rrLA8mGVXXf/xSZ5zzTsACN+nggwcze99sZg7OND0FAADYQmIsAADA6+iO63z0xHrmu9eH10HlMaMAAADA6xNjAQAArlNnvjvJymCSJOmOqyz1x1kbVjd4HQAAAMD1xFgAAIBv8Zn5Tp6c726+P3YKFgAAALgNYiwAALDnLfbHeWl1mG8k1/PtcUYCLAAAAPAGibEAANCYOslU0yN2uNf7O7z5v9/z7VE+dnItE/0VAAAAuIPEWAAAaIwQ+8a93t/hd/5cb1zl6YVuWqNr98Au9MZ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+ }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "f53abdf2", - "metadata": {}, - "source": [ - "## Step 4: Add lighting\n", - "\n", - "This section corresponds to the [Step 4 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-4-lighting).\n", - "Please read that section for an explanation of the trick used to estimate the\n", - "light intensity of pixel based on the angle of intersection between each ray\n", - "and the spheres. The changes are minimal and are primarily about handling this\n", - "intersection angle." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from algorithm import parallelize\n", + "from utils.numerics import inf\n", + "from collections import List\n", + "\n", + "\n", + "fn scene_intersect(\n", + " orig: Vec3f,\n", + " dir: Vec3f,\n", + " spheres: List[Sphere],\n", + " background: Material,\n", + ") -> Material:\n", + " var spheres_dist = inf[DType.float32]()\n", + " var material = background\n", + "\n", + " for i in range(spheres.size):\n", + " var dist = inf[DType.float32]()\n", + " if spheres[i].intersects(orig, dir, dist) and dist < spheres_dist:\n", + " spheres_dist = dist\n", + " material = spheres[i].material\n", + "\n", + " return material\n", + "\n", + "\n", + "fn cast_ray(\n", + " orig: Vec3f, dir: Vec3f, spheres: List[Sphere]\n", + ") -> Material:\n", + " var background = Material(Vec3f(0.02, 0.02, 0.02))\n", + " return scene_intersect(orig, dir, spheres, background)\n", + "\n", + "\n", + "fn create_image_with_spheres(\n", + " spheres: List[Sphere], height: Int, width: Int\n", + ") -> Image:\n", + " var image = Image(height, width)\n", + "\n", + " @parameter\n", + " fn _process_row(row: Int):\n", + " var y = -((Float32(2.0) * row + 1) / height - 1)\n", + " for col in range(width):\n", + " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", + " var dir = Vec3f(x, y, -1).normalize()\n", + " image.set(row, col, cast_ray(Vec3f.zero(), dir, spheres).color)\n", + "\n", + " parallelize[_process_row](height)\n", + "\n", + " return image\n", + "\n", + "var spheres = List[Sphere]()\n", + "spheres.append(Sphere(Vec3f(-3, 0, -16), 2, shiny_yellow))\n", + "spheres.append(Sphere(Vec3f(-1.0, -1.5, -12), 1.8, green_rubber))\n", + "spheres.append(Sphere(Vec3f( 1.5, -0.5, -18), 3, green_rubber))\n", + "spheres.append(Sphere(Vec3f( 7, 5, -18), 4, shiny_yellow))\n", + "\n", + "render(create_image_with_spheres(spheres, H, W))\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f53abdf2", + "metadata": {}, + "source": [ + "## Step 4: Add lighting\n", + "\n", + "This section corresponds to the [Step 4 of the C++\n", + "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-4-lighting).\n", + "Please read that section for an explanation of the trick used to estimate the\n", + "light intensity of pixel based on the angle of intersection between each ray\n", + "and the spheres. The changes are minimal and are primarily about handling this\n", + "intersection angle." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3ed5bc7c-f335-48c4-abf8-31c75d6e79ad", + "metadata": {}, + "outputs": [], + "source": [ + "@value\n", + "@register_passable(\"trivial\")\n", + "struct Light(CollectionElement):\n", + " var position: Vec3f\n", + " var intensity: Float32" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8b99f641", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "id": "3ed5bc7c-f335-48c4-abf8-31c75d6e79ad", - "metadata": {}, - "outputs": [], - "source": [ - "@value\n", - "@register_passable(\"trivial\")\n", - "struct Light(CollectionElement):\n", - " var position: Vec3f\n", - " var intensity: Float32" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "8b99f641", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "fn scene_intersect(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " mut material: Material,\n", - " mut hit: Vec3f,\n", - " mut N: Vec3f,\n", - ") -> Bool:\n", - " var spheres_dist = inf[DType.float32]()\n", - "\n", - " for i in range(0, spheres.size):\n", - " var dist: Float32 = 0\n", - " if spheres[i].intersects(orig, dir, dist) and dist < spheres_dist:\n", - " spheres_dist = dist\n", - " hit = orig + dir * dist\n", - " N = (hit - spheres[i].center).normalize()\n", - " material = spheres[i].material\n", - "\n", - " return (spheres_dist != inf[DType.float32]())\n", - "\n", - "\n", - "fn cast_ray(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - ") -> Material:\n", - " var point = Vec3f.zero()\n", - " var material = Material(Vec3f.zero())\n", - " var N = Vec3f.zero()\n", - " if not scene_intersect(orig, dir, spheres, material, point, N):\n", - " return bg_color\n", - "\n", - " var diffuse_light_intensity: Float32 = 0\n", - " for i in range(lights.size):\n", - " var light_dir = (lights[i].position - point).normalize()\n", - " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", - "\n", - " return material.color * diffuse_light_intensity\n", - "\n", - "\n", - "fn create_image_with_spheres_and_lights(\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " height: Int,\n", - " width: Int,\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(\n", - " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights).color\n", - " )\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "\n", - "var lights = List[Light]()\n", - "lights.append(Light(Vec3f(-20, 20, 20), 1.0))\n", - "lights.append(Light(Vec3f(20, -20, 20), 0.5))\n", - "\n", - "render(create_image_with_spheres_and_lights(spheres, lights, H, W))\n" - ] + "data": { + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "78043756-0ce9-455f-9e49-fb75268d4478", - "metadata": {}, - "source": [ - "## Step 5: Add specular lighting\n", - "\n", - "This section corresponds to the [Step 5 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-5-specular-lighting).\n", - "The changes to the code are quite minimal, but the rendered picture looks much\n", - "more realistic!" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "fn scene_intersect(\n", + " orig: Vec3f,\n", + " dir: Vec3f,\n", + " spheres: List[Sphere],\n", + " mut material: Material,\n", + " mut hit: Vec3f,\n", + " mut N: Vec3f,\n", + ") -> Bool:\n", + " var spheres_dist = inf[DType.float32]()\n", + "\n", + " for i in range(0, spheres.size):\n", + " var dist: Float32 = 0\n", + " if spheres[i].intersects(orig, dir, dist) and dist < spheres_dist:\n", + " spheres_dist = dist\n", + " hit = orig + dir * dist\n", + " N = (hit - spheres[i].center).normalize()\n", + " material = spheres[i].material\n", + "\n", + " return (spheres_dist != inf[DType.float32]())\n", + "\n", + "\n", + "fn cast_ray(\n", + " orig: Vec3f,\n", + " dir: Vec3f,\n", + " spheres: List[Sphere],\n", + " lights: List[Light],\n", + ") -> Material:\n", + " var point = Vec3f.zero()\n", + " var material = Material(Vec3f.zero())\n", + " var N = Vec3f.zero()\n", + " if not scene_intersect(orig, dir, spheres, material, point, N):\n", + " return bg_color\n", + "\n", + " var diffuse_light_intensity: Float32 = 0\n", + " for i in range(lights.size):\n", + " var light_dir = (lights[i].position - point).normalize()\n", + " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", + "\n", + " return material.color * diffuse_light_intensity\n", + "\n", + "\n", + "fn create_image_with_spheres_and_lights(\n", + " spheres: List[Sphere],\n", + " lights: List[Light],\n", + " height: Int,\n", + " width: Int,\n", + ") -> Image:\n", + " var image = Image(height, width)\n", + "\n", + " @parameter\n", + " fn _process_row(row: Int):\n", + " var y = -((Float32(2.0) * row + 1) / height - 1)\n", + " for col in range(width):\n", + " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", + " var dir = Vec3f(x, y, -1).normalize()\n", + " image.set(\n", + " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights).color\n", + " )\n", + "\n", + " parallelize[_process_row](height)\n", + "\n", + " return image\n", + "\n", + "\n", + "var lights = List[Light]()\n", + "lights.append(Light(Vec3f(-20, 20, 20), 1.0))\n", + "lights.append(Light(Vec3f(20, -20, 20), 0.5))\n", + "\n", + "render(create_image_with_spheres_and_lights(spheres, lights, H, W))\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "78043756-0ce9-455f-9e49-fb75268d4478", + "metadata": {}, + "source": [ + "## Step 5: Add specular lighting\n", + "\n", + "This section corresponds to the [Step 5 of the C++\n", + "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-5-specular-lighting).\n", + "The changes to the code are quite minimal, but the rendered picture looks much\n", + "more realistic!" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3ed5bc7c-f335-48c4-abf8-31c75d6e79ad", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "id": "3ed5bc7c-f335-48c4-abf8-31c75d6e79ad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "fn reflect(I: Vec3f, N: Vec3f) -> Vec3f:\n", - " return I - N * (I @ N) * 2.0\n", - "\n", - "\n", - "fn cast_ray(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - ") -> Material:\n", - " var point = Vec3f.zero()\n", - " var material = Material(Vec3f.zero())\n", - " var N = Vec3f.zero()\n", - " if not scene_intersect(orig, dir, spheres, material, point, N):\n", - " return bg_color\n", - "\n", - " var diffuse_light_intensity: Float32 = 0\n", - " var specular_light_intensity: Float32 = 0\n", - " for i in range(lights.size):\n", - " var light_dir = (lights[i].position - point).normalize()\n", - " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", - " specular_light_intensity += (\n", - " pow(\n", - " max(-reflect(-light_dir, N) @ dir, 0.0),\n", - " material.specular_component,\n", - " )\n", - " * lights[i].intensity\n", - " )\n", - "\n", - " var result = material.color * diffuse_light_intensity * material.albedo.data[\n", - " 0\n", - " ] + Vec3f(\n", - " 1.0, 1.0, 1.0\n", - " ) * specular_light_intensity * material.albedo.data[\n", - " 1\n", - " ]\n", - " var result_max = max(result[0], max(result[1], result[2]))\n", - " # Cap the resulting vector\n", - " if result_max > 1:\n", - " return result * (1.0 / result_max)\n", - " return result\n", - "\n", - "\n", - "fn create_image_with_spheres_and_specular_lights(\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " height: Int,\n", - " width: Int,\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(\n", - " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights).color\n", - " )\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "render(create_image_with_spheres_and_specular_lights(spheres, lights, H, W))\n" - ] + "data": { + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "a6e7fc32", - "metadata": {}, - "source": [ - "## Step 6: Add background\n", - "\n", - "As a last step, let's use an image for the background instead of a uniform\n", - "fill. The only code that we need to change is the code where we used to return\n", - "`bg_color`. Now we will determine a point in the background image to which the\n", - "ray is directed and draw that." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "fn reflect(I: Vec3f, N: Vec3f) -> Vec3f:\n", + " return I - N * (I @ N) * 2.0\n", + "\n", + "\n", + "fn cast_ray(\n", + " orig: Vec3f,\n", + " dir: Vec3f,\n", + " spheres: List[Sphere],\n", + " lights: List[Light],\n", + ") -> Material:\n", + " var point = Vec3f.zero()\n", + " var material = Material(Vec3f.zero())\n", + " var N = Vec3f.zero()\n", + " if not scene_intersect(orig, dir, spheres, material, point, N):\n", + " return bg_color\n", + "\n", + " var diffuse_light_intensity: Float32 = 0\n", + " var specular_light_intensity: Float32 = 0\n", + " for i in range(lights.size):\n", + " var light_dir = (lights[i].position - point).normalize()\n", + " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", + " specular_light_intensity += (\n", + " pow(\n", + " max(-reflect(-light_dir, N) @ dir, 0.0),\n", + " material.specular_component,\n", + " )\n", + " * lights[i].intensity\n", + " )\n", + "\n", + " var result = material.color * diffuse_light_intensity * material.albedo.data[\n", + " 0\n", + " ] + Vec3f(\n", + " 1.0, 1.0, 1.0\n", + " ) * specular_light_intensity * material.albedo.data[\n", + " 1\n", + " ]\n", + " var result_max = max(result[0], max(result[1], result[2]))\n", + " # Cap the resulting vector\n", + " if result_max > 1:\n", + " return result * (1.0 / result_max)\n", + " return result\n", + "\n", + "\n", + "fn create_image_with_spheres_and_specular_lights(\n", + " spheres: List[Sphere],\n", + " lights: List[Light],\n", + " height: Int,\n", + " width: Int,\n", + ") -> Image:\n", + " var image = Image(height, width)\n", + "\n", + " @parameter\n", + " fn _process_row(row: Int):\n", + " var y = -((Float32(2.0) * row + 1) / height - 1)\n", + " for col in range(width):\n", + " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", + " var dir = Vec3f(x, y, -1).normalize()\n", + " image.set(\n", + " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights).color\n", + " )\n", + "\n", + " parallelize[_process_row](height)\n", + "\n", + " return image\n", + "\n", + "render(create_image_with_spheres_and_specular_lights(spheres, lights, H, W))\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a6e7fc32", + "metadata": {}, + "source": [ + "## Step 6: Add background\n", + "\n", + "As a last step, let's use an image for the background instead of a uniform\n", + "fill. The only code that we need to change is the code where we used to return\n", + "`bg_color`. Now we will determine a point in the background image to which the\n", + "ray is directed and draw that." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3de30ee3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "id": "3de30ee3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "fn cast_ray(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " bg: Image,\n", - ") -> Material:\n", - " var point = Vec3f.zero()\n", - " var material = Material(Vec3f.zero())\n", - " var N = Vec3f.zero()\n", - " if not scene_intersect(orig, dir, spheres, material, point, N):\n", - " # Background\n", - " # Given a direction vector `dir` we need to find a pixel in the image\n", - " var x = dir[0]\n", - " var y = dir[1]\n", - "\n", - " # Now map x from [-1,1] to [0,w-1] and do the same for y.\n", - " var w = bg.width\n", - " var h = bg.height\n", - " var col = int((1.0 + x) * 0.5 * (w - 1))\n", - " var row = int((1.0 + y) * 0.5 * (h - 1))\n", - " return Material(bg.pixels[bg._pos_to_index(row, col)])\n", - "\n", - " var diffuse_light_intensity: Float32 = 0\n", - " var specular_light_intensity: Float32 = 0\n", - " for i in range(lights.size):\n", - " var light_dir = (lights[i].position - point).normalize()\n", - " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", - " specular_light_intensity += (\n", - " pow(\n", - " max(-reflect(-light_dir, N) @ dir, 0.0),\n", - " material.specular_component,\n", - " )\n", - " * lights[i].intensity\n", - " )\n", - "\n", - " var result = material.color * diffuse_light_intensity * material.albedo.data[\n", - " 0\n", - " ] + Vec3f(\n", - " 1.0, 1.0, 1.0\n", - " ) * specular_light_intensity * material.albedo.data[\n", - " 1\n", - " ]\n", - " var result_max = max(result[0], max(result[1], result[2]))\n", - " # Cap the resulting vector\n", - " if result_max > 1:\n", - " return result * (1.0 / result_max)\n", - " return result\n", - "\n", - "\n", - "fn create_image_with_spheres_and_specular_lights(\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " height: Int,\n", - " width: Int,\n", - " bg: Image,\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(\n", - " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights, bg).color\n", - " )\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "\n", - "var bg = load_image(\"images/background.png\")\n", - "render(\n", - " create_image_with_spheres_and_specular_lights(spheres, lights, H, W, bg)\n", - ")\n" - ] + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" }, { - "attachments": {}, - "cell_type": "markdown", - "id": "766de19f-200c-4dce-8678-b36cc3c3dc93", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "We've only explored the basics of ray tracing here, but you can add shadows,\n", - "reflections and so much more! Fortunately these are explained in [the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing),\n", - "and we leave the corresponding Mojo implementations as an exercise for you." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Mojo", - "language": "mojo", - "name": "mojo-jupyter-kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "mojo" - }, - "file_extension": ".mojo", - "mimetype": "text/x-mojo", - "name": "mojo" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] } + ], + "source": [ + "fn cast_ray(\n", + " orig: Vec3f,\n", + " dir: Vec3f,\n", + " spheres: List[Sphere],\n", + " lights: List[Light],\n", + " bg: Image,\n", + ") -> Material:\n", + " var point = Vec3f.zero()\n", + " var material = Material(Vec3f.zero())\n", + " var N = Vec3f.zero()\n", + " if not scene_intersect(orig, dir, spheres, material, point, N):\n", + " # Background\n", + " # Given a direction vector `dir` we need to find a pixel in the image\n", + " var x = dir[0]\n", + " var y = dir[1]\n", + "\n", + " # Now map x from [-1,1] to [0,w-1] and do the same for y.\n", + " var w = bg.width\n", + " var h = bg.height\n", + " var col = int((1.0 + x) * 0.5 * (w - 1))\n", + " var row = int((1.0 + y) * 0.5 * (h - 1))\n", + " return Material(bg.pixels[bg._pos_to_index(row, col)])\n", + "\n", + " var diffuse_light_intensity: Float32 = 0\n", + " var specular_light_intensity: Float32 = 0\n", + " for i in range(lights.size):\n", + " var light_dir = (lights[i].position - point).normalize()\n", + " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", + " specular_light_intensity += (\n", + " pow(\n", + " max(-reflect(-light_dir, N) @ dir, 0.0),\n", + " material.specular_component,\n", + " )\n", + " * lights[i].intensity\n", + " )\n", + "\n", + " var result = material.color * diffuse_light_intensity * material.albedo.data[\n", + " 0\n", + " ] + Vec3f(\n", + " 1.0, 1.0, 1.0\n", + " ) * specular_light_intensity * material.albedo.data[\n", + " 1\n", + " ]\n", + " var result_max = max(result[0], max(result[1], result[2]))\n", + " # Cap the resulting vector\n", + " if result_max > 1:\n", + " return result * (1.0 / result_max)\n", + " return result\n", + "\n", + "\n", + "fn create_image_with_spheres_and_specular_lights(\n", + " spheres: List[Sphere],\n", + " lights: List[Light],\n", + " height: Int,\n", + " width: Int,\n", + " bg: Image,\n", + ") -> Image:\n", + " var image = Image(height, width)\n", + "\n", + " @parameter\n", + " fn _process_row(row: Int):\n", + " var y = -((Float32(2.0) * row + 1) / height - 1)\n", + " for col in range(width):\n", + " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", + " var dir = Vec3f(x, y, -1).normalize()\n", + " image.set(\n", + " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights, bg).color\n", + " )\n", + "\n", + " parallelize[_process_row](height)\n", + "\n", + " return image\n", + "\n", + "\n", + "var bg = load_image(\"images/background.png\")\n", + "render(\n", + " create_image_with_spheres_and_specular_lights(spheres, lights, H, W, bg)\n", + ")\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "766de19f-200c-4dce-8678-b36cc3c3dc93", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "We've only explored the basics of ray tracing here, but you can add shadows,\n", + "reflections and so much more! Fortunately these are explained in [the C++\n", + "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing),\n", + "and we leave the corresponding Mojo implementations as an exercise for you." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Mojo", + "language": "mojo", + "name": "mojo-jupyter-kernel" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "mojo" + }, + "file_extension": ".mojo", + "mimetype": "text/x-mojo", + "name": "mojo" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } From 4645771c62a6644a6947b4b8e9844664f43a69f7 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sun, 8 Dec 2024 06:29:02 +0000 Subject: [PATCH 056/509] Update lockfiles to point to latest nightly version: 24.6.0.dev2024120805 --- examples/life/magic.lock | 829 ++++++++++++++++--------------- examples/magic.lock | 730 ++++++++++++++------------- examples/notebooks/magic.lock | 906 ++++++++++++++++++---------------- examples/operators/magic.lock | 730 ++++++++++++++------------- magic.lock | 730 ++++++++++++++------------- 5 files changed, 2032 insertions(+), 1893 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 3326e0e536..78ae0c08bf 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -25,11 +25,11 @@ environments: - 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md5: ce9ee67733d1ab8c43031b67b475dba8 + url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-24.6.0.dev2024120805-release.conda + sha256: 1535cecc4930833e604ed39a4450b421914312f4abbb4fc6af9e7aedd5293ffd + md5: ad285352ec9e9061d8facbf62f708d9d depends: - - max-core ==24.6.0.dev2024120705 release + - max-core ==24.6.0.dev2024120805 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 11370 - timestamp: 1733548613220 + size: 11369 + timestamp: 1733635043556 - kind: conda name: multidict version: 6.1.0 From 647133813e6d9c949422d1e7824735dd7153fad7 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sat, 7 Dec 2024 21:26:47 -0800 Subject: [PATCH 057/509] [******][GPU] Add log log2_floor in the bit module MODULAR_ORIG_COMMIT_REV_ID: 78e0005db0bb77465e11d6ce856da56116c839dc --- stdlib/src/bit/__init__.mojo | 1 + stdlib/src/bit/bit.mojo | 22 +++++++++++++++++++++- stdlib/test/bit/test_bit.mojo | 27 +++++++++++++++++++++++++++ 3 files changed, 49 insertions(+), 1 deletion(-) diff --git a/stdlib/src/bit/__init__.mojo b/stdlib/src/bit/__init__.mojo index e41c3ca52c..75004d3618 100644 --- a/stdlib/src/bit/__init__.mojo +++ b/stdlib/src/bit/__init__.mojo @@ -21,6 +21,7 @@ from .bit import ( byte_swap, count_leading_zeros, count_trailing_zeros, + log2_floor, is_power_of_two, pop_count, rotate_bits_left, diff --git a/stdlib/src/bit/bit.mojo b/stdlib/src/bit/bit.mojo index 74a428d3df..fb4170f825 100644 --- a/stdlib/src/bit/bit.mojo +++ b/stdlib/src/bit/bit.mojo @@ -343,10 +343,30 @@ fn bit_width[ return bitwidth - leading_zero +# ===-----------------------------------------------------------------------===# +# log2_floor +# ===-----------------------------------------------------------------------===# + + +@always_inline +fn log2_floor(val: Int) -> Int: + """Returns the floor of the base-2 logarithm of an integer value. + + Args: + val: The input value. + + Returns: + The floor of the base-2 logarithm of the input value, which is equal to + the position of the highest set bit. Returns -1 if val is 0. + """ + if val <= 1: + return 0 + return bitwidthof[Int]() - count_leading_zeros(val) - 1 + + # ===-----------------------------------------------------------------------===# # is_power_of_two # ===-----------------------------------------------------------------------===# -# reference: https://en.cppreference.com/w/cpp/numeric/has_single_bit @always_inline diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index dee81507d1..1f1063eee0 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -25,7 +25,9 @@ from bit import ( pop_count, rotate_bits_left, rotate_bits_right, + log2_floor, ) +from math import log2, floor from testing import assert_equal @@ -497,6 +499,30 @@ def test_rotate_bits_simd(): assert_equal(rotate_bits_right[6](Scalar[type](96)), 129) +fn _log2_floor(n: Int) -> Int: + return int(floor(log2(float(n)))) + + +def test_log2_floor(): + assert_equal(log2_floor(0), 0) + for i in range(1, 100): + assert_equal( + log2_floor(i), + _log2_floor(i), + msg="mismatching value for the input value of " + str(i), + ) + + fn _check_alias[n: Int](expected: Int) raises: + alias res = log2_floor(n) + assert_equal(res, expected) + + _check_alias[0](0) + _check_alias[1](0) + _check_alias[2](1) + _check_alias[15](3) + _check_alias[32](5) + + def main(): test_rotate_bits_int() test_rotate_bits_simd() @@ -519,3 +545,4 @@ def main(): test_pop_count() test_pop_count_simd() test_bit_not_simd() + test_log2_floor() From f87dec5d493bf4cae2db1fab78e264ba80f1f3fe Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 7 Dec 2024 23:41:55 -0800 Subject: [PATCH 058/509] [mojo-lang] Add support for named results using `out` syntax. This adds support for spelling "named functions results" using the same `out` syntax used by initializers (in addition to `-> T as name`). Functions may have at most one named result or return type specified with the usual `->` syntax. `out` arguments may occur anywhere in the argument list, but are typically last (except for `__init__` methods, where they are typically first). ```mojo # This function has type "fn() -> String" fn example(out result: String): result = "foo" ``` The parser still accepts the old syntax as a synonym for this, but that will eventually be deprecated and removed. This was discussed extensively here: https://github.com/modularml/mojo/issues/3623 MODULAR_ORIG_COMMIT_REV_ID: 23b3a120227a42d9550ba76d8cafb63c3a03edcf --- docs/changelog.md | 18 ++++ stdlib/src/builtin/_pybind.mojo | 12 ++- stdlib/src/builtin/coroutine.mojo | 16 ++-- stdlib/src/builtin/int.mojo | 2 +- stdlib/src/builtin/simd.mojo | 2 +- stdlib/src/collections/deque.mojo | 4 +- stdlib/src/memory/unsafe_pointer.mojo | 15 ++-- stdlib/src/utils/index.mojo | 103 ++++++++++++++++------- stdlib/test/os/path/test_expandvars.mojo | 2 +- 9 files changed, 120 insertions(+), 54 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 3673d58fd7..0576359789 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -335,6 +335,24 @@ what we publish. release of Mojo, but will be removed in the future. Please migrate to the new syntax. +- Similarly, the spelling of "named functions results" has switched to use `out` + syntax instead of `-> T as name`. Functions may have at most one named result + or return type specified with the usual `->` syntax. `out` arguments may + occur anywhere in the argument list, but are typically last (except for + `__init__` methods, where they are typically first). + + ```mojo + # This function has type "fn() -> String" + fn example(out result: String): + result = "foo" + ``` + + The parser still accepts the old syntax as a synonym for this, but that will + eventually be deprecated and removed. + + This was [discussed extensively in a public + proposal](https://github.com/modularml/mojo/issues/3623). + - More things have been removed from the auto-exported set of entities in the `prelude` module from the Mojo standard library. - `UnsafePointer` has been removed. Please explicitly import it via diff --git a/stdlib/src/builtin/_pybind.mojo b/stdlib/src/builtin/_pybind.mojo index 24b0fdc619..5e1d8c874c 100644 --- a/stdlib/src/builtin/_pybind.mojo +++ b/stdlib/src/builtin/_pybind.mojo @@ -61,10 +61,13 @@ fn fail_initialization(owned err: Error) -> PythonObject: fn pointer_bitcast[ To: AnyType -](ptr: Pointer) -> Pointer[To, ptr.origin, ptr.address_space, *_, **_] as out: - return __type_of(out)( +]( + ptr: Pointer, + out result: Pointer[To, ptr.origin, ptr.address_space, *_, **_], +): + return __type_of(result)( _mlir_value=__mlir_op.`lit.ref.from_pointer`[ - _type = __type_of(out)._mlir_type + _type = __type_of(result)._mlir_type ]( UnsafePointer(__mlir_op.`lit.ref.to_pointer`(ptr._value)) .bitcast[To]() @@ -162,7 +165,8 @@ fn _try_convert_arg[ type_name_id: StringLiteral, py_args: TypedPythonObject["Tuple"], argidx: Int, -) raises -> T as result: + out result: T, +) raises: try: result = T.try_from_python(py_args[argidx]) except convert_err: diff --git a/stdlib/src/builtin/coroutine.mojo b/stdlib/src/builtin/coroutine.mojo index 04688695c8..c2c0ee1d81 100644 --- a/stdlib/src/builtin/coroutine.mojo +++ b/stdlib/src/builtin/coroutine.mojo @@ -137,7 +137,7 @@ struct Coroutine[type: AnyType, origins: OriginSet]: __disable_del self @always_inline - fn __await__(owned self) -> type as out: + fn __await__(owned self, out result: type): """Suspends the current coroutine until the coroutine is complete. Returns: @@ -150,9 +150,11 @@ struct Coroutine[type: AnyType, origins: OriginSet]: __disable_del self __mlir_op.`co.await`[_type=NoneType]( handle, - __mlir_op.`lit.ref.to_pointer`(__get_mvalue_as_litref(out)), + __mlir_op.`lit.ref.to_pointer`(__get_mvalue_as_litref(result)), + ) + __mlir_op.`lit.ownership.mark_initialized`( + __get_mvalue_as_litref(result) ) - __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(out)) # ===----------------------------------------------------------------------=== # @@ -220,7 +222,7 @@ struct RaisingCoroutine[type: AnyType, origins: OriginSet]: __disable_del self @always_inline - fn __await__(owned self) raises -> type as out: + fn __await__(owned self, out result: type) raises: """Suspends the current coroutine until the coroutine is complete. Returns: @@ -233,7 +235,7 @@ struct RaisingCoroutine[type: AnyType, origins: OriginSet]: __disable_del self if __mlir_op.`co.await`[_type = __mlir_type.i1]( handle, - __mlir_op.`lit.ref.to_pointer`(__get_mvalue_as_litref(out)), + __mlir_op.`lit.ref.to_pointer`(__get_mvalue_as_litref(result)), __mlir_op.`lit.ref.to_pointer`( __get_mvalue_as_litref(__get_nearest_error_slot()) ), @@ -242,4 +244,6 @@ struct RaisingCoroutine[type: AnyType, origins: OriginSet]: __get_mvalue_as_litref(__get_nearest_error_slot()) ) __mlir_op.`lit.raise`() - __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(out)) + __mlir_op.`lit.ownership.mark_initialized`( + __get_mvalue_as_litref(result) + ) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 2c9a13234b..92cb98be75 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -1154,7 +1154,7 @@ struct Int( @doc_private @staticmethod - fn try_from_python(obj: PythonObject) raises -> Self as result: + fn try_from_python(obj: PythonObject, out result: Self) raises: """Construct an `Int` from a Python integer value. Raises: diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 2471fbf58a..d93be3a050 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -2329,7 +2329,7 @@ struct SIMD[type: DType, size: Int]( ](self, value, Int64(offset)) @always_inline("nodebug") - fn join(self, other: Self) -> SIMD[type, 2 * size] as result: + fn join(self, other: Self, out result: SIMD[type, 2 * size]): """Concatenates the two vectors together. Args: diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index a0bb3cd262..284fb630da 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -794,7 +794,7 @@ struct Deque[ElementType: CollectionElement]( return (self._data + self._head)[] - fn pop(mut self) raises -> ElementType as element: + fn pop(mut self, out element: ElementType) raises: """Removes and returns the element from the right side of the deque. Returns: @@ -818,7 +818,7 @@ struct Deque[ElementType: CollectionElement]( return - fn popleft(mut self) raises -> ElementType as element: + fn popleft(mut self, out element: ElementType) raises: """Removes and returns the element from the left side of the deque. Returns: diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index f4971c68af..047bb61ac7 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -154,13 +154,14 @@ struct UnsafePointer[ @always_inline("nodebug") fn address_of( ref [address_space]arg: type, - ) -> UnsafePointer[ - type, - address_space=address_space, - alignment=1, - origin=MutableAnyOrigin - # TODO: Propagate origin of the argument. - ] as result: + out result: UnsafePointer[ + type, + address_space=address_space, + alignment=1, + origin=MutableAnyOrigin + # TODO: Propagate origin of the argument. + ], + ): """Gets the address of the argument. Args: diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index b526aa84ff..0ea7685fe4 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -454,9 +454,10 @@ struct IndexList[ @always_inline("nodebug") fn canonicalize( self, - ) -> IndexList[ - size, element_bitwidth = bitwidthof[Int](), unsigned=False - ] as result: + out result: IndexList[ + size, element_bitwidth = bitwidthof[Int](), unsigned=False + ], + ): """Canonicalizes the IndexList. Returns: @@ -772,11 +773,14 @@ struct IndexList[ @always_inline fn cast[ type: DType - ](self) -> IndexList[ - size, - element_bitwidth = bitwidthof[type](), - unsigned = _is_unsigned[type](), - ] as result: + ]( + self, + out result: IndexList[ + size, + element_bitwidth = bitwidthof[type](), + unsigned = _is_unsigned[type](), + ], + ): """Casts to the target DType. Parameters: @@ -803,9 +807,12 @@ struct IndexList[ *, element_bitwidth: Int = Self.element_bitwidth, unsigned: Bool = Self.unsigned, - ](self) -> IndexList[ - size, element_bitwidth=element_bitwidth, unsigned=unsigned - ] as result: + ]( + self, + out result: IndexList[ + size, element_bitwidth=element_bitwidth, unsigned=unsigned + ], + ): """Casts to the target DType. Parameters: @@ -837,9 +844,12 @@ fn Index[ *, element_bitwidth: Int = bitwidthof[Int](), unsigned: Bool = False, -](x: T0) -> IndexList[ - 1, element_bitwidth=element_bitwidth, unsigned=unsigned -] as result: +]( + x: T0, + out result: IndexList[ + 1, element_bitwidth=element_bitwidth, unsigned=unsigned + ], +): """Constructs a 1-D Index from the given value. Parameters: @@ -859,9 +869,12 @@ fn Index[ @always_inline fn Index[ *, element_bitwidth: Int = bitwidthof[Int](), unsigned: Bool = False -](x: UInt) -> IndexList[ - 1, element_bitwidth=element_bitwidth, unsigned=unsigned -] as result: +]( + x: UInt, + out result: IndexList[ + 1, element_bitwidth=element_bitwidth, unsigned=unsigned + ], +): """Constructs a 1-D Index from the given value. Parameters: @@ -884,9 +897,13 @@ fn Index[ *, element_bitwidth: Int = bitwidthof[Int](), unsigned: Bool = False, -](x: T0, y: T1) -> IndexList[ - 2, element_bitwidth=element_bitwidth, unsigned=unsigned -] as result: +]( + x: T0, + y: T1, + out result: IndexList[ + 2, element_bitwidth=element_bitwidth, unsigned=unsigned + ], +): """Constructs a 2-D Index from the given values. Parameters: @@ -908,9 +925,13 @@ fn Index[ @always_inline fn Index[ *, element_bitwidth: Int = bitwidthof[Int](), unsigned: Bool = False -](x: UInt, y: UInt) -> IndexList[ - 2, element_bitwidth=element_bitwidth, unsigned=unsigned -] as result: +]( + x: UInt, + y: UInt, + out result: IndexList[ + 2, element_bitwidth=element_bitwidth, unsigned=unsigned + ], +): """Constructs a 2-D Index from the given values. Parameters: @@ -935,9 +956,14 @@ fn Index[ *, element_bitwidth: Int = bitwidthof[Int](), unsigned: Bool = False, -](x: T0, y: T1, z: T2) -> IndexList[ - 3, element_bitwidth=element_bitwidth, unsigned=unsigned -] as result: +]( + x: T0, + y: T1, + z: T2, + out result: IndexList[ + 3, element_bitwidth=element_bitwidth, unsigned=unsigned + ], +): """Constructs a 3-D Index from the given values. Parameters: @@ -967,9 +993,15 @@ fn Index[ *, element_bitwidth: Int = bitwidthof[Int](), unsigned: Bool = False, -](x: T0, y: T1, z: T2, w: T3) -> IndexList[ - 4, element_bitwidth=element_bitwidth, unsigned=unsigned -] as result: +]( + x: T0, + y: T1, + z: T2, + w: T3, + out result: IndexList[ + 4, element_bitwidth=element_bitwidth, unsigned=unsigned + ], +): """Constructs a 4-D Index from the given values. Parameters: @@ -1002,9 +1034,16 @@ fn Index[ *, element_bitwidth: Int = bitwidthof[Int](), unsigned: Bool = False, -](x: T0, y: T1, z: T2, w: T3, v: T4) -> IndexList[ - 5, element_bitwidth=element_bitwidth, unsigned=unsigned -] as result: +]( + x: T0, + y: T1, + z: T2, + w: T3, + v: T4, + out result: IndexList[ + 5, element_bitwidth=element_bitwidth, unsigned=unsigned + ], +): """Constructs a 5-D Index from the given values. Parameters: diff --git a/stdlib/test/os/path/test_expandvars.mojo b/stdlib/test/os/path/test_expandvars.mojo index 4dccce1208..fead55656d 100644 --- a/stdlib/test/os/path/test_expandvars.mojo +++ b/stdlib/test/os/path/test_expandvars.mojo @@ -22,7 +22,7 @@ from testing import assert_equal struct EnvVar: var name: String - fn __init__(out self, name: String, value: String) -> None: + fn __init__(out self, name: String, value: String): self.name = name _ = os.setenv(name, value) From a0e2311ebdec34b6b13debb1ee56eb2a3cf1c39a Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Sun, 8 Dec 2024 09:04:39 -0800 Subject: [PATCH 059/509] [Docs] Implicit conversion updates. Updates for the new default explicit constructors & `@implicit` decorator. MODULAR_ORIG_COMMIT_REV_ID: ef4ab402b9e2c63747f26f7ab4e6e3208859a8d7 --- docs/manual/decorators/implicit.md | 40 +++++++++++++++ docs/manual/decorators/index.mdx | 1 + docs/manual/lifecycle/life.mdx | 82 +++++++++++++++--------------- docs/manual/parameters/index.mdx | 2 +- docs/manual/variables.mdx | 16 ++++-- 5 files changed, 94 insertions(+), 47 deletions(-) create mode 100644 docs/manual/decorators/implicit.md diff --git a/docs/manual/decorators/implicit.md b/docs/manual/decorators/implicit.md new file mode 100644 index 0000000000..1e9cd07e0b --- /dev/null +++ b/docs/manual/decorators/implicit.md @@ -0,0 +1,40 @@ +--- +title: '@implicit' +description: Marks a constructor as eligible for implicit conversion. +codeTitle: true + +--- + +You can add the `@implicit` decorator on any single-argument constructor to +identify it as eligible for implicit conversion. + +For example: + +```mojo +struct MyInt: + var value: Int + + @implicit + fn __init__(out self, value: Int): + self.value = value + + fn __init__(out self, value: Float64): + self.value = int(value) + + +``` + +This implicit conversion constructor allows you to pass an `Int` to a function +that takes a `MyInt` argument, or assign an `Int` to a variable of type `MyInt`. +However, the constructor that takes a `Float64` value is **not** an implicit +conversion constructor, so it must be invoked explicitly: + +```mojo +fn func(n: MyInt): + print("MyInt value: ", n.value) + +fn main(): + func(Int(42)) # Implicit conversion from Int: OK + func(MyInt(Float64(4.2))) # Explicit conversion from Float64: OK + func(Float64(4.2)) # Error: can't convert Float64 to MyInt +``` diff --git a/docs/manual/decorators/index.mdx b/docs/manual/decorators/index.mdx index 2a65a9f213..058580e41e 100644 --- a/docs/manual/decorators/index.mdx +++ b/docs/manual/decorators/index.mdx @@ -9,6 +9,7 @@ listing: contents: - always-inline.md - copy-capture.md + - implicit.md - nonmaterializable.md - parameter.md - register-passable.md diff --git a/docs/manual/lifecycle/life.mdx b/docs/manual/lifecycle/life.mdx index 91646caa5f..c852c4c700 100644 --- a/docs/manual/lifecycle/life.mdx +++ b/docs/manual/lifecycle/life.mdx @@ -153,12 +153,16 @@ struct MyPet: Mojo supports implicit conversion from one type to another. Implicit conversion can happen when one of the following occurs: -* You assign a value of one type to a variable with a different type. -* You pass a value of one type to a function that requires a different type. +- You assign a value of one type to a variable with a different type. +- You pass a value of one type to a function that requires a different type. In both cases, implicit conversion is supported when the target type -defines a constructor that takes a single required, non-keyword argument of the -source type. For example: +defines a constructor that meets the following criteria: + +- Is declared with the `@implicit` decorator. +- Has a single required, non-keyword argument of the source type. + +For example: ```mojo var a = Source() @@ -170,7 +174,9 @@ Mojo implicitly converts the `Source` value in `a` to a `Target` value if ```mojo struct Target: - fn __init__(out self, s: Source): ... + + @implicit + fn __init__(out self, s: Source): ... ``` With implicit conversion, the assignment above is essentially identical to: @@ -179,26 +185,22 @@ With implicit conversion, the assignment above is essentially identical to: var b = Target(a) ``` +In general, types should only support implicit conversions when the conversion +lossless, and ideally inexpensive. For example, converting an integer to a +floating-point number is usually lossless (except for very large positive and +negative integers, where the conversion may be approximate), but converting a +floating-point number to an integer is very likely to lose information. So +Mojo supports implicit conversion from `Int` to `Float64`, but not the reverse. + The constructor used for implicit conversion can take optional arguments, so the following constructor would also support implicit conversion from `Source` to `Target`: ```mojo struct Target: - fn __init__(out self, s: Source, reverse: Bool = False): ... -``` - -Implicit conversion also occurs if the type doesn't declare its own constructor, -but instead uses the [`@value` decorator](#value-decorator), *and* the type -has only one field. That's because Mojo automatically creates a member-wise -constructor for each field, and when there is only one field, that synthesized -constructor works exactly like a conversion constructor. For example, this -type also can convert a `Source` value to a `Target` value: -```mojo -@value -struct Target: - var s: Source + @implicit + fn __init__(out self, s: Source, reverse: Bool = False): ... ``` Implicit conversion can fail if Mojo can't unambiguously match the conversion to @@ -209,41 +211,39 @@ convert the values: ```mojo struct A: - fn __init__(out self, s: Source): ... + @implicit + fn __init__(out self, s: Source): ... struct B: - fn __init__(out self, s: Source): ... + @implicit + fn __init__(out self, s: Source): ... -struct Target: - fn __init__(out self, a: A): ... - fn __init__(out self, b: B): ... +struct OverloadedTarget: + @implicit + fn __init__(out self, a: A): ... + @implicit + fn __init__(out self, b: B): ... -# Fails -var t = Target(Source()) +var t = OverloadedTarget(Source()) # Error: ambiguous call to '__init__': each + # candidate requires 1 implicit conversion ``` -In this case, removing either one of the target type's constructors will fix the -problem. - -If you want to define a single-argument constructor, but you **don't** want -the types to implicitly convert, you can define the constructor with a -[keyword-only argument](/mojo/manual/functions#positional-only-and-keyword-only-arguments): +In this case, you can fix the issue by explicitly casting to one of the +intermediate types. For example: ```mojo -struct Target: - # does not support implicit conversion - fn __init__(out self, *, source: Source): ... - -# the constructor must be called with a keyword -var t = Target(source=a) +var t = OverloadedTarget(A(Source())) # OK ``` -:::note +Mojo applies at most one implicit conversion to a variable. For example: -In the future we intend to provide a more explicit method of declaring whether -a constructor should support implicit conversion. +```mojo +var t: OverloadedTarget = Source() # Error: can't implicitly convert Source + # to Target +``` -::: +Would fail because there's no direct conversion from `Source` to +`OverloadedTarget`. ## Copy constructor diff --git a/docs/manual/parameters/index.mdx b/docs/manual/parameters/index.mdx index 9b3ba3250f..fae5e5f72c 100644 --- a/docs/manual/parameters/index.mdx +++ b/docs/manual/parameters/index.mdx @@ -378,7 +378,7 @@ struct MyInt: """A type that is implicitly convertible to `Int`.""" var value: Int - @always_inline("nodebug") + @implicit fn __init__(out self, _a: Int): self.value = _a diff --git a/docs/manual/variables.mdx b/docs/manual/variables.mdx index 6b77b88f0f..9247ff5c9f 100644 --- a/docs/manual/variables.mdx +++ b/docs/manual/variables.mdx @@ -183,7 +183,7 @@ its own type. For example, if you assign an integer to a variable that has a floating-point type, it converts the value instead of giving a compiler error: ```mojo -var number: Float64 = 1 +var number: Float64 = Int(1) ``` ```output @@ -191,8 +191,13 @@ var number: Float64 = 1 ``` As shown above, value assignment can be converted into a constructor call if the -target type has a constructor that takes a single argument that matches the -value being assigned. So, this code uses the `Float64` constructor that takes an +target type has a constructor that meets the following criteria: + +- It's decorated with the `@implicit` decorator. + +- It takes a single required argument that matches the value being assigned. + +So, this code uses the `Float64` constructor that takes an integer: `__init__(out self, value: Int)`. In general, implicit conversions should only be supported where the conversion @@ -200,7 +205,7 @@ is lossless. Implicit conversion follows the logic of [overloaded functions](/mojo/manual/functions#overloaded-functions). If the destination -type has a single-argument constructor that takes an argument of the source +type has a viable implicit conversion constructor for the source type, it can be invoked for implicit conversion. So assigning an integer to a `Float64` variable is exactly the same as this: @@ -215,7 +220,8 @@ implicitly convert to the required type (using one of the type's overloaded constructors). For example, you can pass an `Int` to a function that expects a `Float64`, -because `Float64` includes a constructor that takes an `Int`: +because `Float64` includes an implicit conversion constructor that takes an +`Int`: ```mojo fn take_float(value: Float64): From df36841822bc04ba3e757b773ef5ac622219165d Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sun, 8 Dec 2024 09:54:14 -0800 Subject: [PATCH 060/509] [******][GPU] Do not run gpu tests with assertions on MODULAR_ORIG_COMMIT_REV_ID: ae0b65162253dd2e39cf820d2be615f4678da830 --- stdlib/test/lit.cfg.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/stdlib/test/lit.cfg.py b/stdlib/test/lit.cfg.py index 55976ecfe6..67d1f613b2 100644 --- a/stdlib/test/lit.cfg.py +++ b/stdlib/test/lit.cfg.py @@ -78,8 +78,9 @@ def has_not(): # with assertions enabled. config.substitutions.insert(1, ("%bare-mojo", "mojo")) - # NOTE: Right now this is the same as %mojo but we should start testing + # NOTE: Right now these are the same as %mojo but we should start testing # with debug info as well + config.substitutions.insert(0, ("%mojo-no-debug-no-assert", "mojo")) config.substitutions.insert(0, ("%mojo-no-debug", base_mojo_command)) # The `mojo` nightly compiler ships with its own `stdlib.mojopkg`. For the From 58fcc1a39ae19694ab77859486d7ea9159771283 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sun, 8 Dec 2024 22:47:27 -0600 Subject: [PATCH 061/509] [stdlib] docs: Update changelog to reflect recent changes to `Origin` MODULAR_ORIG_COMMIT_REV_ID: 0d3fca509ce444c56e0cb65814e2696150e86b18 --- docs/changelog.md | 13 +++++++++++++ stdlib/src/builtin/type_aliases.mojo | 12 ++++++++++++ 2 files changed, 25 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 0576359789..a57e0db1b2 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -485,6 +485,19 @@ what we publish. for more information and rationale. As a consequence the `__lifetime_of()` operator is now named `__origin_of()`. +- `Origin` is now a complete wrapper around the MLIR origin type. + + - The `Origin.type` alias has been renamed to `_mlir_origin`. In parameter + lists, you can now write just `Origin[..]`, instead of `Origin[..].type`. + + - `ImmutableOrigin` and `MutableOrigin` are now, respectively, just aliases + for `Origin[False]` and `Origin[True]`. + + - `Origin` struct values are now supported in the brackets of a `ref [..]` + argument. + + - Added `Origin.cast_from` for casting the mutability of an origin value. + - You can now use the `+=` and `*` operators on a `StringLiteral` at compile time using the `alias` keyword: diff --git a/stdlib/src/builtin/type_aliases.mojo b/stdlib/src/builtin/type_aliases.mojo index b3358188b5..762746f8c2 100644 --- a/stdlib/src/builtin/type_aliases.mojo +++ b/stdlib/src/builtin/type_aliases.mojo @@ -67,6 +67,18 @@ struct Origin[is_mutable: Bool]: Safety: This is an UNSAFE operation if used to cast an immutable origin to a mutable origin. + + Examples: + + Cast a mutable origin to be immutable: + + ```mojo + struct Container[mut: Bool, //, origin: Origin[mut]]: + var data: Int + + fn imm_borrow(self) -> Container[ImmutableOrigin.cast_from[origin].result]: + # ... + ``` """ # ===-------------------------------------------------------------------===# From 30bf31e81eaf98d5bb6e12c5fd1576780839a1ed Mon Sep 17 00:00:00 2001 From: modularbot Date: Mon, 9 Dec 2024 17:09:00 +0000 Subject: [PATCH 062/509] Update lockfiles to point to latest nightly version: 24.6.0.dev2024120905 --- examples/life/magic.lock | 1085 ++++++++++++++++----------------- examples/magic.lock | 899 ++++++++++++++------------- examples/notebooks/magic.lock | 897 ++++++++++++++------------- examples/operators/magic.lock | 899 ++++++++++++++------------- magic.lock | 899 ++++++++++++++------------- 5 files changed, 2314 insertions(+), 2365 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 78ae0c08bf..c3b6a5b2ae 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -17,18 +17,18 @@ environments: - 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md5: d95dd6e8a70417e394bb16dad5cff408 + url: https://conda.anaconda.org/conda-forge/noarch/opentelemetry-semantic-conventions-0.49b2-pyh3cfb1c2_1.conda + sha256: 28180ffa6611f117c782c7d72066b50332c1df0bdcfed0dea4e446a20c4b7d10 + md5: e0ada55d18e6bd5a8e61943b4b5d3a8f depends: - deprecated >=1.2.6 - opentelemetry-api 1.28.2 - python >=3.9 license: Apache-2.0 - license_family: APACHE - size: 81534 - timestamp: 1732067304518 + size: 81099 + timestamp: 1733749104727 - kind: conda name: orc version: 2.0.3 @@ -8214,12 +8211,12 @@ packages: timestamp: 1699202167581 - kind: conda name: tokenizers - version: 0.20.4 + version: 0.21.0 build: py312h8360d73_0 subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/tokenizers-0.20.4-py312h8360d73_0.conda - sha256: 7b59759739b5426e17825b26d6ce9df487d1dc254d4e6a92b14a97107eb6daca - md5: 035c6de3c821559bf1af5ec072593bfd + url: https://conda.anaconda.org/conda-forge/linux-64/tokenizers-0.21.0-py312h8360d73_0.conda + sha256: 4f504a5e9d77c6d88a8f735c4319429d8bf40b742384f908a2efe0a09acc3cc5 + md5: f953aa733207f3d37acf4a3efbedba89 depends: - __glibc >=2.17,<3.0.a0 - huggingface_hub >=0.16.4,<1.0 @@ -8232,16 +8229,16 @@ packages: - __glibc >=2.17 license: Apache-2.0 license_family: APACHE - size: 2259332 - timestamp: 1732657962717 + size: 2258007 + timestamp: 1732734202127 - kind: conda name: tokenizers - version: 0.20.4 + version: 0.21.0 build: py312ha0d6ea1_0 subdir: linux-aarch64 - url: https://conda.anaconda.org/conda-forge/linux-aarch64/tokenizers-0.20.4-py312ha0d6ea1_0.conda - sha256: 6dbb7e5f3f152210817e8658bc454056e464dee16fd9757508f2417edd44d8b8 - md5: b135e3d9178b255993ea4d72741192f8 + url: https://conda.anaconda.org/conda-forge/linux-aarch64/tokenizers-0.21.0-py312ha0d6ea1_0.conda + sha256: ef0f4d4e2c798b1821187ea0ba4c86484e48abaa0e9a19fe68030fa7ff5dde84 + md5: 077f48c9e0c08a30d842e15c51df4143 depends: - huggingface_hub >=0.16.4,<1.0 - libgcc >=13 @@ -8254,16 +8251,16 @@ packages: - __glibc >=2.17 license: Apache-2.0 license_family: APACHE - size: 2330899 - timestamp: 1732658129796 + size: 2331194 + timestamp: 1732734303196 - kind: conda name: tokenizers - version: 0.20.4 + version: 0.21.0 build: py312hf3e4074_0 subdir: osx-arm64 - url: https://conda.anaconda.org/conda-forge/osx-arm64/tokenizers-0.20.4-py312hf3e4074_0.conda - sha256: fd86f8a5d41622e8f5092913dea48f41de86258583ee449045905a725672fdc2 - md5: 40e8bc575721b4da76712e90e4a9b7fa + url: https://conda.anaconda.org/conda-forge/osx-arm64/tokenizers-0.21.0-py312hf3e4074_0.conda + sha256: 5d395333fcb22dc611140286c1f2ea8b3fa220a4931c583587cb612238091555 + md5: 4c732c74b485ef7ac8ec1c548dd45e8e depends: - __osx >=11.0 - huggingface_hub >=0.16.4,<1.0 @@ -8275,8 +8272,8 @@ packages: - __osx >=11.0 license: Apache-2.0 license_family: APACHE - size: 1933005 - timestamp: 1732658406859 + size: 1931389 + timestamp: 1732734727624 - kind: conda name: tornado version: 6.4.2 @@ -8360,13 +8357,13 @@ packages: timestamp: 1733367480074 - kind: conda name: transformers - version: 4.46.3 + version: 4.47.0 build: pyhd8ed1ab_0 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/transformers-4.46.3-pyhd8ed1ab_0.conda - sha256: 6ae73c0d1197812d8fd6a2c64309fe9abe822feb66b2d330cc61ce9fa60dee0c - md5: 457af723774f077a128515a6fdd536a2 + url: https://conda.anaconda.org/conda-forge/noarch/transformers-4.47.0-pyhd8ed1ab_0.conda + sha256: b9cf6ae5fcd6c78dcaa24ebfd41580a4a10b0649ac726a44d3521f70fdece218 + md5: 495745078b8e18fe2dcc3267f4baae0d depends: - datasets !=2.5.0 - filelock @@ -8378,12 +8375,12 @@ packages: - regex !=2019.12.17 - requests - safetensors >=0.4.1 - - tokenizers >=0.20,<0.21 + - tokenizers >=0.21,<0.22 - tqdm >=4.27 license: Apache-2.0 license_family: APACHE - size: 3622494 - timestamp: 1731981383171 + size: 3721837 + timestamp: 1733708797762 - kind: conda name: typer version: 0.15.1 From 3368a633da459f20d5eccb600df6e88b52ee4955 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 9 Dec 2024 09:11:31 -0800 Subject: [PATCH 063/509] [mojo-stdlib] Minor code cleanups, NFC. This just tidies up some code to avoid using patterns that will soon be invalid: as the 'out' argument on inits is becoming more normal, you won't be able to pass a value in at a call site, it is always a result. MODULAR_ORIG_COMMIT_REV_ID: b9e5685612246c8c758b4191f3a70f61a4eecff5 --- stdlib/src/builtin/builtin_slice.mojo | 2 +- stdlib/src/builtin/simd.mojo | 2 +- stdlib/src/collections/deque.mojo | 4 ++-- stdlib/src/collections/list.mojo | 2 +- stdlib/src/collections/string.mojo | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/stdlib/src/builtin/builtin_slice.mojo b/stdlib/src/builtin/builtin_slice.mojo index 0d0fe96750..b663b9350e 100644 --- a/stdlib/src/builtin/builtin_slice.mojo +++ b/stdlib/src/builtin/builtin_slice.mojo @@ -88,7 +88,7 @@ struct Slice( Args: other: The slice to copy. """ - self.__init__(start=other.start, end=other.end, step=other.step) + self = Self(start=other.start, end=other.end, step=other.step) # ===-------------------------------------------------------------------===# # Trait implementations diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index d93be3a050..18a87f1903 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -1584,7 +1584,7 @@ struct SIMD[type: DType, size: Int]( output.write(", ") _write_scalar(output, element) output.write(")") - return output + return output^ @always_inline("nodebug") fn __floor__(self) -> Self: diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index 284fb630da..6415979277 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -88,8 +88,8 @@ struct Deque[ElementType: CollectionElement]( out self, *, owned elements: Optional[List[ElementType]] = None, - capacity: Int = self.default_capacity, - min_capacity: Int = self.default_capacity, + capacity: Int = Self.default_capacity, + min_capacity: Int = Self.default_capacity, maxlen: Int = -1, shrink: Bool = True, ): diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 13ee261638..10490c16c7 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -121,7 +121,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Args: other: The list to copy. """ - self.__init__(capacity=other.capacity) + self = Self(capacity=other.capacity) for e in other: self.append(e[]) diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 86449179a3..fe9746584d 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -867,7 +867,7 @@ struct String( Args: other: The value to copy. """ - self.__copyinit__(other) + self = other # Just use the implicit copyinit. @implicit fn __init__(out self, str: StringRef): From 51fe9e69ff7d179bf44b3cc266b1ca1b1c7cb8ad Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Mon, 9 Dec 2024 10:45:48 -0700 Subject: [PATCH 064/509] [stdlib] Skip trivial destructors in `List` If the elements are hinted as "trivial", skip running the destructors for them. Note that we don't have a checked equivalent of `std::is_trivially_destructible`, so this is just relying on the hint provided to `List`. For non-trivial types, we still run the destructors on each of the elements, of course. Future PRs will take advantage of the `hint` parameter in `copyinit`. MODULAR_ORIG_COMMIT_REV_ID: c92f98b69f1a40b7b671b6392a7b71b54cfb61b8 --- stdlib/src/collections/list.mojo | 7 +++++-- stdlib/test/collections/test_list.mojo | 14 ++++++++++++++ 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 10490c16c7..bcfca0c2fa 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -212,8 +212,11 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( fn __del__(owned self): """Destroy all elements in the list and free its memory.""" - for i in range(self.size): - (self.data + i).destroy_pointee() + + @parameter + if not hint_trivial_type: + for i in range(self.size): + (self.data + i).destroy_pointee() self.data.free() # ===-------------------------------------------------------------------===# diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index 1def6849ea..56dab6510b 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -915,6 +915,19 @@ def test_list_dtor(): assert_equal(g_dtor_count, 1) +# Verify we skip calling destructors for the trivial elements +def test_destructor_trivial_elements(): + # explicitly reset global counter + g_dtor_count = 0 + + var l = List[DtorCounter, hint_trivial_type=True]() + l.append(DtorCounter()) + + l^.__del__() + + assert_equal(g_dtor_count, 0) + + def test_list_repr(): var l = List(1, 2, 3) assert_equal(l.__repr__(), "[1, 2, 3]") @@ -959,4 +972,5 @@ def main(): test_list_contains() test_indexing() test_list_dtor() + test_destructor_trivial_elements() test_list_repr() From fc9fe840e4b682c372ca25ce27cba95bddfb307e Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Mon, 9 Dec 2024 12:25:44 -0600 Subject: [PATCH 065/509] [External] [stdlib] Micro-optimize string `splitlines` and `isnewline` (#52307) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [External] [stdlib] Micro-optimize string `splitlines` and `isnewline` ## Micro-optimize string `splitlines` and `isnewline`. Benchmark results: CPU: Intel® Core™ i7-7700HQ improvement metric: markdown percentage improvement (`(old_value - new_value) / old_value`) Average improvement: 1.2% . Many of the discrepancies are likely because of runtime (nano-second scale) pipelining (and other) differences during testing. The most robust estimates are the biggest tests which show around 4.2% improvement. |Name | old_value (ms) | new_value (ms) | improvement| |:-----------------------------|---------------------:|----------------:|------:| |`bench_string_splitlines[10]` | 0.000100497059839818 | 0.00010261460376 | -2.11% | |`bench_string_splitlines[30]` | 0.000171998352418428 | 0.000174028055158599 | -1.18% | |`bench_string_splitlines[50]` | 0.000264958575880379 | 0.000251598931395228 | 5.04% | |`bench_string_splitlines[100]` | 0.000417350538881514 | 0.000415764808815687 | 0.38% | |`bench_string_splitlines[1000]` | 0.00320600819966129 | 0.00322517645847026 | -0.60% | |`bench_string_splitlines[10000]` | 0.0302790238107332 | 0.0305872863513719 | -1.02% | |`bench_string_splitlines[100000]` | 0.319274428592796 | 0.304513179279761 | 4.62% | |`bench_string_splitlines[1000000]` | 3.24098357344348 | 3.10220707742267 | 4.28% | Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3825 MODULAR_ORIG_COMMIT_REV_ID: 433b5ea2f49164bf08572004da1541ac42f1eed2 --- .../benchmarks/collections/bench_string.mojo | 2 +- stdlib/src/utils/string_slice.mojo | 133 +++++++++--------- 2 files changed, 71 insertions(+), 64 deletions(-) diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index 3cee895b73..fc5dfc718e 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -227,7 +227,7 @@ fn bench_string_is_valid_utf8[ # ===-----------------------------------------------------------------------===# def main(): seed() - var m = Bench(BenchConfig(num_repetitions=5)) + var m = Bench(BenchConfig(num_repetitions=1)) alias filenames = ( "UN_charter_EN", "UN_charter_ES", diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index e572747d4e..03ed9be490 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -24,7 +24,7 @@ from utils import StringSlice from collections import List, Optional from collections.string import _atof, _atol, _isspace from sys import bitwidthof, simdwidthof -from sys.intrinsics import unlikely +from sys.intrinsics import unlikely, likely from bit import count_leading_zeros from memory import UnsafePointer, memcmp, memcpy, Span @@ -1010,40 +1010,22 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( listed above, otherwise False. """ - fn _is_newline_char(s: StringSlice) -> Bool: - # sorry for readability, but this has less overhead than memcmp - # highly performance sensitive code, benchmark before touching - alias `\t` = UInt8(ord("\t")) - alias `\r` = UInt8(ord("\r")) - alias `\n` = UInt8(ord("\n")) - alias `\x1c` = UInt8(ord("\x1c")) - alias `\x1e` = UInt8(ord("\x1e")) - no_null_len = s.byte_length() - ptr = s.unsafe_ptr() - if no_null_len == 1: - v = ptr[0] - return `\t` <= v <= `\x1e` and not (`\r` < v < `\x1c`) - elif no_null_len == 2: - v0 = ptr[0] - v1 = ptr[1] - next_line = v0 == 0xC2 and v1 == 0x85 # next line: \x85 - r_n = v0 == `\r` and v1 == `\n` - return next_line or r_n - elif no_null_len == 3: - # unicode line sep or paragraph sep: \u2028 , \u2029 - v2 = ptr[2] - lastbyte = v2 == 0xA8 or v2 == 0xA9 - return ptr[0] == 0xE2 and ptr[1] == 0x80 and lastbyte - return False + var ptr = self.unsafe_ptr() + var length = self.byte_length() @parameter if single_character: - return _is_newline_char(self) + return length != 0 and _is_newline_char[include_r_n=True]( + ptr, 0, ptr[0], length + ) else: + var offset = 0 for s in self: - if not _is_newline_char(s): + var b_len = s.byte_length() + if not _is_newline_char(ptr, offset, ptr[offset], b_len): return False - return self.byte_length() != 0 + offset += b_len + return length != 0 fn splitlines[ O: ImmutableOrigin, // @@ -1063,54 +1045,41 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( A List of Strings containing the input split by line boundaries. """ + # highly performance sensitive code, benchmark before touching alias `\r` = UInt8(ord("\r")) alias `\n` = UInt8(ord("\n")) - alias `\t` = UInt8(ord("\t")) - alias `\x1c` = UInt8(ord("\x1c")) - alias `\x1e` = UInt8(ord("\x1e")) - output = List[StringSlice[O]](capacity=128) # guessing - ptr = self.unsafe_ptr() - length = self.byte_length() - offset = 0 - @always_inline - @parameter - fn _is_newline_char(p: UnsafePointer[Byte], l: Int, b0: Byte) -> Bool: - # sorry for readability, but this has less overhead than memcmp - # highly performance sensitive code, benchmark before touching - if l == 1: - return `\t` <= b0 <= `\x1e` and not (`\r` < b0 < `\x1c`) - elif l == 2: - return b0 == 0xC2 and p[1] == 0x85 # next line: \x85 - elif l == 3: - # unicode line sep or paragraph sep: \u2028 , \u2029 - v2 = p[2] - lastbyte = v2 == 0xA8 or v2 == 0xA9 - return b0 == 0xE2 and p[1] == 0x80 and lastbyte - return False + output = List[StringSlice[O]](capacity=128) # guessing + var ptr = self.unsafe_ptr() + var length = self.byte_length() + var offset = 0 while offset < length: - eol_start = offset - eol_length = 0 + var eol_start = offset + var eol_length = 0 while eol_start < length: - b0 = ptr[eol_start] - char_len = _utf8_first_byte_sequence_length(b0) + var b0 = ptr[eol_start] + var char_len = _utf8_first_byte_sequence_length(b0) debug_assert( eol_start + char_len <= length, "corrupted sequence causing unsafe memory access", ) - isnewline = int(_is_newline_char(ptr + eol_start, char_len, b0)) - char_end = isnewline * (eol_start + char_len) - next_idx = char_end * int(char_end < length) - is_r_n = b0 == `\r` and next_idx != 0 and ptr[next_idx] == `\n` - eol_length = isnewline * char_len + int(is_r_n) - if unlikely(isnewline == 1): + var isnewline = unlikely( + _is_newline_char(ptr, eol_start, b0, char_len) + ) + var char_end = int(isnewline) * (eol_start + char_len) + var next_idx = char_end * int(char_end < length) + var is_r_n = b0 == `\r` and next_idx != 0 and ptr[ + next_idx + ] == `\n` + eol_length = int(isnewline) * char_len + int(is_r_n) + if isnewline: break eol_start += char_len - str_len = eol_start - offset + int(keepends) * eol_length - s = StringSlice[O](ptr=ptr + offset, length=str_len) + var str_len = eol_start - offset + int(keepends) * eol_length + var s = StringSlice[O](ptr=ptr + offset, length=str_len) output.append(s) offset = eol_start + eol_length @@ -1191,3 +1160,41 @@ fn _to_string_list[ return len(v) return _to_string_list[items.T, len_fn, unsafe_ptr_fn](items) + + +@always_inline +fn _is_newline_char[ + include_r_n: Bool = False +](p: UnsafePointer[Byte], eol_start: Int, b0: Byte, char_len: Int) -> Bool: + """Returns whether the char is a newline char. + + Safety: + This assumes valid utf-8 is passed. + """ + # highly performance sensitive code, benchmark before touching + alias `\r` = UInt8(ord("\r")) + alias `\n` = UInt8(ord("\n")) + alias `\t` = UInt8(ord("\t")) + alias `\x1c` = UInt8(ord("\x1c")) + alias `\x1e` = UInt8(ord("\x1e")) + + # here it's actually faster to have branching due to the branch predictor + # "realizing" that the char_len == 1 path is often taken. Using the likely + # intrinsic is to make the machine code be ordered to optimize machine + # instruction fetching, which is an optimization for the CPU front-end. + if likely(char_len == 1): + return `\t` <= b0 <= `\x1e` and not (`\r` < b0 < `\x1c`) + elif char_len == 2: + var b1 = p[eol_start + 1] + var is_next_line = b0 == 0xC2 and b1 == 0x85 # unicode next line \x85 + + @parameter + if include_r_n: + return is_next_line or (b0 == `\r` and b1 == `\n`) + else: + return is_next_line + elif char_len == 3: # unicode line sep or paragraph sep: \u2028 , \u2029 + var b1 = p[eol_start + 1] + var b2 = p[eol_start + 2] + return b0 == 0xE2 and b1 == 0x80 and (b2 == 0xA8 or b2 == 0xA9) + return False From 9949837c5099001633d0a992ec4f8c9422b913d4 Mon Sep 17 00:00:00 2001 From: Manuel Saelices Date: Mon, 9 Dec 2024 12:27:36 -0600 Subject: [PATCH 066/509] [External] [stdlib] Snake casing stdlib (#48873) [External] [stdlib] Snake casing stdlib Convert camelCase code to a pythonic snake_case throughout the stdlib Co-authored-by: Manuel Saelices Closes modularml/mojo#3631 MODULAR_ORIG_COMMIT_REV_ID: f308889bd1a2221371fa2b2d85ea2f30da333667 --- stdlib/src/builtin/object.mojo | 64 ++++++++++++------------ stdlib/src/python/_cpython.mojo | 9 ++-- stdlib/src/python/python_object.mojo | 46 ++++++++--------- stdlib/src/time/time.mojo | 16 +++--- stdlib/src/utils/_serialize.mojo | 2 +- stdlib/test/builtin/test_error.mojo | 4 +- stdlib/test/python/test_python_info.mojo | 8 +-- 7 files changed, 75 insertions(+), 74 deletions(-) diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index fce05d2476..c2337934a1 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -523,9 +523,9 @@ struct _ObjectImpl( lowest-common denominator type for performing comparisons, in order of increasing priority: bool, int, and then float. """ - var lhsId = lhs.get_type_id() - var rhsId = rhs.get_type_id() - if lhsId == rhsId: + var lhs_id = lhs.get_type_id() + var rhs_id = rhs.get_type_id() + if lhs_id == rhs_id: return @parameter @@ -538,10 +538,10 @@ struct _ObjectImpl( else: value = value.convert_int_to_float() - if lhsId > rhsId: - convert(rhs, rhsId, lhsId) + if lhs_id > rhs_id: + convert(rhs, rhs_id, lhs_id) else: - convert(lhs, lhsId, rhsId) + convert(lhs, lhs_id, rhs_id) @staticmethod fn coerce_arithmetic_type(mut lhs: _ObjectImpl, mut rhs: _ObjectImpl): @@ -1075,15 +1075,15 @@ struct object( """ lhs._comparison_type_check() rhs._comparison_type_check() - var lhsValue = lhs._value - var rhsValue = rhs._value - _ObjectImpl.coerce_comparison_type(lhsValue, rhsValue) - if lhsValue.is_float(): - return fp_func(lhsValue.get_as_float(), rhsValue.get_as_float()) - if lhsValue.is_int(): - return int_func(lhsValue.get_as_int(), rhsValue.get_as_int()) - debug_assert(lhsValue.is_bool(), "expected both values to be bool") - return bool_func(lhsValue.get_as_bool(), rhsValue.get_as_bool()) + var lhs_value = lhs._value + var rhs_value = rhs._value + _ObjectImpl.coerce_comparison_type(lhs_value, rhs_value) + if lhs_value.is_float(): + return fp_func(lhs_value.get_as_float(), rhs_value.get_as_float()) + if lhs_value.is_int(): + return int_func(lhs_value.get_as_int(), rhs_value.get_as_int()) + debug_assert(lhs_value.is_bool(), "expected both values to be bool") + return bool_func(lhs_value.get_as_bool(), rhs_value.get_as_bool()) @always_inline fn _string_compare(self, rhs: object) -> Int: @@ -1286,12 +1286,12 @@ struct object( """ lhs._arithmetic_type_check() rhs._arithmetic_type_check() - var lhsValue = lhs._value - var rhsValue = rhs._value - _ObjectImpl.coerce_arithmetic_type(lhsValue, rhsValue) - if lhsValue.is_float(): - return fp_func(lhsValue.get_as_float(), rhsValue.get_as_float()) - return int_func(lhsValue.get_as_int(), rhsValue.get_as_int()) + var lhs_value = lhs._value + var rhs_value = rhs._value + _ObjectImpl.coerce_arithmetic_type(lhs_value, rhs_value) + if lhs_value.is_float(): + return fp_func(lhs_value.get_as_float(), rhs_value.get_as_float()) + return int_func(lhs_value.get_as_int(), rhs_value.get_as_int()) @staticmethod @always_inline @@ -1310,12 +1310,12 @@ struct object( """ lhs._arithmetic_integral_type_check() rhs._arithmetic_integral_type_check() - var lhsValue = lhs._value - var rhsValue = rhs._value - _ObjectImpl.coerce_integral_type(lhsValue, rhsValue) - if lhsValue.is_int(): - return int_func(lhsValue.get_as_int(), rhsValue.get_as_int()) - return bool_func(lhsValue.get_as_bool(), rhsValue.get_as_bool()) + var lhs_value = lhs._value + var rhs_value = rhs._value + _ObjectImpl.coerce_integral_type(lhs_value, rhs_value) + if lhs_value.is_int(): + return int_func(lhs_value.get_as_int(), rhs_value.get_as_int()) + return bool_func(lhs_value.get_as_bool(), rhs_value.get_as_bool()) @always_inline fn __neg__(self) raises -> object: @@ -1360,14 +1360,14 @@ struct object( The sum or concatenated values. """ if self._value.is_str() and rhs._value.is_str(): - var lhsStr = self._value.get_as_string() - var rhsStr = rhs._value.get_as_string() - var length = lhsStr.length + rhsStr.length + var lhs_str = self._value.get_as_string() + var rhs_str = rhs._value.get_as_string() + var length = lhs_str.length + rhs_str.length var impl = _ImmutableString( UnsafePointer[UInt8].alloc(length), length ) - memcpy(impl.data, lhsStr.data, lhsStr.length) - memcpy(impl.data + lhsStr.length, rhsStr.data, rhsStr.length) + memcpy(impl.data, lhs_str.data, lhs_str.length) + memcpy(impl.data + lhs_str.length, rhs_str.data, rhs_str.length) var result = object() result._value = impl return result diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index 05426aa8eb..d047c84b3d 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -1478,14 +1478,13 @@ struct CPython: return int(self.lib.call["PyObject_Hash", Int](obj)) fn PyObject_GetIter( - mut self, traversablePyObject: PyObjectPtr + mut self, traversable_py_object: PyObjectPtr ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_GetIter). """ - var iterator = self.lib.call["PyObject_GetIter", PyObjectPtr]( - traversablePyObject + traversable_py_object ) self.log( @@ -1493,9 +1492,9 @@ struct CPython: " NEWREF PyObject_GetIter, refcnt:", self._Py_REFCNT(iterator), "referencing ", - traversablePyObject._get_ptr_as_int(), + traversable_py_object._get_ptr_as_int(), "refcnt of traversable: ", - self._Py_REFCNT(traversablePyObject), + self._Py_REFCNT(traversable_py_object), ) self._inc_total_rc() diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index a6b3a8a45f..42b2dc6bed 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -41,9 +41,9 @@ struct _PyIter(Sized): var iterator: PythonObject """The iterator object that stores location.""" - var preparedNextItem: PythonObject + var prepared_next_item: PythonObject """The next item to vend or zero if there are no items.""" - var isDone: Bool + var is_done: Bool """Stores True if the iterator is pointing to the last item.""" # ===-------------------------------------------------------------------===# @@ -57,8 +57,8 @@ struct _PyIter(Sized): existing: Initialized _PyIter instance. """ self.iterator = existing.iterator - self.preparedNextItem = existing.preparedNextItem - self.isDone = existing.isDone + self.prepared_next_item = existing.prepared_next_item + self.is_done = existing.is_done @implicit fn __init__(out self, iter: PythonObject): @@ -69,19 +69,19 @@ struct _PyIter(Sized): """ var cpython = _get_global_python_itf().cpython() self.iterator = iter - var maybeNextItem = cpython.PyIter_Next(self.iterator.py_object) - if maybeNextItem.is_null(): - self.isDone = True - self.preparedNextItem = PythonObject(PyObjectPtr()) + var maybe_next_item = cpython.PyIter_Next(self.iterator.py_object) + if maybe_next_item.is_null(): + self.is_done = True + self.prepared_next_item = PythonObject(PyObjectPtr()) else: - self.preparedNextItem = PythonObject(maybeNextItem) - self.isDone = False + self.prepared_next_item = PythonObject(maybe_next_item) + self.is_done = False fn __init__(out self): """Initialize an empty iterator.""" self.iterator = PythonObject(PyObjectPtr()) - self.isDone = True - self.preparedNextItem = PythonObject(PyObjectPtr()) + self.is_done = True + self.prepared_next_item = PythonObject(PyObjectPtr()) # ===-------------------------------------------------------------------===# # Trait implementations @@ -97,12 +97,12 @@ struct _PyIter(Sized): if not self.iterator: return self.iterator var cpython = _get_global_python_itf().cpython() - var current = self.preparedNextItem - var maybeNextItem = cpython.PyIter_Next(self.iterator.py_object) - if maybeNextItem.is_null(): - self.isDone = True + var current = self.prepared_next_item + var maybe_next_item = cpython.PyIter_Next(self.iterator.py_object) + if maybe_next_item.is_null(): + self.is_done = True else: - self.preparedNextItem = PythonObject(maybeNextItem) + self.prepared_next_item = PythonObject(maybe_next_item) return current @always_inline @@ -115,7 +115,7 @@ struct _PyIter(Sized): Returns: 0 if the traversal is complete and 1 otherwise. """ - if self.isDone: + if self.is_done: return 0 else: return 1 @@ -596,19 +596,19 @@ struct PythonObject( raise Error("Attribute is not found.") return PythonObject(result) - fn __setattr__(self, name: StringLiteral, newValue: PythonObject) raises: + fn __setattr__(self, name: StringLiteral, new_value: PythonObject) raises: """Set the given value for the object attribute with the given name. Args: name: The name of the object attribute to set. - newValue: The new value to be set for that attribute. + new_value: The new value to be set for that attribute. """ - return self._setattr(name, newValue.py_object) + return self._setattr(name, new_value.py_object) - fn _setattr(self, name: StringLiteral, newValue: PyObjectPtr) raises: + fn _setattr(self, name: StringLiteral, new_value: PyObjectPtr) raises: var cpython = _get_global_python_itf().cpython() var result = cpython.PyObject_SetAttrString( - self.py_object, name, newValue + self.py_object, name, new_value ) Python.throw_python_exception_if_error_state(cpython) if result < 0: diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 0167c26548..f6221590ac 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -84,21 +84,23 @@ struct _CTimeSpec(Stringable): @value @register_passable("trivial") struct _FILETIME: - var dwLowDateTime: UInt32 - var dwHighDateTime: UInt32 + var dw_low_date_time: UInt32 + var dw_high_date_time: UInt32 fn __init__(out self): - self.dwLowDateTime = 0 - self.dwHighDateTime = 0 + self.dw_low_date_time = 0 + self.dw_high_date_time = 0 fn as_nanoseconds(self) -> UInt: # AFTER subtracting windows offset the return value fits in a signed int64 # BEFORE subtracting windows offset the return value does not fit in a signed int64 # Taken from https://github.com/microsoft/STL/blob/c8d1efb6d504f6392acf8f6d01fd703f7c8826c0/stl/src/xtime.cpp#L50 - alias windowsToUnixEpochOffsetNs: Int = 0x19DB1DED53E8000 + alias windows_to_unix_epoch_offset_ns: Int = 0x19DB1DED53E8000 var interval_count: UInt64 = ( - self.dwHighDateTime.cast[DType.uint64]() << 32 - ) + self.dwLowDateTime.cast[DType.uint64]() - windowsToUnixEpochOffsetNs + self.dw_high_date_time.cast[DType.uint64]() << 32 + ) + self.dw_low_date_time.cast[ + DType.uint64 + ]() - windows_to_unix_epoch_offset_ns return int(interval_count * 100) diff --git a/stdlib/src/utils/_serialize.mojo b/stdlib/src/utils/_serialize.mojo index 1cc0eea138..f6e5baf18c 100644 --- a/stdlib/src/utils/_serialize.mojo +++ b/stdlib/src/utils/_serialize.mojo @@ -134,7 +134,7 @@ fn _serialize[ if row_idx != row_elem_count: serialize_fn(",") - # Intermediate rows are filled with "..." and rowIdx is advanced to third + # Intermediate rows are filled with "..." and row_idx is advanced to third # from last. if ( row_elem_count >= _kCompactMaxElemsToPrint diff --git a/stdlib/test/builtin/test_error.mojo b/stdlib/test/builtin/test_error.mojo index 87dd096cd4..eb24ee7e4b 100644 --- a/stdlib/test/builtin/test_error.mojo +++ b/stdlib/test/builtin/test_error.mojo @@ -27,8 +27,8 @@ def test_error_raising(): def test_from_and_to_string(): - var myString: String = "FOO" - var error = Error(myString) + var my_string: String = "FOO" + var error = Error(my_string) assert_equal(str(error), "FOO") assert_equal(str(Error("bad")), "bad") diff --git a/stdlib/test/python/test_python_info.mojo b/stdlib/test/python/test_python_info.mojo index b1b7eb29e7..5e1a4a1c7c 100644 --- a/stdlib/test/python/test_python_info.mojo +++ b/stdlib/test/python/test_python_info.mojo @@ -21,10 +21,10 @@ from testing import assert_equal fn test_python_version(mut python: Python) raises: var version = "3.10.8 (main, Nov 24 2022, 08:08:27) [Clang 14.0.6 ]" - var pythonVersion = PythonVersion(version) - assert_equal(pythonVersion.major, 3) - assert_equal(pythonVersion.minor, 10) - assert_equal(pythonVersion.patch, 8) + var python_version = PythonVersion(version) + assert_equal(python_version.major, 3) + assert_equal(python_version.minor, 10) + assert_equal(python_version.patch, 8) def main(): From bd2384cd0f9ed154e71eb4a691b14332283880c2 Mon Sep 17 00:00:00 2001 From: Joshua James Venter Date: Mon, 9 Dec 2024 12:29:02 -0600 Subject: [PATCH 067/509] [External] [docs] Prevent unwanted Latex interpretation in `Path` docs (#52407) [External] [docs] Prevent unwanted Latex interpretation in `Path` docs As title, see [docs](https://docs.modular.com/mojo/stdlib/pathlib/path/Path#home). Co-authored-by: Joshua James Venter Closes modularml/mojo#3844 MODULAR_ORIG_COMMIT_REV_ID: d3cc7796c2b2fc8991bd958984c0d2313a9bfcd5 --- stdlib/src/pathlib/path.mojo | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index c6e9016b5e..5a0f285db1 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -264,7 +264,7 @@ struct Path( return os.path.exists(self) fn expanduser(self) raises -> Path: - """Expands a prefixed `~` with $HOME on posix or $USERPROFILE on + """Expands a prefixed `~` with `$HOME` on posix or `$USERPROFILE` on windows. If environment variables are not set or the `path` is not prefixed with `~`, returns the `path` unmodified. @@ -275,7 +275,7 @@ struct Path( @staticmethod fn home() raises -> Path: - """Returns $HOME on posix or $USERPROFILE on windows. If environment + """Returns `$HOME` on posix or `$USERPROFILE` on windows. If environment variables are not set it returns `~`. Returns: From ebfbf2a0c3a78ae721cc8d7c50b951b3756c05e1 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Mon, 9 Dec 2024 15:01:03 -0800 Subject: [PATCH 068/509] New Get started with Mojo tutorial The tutorial gives new Mojo programmers a tour of some of the language's features by guiding them through the process of creating an implementation of Conway's Game of Life from scratch. MODULAR_ORIG_COMMIT_REV_ID: 3548d7d23c6f612ef22b730106646b8332ce8dfa --- docs/manual/basics.mdx | 34 +- docs/manual/get-started.mdx | 1310 +++++++++++++++++--- docs/manual/images/game-of-life-screen.png | Bin 0 -> 85325 bytes 3 files changed, 1137 insertions(+), 207 deletions(-) create mode 100644 docs/manual/images/game-of-life-screen.png diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index 938701d322..edeb731899 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -1,24 +1,21 @@ --- -title: Introduction to Mojo +title: Mojo language basics sidebar_position: 1 +sidebar_label: Overview description: Introduction to Mojo's basic language features. --- -At this point, you should have already set up the Mojo -SDK and run ["Hello -world"](/mojo/manual/get-started). Now let's talk about how -to write Mojo code. +At this point, you should have already installed [Magic](/magic) to manage the +virtual environment and packages for your MAX and Mojo development (or +alternatively added the `max` package to a [conda](/magic/conda) development +environment). It's also a good idea to go through the [Get Started with +Mojo](/mojo/manual/get-started) tutorial to get a taste of what Mojo programming +is like. Now let's talk about how to write Mojo code. -If -you know Python, then a lot of Mojo code will look familiar. However, Mojo -is—first and foremost—designed for high-performance systems programming, with -features like strong type checking, memory safety, next-generation compiler -technologies, and more. As such, Mojo also has a lot in common with languages -like C++ and Rust. - -Yet, we've designed Mojo to be flexible, so you can incrementally adopt -systems-programming features like strong type checking as you see fit—Mojo does -not *require* strong type checking. +If you know Python, then a lot of Mojo code will look familiar. However, Mojo +incorporates features like strong type checking, memory safety, next-generation +compiler technologies, and more. As such, Mojo also has a lot in common with +languages like C++ and Rust. On this page, we'll introduce the essential Mojo syntax, so you can start coding quickly and understand other Mojo code you encounter. Subsequent @@ -181,8 +178,9 @@ each characteristic (such as each method). Each characteristic in a trait is a "requirement" for the struct, and when your struct implements each requirement, it's said to "conform" to the trait. -Currently, the only characteristics that traits can define are method signatures. Also, traits -currently cannot implement default behaviors for methods. +Currently, the only characteristics that traits can define are method +signatures. Also, traits currently cannot implement default behaviors for +methods. Using traits allows you to write generic functions that can accept any type that conforms to a trait, rather than accept only specific types. @@ -355,7 +353,7 @@ from docstrings using the [`mojo doc` command](/mojo/cli/doc). ## Python integration -Mojo does not yet adopt the full syntax of Python, but we've built a mechanism to import +Mojo supports the ability to import Python modules as-is, so you can leverage existing Python code right away. For example, here's how you can import and use NumPy (you must have Python diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 363fef9d10..469542567d 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -1,295 +1,1227 @@ --- -title: Get started with Mojo🔥 -sidebar_label: Get started -description: Install Mojo now and start developing +title: "Get started with Mojo" +sidebar_label: "Tutorial: Get started with Mojo" +description: "Install Mojo and learn the language basics by building a complete Mojo program" +github_url: https://github.com/modularml/mojo/tree/nightly/examples/life --- -import Tabs from '@theme/Tabs'; -import TabItem from '@theme/TabItem'; -import MaxInstall from '@site/src/components/MaxInstall'; +import GetMagic from '@site/src/includes/get_magic.mdx'; -On this page, we'll show you how to create the classic "Hello world" starter -program with Mojo. If you'd rather read how to write Mojo code, see the -[introduction to Mojo](/mojo/manual/basics). +Get ready to learn Mojo! This tutorial is designed to give you a tour of several +features of Mojo by building a complete program that does much more than simply +printing "Hello, world!" -By installing Mojo, you understand and agree to our [software -license](https://www.modular.com/legal/max). +In fact, we'll build a version of [Conway's Game of +Life](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life), which is a simple +simulation to explore self-replicating systems. If you haven't heard of it +before, don't worry, it will make sense when you see it in action. Let's just +get started so you can learn Mojo programming basics, including the following: -## 1. Create a new project +- Using basic built-in types like `Int` and `String` +- Using a `List` to manage a sequence of values +- Creating custom types in the form of structs (data structures) +- Creating and importing Mojo modules +- Importing and using Python libraries -To create a new Mojo project, we'll use [Magic](/magic)—a virtual environment -manager and package manager based on conda. +This tutorial might be a little long because there's a lot to learn, but we +tried to keep the explanations simple, and we included links along the way for +you to go learn more about each topic. -1. Install Magic on macOS or Ubuntu Linux with this command: +And if you just want to see the finished code, you can [get it on +GitHub](https://github.com/modularml/mojo/tree/nightly/examples/life). - +## 1. Create a Mojo project with `magic` - Then run the `source` command printed in your terminal. +We'll start by using the [`magic` CLI](/magic) to create a virtual environment +and generate our initial project directory. -2. Create a Mojo project called "hello-world": + - ```sh - magic init hello-world --format mojoproject - ``` +In your terminal, go to the directory in which you want to create the project +and execute: - This creates a directory named `hello-world` and installs the Mojo project - dependencies—the only dependency for a Mojo project is the `max` package - (Mojo is [bundled with MAX](/max/faq#why-bundle-mojo-with-max)). +```bash +magic init life --format mojoproject +``` -3. Start a shell in the project virtual environment: +This creates a project directory named `life`. Let's go to the directory and +list its contents: - ```sh - cd hello-world && magic shell - ``` +```bash +cd life +``` -That's it! The `magic shell` command activates the virtual environment so you -can now start using Mojo. For example, you can check your Mojo version like -this: +```bash +ls -A +``` -```sh -mojo --version +```output +.gitattributes +.gitignore +.magic +magic.lock +mojoproject.toml ``` +You should see that the project directory contains: -
- Click here to install the Mojo nightly build. -
+- An initial `mojoproject.toml` manifest file, which defines the project + dependencies and other features +- A [lock file](/magic#the-magiclock-file) named `magic.lock`, which specifies + the transitive dependencies and actual package versions installed in the + project's virtual environment + + :::note + + Never edit the lock file directly. The `magic` command automatically updates the lock file if you edit the manifest file. + + ::: + +- A `.magic` subdirectory containing the conda virtual environment for the + project +- Initial `.gitignore` and `.gitattributes` files that you can optionally use if + you plan to use `git` version control with the project -To install the latest nightly build, specify the `max-nightly` channel when you -initialize your project: +Because we used the `--format mojoproject` option when creating the project, +`magic` automatically added the `max` package as a dependency, which includes +Mojo. Let's verify that our project is configured correctly by checking the +version of Mojo that's installed within our project's virtual environment. +`magic run` executes a command in the project's virtual environment, so let's +use it to execute `mojo --version`: -```sh -magic init hello-world-nightly --format mojoproject \ - -c conda-forge -c https://conda.modular.com/max-nightly +```bash +magic run mojo --version ``` -When you include the `-c` (`--channel`) option, you must specify all channels. -Be sure to specify the `conda-forge` channel (or another conda channel) where -Magic should look for all packages other than MAX/Mojo. +You should see a version string indicating the version of Mojo installed, which +by default should be the latest released version. -Then start a shell in the new environment: +Great! Now let's write our first Mojo program. -```sh -cd hello-world-nightly && magic shell +## 2. Create a "Hello, world" program + +You can use any editor or IDE that you like. If you're using [Visual Studio +Code](https://code.visualstudio.com/) you can take advantage of the [Mojo for +Visual Studio Code +extension](https://marketplace.visualstudio.com/items?itemName=modular-mojotools.vscode-mojo), +which provides features like syntax highlighting, code completion, and debugging +support. + +In the project directory, create a file named `life.mojo` containing the +following lines of code: + +```mojo title="life.mojo" +# My first Mojo program! +def main(): + print("Hello, World!") ``` -The nightly version of Mojo installed in this project is fully independent, so -it will not interfere with other projects that use the latest stable build. +If you've programmed before in Python, this should look familiar. -
-
+- We're using the `def` keyword to define a function named `main`. +- You can use any number of spaces or tabs for indentation as long as you use + the same indentation for the entire code block. We'll follow the [Python style + guide](https://peps.python.org/pep-0008/) and use 4 spaces. +- This [`print()`](/mojo/stdlib/builtin/io/print) function a Mojo built-in so it + doesn't require an import. +An executable Mojo program *requires* you to define a no-argument `main()` as +its entry point. Running the program automatically invokes the `main()` +function, and your program exits when the `main()` function returns. -## 2. Run code in the REPL +To run the program, we first need to start a shell session in our project's +virtual environment: -First, let's use the Mojo -[REPL](https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop), -which allows you to write and run Mojo code in a command prompt: +```bash +magic shell +``` + +Later on, when you want to exit the virtual environment, just type `exit`. -1. To start a REPL session, type `mojo` and press Enter. +Now we can use the `mojo` command to run our program. -2. Type `print("Hello, world!")` and press Enter twice -(a blank line is required to indicate the end of an expression). +```bash +mojo life.mojo +``` - The result looks like this: +```output +Hello, World! +``` - ```text - $ mojo - Welcome to Mojo! 🔥 +Mojo is a compiled language, not an interpreted one like Python. So when we run +our program like this, `mojo` performs [just-in-time +compilation](https://en.wikipedia.org/wiki/Just-in-time_compilation) (JIT) and +then runs the result. - Expressions are delimited by a blank line. - Type `:quit` to exit the REPL and `:mojo help repl` for further assistance. +We can also compile our program into an executable file using [`mojo +build`](/mojo/cli/build) like this: - 1> print("Hello world") - Hello world - ``` +```bash +mojo build life.mojo +``` -3. To exit REPL, type `:quit` and press Enter, or press -Ctrl + D. +By default, this saves an executable file to the current directory named `life`. -You can write as much code as you want in the REPL. You can press -Enter to start a new line and continue writing code, and when you -want Mojo to evaluate the code, press Enter twice. If there's -something to print, Mojo prints it and then returns the prompt to you. +```bash +./life +``` -The REPL is primarily useful for short experiments because the code isn't -saved. So when you want to write a real program, you need to write the code in -a `.mojo` source file. +```output +Hello, World! +``` -## 3. Run a Mojo file +## 3. Create and use variables -Now let's write the code in a Mojo source file and run it with the -[`mojo`](/mojo/cli/) command: +Let's extend this basic program by prompting the user for their name and +including that in the greeting printed. The built-in +[`input()`](/mojo/stdlib/builtin/io/input) function accepts an optional +[`String`](/mojo/stdlib/collections/string/String) argument to use as a prompt, +and returns a `String` consisting of the characters the user entered (with the +newline character at the end stripped off). -1. Create a file named `hello.mojo` (or `hello.🔥`) and add the following code: +So let's declare a variable, assign the return value from `input()` to it, and +build a customized greeting. - ```mojo - fn main(): - print("Hello, world!") - ``` +```mojo title="life.mojo" +def main(): + var name: String = input("Who are you? ") + var greeting: String = "Hi, " + name + "!" + print(greeting) +``` - That's all you need. Save the file and return to your terminal. +Go ahead and run it: -2. Now run it with the `mojo` command: +```bash +mojo life.mojo +``` - ```sh - mojo hello.mojo - ``` +```output +Who are you? Edna +Hi, Edna! +``` - ```output - Hello, world! - ``` +Notice that this code uses a `String` type annotation indicating the type of +value that the variable can contain. The Mojo compiler performs [static type +checking](https://en.wikipedia.org/wiki/Type_system#Static_type_checking), which +means that you'll encounter a compile-time error if your code tries to assign a +value of one type to a variable of a different type. + +Mojo also supports implicitly declared variables, where you simply assign a +value to a new variable without using the `var` keyword or indicating its type. +So we can replace the code we just entered with the following, and it works +exactly the same. + +```mojo title="life.mojo" +def main(): + name = input("Who are you? ") + greeting = "Hi, " + name + "!" + print(greeting) +``` -## 4. Build an executable binary +However, implicitly declared variables still have a fixed type, which Mojo +automatically infers from the initial value assignment. So in this example both +`name` and `greeting` are inferred as `String` type variables. If you then try +to assign an integer value like 42 to the `name` variable, you'll get a +compile-time error because of the type mismatch. You can learn more about Mojo +variables in the [Variables](/mojo/manual/variables) section of the Mojo manual. + +## 4. Use Mojo `Int` and `List` types to represent the game state + +As originally envisioned by John Conway, the game's "world" is an infinite, +two-dimensional grid of square cells, but for our implementation we'll constrain +the grid to a finite size. A drawback to making the edges of the grid a hard +boundary is that there are fewer neighboring cells around the edges compared to +the interior, which tends to cause die offs. Therefore, we'll model the world as +a toroid (a donut shape), where the top row is considered adjacent to the bottom +row, and the left column is considered adjacent to the right column. This will +come into play later when we implement the algorithm for calculating each +subsequent generation. + +To keep track of the height and width of our grid we'll use +[`Int`](/mojo/stdlib/builtin/int/Int), which represents a signed integer of the +[word size](https://en.wikipedia.org/wiki/Word_(computer_architecture)) of the +CPU, typically 32 or 64 bits. + +To represent the state of an individual cell, we'll represent the cell state +with an `Int` value of 1 (populated) or 0 (unpopulated). Later, when we need to +determine the number of populated neighbors surrounding a cell, we can simply +add the values of the neighboring cells. + +To represent the state of the entire grid, we need a [collection +type](/mojo/manual/types#collection-types). The most appropriate for this use +case is [`List`](/mojo/stdlib/collections/list/List), which is a +dynamically-sized sequence of values. + +All of the values in a Mojo `List` must be the same type so that the Mojo +compiler can ensure type safety. (For example, when we retrieve a value from a +`List[Int]`, the compiler knows that the value is an `Int` and can verify that +we then use it correctly). Mojo collections are implemented as [generic +types](https://en.wikipedia.org/wiki/Generic_programming), so that we can +indicate the type of values the specific collection will hold by specifying a +[type parameter](/mojo/manual/parameters/#parameterized-structs) in square +brackets like this: + +```mojo +# The List in row can contain only Int values +row = List[Int]() + +# The List in names can contain only String values +names = List[String]() +``` -Finally, let's build and run that same code as an executable: +We can also create a `List` with an initial set of values and let the compiler +infer the type. -1. Create an executable file with the [`build`](/mojo/cli/build) command: +```mojo +nums = List(12, -7, 64) # A List[Int] containing 3 Int values +``` - ```sh - mojo build hello.mojo - ``` +The Mojo `List` type includes the ability to append to the list, pop values out +of the list, and access list items using subscript notation. Here's a taste of +those operations: + +```mojo +nums = List(12, -7, 64) +nums.append(-937) +print("Number of elements in the list:", len(nums)) +print("Popping last element off the list:", nums.pop()) +print("First element of the list:", nums[0]) +print("Second element of the list:", nums[1]) +print("Last element of the list:", nums[-1]) +``` - The executable file uses the same name as the `.mojo` file, but - you can change that with the `-o` option. +```output +Number of elements in the list: 4 +Popping last element off the list: -937 +First element of the list: 12 +Second element of the list: -7 +Last element of the list: 64 +``` -2. Then run the executable: +We can also nest `List`s: + +```mojo +grid = List( + List(11, 22), + List(33, 44) +) +print("Row 0, Column 0:", grid[0][0]) +print("Row 0, Column 1:", grid[0][1]) +print("Row 1, Column 0:", grid[1][0]) +print("Row 1, Column 1:", grid[1][1]) +``` - ```sh - ./hello - ``` +```output +Row 0, Column 0: 11 +Row 0, Column 1: 22 +Row 1, Column 0: 33 +Row 1, Column 1: 44 +``` - ```output - Hello, world! - ``` +This looks like a good way to represent the state of the grid for our program. +So let's update the `main()` function with the following code that defines an +8x8 grid containing the initial state of a +"[glider](https://en.wikipedia.org/wiki/Glider_(Conway%27s_Game_of_Life))" +pattern. + +```mojo title="life.mojo" +def main(): + num_rows = 8 + num_cols = 8 + glider = List( + List(0, 1, 0, 0, 0, 0, 0, 0), + List(0, 0, 1, 0, 0, 0, 0, 0), + List(1, 1, 1, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + ) +``` -The [`build`](/mojo/cli/build) command creates a statically compiled binary -file, so it contains all the code and libraries it needs to run. +## 5. Create and use a function to print the grid + +Now let's create a function to generate a string representation of the game grid +that we can print it to the terminal. + +There are actually two different keywords that we can use to define functions in +Mojo: `def` and `fn`. Using `fn` gives us finer level control over the function +definition, whereas `def` provides a good set of default behaviors for most use +cases. + +Let's add the following definition of a function named `grid_str()` to our +program. The Mojo compiler doesn't care whether we add our function before or +after `main()`, but the convention is to put `main()` at the end. + +```mojo title="life.mojo" +def grid_str(rows: Int, cols: Int, grid: List[List[Int]]) -> String: + # Create an empty String + str = String() + + # Iterate through rows 0 through rows-1 + for row in range(rows): + # Iterate through columns 0 through cols-1 + for col in range(cols): + if grid[row][col] == 1: + str += "*" # If cell is populated, append an asterisk + else: + str += " " # If cell is not populated, append a space + if row != rows-1: + str += "\n" # Add a newline between rows, but not at the end + return str +``` -You can now deactivate the virtual environment by just typing `exit`: +When we pass a value to a Mojo function, the default behavior for `def` is that +an argument is treated as a read-only reference to the value. However, if the +Mojo compiler determines that there is code in the function that can change the +value, then the argument gets a copy of the original value assigned to it. As +we'll see later, we can specify a different behavior by including an explicit +[argument convention](/mojo/manual/values/ownership#argument-conventions). In +contrast, when you define a function with `fn` Mojo simply treats each argument +as a read-only reference by default unless you provide an explicit argument +convention. + +Each argument name is followed by a type annotation indicating the type of value +you can pass to the argument. Just like when you're assigning a value to a +variable, you'll encounter a compile-time error if your code tries to pass a +value of one type to an argument of a different type. Finally, the `-> String` +following the argument list indicates that this function has a `String` type +return value. + +In the body of the function, we generate a `String` by appending an asterisk for +each populated cell and a space for each unpopulated cell, separating each row +of the grid with a newline character. We use nested `for` loops to iterate +through each row and column of the grid, using +[`range()`](/mojo/stdlib/builtin/range/range) to generate a sequence of integers +from 0 up to but not including the given end value. Then we append the correct +characters to the `String` representation. See [Control +flow](/mojo/manual/control-flow) for more information on `if`, `for`, and other +control flow structures in Mojo. -```sh -exit +:::note + +As described in [The `for` +statement](/mojo/manual/control-flow#the-for-statement) section of the Mojo +manual, it's possible to iterate over the elements of a `List` directly instead +of iterating over the values of a `range()` and then accessing the `List` +elements by their numeric index. However, iterating over a `List` directly +currently returns a *reference* to the element, which then requires using the +dereference operator, `[]`, to access the actual element value. The code looks +like this: + +```mojo +nums = List(12, -7, 64) +for value in nums: + print("Value:", value[]) ``` -Now let's try running an existing code example. +This behavior is likely to change in the future, at which point iterating over a +`List` won't require using the dereference operator. But for this tutorial, +we'll stick with iterating over a `range()` and accessing the `List` elements by +their numeric index. + +::: + +Now that we've defined our `grid_str()` function, let's invoke it from `main()`. -## 5. Run an example from GitHub +```mojo title="life.mojo" +def main(): + ... + result = grid_str(num_rows, num_cols, glider) + print(result) +``` -Our Mojo code examples in GitHub include a Magic configuration file so you can -simply clone the repo and run the code with `magic`. For example: +Then run the program to see the result: -1. Clone the Mojo repo: +```bash +mojo life.mojo +``` - ```sh - git clone https://github.com/modularml/mojo.git - ``` +```output + * + * +*** - Only if you installed the nightly build, also checkout the nightly branch: - ```sh - git checkout nightly - ``` -2. Navigate to the examples: - ```sh - cd mojo/examples - ``` -3. Run some code: +``` - ```sh - magic run mojo hello_interop.mojo - ``` +We can see that the position of the asterisks matches the location of the 1s in +the `glider` grid. - ```output - Hello Mojo 🔥! - 9 - 6 - 3 - Hello from Python! - I can even print a numpy array: [1 2 3] - ``` +## 6. Create a module and define a custom type -## 6. Install our VS Code extension (optional) +We're currently passing three arguments to `grid_str()` to describe the size and +state of the grid to print. A better approach would be to define our own custom +type that encapsulates all information about the grid. Then any function that +needs to manipulate a grid can accept just a single argument. We can do this by +defining a Mojo *struct*, which is a custom data structure. -To provide a first-class developer experience with features like code -completion, quick fixes, and hover help, we've created a [Mojo extension for -Visual Studio -Code](https://marketplace.visualstudio.com/items?itemName=modular-mojotools.vscode-mojo). +A [Mojo struct](/mojo/manual/structs) is a custom type consisting of: -![](./images/mojo-vscode.png) +- Fields, which are variables containing the data associated with the structure +- Methods, which are functions that we can optionally define to manipulate + instances of the struct that we create +:::note -## Update Mojo +Mojo structs are similar to classes. However, Mojo structs do *not* support +inheritance. Mojo doesn't support classes at this time. -To update the Mojo version in your project, use `magic add` -and specify the `max` package version. +::: -For example, if you want to always use the latest version of Mojo, you can use -the `*` wildcard as the version and then simply run `magic update` (you must -run `magic add` within the project path): +We could define the struct in our existing `life.mojo` source file, but let's +create a separate *module* for the struct. A module is simply a Mojo source file +containing struct and function definitions that can be imported into other Mojo +source files. To learn more about creating and importing modules, see the +[Modules and packages](/mojo/manual/packages) section of the Mojo manual . + +So create a new source file named `gridv1.mojo` in the project directory +containing the following definition of a struct named `Grid` consisting of three +fields: + +```mojo title="gridv1.mojo" +@value +struct Grid(): + var rows: Int + var cols: Int + var data: List[List[Int]] +``` -```sh -cd hello-world +Mojo requires you to declare all of the fields in the struct definition. You +can't add fields dynamically at run-time. You must declare the type for each +field, but you cannot assign a value as part of the field declaration. Instead, +the [constructor](/mojo/manual/lifecycle/life#constructor) is responsible for +initializing the value of all fields. + +Mojo structs support several different [lifecycle +methods](/mojo/manual/lifecycle/) defining the behavior when an instance of the +struct is created, moved, copied, and destroyed. For structs that are basic +aggregations of other types and don't require custom resource management or +lifecycle behaviors, you can simply add the +[`@value`](/mojo/manual/structs#value-decorator) decorator to your struct +definition to have the Mojo compiler automatically generate lifecycle methods +for you. + +Because we used the `@value` decorator, `Grid` includes a "member-wise" +[constructor](/mojo/manual/lifecycle/life#constructor) . The constructor's +arguments are the same names and types as the struct's fields and appear in the +same order. So this means that we can create an instance of `Grid` like this: + +```mojo +my_grid = Grid(2, 2, List(List(0, 1), List(1, 1))) ``` -```sh -magic add max +We can then access the field values with "dot" syntax like this: + +```mojo +print("Rows:", my_grid.rows) ``` -```sh -magic update +```output +Rows: 2 ``` -:::note +## 7. Import a module and use our custom `Grid` type + +Now let's edit `life.mojo` to import `Grid` from our new module and update our +code to use it. + +```mojo title="life.mojo" +from gridv1 import Grid + +def grid_str(grid: Grid) -> String: + # Create an empty String + str = String() + + # Iterate through rows 0 through rows-1 + for row in range(grid.rows): + # Iterate through columns 0 through cols-1 + for col in range(grid.cols): + if grid.data[row][col] == 1: + str += "*" # If cell is populated, append an asterisk + else: + str += " " # If cell is not populated, append a space + if row != grid.rows - 1: + str += "\n" # Add a newline between rows, but not at the end + return str + +def main(): + glider = List( + List(0, 1, 0, 0, 0, 0, 0, 0), + List(0, 0, 1, 0, 0, 0, 0, 0), + List(1, 1, 1, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + List(0, 0, 0, 0, 0, 0, 0, 0), + ) + start = Grid(8, 8, glider) + result = grid_str(start) + print(result) +``` + +At this point we've made several changes to improve the structure of our +program, but the output should remain the same. + +```bash +mojo life.mojo +``` + +```output + * + * +*** + -Although the wildcard option allows `magic update` to always install the latest -version, it also updates the `magic.lock` file in your project with the -explicit version you've installed. This ensures that anybody else who -initializes the project also gets the same package version (until you run -`magic update` again). -::: -To be more specific with your package version, you can use any of the [Python -package version -specifiers](https://packaging.python.org/en/latest/specifications/version-specifiers/#id5). -For example: -```sh -magic add "max~=24.4" ``` -```sh -magic add "max>=24.4,<24.5" +## 8. Implement `grid_str()` as a method + +Our `grid_str()` function is really a utility function unique to the `Grid` +type. So rather than defining it as a standalone function, it makes more sense +to define it as part of the `Grid` type as a method. + +To do so, move the function into `gridv1.mojo` and edit it to look like this (or +simply copy the code below into `gridv1.mojo`): + +```mojo title="gridv1.mojo" +@value +struct Grid(): + var rows: Int + var cols: Int + var data: List[List[Int]] + + def grid_str(self) -> String: + # Create an empty String + str = String() + + # Iterate through rows 0 through rows-1 + for row in range(self.rows): + # Iterate through columns 0 through cols-1 + for col in range(self.cols): + if self.data[row][col] == 1: + str += "*" # If cell is populated, append an asterisk + else: + str += " " # If cell is not populated, append a space + if row != self.rows - 1: + str += "\n" # Add a newline between rows, but not at the end + return str ``` +So aside from moving the code from one source file to another, there are a few +other changes that we made. + +- The function definition is indented to indicate that it's a method defined by + the `Grid` struct. This also changes the way that we invoke the function. + Instead of `grid_str(my_grid)` we now write `my_grid.grid_str()`. +- We've changed the argument name to `self`. When you invoke an instance method, + Mojo automatically passes the instance as the first argument, followed by any + explicit arguments that you provide. Although we could use any name we like + for this argument, the convention is to call it `self`. +- We've deleted the argument's type annotation. The compiler knows that the + first argument of the method is an instance of the struct, so it doesn't + require an explicit type annotation. + +Now that we've refactored the function into an instance method, we also need to +update the code in `life.mojo` where we invoke it from `main()`: + +```mojo title="life.mojo" +def main(): + ... + start = Grid(8, 8, glider) + print(start.grid_str()) +``` + +Once again, our refactoring has improved the structure of our code, but it still +produces the same output. You can verify that by running the program again. + +## 9. Implement support for the `StringableRaising` trait + +You can pass most Mojo types to the built-in `str()` function to produce a +`String` representation of that instance. But you'll get an error if you try to +do that with our current implementation of `Grid`. So let's fix that. + +Because the Mojo compiler performs static type checking, a function like `str()` +can accept a value only if its type implements some required behavior—in this +case, it only accepts types that can generate a `String` representation. + +To enable that, Mojo supports [*traits*](/mojo/manual/traits). A trait is a set +of requirements in the form of one or more method signatures. A type can +*conform* to that trait by implementing all of the method signatures declared in +the trait. Then we can have a function that indicates that it accepts values of +any type that conform to a specified trait. (This type of function is sometimes +referred to as a [*generic* +function](/mojo/manual/parameters/#parameters-and-generics).) + +In the case of `str()`, it requires a type to conform to either the `Stringable` +or `StringableRaising` trait. Each trait requires a conforming type to implement +a `__str__()` method that returns a `String` representation. The only difference +between the two traits is that `Stringable` requires that the method *cannot* +raise and error, whereas `StringableRaising` indicates that the method *might* +raise an error. (To learn more, read [The `Stringable`, `Representable`, and +`Writable` +traits](/mojo/manual/traits#the-stringable-representable-and-writable-traits).) + +Our `grid_str()` method already returns a `String` representation, so it looks +like we just have to rename it to `__str__()`. But we also need to indicate +which trait `Grid` conforms to. In our case, it's `StringableRaising` because we +used `def` to define the method. If you define a function or method with `def`, +the compiler *always* assumes that the function *can* raise an error. In +contrast, if you define a function or method with `fn` you must explicitly +indicate with a `raises` keyword if it can raise an error. + +So in `gridv1.mojo` we need to update the `Grid` declaration to indicate that +the type conforms to `StringableRaising` and rename the `grid_str()` method to +`__str__()`: + +```mojo title="gridv1.mojo" +@value +struct Grid(StringableRaising): + ... + def __str__(self) -> String: + ... +``` + +Now let's verify that `str()` works with an instance of `Grid`. + +```mojo title="life.mojo" +def main(): + ... + start = Grid(8, 8, glider) + print(str(start)) +``` + +If you run the program again, you should still see the same glider pattern as before. + +```bash +mojo life.mojo +``` + +```output + * + * +*** -## Next steps -- If you're new to Mojo, we suggest you learn the language basics in the - [introduction to Mojo](/mojo/manual/basics). -- To learn more about the `magic` tool, read [Get started with Magic](/magic/). -- Explore more code examples in the [the Mojo -repo](https://github.com/modularml/mojo/). In addition to several `.mojo` -examples, the repo includes [Jupyter -notebooks](https://github.com/modularml/mojo/tree/main/examples/notebooks#readme) -that teach advanced Mojo features. -- To see all the available Mojo APIs, check out the [Mojo standard library - reference](/mojo/lib). +``` + +## 10. Implement methods to support indexing + +Looking at the implementation of `__str__()` you'll notice that we use +`self.data[row][col]` to retrieve the value of a cell in the grid. And if +`my_grid` is an instance of `Grid`, we would use `my_grid.data[row][col]` to +refer to a cell in the grid. This breaks a fundamental principle of +encapsulation in that we need to know that `Grid` stores the game state in a +field called `data`, and that field is a `List[List[Int]]`. If we later decide +to change the internal implementation of `Grid`, then there could be a lot of +code that would need to be changed. + +A cleaner approach is to provide "getter" and "setter" methods to access cell +values. We could simply define methods like `get_cell()` and `set_cell()`, but +this is a good opportunity to show how we can define the behavior of built-in +operators for custom Mojo types. Specifically, we'll implement support for +indexing, so that we can refer to a cell with syntax like `my_grid[row, col]`. +This will be useful when we implement support for evolving the state of the +grid. + +As described in [Operators, expressions, and dunder +methods](/mojo/manual/operators), Mojo allows us to define the behavior of many +of the built-in operators for a custom type by implementing special *dunder* +(double underscore) methods. In the case of indexing, the two methods are +`__getitem__()` and `__setitem__()`. So let's add the following methods to the +`Grid` struct in `gridv1.mojo`: + +```mojo title="gridv1.mojo" +@value +struct Grid(StringableRaising): + ... + def __getitem__(self, row: Int, col: Int) -> Int: + return self.data[row][col] + + def __setitem__(mut self, row: Int, col: Int, value: Int) -> None: + self.data[row][col] = value +``` + +The implementation of `__getitem__()` is easy. For the given values of `row` and +`col` we just need to retrieve and return the corresponding value from the +nested `List[List[Int]]` stored in the `data` field of the instance. + +The body of `__setitem__()` is similarly straightforward. We just take the given +`value` and store it in the corresponding `row` and `col` in `data`. One thing +new in the declaration is that we set the return type to `None` to indicate that +the method doesn't have a return value. But more notable is that we've added the +`mut` [argument convention](/mojo/manual/values/ownership#argument-conventions) +to the `self` argument to explicitly tell the Mojo compiler that we want to +mutate the state of the current instance. If we were to omit `mut`, we would get +an error because the compiler would default to read-only access for the +argument. + +Now that we've implemented these methods, we can update `__str__()` to use +indexing syntax to access the cell value. + +```mojo title="gridv1.mojo" +@value +struct Grid(StringableRaising): + ... + def __str__(self) -> String: + ... + # Iterate through columns 0 through cols-1 + for col in range(self.cols): + if self[row, col] == 1: + ... +``` + +
+ Click here to see the complete `gridv1.mojo` so far: + +```mojo title="gridv1.mojo" +import random + +@value +struct Grid(StringableRaising): + var rows: Int + var cols: Int + var data: List[List[Int]] + + def __str__(self) -> String: + # Create an empty String + str = String() + + # Iterate through rows 0 through rows-1 + for row in range(self.rows): + # Iterate through columns 0 through cols-1 + for col in range(self.cols): + if self[row, col] == 1: + str += "*" # If cell is populated, append an asterisk + else: + str += " " # If cell is not populated, append a space + if row != self.rows - 1: + str += "\n" # Add a newline between rows, but not at the end + return str + + def __getitem__(self, row: Int, col: Int) -> Int: + return self.data[row][col] + + def __setitem__(mut self, row: Int, col: Int, value: Int) -> None: + self.data[row][col] = value +``` + +
+ +Our refactoring hasn't changed our program's behavior, but it's still a good +idea to run it to be sure that we don't have any errors in our code. + +## 11. Define a static method to generate random grids + +So far, we've used the glider to build the basic functionality of our `Grid` +type. But what's much more interesting is to start with a grid in a random state +and see how it evolves over time. + +Let's add a *static method* named `random()` to the `Grid` struct to generate +and return an instance of `Grid` with a random state. A static method doesn't +operate on specific instances of the type, so it can be invoked as a utility +function. We indicate that a method is a static method by using the +`@staticmethod` decorator. + +```mojo title="gridv1.mojo" +import random + +@value +struct Grid(StringableRaising): + ... + @staticmethod + def random(rows: Int, cols: Int) -> Self: + # Seed the random number generator using the current time. + random.seed() + + data = List[List[Int]]() + + for row in range(rows): + row_data = List[Int]() + for col in range(cols): + # Generate a random 0 or 1 and append it to the row. + row_data.append(int(random.random_si64(0, 1))) + data.append(row_data) + + return Self(rows, cols, data) +``` + +At the top of the file we're importing the `random` package from the Mojo +standard library. It includes several functions related to random number +generation. + +By default, the [pseudorandom number +generator](https://en.wikipedia.org/wiki/Pseudorandom_number_generator) used by +the Mojo standard library currently uses a fixed seed. This means that it +generates the same sequence of numbers unless you provide a different seed, +which is useful for testing purposes. But for this application we want to call +`random.seed()` to set a seed value based on the current time, which gives us a +unique value every time. + +Then we create an empty `List[List[Int]]` that we populate with a random initial +state. For each cell, we call +[`random.random_si64()`](/mojo/stdlib/random/random/random_si64), which returns +a random integer value from the provided minimum and maximum values of 0 and 1, +respectively. This function actually returns a value of type `Int64`, which is a +signed 64-bit integer value. As described in [Numeric +types](/mojo/manual/types#numeric-types), this is *not* the same as the `Int` +type whose precision is dependent on the native word size of the system. +Therefore we're passing this value to the built-in +[`int()`](/mojo/stdlib/builtin/int/int-function/) function, which explicitly +converts a numeric value to an `Int`. + +The return type of the method is `Self`, which is an alias for the type of the +struct. This is a convenient shortcut if the actual name of the struct is long +or includes parameters. + +The last line uses `Self()` to invoke the struct's constructor and return a +newly created instance with random data. + +Now we can update the `main()` function in `life.mojo` to create a random `Grid` +and print it. + +```mojo title="life.mojo" +... + +def main(): + start = Grid.random(8, 16) + print(str(start)) +``` + +Run the program a few times to verify that it generates a different grid each +time. + +```bash +mojo life.mojo +``` + +```output +*** * **** +* **** ****** +* * ***** +* * ** ** + * * ** **** +* ** * * * *** + * * ** ** ** + * ***** ** +``` + +## 12. Implement a method to evolve the grid + +It's finally time to let our world evolve. We'll implement an `evolve()` method +to calculate the state of the grid for the next generation. One option would be +to do an in-place modification of the existing `Grid` instance. But instead +we'll have `evolve()` return a new instance of `Grid` for the next generation. + +```mojo title="gridv1.mojo" +... +struct Grid(StringableRaising): + ... + def evolve(self) -> Self: + next_generation = List[List[Int]]() + + for row in range(self.rows): + row_data = List[Int]() + + # Calculate neighboring row indices, handling "wrap-around" + row_above = (row - 1) % self.rows + row_below = (row + 1) % self.rows + + for col in range(self.cols): + # Calculate neighboring column indices, handling "wrap-around" + col_left = (col - 1) % self.cols + col_right = (col + 1) % self.cols + + # Determine number of populated cells around the current cell + num_neighbors = ( + self[row_above, col_left] + + self[row_above, col] + + self[row_above, col_right] + + self[row, col_left] + + self[row, col_right] + + self[row_below, col_left] + + self[row_below, col] + + self[row_below, col_right] + ) + + # Determine the state of the current cell for the next generation + new_state = 0 + if self[row, col] == 1 and (num_neighbors == 2 or num_neighbors == 3): + new_state = 1 + elif self[row, col] == 0 and num_neighbors == 3: + new_state = 1 + row_data.append(new_state) + + next_generation.append(row_data) + + return Self(self.rows, self.cols, next_generation) +``` + +We start out with an empty `List[List[Int]]` to represent the state of the next +generation. Then we use nested `for` loops to iterate over each row and each +column of the existing `Grid` to determine the state of each cell in the next +generation. + +For each cell in the grid we need to count the number of populated neighboring +cells. Because we're modeling the world as a toroid, we need to consider the top +and bottom rows as adjacent and the left-most and right-most columns as +adjacent. So as we iterate through each row and column, we're using the modulo +operator, `%`, to handle "wrap-around" when we calculate the indices of the rows +above and below and the columns to the left and right of the current cell. (For +example, if there are 8 rows, then `-1 % 8` is 7.) + +Then we apply the Game of Life rules that determines if the current cell is +populated (1) or unpopulated (0) for the next generation: + +- A populated cell with either 2 or 3 populated neighbors remains populated in + the next generation +- An unpopulated cell with exactly 3 populated neighbors becomes populated in + the next generation +- All other cells become unpopulated in the next generation + +After calculating the state of the next generation, we use `Self()` to create an +new instance of `Grid`, and return the newly created instance. + +Now that we can evolve the grid, let's use it in `life.mojo`. We'll add a +`run_display()` function to control the game's main loop: + +- Display the current `Grid` +- Prompt the user to continue or quit +- Break out of the loop if the user enters `q` +- Otherwise, calculate the next generation and loop again + +Then we'll update `main()` to create a random initial `Grid` and pass it to +`run_display()`. Here is the updated version of `life.mojo`: + +```mojo title="life.mojo" +from gridv1 import Grid + +def run_display(owned grid: Grid) -> None: + while True: + print(str(grid)) + print() + if input("Enter 'q' to quit or press to continue: ") == "q": + break + grid = grid.evolve() + +def main(): + start = Grid.random(16, 16) + run_display(start) +``` + +Run the program and verify that each call to `evolve()` successfully produces a +new generation. -If you have issues during install, check our [known -issues](/mojo/roadmap#mojo-sdk-known-issues). +So now we have a working version of the Game of Life, but the terminal interface +is not very pretty. Let's spice things up with a nicer graphical user interface, +using a Python library. + +## 13. Import and use a Python package + +Mojo lets you import Python modules, call Python functions, and interact with +Python objects from Mojo code. To demonstrate this capability, we're going to +use a Python package called [pygame](https://www.pygame.org) to create and +manage a graphical user interface for our Game of Life program. + +First, we need to update our `mojoproject.toml` file to add a dependency on +Python and the `pygame` package. So in the project directory, execute the +following command from the terminal: + +```bash +magic add "python>=3.11,<3.13" "pygame>=2.6.1,<3" +``` + +:::note + +When you use Python code and packages as part of your Mojo program, you create a +run-time dependency on a compatible Python runtime and packages. Building an +executable version of your program with `mojo build` does *not* incorporate a +Python runtime or Python packages into the resulting executable file. These +run-time Python dependencies must be provided by the environment where you run +the executable. The easiest way to ensure this requirement is to deploy and run +your Mojo executable in a virtual environment, such as one managed by +[Magic](/magic/) or [conda](https://docs.conda.io/). + +::: + +You can import a Python module in Mojo using +[`Python.import_module()`](/mojo/stdlib/python/python/Python#import_module). +This returns a reference to the module in the form of a `PythonObject` wrapper. +You must store the reference in a variable so that you can then access the +functions and objects in the module. For example: + +```mojo +from python import Python + +def run_display(): + # This is roughly equivalent to Python's `import pygame` + pygame = Python.import_module("pygame") + pygame.init() +``` :::note -To help us improve Mojo, we collect some basic system information and -crash reports. [Learn -more](/mojo/faq#does-the-mojo-sdk-collect-telemetry). +Because Mojo doesn't support globally scoped variables, you must either import a +Python module into each Mojo function that needs to use it or else pass the +`PythonObject` wrapped module as an argument between functions. ::: + +You can learn more about importing and using Python modules in Mojo by reading +[Python integration](/mojo/manual/python/). + +Once we import `pygame`, we can call its APIs as if we were writing Python code. +For this project, we'll use `pygame` to create a new window and draw the whole +game UI. This requires a complete rewrite of the `run_display()` function. Take +a look at the updated code for `life.mojo` and we'll explain more of it below: + +```mojo title="life.mojo" +from gridv1 import Grid +from python import Python +import time + +def run_display( + owned grid: Grid, + window_height: Int = 600, + window_width: Int = 600, + background_color: String = "black", + cell_color: String = "green", + pause: Float64 = 0.1, +) -> None: + # Import the pygame Python package + pygame = Python.import_module("pygame") + + # Initialize pygame modules + pygame.init() + + # Create a window and set its title + window = pygame.display.set_mode((window_height, window_width)) + pygame.display.set_caption("Conway's Game of Life") + + cell_height = window_height / grid.rows + cell_width = window_width / grid.cols + border_size = 1 + cell_fill_color = pygame.Color(cell_color) + background_fill_color = pygame.Color(background_color) + + running = True + while running: + # Poll for events + event = pygame.event.poll() + if event.type == pygame.QUIT: + # Quit if the window is closed + running = False + elif event.type == pygame.KEYDOWN: + # Also quit if the user presses or 'q' + if event.key == pygame.K_ESCAPE or event.key == pygame.K_q: + running = False + + # Clear the window by painting with the background color + window.fill(background_fill_color) + + # Draw each live cell in the grid + for row in range(grid.rows): + for col in range(grid.cols): + if grid[row, col]: + x = col * cell_width + border_size + y = row * cell_height + border_size + width = cell_width - border_size + height = cell_height - border_size + pygame.draw.rect( + window, cell_fill_color, (x, y, width, height) + ) + + # Update the display + pygame.display.flip() + + # Pause to let the user appreciate the scene + time.sleep(pause) + + # Next generation + grid = grid.evolve() + + # Shut down pygame cleanly + pygame.quit() + +def main(): + start = Grid.random(128, 128) + run_display(start) +``` + +Each argument for `run_display()` other than `grid` has a default value +associated with it (for example, the default `window_height` is 600 pixels). If +you don't explicitly pass a value for an argument when you invoke +`run_display()`, Mojo uses the default value specified in the function +definition. + +After importing the `pygame` module, we call `pygame.init()` to initialize all +the pygame subsystems. + +The `set_mode()` function creates and initializes a window, with the height and +width passed as a Mojo tuple of two values. This returns a +[`PythonObject`](/mojo/stdlib/python/python_object/PythonObject) wrapper for the +window, which we can then use to call functions and set attributes to manipulate +the window. (For more information about interacting with Python objects from +Mojo, see [Python types](/mojo/manual/python/types).) + +The bulk of the `run_display()` function is a loop that uses `pygame` to poll +for events like key presses and mouse clicks. If it detects that the user +presses `q` or the `` key or closes the display window, it ends the +program with `pygame.quit()`. Otherwise, it clears the window and then iterates +through all cells in the grid to display the populated cells. After sleeping for +`pause` seconds, it evolves the grid to the next generation and loops again. + +So it's finally time to try it out. + +```bash +mojo life.mojo +``` + +Now when you run the program you should see a new window appear on screen +displaying your evolving grid. We now have a fully functional implementation of +the Game of Life with a nice interface. We've come quite a way from just +displaying a few asterisks on the terminal! + +![game_of_life_screen.png](images/game-of-life-screen.png) + +To quit the program press the `q` or `` key, or close the window. + +And now that we're done with the tutorial, exit our project's virtual +environment: + +```bash +exit +``` + +## Summary + +Congratulations on writing a complete Mojo application from scratch! Along the +way, you got a taste of: + +- Using Magic to create, build, and run a Mojo program +- Using Mojo built-in types like `Int`, `String`, and `List` +- Creating and using variables and functions +- Using control structures like `if`, `while`, and `for` +- Defining and using a custom Mojo struct +- Creating and importing a Mojo module +- Using modules from the Mojo standard library +- Importing and using a Python module + +## Next steps + +The [Mojo manual](/mojo/manual/) and the Mojo standard library [API +reference](/mojo/lib) contains much more information about these and other +features of Mojo programming. 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MODULAR_ORIG_COMMIT_REV_ID: 4f58b6a25ba4138a25fddf691e7aaaf1fb333d02 --- docs/manual/python/types.mdx | 182 +++++++++++++++++++++-------------- 1 file changed, 111 insertions(+), 71 deletions(-) diff --git a/docs/manual/python/types.mdx b/docs/manual/python/types.mdx index a7d25d0f9a..4ef8bee6aa 100644 --- a/docs/manual/python/types.mdx +++ b/docs/manual/python/types.mdx @@ -12,26 +12,28 @@ method. ## Mojo types in Python -Mojo primitive types implicitly convert into Python objects. -Today we support lists, tuples, integers, floats, booleans, and strings. +Mojo primitive types implicitly convert into Python objects. Today we support +integers, floats, booleans, strings, tuples, and `ListLiteral` instances +(described below in [Mojo wrapper objects](#mojo-wrapper-objects)). -For example, given this Python function that prints Python types: +To demonstrate, the following example dynamically creates an in-memory Python +module named `py_utils` containing a `type_printer()` function, which simply +prints the type of a given value. Then you can see how different Mojo values +convert into corresponding Python types. ```mojo -%%python +from python import Python + +def main(): + py_module = """ def type_printer(value): print(type(value)) -``` - -(You can ignore the `%%python` at the start of the code sample; it's explained -in the note below.) - -You can pass this Python function Mojo types with no problem: +""" + py_utils = Python.evaluate(py_module, file=True, name="py_utils") -```mojo -type_printer(4) -type_printer(3.14) -type_printer(("Mojo", True)) + py_utils.type_printer(4) + py_utils.type_printer(3.14) + py_utils.type_printer(("Mojo", True)) ``` ```output @@ -40,15 +42,6 @@ type_printer(("Mojo", True)) ``` -:::note - -This is a simplified code example written as a set of Jupyter -notebook cells. The first cell includes the `%%python` directive so it's -interpreted as Python. The second cell includes top-level Mojo code. You'd need -to adjust this code to run it elsewhere. - -::: - ## Python types in Mojo You can also use Python objects from Mojo. For example, Mojo's @@ -58,18 +51,21 @@ heterogeneous collections. One alternative is to use a Python dictionary or list. For example, to create a Python dictionary, use the -[`dict()`](/mojo/stdlib/python/python/Python#dict) method: +[`Python.dict()`](/mojo/stdlib/python/python/Python#dict) method: ```mojo from python import Python -def use_dict(): - var dictionary = Python.dict() - dictionary["item_name"] = "whizbang" - dictionary["price"] = 11.75 - dictionary["inventory"] = 100 - print(dictionary) +def main(): + py_dict = Python.dict() + py_dict["item_name"] = "whizbang" + py_dict["price"] = 11.75 + py_dict["inventory"] = 100 + print(py_dict) +``` +```output +{'item_name': 'whizbang', 'price': 11.75, 'inventory': 100} ``` ### Mojo wrapper objects @@ -81,39 +77,69 @@ methods (dunder methods) like `__getitem__()` and `__getattr__()`, passing them through to the underlying Python object. You can explicitly create a wrapped Python object by initializing a -`PythonObject` with a Mojo literal: +`PythonObject` with a Mojo literal. Most of the time, you can treat the wrapped +object just like you'd treat it in Python. You can use dot-notation to access +attributes and call methods, and use the `[]` operator to access an item in a +sequence. For example: ```mojo from python import PythonObject -var py_list: PythonObject = [1, 2, 3, 4] +def main(): + var py_list: PythonObject = ["cat", 2, 3.14159, 4] # A ListLiteral + n = py_list[2] + print("n =", n) + py_list.append(5) + py_list[0] = "aardvark" + print(py_list) ``` -Most of the time, you can treat the wrapped object just like you'd treat it in -Python. You can use Python's `[]` operators to access an item in a list, and use -dot-notation to access attributes and call methods. For example: +```output +n = 3.14159 +['aardvark', 2, 3.14159, 4, 5] +``` -```mojo -var n = py_list[2] -py_list.append(5) +:::note + +In the example above, `["cat", 2, 3.14159, 4]` is an instance of +[`ListLiteral`](/mojo/stdlib/builtin/builtin_list/ListLiteral/), which supports +lists of heterogeneous values. The `ListLiteral` type is intended only for +Python interoperability, and it is not compatible with the native Mojo +[`List`](/mojo/stdlib/collections/list/List) type. For example, the following +line results in a compilation error: + +``` +var mojo_list: List[Int] = [1, 2, 3, 4] +``` + +```output +error: cannot implicitly convert 'ListLiteral[Int, Int, Int, Int]' value to 'List[Int]' +var mojo_list: List[Int] = [1, 2, 3, 4] + ^~~~~~~~~~~~ ``` +::: + If you want to construct a Python type that doesn't have a literal Mojo equivalent, you can also use the [`Python.evaluate()`](/mojo/stdlib/python/python/Python#evaluate) method. For example, to create a Python `set`: ```mojo -def use_py_set(): - var py_set = Python.evaluate('set([2, 3, 5, 7, 11])') - var num_items = len(py_set) - print(num_items, " items in set.") # prints "5 items in set" - print(py_set.__contains__(6)) # prints "False" +from python import Python + +def main(): + var py_set = Python.evaluate('{2, 3, 2, 7, 11, 3}') + num_items = len(py_set) + print(num_items, "items in the set.") + contained = 7 in py_set + print("Is 7 in the set:", contained) ``` -TODO: You should be able to use the expression `6 in py_set`. However, because -of the way `PythonObject` currently works, you need to call the -`__contains__()` method directly. +```output +4 items in the set. +Is 7 in the set: True +``` Some Mojo APIs handle `PythonObject` just fine, but sometimes you'll need to explicitly convert a Python value into a native Mojo value. @@ -121,12 +147,11 @@ explicitly convert a Python value into a native Mojo value. Currently `PythonObject` conforms to the [`Intable`](/mojo/stdlib/builtin/int/Intable), [`Stringable`](/mojo/stdlib/builtin/str/Stringable), and -[`Boolable`](/mojo/stdlib/builtin/bool/Boolable) traits, which -means you can convert Python values to Mojo `Int`, `String`, and `Bool` types -using the built-in -[`int()`](/mojo/stdlib/builtin/int/int-function), -[`str()`](/mojo/stdlib/builtin/str/str), -and [`bool()`](/mojo/stdlib/builtin/bool/bool-function) functions, and print Python +[`Boolable`](/mojo/stdlib/builtin/bool/Boolable) traits, which means you can +convert Python values to Mojo `Int`, `String`, and `Bool` types using the +built-in [`int()`](/mojo/stdlib/builtin/int/int-function), +[`str()`](/mojo/stdlib/builtin/str/str), and +[`bool()`](/mojo/stdlib/builtin/bool/bool-function) functions, and print Python values using the built-in [`print()`](/mojo/stdlib/builtin/io/print) function. `PythonObject` also provides the @@ -142,31 +167,46 @@ var f: Float64 = py_float.to_float64() ### Comparing Python types in Mojo -In conditionals, Python objects act like you'd expect them to: Python values -like `False` and `None` evaluate as false in Mojo, too. +You can use Python objects in Mojo comparison expressions, and the Mojo `is` +operator also works to compare the identity of two Python objects. Python values +like `False` and `None` evaluate as false in Mojo boolean expressions as well. If you need to know the type of the underlying Python object, you can use the [`Python.type()`](/mojo/stdlib/python/python/Python#type) method, which is -equivalent to the Python `type()` builtin. You can compare the identity of two -Python objects using the -[`Python.is_type()`](/mojo/stdlib/python/python/Python#is_type) method (which is -equivalent to the Python `is` operator): +equivalent to the Python `type()` builtin. You can test if a Python +object is of a particular type by performing an identity comparison against the +type as shown below: ```mojo -def python_types(): - from python import Python - from python import PythonObject +from python import Python +from python import PythonObject +def main(): var value1: PythonObject = 3.7 - var value2 = Python.evaluate("10/3") - var float_type = Python.evaluate("float") - - print(Python.type(value1)) # - print(Python.is_type(Python.type(value1), Python.type(value2))) # True - print(Python.is_type(Python.type(value1), float_type)) # True - print(Python.is_type(Python.type(value1), Python.none())) # False - + value2 = Python.evaluate("10/3") + + # Compare values + print("Is value1 greater than 3:", value1 > 3) + print("Is value1 greater than value2:", value1 > value2) + + # Compare identities + value3 = value2 + print("value1 is value2:", value1 is value2) + print("value2 is value3:", value2 is value3) + + # Compare types + py_float_type = Python.evaluate("float") + print("Python float type:", py_float_type) + print("value1 type:", Python.type(value1)) + print("Is value1 a Python float:", Python.type(value1) is py_float_type) ``` -One TODO item here: The `Python.is_type()` method is misleadingly named, since -it doesn't compare *types*, but object identity. +```output +Is value1 greater than 3: True +Is value1 greater than value2: True +value1 is value2: False +value2 is value3: True +Python float type: +value1 type: +Is value1 a Python float: True +``` From abd150e2d1b8c20f4a8aeb705a3ff59f54a8916b Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Mon, 9 Dec 2024 16:22:39 -0800 Subject: [PATCH 070/509] [Docs] Origin changes: add suggestions from code review. MODULAR_ORIG_COMMIT_REV_ID: 9e019cdabf2bd6d46df078522f1633df94388920 --- docs/manual/values/lifetimes.mdx | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/docs/manual/values/lifetimes.mdx b/docs/manual/values/lifetimes.mdx index 5dc05d799a..baf5f1e860 100644 --- a/docs/manual/values/lifetimes.mdx +++ b/docs/manual/values/lifetimes.mdx @@ -85,18 +85,16 @@ struct ParametricRef[ pass ``` -Note that `Origin` isn't an origin **value**, it's a helper for specifying a -origin **type**. Origin types carry the mutability of a reference as a -boolean parameter value, indicating whether the origin is mutable, immutable, -or even with mutability depending on a parameter specified by the enclosing API. +Origin types carry the mutability of a reference as a boolean parameter value, +indicating whether the origin is mutable, immutable, or even with mutability +depending on a parameter specified by the enclosing API. The `is_mutable` parameter here is an [infer-only -parameter](/mojo/manual/parameters/#infer-only-parameters). It can't be passed -as a positional parameter—it's either inferred from context or specified by -keyword. The `origin` value is often inferred, as well. For example, the -following code creates a [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) to an -existing value, but doesn't need to specify an origin—the `origin` is inferred -from the variable passed in to the `address_of()` method. +parameter](/mojo/manual/parameters/#infer-only-parameters). The `origin` value +is often inferred, as well. For example, the following code creates a +[`Pointer`](/mojo/stdlib/memory/pointer/Pointer) to an existing value, but +doesn't need to specify an origin—the `origin` is inferred from the variable +passed in to the `address_of()` method. ```mojo from memory import Pointer @@ -474,8 +472,5 @@ that's a union of both origins. ```mojo def pick_one(cond: Bool, ref a: String, ref b: String) -> ref [a, b] String: - if cond: - return a - else: - return b + return a if cond else b ``` From 8486a84c6862bf8b5f907670e5f898beeb3b8cf9 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Mon, 9 Dec 2024 16:40:56 -0800 Subject: [PATCH 071/509] [Docs] Add doc on associated aliases. Fixes DOCS-495. MODULAR_ORIG_COMMIT_REV_ID: 20fb09bc7db26baf4f6f30bef9d67037b41fdec2 --- docs/manual/traits.mdx | 113 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 108 insertions(+), 5 deletions(-) diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index 99da582c4b..4c33a10578 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -75,7 +75,7 @@ Quack Moo! ``` -This isn't too bad with only two classes. But the more classes you want to +This isn't too bad with only two types. But the more types you want to support, the less practical this approach is. You might notice that the Mojo versions of `make_it_quack()` don't include the @@ -101,11 +101,15 @@ keyword. Right now, a trait can only contain method signatures, and cannot include method implementations. Each method signature must be followed by three dots (`...`) to indicate that the method is unimplemented. +A trait can also include associated aliases—compile-time constant values that +must be defined by conforming structs. Associated aliases are useful for writing +traits that describe generic types. For more information, see +[Associated aliases for generics](#associated-aliases-for-generics). + :::note TODO In the future, we plan to support defining fields and default method -implementations inside a trait. Right now, though, a trait can only declare -method signatures. +implementations inside a trait. ::: @@ -286,9 +290,9 @@ copyable and movable. ## Built-in traits -The Mojo standard library currently includes a few traits. They're implemented +The Mojo standard library includes many traits. They're implemented by a number of standard library types, and you can also implement these on your -own types: +own types. These standard library traits include: * [`Absable`](/mojo/stdlib/builtin/math/Absable) * [`AnyType`](/mojo/stdlib/builtin/anytype/AnyType) @@ -513,3 +517,102 @@ the `CollectionElement` trait, which requires a Building generic containers is an advanced topic. For an introduction, see the section on [parameterized structs](/mojo/manual/parameters/#parameterized-structs). + +### Associated aliases for generics + +In addition to methods, a trait can include _associated aliases_, which must be +defined by any conforming struct. For example: + +```mojo +trait Repeater: + alias count: Int +``` + +An implementing struct must define a concrete constant value for the alias, +using any compile-time parameter value. For example, it can use a literal +constant or a compile-time expression, including one that uses the struct's +parameters. + +```mojo +struct Doublespeak(Repeater): + alias count: Int = 2 + +struct Multispeak[verbosity: Int](Repeater): + alias count: Int = verbosity*2+1 +``` + +The `Doublespeak` struct has a constant value for the alias, but the `Multispeak` +struct lets the user set the value using a parameter: + +```mojo +repeater = Multispeak[12]() +``` + +Note that the alias is named `count`, and the `Multispeak` parameter is named +`verbosity`. Parameters and aliases are in the same namespace, so the parameter +can't have the same name as the associated alias. + +Associated aliases are most useful for writing traits for generic types. For +example, imagine that you want to write a trait that describes a generic stack +data structure that stores elements that conform to the `CollectionElement` +trait. + +By adding the element type as an associated alias to the trait, you can specify +generic methods on the trait: + +```mojo +trait Stacklike: + alias EltType: CollectionElement + + def push(mut self, owned item: Self.EltType): + pass + + def pop(mut self) -> Self.EltType: + pass +``` + +The following struct implements the `Stacklike` trait using a `List` as the +underlying storage: + +```mojo +struct MyStack[T: CollectionElement](Stacklike): + """A simple Stack built using a List.""" + alias EltType = T + alias list_type = List[Self.EltType] + + var list: Self.list_type + + fn __init__(out self): + self.list = Self.list_type() + + def push(mut self, owned item: Self.EltType): + self.list.append(item) + + def pop(mut self) -> Self.EltType: + return self.list.pop() + + def dump[U: RepresentableCollectionElement](self: MyStack[U]): + print(self.list.__repr__()) +``` + +The `MyStack` type adds a `dump()` method that prints the contents of the stack. +Because a struct that conforms to `CollectionElement` is not necessarily +printable, `MyStack` uses +[conditional conformance](/mojo/manual/parameters/#conditional-conformance) to +define a `dump()` method that works as long as the element type is +_representable_. + +The following code exercises this new trait by defining a generic method, +`add_to_stack()` that adds an item to any `Stacklike` type. + +```mojo +def add_to_stack[S: Stacklike](mut stack: S, item: S.EltType): + stack.push(item) + +def main(): + s = MyStack[Int]() + add_to_stack(s, 12) + add_to_stack(s, 33) + s.dump() # [12, 33] + print(s.pop()) # 33 +``` From daa16c7107786d66e23f6f2126c52dfa96a134f3 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Mon, 9 Dec 2024 17:15:40 -0800 Subject: [PATCH 072/509] [Docs] Document named result values MODULAR_ORIG_COMMIT_REV_ID: 1d176b9be734cfcaef30f4da54bb748ccb0e1106 --- docs/manual/functions.mdx | 116 +++++++++++++++++++++++++++++-- docs/manual/index.md | 7 +- docs/manual/lifecycle/life.mdx | 2 + docs/manual/structs.mdx | 18 ++--- docs/manual/values/ownership.mdx | 19 +++-- 5 files changed, 141 insertions(+), 21 deletions(-) diff --git a/docs/manual/functions.mdx b/docs/manual/functions.mdx index 19d4c2e067..071255ec24 100644 --- a/docs/manual/functions.mdx +++ b/docs/manual/functions.mdx @@ -4,7 +4,7 @@ sidebar_position: 2 description: Introduction to Mojo `fn` and `def` functions. --- -As mentioned in [Language basics](/mojo/manual/basics), Mojo supports two +As mentioned in the [syntax overview](/mojo/manual/basics), Mojo supports two types of functions: `def` and `fn` functions. You can use either declaration with any function, including the `main()` function, but they have different default behaviors, as described on this page. @@ -556,10 +556,10 @@ that the call is ambiguous (if it can’t figure out which one to pick). If the compiler can't figure out which function to use, you can resolve the ambiguity by explicitly casting your value to a supported argument type. For -example, in the following code, we want to call the overloaded `foo()` -function, but both implementations accept an argument that supports [implicit -conversion](/mojo/manual/variables#implicit-type-conversion) from -`StringLiteral`. So, the call to `foo(string)` is ambiguous and creates a +example, in the following code, we want to call the overloaded `foo()` function, +but both implementations accept an argument that supports [implicit +conversion](/mojo/manual/lifecycle/life#constructors-and-implicit-conversion) +from `StringLiteral`. So, the call to `foo(string)` is ambiguous and creates a compiler error. We can fix it by casting the value to the type we really want: ```mojo @@ -596,3 +596,109 @@ different from function arguments, and used for compile-time metaprogramming), you can also overload functions based on parameter types. ::: + +## Return values + +Return value types are declared in the signature using the +-> type syntax. Values are +passed using the `return` keyword, which ends the function and returns the +identified value (if any) to the caller. + +```mojo +def get_greeting() -> String: + return "Hello" +``` + +By default, the value is returned to the caller as an owned value. As with +arguments, a return value may be [implicitly +converted](/mojo/manual/lifecycle/life#constructors-and-implicit-conversion) to +the named return type. For example, the previous example calls `return` with a +string literal, `"Hello"`, which is implicitly converted to a `String`. + +:::note Returning a reference + +A function can also return a mutable or immutable reference using a `ref` return +value. For details, see +[Lifetimes, origins, and references](/mojo/manual/values/lifetimes). + +::: + +### Named results + +Named function results allow a function to return a value that can't be moved or +copied. Named result syntax lets you specify a named, uninitialized variable to +return to the caller using the `out` argument convention: + +```mojo +def get_name_tag(owned name: String, out name_tag: NameTag): + name_tag = NameTag(name^) +``` + +The `out` argument convention identifies an uninitialized variable that the +function must initialize. (This is the same as the `out` convention used in +[struct constructors](/mojo/manual/lifecycle/life#constructor).) A function can +have only one `out` argument. The `out` argument for a named result can appear +anywhere in the argument list, but by convention, it should be the last argument +in the list. + +A function with a named result argument doesn't need to include an explicit +`return` statement, as shown above. If the function terminates without a `return`, +or at a `return` statement with no value, the value of the `out` argument is +returned to the caller. If it includes a `return` statement with a value, that +value is returned to the caller, as usual. + +The fact that a function uses a named result is transparent to the caller. That +is, these two signatures are interchangeable to the caller: + +```mojo +def get_name_tag(owned name: String) -> NameTag: + ... +def get_name_tag(owned name: String, out name_tag: NameTag): + ... +``` + +In both cases, the call looks like this: + +```mojo +tag = get_name_tag("Judith") +``` + +Because the return value is assigned to this special `out` variable, it doesn't +need to be moved or copied when it's returned to the caller. This means that you +can create a function that returns a type that can't be moved or copied, and +which takes several steps to initialize: + +```mojo +struct ImmovableObject: + var name: String + + fn __init__(out self, owned name: String): + self.name = name^ + +def create_immovable_object(owned name: String, out obj: ImmovableObject): + obj = ImmovableObject(name^) + obj.name += "!" + # obj is implicitly returned + +def main(): + my_obj = create_immutable_object("Blob") +``` + +By contrast, the following function with a standard return value doesn't work: + +```mojo +def create_immovable_object2(owned name: String) -> ImmovableObject: + obj = ImmovableObject(name^) + obj.name += "!" + return obj^ # Error: ImmovableObject is not copyable or movable +``` + +Because `create_immovable_object2` uses a local variable to store the object +while it's under construction, the return call requires it to be either moved +or copied to the callee. This isn't an issue if the newly-created value is +returned immediately: + +```mojo +def create_immovable_object3(owned name: String) -> ImmovableObject: + return ImmovableObject(name^) # OK +``` diff --git a/docs/manual/index.md b/docs/manual/index.md index 7d152a1b2b..54a7c53af7 100644 --- a/docs/manual/index.md +++ b/docs/manual/index.md @@ -29,11 +29,13 @@ feedback](https://www.modular.com/community). - **Language basics** - - [Introduction to Mojo](/mojo/manual/basics) + - [Overview](/mojo/manual/basics) - [Functions](/mojo/manual/functions) - [Variables](/mojo/manual/variables) - [Types](/mojo/manual/types) + - [Operators and expressions](/mojo/manual/operators) - [Control flow](/mojo/manual/control-flow) + - [Errors and context managers](/mojo/manual/errors) - [Structs](/mojo/manual/structs) - [Modules and packages](/mojo/manual/packages) @@ -41,7 +43,8 @@ feedback](https://www.modular.com/community). - [Intro to value ownership](/mojo/manual/values/) - [Value semantics](/mojo/manual/values/value-semantics) - - [Ownership and borrowing](/mojo/manual/values/ownership) + - [Ownership](/mojo/manual/values/ownership) + - [Lifetimes, origins, and references](/mojo/manual/values/lifetimes) - **Value lifecycle** diff --git a/docs/manual/lifecycle/life.mdx b/docs/manual/lifecycle/life.mdx index c852c4c700..360ec57c33 100644 --- a/docs/manual/lifecycle/life.mdx +++ b/docs/manual/lifecycle/life.mdx @@ -155,6 +155,8 @@ can happen when one of the following occurs: - You assign a value of one type to a variable with a different type. - You pass a value of one type to a function that requires a different type. +- You return a value of one type from a function that specifies a different + return type. In both cases, implicit conversion is supported when the target type defines a constructor that meets the following criteria: diff --git a/docs/manual/structs.mdx b/docs/manual/structs.mdx index 92f0fdc1c0..7a32bb323d 100644 --- a/docs/manual/structs.mdx +++ b/docs/manual/structs.mdx @@ -50,14 +50,16 @@ struct MyPair: self.second = second ``` -Notice that the first argument in the `__init__()` method is `out self`. For -now, ignore `out` (it's an [argument -convention](/mojo/manual/values/ownership#argument-conventions) that -declares `self` as a mutable reference); all you need to know right now is that -`self` must be the first argument. It references the current struct instance -(it allows code in the method to refer to "itself"). *When you call the -constructor, you never pass a value for `self`—Mojo passes it in -automatically.* +Notice that the first argument in the `__init__()` method is `out self`. You'll +have a `self` argument as the first argument on all struct methods. It +references the current struct instance (it allows code in the method to refer to +"itself"). *When you call the constructor, you never pass a value for +`self`—Mojo passes it in automatically.* + +The `out` portion of `out self` is an [argument +convention](/mojo/manual/values/ownership#argument-conventions) that declares +`self` as a mutable reference that starts out as uninitialized and must be +initialized before the function returns. The `__init__()` method is one of many [special methods](#special-methods) (also known as "dunder methods" because they have *d*ouble *under*scores) with diff --git a/docs/manual/values/ownership.mdx b/docs/manual/values/ownership.mdx index d45ecc7c70..e8d201e8b6 100644 --- a/docs/manual/values/ownership.mdx +++ b/docs/manual/values/ownership.mdx @@ -1,5 +1,5 @@ --- -title: Ownership and borrowing +title: Ownership sidebar_position: 3 description: How Mojo shares references through function arguments. --- @@ -84,6 +84,13 @@ keyword at the beginning of an argument declaration: `ref` arguments are an advanced topic, and they're described in more detail in [Lifetimes and references](/mojo/manual/values/lifetimes). +* `out`: A special convention used for the `self` argument in + [constructors](/mojo/manual/lifecycle/life#constructor) and for + [named results](/mojo/manual/functions#named-function-results). An `out` + argument is uninitialized at the beginning of the function, and must be + initialized before the function returns. Although `out` arguments show up in + the argument list, they're never passed in by the caller. + For example, this function has one argument that's a mutable reference and one that's immutable: @@ -148,7 +155,7 @@ doesn't do any copying. In general, passing an immutable reference is much more efficient when handling large or expensive-to-copy values, because the copy constructor -and destructor are not invoked for a borrow. +and destructor are not invoked for a `read` argument. ### Compared to C++ and Rust @@ -168,9 +175,9 @@ mutability in the callee. However, the read convention differs from when compared to languages like C++ and Rust, and moves this optimization from every call site to a declaration on the type definition. -The major difference between Rust and Mojo is that Mojo does not require a -sigil on the caller side to pass by borrow. Also, Mojo is more efficient when -passing small values, and Rust defaults to moving values instead of passing +The major difference between Rust and Mojo is that Mojo does not require a sigil +on the caller side to pass by immutable reference. Also, Mojo is more efficient +when passing small values, and Rust defaults to moving values instead of passing them around by borrow. These policy and syntax decisions allow Mojo to provide an easier-to-use programming model. @@ -448,7 +455,7 @@ like `object` are cheap to copy. However, copying large types that allocate heap storage can be expensive. (For example, copying `List` or `Dict` types, or copying large numbers of strings.) -### Borrowed versus owned in `def` functions +### `read` versus `owned` in `def` functions The difference between `read` and `owned` in a `def` function may be a little subtle. In both cases, you can end up with a uniquely-owned value that's From ed76b83b6ce398ed4d6ed9ad979ca14aba42e07c Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Mon, 9 Dec 2024 17:17:49 -0800 Subject: [PATCH 073/509] Updated solution code for the life example to match recent tutorial changes MODULAR_ORIG_COMMIT_REV_ID: d87d1ea4e81ffc5625accbab753d23ef0fd03c9a --- examples/life/gridv1.mojo | 28 +++++++++++++++++++++------- examples/life/gridv2.mojo | 3 +++ examples/life/lifev1.mojo | 2 +- examples/life/lifev2.mojo | 2 +- examples/life/mojoproject.toml | 2 +- 5 files changed, 27 insertions(+), 10 deletions(-) diff --git a/examples/life/gridv1.mojo b/examples/life/gridv1.mojo index b06cb51315..0eded6f24d 100644 --- a/examples/life/gridv1.mojo +++ b/examples/life/gridv1.mojo @@ -40,15 +40,19 @@ struct Grid(StringableRaising): # ===-------------------------------------------------------------------===# def __str__(self) -> String: + # Create an empty String str = String() + + # Iterate through rows 0 through rows-1 for row in range(self.rows): + # Iterate through columns 0 through cols-1 for col in range(self.cols): if self[row, col] == 1: - str += "*" + str += "*" # If cell is populated, append an asterisk else: - str += " " + str += " " # If cell is not populated, append a space if row != self.rows - 1: - str += "\n" + str += "\n" # Add a newline between rows, but not at the end return str # ===-------------------------------------------------------------------===# @@ -74,6 +78,7 @@ struct Grid(StringableRaising): if seed: random.seed(seed.value()) else: + # Seed the random number generator using the current time. random.seed() data = List[List[Int]]() @@ -81,6 +86,7 @@ struct Grid(StringableRaising): for row in range(rows): row_data = List[Int]() for col in range(cols): + # Generate a random 0 or 1 and append it to the row. row_data.append(int(random.random_si64(0, 1))) data.append(row_data) @@ -96,13 +102,16 @@ struct Grid(StringableRaising): for row in range(self.rows): row_data = List[Int]() + # Calculate neighboring row indices, handling "wrap-around" row_above = (row - 1) % self.rows row_below = (row + 1) % self.rows for col in range(self.cols): + # Calculate neighboring column indices, handling "wrap-around" col_left = (col - 1) % self.cols col_right = (col + 1) % self.cols + # Determine number of populated cells around the current cell num_neighbors = ( self[row_above, col_left] + self[row_above, col] @@ -114,10 +123,15 @@ struct Grid(StringableRaising): + self[row_below, col_right] ) - if num_neighbors | self[row, col] == 3: - row_data.append(1) - else: - row_data.append(0) + # Determine the state of the current cell for the next generation + new_state = 0 + if self[row, col] == 1 and ( + num_neighbors == 2 or num_neighbors == 3 + ): + new_state = 1 + elif self[row, col] == 0 and num_neighbors == 3: + new_state = 1 + row_data.append(new_state) next_generation.append(row_data) diff --git a/examples/life/gridv2.mojo b/examples/life/gridv2.mojo index 3e43b5988a..fa5807974f 100644 --- a/examples/life/gridv2.mojo +++ b/examples/life/gridv2.mojo @@ -98,13 +98,16 @@ struct Grid[rows: Int, cols: Int](StringableRaising): next_generation = Self() for row in range(rows): + # Calculate neighboring row indices, handling "wrap-around" row_above = (row - 1) % rows row_below = (row + 1) % rows for col in range(cols): + # Calculate neighboring column indices, handling "wrap-around" col_left = (col - 1) % cols col_right = (col + 1) % cols + # Determine number of populated cells around the current cell num_neighbors = ( self[row_above, col_left] + self[row_above, col] diff --git a/examples/life/lifev1.mojo b/examples/life/lifev1.mojo index 9056388874..04e78df7c4 100644 --- a/examples/life/lifev1.mojo +++ b/examples/life/lifev1.mojo @@ -59,7 +59,7 @@ def run_display( # Draw each live cell in the grid for row in range(grid.rows): for col in range(grid.cols): - if grid.data[row][col]: + if grid[row, col]: x = col * cell_width + border_size y = row * cell_height + border_size width = cell_width - border_size diff --git a/examples/life/lifev2.mojo b/examples/life/lifev2.mojo index ca2071ae06..e78306c0d9 100644 --- a/examples/life/lifev2.mojo +++ b/examples/life/lifev2.mojo @@ -83,4 +83,4 @@ def run_display( def main(): start = Grid[128, 128].random() - run_display(start, window_height=600, window_width=600) + run_display(start) diff --git a/examples/life/mojoproject.toml b/examples/life/mojoproject.toml index 2d38853796..4a1ffb3026 100644 --- a/examples/life/mojoproject.toml +++ b/examples/life/mojoproject.toml @@ -8,7 +8,7 @@ version = "0.1.0" [dependencies] max = "*" -python = "3.12.*" +python = ">=3.11,<3.13" pygame = ">=2.6.1,<3" [tasks] From 2ace785ef7b5cafda55a93b844a752a05137caba Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 10 Dec 2024 18:15:54 +0000 Subject: [PATCH 074/509] Update lockfiles to point to latest nightly version: 24.6.0.dev2024121016 --- examples/life/magic.lock | 2283 +++++++++++++++------------------ examples/magic.lock | 1781 ++++++++++++------------- examples/notebooks/magic.lock | 1799 +++++++++++++------------- examples/operators/magic.lock | 1781 ++++++++++++------------- magic.lock | 1781 ++++++++++++------------- 5 files changed, 4628 insertions(+), 4797 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index c3b6a5b2ae..e9b082c770 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -9,7 +9,7 @@ environments: - 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We're leaning towards restricting __disable_del to only work on Self, so skipping remaining mark_destroyed usages for now. MODULAR_ORIG_COMMIT_REV_ID: 0e5c5d0f92dc0be4c9e2e88f6d1e8dc4ba60dc5e --- stdlib/src/collections/dict.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 99aa18c45f..bed109d221 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -242,14 +242,14 @@ struct DictEntry[K: KeyElement, V: CollectionElement]( self.key = other.key self.value = other.value - fn reap_value(owned self) -> V: + fn reap_value(owned self) -> V as out: """Take the value from an owned entry. Returns: The value of the entry. """ - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(self)) - return self.value^ + out = self.value^ + __disable_del self alias _EMPTY = -1 From 52e25220a34a9a1a12bca01548b45e2f921d8af2 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Tue, 10 Dec 2024 13:20:58 -0600 Subject: [PATCH 076/509] [External] [stdlib] Fix allow immutable `UnsafePointer` and adjust `.unsafe_ptr()` methods (#50547) [External] [stdlib] Allow immutable `UnsafePointer` and adjust `.unsafe_ptr()` methods This changes `UnsafePointer` to support immutable origin parameters, and updates the `unsafe_ptr()` methods of many collection types to more accurately report their origin as being tied to the collection instance, instead of being unspecified `MutableAnyOrigin`. Additionally, `UnsafePointer.address_of()` has been updated to infer the origin of the returned `UnsafePointer` from the memory location being referenced. With these changes, the most common places where `MutableAnyOrigin` origins were constructed have been fixed. Although this should not break anything in well-behaved Mojo code, it may change where ASAP destruction is performed, due to the narrower origin references meaning destructors will no longer be deferred unnecessarily. Fixes MSTDL-956 ORIGINAL_AUTHOR=martinvuyk <110240700+martinvuyk@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3734 --------- Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Co-authored-by: Connor Gray Closes modularml/mojo#3734 MODULAR_ORIG_COMMIT_REV_ID: a5023a5464a9c9b0608734af096fec165322dcd8 --- stdlib/scripts/run-tests.sh | 2 +- stdlib/src/builtin/sort.mojo | 16 +++--- stdlib/src/builtin/string_literal.mojo | 14 +++-- stdlib/src/collections/dict.mojo | 4 +- stdlib/src/collections/list.mojo | 12 ++-- stdlib/src/collections/string.mojo | 10 +++- stdlib/src/memory/memory.mojo | 4 +- stdlib/src/memory/span.mojo | 12 ++-- stdlib/src/memory/unsafe_pointer.mojo | 65 ++++++++++++++-------- stdlib/src/random/random.mojo | 2 +- stdlib/src/utils/string_slice.mojo | 5 +- stdlib/test/collections/test_string.mojo | 2 +- stdlib/test/memory/test_unsafepointer.mojo | 6 +- 13 files changed, 97 insertions(+), 57 deletions(-) diff --git a/stdlib/scripts/run-tests.sh b/stdlib/scripts/run-tests.sh index 70a7bf52db..a13d2e03ef 100755 --- a/stdlib/scripts/run-tests.sh +++ b/stdlib/scripts/run-tests.sh @@ -29,7 +29,7 @@ TEST_UTILS_PATH="${REPO_ROOT}/stdlib/test/test_utils" # uses the stdlib that's given in the nightly, and will fail compilation # if some breaking changes are made. export MODULAR_MOJO_NIGHTLY_IMPORT_PATH=$BUILD_DIR -mojo package "${TEST_UTILS_PATH}" -o "${BUILD_DIR}/test_utils.mojopkg" +mojo package "${TEST_UTILS_PATH}" -I ${BUILD_DIR} -o "${BUILD_DIR}/test_utils.mojopkg" TEST_PATH="${REPO_ROOT}/stdlib/test" if [[ $# -gt 0 ]]; then diff --git a/stdlib/src/builtin/sort.mojo b/stdlib/src/builtin/sort.mojo index 23429d9b4f..f3f19ba9b2 100644 --- a/stdlib/src/builtin/sort.mojo +++ b/stdlib/src/builtin/sort.mojo @@ -48,7 +48,7 @@ fn _insertion_sort[ cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): """Sort the array[start:end] slice""" - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var size = len(span) for i in range(1, size): @@ -72,7 +72,7 @@ fn _quicksort_partition_right[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]) -> Int: - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var size = len(span) var left = 1 @@ -101,7 +101,7 @@ fn _quicksort_partition_left[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]) -> Int: - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var size = len(span) var left = 1 @@ -127,7 +127,7 @@ fn _heap_sort_fix_down[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin], idx: Int): - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var size = len(span) var i = idx var j = i * 2 + 1 @@ -148,7 +148,7 @@ fn _heap_sort[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var size = len(span) # heapify for i in range(size // 2 - 1, -1, -1): @@ -177,7 +177,7 @@ fn _delegate_small_sort[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var size = len(span) if size == 2: _small_sort[2, type, cmp_fn](array) @@ -209,7 +209,7 @@ fn _quicksort[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var size = len(span) if size == 0: return @@ -379,7 +379,7 @@ fn _partition[ if size <= 1: return 0 - var array = span.unsafe_ptr() + var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() var pivot = size // 2 var pivot_value = array[pivot] diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 377173470f..fc3ce8e14a 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -485,8 +485,9 @@ struct StringLiteral( return __mlir_op.`pop.string.size`(self.value) @always_inline("nodebug") - # FIXME(MSTDL-956): This should return a pointer with StaticConstantOrigin. - fn unsafe_ptr(self) -> UnsafePointer[UInt8]: + fn unsafe_ptr( + self, + ) -> UnsafePointer[Byte, is_mutable=False, origin=StaticConstantOrigin]: """Get raw pointer to the underlying data. Returns: @@ -497,11 +498,14 @@ struct StringLiteral( # TODO(MSTDL-555): # Remove bitcast after changing pop.string.address # return type. - return ptr.bitcast[UInt8]() + return ptr.bitcast[ + Byte, is_mutable=False, origin=StaticConstantOrigin + ]() @always_inline - # FIXME(MSTDL-956): This should return a pointer with StaticConstantOrigin. - fn unsafe_cstr_ptr(self) -> UnsafePointer[c_char]: + fn unsafe_cstr_ptr( + self, + ) -> UnsafePointer[c_char, is_mutable=False, origin=StaticConstantOrigin]: """Retrieves a C-string-compatible pointer to the underlying memory. The returned pointer is guaranteed to be NUL terminated, and not null. diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index bed109d221..f6b36900dc 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -192,7 +192,9 @@ struct _DictValueIter[ # Cast through a pointer to grant additional mutability because # _DictEntryIter.next erases it. return Self.ref_type.address_of( - UnsafePointer.address_of(entry_ref[].value)[] + UnsafePointer.address_of(entry_ref[].value).bitcast[ + origin=dict_origin + ]()[] ) @always_inline diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index bcfca0c2fa..a3a64643a3 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -22,7 +22,6 @@ from collections import List from os import abort from sys import sizeof -from sys.intrinsics import _type_is_eq from memory import Pointer, UnsafePointer, memcpy, Span @@ -924,12 +923,17 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( if elt_idx_1 != elt_idx_2: swap((self.data + elt_idx_1)[], (self.data + elt_idx_2)[]) - @always_inline - fn unsafe_ptr(self) -> UnsafePointer[T]: + fn unsafe_ptr( + ref self, + ) -> UnsafePointer[ + T, + is_mutable = Origin(__origin_of(self)).is_mutable, + origin = __origin_of(self), + ]: """Retrieves a pointer to the underlying memory. Returns: - The UnsafePointer to the underlying memory. + The pointer to the underlying memory. """ return self.data diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index fe9746584d..cef5b9a42c 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -1594,13 +1594,19 @@ struct String( buf.append(0) return String(buf^) - fn unsafe_ptr(self) -> UnsafePointer[UInt8]: + fn unsafe_ptr( + ref self, + ) -> UnsafePointer[ + Byte, + is_mutable = Origin(__origin_of(self)).is_mutable, + origin = __origin_of(self), + ]: """Retrieves a pointer to the underlying memory. Returns: The pointer to the underlying memory. """ - return self._buffer.data + return self._buffer.unsafe_ptr() fn unsafe_cstr_ptr(self) -> UnsafePointer[c_char]: """Retrieves a C-string-compatible pointer to the underlying memory. diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 8d3a525cb4..0a23477416 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -153,7 +153,9 @@ fn memcmp[ @always_inline fn _memcpy_impl( - dest_data: UnsafePointer[Byte, **_], src_data: __type_of(dest_data), n: Int + dest_data: UnsafePointer[Byte, is_mutable=True, **_], + src_data: __type_of(dest_data), + n: Int, ): """Copies a memory area. diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 497a084c88..03f860f899 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -107,7 +107,7 @@ struct Span[ """ # Field - var _data: UnsafePointer[T] + var _data: UnsafePointer[T, is_mutable=is_mutable, origin=origin] var _len: Int # ===------------------------------------------------------------------===# @@ -250,14 +250,14 @@ struct Span[ # Methods # ===------------------------------------------------------------------===# - fn unsafe_ptr(self) -> UnsafePointer[T]: - """ - Gets a pointer to the first element of this slice. + fn unsafe_ptr( + self, + ) -> UnsafePointer[T, is_mutable=is_mutable, origin=origin]: + """Retrieves a pointer to the underlying memory. Returns: - A pointer pointing at the first element of this slice. + The pointer to the underlying memory. """ - return self._data fn as_ref(self) -> Pointer[T, origin]: diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 047bb61ac7..a5feb34736 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -28,7 +28,6 @@ from sys.intrinsics import ( strided_load, strided_store, ) - from bit import is_power_of_two from memory.memory import _free, _malloc @@ -47,13 +46,19 @@ fn _default_alignment[type: DType, width: Int = 1]() -> Int: return _default_alignment[Scalar[type]]() +alias _must_be_mut_err = "UnsafePointer must be mutable for this operation" + + @register_passable("trivial") struct UnsafePointer[ type: AnyType, *, address_space: AddressSpace = AddressSpace.GENERIC, alignment: Int = _default_alignment[type](), - origin: Origin[True] = MutableAnyOrigin, + is_mutable: Bool = True, + origin: Origin[is_mutable] = Origin[is_mutable] + .cast_from[MutableAnyOrigin] + .result, ]( ImplicitlyBoolable, CollectionElement, @@ -79,6 +84,7 @@ struct UnsafePointer[ type: The type the pointer points to. address_space: The address space associated with the UnsafePointer allocated memory. alignment: The minimum alignment of this pointer known statically. + is_mutable: Whether the origin is mutable. origin: The origin of the memory being addressed. """ @@ -158,8 +164,8 @@ struct UnsafePointer[ type, address_space=address_space, alignment=1, - origin=MutableAnyOrigin - # TODO: Propagate origin of the argument. + is_mutable = Origin(__origin_of(arg)).is_mutable, + origin = __origin_of(arg), ], ): """Gets the address of the argument. @@ -198,9 +204,7 @@ struct UnsafePointer[ # ===-------------------------------------------------------------------===# @always_inline - fn __getitem__( - self, - ) -> ref [origin, address_space] type: + fn __getitem__(self) -> ref [origin, address_space] type: """Return a reference to the underlying data. Returns: @@ -211,12 +215,7 @@ struct UnsafePointer[ alias _ref_type = Pointer[type, origin, address_space] return __get_litref_as_mvalue( __mlir_op.`lit.ref.from_pointer`[_type = _ref_type._mlir_type]( - UnsafePointer[ - type, - address_space=address_space, - alignment=alignment, - origin=origin, - ](self).address + self.address ) ) @@ -372,7 +371,8 @@ struct UnsafePointer[ rhs: The value of the other pointer. Returns: - True if this pointer represents a higher than or equal address and False otherwise. + True if this pointer represents a higher than or equal address and + False otherwise. """ return int(self) > int(rhs) @@ -386,7 +386,8 @@ struct UnsafePointer[ rhs: The value of the other pointer. Returns: - True if this pointer represents a higher than or equal address and False otherwise. + True if this pointer represents a higher than or equal address and + False otherwise. """ return int(self) >= int(rhs) @@ -450,11 +451,7 @@ struct UnsafePointer[ # ===-------------------------------------------------------------------===# @always_inline("nodebug") - fn as_noalias_ptr( - self, - ) -> UnsafePointer[ - type, address_space=address_space, alignment=alignment, origin=origin - ]: + fn as_noalias_ptr(self) -> Self: """Cast the pointer to a new pointer that is known not to locally alias any other pointer. In other words, the pointer transitively does not alias any other memory value declared in the local function context. @@ -641,6 +638,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ + constrained[is_mutable, _must_be_mut_err]() self.offset(offset)._store[alignment=alignment, volatile=volatile](val) @always_inline @@ -673,6 +671,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ + constrained[is_mutable, _must_be_mut_err]() self.offset(offset).store[alignment=alignment, volatile=volatile](val) @always_inline @@ -702,6 +701,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ + constrained[is_mutable, _must_be_mut_err]() constrained[offset_type.is_integral(), "offset must be integer"]() self.offset(int(offset))._store[alignment=alignment, volatile=volatile]( val @@ -736,6 +736,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ + constrained[is_mutable, _must_be_mut_err]() constrained[offset_type.is_integral(), "offset must be integer"]() self.offset(int(offset))._store[alignment=alignment, volatile=volatile]( val @@ -761,6 +762,7 @@ struct UnsafePointer[ Args: val: The value to store. """ + constrained[is_mutable, _must_be_mut_err]() self._store[alignment=alignment, volatile=volatile](val) @always_inline("nodebug") @@ -785,6 +787,7 @@ struct UnsafePointer[ Args: val: The value to store. """ + constrained[is_mutable, _must_be_mut_err]() self._store[alignment=alignment, volatile=volatile](val) @always_inline("nodebug") @@ -795,6 +798,7 @@ struct UnsafePointer[ alignment: Int = _default_alignment[type, width](), volatile: Bool = False, ](self: UnsafePointer[Scalar[type], **_], val: SIMD[type, width]): + constrained[is_mutable, _must_be_mut_err]() constrained[width > 0, "width must be a positive integer value"]() constrained[ alignment > 0, "alignment must be a positive integer value" @@ -850,6 +854,7 @@ struct UnsafePointer[ val: The SIMD value to store. stride: The stride between stores. """ + constrained[is_mutable, _must_be_mut_err]() strided_store(val, self, int(stride), True) @always_inline("nodebug") @@ -949,6 +954,7 @@ struct UnsafePointer[ mask: The SIMD vector of boolean values, indicating for each element whether to store at memory or not. """ + constrained[is_mutable, _must_be_mut_err]() constrained[ offset.type.is_integral(), "offset type must be an integral type", @@ -972,9 +978,17 @@ struct UnsafePointer[ /, address_space: AddressSpace = Self.address_space, alignment: Int = Self.alignment, - origin: Origin[True] = Self.origin, + *, + is_mutable: Bool = Self.is_mutable, + origin: Origin[is_mutable] = Origin[is_mutable] + .cast_from[Self.origin] + .result, ](self) -> UnsafePointer[ - T, address_space=address_space, alignment=alignment, origin=origin + T, + address_space=address_space, + alignment=alignment, + is_mutable=is_mutable, + origin=origin, ]: """Bitcasts a UnsafePointer to a different type. @@ -982,6 +996,7 @@ struct UnsafePointer[ T: The target type. address_space: The address space of the result. alignment: Alignment of the destination pointer. + is_mutable: Whether the origin is mutable. origin: Origin of the destination pointer. Returns: @@ -1006,6 +1021,7 @@ struct UnsafePointer[ more efficient because it doesn't invoke `__moveinit__`. """ + constrained[is_mutable, _must_be_mut_err]() _ = __get_address_as_owned_value(self.address) @always_inline @@ -1028,6 +1044,7 @@ struct UnsafePointer[ Returns: The value at the pointer. """ + constrained[is_mutable, _must_be_mut_err]() return __get_address_as_owned_value(self.address) # TODO: Allow overloading on more specific traits @@ -1054,6 +1071,7 @@ struct UnsafePointer[ Args: value: The value to emplace. """ + constrained[is_mutable, _must_be_mut_err]() __get_address_as_uninit_lvalue(self.address) = value^ @always_inline @@ -1079,6 +1097,7 @@ struct UnsafePointer[ Args: value: The value to emplace. """ + constrained[is_mutable, _must_be_mut_err]() __get_address_as_uninit_lvalue(self.address) = value @always_inline @@ -1105,6 +1124,7 @@ struct UnsafePointer[ Args: value: The value to emplace. """ + constrained[is_mutable, _must_be_mut_err]() __get_address_as_uninit_lvalue(self.address) = T(other=value) @always_inline @@ -1139,6 +1159,7 @@ struct UnsafePointer[ Args: dst: Destination pointer that the value will be moved into. """ + constrained[is_mutable, _must_be_mut_err]() __get_address_as_uninit_lvalue( dst.address ) = __get_address_as_owned_value(self.address) diff --git a/stdlib/src/random/random.mojo b/stdlib/src/random/random.mojo index 8ac75d5a8f..e64ec3373b 100644 --- a/stdlib/src/random/random.mojo +++ b/stdlib/src/random/random.mojo @@ -122,7 +122,7 @@ fn randint[ fn rand[ type: DType ]( - ptr: UnsafePointer[Scalar[type], **_], + ptr: UnsafePointer[Scalar[type], is_mutable=True, **_], size: Int, /, *, diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 03ed9be490..454f7968d9 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -758,13 +758,14 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( return self._slice @always_inline - fn unsafe_ptr(self) -> UnsafePointer[UInt8]: + fn unsafe_ptr( + self, + ) -> UnsafePointer[Byte, is_mutable=is_mutable, origin=origin]: """Gets a pointer to the first element of this string slice. Returns: A pointer pointing at the first element of this string slice. """ - return self._slice.unsafe_ptr() @always_inline diff --git a/stdlib/test/collections/test_string.mojo b/stdlib/test/collections/test_string.mojo index da23f5d9c7..ff5da33ee7 100644 --- a/stdlib/test/collections/test_string.mojo +++ b/stdlib/test/collections/test_string.mojo @@ -1263,7 +1263,7 @@ def test_string_iter(): assert_equal(321, atol(concat)) for v in vs: - v.unsafe_ptr()[] = ord("1") + v.unsafe_ptr().bitcast[is_mutable=True]()[] = ord("1") # Borrow immutably for v in vs: diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index d6339f7e18..3331764e47 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -150,15 +150,15 @@ def test_unsafepointer_string(): def test_eq(): var local = 1 - var p1 = UnsafePointer[Int].address_of(local) + var p1 = UnsafePointer.address_of(local).bitcast[is_mutable=False]() var p2 = p1 assert_equal(p1, p2) var other_local = 2 - var p3 = UnsafePointer[Int].address_of(other_local) + var p3 = UnsafePointer.address_of(other_local).bitcast[is_mutable=False]() assert_not_equal(p1, p3) - var p4 = UnsafePointer[Int].address_of(local) + var p4 = UnsafePointer.address_of(local).bitcast[is_mutable=False]() assert_equal(p1, p4) _ = local _ = other_local From 0c26ac9014c1e6b799daa7983cbbe306375808b6 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Tue, 10 Dec 2024 11:51:20 -0800 Subject: [PATCH 077/509] [Docs] Mojo 24.6 changelog. That's all, folks. MODULAR_ORIG_COMMIT_REV_ID: 98f4a7713b4767bb8a1b244adb187414eb1d200f --- docs/changelog-released.md | 736 +++++++++++++++++++++++++++++++++++++ docs/changelog.md | 687 +--------------------------------- 2 files changed, 744 insertions(+), 679 deletions(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 79b64934eb..44376a6153 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -20,6 +20,742 @@ conda](/magic/conda). ::: +## v24.6 (2024-12-17) + +### ✨ Highlights + +Here's a brief summary of some of the major changes in this release, with more +detailed information in the following sections: + +- The `inout` and `borrowed` argument conventions have been renamed to `mut` + and `read`, respectively. See Language changes for details. + +- `Lifetime` and related types have been renamed to `Origin` in the standard + library to better clarify that parameters of this type indicate where a + reference is derived from, not the more complicated notion of where a variable + is initialized and destroyed. As a consequence the `__lifetime_of()` operator + is now named `__origin_of()`. There are also a number of other origin-related + improvements in this release, described under Language changes and Standard + library changes below. + + For background information and rationale on the name change see + [the proposal](https://github.com/modularml/mojo/issues/3623). For more + information on origins, see + [Lifetimes, origins and references](/mojo/manual/values/lifetimes) in the Mojo + Manual. + +- Implicit conversions are now opt-in using the `@implicit` decorator. See + Language changes for details. + +- Lots of new docs, including a brand new + [Mojo tutorial](/mojo/manual/get-started), new pages on + [operators and expressions](/mojo/manual/operators), + [error handling](/mojo/manual/errors), and + [pointers](/mojo/manual/pointers/), and many smaller additions and + improvements. + +### Language changes + +- The `inout` and `borrowed` argument conventions have been renamed to the `mut` + and `read` argument conventions (respectively). These verbs reflect + declaratively what the callee can do to the argument value passed into the + caller, without tying in the requirement for the programmer to know about + advanced features like references. + +- The argument convention for the `self` argument in `__init__()`, + `__copyinit__()`, and `__moveinit__()` methods has been changed from `inout` + to `out`, reflecting that a constructor method initializes its `self` value + without reading from it. This also enables spelling the type of an initializer + correctly, which was not supported before: + + ```mojo + struct Foo: + fn __init__(out self): pass + + fn test(): + # This works now + var fnPtr : fn(out x: Foo)->None = Foo.__init__ + + var someFoo : Foo + fnPtr(someFoo) # initializes someFoo. + ``` + + The previous `fn __init__(inout self)` syntax is still supported in this + release of Mojo, but will be removed in the future. Please migrate to the + new syntax. + +- Similarly, the spelling of "named functions results" has switched to use `out` + syntax instead of `-> T as name`. Functions may have at most one named result + or return type specified with the usual `->` syntax. `out` arguments may + occur anywhere in the argument list, but are typically last (except for + `__init__` methods, where they are typically first). + + ```mojo + # This function has type "fn() -> String" + fn example(out result: String): + result = "foo" + ``` + + The parser still accepts the old syntax as a synonym for this, but that will + eventually be deprecated and removed. + + This was [discussed extensively in a public + proposal](https://github.com/modularml/mojo/issues/3623). For more + information, see + [Named results](/nightly/mojo/manual/functions#named-results) in the Mojo + Manual. + +- Single argument constructors now require a `@implicit` decorator to allow + for implicit conversions. Previously you could define an `__init__` that + takes a single argument: + + ```mojo + struct Foo: + var value: Int + + fn __init__(out self, value: Int): + self.value = value + ``` + + And this would allow you to pass an `Int` in the position of a `Foo`: + + ```mojo + fn func(foo: Foo): + print("implicitly converted Int to Foo:", foo.value) + + fn main(): + func(Int(42)) + ``` + + This can result in complicated errors that are difficult to debug. By default + this implicit behavior is now turned off, so you have to explicitly construct + `Foo`: + + ```mojo + fn main(): + func(Foo(42)) + ``` + + You can still opt into implicit conversions by adding the `@implicit` + decorator. For example, to enable implicit conversions from `Int` to `Foo`: + + ```mojo + struct Foo: + var value: Int + + @implicit + fn __init__(out self, value: Int): + self.value = value + ``` + + For more information see + [Constructors and implicit conversion](/mojo/manual/lifecycle/life#constructors-and-implicit-conversion) + in the Mojo Manual. + +- Various improvements to origin handling and syntax have landed, including + support for the ternary operator and allowing multiple arguments in a `ref` + specifier (which are implicitly unions). This enables expression of simple + algorithms cleanly: + + ```mojo + fn my_min[T: Comparable](ref a: T, ref b: T) -> ref [a, b] T: + return a if a < b else b + ``` + + It is also nice that `my_min` automatically and implicitly propagates the + mutability of its arguments, so things like `my_min(str1, str2) += "foo"` is + valid. + +- `Origin` is now a complete wrapper around the MLIR origin type. + + - The `Origin.type` alias has been renamed to `_mlir_origin`. In parameter + lists, you can now write just `Origin[..]`, instead of `Origin[..].type`. + + - `ImmutableOrigin` and `MutableOrigin` are now, respectively, just aliases + for `Origin[False]` and `Origin[True]`. + + - `Origin` struct values are now supported in the brackets of a `ref [..]` + argument. + + - Added `Origin.cast_from` for casting the mutability of an origin value. + +- `ref` argument and result specifiers now allow providing a memory value + directly in the origin specifier, rather than requiring the use of + `__origin_of()`. It is still fine to use `__origin_of()` explicitly though, + and this is required when specifying origins for parameters (e.g. to the + `Pointer` type). For example, this is now valid without `__origin_of()`: + + ```mojo + fn return_ref(a: String) -> ref [a] String: + return a + ``` + +- `ref` function arguments without an origin clause are now treated as + `ref [_]`, which is more syntactically convenient and consistent: + + ```mojo + fn takes_and_return_ref(ref a: String) -> ref [a] String: + return a + ``` + +- The `__type_of(x)` and `__origin_of(x)` operators are much more general now: + they allow arbitrary expressions inside of them, allow referring to dynamic + values in parameter contexts, and even allow referring to raising functions + in non-raising contexts. These operations never evaluate their expression, so + any side effects that occur in the expression are never evaluated at runtime, + eliminating concerns about `__type_of(expensive())` being a problem. + +- The destructor insertion logic in Mojo is now aware that types that take an + `MutableAnyOrigin` or `ImmutableAnyOrigin` as part of their signature could + potentially access any live value that destructor insertion is tracking, + eliminating a significant usability issue with unsafe APIs like + `UnsafePointer`. Consider a typical example working with strings before this + change: + + ```mojo + var str = String(...) + var ptr = str.unsafe_ptr() + some_low_level_api(ptr) + _ = str^ # OLD HACK: Explicitly keep string alive until here! + ``` + + The `_ = str^` pattern was formerly required because the Mojo compiler has no + idea what "ptr" might reference. As a consequence, it had no idea that + `some_low_level_api()` might access `str` and therefore thought it was ok to + destroy the `String` before the call - this is why the explicit lifetime + extension was required. + + Mojo now knows that `UnsafePointer` may access the `MutableAnyOrigin` origin, + and now assumes that any API that uses that origin could use live values. + In this case, it assumes that `some_low_level_api()` might access `str` and + because it might be using it, it cannot destroy `str` until after the call. + The consequence of this is that the old hack is no longer needed for these + cases! + +- Infer-only parameters may now be explicitly bound with keywords, enabling + some important patterns in the standard library: + + ```mojo + struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]: ... + alias ImmStringSlice = StringSlice[is_mutable=False] + # This auto-parameterizes on the origin, but constrains it to being an + # immutable slice instead of a potentially mutable one. + fn take_imm_slice(a: ImmStringSlice): ... + ``` + +- Automatic parameterization of parameters is now supported. Specifying a + parameterized type with unbound parameters causes them to be implicitly added + to the function signature as infer-only parameters. + + ```mojo + fn foo[value: SIMD[DType.int32, _]](): + pass + + # Equivalent to + fn foo[size: Int, //, value: SIMD[DType.int32, size]](): + pass + ``` + +- Function types now accept an origin set parameter. This parameter represents + the origins of values captured by a parameter closure. The compiler + automatically tags parameter closures with the right set of origins. This + enables lifetimes and parameter closures to correctly compose. + + ```mojo + fn call_it[f: fn() capturing [_] -> None](): + f() + + fn test(): + var msg = String("hello world") + + @parameter + fn say_hi(): + print(msg) + + call_it[say_hi]() + # no longer need to write `_ = msg^`!! + ``` + + Note that this only works for higher-order functions which have explicitly + added `[_]` as the capture origins. By default, the compiler still assumes + a `capturing` closure does not reference any origins. This will soon change. + +- Mojo can now interpret simple LLVM intrinsics in parameter expressions, + enabling things like `count_leading_zeros` to work at compile time: + [Issue #933](https://github.com/modularml/mojo/issues/933). + +- Introduced the `@explicit_destroy` annotation, the `__disable_del` keyword, + the `UnknownDestructibility` trait, and the `ImplicitlyDestructible` keyword, + for the experimental explicitly destroyed types feature. + +- Added associated types; we can now have aliases like `alias T: AnyType`, + `alias N: Int`, etc. in a trait, and then specify them in structs that conform + to that trait. For more information, see + [Associated aliases for generics](/mojo/manual/traits#associated-aliases-for-generics). + +### Standard library changes {#24-6-standard-library-changes} + +- Add the `Floatable` and `FloatableRaising` traits to denote types that can + be converted to a `Float64` value using the builtin `float` function. + - Make `SIMD` and `FloatLiteral` conform to the `Floatable` trait. + + ```mojo + fn foo[F: Floatable](v: F): + ... + + var f = float(Int32(45)) + ``` + + ([PR #3163](https://github.com/modularml/mojo/pull/3163)) + +- Add `DLHandle.get_symbol()`, for getting a pointer to a symbol in a dynamic + library. This is more general purpose than the existing methods for getting + function pointers. + +- Introduce `TypedPythonObject` as a light-weight way to annotate `PythonObject` + values with static type information. This design will likely evolve and + change significantly. + + - Added `TypedPythonObject["Tuple].__getitem__` for accessing the elements of + a Python tuple. + +- Added `Python.add_object()`, to add a named `PythonObject` value to a Python + 'module' object instance. + +- Added `Python.unsafe_get_python_exception()`, as an efficient low-level + utility to get the Mojo `Error` equivalent of the current CPython error state. + +- Add `PythonObject.from_borrowed_ptr()`, to simplify the construction of + `PythonObject` values from CPython 'borrowed reference' pointers. + + The existing `PythonObject.__init__(PyObjectPtr)` should continue to be used + for the more common case of constructing a `PythonObject` from a + 'strong reference' pointer. + +- The `rebind` standard library function now works with memory-only types in + addition to `@register_passable("trivial")` ones, without requiring a copy. + +- Introduce `random.shuffle` for `List`. + ([PR #3327](https://github.com/modularml/mojo/pull/3327)) + + Example: + + ```mojo + from random import shuffle + + var l = List[Int](1, 2, 3, 4, 5) + shuffle(l) + ``` + +- The `Dict.__getitem__` method now returns a reference instead of a copy of + the value (or raises). This improves the performance of common code that + uses `Dict` by allowing borrows from the `Dict` elements. + +- `Slice.step` is now an `Optional[Int]`, matching the optionality of + `slice.step` in Python. + ([PR #3160](https://github.com/modularml/mojo/pull/3160)) + +- `StringRef` now implements `split()` which can be used to split a + `StringRef` into a `List[StringRef]` by a delimiter. + ([PR #2705](https://github.com/modularml/mojo/pull/2705)) + +- Support for multi-dimensional indexing for `PythonObject` + ([PR #3583](https://github.com/modularml/mojo/pull/3583)). + +- Support for multi-dimensional indexing and slicing for `PythonObject` + (PR [#3549](https://github.com/modularml/mojo/pull/3549), + PR [#3583](https://github.com/modularml/mojo/pull/3583)). + + ```mojo + var np = Python.import_module("numpy") + var a = np.array(PythonObject([1,2,3,4,5,6])).reshape(2,3) + print((a[0, 1])) # 2 + print((a[1][::-1])) # [6 5 4] + ``` + + Note, that the syntax, `a[1, ::-1]`, is currently not supported. + +- There is now a [`Byte`](/mojo/stdlib/builtin/simd/Byte) alias to better + express intent when working with a pack of bits. + ([PR #3670](https://github.com/modularml/mojo/pull/3670)). + +- Expanded `os.path` with new functions (by [@thatstoasty](https://github.com/thatstoasty)): + - `os.path.expandvars`: Expands environment variables in a path ([PR #3735](https://github.com/modularml/mojo/pull/3735)). + - `os.path.splitroot`: Split a path into drive, root and tail. + ([PR #3780](https://github.com/modularml/mojo/pull/3780)). + +- Added a `reserve` method and new constructor to the `String` struct to + allocate additional capacity. + ([PR #3755](https://github.com/modularml/mojo/pull/3755)). + +- Introduced a new `Deque` (double-ended queue) collection type, based on a + dynamically resizing circular buffer for efficient O(1) additions and removals + at both ends as well as O(1) direct access to all elements. + + The `Deque` supports the full Python `collections.deque` API, ensuring that all + expected deque operations perform as in Python. + + Enhancements to the standard Python API include `peek()` and `peekleft()` + methods for non-destructive access to the last and first elements, and advanced + constructor options (`capacity`, `min_capacity`, and `shrink`) for customizing + memory allocation and performance. These options allow for optimized memory usage + and reduced buffer reallocations, providing flexibility based on application requirements. + +- A new `StringLiteral.get[some_stringable]()` method is available. It + allows forming a runtime-constant StringLiteral from a compile-time-dynamic + `Stringable` value. + +- `Span` now implements `__reversed__`. This means that one can get a + reverse iterator over a `Span` using `reversed(my_span)`. Users should + currently prefer this method over `my_span[::-1]`. + +- `StringSlice` now implements `strip`, `rstrip`, and `lstrip`. + +- `StringRef` is now representable so `repr(StringRef("hello"))` will return + `StringRef('hello')`. + +- The `UnsafePointer` type now has an `origin` parameter that can be used when + the `UnsafePointer` is known to point to a value with a known origin. This + origin is propagated through the `ptr[]` indirection operation. + +- You can now index into `UnsafePointer` using SIMD scalar integral types: + + ```mojo + p = UnsafePointer[Int].alloc(1) + i = UInt8(1) + p[i] = 42 + print(p[i]) + ``` + +- `UnsafePointer` parameters (other than the type) are now keyword-only. + +- Added a new [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) + type as a safe, single-owner, non-nullable smart pointer with similar + semantics to Rust's + [`Box<>`](https://doc.rust-lang.org/std/boxed/struct.Box.html) and C++'s + [`std::unique_ptr`](https://en.cppreference.com/w/cpp/memory/unique_ptr). + ([PR #3524](https://github.com/modularml/mojo/pull/3524)) + +- `Arc` has been renamed to [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer), + for consistency with `OwnedPointer`. + +- [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer) now implements + [`Identifiable`](/mojo/stdlib/builtin/identifiable/Identifiable), and can be + compared for pointer equivalence using `a is b`. + +- Added `PythonObject.__contains__`. + ([PR #3101](https://github.com/modularml/mojo/pull/3101)) + + Example usage: + + ```mojo + x = PythonObject([1,2,3]) + if 1 in x: + print("1 in x") + +- `Span` has moved from the `utils` module to the `memory` module. + + - More things have been removed from the auto-exported set of entities in the + `prelude` module from the Mojo standard library: + - `UnsafePointer` has been removed. Please explicitly import it via + `from memory import UnsafePointer`. + - `StringRef` has been removed. Please explicitly import it via + `from utils import StringRef`. + +- The `Reference` type has been renamed to `Pointer`: a memory safe complement + to `UnsafePointer`. This change is motivated by the fact that `Pointer` + is assignable and requires an explicit dereference with `ptr[]`. Renaming + to `Pointer` clarifies that "references" means `ref` arguments and results, + and gives us a model that is more similar to what the C++ community would + expect. + +- A new `as_noalias_ptr` method as been added to `UnsafePointer`. This method + specifies to the compiler that the resultant pointer is a distinct + identifiable object that does not alias any other memory in the local scope. + +- The `AnyLifetime` type (useful for declaring origin types as parameters) has + been renamed to `Origin`. + +- Restored implicit copyability of `Tuple` and `ListLiteral`. + +- The aliases for C FFI have been renamed: `C_int` -> `c_int`, `C_long` -> `c_long` + and so on. + +- The `Formatter` struct has changed to a `Writer` trait to enable buffered IO, + increasing print and file writing perf to the same speed as C. It's now more + general purpose and can write any `Span[Byte]`. To align with this the + `Formattable` trait is now named `Writable`, and the `String.format_sequence` + static methods to initialize a new `String` have been renamed to + `String.write`. Here's an example of using all the changes: + + ```mojo + from memory import Span + + @value + struct NewString(Writer, Writable): + var s: String + + # Writer requirement to write a Span of Bytes + fn write_bytes(inout self, bytes: Span[Byte, _]): + self.s._iadd[False](bytes) + + # Writer requirement to take multiple args + fn write[*Ts: Writable](inout self, *args: *Ts): + @parameter + fn write_arg[T: Writable](arg: T): + arg.write_to(self) + + args.each[write_arg]() + + # Also make it Writable to allow `print` to write the inner String + fn write_to[W: Writer](self, inout writer: W): + writer.write(self.s) + + + @value + struct Point(Writable): + var x: Int + var y: Int + + # Pass multiple args to the Writer. The Int and StringLiteral types call + # `writer.write_bytes` in their own `write_to` implementations. + fn write_to[W: Writer](self, inout writer: W): + writer.write("Point(", self.x, ", ", self.y, ")") + + # Enable conversion to a String using `str(point)` + fn __str__(self) -> String: + return String.write(self) + + + fn main(): + var point = Point(1, 2) + var new_string = NewString(str(point)) + new_string.write("\n", Point(3, 4)) + print(new_string) + ``` + + Point(1, 2) + Point(3, 4) + +- You can now use the `+=` and `*` operators on a `StringLiteral` at compile + time using the `alias` keyword: + + ```mojo + alias original = "mojo" + alias concat = original * 3 + assert_equal("mojomojomojo", concat) + ``` + + Or inside a `fn` that is being evaluated at compile time: + + ```mojo + fn add_literal( + owned original: StringLiteral, add: StringLiteral, n: Int + ) -> StringLiteral: + for _ in range(n): + original += add + return original + + + fn main(): + alias original = "mojo" + alias concat = add_literal(original, "!", 4) + assert_equal("mojo!!!!", concat) + ``` + + These operators can't be evaluated at runtime, as a `StringLiteral` must be + written into the binary during compilation. + +- The `StaticIntTuple` data structure in the `utils` package has been renamed to + `IndexList`. The data structure now allows one to specify the index bitwidth + of the elements along with whether the underlying indices are signed or + unsigned. + +- A new `AsBytes` trait has been added to enable taking a `Span[Byte]` of a + type with `s.as_bytes()`. `String.as_bytes` and `String.as_bytes_slice` have + been consolidated under `s.as_bytes` to return a `Span[Byte]`, you can convert + it to a `List` if you require a copy with `List(s.as_bytes())`. + +### Tooling changes + +- The VS Code Mojo Debugger now has a `buildArgs` JSON debug configuration + setting that can be used in conjunction with `mojoFile` to define the build + arguments when compiling the Mojo file. + +- The VS Code extension now supports a `Configure Build and Run Args` command + that helps set the build and run args for actions file `Run Mojo File` and + `Debug Mojo File`. A corresponding button appears in `Run and Debug` selector + in the top right corner of a Mojo File. + +- The VS Code extension now has the `mojo.run.focusOnTerminalAfterLaunch` + setting, which controls whether to focus on the terminal used by the + `Mojo: Run Mojo File` command or on the editor after launch. + [Issue #3532](https://github.com/modularml/mojo/issues/3532). + +- The VS Code extension now has the `mojo.SDK.additionalSDKs` setting, which + allows the user to provide a list of MAX SDKs that the extension can use when + determining a default SDK to use. The user can select the default SDK to use + with the `Mojo: Select the default MAX SDK` command. + +- The VS Code extension now supports setting [data breakpoints](https://code.visualstudio.com/docs/editor/debugging#_data-breakpoints) + as well as [function breakpoints](https://code.visualstudio.com/docs/editor/debugging#_function-breakpoints). + +- The Mojo LLDB debugger now supports symbol breakpoints, e.g. `b main` or + `b my_module::main`. + +- The VS Code extension now allows cancelling the installation of its private + MAX SDK. + +- The VS Code extension now opens the Run and Debug tab automatically whenever + a debug session starts. + +- The `mojo debug --vscode` command now support the `--init-command` and + `--stop-on-entry` flags. Execute `mojo debug --help` for more information. + +- The Mojo LLDB debugger on VS Code now supports inspecting the raw attributes + of variables that are handled as synthetic types, e.g. `List` from Mojo or + `std::vector` from C++. + +- The VS Code extension now allows selecting a default SDK when multiple are + available. + +- The flag for turning on asserts has changed, e.g. to enable all checks: + + ```bash + mojo -D ASSERT=all main.mojo + ``` + + The levels are: + + - `none`: all assertions off + - `warn`: print assertion errors e.g. for multithreaded tests (previously `-D + ASSERT_WARNING`) + - `safe`: the default mode for standard CPU safety assertions + - `all`: turn on all assertions (previously `-D MOJO_ENABLE_ASSERTIONS`) + + You can now also pass `Stringable` args to format a message, which will have + no runtime penalty or IR bloat cost when assertions are off. Previously you + had to: + + ```mojo + x = -1 + debug_assert( + x > 0, String.format_sequence(“expected x to be more than 0 but got: ”, x) + ) + ``` + + Which can't be optimized away by the compiler in release builds, you can now + pass multiple args for a formatted message at no runtime cost: + + ```mojo + debug_assert(x > 0, “expected x to be more than 0 but got: ”, x) + ``` + +- Float32 and Float64 are now printed and converted to strings with roundtrip + guarantee and shortest representation: + + ```plaintext + Value Old New + Float64(0.3) 0.29999999999999999 0.3 + Float32(0.3) 0.30000001192092896 0.3 + Float64(0.0001) 0.0001 0.0001 + Float32(0.0001) 9.9999997473787516e-05 0.0001 + Float64(-0.00001) -1.0000000000000001e-05 -1e-05 + Float32(-0.00001) -9.9999997473787516e-06 -1e-05 + Float32(0.00001234) 1.2339999557298142e-05 1.234e-05 + Float32(-0.00000123456) -1.2345600453045336e-06 -1.23456e-06 + Float64(1.1234567e-320) 1.1235052786429946e-320 1.1235e-320 + Float64(1.234 * 10**16) 12340000000000000.0 1.234e+16 + ``` + +### ❌ Removed + +- The `UnsafePointer.bitcast` overload for `DType` has been removed. Wrap your + `DType` in a `Scalar[my_dtype]` to call the only overload of `bitcast` now. + +### 🛠️ Fixed + +- Lifetime tracking is now fully field sensitive, which makes the uninitialized + variable checker more precise. + +- [Issue #1310](https://github.com/modularml/mojo/issues/1310) - Mojo permits + the use of any constructor for implicit conversions + +- [Issue #1632](https://github.com/modularml/mojo/issues/1632) - Mojo produces + weird error when inout function is used in non mutating function + +- [Issue #3444](https://github.com/modularml/mojo/issues/3444) - Raising init + causing use of uninitialized variable + +- [Issue #3544](https://github.com/modularml/mojo/issues/3544) - Known + mutable `ref` argument are not optimized as `noalias` by LLVM. + +- [Issue #3559](https://github.com/modularml/mojo/issues/3559) - VariadicPack + doesn't extend the lifetimes of the values it references. + +- [Issue #3627](https://github.com/modularml/mojo/issues/3627) - Compiler + overlooked exclusivity violation caused by `ref [MutableAnyOrigin] T` + +- [Issue #3710](https://github.com/modularml/mojo/issues/3710) - Mojo frees + memory while reference to it is still in use. + +- [Issue #3805](https://github.com/modularml/mojo/issues/3805) - Crash When + Initializing !llvm.ptr. + +- [Issue #3816](https://github.com/modularml/mojo/issues/3816) - Ternary + if-operator doesn't propagate origin information. + +- [Issue #3815](https://github.com/modularml/mojo/issues/3815) - + [BUG] Mutability not preserved when taking the union of two origins. + +- [Issue #3829](https://github.com/modularml/mojo/issues/3829) - Poor error + message when invoking a function pointer upon an argument of the wrong origin + +- [Issue #3830](https://github.com/modularml/mojo/issues/3830) - Failures + emitting register RValues to ref arguments. + +- The VS Code extension now auto-updates its private copy of the MAX SDK. + +- The variadic initializer for `SIMD` now works in parameter expressions. + +- The VS Code extension now downloads its private copy of the MAX SDK in a way + that prevents ETXTBSY errors on Linux. + +- The VS Code extension now allows invoking a mojo formatter from SDK + installations that contain white spaces in their path. + +- Error messages that include type names no longer include inferred or defaulted + parameters when they aren't needed. For example, previously Mojo complained + about things like: + + ```plaintext + ... cannot be converted from 'UnsafePointer[UInt, 0, _default_alignment::AnyType](), MutableAnyOrigin]' to 'UnsafePointer[Int, 0, _default_alignment[::AnyType](), MutableAnyOrigin]' + ``` + + it now complains more helpfully that: + + ```plaintext + ... cannot be converted from 'UnsafePointer[UInt]' to 'UnsafePointer[Int]' + ``` + +- Tooling now prints the origins of `ref` arguments and results correctly, and + prints `self` instead of `self: Self` in methods. + +- The LSP and generated documentation now print parametric result types + correctly, e.g. showing `SIMD[type, simd_width]` instead of `SIMD[$0, $1]`. + +### Special thanks + +Special thanks to our community contributors: +[@soraos](https://github.com/soraros), [@jjvraw](https://github.com/jjvraw), +[@bgreni](https://github.com/bgreni), +[@thatstoasty](https://github.com/thatstoasty), +[@szbergeron](https://github.com/szbergeron), +[@rd4com](https://github.com/rd4com), +[@fknfilewalker](https://github.com/fknfilewalker), +[@gabrieldemarmiesse](https://github.com/gabrieldemarmiesse), +[@avitkauskas](https://github.com/avitkauskas) + ## v24.5 (2024-09-13) ### ✨ Highlights diff --git a/docs/changelog.md b/docs/changelog.md index a57e0db1b2..9f6a1c4e3e 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -7,694 +7,23 @@ what we publish. [//]: # Here's the template to use when starting a new batch of notes: [//]: ## UNRELEASED -[//]: ### ⭐️ New -[//]: ### 🦋 Changed +[//]: ### ✨ Highlights +[//]: ### Language changes +[//]: ### Standard library changes +[//]: ### Tooling changes [//]: ### ❌ Removed [//]: ### 🛠️ Fixed ## UNRELEASED -### ⭐️ New +### ✨ Highlights -- `StringRef` is now representable so `repr(StringRef("hello"))` will return - `StringRef('hello')`. +### Language changes -- Mojo can now interpret simple LLVM intrinsics in parameter expressions, - enabling things like `count_leading_zeros` to work at compile time: - [Issue #933](https://github.com/modularml/mojo/issues/933). +### Standard library changes -- The destructor insertion logic in Mojo is now aware that types that take an - `MutableAnyOrigin` or `ImmutableAnyOrigin` as part of their signature could - potentially access any live value that destructor insertion is tracking, - eliminating a significant usability issue with unsafe APIs like - `UnsafePointer`. Consider a typical example working with strings before this - change: - - ```mojo - var str = String(...) - var ptr = str.unsafe_ptr() - some_low_level_api(ptr) - _ = str^ # OLD HACK: Explicitly keep string alive until here! - ``` - - The `_ = str^` pattern was formerly required because the Mojo compiler has no - idea what "ptr" might reference. As a consequence, it had no idea that - `some_low_level_api()` might access `str` and therefore thought it was ok to - destroy the `String` before the call - this is why the explicit lifetime - extension was required. - - Mojo now knows that `UnsafePointer` may access the `MutableAnyOrigin` origin, - and now assumes that any API that uses that origin could use live values. - In this case, it assumes that `some_low_level_api()` might access `str` and - because it might be using it, it cannot destroy `str` until after the call. - The consequence of this is that the old hack is no longer needed for these - cases! - -- Various improvements to origin handling and syntax have landed, including - support for the ternary operator and allowing multiple arguments in a `ref` - specifier (which are implicitly unions). This enables expression of simple - algorithms cleanly: - - ```mojo - fn my_min[T: Comparable](ref a: T, ref b: T) -> ref [a, b] T: - return a if a < b else b - ``` - - It is also nice that `my_min` automatically and implicitly propagates the - mutability of its arguments, so things like `my_min(str1, str2) += "foo"` is - valid. - -- The `UnsafePointer` type now has an `origin` parameter that can be used when - the `UnsafePointer` is known to point to a value with a known origin. This - origin is propagated through the `ptr[]` indirection operation. - -- The VS Code Mojo Debugger now has a `buildArgs` JSON debug configuration - setting that can be used in conjunction with `mojoFile` to define the build - arguments when compiling the Mojo file. - -- The VS Code extension now supports a `Configure Build and Run Args` command - that helps set the build and run args for actions file `Run Mojo File` and - `Debug Mojo File`. A corresponding button appears in `Run and Debug` selector - in the top right corner of a Mojo File. - -- Add the `Floatable` and `FloatableRaising` traits to denote types that can - be converted to a `Float64` value using the builtin `float` function. - - Make `SIMD` and `FloatLiteral` conform to the `Floatable` trait. - - ```mojo - fn foo[F: Floatable](v: F): - ... - - var f = float(Int32(45)) - ``` - - ([PR #3163](https://github.com/modularml/mojo/pull/3163) by [@bgreni](https://github.com/bgreni)) - -- Add `DLHandle.get_symbol()`, for getting a pointer to a symbol in a dynamic - library. This is more general purpose than the existing methods for getting - function pointers. - -- Introduce `TypedPythonObject` as a light-weight way to annotate `PythonObject` - values with static type information. This design will likely evolve and - change significantly. - - - Added `TypedPythonObject["Tuple].__getitem__` for accessing the elements of - a Python tuple. - -- Added `Python.add_object()`, to add a named `PythonObject` value to a Python - 'module' object instance. - -- Added `Python.unsafe_get_python_exception()`, as an efficient low-level - utility to get the Mojo `Error` equivalent of the current CPython error state. - -- The `__type_of(x)` and `__origin_of(x)` operators are much more general now: - they allow arbitrary expressions inside of them, allow referring to dynamic - values in parameter contexts, and even allow referring to raising functions - in non-raising contexts. These operations never evaluate their expression, so - any side effects that occur in the expression are never evaluated at runtime, - eliminating concerns about `__type_of(expensive())` being a problem. - -- Add `PythonObject.from_borrowed_ptr()`, to simplify the construction of - `PythonObject` values from CPython 'borrowed reference' pointers. - - The existing `PythonObject.__init__(PyObjectPtr)` should continue to be used - for the more common case of constructing a `PythonObject` from a - 'strong reference' pointer. - -- The `rebind` standard library function now works with memory-only types in - addition to `@register_passable("trivial")` ones, without requiring a copy. - -- Introduce `random.shuffle` for `List`. - ([PR #3327](https://github.com/modularml/mojo/pull/3327) by [@jjvraw](https://github.com/jjvraw)) - - Example: - - ```mojo - from random import shuffle - - var l = List[Int](1, 2, 3, 4, 5) - shuffle(l) - ``` - -- The `Dict.__getitem__` method now returns a reference instead of a copy of - the value (or raises). This improves the performance of common code that - uses `Dict` by allowing borrows from the `Dict` elements. - -- Autoparameterization of parameters is now supported. Specifying a parameter - type with unbound parameters causes them to be implicitly added to the - function signature as inferred parameters. - - ```mojo - fn foo[value: SIMD[DType.int32, _]](): - pass - - # Equivalent to - fn foo[size: Int, //, value: SIMD[DType.int32, size]](): - pass - ``` - -- Function types now accept an origin set parameter. This parameter represents - the origins of values captured by a parameter closure. The compiler - automatically tags parameter closures with the right set of origins. This - enables lifetimes and parameter closures to correctly compose. - - ```mojo - fn call_it[f: fn() capturing [_] -> None](): - f() - - fn test(): - var msg = String("hello world") - - @parameter - fn say_hi(): - print(msg) - - call_it[say_hi]() - # no longer need to write `_ = msg^`!! - ``` - - Note that this only works for higher-order functions which have explicitly - added `[_]` as the capture origins. By default, the compiler still assumes - a `capturing` closure does not reference any origins. This will soon change. - -- The VS Code extension now has the `mojo.run.focusOnTerminalAfterLaunch` - setting, which controls whether to focus on the terminal used by the - `Mojo: Run Mojo File` command or on the editor after launch. - [Issue #3532](https://github.com/modularml/mojo/issues/3532). - -- The VS Code extension now has the `mojo.SDK.additionalSDKs` setting, which - allows the user to provide a list of MAX SDKs that the extension can use when - determining a default SDK to use. The user can select the default SDK to use - with the `Mojo: Select the default MAX SDK` command. - -- Added a new [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) - type as a safe, single-owner, non-nullable smart pointer with similar - semantics to Rust's - [`Box<>`](https://doc.rust-lang.org/std/boxed/struct.Box.html) and C++'s - [`std::unique_ptr`](https://en.cppreference.com/w/cpp/memory/unique_ptr). - - ([PR #3524](https://github.com/modularml/mojo/pull/3524) by [@szbergeron](https://github.com/szbergeron)) - -- `ref` argument and result specifiers now allow providing a memory value - directly in the origin specifier, rather than requiring the use of - `__origin_of()`. It is still fine to use `__origin_of()` explicitly though, - and this is required when specifying origins for parameters (e.g. to the - `Pointer` type). For example, this is now valid without `__origin_of()`: - - ```mojo - fn return_ref(a: String) -> ref [a] String: - return a - ``` - -- `ref` function arguments without an origin clause are now treated as - `ref [_]`, which is more syntactically convenient and consistent: - - ```mojo - fn takes_and_return_ref(ref a: String) -> ref [a] String: - return a - ``` - -- `Slice.step` is now an `Optional[Int]`, matching the optionality of - `slice.step` in Python. - ([PR #3160](https://github.com/modularml/mojo/pull/3160) by - [@bgreni](https://github.com/bgreni)) - -- `StringRef` now implements `split()` which can be used to split a - `StringRef` into a `List[StringRef]` by a delimiter. - ([PR #2705](https://github.com/modularml/mojo/pull/2705) by [@fknfilewalker](https://github.com/fknfilewalker)) - -- Support for multi-dimensional indexing for `PythonObject` - ([PR #3583](https://github.com/modularml/mojo/pull/3583) by [@jjvraw](https://github.com/jjvraw)). - -- Support for multi-dimensional indexing and slicing for `PythonObject` - (PRs [#3549](https://github.com/modularml/mojo/pull/3549), - [#3583](https://github.com/modularml/mojo/pull/3583) by [@jjvraw](https://github.com/jjvraw)). - - ```mojo - var np = Python.import_module("numpy") - var a = np.array(PythonObject([1,2,3,4,5,6])).reshape(2,3) - print((a[0, 1])) # 2 - print((a[1][::-1])) # [6 5 4] - ``` - - Note, that the syntax, `a[1, ::-1]`, is currently not supported. - -- [`Arc`](/mojo/stdlib/memory/arc/Arc) now implements - [`Identifiable`](/mojo/stdlib/builtin/identifiable/Identifiable), and can be - compared for pointer equivalence using `a is b`. - -- There is now a [`Byte`](/mojo/stdlib/builtin/simd/Byte) alias to better - express intent when working with a pack of bits. - ([PR #3670](https://github.com/modularml/mojo/pull/3670) by [@soraos](https://github.com/soraros)). - -- The VS Code extension now supports setting [data breakpoints](https://code.visualstudio.com/docs/editor/debugging#_data-breakpoints) - as well as [function breakpoints](https://code.visualstudio.com/docs/editor/debugging#_function-breakpoints). - -- The Mojo LLDB debugger now supports symbol breakpoints, e.g. `b main` or - `b my_module::main`. - -- The VS Code extension now allows cancelling the installation of its private - MAX SDK. - -- The VS Code extension now opens the Run and Debug tab automatically whenever - a debug session starts. - -- The `mojo debug --vscode` command now support the `--init-command` and - `--stop-on-entry` flags. Execute `mojo debug --help` for more information. - -- The Mojo LLDB debugger on VS Code now supports inspecting the raw attributes - of variables that are handled as synthetic types, e.g. `List` from Mojo or - `std::vector` from C++. - -- Expanded `os.path` with new functions (by [@thatstoasty](https://github.com/thatstoasty)): - - `os.path.expandvars`: Expands environment variables in a path ([PR #3735](https://github.com/modularml/mojo/pull/3735)). - - `os.path.splitroot`: Split a path into drive, root and tail. - ([PR #3780](https://github.com/modularml/mojo/pull/3780)). - -- Added a `reserve` method and new constructor to the `String` struct to - allocate additional capacity. - ([PR #3755](https://github.com/modularml/mojo/pull/3755) by [@thatstoasty](https://github.com/thatstoasty)). - -- Introduced a new `Deque` (double-ended queue) collection type, based on a - dynamically resizing circular buffer for efficient O(1) additions and removals - at both ends as well as O(1) direct access to all elements. - - The `Deque` supports the full Python `collections.deque` API, ensuring that all - expected deque operations perform as in Python. - - Enhancements to the standard Python API include `peek()` and `peekleft()` - methods for non-destructive access to the last and first elements, and advanced - constructor options (`capacity`, `min_capacity`, and `shrink`) for customizing - memory allocation and performance. These options allow for optimized memory usage - and reduced buffer reallocations, providing flexibility based on application requirements. - -- A new `StringLiteral.get[some_stringable]()` method is available. It - allows forming a runtime-constant StringLiteral from a compile-time-dynamic - `Stringable` value. - -- `Span` now implements `__reversed__`. This means that one can get a - reverse iterator over a `Span` using `reversed(my_span)`. Users should - currently prefer this method over `my_span[::-1]`. - -- `StringSlice` now implements `strip`, `rstrip`, and `lstrip`. - -- Introduced the `@explicit_destroy` annotation, the `__disable_del` keyword, - the `UnknownDestructibility` trait, and the `ImplicitlyDestructible` keyword, - for the experimental explicitly destroyed types feature. - -- Added associated types; we can now have aliases like `alias T: AnyType`, - `alias N: Int`, etc. in a trait, and then specify them in structs that conform - to that trait. - -### 🦋 Changed - -- The `inout` and `borrowed` argument conventions have been renamed to the `mut` - and `read` argument conventions (respectively). These verbs reflect - declaratively what the callee can do to the argument value passed into the - caller, without tying in the requirement for the programmer to know about - advanced features like references. - -- The argument convention for `__init__` methods has been changed from `inout` - to `out`, reflecting that an `__init__` method initializes its `self` without - reading from it. This also enables spelling the type of an initializer - correctly, which was not supported before: - - ```mojo - struct Foo: - fn __init__(out self): pass - - fn test(): - # This works now - var fnPtr : fn(out x: Foo)->None = Foo.__init__ - - var someFoo : Foo - fnPtr(someFoo) # initializes someFoo. - ``` - - The previous `fn __init__(inout self)` syntax is still supported in this - release of Mojo, but will be removed in the future. Please migrate to the - new syntax. - -- Similarly, the spelling of "named functions results" has switched to use `out` - syntax instead of `-> T as name`. Functions may have at most one named result - or return type specified with the usual `->` syntax. `out` arguments may - occur anywhere in the argument list, but are typically last (except for - `__init__` methods, where they are typically first). - - ```mojo - # This function has type "fn() -> String" - fn example(out result: String): - result = "foo" - ``` - - The parser still accepts the old syntax as a synonym for this, but that will - eventually be deprecated and removed. - - This was [discussed extensively in a public - proposal](https://github.com/modularml/mojo/issues/3623). - -- More things have been removed from the auto-exported set of entities in the `prelude` - module from the Mojo standard library. - - `UnsafePointer` has been removed. Please explicitly import it via - `from memory import UnsafePointer`. - - `StringRef` has been removed. Please explicitly import it via - `from utils import StringRef`. - -- The `Reference` type has been renamed to `Pointer`: a memory safe complement - to `UnsafePointer`. This change is motivated by the fact that `Pointer` - is assignable and requires an explicit dereference with `ptr[]`. Renaming - to `Pointer` clarifies that "references" means `ref` arguments and results, - and gives us a model that is more similar to what the C++ community would - expect. - -- A new `as_noalias_ptr` method as been added to `UnsafePointer`. This method - specifies to the compiler that the resultant pointer is a distinct - identifiable object that does not alias any other memory in the local scope. - -- The `AnyLifetime` type (useful for declaring origin types as parameters) has - been renamed to `Origin`. - -- Restore implicit copyability of `Tuple` and `ListLiteral`. - -- The aliases for C FFI have been renamed: `C_int` -> `c_int`, `C_long` -> `c_long` - and so on. - -- The VS Code extension now allows selecting a default SDK when multiple are available. - -- The `Formatter` struct has changed to a `Writer` trait to enable buffered IO, - increasing print and file writing perf to the same speed as C. It's now more - general purpose and can write any `Span[Byte]`. To align with this the - `Formattable` trait is now named `Writable`, and the `String.format_sequence` - static methods to initialize a new `String` have been renamed to - `String.write`. Here's an example of using all the changes: - - ```mojo - from memory import Span - - @value - struct NewString(Writer, Writable): - var s: String - - # Writer requirement to write a Span of Bytes - fn write_bytes(inout self, bytes: Span[Byte, _]): - self.s._iadd[False](bytes) - - # Writer requirement to take multiple args - fn write[*Ts: Writable](inout self, *args: *Ts): - @parameter - fn write_arg[T: Writable](arg: T): - arg.write_to(self) - - args.each[write_arg]() - - # Also make it Writable to allow `print` to write the inner String - fn write_to[W: Writer](self, inout writer: W): - writer.write(self.s) - - - @value - struct Point(Writable): - var x: Int - var y: Int - - # Pass multiple args to the Writer. The Int and StringLiteral types call - # `writer.write_bytes` in their own `write_to` implementations. - fn write_to[W: Writer](self, inout writer: W): - writer.write("Point(", self.x, ", ", self.y, ")") - - # Enable conversion to a String using `str(point)` - fn __str__(self) -> String: - return String.write(self) - - - fn main(): - var point = Point(1, 2) - var new_string = NewString(str(point)) - new_string.write("\n", Point(3, 4)) - print(new_string) - ``` - - Point(1, 2) - Point(3, 4) - -- The flag for turning on asserts has changed, e.g. to enable all checks: - - ```bash - mojo -D ASSERT=all main.mojo - ``` - - The levels are: - - - none: all assertions off - - warn: print assertion errors e.g. for multithreaded tests (previously -D - ASSERT_WARNING) - - safe: the default mode for standard CPU safety assertions - - all: turn on all assertions (previously -D MOJO_ENABLE_ASSERTIONS) - - You can now also pass `Stringable` args to format a message, which will have - no runtime penalty or IR bloat cost when assertions are off. Previously you - had to: - - ```mojo - x = -1 - debug_assert( - x > 0, String.format_sequence(“expected x to be more than 0 but got: ”, x) - ) - ``` - - Which can't be optimized away by the compiler in release builds, you can now - pass multiple args for a formatted message at no runtime cost: - - ```mojo - debug_assert(x > 0, “expected x to be more than 0 but got: ”, x) - ``` - -- The `StaticIntTuple` datastructure in the `utils` package has been renamed to - `IndexList`. The datastructure now allows one to specify the index bitwidth of - the elements along with whether the underlying indices are signed or unsigned. - -- A new trait has been added `AsBytes` to enable taking a `Span[Byte]` of a - type with `s.as_bytes()`. `String.as_bytes` and `String.as_bytes_slice` have - been consolidated under `s.as_bytes` to return a `Span[Byte]`, you can convert - it to a `List` if you require a copy with `List(s.as_bytes())`. - -- `Lifetime` and related types have been renamed to `Origin` in the standard - library to better clarify that parameters of this type indicate where a - reference is derived from, not the more complicated notion of where a variable - is initialized and destroyed. Please see [the proposal](https://github.com/modularml/mojo/blob/main/proposals/lifetimes-keyword-renaming.md) - for more information and rationale. As a consequence the `__lifetime_of()` - operator is now named `__origin_of()`. - -- `Origin` is now a complete wrapper around the MLIR origin type. - - - The `Origin.type` alias has been renamed to `_mlir_origin`. In parameter - lists, you can now write just `Origin[..]`, instead of `Origin[..].type`. - - - `ImmutableOrigin` and `MutableOrigin` are now, respectively, just aliases - for `Origin[False]` and `Origin[True]`. - - - `Origin` struct values are now supported in the brackets of a `ref [..]` - argument. - - - Added `Origin.cast_from` for casting the mutability of an origin value. - -- You can now use the `+=` and `*` operators on a `StringLiteral` at compile - time using the `alias` keyword: - - ```mojo - alias original = "mojo" - alias concat = original * 3 - assert_equal("mojomojomojo", concat) - ``` - - Or inside a `fn` that is being evaluated at compile time: - - ```mojo - fn add_literal( - owned original: StringLiteral, add: StringLiteral, n: Int - ) -> StringLiteral: - for _ in range(n): - original += add - return original - - - fn main(): - alias original = "mojo" - alias concat = add_literal(original, "!", 4) - assert_equal("mojo!!!!", concat) - ``` - - These operators can't be evaluated at runtime, as a `StringLiteral` must be - written into the binary during compilation. - -- You can now index into `UnsafePointer` using SIMD scalar integral types: - - ```mojo - p = UnsafePointer[Int].alloc(1) - i = UInt8(1) - p[i] = 42 - print(p[i]) - ``` - -- Float32 and Float64 are now printed and converted to strings with roundtrip - guarantee and shortest representation: - - ```plaintext - Value Old New - Float64(0.3) 0.29999999999999999 0.3 - Float32(0.3) 0.30000001192092896 0.3 - Float64(0.0001) 0.0001 0.0001 - Float32(0.0001) 9.9999997473787516e-05 0.0001 - Float64(-0.00001) -1.0000000000000001e-05 -1e-05 - Float32(-0.00001) -9.9999997473787516e-06 -1e-05 - Float32(0.00001234) 1.2339999557298142e-05 1.234e-05 - Float32(-0.00000123456) -1.2345600453045336e-06 -1.23456e-06 - Float64(1.1234567e-320) 1.1235052786429946e-320 1.1235e-320 - Float64(1.234 * 10**16) 12340000000000000.0 1.234e+16 - ``` - -- Single argument constructors now require a `@implicit` decorator to allow - for implicit conversions. Previously you could define an `__init__` that - takes a single argument: - - ```mojo - struct Foo: - var value: Int - - fn __init__(out self, value: Int): - self.value = value - ``` - - And this would allow you to pass an `Int` in the position of a `Foo`: - - ```mojo - fn func(foo: Foo): - print("implicitly converted Int to Foo:", foo.value) - - fn main(): - func(Int(42)) - ``` - - This can result in complicated errors that are difficult to debug. By default - this implicit behavior is now turned off, so you have to explicitly construct - `Foo`: - - ```mojo - fn main(): - func(Foo(42)) - ``` - - You can still opt into implicit conversions by adding the `@implicit` - decorator. For example, to enable implicit conversions from `Int` to `Foo`: - - ```mojo - struct Foo: - var value: Int - - @implicit - fn __init__(out self, value: Int): - self.value = value - ``` - -- `Arc` has been renamed to `ArcPointer`, for consistency with `OwnedPointer`. - -- `UnsafePointer` parameters (other than the type) are now keyword-only. - -- Inferred-only parameters may now be explicitly bound with keywords, enabling - some important patterns in the standard library: - - ```mojo - struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]: ... - alias ImmStringSlice = StringSlice[is_mutable=False] - # This auto-parameterizes on the origin, but constrains it to being an - # immutable slice instead of a potentially mutable one. - fn take_imm_slice(a: ImmStringSlice): ... - ``` - -- Added `PythonObject.__contains__`. - ([PR #3101](https://github.com/modularml/mojo/pull/3101) by [@rd4com](https://github.com/rd4com)) - - Example usage: - - ```mojo - x = PythonObject([1,2,3]) - if 1 in x: - print("1 in x") - -- `Span` has moved from the `utils` module to the `memory` module. +### Tooling changes ### ❌ Removed -- The `UnsafePointer.bitcast` overload for `DType` has been removed. Wrap your - `DType` in a `Scalar[my_dtype]` to call the only overload of `bitcast` now. - ### 🛠️ Fixed - -- Lifetime tracking is now fully field sensitive, which makes the uninitialized - variable checker more precise. - -- [Issue #1310](https://github.com/modularml/mojo/issues/1310) - Mojo permits - the use of any constructor for implicit conversions - -- [Issue #1632](https://github.com/modularml/mojo/issues/1632) - Mojo produces - weird error when inout function is used in non mutating function - -- [Issue #3444](https://github.com/modularml/mojo/issues/3444) - Raising init - causing use of uninitialized variable - -- [Issue #3544](https://github.com/modularml/mojo/issues/3544) - Known - mutable `ref` argument are not optimized as `noalias` by LLVM. - -- [Issue #3559](https://github.com/modularml/mojo/issues/3559) - VariadicPack - doesn't extend the lifetimes of the values it references. - -- [Issue #3627](https://github.com/modularml/mojo/issues/3627) - Compiler - overlooked exclusivity violation caused by `ref [MutableAnyOrigin] T` - -- [Issue #3710](https://github.com/modularml/mojo/issues/3710) - Mojo frees - memory while reference to it is still in use. - -- [Issue #3805](https://github.com/modularml/mojo/issues/3805) - Crash When - Initializing !llvm.ptr. - -- [Issue #3816](https://github.com/modularml/mojo/issues/3816) - Ternary - if-operator doesn't propagate origin information. - -- [Issue #3815](https://github.com/modularml/mojo/issues/3815) - - [BUG] Mutability not preserved when taking the union of two origins. - -- [Issue #3829](https://github.com/modularml/mojo/issues/3829) - Poor error - message when invoking a function pointer upon an argument of the wrong origin - -- [Issue #3830](https://github.com/modularml/mojo/issues/3830) - Failures - emitting register RValues to ref arguments. - -- The VS Code extension now auto-updates its private copy of the MAX SDK. - -- The variadic initializer for `SIMD` now works in parameter expressions. - -- The VS Code extension now downloads its private copy of the MAX SDK in a way - that prevents ETXTBSY errors on Linux. - -- The VS Code extension now allows invoking a mojo formatter from SDK - installations that contain white spaces in their path. - -- Error messages that include type names no longer include inferred or defaulted - parameters when they aren't needed. For example, previously Mojo complained - about things like: - - ```plaintext - ... cannot be converted from 'UnsafePointer[UInt, 0, _default_alignment::AnyType](), MutableAnyOrigin]' to 'UnsafePointer[Int, 0, _default_alignment[::AnyType](), MutableAnyOrigin]' - ``` - - it now complains more helpfully that: - - ```plaintext - ... cannot be converted from 'UnsafePointer[UInt]' to 'UnsafePointer[Int]' - ``` - -- Tooling now prints the origins of `ref` arguments and results correctly, and - prints `self` instead of `self: Self` in methods. - -- The LSP and generated documentation now print parametric result types - correctly, e.g. showing `SIMD[type, simd_width]` instead of `SIMD[$0, $1]`. From eabc67cc865ac692ff140e42ba5d68cda82f4a65 Mon Sep 17 00:00:00 2001 From: Christian Hernandez <95982760+cnhz95@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:13:55 -0600 Subject: [PATCH 078/509] [External] [docs] Typo fix (#52539) [External] [docs] Typo fix Co-authored-by: Christian Hernandez <95982760+cnhz95@users.noreply.github.com> Closes modularml/mojo#3853 MODULAR_ORIG_COMMIT_REV_ID: cb525f70b5a3dfa9afd457080a4fb62d3615c429 --- docs/manual/decorators/parameter.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/manual/decorators/parameter.md b/docs/manual/decorators/parameter.md index c82d9c6783..2b9f27e9e0 100644 --- a/docs/manual/decorators/parameter.md +++ b/docs/manual/decorators/parameter.md @@ -29,9 +29,9 @@ this will be included in the binary ## Parametric for statement -You can add the `@parameter` decorator to an `for` loop to create a loop that's +You can add the `@parameter` decorator to a `for` loop to create a loop that's evaluated at compile time. The loop sequence and induction values must be -a valid parameter expressions (that is, an expressions that evaluate at compile +valid parameter expressions (that is, expressions that evaluate at compile time). This has the effect of "unrolling" the loop. From 9930b676cee4455242e8f0eec38609a5a002e2fc Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:20:18 -0600 Subject: [PATCH 079/509] [External] [stdlib] [NFC] Fix docstrings for `string.strip()` function family (#52540) [External] [stdlib] [NFC] Fix docstrings for `string.strip()` function family Fix docstrings for `string.strip()` function family. Was deleted in b0094d3d9dfc6e38154393862098c22a0c1dec89. @lsh I really don't want that documentation deleted, this is very often not included and caused me quite a couple of headaches in finding the correct information and backward compatibility reasons for using those characters. And we should be explicit that this method does not yet support full Python spaces until we can run the methods with `.isspace()` at compile time. Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3843 MODULAR_ORIG_COMMIT_REV_ID: 95456c663bc28381d8c9fc1dd98c4d03d38d0ca4 --- stdlib/src/builtin/string_literal.mojo | 13 +++++++++---- stdlib/src/collections/string.mojo | 11 ++++++++--- stdlib/src/utils/string_slice.mojo | 11 ++++++++--- 3 files changed, 25 insertions(+), 10 deletions(-) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index fc3ce8e14a..727ac65555 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -893,8 +893,9 @@ struct StringLiteral( return str(self).islower() fn strip(self) -> String: - """Return a copy of the string literal with leading and trailing whitespaces - removed. + """Return a copy of the string literal with leading and trailing + whitespaces removed. This only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A string with no leading or trailing whitespaces. @@ -926,7 +927,9 @@ struct StringLiteral( return str(self).rstrip(chars) fn rstrip(self) -> String: - """Return a copy of the string with trailing whitespaces removed. + """Return a copy of the string with trailing whitespaces removed. This + only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no trailing whitespaces. @@ -945,7 +948,9 @@ struct StringLiteral( return str(self).lstrip(chars) fn lstrip(self) -> String: - """Return a copy of the string with leading whitespaces removed. + """Return a copy of the string with leading whitespaces removed. This + only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no leading whitespaces. diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index cef5b9a42c..7e09a482ff 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -1968,7 +1968,8 @@ struct String( fn strip(self) -> StringSlice[__origin_of(self)]: """Return a copy of the string with leading and trailing whitespaces - removed. + removed. This only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no leading or trailing whitespaces. @@ -1988,7 +1989,9 @@ struct String( return self.as_string_slice().rstrip(chars) fn rstrip(self) -> StringSlice[__origin_of(self)]: - """Return a copy of the string with trailing whitespaces removed. + """Return a copy of the string with trailing whitespaces removed. This + only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no trailing whitespaces. @@ -2008,7 +2011,9 @@ struct String( return self.as_string_slice().lstrip(chars) fn lstrip(self) -> StringSlice[__origin_of(self)]: - """Return a copy of the string with leading whitespaces removed. + """Return a copy of the string with leading whitespaces removed. This + only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no leading whitespaces. diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 454f7968d9..09372e41e1 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -638,7 +638,8 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( @always_inline fn strip(self) -> Self: """Return a copy of the string with leading and trailing whitespaces - removed. + removed. This only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no leading or trailing whitespaces. @@ -678,7 +679,9 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( @always_inline fn rstrip(self) -> Self: - """Return a copy of the string with trailing whitespaces removed. + """Return a copy of the string with trailing whitespaces removed. This + only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no trailing whitespaces. @@ -726,7 +729,9 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( @always_inline fn lstrip(self) -> Self: - """Return a copy of the string with leading whitespaces removed. + """Return a copy of the string with leading whitespaces removed. This + only takes ASCII whitespace into account: + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e"`. Returns: A copy of the string with no leading whitespaces. From 8123754e2dc08b0fe78506291c756bf053b18ab7 Mon Sep 17 00:00:00 2001 From: Alvydas Vitkauskas Date: Tue, 10 Dec 2024 15:56:30 -0600 Subject: [PATCH 080/509] [External] [stdlib] Deque - don't use non-self out args (#52550) [External] [stdlib] Deque - don't use non-self out args As the original author of the Deque, I would like to propose to not use the `out` args instead of `-> ElementType as element` in `pop()` and `popleft()` methods. The named return variable was just a simple convenience in these functions and not needed at all. In general, having something in the args list of the function that you do not pass to the function at the call site looks weird (except for constructors). In my opinion, it makes the function signature just less clear to read and the return type of the function unnecessarily hidden inside the args list. So, let's just use normal return statements here and have a clear function signatures. Co-authored-by: Alvydas Vitkauskas Closes modularml/mojo#3851 MODULAR_ORIG_COMMIT_REV_ID: acae8a18cea7f91639d059e03dff88e3e3d1f5a5 --- stdlib/src/collections/deque.mojo | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index 6415979277..e8c1138071 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -794,7 +794,7 @@ struct Deque[ElementType: CollectionElement]( return (self._data + self._head)[] - fn pop(mut self, out element: ElementType) raises: + fn pop(mut self) raises -> ElementType: """Removes and returns the element from the right side of the deque. Returns: @@ -816,9 +816,9 @@ struct Deque[ElementType: CollectionElement]( ): self._realloc(self._capacity >> 1) - return + return element - fn popleft(mut self, out element: ElementType) raises: + fn popleft(mut self) raises -> ElementType: """Removes and returns the element from the left side of the deque. Returns: @@ -840,7 +840,7 @@ struct Deque[ElementType: CollectionElement]( ): self._realloc(self._capacity >> 1) - return + return element fn reverse(mut self): """Reverses the elements of the deque in-place.""" From ef4e86f7ab05a628e80c4a7b5da29e75db4f3b7d Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Tue, 10 Dec 2024 16:39:19 -0700 Subject: [PATCH 081/509] [stdlib] [GPU] Fix address spaces for AMD The address space values for AMD are incorrect, so adjust the definitions to be correct for both Nvidia and AMD. Note that there are some other oddities with address spaces we'll deal with in a follow-up. MODULAR_ORIG_COMMIT_REV_ID: a9feb989eba84e8cd69242dcd82e6188bb0c8437 --- stdlib/src/memory/pointer.mojo | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 16a4eadd29..007343d63f 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -19,6 +19,8 @@ from memory import Pointer ``` """ +from sys import is_nvidia_gpu + # ===-----------------------------------------------------------------------===# # AddressSpace @@ -36,13 +38,13 @@ struct _GPUAddressSpace(EqualityComparable): """Generic address space.""" alias GLOBAL = AddressSpace(1) """Global address space.""" - alias CONSTANT = AddressSpace(2) + alias CONSTANT = AddressSpace(2) if is_nvidia_gpu() else AddressSpace(4) """Constant address space.""" alias SHARED = AddressSpace(3) """Shared address space.""" alias PARAM = AddressSpace(4) """Param address space.""" - alias LOCAL = AddressSpace(5) + alias LOCAL = AddressSpace(5) if is_nvidia_gpu() else AddressSpace(3) """Local address space.""" @always_inline("nodebug") From 3efbf0908e84feff332bed7003cebc7c9983e960 Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Tue, 10 Dec 2024 17:41:12 -0700 Subject: [PATCH 082/509] [stdlib][GPU] Fix invalid constant addr space On Nvidia, address space value of 2 in the IR is reserved. So, it's invalid for us to represent the constant address space with the value of `2`. Good news: nobody is setting this value currently. So, just remove it. While here, rename the existing `PARAM` address space name to be called "CONSTANT" instead, which is more consistent with the [docs](https://docs.nvidia.com/cuda/nvvm-ir-spec/#address-space). Note that AMD calls address space 4 "constant" as well. MODULAR_ORIG_COMMIT_REV_ID: 35bbc8ad2b82efdddf6fa4cf537132af2ca2d3af --- stdlib/src/memory/memory.mojo | 7 +++++-- stdlib/src/memory/pointer.mojo | 6 ++---- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 0a23477416..ed72524482 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -414,9 +414,12 @@ fn stack_allocation[ @parameter if is_gpu(): - # On NVGPU, SHARED and PARAM address spaces lower to global memory. + # On NVGPU, SHARED and CONSTANT address spaces lower to global memory. @parameter - if address_space in (_GPUAddressSpace.SHARED, _GPUAddressSpace.PARAM): + if address_space in ( + _GPUAddressSpace.SHARED, + _GPUAddressSpace.CONSTANT, + ): alias global_name = name.value() if name else "_global_alloc" return __mlir_op.`pop.global_alloc`[ name = global_name.value, diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 007343d63f..f3833563f3 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -38,12 +38,10 @@ struct _GPUAddressSpace(EqualityComparable): """Generic address space.""" alias GLOBAL = AddressSpace(1) """Global address space.""" - alias CONSTANT = AddressSpace(2) if is_nvidia_gpu() else AddressSpace(4) - """Constant address space.""" alias SHARED = AddressSpace(3) """Shared address space.""" - alias PARAM = AddressSpace(4) - """Param address space.""" + alias CONSTANT = AddressSpace(4) + """Constant address space.""" alias LOCAL = AddressSpace(5) if is_nvidia_gpu() else AddressSpace(3) """Local address space.""" From ddf1aca9fb21f78cc9c308ca4cf479abf1863f3f Mon Sep 17 00:00:00 2001 From: Walter Erquinigo Date: Tue, 10 Dec 2024 23:24:23 -0500 Subject: [PATCH 083/509] [mblack] Ignore non mojo files For context, in https://github.com/modularml/mojo/pull/3832 we decided that mblack (aka mojo format) will no longer format non mojo files. The change is trivial fortunately. However, this doesn't solve the problem if formatting mojo cells in notebooks, but that's a problem for the future. MODULAR_ORIG_COMMIT_REV_ID: a28bc715affc0d83bff92b72c7eb313e1f80db96 --- docs/changelog.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 9f6a1c4e3e..cbae1be090 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -24,6 +24,9 @@ what we publish. ### Tooling changes +- mblack (aka `mojo format`) no longer formats non-mojo files. This prevents + unexpected formatting of python files. + ### ❌ Removed ### 🛠️ Fixed From 7ea103c905765610c9ff88b60bddb4eacc07d1aa Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 11 Dec 2024 17:11:19 +0000 Subject: [PATCH 084/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024121105 --- examples/life/magic.lock | 260 ++++++++++++++++++++-------------- examples/magic.lock | 260 ++++++++++++++++++++-------------- examples/notebooks/magic.lock | 258 +++++++++++++++++++-------------- examples/operators/magic.lock | 260 ++++++++++++++++++++-------------- magic.lock | 260 ++++++++++++++++++++-------------- 5 files changed, 754 insertions(+), 544 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index e9b082c770..9fa015dd3f 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -57,7 +57,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.6-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-cli-0.0.5-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-cli-0.0.6-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.16.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/fluidsynth-2.3.7-hd992666_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 @@ -170,14 +170,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-24.6.0.dev2024121016-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-24.6.0.dev2024121016-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-24.6.0.dev2024121016-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-24.6.0.dev2024121016-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2024121105-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2024121105-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2024121105-3.12release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2024121105-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-24.6.0.dev2024121016-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2024121105-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.15-py312h98912ed_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda @@ -234,6 +234,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/regex-2024.11.6-py312h66e93f0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/rich-13.9.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rich-toolkit-0.11.3-pyh29332c3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.9-h0fd0ee4_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/safetensors-0.4.5-py312h12e396e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2-2.30.10-h63c27ac_0.conda @@ -333,7 +334,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/expat-2.6.4-h5ad3122_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.6-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-cli-0.0.5-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-cli-0.0.6-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.16.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/fluidsynth-2.3.7-h4f58cef_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 @@ -446,14 +447,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-c-1.10.0-h5ad3122_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/markupsafe-3.0.2-py312h74ce7d3_1.conda - 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conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2024121105-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/mpg123-1.32.9-h65af167_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/multidict-6.1.0-py312hcc812fe_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/multidict-6.1.0-py312hcc812fe_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/multiprocess-0.70.15-py312hdd3e373_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-hcccb83c_1.conda @@ -510,6 +511,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/regex-2024.11.6-py312hb2c0f52_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/rich-13.9.4-pyhd8ed1ab_1.conda + - 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size: 112564915 - timestamp: 1733850300425 + size: 112620525 + timestamp: 1733894448573 - kind: conda name: mblack - version: 24.6.0.dev2024121016 + version: 25.1.0.dev2024121105 build: release subdir: noarch noarch: python - url: https://conda.modular.com/max-nightly/noarch/mblack-24.6.0.dev2024121016-release.conda - sha256: f9acaa65326bbc7f7e3158b1e20b0cdc7ac7da0ca925278566da7f80731146b5 - md5: 6e4496ad926e9f2e8c187f3c1cdd6a0c + url: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2024121105-release.conda + sha256: ee5c623d7908731bc5c42079cbb5d7ac461481c5bcaf615e25c4c8263f041793 + md5: 1271b7b52a8cc538e26ffbdd22743d3a depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -6084,8 +6103,8 @@ packages: - platformdirs >=2 - python license: MIT - size: 130747 - timestamp: 1733849930259 + size: 130789 + timestamp: 1733894234681 - kind: conda name: mdurl version: 0.1.2 @@ -6104,30 +6123,30 @@ packages: timestamp: 1733255681319 - kind: conda name: mojo-jupyter - version: 24.6.0.dev2024121016 + version: 25.1.0.dev2024121105 build: release subdir: noarch noarch: python - url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-24.6.0.dev2024121016-release.conda - sha256: e2a2514e4570f219e25f3f6f948d9035723b80df4fb9209598f34af17239a4f6 - md5: 0b20302cee375040ce15e8b795d7cda4 + url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2024121105-release.conda + sha256: c59130a6e8190e55b8881bd43429377da5368df6afed78b04c8df236d253a047 + md5: 35cc55bad72e3b9d0d490ff7af58c446 depends: - - max-core ==24.6.0.dev2024121016 release + - max-core ==25.1.0.dev2024121105 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 23064 - timestamp: 1733849930259 + size: 22932 + timestamp: 1733894234682 - kind: conda name: multidict version: 6.1.0 - build: py312h178313f_1 - build_number: 1 + build: py312h178313f_2 + build_number: 2 subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_1.conda - sha256: bf9cb8487f447098bd4a8248b4f176f34dd55be729a67b8ac2fdb984b80c5d46 - md5: e397d9b841c37fc3180b73275ce7e990 + url: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda + sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 + md5: 5b5e3267d915a107eca793d52e1b780a depends: - __glibc >=2.17,<3.0.a0 - libgcc >=13 @@ -6135,17 +6154,17 @@ packages: - python_abi 3.12.* *_cp312 license: Apache-2.0 license_family: APACHE - size: 61519 - timestamp: 1729065799315 + size: 61507 + timestamp: 1733913288935 - kind: conda name: multidict version: 6.1.0 - build: py312hcc812fe_1 - build_number: 1 + build: py312hcc812fe_2 + build_number: 2 subdir: linux-aarch64 - url: https://conda.anaconda.org/conda-forge/linux-aarch64/multidict-6.1.0-py312hcc812fe_1.conda - sha256: 39264fd518c5dcda3affed162b874a58c775a5f5eb81e0aaf2387e92408a3490 - md5: 7629c9ce86495fa01cdfc3ea5418d03f + url: https://conda.anaconda.org/conda-forge/linux-aarch64/multidict-6.1.0-py312hcc812fe_2.conda + sha256: ff9f767ba4df68e9ac2a380529a83a2fb6abd985beee9eab16608f7e2c3ccc6e + md5: dcf3ae213cf0ab40ebcc10452e1ed9fa depends: - libgcc >=13 - python >=3.12,<3.13.0a0 @@ -6153,8 +6172,8 @@ packages: - python_abi 3.12.* *_cp312 license: Apache-2.0 license_family: APACHE - size: 62830 - timestamp: 1729065694252 + size: 63077 + timestamp: 1733913233032 - kind: conda name: multidict version: 6.1.0 @@ -6369,6 +6388,7 @@ packages: - libtiff >=4.7.0,<4.8.0a0 - libzlib >=1.3.1,<2.0a0 license: BSD-2-Clause + license_family: BSD size: 377796 timestamp: 1733816683252 - kind: conda @@ -6387,6 +6407,7 @@ packages: - libtiff >=4.7.0,<4.8.0a0 - libzlib >=1.3.1,<2.0a0 license: BSD-2-Clause + license_family: BSD size: 342988 timestamp: 1733816638720 - kind: conda @@ -6404,6 +6425,7 @@ packages: - libtiff >=4.7.0,<4.8.0a0 - libzlib >=1.3.1,<2.0a0 license: BSD-2-Clause + license_family: BSD size: 319362 timestamp: 1733816781741 - kind: conda @@ -7255,6 +7277,7 @@ packages: - python >=3.9 - python-dotenv >=0.21.0 license: MIT + license_family: MIT size: 30832 timestamp: 1733851937909 - kind: conda @@ -7930,6 +7953,25 @@ packages: license_family: MIT size: 185646 timestamp: 1733342347277 +- kind: conda + name: rich-toolkit + version: 0.11.3 + build: pyh29332c3_0 + subdir: noarch + noarch: python + url: https://conda.anaconda.org/conda-forge/noarch/rich-toolkit-0.11.3-pyh29332c3_0.conda + sha256: e558f8c254a9ff9164d069110da162fc79497d70c60f2c09a5d3d0d7101c5628 + md5: 4ba15ae9388b67d09782798347481f69 + depends: + - python >=3.9 + - rich >=13.7.1 + - click >=8.1.7 + - typing_extensions >=4.12.2 + - python + license: MIT + license_family: MIT + size: 17357 + timestamp: 1733750834072 - kind: conda name: s2n version: 1.5.9 From 9288faa77fbdb109dc9b198f8effbeb35876f3f4 Mon Sep 17 00:00:00 2001 From: Walter Erquinigo Date: Wed, 11 Dec 2024 12:27:06 -0500 Subject: [PATCH 085/509] [Mojo tooling] Update changelog MODULAR_ORIG_COMMIT_REV_ID: 8d06b242b62b243f9a09f1dd8a20d75f93c40a64 --- docs/changelog.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index cbae1be090..341b25367e 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -30,3 +30,11 @@ what we publish. ### ❌ Removed ### 🛠️ Fixed + +- The Mojo Kernel for Jupyter Notebooks is working again on nightly releases. + +- The command `mojo debug --vscode` now sets the current working directory + properly. + +- The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. + [Issue #3826](https://github.com/modularml/mojo/issues/3826). From bf8869c54eff95a1c8fb6401234860c264fd6e31 Mon Sep 17 00:00:00 2001 From: Patrick Rachford Date: Wed, 11 Dec 2024 13:11:47 -0800 Subject: [PATCH 086/509] [docs] Standardize straight quotation marks and apostrophes Replace curly quotes/apostrophes (`'`) with straight ones (`'`) throughout developer documentation for consistency. MODULAR_ORIG_COMMIT_REV_ID: 7177e1beaa2aaaff3e1421f69d623f1df6eb37e4 --- docs/manual/functions.mdx | 2 +- docs/manual/lifecycle/death.mdx | 6 +++--- docs/manual/lifecycle/life.mdx | 2 +- docs/manual/pointers/index.mdx | 30 +++++++++++++++--------------- docs/manual/structs.mdx | 8 ++++---- docs/manual/types.mdx | 2 +- docs/tools/debugging.mdx | 2 +- docs/tools/testing.mdx | 8 ++++---- 8 files changed, 30 insertions(+), 30 deletions(-) diff --git a/docs/manual/functions.mdx b/docs/manual/functions.mdx index 071255ec24..46cd3f5aaa 100644 --- a/docs/manual/functions.mdx +++ b/docs/manual/functions.mdx @@ -552,7 +552,7 @@ Thus, you can also pass a `StringLiteral` to a function that expects a `String`. When resolving an overloaded function call, the Mojo compiler tries each candidate function and uses the one that works (if only one version works), or it picks the closest match (if it can determine a close match), or it reports -that the call is ambiguous (if it can’t figure out which one to pick). +that the call is ambiguous (if it can't figure out which one to pick). If the compiler can't figure out which function to use, you can resolve the ambiguity by explicitly casting your value to a supported argument type. For diff --git a/docs/manual/lifecycle/death.mdx b/docs/manual/lifecycle/death.mdx index 2f7ee808f7..864f06e365 100644 --- a/docs/manual/lifecycle/death.mdx +++ b/docs/manual/lifecycle/death.mdx @@ -105,7 +105,7 @@ the end of the code scope to destroy values): because the destructor call always happens before the tail call. Additionally, Mojo's ASAP destruction works great within Python-style `def` -functions. That's because Python doesn’t really provide scopes beyond a +functions. That's because Python doesn't really provide scopes beyond a function scope, so the Python garbage collector cleans up resources more often than a scope-based destruction policy would. However, Mojo does not use a garbage collector, so the ASAP destruction policy provides destruction @@ -276,7 +276,7 @@ object. Mojo's policy here is powerful and intentionally straight-forward: fields can be temporarily transferred, but the "whole object" must be constructed with the -aggregate type’s initializer and destroyed with the aggregate destructor. This +aggregate type's initializer and destroyed with the aggregate destructor. This means it's impossible to create an object by initializing only its fields, and it's likewise impossible to destroy an object by destroying only its fields. @@ -316,7 +316,7 @@ to a new instance (read more about the [move constructor](/mojo/manual/lifecycle/life#move-constructor)), while `__del__()` implements the deletion logic for its `self`. As such, they both need to own and transform elements of the `owned` value, and they -definitely don’t want the original `owned` value's destructor to also run—that +definitely don't want the original `owned` value's destructor to also run—that could result in a double-free error, and in the case of the `__del__()` method, it would become an infinite loop. diff --git a/docs/manual/lifecycle/life.mdx b/docs/manual/lifecycle/life.mdx index 360ec57c33..dbdd5f11cd 100644 --- a/docs/manual/lifecycle/life.mdx +++ b/docs/manual/lifecycle/life.mdx @@ -656,7 +656,7 @@ things like a single integer or floating point number. We call these types and destroyed without invoking any custom lifecycle methods. Trivial types are the most common types that surround us, and from a language -perspective, Mojo doesn’t need special support for these written in a struct. +perspective, Mojo doesn't need special support for these written in a struct. Usually, these values are so tiny that they should be passed around in CPU registers, not indirectly through memory. diff --git a/docs/manual/pointers/index.mdx b/docs/manual/pointers/index.mdx index 075fa33ea5..a254c77bc5 100644 --- a/docs/manual/pointers/index.mdx +++ b/docs/manual/pointers/index.mdx @@ -45,7 +45,7 @@ print(ptr[]) ## Pointer terminology -Before we jump into the pointer types, here are a few terms you’ll run across. Some +Before we jump into the pointer types, here are a few terms you'll run across. Some of them may already be familiar to you. - **Safe pointers**: are designed to prevent memory errors. Unless you use one @@ -54,7 +54,7 @@ of them may already be familiar to you. use-after-free. - **Nullable pointers**: can point to an invalid memory location (typically 0, -or a “null pointer”). Safe pointers aren’t nullable. +or a “null pointer”). Safe pointers aren't nullable. - **Smart pointers**: own their pointees, which means that the value they point to may be deallocated when the pointer itself is destroyed. Non-owning @@ -66,10 +66,10 @@ or a “null pointer”). Safe pointers aren’t nullable. allocation can either be implicit (that is, performed automatically when initializing a pointer with a value) or explicit. -- **Uninitialized memory**: refers to memory locations that haven’t been +- **Uninitialized memory**: refers to memory locations that haven't been initialized with a value, which may therefore contain random data. - Newly-allocated memory is uninitialized. The safe pointer types don’t allow - users to access memory that’s uninitialized. Unsafe pointers can allocate a + Newly-allocated memory is uninitialized. The safe pointer types don't allow + users to access memory that's uninitialized. Unsafe pointers can allocate a block of uninitialized memory locations and then initialize them one at a time. Being able to access uninitialized memory is unsafe by definition. @@ -93,7 +93,7 @@ The Mojo standard library includes several pointer types with different characteristics: - [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) is a safe pointer that points - to a single value that it doesn’t own. + to a single value that it doesn't own. - [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) is a smart pointer that points to a single value, and maintains exclusive ownership of @@ -139,10 +139,10 @@ The following sections provide more details on each pointer type. ## `Pointer` The [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) type is a safe pointer that -points to a initialized value that it doesn’t own. Some example use cases for a +points to a initialized value that it doesn't own. Some example use cases for a `Pointer` include: -- Storing a reference to a related type. For example, a list’s iterator object +- Storing a reference to a related type. For example, a list's iterator object might hold a `Pointer` back to the original list. - Passing the memory location for a single value to external code via @@ -181,11 +181,11 @@ either `Movable`, `Copyable`, or `ExplicitlyCopyable`. Since an `OwnedPointer` is designed to enforce single ownership, the pointer itself can be moved, but not copied. -Note: Currently, you can’t create an `Optional[OwnedPointer[T]]` because the +Note: Currently, you can't create an `Optional[OwnedPointer[T]]` because the `Optional` type only works with types that are both movable and copyable. This restricts some use cases that would otherwise be a natural fit for`OwnedPointer`, including self-referential data structures like linked lists -and trees. (Until this use case is supported for `OwnedPointer`, it’s possible +and trees. (Until this use case is supported for `OwnedPointer`, it's possible to use`ArcPointer` where you need a smart pointer that can be `Optional`.) ## `ArcPointer` @@ -241,19 +241,19 @@ def main(): Note: The reference count is stored using an [`Atomic`]([/mojo/stdlib/os/atomic/Atomic](https://docs.modular-staging.com/mojo/stdlib/os/atomic/Atomic)) value to ensure that updates to the reference count are thread-safe. However, -Mojo doesn’t currently enforce exclusive access across thread boundaries, so -it’s possible to form race conditions. +Mojo doesn't currently enforce exclusive access across thread boundaries, so +it's possible to form race conditions. ## UnsafePointer [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) is a low-level pointer that can access a block of contiguous memory locations, which -might be uninitialized. It’s analogous to a raw pointer in the C and C++ +might be uninitialized. It's analogous to a raw pointer in the C and C++ programming languages. `UnsafePointer` provides unsafe methods for initializing -and destroying stored values, as well as for accessing the values once they’re +and destroying stored values, as well as for accessing the values once they're initialized. -As the name suggests, `UnsafePointer` doesn’t provide any memory safety +As the name suggests, `UnsafePointer` doesn't provide any memory safety guarantees, so you should reserve it for cases when none of the other pointer types will do the job. Here are some use cases where you might want to use an `UnsafePointer`: diff --git a/docs/manual/structs.mdx b/docs/manual/structs.mdx index 7a32bb323d..818058ba4c 100644 --- a/docs/manual/structs.mdx +++ b/docs/manual/structs.mdx @@ -195,15 +195,15 @@ Syntactically, the biggest difference compared to a Python class is that all fields in a struct must be explicitly declared with `var`. In Mojo, the structure and contents of a struct are set at compile time and -can’t be changed while the program is running. Unlike in Python, where you can -add, remove, or change attributes of an object on the fly, Mojo doesn’t allow +can't be changed while the program is running. Unlike in Python, where you can +add, remove, or change attributes of an object on the fly, Mojo doesn't allow that for structs. However, the static nature of structs helps Mojo run your code faster. The -program knows exactly where to find the struct’s information and how to use it +program knows exactly where to find the struct's information and how to use it without any extra steps or delays at runtime. -Mojo’s structs also work really well with features you might already know from +Mojo's structs also work really well with features you might already know from Python, like operator overloading (which lets you change how math symbols like `+` and `-` work with your own data, using [special methods](#special-methods)). diff --git a/docs/manual/types.mdx b/docs/manual/types.mdx index 4f276c82f9..ee6ddcf586 100644 --- a/docs/manual/types.mdx +++ b/docs/manual/types.mdx @@ -16,7 +16,7 @@ There are a some types that aren't defined as structs: signal "no value." Mojo comes with a standard library that provides a number of useful types and -utility functions. These standard types aren’t privileged. Each of the standard +utility functions. These standard types aren't privileged. Each of the standard library types is defined just like user-defined types—even basic types like [`Int`](/mojo/stdlib/builtin/int/Int) and [`String`](/mojo/stdlib/collections/string/String). But these standard library diff --git a/docs/tools/debugging.mdx b/docs/tools/debugging.mdx index 8631b5cc71..6672ded32d 100644 --- a/docs/tools/debugging.mdx +++ b/docs/tools/debugging.mdx @@ -341,7 +341,7 @@ The debugger currently has the following limitations: * When stepping out of a function, the returned value is not displayed. -* LLDB doesn’t support stopping or resuming individual threads. +* LLDB doesn't support stopping or resuming individual threads. ### Breakpoints diff --git a/docs/tools/testing.mdx b/docs/tools/testing.mdx index ef10b459cf..287e40c690 100644 --- a/docs/tools/testing.mdx +++ b/docs/tools/testing.mdx @@ -14,7 +14,7 @@ consists of a set of assertions defined as part of the ## Get started -Let’s start with a simple example of writing and running Mojo tests. +Let's start with a simple example of writing and running Mojo tests. ### 1. Write tests @@ -106,7 +106,7 @@ its error message. The Mojo standard library includes a [`testing`](/mojo/stdlib/testing/testing/) module that defines several assertion functions for implementing tests. Each -assertion returns `None` if its condition is met or raises an error if it isn’t. +assertion returns `None` if its condition is met or raises an error if it isn't. * [`assert_true()`](/mojo/stdlib/testing/testing/assert_true): Asserts that the input value is `True`. @@ -597,8 +597,8 @@ a += 1 Test suite scopes do *not* nest. In other words, the test suite scope of a module is completely independent of the test suite scope of a function or struct -defined within that module. For example, this means that if a module’s test -suite creates a variable, that variable is *not* accessible to a function’s test +defined within that module. For example, this means that if a module's test +suite creates a variable, that variable is *not* accessible to a function's test suite within the same module. ::: From 9e31af8b08571477777273d3cf3372ba0808ea02 Mon Sep 17 00:00:00 2001 From: Anand Pratap Singh <628125+anandpratap@users.noreply.github.com> Date: Wed, 11 Dec 2024 16:47:58 -0500 Subject: [PATCH 087/509] [stdlib] Selectively revert "[stdlib][GPU] Fix invalid constant addr space" Use address space value of 5 for shared memory on all devices. MODULAR_ORIG_COMMIT_REV_ID: b2dfc4c7ec71e33053b09449a338a795ef322aff --- stdlib/src/memory/pointer.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index f3833563f3..1ffd5528f8 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -42,7 +42,7 @@ struct _GPUAddressSpace(EqualityComparable): """Shared address space.""" alias CONSTANT = AddressSpace(4) """Constant address space.""" - alias LOCAL = AddressSpace(5) if is_nvidia_gpu() else AddressSpace(3) + alias LOCAL = AddressSpace(5) """Local address space.""" @always_inline("nodebug") From e166e905986c394739bdc5cd29840b80560b7a07 Mon Sep 17 00:00:00 2001 From: Walter Erquinigo Date: Wed, 11 Dec 2024 20:27:15 -0500 Subject: [PATCH 088/509] [Mojo tooling] Expose struct signatures in the LSP and doc gen - Add a signature field in struct fields in the doc generator - Expose this signature in the symbol outline throught the LSP - Expose the list of traits in the hover popup thorugh the LSP MODULAR_ORIG_COMMIT_REV_ID: 5ef3f95a520cebda8000daeea2873a6fe55ccaf8 --- docs/changelog.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 341b25367e..27f3f08ff6 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -27,6 +27,10 @@ what we publish. - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents unexpected formatting of python files. +- Full struct signature information is now exposed in the documentation + generator, and in the symbol outline and hover markdown via the Mojo Language + Server. + ### ❌ Removed ### 🛠️ Fixed From 4d177f12f4b97788e3b8f038ee071f2b2303a3ce Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 12 Dec 2024 16:18:56 +0000 Subject: [PATCH 089/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024121214 --- examples/life/magic.lock | 301 +++++++++++++++--------------- examples/magic.lock | 301 +++++++++++++++--------------- examples/notebooks/magic.lock | 341 +++++++++++++++++----------------- examples/operators/magic.lock | 301 +++++++++++++++--------------- magic.lock | 301 +++++++++++++++--------------- 5 files changed, 775 insertions(+), 770 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 9fa015dd3f..11ef646bda 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -113,7 +113,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.71-h39aace5_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-25_linux64_openblas.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.10.1-hbbe4b11_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.11.1-h332b0f4_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libdb-6.2.32-h9c3ff4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.22-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2 @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - 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fastapi - httpx @@ -6049,18 +6049,18 @@ packages: arch: aarch64 platform: linux license: LicenseRef-Modular-Proprietary - size: 127725024 - timestamp: 1734015642970 + size: 127728932 + timestamp: 1734067047906 - kind: conda name: max-python - version: 25.1.0.dev2024121214 + version: 25.1.0.dev2024121305 build: 3.12release subdir: osx-arm64 - url: https://conda.modular.com/max-nightly/osx-arm64/max-python-25.1.0.dev2024121214-3.12release.conda - sha256: 289119c20859195f0d86fbd67c4814d65604913c12086cac9591a3371323d1d6 - md5: 8f2676a5b6775eeb835c6e0db97bc3b2 + url: https://conda.modular.com/max-nightly/osx-arm64/max-python-25.1.0.dev2024121305-3.12release.conda + sha256: a273ba2302364c8a3163ce7f26dd7b787194c3c3f1bc9ed77e77b2edf483522d + md5: 09d62b97c29133fadc721fd9cb7b0388 depends: - - max-core ==25.1.0.dev2024121214 release + - max-core ==25.1.0.dev2024121305 release - python 3.12.* - fastapi - httpx @@ -6083,17 +6083,17 @@ packages: arch: arm64 platform: osx license: LicenseRef-Modular-Proprietary - size: 112807365 - timestamp: 1734016112401 + size: 112812970 + timestamp: 1734067231662 - kind: conda name: mblack - version: 25.1.0.dev2024121214 + version: 25.1.0.dev2024121305 build: release subdir: noarch noarch: python - url: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2024121214-release.conda - sha256: 92acaf798bb3834675c7358f2d68f4d792036e166957fa6dbc6ff14f843ede4f - md5: 4de530f1fe2a901756c581249180eca9 + url: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2024121305-release.conda + sha256: abb1e77a5677ba3dff4645ce9d9867da248690f7a64badbd6678afcdb8ba3f35 + md5: 665d00da19a5c6902c00afa7c456a8da depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -6103,8 +6103,8 @@ packages: - platformdirs >=2 - python license: MIT - size: 130794 - timestamp: 1734015648615 + size: 130799 + timestamp: 1734067047902 - kind: conda name: mdurl version: 0.1.2 @@ -6123,21 +6123,21 @@ packages: timestamp: 1733255681319 - kind: conda name: mojo-jupyter - version: 25.1.0.dev2024121214 + version: 25.1.0.dev2024121305 build: release subdir: noarch noarch: python - url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2024121214-release.conda - sha256: 72f123af334c1bcfe47977593401441d281156684350568147ab6c62f7717dc0 - md5: a631623d32a0b423b4ac774dbe79a918 + url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2024121305-release.conda + sha256: 4695f084795165a27b510a6b7c4174141cabad2f84c76c785529b0d6e9cc45ea + md5: 86028b0c146cc15b505e17670db7f139 depends: - - max-core ==25.1.0.dev2024121214 release + - max-core ==25.1.0.dev2024121305 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22936 - timestamp: 1734015648615 + size: 22933 + timestamp: 1734067047904 - kind: conda name: multidict version: 6.1.0 From 6c25e6547e0525bc37639fad999fd680f2b15198 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 13 Dec 2024 12:20:05 -0600 Subject: [PATCH 091/509] [stdlib] feat: Rename of `StringSlice.is_mutable` => `mut` (#52091) This enables a concise syntax for specifying string argument mutability without requiring explicit type parameters. Instead the mutability of the string can be specified using `StringSlice[mut=True]`, and autoparameterization will do the rest. MODULAR_ORIG_COMMIT_REV_ID: 6f495f641a46c50b44cf3c66664e4b018be1d66d --- stdlib/src/builtin/io.mojo | 1 - stdlib/src/builtin/simd.mojo | 2 +- stdlib/src/collections/string.mojo | 6 +++--- stdlib/src/utils/inline_string.mojo | 4 ++-- stdlib/src/utils/string_slice.mojo | 32 +++++++++++++++++++++-------- 5 files changed, 29 insertions(+), 16 deletions(-) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index bb15718f20..fc363fc15c 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -35,7 +35,6 @@ from memory import UnsafePointer, memcpy from utils import ( StaticString, StringRef, - StringSlice, write_args, write_buffered, ) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 18a87f1903..7bb4c061b6 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -52,7 +52,7 @@ from builtin.io import _snprintf from documentation import doc_private from memory import UnsafePointer, bitcast, Span -from utils import IndexList, StaticTuple, StringSlice +from utils import IndexList, StaticTuple from utils._visualizers import lldb_formatter_wrapping_type from utils.numerics import FPUtils from utils.numerics import isnan as _isnan diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 7e09a482ff..716ddc9756 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -396,7 +396,7 @@ fn _str_to_base_error(base: Int, str_slice: StringSlice) -> String: ) -fn _identify_base(str_slice: StringSlice[_], start: Int) -> Tuple[Int, Int]: +fn _identify_base(str_slice: StringSlice, start: Int) -> Tuple[Int, Int]: var length = str_slice.byte_length() # just 1 digit, assume base 10 if start == (length - 1): @@ -468,11 +468,11 @@ fn atol(str: String, base: Int = 10) raises -> Int: return _atol(str.as_string_slice(), base) -fn _atof_error(str_ref: StringSlice[_]) -> Error: +fn _atof_error(str_ref: StringSlice) -> Error: return Error("String is not convertible to float: '" + str(str_ref) + "'") -fn _atof(str_ref: StringSlice[_]) raises -> Float64: +fn _atof(str_ref: StringSlice) raises -> Float64: """Implementation of `atof` for StringRef inputs. Please see its docstring for details. diff --git a/stdlib/src/utils/inline_string.mojo b/stdlib/src/utils/inline_string.mojo index 8c6cfb3166..7ddb0df082 100644 --- a/stdlib/src/utils/inline_string.mojo +++ b/stdlib/src/utils/inline_string.mojo @@ -119,7 +119,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): """ self.__iadd__(string.as_string_slice()) - fn __iadd__(mut self, str_slice: StringSlice[_]): + fn __iadd__(mut self, str_slice: StringSlice): """Appends another string to this string. Args: @@ -412,7 +412,7 @@ struct _FixedString[CAP: Int]( self.__iadd__(string.as_string_slice()) @always_inline - fn __iadd__(mut self, str_slice: StringSlice[_]) raises: + fn __iadd__(mut self, str_slice: StringSlice) raises: """Appends another string to this string. Args: diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 09372e41e1..64fbc94437 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -168,14 +168,14 @@ fn _memrmem[ @value struct _StringSliceIter[ - is_mutable: Bool, //, - origin: Origin[is_mutable], + mut: Bool, //, + origin: Origin[mut], forward: Bool = True, ]: """Iterator for `StringSlice` over unicode characters. Parameters: - is_mutable: Whether the slice is mutable. + mut: Whether the slice is mutable. origin: The origin of the underlying string data. forward: The iteration direction. `False` is backwards. """ @@ -234,7 +234,7 @@ struct _StringSliceIter[ @value @register_passable("trivial") -struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( +struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Stringable, Sized, Writable, @@ -245,7 +245,7 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( """A non-owning view to encoded string data. Parameters: - is_mutable: Whether the slice is mutable. + mut: Whether the slice is mutable. origin: The origin of the underlying string data. Notes: @@ -559,7 +559,7 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( buf.append(0) return String(buf^) - fn __contains__(ref self, substr: StringSlice[_]) -> Bool: + fn __contains__(ref self, substr: StringSlice) -> Bool: """Returns True if the substring is contained within the current string. Args: @@ -765,7 +765,7 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( @always_inline fn unsafe_ptr( self, - ) -> UnsafePointer[Byte, is_mutable=is_mutable, origin=origin]: + ) -> UnsafePointer[Byte, is_mutable=mut, origin=origin]: """Gets a pointer to the first element of this string slice. Returns: @@ -783,8 +783,22 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( return len(self.as_bytes()) + fn get_immutable( + self, + ) -> StringSlice[ImmutableOrigin.cast_from[origin].result]: + """ + Return an immutable version of this string slice. + + Returns: + A string slice covering the same elements, but without mutability. + """ + return StringSlice[ImmutableOrigin.cast_from[origin].result]( + ptr=self._slice.unsafe_ptr(), + length=len(self), + ) + fn startswith( - self, prefix: StringSlice[_], start: Int = 0, end: Int = -1 + self, prefix: StringSlice, start: Int = 0, end: Int = -1 ) -> Bool: """Verify if the `StringSlice` starts with the specified prefix between start and end positions. @@ -804,7 +818,7 @@ struct StringSlice[is_mutable: Bool, //, origin: Origin[is_mutable]]( ).startswith(prefix) fn endswith( - self, suffix: StringSlice[_], start: Int = 0, end: Int = -1 + self, suffix: StringSlice, start: Int = 0, end: Int = -1 ) -> Bool: """Verify if the `StringSlice` end with the specified suffix between start and end positions. From 4aea0fe704820fee74372a4dc4f2aa0c0abce974 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Fri, 13 Dec 2024 11:08:01 -0800 Subject: [PATCH 092/509] [Docs] Changelog: edit & apply suggestions from review. MODULAR_ORIG_COMMIT_REV_ID: e318d2b9551fbe5725e659a286ab59086e2f1662 --- docs/changelog-released.md | 893 +++++++++++++++++++------------------ 1 file changed, 469 insertions(+), 424 deletions(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 44376a6153..a0d9609952 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -28,15 +28,20 @@ Here's a brief summary of some of the major changes in this release, with more detailed information in the following sections: - The `inout` and `borrowed` argument conventions have been renamed to `mut` - and `read`, respectively. See Language changes for details. + and `read`, respectively. A new `out` convention has been added for the `self` + argument in constructors and for named results. See + [Language changes](#24-6-language-changes) for details. -- `Lifetime` and related types have been renamed to `Origin` in the standard - library to better clarify that parameters of this type indicate where a - reference is derived from, not the more complicated notion of where a variable - is initialized and destroyed. As a consequence the `__lifetime_of()` operator - is now named `__origin_of()`. There are also a number of other origin-related - improvements in this release, described under Language changes and Standard - library changes below. +- `Lifetime` and related types in the standard library have been renamed to + [`Origin`](/mojo/stdlib/builtin/type_aliases/Origin) to better clarify that + parameters of this type indicate where a reference is derived from, not the + more complicated notion of where a variable is initialized and destroyed. As a + consequence the `__lifetime_of()` operator is now named `__origin_of()`. + + There are also a number of other origin-related improvements in this release, + including being able to specify a union of origins by listing multiple values + in the `__origin_of()` operator or inside the `ref` origin specifier + (`ref [a, b]`). For details, see [Language changes](#24-6-language-changes). For background information and rationale on the name change see [the proposal](https://github.com/modularml/mojo/issues/3623). For more @@ -44,70 +49,92 @@ detailed information in the following sections: [Lifetimes, origins and references](/mojo/manual/values/lifetimes) in the Mojo Manual. -- Implicit conversions are now opt-in using the `@implicit` decorator. See - Language changes for details. +- Implicit conversions are now opt-in using the + [`@implicit`](/mojo/manual/decorators/implicit) decorator. See + [Language changes](#24-6-language-changes) for details. + +- The standard library has added several new types, including + [`Deque`](/mojo/stdlib/collections/deque/Deque) (a double-ended queue) and + [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) (safe, + single-owner, non-nullable smart pointer). See + [Standard library changes](#standard-library-changes-24-6-standard-library-changes) + for details. + +- The VS Code extension now supports setting data breakpoints and function + breakpoints, and the Mojo LLDB debugger supports symbol breakpoints, such + as `b main` or `b my_module::main`. -- Lots of new docs, including a brand new +- We've made a number of improvement to how information is displayed in error + messages, LSP, and generated API documentation. For details, see + [Tooling changes](#24-6-tooling-changes). + +- And we've added a number of new docs, including a brand new [Mojo tutorial](/mojo/manual/get-started), new pages on [operators and expressions](/mojo/manual/operators), [error handling](/mojo/manual/errors), and [pointers](/mojo/manual/pointers/), and many smaller additions and improvements. -### Language changes +### Language changes {#24-6-language-changes} -- The `inout` and `borrowed` argument conventions have been renamed to the `mut` - and `read` argument conventions (respectively). These verbs reflect - declaratively what the callee can do to the argument value passed into the - caller, without tying in the requirement for the programmer to know about - advanced features like references. +- Argument convention changes: -- The argument convention for the `self` argument in `__init__()`, - `__copyinit__()`, and `__moveinit__()` methods has been changed from `inout` - to `out`, reflecting that a constructor method initializes its `self` value - without reading from it. This also enables spelling the type of an initializer - correctly, which was not supported before: + - The `inout` and `borrowed` argument conventions have been renamed to `mut` + (for "mutate") and `read`, respectively. These verbs reflect what the callee + can do to the argument value passed in by the caller, without requiring the + programmer to know about advanced features like references. - ```mojo - struct Foo: - fn __init__(out self): pass + For information on Mojo's argument conventions, see + [Argument conventions](/mojo/manual/values/ownership/#argument-conventions) + in the Mojo Manual. - fn test(): - # This works now - var fnPtr : fn(out x: Foo)->None = Foo.__init__ + - The argument convention for the `self` argument in the `__init__()`, + `__copyinit__()`, and `__moveinit__()` methods has been changed from `inout` + to `out`, reflecting that a constructor method initializes its `self` value + without reading from it. This also enables spelling the type of an + initializer correctly, which was not supported before: - var someFoo : Foo - fnPtr(someFoo) # initializes someFoo. - ``` + ```mojo + struct Foo: + fn __init__(out self): pass + + fn test(): + # This works now + var fnPtr : fn(out x: Foo)->None = Foo.__init__ - The previous `fn __init__(inout self)` syntax is still supported in this - release of Mojo, but will be removed in the future. Please migrate to the - new syntax. + var someFoo : Foo + fnPtr(someFoo) # initializes someFoo. + ``` -- Similarly, the spelling of "named functions results" has switched to use `out` - syntax instead of `-> T as name`. Functions may have at most one named result - or return type specified with the usual `->` syntax. `out` arguments may - occur anywhere in the argument list, but are typically last (except for - `__init__` methods, where they are typically first). + The previous `fn __init__(inout self)` syntax is still supported in this + release of Mojo, but will be removed in the future. Please migrate to the + new syntax. - ```mojo - # This function has type "fn() -> String" - fn example(out result: String): - result = "foo" - ``` + - Similarly, the spelling of named results has switched to use + `out` syntax instead of `-> T as name`. Functions may have at most one named + result or return type specified with the usual `->` syntax. `out` arguments + may occur anywhere in the argument list, but are typically last (except for + `__init__` methods, where they are typically first). - The parser still accepts the old syntax as a synonym for this, but that will - eventually be deprecated and removed. + ```mojo + # This function has type "fn() -> String" + fn example(out result: String): + result = "foo" + ``` - This was [discussed extensively in a public - proposal](https://github.com/modularml/mojo/issues/3623). For more - information, see - [Named results](/nightly/mojo/manual/functions#named-results) in the Mojo - Manual. + The parser still accepts the old syntax as a synonym for this, but that will + eventually be deprecated and removed. + + This was [discussed extensively in a public + proposal](https://github.com/modularml/mojo/issues/3623). For more + information, see + [Named results](/nightly/mojo/manual/functions#named-results) in the Mojo + Manual. -- Single argument constructors now require a `@implicit` decorator to allow - for implicit conversions. Previously you could define an `__init__` that - takes a single argument: +- Single argument constructors now require the + [`@implicit`](/mojo/manual/decorators/implicit) decorator to allow for + implicit conversions. Previously you could define an `__init__` that takes a + single argument: ```mojo struct Foo: @@ -152,85 +179,117 @@ detailed information in the following sections: [Constructors and implicit conversion](/mojo/manual/lifecycle/life#constructors-and-implicit-conversion) in the Mojo Manual. -- Various improvements to origin handling and syntax have landed, including - support for the ternary operator and allowing multiple arguments in a `ref` - specifier (which are implicitly unions). This enables expression of simple - algorithms cleanly: +- Origin-related changes: - ```mojo - fn my_min[T: Comparable](ref a: T, ref b: T) -> ref [a, b] T: - return a if a < b else b - ``` + - The `AnyLifetime` type (useful for declaring origin types as parameters) has + has been renamed to [`Origin`](/mojo/stdlib/builtin/type_aliases/Origin) and + the `__lifetime_of()` operator renamed to `__origin_of()`. - It is also nice that `my_min` automatically and implicitly propagates the - mutability of its arguments, so things like `my_min(str1, str2) += "foo"` is - valid. + - `Origin` is now a complete wrapper around the MLIR origin type. -- `Origin` is now a complete wrapper around the MLIR origin type. + - The `Origin.type` alias has been renamed to `_mlir_origin`. In parameter + lists, you can now write just `Origin[..]`, instead of `Origin[..].type`. - - The `Origin.type` alias has been renamed to `_mlir_origin`. In parameter - lists, you can now write just `Origin[..]`, instead of `Origin[..].type`. + - `ImmutableOrigin` and `MutableOrigin` are now, respectively, just aliases + for `Origin[False]` and `Origin[True]`. - - `ImmutableOrigin` and `MutableOrigin` are now, respectively, just aliases - for `Origin[False]` and `Origin[True]`. + - `Origin` struct values are now supported in the origin specifier of a + `ref [..]` argument. - - `Origin` struct values are now supported in the brackets of a `ref [..]` - argument. + - Added `Origin.cast_from` for casting the mutability of an origin value. - - Added `Origin.cast_from` for casting the mutability of an origin value. + - `ref` arguments and results now allow for providing a memory value + directly in the origin specifier, rather than requiring the use of + `__origin_of()`. It is still fine to use `__origin_of()` explicitly though, + and this is required when specifying origins for parameters (e.g. to the + `Pointer` type). For example, this is now valid without `__origin_of()`: -- `ref` argument and result specifiers now allow providing a memory value - directly in the origin specifier, rather than requiring the use of - `__origin_of()`. It is still fine to use `__origin_of()` explicitly though, - and this is required when specifying origins for parameters (e.g. to the - `Pointer` type). For example, this is now valid without `__origin_of()`: + ```mojo + fn return_ref(a: String) -> ref [a] String: + return a + ``` - ```mojo - fn return_ref(a: String) -> ref [a] String: - return a - ``` + - Various improvements to origin handling and syntax have landed, including + support for the ternary operator and allowing multiple arguments in a `ref` + specifier (which are implicitly unions). This enables expression of simple + algorithms cleanly: -- `ref` function arguments without an origin clause are now treated as - `ref [_]`, which is more syntactically convenient and consistent: + ```mojo + fn my_min[T: Comparable](ref a: T, ref b: T) -> ref [a, b] T: + return a if a < b else b + ``` - ```mojo - fn takes_and_return_ref(ref a: String) -> ref [a] String: - return a - ``` + It is also nice that `my_min` automatically and implicitly propagates the + mutability of its arguments, so things like `my_min(str1, str2) += "foo"` is + valid. -- The `__type_of(x)` and `__origin_of(x)` operators are much more general now: - they allow arbitrary expressions inside of them, allow referring to dynamic - values in parameter contexts, and even allow referring to raising functions - in non-raising contexts. These operations never evaluate their expression, so - any side effects that occur in the expression are never evaluated at runtime, - eliminating concerns about `__type_of(expensive())` being a problem. + - `ref` function arguments without an origin clause are now treated as + `ref [_]`, which is more syntactically convenient and consistent: -- The destructor insertion logic in Mojo is now aware that types that take an - `MutableAnyOrigin` or `ImmutableAnyOrigin` as part of their signature could - potentially access any live value that destructor insertion is tracking, - eliminating a significant usability issue with unsafe APIs like - `UnsafePointer`. Consider a typical example working with strings before this - change: + ```mojo + fn takes_and_return_ref(ref a: String) -> ref [a] String: + return a + ``` - ```mojo - var str = String(...) - var ptr = str.unsafe_ptr() - some_low_level_api(ptr) - _ = str^ # OLD HACK: Explicitly keep string alive until here! - ``` + - The `__type_of(x)` and `__origin_of(x)` operators are much more general now: + they allow arbitrary expressions inside of them, allow referring to dynamic + values in parameter contexts, and even allow referring to raising functions + in non-raising contexts. These operations never evaluate their expression, + so any side effects that occur in the expression are never evaluated at + runtime, eliminating concerns about `__type_of(expensive())` being a + problem. + + - The destructor insertion logic in Mojo is now aware that types that take an + `MutableAnyOrigin` or `ImmutableAnyOrigin` as part of their signature could + potentially access any live value that destructor insertion is tracking, + eliminating a significant usability issue with unsafe APIs like + [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer). + Consider a typical example working with strings before this change: - The `_ = str^` pattern was formerly required because the Mojo compiler has no - idea what "ptr" might reference. As a consequence, it had no idea that - `some_low_level_api()` might access `str` and therefore thought it was ok to - destroy the `String` before the call - this is why the explicit lifetime - extension was required. + ```mojo + var str = String(...) + var ptr = str.unsafe_ptr() + some_low_level_api(ptr) + _ = str^ # OLD HACK: Explicitly keep string alive until here! + ``` + + The `_ = str^` pattern was formerly required because the Mojo compiler has + no idea what "ptr" might reference. As a consequence, it had no idea that + `some_low_level_api()` might access `str` and therefore thought it was ok to + destroy the `String` before the call - this is why the explicit lifetime + extension was required. + + Mojo now knows that + [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) may + access the `MutableAnyOrigin` origin, and now assumes that any API that uses + that origin could use live values. In this case, it assumes that + `some_low_level_api()` might access `str` and because it might be using it, + it cannot destroy `str` until after the call. The consequence of this is + that the old hack is no longer needed for these cases! + + - Function types now accept an origin set parameter. This parameter represents + the origins of values captured by a parameter closure. The compiler + automatically tags parameter closures with the right set of origins. This + enables lifetimes and parameter closures to correctly compose. + + ```mojo + fn call_it[f: fn() capturing [_] -> None](): + f() + + fn test(): + var msg = String("hello world") + + @parameter + fn say_hi(): + print(msg) + + call_it[say_hi]() + # no longer need to write `_ = msg^`!! + ``` - Mojo now knows that `UnsafePointer` may access the `MutableAnyOrigin` origin, - and now assumes that any API that uses that origin could use live values. - In this case, it assumes that `some_low_level_api()` might access `str` and - because it might be using it, it cannot destroy `str` until after the call. - The consequence of this is that the old hack is no longer needed for these - cases! + Note that this only works for higher-order functions which have explicitly + added `[_]` as the capture origins. By default, the compiler still assumes + a `capturing` closure does not reference any origins. This will soon change. - Infer-only parameters may now be explicitly bound with keywords, enabling some important patterns in the standard library: @@ -243,6 +302,38 @@ detailed information in the following sections: fn take_imm_slice(a: ImmStringSlice): ... ``` +- The flag for turning on asserts has changed, e.g. to enable all checks: + + ```bash + mojo -D ASSERT=all main.mojo + ``` + + The levels are: + + - `none`: all assertions off + - `warn`: print assertion errors e.g. for multithreaded tests (previously `-D + ASSERT_WARNING`) + - `safe`: the default mode for standard CPU safety assertions + - `all`: turn on all assertions (previously `-D MOJO_ENABLE_ASSERTIONS`) + + You can now also pass `Stringable` args to format a message, which will have + no runtime penalty or IR bloat cost when assertions are off. Previously you + had to: + + ```mojo + x = -1 + debug_assert( + x > 0, String.format_sequence(“expected x to be more than 0 but got: ”, x) + ) + ``` + + Which can't be optimized away by the compiler in release builds, you can now + pass multiple args for a formatted message at no runtime cost: + + ```mojo + debug_assert(x > 0, “expected x to be more than 0 but got: ”, x) + ``` + - Automatic parameterization of parameters is now supported. Specifying a parameterized type with unbound parameters causes them to be implicitly added to the function signature as infer-only parameters. @@ -256,30 +347,6 @@ detailed information in the following sections: pass ``` -- Function types now accept an origin set parameter. This parameter represents - the origins of values captured by a parameter closure. The compiler - automatically tags parameter closures with the right set of origins. This - enables lifetimes and parameter closures to correctly compose. - - ```mojo - fn call_it[f: fn() capturing [_] -> None](): - f() - - fn test(): - var msg = String("hello world") - - @parameter - fn say_hi(): - print(msg) - - call_it[say_hi]() - # no longer need to write `_ = msg^`!! - ``` - - Note that this only works for higher-order functions which have explicitly - added `[_]` as the capture origins. By default, the compiler still assumes - a `capturing` closure does not reference any origins. This will soon change. - - Mojo can now interpret simple LLVM intrinsics in parameter expressions, enabling things like `count_leading_zeros` to work at compile time: [Issue #933](https://github.com/modularml/mojo/issues/933). @@ -295,102 +362,10 @@ detailed information in the following sections: ### Standard library changes {#24-6-standard-library-changes} -- Add the `Floatable` and `FloatableRaising` traits to denote types that can - be converted to a `Float64` value using the builtin `float` function. - - Make `SIMD` and `FloatLiteral` conform to the `Floatable` trait. - - ```mojo - fn foo[F: Floatable](v: F): - ... - - var f = float(Int32(45)) - ``` - - ([PR #3163](https://github.com/modularml/mojo/pull/3163)) - -- Add `DLHandle.get_symbol()`, for getting a pointer to a symbol in a dynamic - library. This is more general purpose than the existing methods for getting - function pointers. - -- Introduce `TypedPythonObject` as a light-weight way to annotate `PythonObject` - values with static type information. This design will likely evolve and - change significantly. - - - Added `TypedPythonObject["Tuple].__getitem__` for accessing the elements of - a Python tuple. - -- Added `Python.add_object()`, to add a named `PythonObject` value to a Python - 'module' object instance. - -- Added `Python.unsafe_get_python_exception()`, as an efficient low-level - utility to get the Mojo `Error` equivalent of the current CPython error state. - -- Add `PythonObject.from_borrowed_ptr()`, to simplify the construction of - `PythonObject` values from CPython 'borrowed reference' pointers. - - The existing `PythonObject.__init__(PyObjectPtr)` should continue to be used - for the more common case of constructing a `PythonObject` from a - 'strong reference' pointer. - -- The `rebind` standard library function now works with memory-only types in - addition to `@register_passable("trivial")` ones, without requiring a copy. - -- Introduce `random.shuffle` for `List`. - ([PR #3327](https://github.com/modularml/mojo/pull/3327)) - - Example: - - ```mojo - from random import shuffle - - var l = List[Int](1, 2, 3, 4, 5) - shuffle(l) - ``` - -- The `Dict.__getitem__` method now returns a reference instead of a copy of - the value (or raises). This improves the performance of common code that - uses `Dict` by allowing borrows from the `Dict` elements. - -- `Slice.step` is now an `Optional[Int]`, matching the optionality of - `slice.step` in Python. - ([PR #3160](https://github.com/modularml/mojo/pull/3160)) - -- `StringRef` now implements `split()` which can be used to split a - `StringRef` into a `List[StringRef]` by a delimiter. - ([PR #2705](https://github.com/modularml/mojo/pull/2705)) - -- Support for multi-dimensional indexing for `PythonObject` - ([PR #3583](https://github.com/modularml/mojo/pull/3583)). - -- Support for multi-dimensional indexing and slicing for `PythonObject` - (PR [#3549](https://github.com/modularml/mojo/pull/3549), - PR [#3583](https://github.com/modularml/mojo/pull/3583)). - - ```mojo - var np = Python.import_module("numpy") - var a = np.array(PythonObject([1,2,3,4,5,6])).reshape(2,3) - print((a[0, 1])) # 2 - print((a[1][::-1])) # [6 5 4] - ``` - - Note, that the syntax, `a[1, ::-1]`, is currently not supported. - -- There is now a [`Byte`](/mojo/stdlib/builtin/simd/Byte) alias to better - express intent when working with a pack of bits. - ([PR #3670](https://github.com/modularml/mojo/pull/3670)). - -- Expanded `os.path` with new functions (by [@thatstoasty](https://github.com/thatstoasty)): - - `os.path.expandvars`: Expands environment variables in a path ([PR #3735](https://github.com/modularml/mojo/pull/3735)). - - `os.path.splitroot`: Split a path into drive, root and tail. - ([PR #3780](https://github.com/modularml/mojo/pull/3780)). - -- Added a `reserve` method and new constructor to the `String` struct to - allocate additional capacity. - ([PR #3755](https://github.com/modularml/mojo/pull/3755)). - -- Introduced a new `Deque` (double-ended queue) collection type, based on a - dynamically resizing circular buffer for efficient O(1) additions and removals - at both ends as well as O(1) direct access to all elements. +- Introduced a new [`Deque`](/mojo/stdlib/collections/deque/Deque) (double-ended + queue) collection type, based on a dynamically resizing circular buffer for + efficient O(1) additions and removals at both ends as well as O(1) direct + access to all elements. The `Deque` supports the full Python `collections.deque` API, ensuring that all expected deque operations perform as in Python. @@ -401,92 +376,15 @@ detailed information in the following sections: memory allocation and performance. These options allow for optimized memory usage and reduced buffer reallocations, providing flexibility based on application requirements. -- A new `StringLiteral.get[some_stringable]()` method is available. It - allows forming a runtime-constant StringLiteral from a compile-time-dynamic - `Stringable` value. - -- `Span` now implements `__reversed__`. This means that one can get a - reverse iterator over a `Span` using `reversed(my_span)`. Users should - currently prefer this method over `my_span[::-1]`. - -- `StringSlice` now implements `strip`, `rstrip`, and `lstrip`. - -- `StringRef` is now representable so `repr(StringRef("hello"))` will return - `StringRef('hello')`. - -- The `UnsafePointer` type now has an `origin` parameter that can be used when - the `UnsafePointer` is known to point to a value with a known origin. This - origin is propagated through the `ptr[]` indirection operation. - -- You can now index into `UnsafePointer` using SIMD scalar integral types: - - ```mojo - p = UnsafePointer[Int].alloc(1) - i = UInt8(1) - p[i] = 42 - print(p[i]) - ``` - -- `UnsafePointer` parameters (other than the type) are now keyword-only. - -- Added a new [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) - type as a safe, single-owner, non-nullable smart pointer with similar - semantics to Rust's - [`Box<>`](https://doc.rust-lang.org/std/boxed/struct.Box.html) and C++'s - [`std::unique_ptr`](https://en.cppreference.com/w/cpp/memory/unique_ptr). - ([PR #3524](https://github.com/modularml/mojo/pull/3524)) - -- `Arc` has been renamed to [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer), - for consistency with `OwnedPointer`. - -- [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer) now implements - [`Identifiable`](/mojo/stdlib/builtin/identifiable/Identifiable), and can be - compared for pointer equivalence using `a is b`. - -- Added `PythonObject.__contains__`. - ([PR #3101](https://github.com/modularml/mojo/pull/3101)) - - Example usage: - - ```mojo - x = PythonObject([1,2,3]) - if 1 in x: - print("1 in x") - -- `Span` has moved from the `utils` module to the `memory` module. - - - More things have been removed from the auto-exported set of entities in the - `prelude` module from the Mojo standard library: - - `UnsafePointer` has been removed. Please explicitly import it via - `from memory import UnsafePointer`. - - `StringRef` has been removed. Please explicitly import it via - `from utils import StringRef`. - -- The `Reference` type has been renamed to `Pointer`: a memory safe complement - to `UnsafePointer`. This change is motivated by the fact that `Pointer` - is assignable and requires an explicit dereference with `ptr[]`. Renaming - to `Pointer` clarifies that "references" means `ref` arguments and results, - and gives us a model that is more similar to what the C++ community would - expect. - -- A new `as_noalias_ptr` method as been added to `UnsafePointer`. This method - specifies to the compiler that the resultant pointer is a distinct - identifiable object that does not alias any other memory in the local scope. - -- The `AnyLifetime` type (useful for declaring origin types as parameters) has - been renamed to `Origin`. - -- Restored implicit copyability of `Tuple` and `ListLiteral`. - -- The aliases for C FFI have been renamed: `C_int` -> `c_int`, `C_long` -> `c_long` - and so on. - -- The `Formatter` struct has changed to a `Writer` trait to enable buffered IO, +- The `Formatter` struct has been replaced with a + [`Writer`](/mojo/stdlib/utils/write/Writer) trait to enable buffered IO, increasing print and file writing perf to the same speed as C. It's now more general purpose and can write any `Span[Byte]`. To align with this the - `Formattable` trait is now named `Writable`, and the `String.format_sequence` - static methods to initialize a new `String` have been renamed to - `String.write`. Here's an example of using all the changes: + `Formattable` trait is now named + [`Writable`](/mojo/stdlib/utils/write/Writable), and the + `String.format_sequence()` static method to initialize a new `String` has been + renamed to [`String.write()`](/mojo/stdlib/collections/string/String/#write). + Here's an example of using all of the changes: ```mojo from memory import Span @@ -534,49 +432,239 @@ detailed information in the following sections: print(new_string) ``` + ```output Point(1, 2) Point(3, 4) + ``` + +- Python interop changes: + + - Introduced + [`TypedPythonObject`](/mojo/stdlib/python/python_object/TypedPythonObject) + as a light-weight way to annotate + [`PythonObject`](/mojo/stdlib/python/python_object/PythonObject) values with + static type information. This design will likely evolve and change + significantly. + + - Added `TypedPythonObject["Tuple].__getitem__()` for accessing the elements + of a Python tuple. + + - Added + [`Python.add_object()`](/mojo/stdlib/python/python/Python#add_object), to + add a named `PythonObject` value to a Python 'module' object instance. + + - Added + [`Python.unsafe_get_python_exception()`](/mojo/stdlib/python/python/Python#unsafe_get_python_exception), + as an efficient low-level utility to get the Mojo `Error` equivalent of the + current CPython error state. -- You can now use the `+=` and `*` operators on a `StringLiteral` at compile - time using the `alias` keyword: + - Add + [`PythonObject.from_borrowed_ptr()`](/mojo/stdlib/python/python_object/PythonObject#from_borrowed_ptr), + to simplify the construction of `PythonObject` values from CPython 'borrowed + reference' pointers. + + The existing `PythonObject.__init__(PyObjectPtr)` should continue to be used + for the more common case of constructing a `PythonObject` from a + 'strong reference' pointer. + + - Support for multi-dimensional indexing and slicing for `PythonObject` + (PR [#3549](https://github.com/modularml/mojo/pull/3549), + PR [#3583](https://github.com/modularml/mojo/pull/3583)). + + ```mojo + var np = Python.import_module("numpy") + var a = np.array(PythonObject([1,2,3,4,5,6])).reshape(2,3) + print((a[0, 1])) # 2 + print((a[1][::-1])) # [6 5 4] + ``` + + Note that the syntax, `a[1, ::-1]`, is currently not supported. + + - Added + [`PythonObject.__contains__()`](/mojo/stdlib/python/python_object/PythonObject#__contains__). + ([PR #3101](https://github.com/modularml/mojo/pull/3101)) + + Example usage: + + ```mojo + x = PythonObject([1,2,3]) + if 1 in x: + print("1 in x") + ``` + +- Pointer related changes: + + - The [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer) type + now has an `origin` parameter that can be used when the `UnsafePointer` + points to a value with a known origin. This origin is propagated through the + `ptr[]` indirection operation. This parameter and other `UnsafePointer` + parameters (other than the type) are now keyword-only. + + - You can now index into `UnsafePointer` using `SIMD` scalar integral types: + + ```mojo + p = UnsafePointer[Int].alloc(1) + i = UInt8(1) + p[i] = 42 + print(p[i]) + ``` + + - Added a new [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) + type as a safe, single-owner, non-nullable smart pointer with similar + semantics to Rust's + [`Box<>`](https://doc.rust-lang.org/std/boxed/struct.Box.html) and C++'s + [`std::unique_ptr`](https://en.cppreference.com/w/cpp/memory/unique_ptr). + ([PR #3524](https://github.com/modularml/mojo/pull/3524)) + + - `Arc` has been renamed to [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer), + for consistency with `OwnedPointer`. + + - [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer) now implements + [`Identifiable`](/mojo/stdlib/builtin/identifiable/Identifiable), and can be + compared for pointer equivalence using `a is b`. + + - The `Reference` type has been renamed to + [`Pointer`](/mojo/stdlib/memory/pointer/Pointer): a memory safe complement + to `UnsafePointer`. This change is motivated by the fact that `Pointer` is + assignable and requires an explicit dereference with `ptr[]`. Renaming to + `Pointer` clarifies that "references" means `ref` arguments and results, and + gives us a model that is more similar to what the C++ community would + expect. + + For an overview of Mojo's pointer types, see the new + [Intro to pointers](/mojo/manual/pointers/) page in the Mojo Manual. + + - A new + [`as_noalias_ptr()`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer#as_noalias_ptr) + method as been added to `UnsafePointer`. This method specifies to the + compiler that the resultant pointer is a distinct identifiable object that + does not alias any other memory in the local scope. + +- Added the [`Floatable`](/mojo/stdlib/builtin/floatable/Floatable) and + [`FloatableRaising`](/mojo/stdlib/builtin/floatable/FloatableRaising) traits to + denote types that can be converted to a `Float64` value using the builtin + `float` function. Made `SIMD` and `FloatLiteral` conform to the `Floatable` + trait. ([PR #3163](https://github.com/modularml/mojo/pull/3163)) ```mojo - alias original = "mojo" - alias concat = original * 3 - assert_equal("mojomojomojo", concat) + fn foo[F: Floatable](v: F): + ... + + var f = float(Int32(45)) ``` - Or inside a `fn` that is being evaluated at compile time: +- The [`rebind()`](/mojo/stdlib/builtin/rebind/rebind) standard library function + now works with memory-only types in addition to + `@register_passable("trivial")` ones, without requiring a copy. For more + information, see + [The `rebind()` builtin](/mojo/manual/parameters/#the-rebind-builtin) in the + Mojo Manual. - ```mojo - fn add_literal( - owned original: StringLiteral, add: StringLiteral, n: Int - ) -> StringLiteral: - for _ in range(n): - original += add - return original +- Introduced the [`random.shuffle()`](/mojo/stdlib/random/random/shuffle) + function for randomizing the elements of a `List`. + ([PR #3327](https://github.com/modularml/mojo/pull/3327)) + Example: - fn main(): - alias original = "mojo" - alias concat = add_literal(original, "!", 4) - assert_equal("mojo!!!!", concat) + ```mojo + from random import shuffle + + var l = List[Int](1, 2, 3, 4, 5) + shuffle(l) ``` - These operators can't be evaluated at runtime, as a `StringLiteral` must be - written into the binary during compilation. +- The [`Dict.__getitem__()`](/mojo/stdlib/collections/dict/Dict#__getitem__) + method now returns a reference instead of a copy of the value (or raises). + This improves the performance of common code that uses `Dict` by allowing + borrows from the `Dict` elements. + +- [`Slice.step`](/mojo/stdlib/builtin/builtin_slice/Slice#fields) is now an + `Optional[Int]`, matching the optionality of `slice.step` in Python. + ([PR #3160](https://github.com/modularml/mojo/pull/3160)) + +- There is now a [`Byte`](/mojo/stdlib/builtin/simd/#aliases) alias to better + express intent when working with a pack of bits. + ([PR #3670](https://github.com/modularml/mojo/pull/3670)). + +- Expanded [`os.path`](/mojo/stdlib/os/path/path/) with new functions: + - `os.path.expandvars()`: Expands environment variables in a path ([PR #3735](https://github.com/modularml/mojo/pull/3735)). + - `os.path.splitroot()`: Split a path into drive, root and tail. + ([PR #3780](https://github.com/modularml/mojo/pull/3780)). + +- Added a [`reserve()`](/mojo/stdlib/collections/string/String#reserve) method + and new constructor to the `String` struct to allocate additional capacity. + ([PR #3755](https://github.com/modularml/mojo/pull/3755)). + +- A new + [`StringLiteral.get[some_stringable]()`](/mojo/stdlib/builtin/string_literal/StringLiteral#get) + method is available. It allows forming a runtime-constant `StringLiteral` from + a compile-time-dynamic `Stringable` value. + +- [`Span`](/mojo/stdlib/memory/span/Span) has moved from the `utils` module to + the `memory` module. + +- [`Span`](/mojo/stdlib/memory/span/Span) now implements `__reversed__()`. This + means that one can get a reverse iterator over a `Span` using + `reversed(my_span)`. Users should currently prefer this method over + `my_span[::-1]`. + +- A new [`AsBytes`](/mojo/stdlib/memory/span/AsBytes) trait has been added to + enable taking a `Span[Byte]` from any type that implements `as_bytes()`. + `String.as_bytes()` and `String.as_bytes_slice()` have been consolidated under + `String.as_bytes()` to return a `Span[Byte]`. If you require a copy, you can + convert the `Span` to a `List` with `List(my_string.as_bytes())`. + +- [`StringSlice`](/mojo/stdlib/utils/string_slice/StringSlice) now implements + `strip()`, `rstrip()`, and `lstrip()`. + +- [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) now implements `split()` + which can be used to split a `StringRef` into a `List[StringRef]` by a + delimiter. ([PR #2705](https://github.com/modularml/mojo/pull/2705)) + +- [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) is now representable so + `repr(StringRef("hello"))` will return `StringRef('hello')`. + +- More things have been removed from the auto-exported set of entities in the + `prelude` module from the Mojo standard library: + - `UnsafePointer` has been removed. Please explicitly import it via + `from memory import UnsafePointer`. + - `StringRef` has been removed. Please explicitly import it via + `from utils import StringRef`. + +- Restored implicit copyability of [`Tuple`](/mojo/stdlib/builtin/tuple/Tuple) + and [`ListLiteral`](/mojo/stdlib/builtin/builtin_list/ListLiteral). + +- The + [aliases for C foreign function interface (FFI)](/mojo/stdlib/sys/ffi/#aliases) + have been renamed: `C_int` -> `c_int`, `C_long` -> `c_long` and so on. + +- `Float32` and `Float64` are now printed and converted to strings with + roundtrip guarantee and shortest representation: + + ```plaintext + Value Old New + Float64(0.3) 0.29999999999999999 0.3 + Float32(0.3) 0.30000001192092896 0.3 + Float64(0.0001) 0.0001 0.0001 + Float32(0.0001) 9.9999997473787516e-05 0.0001 + Float64(-0.00001) -1.0000000000000001e-05 -1e-05 + Float32(-0.00001) -9.9999997473787516e-06 -1e-05 + Float32(0.00001234) 1.2339999557298142e-05 1.234e-05 + Float32(-0.00000123456) -1.2345600453045336e-06 -1.23456e-06 + Float64(1.1234567e-320) 1.1235052786429946e-320 1.1235e-320 + Float64(1.234 * 10**16) 12340000000000000.0 1.234e+16 + ``` - The `StaticIntTuple` data structure in the `utils` package has been renamed to - `IndexList`. The data structure now allows one to specify the index bitwidth - of the elements along with whether the underlying indices are signed or - unsigned. + [`IndexList`](/mojo/stdlib/utils/index_/IndexList). The data structure now + allows one to specify the index bitwidth of the elements along with whether + the underlying indices are signed or unsigned. -- A new `AsBytes` trait has been added to enable taking a `Span[Byte]` of a - type with `s.as_bytes()`. `String.as_bytes` and `String.as_bytes_slice` have - been consolidated under `s.as_bytes` to return a `Span[Byte]`, you can convert - it to a `List` if you require a copy with `List(s.as_bytes())`. +- Added [`DLHandle.get_symbol()`](/mojo/stdlib/sys/ffi/DLHandle#get_symbol), for + getting a pointer to a symbol in a dynamic library. This is more general + purpose than the existing methods for getting function pointers. -### Tooling changes +### Tooling changes {#24-6-tooling-changes} - The VS Code Mojo Debugger now has a `buildArgs` JSON debug configuration setting that can be used in conjunction with `mojoFile` to define the build @@ -597,11 +685,37 @@ detailed information in the following sections: determining a default SDK to use. The user can select the default SDK to use with the `Mojo: Select the default MAX SDK` command. -- The VS Code extension now supports setting [data breakpoints](https://code.visualstudio.com/docs/editor/debugging#_data-breakpoints) - as well as [function breakpoints](https://code.visualstudio.com/docs/editor/debugging#_function-breakpoints). +- The VS Code extension now supports setting + [data breakpoints](https://code.visualstudio.com/docs/editor/debugging#_data-breakpoints) + as well as + [function breakpoints](https://code.visualstudio.com/docs/editor/debugging#_function-breakpoints). + +- The Mojo LLDB debugger now supports symbol breakpoints, for example, `b main` + or `b my_module::main`. + +- Error messages that include type names no longer include inferred or defaulted + parameters when they aren't needed. For example, previously Mojo complained + about things like: + + ```plaintext + ... cannot be converted from 'UnsafePointer[UInt, 0, _default_alignment::AnyType](), MutableAnyOrigin]' to 'UnsafePointer[Int, 0, _default_alignment[::AnyType](), MutableAnyOrigin]' + ``` + + it now complains more helpfully that: -- The Mojo LLDB debugger now supports symbol breakpoints, e.g. `b main` or - `b my_module::main`. + ```plaintext + ... cannot be converted from 'UnsafePointer[UInt]' to 'UnsafePointer[Int]' + ``` + +- Tooling now prints the origins of `ref` arguments and results correctly, and + prints `self` instead of `self: Self` in methods. + +- The Mojo Language Server and generated documentation now print parametric + result types correctly, e.g. showing `SIMD[type, simd_width]` instead of + `SIMD[$0, $1]`. + +- Generated API documentation now shows the signatures for structs, and + identifies `@register_passable` and `@register_passable("trivial")` types. - The VS Code extension now allows cancelling the installation of its private MAX SDK. @@ -619,59 +733,10 @@ detailed information in the following sections: - The VS Code extension now allows selecting a default SDK when multiple are available. -- The flag for turning on asserts has changed, e.g. to enable all checks: - - ```bash - mojo -D ASSERT=all main.mojo - ``` - - The levels are: - - - `none`: all assertions off - - `warn`: print assertion errors e.g. for multithreaded tests (previously `-D - ASSERT_WARNING`) - - `safe`: the default mode for standard CPU safety assertions - - `all`: turn on all assertions (previously `-D MOJO_ENABLE_ASSERTIONS`) - - You can now also pass `Stringable` args to format a message, which will have - no runtime penalty or IR bloat cost when assertions are off. Previously you - had to: - - ```mojo - x = -1 - debug_assert( - x > 0, String.format_sequence(“expected x to be more than 0 but got: ”, x) - ) - ``` - - Which can't be optimized away by the compiler in release builds, you can now - pass multiple args for a formatted message at no runtime cost: - - ```mojo - debug_assert(x > 0, “expected x to be more than 0 but got: ”, x) - ``` - -- Float32 and Float64 are now printed and converted to strings with roundtrip - guarantee and shortest representation: - - ```plaintext - Value Old New - Float64(0.3) 0.29999999999999999 0.3 - Float32(0.3) 0.30000001192092896 0.3 - Float64(0.0001) 0.0001 0.0001 - Float32(0.0001) 9.9999997473787516e-05 0.0001 - Float64(-0.00001) -1.0000000000000001e-05 -1e-05 - Float32(-0.00001) -9.9999997473787516e-06 -1e-05 - Float32(0.00001234) 1.2339999557298142e-05 1.234e-05 - Float32(-0.00000123456) -1.2345600453045336e-06 -1.23456e-06 - Float64(1.1234567e-320) 1.1235052786429946e-320 1.1235e-320 - Float64(1.234 * 10**16) 12340000000000000.0 1.234e+16 - ``` - ### ❌ Removed -- The `UnsafePointer.bitcast` overload for `DType` has been removed. Wrap your - `DType` in a `Scalar[my_dtype]` to call the only overload of `bitcast` now. +- The `UnsafePointer.bitcast()` overload for `DType` has been removed. Wrap your + `DType` in a `Scalar[my_dtype]` to call the only overload of `bitcast()` now. ### 🛠️ Fixed @@ -719,31 +784,11 @@ detailed information in the following sections: - The variadic initializer for `SIMD` now works in parameter expressions. - The VS Code extension now downloads its private copy of the MAX SDK in a way - that prevents ETXTBSY errors on Linux. + that prevents `ETXTBSY` errors on Linux. - The VS Code extension now allows invoking a mojo formatter from SDK installations that contain white spaces in their path. -- Error messages that include type names no longer include inferred or defaulted - parameters when they aren't needed. For example, previously Mojo complained - about things like: - - ```plaintext - ... cannot be converted from 'UnsafePointer[UInt, 0, _default_alignment::AnyType](), MutableAnyOrigin]' to 'UnsafePointer[Int, 0, _default_alignment[::AnyType](), MutableAnyOrigin]' - ``` - - it now complains more helpfully that: - - ```plaintext - ... cannot be converted from 'UnsafePointer[UInt]' to 'UnsafePointer[Int]' - ``` - -- Tooling now prints the origins of `ref` arguments and results correctly, and - prints `self` instead of `self: Self` in methods. - -- The LSP and generated documentation now print parametric result types - correctly, e.g. showing `SIMD[type, simd_width]` instead of `SIMD[$0, $1]`. - ### Special thanks Special thanks to our community contributors: From 9557816183c981704dbaf49c11795d095e60282d Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 13 Dec 2024 14:36:06 -0600 Subject: [PATCH 093/509] [stdlib] cleanup: Rename `UnsafePointer.is_mutable` to `mut` + changelog recent ptr origin improvements MODULAR_ORIG_COMMIT_REV_ID: ea8c7e5271924305c3397eb483d31ca19886a4a1 --- docs/changelog.md | 40 +++++++++++++++++ stdlib/src/builtin/string_literal.mojo | 8 ++-- stdlib/src/collections/list.mojo | 2 +- stdlib/src/collections/string.mojo | 2 +- stdlib/src/memory/memory.mojo | 2 +- stdlib/src/memory/span.mojo | 6 +-- stdlib/src/memory/unsafe_pointer.mojo | 50 ++++++++++------------ stdlib/src/random/random.mojo | 2 +- stdlib/src/utils/string_slice.mojo | 2 +- stdlib/test/collections/test_string.mojo | 2 +- stdlib/test/memory/test_unsafepointer.mojo | 6 +-- 11 files changed, 77 insertions(+), 45 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 27f3f08ff6..4846564f3b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -22,6 +22,46 @@ what we publish. ### Standard library changes +- `UnsafePointer` is now parameterized on mutability. Previously, + `UnsafePointer` could only represent mutable pointers. + + The new `mut` parameter can be used to restrict an `UnsafePointer` to a + specific mutability: `UnsafePointer[T, mut=False]` represents a pointer to + an immutable `T` value. This is analogous to a `const *` pointer in C++. + + - `UnsafePointer.address_of()` will now infer the origin and mutability + of the resulting pointer from the argument. For example: + + ```mojo + var local = 10 + # Constructs a mutable pointer, because `local` is a mutable memory location + var ptr = UnsafePointer.address_of(local) + ``` + + To force the construction of an immutable pointer to an otherwise mutable + memory location, use a cast: + + ```mojo + var local = 10 + # Cast the mutable pointer to be immutable. + var ptr = UnsafePointer.address_of(local).bitcast[mut=False]() + ``` + + - The `unsafe_ptr()` method on several standard library collection types have + been updated to use parametric mutability: they will return an `UnsafePointer` + whose mutability is inherited from the mutability of the `ref self` of the + receiver at the call site. For example, `ptr1` will be immutable, while + `ptr2` will be mutable: + + ```mojo + fn take_lists(read list1: List[Int], mut list2: List[Int]): + # Immutable pointer, since receiver is immutable `read` reference + var ptr1 = list1.unsafe_ptr() + + # Mutable pointer, since receiver is mutable `mut` reference + var ptr2 = list2.unsafe_ptr() + ``` + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 727ac65555..581e21de5e 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -487,7 +487,7 @@ struct StringLiteral( @always_inline("nodebug") fn unsafe_ptr( self, - ) -> UnsafePointer[Byte, is_mutable=False, origin=StaticConstantOrigin]: + ) -> UnsafePointer[Byte, mut=False, origin=StaticConstantOrigin]: """Get raw pointer to the underlying data. Returns: @@ -498,14 +498,12 @@ struct StringLiteral( # TODO(MSTDL-555): # Remove bitcast after changing pop.string.address # return type. - return ptr.bitcast[ - Byte, is_mutable=False, origin=StaticConstantOrigin - ]() + return ptr.bitcast[Byte, mut=False, origin=StaticConstantOrigin]() @always_inline fn unsafe_cstr_ptr( self, - ) -> UnsafePointer[c_char, is_mutable=False, origin=StaticConstantOrigin]: + ) -> UnsafePointer[c_char, mut=False, origin=StaticConstantOrigin]: """Retrieves a C-string-compatible pointer to the underlying memory. The returned pointer is guaranteed to be NUL terminated, and not null. diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index a3a64643a3..f0bc3d0c6a 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -927,7 +927,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( ref self, ) -> UnsafePointer[ T, - is_mutable = Origin(__origin_of(self)).is_mutable, + mut = Origin(__origin_of(self)).is_mutable, origin = __origin_of(self), ]: """Retrieves a pointer to the underlying memory. diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 716ddc9756..06cd2afeb5 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -1598,7 +1598,7 @@ struct String( ref self, ) -> UnsafePointer[ Byte, - is_mutable = Origin(__origin_of(self)).is_mutable, + mut = Origin(__origin_of(self)).is_mutable, origin = __origin_of(self), ]: """Retrieves a pointer to the underlying memory. diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index ed72524482..574c13af04 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -153,7 +153,7 @@ fn memcmp[ @always_inline fn _memcpy_impl( - dest_data: UnsafePointer[Byte, is_mutable=True, **_], + dest_data: UnsafePointer[Byte, mut=True, **_], src_data: __type_of(dest_data), n: Int, ): diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 03f860f899..0d9ea2f811 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -107,7 +107,7 @@ struct Span[ """ # Field - var _data: UnsafePointer[T, is_mutable=is_mutable, origin=origin] + var _data: UnsafePointer[T, mut=is_mutable, origin=origin] var _len: Int # ===------------------------------------------------------------------===# @@ -250,9 +250,7 @@ struct Span[ # Methods # ===------------------------------------------------------------------===# - fn unsafe_ptr( - self, - ) -> UnsafePointer[T, is_mutable=is_mutable, origin=origin]: + fn unsafe_ptr(self) -> UnsafePointer[T, mut=is_mutable, origin=origin]: """Retrieves a pointer to the underlying memory. Returns: diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index a5feb34736..e6e3809b64 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -55,10 +55,8 @@ struct UnsafePointer[ *, address_space: AddressSpace = AddressSpace.GENERIC, alignment: Int = _default_alignment[type](), - is_mutable: Bool = True, - origin: Origin[is_mutable] = Origin[is_mutable] - .cast_from[MutableAnyOrigin] - .result, + mut: Bool = True, + origin: Origin[mut] = Origin[mut].cast_from[MutableAnyOrigin].result, ]( ImplicitlyBoolable, CollectionElement, @@ -84,7 +82,7 @@ struct UnsafePointer[ type: The type the pointer points to. address_space: The address space associated with the UnsafePointer allocated memory. alignment: The minimum alignment of this pointer known statically. - is_mutable: Whether the origin is mutable. + mut: Whether the origin is mutable. origin: The origin of the memory being addressed. """ @@ -164,7 +162,7 @@ struct UnsafePointer[ type, address_space=address_space, alignment=1, - is_mutable = Origin(__origin_of(arg)).is_mutable, + mut = Origin(__origin_of(arg)).is_mutable, origin = __origin_of(arg), ], ): @@ -638,7 +636,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() self.offset(offset)._store[alignment=alignment, volatile=volatile](val) @always_inline @@ -671,7 +669,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() self.offset(offset).store[alignment=alignment, volatile=volatile](val) @always_inline @@ -701,7 +699,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() constrained[offset_type.is_integral(), "offset must be integer"]() self.offset(int(offset))._store[alignment=alignment, volatile=volatile]( val @@ -736,7 +734,7 @@ struct UnsafePointer[ offset: The offset to store to. val: The value to store. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() constrained[offset_type.is_integral(), "offset must be integer"]() self.offset(int(offset))._store[alignment=alignment, volatile=volatile]( val @@ -762,7 +760,7 @@ struct UnsafePointer[ Args: val: The value to store. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() self._store[alignment=alignment, volatile=volatile](val) @always_inline("nodebug") @@ -787,7 +785,7 @@ struct UnsafePointer[ Args: val: The value to store. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() self._store[alignment=alignment, volatile=volatile](val) @always_inline("nodebug") @@ -798,7 +796,7 @@ struct UnsafePointer[ alignment: Int = _default_alignment[type, width](), volatile: Bool = False, ](self: UnsafePointer[Scalar[type], **_], val: SIMD[type, width]): - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() constrained[width > 0, "width must be a positive integer value"]() constrained[ alignment > 0, "alignment must be a positive integer value" @@ -854,7 +852,7 @@ struct UnsafePointer[ val: The SIMD value to store. stride: The stride between stores. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() strided_store(val, self, int(stride), True) @always_inline("nodebug") @@ -954,7 +952,7 @@ struct UnsafePointer[ mask: The SIMD vector of boolean values, indicating for each element whether to store at memory or not. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() constrained[ offset.type.is_integral(), "offset type must be an integral type", @@ -979,15 +977,13 @@ struct UnsafePointer[ address_space: AddressSpace = Self.address_space, alignment: Int = Self.alignment, *, - is_mutable: Bool = Self.is_mutable, - origin: Origin[is_mutable] = Origin[is_mutable] - .cast_from[Self.origin] - .result, + mut: Bool = Self.mut, + origin: Origin[mut] = Origin[mut].cast_from[Self.origin].result, ](self) -> UnsafePointer[ T, address_space=address_space, alignment=alignment, - is_mutable=is_mutable, + mut=mut, origin=origin, ]: """Bitcasts a UnsafePointer to a different type. @@ -996,7 +992,7 @@ struct UnsafePointer[ T: The target type. address_space: The address space of the result. alignment: Alignment of the destination pointer. - is_mutable: Whether the origin is mutable. + mut: Whether the origin is mutable. origin: Origin of the destination pointer. Returns: @@ -1021,7 +1017,7 @@ struct UnsafePointer[ more efficient because it doesn't invoke `__moveinit__`. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() _ = __get_address_as_owned_value(self.address) @always_inline @@ -1044,7 +1040,7 @@ struct UnsafePointer[ Returns: The value at the pointer. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() return __get_address_as_owned_value(self.address) # TODO: Allow overloading on more specific traits @@ -1071,7 +1067,7 @@ struct UnsafePointer[ Args: value: The value to emplace. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() __get_address_as_uninit_lvalue(self.address) = value^ @always_inline @@ -1097,7 +1093,7 @@ struct UnsafePointer[ Args: value: The value to emplace. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() __get_address_as_uninit_lvalue(self.address) = value @always_inline @@ -1124,7 +1120,7 @@ struct UnsafePointer[ Args: value: The value to emplace. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() __get_address_as_uninit_lvalue(self.address) = T(other=value) @always_inline @@ -1159,7 +1155,7 @@ struct UnsafePointer[ Args: dst: Destination pointer that the value will be moved into. """ - constrained[is_mutable, _must_be_mut_err]() + constrained[mut, _must_be_mut_err]() __get_address_as_uninit_lvalue( dst.address ) = __get_address_as_owned_value(self.address) diff --git a/stdlib/src/random/random.mojo b/stdlib/src/random/random.mojo index e64ec3373b..03d2f57275 100644 --- a/stdlib/src/random/random.mojo +++ b/stdlib/src/random/random.mojo @@ -122,7 +122,7 @@ fn randint[ fn rand[ type: DType ]( - ptr: UnsafePointer[Scalar[type], is_mutable=True, **_], + ptr: UnsafePointer[Scalar[type], mut=True, **_], size: Int, /, *, diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 64fbc94437..c465811f1b 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -765,7 +765,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( @always_inline fn unsafe_ptr( self, - ) -> UnsafePointer[Byte, is_mutable=mut, origin=origin]: + ) -> UnsafePointer[Byte, mut=mut, origin=origin]: """Gets a pointer to the first element of this string slice. Returns: diff --git a/stdlib/test/collections/test_string.mojo b/stdlib/test/collections/test_string.mojo index ff5da33ee7..4d9151b279 100644 --- a/stdlib/test/collections/test_string.mojo +++ b/stdlib/test/collections/test_string.mojo @@ -1263,7 +1263,7 @@ def test_string_iter(): assert_equal(321, atol(concat)) for v in vs: - v.unsafe_ptr().bitcast[is_mutable=True]()[] = ord("1") + v.unsafe_ptr().bitcast[mut=True]()[] = ord("1") # Borrow immutably for v in vs: diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 3331764e47..168ffc7bdf 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -150,15 +150,15 @@ def test_unsafepointer_string(): def test_eq(): var local = 1 - var p1 = UnsafePointer.address_of(local).bitcast[is_mutable=False]() + var p1 = UnsafePointer.address_of(local).bitcast[mut=False]() var p2 = p1 assert_equal(p1, p2) var other_local = 2 - var p3 = UnsafePointer.address_of(other_local).bitcast[is_mutable=False]() + var p3 = UnsafePointer.address_of(other_local).bitcast[mut=False]() assert_not_equal(p1, p3) - var p4 = UnsafePointer.address_of(local).bitcast[is_mutable=False]() + var p4 = UnsafePointer.address_of(local).bitcast[mut=False]() assert_equal(p1, p4) _ = local _ = other_local From 597e07b6441b28a408985388b88f512b02818350 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 13 Dec 2024 15:15:45 -0600 Subject: [PATCH 094/509] [stdlib] polish: Rename `Span.is_mutable` to `mut` MODULAR_ORIG_COMMIT_REV_ID: c6efdc7bf6a5d3c8bc0a1da04b7b563a2b31980a --- stdlib/src/memory/span.mojo | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 0d9ea2f811..66107ea66d 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -45,15 +45,15 @@ trait AsBytes: @value struct _SpanIter[ - is_mutable: Bool, //, + mut: Bool, //, T: CollectionElement, - origin: Origin[is_mutable], + origin: Origin[mut], forward: Bool = True, ]: """Iterator for Span. Parameters: - is_mutable: Whether the reference to the span is mutable. + mut: Whether the reference to the span is mutable. T: The type of the elements in the span. origin: The origin of the Span. forward: The iteration direction. `False` is backwards. @@ -94,20 +94,20 @@ struct _SpanIter[ @value @register_passable("trivial") struct Span[ - is_mutable: Bool, //, + mut: Bool, //, T: CollectionElement, - origin: Origin[is_mutable], + origin: Origin[mut], ](CollectionElementNew): """A non owning view of contiguous data. Parameters: - is_mutable: Whether the span is mutable. + mut: Whether the span is mutable. T: The type of the elements in the span. origin: The origin of the Span. """ # Field - var _data: UnsafePointer[T, mut=is_mutable, origin=origin] + var _data: UnsafePointer[T, mut=mut, origin=origin] var _len: Int # ===------------------------------------------------------------------===# @@ -250,7 +250,7 @@ struct Span[ # Methods # ===------------------------------------------------------------------===# - fn unsafe_ptr(self) -> UnsafePointer[T, mut=is_mutable, origin=origin]: + fn unsafe_ptr(self) -> UnsafePointer[T, mut=mut, origin=origin]: """Retrieves a pointer to the underlying memory. Returns: From c61a7ad6e914ab2d8757cb8b60ed78237c91ac15 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Fri, 13 Dec 2024 15:50:33 -0600 Subject: [PATCH 095/509] [External] [stdlib] Add `Span[Scalar[type]].__contains__()` (#52752) MODULAR_ORIG_COMMIT_REV_ID: cae378ed6401e0c400fc4290cbba46227d361daf --- stdlib/src/memory/span.mojo | 37 +++++++++++++++++++++++++++++++ stdlib/test/memory/test_span.mojo | 10 +++++++++ 2 files changed, 47 insertions(+) diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 66107ea66d..fcdf2d7aa9 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -23,6 +23,7 @@ from memory import Span from collections import InlineArray from memory import Pointer, UnsafePointer +from sys.info import simdwidthof trait AsBytes: @@ -246,6 +247,42 @@ struct Span[ """ return self._len + fn __contains__[ + type: DType, // + ](self: Span[Scalar[type]], value: Scalar[type]) -> Bool: + """Verify if a given value is present in the Span. + + Parameters: + type: The DType of the scalars stored in the Span. + + Args: + value: The value to find. + + Returns: + True if the value is contained in the list, False otherwise. + """ + + alias widths = InlineArray[Int, 6](256, 128, 64, 32, 16, 8) + var ptr = self.unsafe_ptr() + var length = len(self) + var processed = 0 + + @parameter + for i in range(len(widths)): + alias width = widths[i] + + @parameter + if simdwidthof[type]() >= width: + for _ in range((length - processed) // width): + if value in (ptr + processed).load[width=width](): + return True + processed += width + + for i in range(length - processed): + if ptr[processed + i] == value: + return True + return False + # ===------------------------------------------------------------------===# # Methods # ===------------------------------------------------------------------===# diff --git a/stdlib/test/memory/test_span.mojo b/stdlib/test/memory/test_span.mojo index 92c49210c6..4a3b6dd980 100644 --- a/stdlib/test/memory/test_span.mojo +++ b/stdlib/test/memory/test_span.mojo @@ -156,6 +156,15 @@ def test_bool(): assert_true(not s[0:0]) +def test_contains(): + items = List[Byte](1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15) + span = Span(items) + assert_true(0 not in span) + assert_true(16 not in span) + for item in items: + assert_true(item[] in span) + + def test_equality(): var l = InlineArray[String, 7]("a", "b", "c", "d", "e", "f", "g") var l2 = List[String]("a", "b", "c", "d", "e", "f", "g") @@ -208,6 +217,7 @@ def main(): test_span_slice() test_equality() test_bool() + test_contains() test_fill() test_ref() test_reversed() From 08a874dfaf09166213ef81ff7a5d4ec5eb56b5b8 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Fri, 13 Dec 2024 17:16:57 -0600 Subject: [PATCH 096/509] [External] [stdlib] Remove `StringRef` starts and endswith (#52785) [External] [stdlib] Remove `StringRef` starts and endswith Remove `StringRef` `startswith` and `endswith`. We already have this in `StringSlice`. Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3867 MODULAR_ORIG_COMMIT_REV_ID: 6e510ad4bd401110aa4cb1172f3c4c60dfef2628 --- docs/changelog.md | 3 ++ stdlib/src/utils/stringref.mojo | 40 ---------------------- stdlib/test/utils/test_string_slice.mojo | 42 ++++++++++++++++++++++++ stdlib/test/utils/test_stringref.mojo | 21 ------------ 4 files changed, 45 insertions(+), 61 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 4846564f3b..6babee4c83 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -73,6 +73,9 @@ what we publish. ### ❌ Removed +- `StringRef` is being deprecated. Use `StringSlice` instead. + - removed `StringRef.startswith()` and `StringRef.endswith()` + ### 🛠️ Fixed - The Mojo Kernel for Jupyter Notebooks is working again on nightly releases. diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 75e864b405..4e92e96848 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -650,46 +650,6 @@ struct StringRef( current_offset = loc + len(delimiter) return output - fn startswith( - self, prefix: StringRef, start: Int = 0, end: Int = -1 - ) -> Bool: - """Checks if the StringRef starts with the specified prefix between start - and end positions. Returns True if found and False otherwise. - - Args: - prefix: The prefix to check. - start: The start offset from which to check. - end: The end offset from which to check. - - Returns: - True if the self[start:end] is prefixed by the input prefix. - """ - if end == -1: - return self.find(prefix, start) == start - return StringRef(self.unsafe_ptr() + start, end - start).startswith( - prefix - ) - - fn endswith(self, suffix: StringRef, start: Int = 0, end: Int = -1) -> Bool: - """Checks if the StringRef end with the specified suffix between start - and end positions. Returns True if found and False otherwise. - - Args: - suffix: The suffix to check. - start: The start offset from which to check. - end: The end offset from which to check. - - Returns: - True if the self[start:end] is suffixed by the input suffix. - """ - if len(suffix) > len(self): - return False - if end == -1: - return self.rfind(suffix, start) + len(suffix) == len(self) - return StringRef(self.unsafe_ptr() + start, end - start).endswith( - suffix - ) - # ===-----------------------------------------------------------------------===# # Utilities diff --git a/stdlib/test/utils/test_string_slice.mojo b/stdlib/test/utils/test_string_slice.mojo index dfeb37b12f..f88ed32dbb 100644 --- a/stdlib/test/utils/test_string_slice.mojo +++ b/stdlib/test/utils/test_string_slice.mojo @@ -584,6 +584,46 @@ def test_strip(): assert_true(comp_str4_stripped == "\n mississippimississippi \n") +def test_startswith(): + var empty = StringSlice("") + assert_true(empty.startswith("")) + assert_false(empty.startswith("a")) + assert_false(empty.startswith("ab")) + + var a = StringSlice("a") + assert_true(a.startswith("")) + assert_true(a.startswith("a")) + assert_false(a.startswith("ab")) + + var ab = StringSlice("ab") + assert_true(ab.startswith("")) + assert_true(ab.startswith("a")) + assert_false(ab.startswith("b")) + assert_true(ab.startswith("b", start=1)) + assert_true(ab.startswith("a", end=1)) + assert_true(ab.startswith("ab")) + + +def test_endswith(): + var empty = StringSlice("") + assert_true(empty.endswith("")) + assert_false(empty.endswith("a")) + assert_false(empty.endswith("ab")) + + var a = StringSlice("a") + assert_true(a.endswith("")) + assert_true(a.endswith("a")) + assert_false(a.endswith("ab")) + + var ab = StringSlice("ab") + assert_true(ab.endswith("")) + assert_false(ab.endswith("a")) + assert_true(ab.endswith("b")) + assert_true(ab.endswith("b", start=1)) + assert_true(ab.endswith("a", end=1)) + assert_true(ab.endswith("ab")) + + def main(): test_string_literal_byte_span() test_string_byte_span() @@ -605,3 +645,5 @@ def main(): test_rstrip() test_lstrip() test_strip() + test_startswith() + test_endswith() diff --git a/stdlib/test/utils/test_stringref.mojo b/stdlib/test/utils/test_stringref.mojo index 0dc7686197..67965d22a4 100644 --- a/stdlib/test/utils/test_stringref.mojo +++ b/stdlib/test/utils/test_stringref.mojo @@ -108,26 +108,6 @@ def test_find(): assert_equal(StringRef("").find("abc"), -1) -def test_endswith(): - var empty = StringRef("") - assert_true(empty.endswith("")) - assert_false(empty.endswith("a")) - assert_false(empty.endswith("ab")) - - var a = StringRef("a") - assert_true(a.endswith("")) - assert_true(a.endswith("a")) - assert_false(a.endswith("ab")) - - var ab = StringRef("ab") - assert_true(ab.endswith("")) - assert_false(ab.endswith("a")) - assert_true(ab.endswith("b")) - assert_true(ab.endswith("b", start=1)) - assert_true(ab.endswith("a", end=1)) - assert_true(ab.endswith("ab")) - - fn test_stringref_split() raises: # Reject empty delimiters with assert_raises( @@ -195,5 +175,4 @@ def main(): test_intable() test_indexing() test_find() - test_endswith() test_str_and_ref() From 9f4544a9cbb6b023ab907b6942ba1c21a049a534 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 14 Dec 2024 19:15:22 +0000 Subject: [PATCH 097/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024121405 --- examples/life/magic.lock | 979 ++++++++++++++++--------------- examples/magic.lock | 979 ++++++++++++++++--------------- examples/notebooks/magic.lock | 1019 ++++++++++++++++----------------- examples/operators/magic.lock | 979 ++++++++++++++++--------------- magic.lock | 979 ++++++++++++++++--------------- 5 files changed, 2455 insertions(+), 2480 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index fa908ca728..c9b212466d 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -17,19 +17,19 @@ environments: - 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sha256: 8cc37e827f0098d07743f57f968283cefce6c11562d9241aba990acc23aedb56 - md5: deabf8afc8d987f20174ef0d8d9b549e + url: https://conda.anaconda.org/conda-forge/noarch/pydantic-settings-2.7.0-pyh3cfb1c2_0.conda + sha256: dd1ac7c8b6a189c8aa18f6c7df019d8f6df495300a259e3fbebdb542fc955c3b + md5: d9f19a7c4199249fa229891b573b6f9b depends: - pydantic >=2.7.0 - python >=3.9 - python-dotenv >=0.21.0 license: MIT - license_family: MIT - size: 30832 - timestamp: 1733851937909 + size: 31426 + timestamp: 1734127929720 - kind: conda name: pygments version: 2.18.0 From 8882e1b507cff885d4a70aaeb286919cfa1d6f48 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 14 Dec 2024 12:13:14 -0800 Subject: [PATCH 098/509] [mojo-lang][LowerSemanticCF] Fix a bug with for/else lowering This changes for/else lowering to not multiply lower the 'else' block. SemanticCF lowering cannot run on pre-lowered operations, e.g. it will hork on pre-lowered finalies. Handle this by dancing around the IR transformation a bit more carefully. This fixes MOCO-1475 and fixes https://github.com/modularml/mojo/issues/3796 MODULAR_ORIG_COMMIT_REV_ID: 521dae77147f52a48d794ca20ff6047a498983cc --- docs/changelog.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 6babee4c83..15a2b2a1e0 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -83,5 +83,8 @@ what we publish. - The command `mojo debug --vscode` now sets the current working directory properly. +- [Issue #3796](https://github.com/modularml/mojo/issues/3796) - Compiler crash + handling for-else statement. + - The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. [Issue #3826](https://github.com/modularml/mojo/issues/3826). From 000c0a091f409f3da83bce0a4feb46cfa5d0fd4a Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 14 Dec 2024 20:43:53 -0800 Subject: [PATCH 099/509] [mojo-stdlib] Modernize initializer calls. initializers return 'self', they don't take self as an argument. Modernize all calls to it, in preparation for making our behavior more strict. MODULAR_ORIG_COMMIT_REV_ID: a1ef5ff41abc46cbc1d8e4b7be9c983409477846 --- stdlib/src/builtin/file.mojo | 2 +- stdlib/src/collections/optional.mojo | 2 +- stdlib/src/collections/set.mojo | 4 ++-- stdlib/src/memory/owned_pointer.mojo | 2 +- stdlib/src/python/_bindings.mojo | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index f5f4e6e5f6..1692a3458d 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -85,7 +85,7 @@ struct FileHandle: path: The file path. mode: The mode to open the file in (the mode can be "r" or "w" or "rw"). """ - self.__init__(path.as_string_slice(), mode.as_string_slice()) + self = Self(path.as_string_slice(), mode.as_string_slice()) fn __init__(out self, path: StringSlice, mode: StringSlice) raises: """Construct the FileHandle using the file path and string. diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index 73518b55a5..0e318980e9 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -130,7 +130,7 @@ struct Optional[T: CollectionElement]( Args: other: The Optional to copy. """ - self.__copyinit__(other) + self = other # ===-------------------------------------------------------------------===# # Operator dunders diff --git a/stdlib/src/collections/set.mojo b/stdlib/src/collections/set.mojo index 9c017dbfe8..0defaa9f51 100644 --- a/stdlib/src/collections/set.mojo +++ b/stdlib/src/collections/set.mojo @@ -72,7 +72,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): Args: elements: An existing set to copy. """ - self.__init__() + self = Self() for e in elements: self.add(e[]) @@ -83,7 +83,7 @@ struct Set[T: KeyElement](Sized, Comparable, Hashable, Boolable): Args: elements: A vector of elements to add to the set. """ - self.__init__() + self = Self() for e in elements: self.add(e[]) diff --git a/stdlib/src/memory/owned_pointer.mojo b/stdlib/src/memory/owned_pointer.mojo index 4dd473023c..0eb0c33a3c 100644 --- a/stdlib/src/memory/owned_pointer.mojo +++ b/stdlib/src/memory/owned_pointer.mojo @@ -86,7 +86,7 @@ struct OwnedPointer[T: AnyType]: Args: other: The OwnedPointer[] to copy. """ - self.__init__(copy_value=other[]) + self = OwnedPointer[T](copy_value=other[]) fn __moveinit__(out self, owned existing: Self): """Move this OwnedPointer[]. diff --git a/stdlib/src/python/_bindings.mojo b/stdlib/src/python/_bindings.mojo index f5e14cd329..2d0156aa70 100644 --- a/stdlib/src/python/_bindings.mojo +++ b/stdlib/src/python/_bindings.mojo @@ -174,7 +174,7 @@ fn empty_tp_init_wrapper[ # ------------------------------------------------ # TODO(MSTDL-950): Avoid forming ref through uninit pointee. - T.__init__(obj_ptr[]) + obj_ptr[] = T() return 0 except e: From cdeaa479aa8f52d2822732cd7b73d08a6dbb78a3 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 15 Dec 2024 16:19:46 -0800 Subject: [PATCH 100/509] [mojo-stdlib] Modernize init calls. Initializers are moving to their out parameter being a result instead of begin a weird mutating argument. Upgrade soon-to-be invalid syntax to something that has always worked. MODULAR_ORIG_COMMIT_REV_ID: 778269b541364e3264c5dc4afbc526c3fef63a18 --- stdlib/src/builtin/simd.mojo | 2 +- stdlib/src/python/python_object.mojo | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 7bb4c061b6..0f2508229e 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -304,7 +304,7 @@ struct SIMD[type: DType, size: Int]( # Args: # other: The value to copy. # """ - # self.__copyinit__(other) + # self = other @always_inline("nodebug") @implicit diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index 42b2dc6bed..5fb1847f8f 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -252,7 +252,7 @@ struct PythonObject( fn __init__(out self): """Initialize the object with a `None` value.""" - self.__init__(None) + self = Self(None) fn __init__(out self, *, other: Self): """Copy the object. From c5c4943cbf92707fcd3290da39ea089aa010962a Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 15 Dec 2024 16:24:39 -0800 Subject: [PATCH 101/509] [mojo-stdlib] Remove pointless code. This removes some code that is problematic with other changes that looks like it is dead. `x|y|1` is always non-zero so the code in the "then" block isn't reachable. I filed MSTDL-1064 to investigate this. MODULAR_ORIG_COMMIT_REV_ID: f091dcf225cf78256f6d218893123f43c73ecb97 --- stdlib/src/builtin/_format_float.mojo | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index 95d7e8a30e..bbbc41f239 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -317,15 +317,8 @@ fn _to_decimal[ while True: if r < deltai: - # Exclude the right endpoint if necessary - if ( - r - | Scalar[CarrierDType](not z_result.is_integer) - | Scalar[CarrierDType](1) - ) == 0: - sig -= 1 - r = FP[type].big_divisor - break + # TODO(MSTDL-1064): Exclude the right endpoint if necessary + pass elif r > deltai: break else: From 6c7310300373b81578f9a6808a528879d4fdbbc3 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Mon, 16 Dec 2024 11:56:07 -0800 Subject: [PATCH 102/509] [Docs] Copyedit API doc for memory package. Fixes DOCS-606. MODULAR_ORIG_COMMIT_REV_ID: 08ae06edd62799e04926efe1c27a2b33568227db --- stdlib/src/memory/__init__.mojo | 3 +- stdlib/src/memory/owned_pointer.mojo | 56 ++++++++++++++++----------- stdlib/src/memory/span.mojo | 32 +++++++-------- stdlib/src/memory/unsafe.mojo | 33 +++++++++++++++- stdlib/src/memory/unsafe_pointer.mojo | 15 +++---- 5 files changed, 90 insertions(+), 49 deletions(-) diff --git a/stdlib/src/memory/__init__.mojo b/stdlib/src/memory/__init__.mojo index 2717a5127a..80684d45b5 100644 --- a/stdlib/src/memory/__init__.mojo +++ b/stdlib/src/memory/__init__.mojo @@ -10,7 +10,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -"""Implements the memory package.""" +"""The memory package provides several pointer types, as well +as utility functions for dealing with memory.""" from .arc import ArcPointer from .memory import memcmp, memcpy, memset, memset_zero, stack_allocation diff --git a/stdlib/src/memory/owned_pointer.mojo b/stdlib/src/memory/owned_pointer.mojo index 0eb0c33a3c..fde72fa4bd 100644 --- a/stdlib/src/memory/owned_pointer.mojo +++ b/stdlib/src/memory/owned_pointer.mojo @@ -10,6 +10,15 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # +"""Implements `OwnedPointer`, a safe, single-ownership smart pointer. + +You can import these APIs from the `memory` package. For example: + +```mojo +from memory import OwnedPointer +``` +""" + from memory import UnsafePointer, memcpy, stack_allocation @@ -25,7 +34,7 @@ struct OwnedPointer[T: AnyType]: pointers](/mojo/manual/pointers/) in the Mojo Manual. Parameters: - T: The type to be stored in the OwnedPointer[]. + T: The type to be stored in the `OwnedPointer`. """ var _inner: UnsafePointer[T, address_space = AddressSpace.GENERIC] @@ -35,13 +44,13 @@ struct OwnedPointer[T: AnyType]: # ===-------------------------------------------------------------------===# fn __init__[T: Movable](mut self: OwnedPointer[T], owned value: T): - """Construct a new OwnedPointer[] by moving the passed value into a new backing allocation. + """Construct a new `OwnedPointer` by moving the passed value into a new backing allocation. Parameters: T: The type of the data to store. It is restricted to `Movable` here to allow efficient move construction. Args: - value: The value to move into the OwnedPointer[]. + value: The value to move into the `OwnedPointer`. """ self._inner = UnsafePointer[T].alloc(1) self._inner.init_pointee_move(value^) @@ -49,13 +58,14 @@ struct OwnedPointer[T: AnyType]: fn __init__[ T: ExplicitlyCopyable ](mut self: OwnedPointer[T], *, copy_value: T): - """Construct a new OwnedPointer[] by explicitly copying the passed value into a new backing allocation. + """Construct a new `OwnedPointer` by explicitly copying the passed value into a new backing allocation. Parameters: - T: The type of the data to store. + T: The type of the data to store, which must be + `ExplicitlyCopyable`. Args: - copy_value: The value to explicitly copy into the OwnedPointer[]. + copy_value: The value to explicitly copy into the `OwnedPointer`. """ self._inner = UnsafePointer[T].alloc(1) self._inner.init_pointee_explicit_copy(copy_value) @@ -63,14 +73,14 @@ struct OwnedPointer[T: AnyType]: fn __init__[ T: Copyable, U: NoneType = None ](mut self: OwnedPointer[T], value: T): - """Construct a new OwnedPointer[] by copying the passed value into a new backing allocation. + """Construct a new `OwnedPointer` by copying the passed value into a new backing allocation. Parameters: T: The type of the data to store. U: A dummy type parameter, to lower the selection priority of this ctor. Args: - value: The value to copy into the OwnedPointer[]. + value: The value to copy into the `OwnedPointer`. """ self._inner = UnsafePointer[T].alloc(1) self._inner.init_pointee_copy(value) @@ -78,18 +88,18 @@ struct OwnedPointer[T: AnyType]: fn __init__[ T: ExplicitlyCopyable ](mut self: OwnedPointer[T], *, other: OwnedPointer[T],): - """Construct a new OwnedPointer[] by explicitly copying the value from another OwnedPointer[]. + """Construct a new `OwnedPointer` by explicitly copying the value from another `OwnedPointer`. Parameters: T: The type of the data to store. Args: - other: The OwnedPointer[] to copy. + other: The `OwnedPointer` to copy. """ self = OwnedPointer[T](copy_value=other[]) fn __moveinit__(out self, owned existing: Self): - """Move this OwnedPointer[]. + """Move this `OwnedPointer`. Args: existing: The value to move. @@ -112,7 +122,7 @@ struct OwnedPointer[T: AnyType]: """Returns a reference to the pointers's underlying data with parametric mutability. Returns: - A reference to the data underlying the OwnedPointer[]. + A reference to the data underlying the `OwnedPointer`. """ # This should have a widening conversion here that allows # the mutable ref that is always (potentially unsafely) @@ -126,25 +136,25 @@ struct OwnedPointer[T: AnyType]: # ===-------------------------------------------------------------------===# fn unsafe_ptr(self) -> UnsafePointer[T]: - """UNSAFE: returns the backing pointer for this OwnedPointer[]. + """UNSAFE: returns the backing pointer for this `OwnedPointer`. Returns: - An UnsafePointer to the backing allocation for this OwnedPointer[]. + An UnsafePointer to the backing allocation for this `OwnedPointer`. """ return self._inner fn take[T: Movable](owned self: OwnedPointer[T]) -> T: - """Move the value within the OwnedPointer[] out of it, consuming the - OwnedPointer[] in the process. + """Move the value within the `OwnedPointer` out of it, consuming the + `OwnedPointer` in the process. Parameters: - T: The type of the data backing this OwnedPointer[]. `take()` only exists for T: Movable + T: The type of the data backing this `OwnedPointer`. `take()` only exists for `T: Movable` since this consuming operation only makes sense for types that you want to avoid copying. - For types that are Copy or ExplicitlyCopy but are not Movable, you can copy them through + For types that are `Copyable` or `ExplicitlyCopyable` but are not `Movable`, you can copy them through `__getitem__` as in `var v = some_ptr_var[]`. Returns: - The data that is (was) backing the OwnedPointer[]. + The data that is (was) backing the `OwnedPointer`. """ var r = self._inner.take_pointee() self._inner.free() @@ -156,13 +166,13 @@ struct OwnedPointer[T: AnyType]: """Take ownership over the heap allocated pointer backing this `OwnedPointer`. - Safety: + **Safety:** This function is not unsafe to call, as a memory leak is not considered unsafe. - However, to avoid a memory leak, callers should ensure that the - returned pointer is eventually deinitialized and deallocated. - Failure to do so will leak memory. + However, to avoid a memory leak, callers should ensure that the + returned pointer is eventually deinitialized and deallocated. + Failure to do so will leak memory. Returns: The pointer owned by this instance. diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index fcdf2d7aa9..2a0c2d020b 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -11,7 +11,7 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # -"""Implements the Span type. +"""Implements the `Span` type. You can import these APIs from the `memory` module. For example: @@ -56,8 +56,8 @@ struct _SpanIter[ Parameters: mut: Whether the reference to the span is mutable. T: The type of the elements in the span. - origin: The origin of the Span. - forward: The iteration direction. `False` is backwards. + origin: The origin of the `Span`. + forward: The iteration direction. False is backwards. """ var index: Int @@ -99,7 +99,7 @@ struct Span[ T: CollectionElement, origin: Origin[mut], ](CollectionElementNew): - """A non owning view of contiguous data. + """A non-owning view of contiguous data. Parameters: mut: Whether the span is mutable. @@ -128,10 +128,10 @@ struct Span[ @always_inline fn __init__(out self, *, other: Self): - """Explicitly construct a deep copy of the provided Span. + """Explicitly construct a copy of the provided `Span`. Args: - other: The Span to copy. + other: The `Span` to copy. """ self._data = other._data self._len = other._len @@ -139,7 +139,7 @@ struct Span[ @always_inline @implicit fn __init__(out self, ref [origin]list: List[T, *_]): - """Construct a Span from a List. + """Construct a `Span` from a `List`. Args: list: The list to which the span refers. @@ -151,10 +151,10 @@ struct Span[ fn __init__[ size: Int, // ](mut self, ref [origin]array: InlineArray[T, size]): - """Construct a Span from an InlineArray. + """Construct a `Span` from an `InlineArray`. Parameters: - size: The size of the InlineArray. + size: The size of the `InlineArray`. Args: array: The array to which the span refers. @@ -218,19 +218,19 @@ struct Span[ @always_inline fn __iter__(self) -> _SpanIter[T, origin]: - """Get an iterator over the elements of the Span. + """Get an iterator over the elements of the `Span`. Returns: - An iterator over the elements of the Span. + An iterator over the elements of the `Span`. """ return _SpanIter(0, self) @always_inline fn __reversed__(self) -> _SpanIter[T, origin, forward=False]: - """Iterate backwards over the Span. + """Iterate backwards over the `Span`. Returns: - A reversed iterator of the Span elements. + A reversed iterator of the `Span` elements. """ return _SpanIter[forward=False](len(self), self) @@ -297,10 +297,10 @@ struct Span[ fn as_ref(self) -> Pointer[T, origin]: """ - Gets a Pointer to the first element of this slice. + Gets a `Pointer` to the first element of this span. Returns: - A Pointer pointing at the first element of this slice. + A `Pointer` pointing at the first element of this span. """ return Pointer[T, origin].address_of(self._data[0]) @@ -316,7 +316,7 @@ struct Span[ origin: The inferred mutable origin of the data within the Span. Args: - other: The Span to copy all elements from. + other: The `Span` to copy all elements from. """ debug_assert(len(self) == len(other), "Spans must be of equal length") for i in range(len(self)): diff --git a/stdlib/src/memory/unsafe.mojo b/stdlib/src/memory/unsafe.mojo index 450c18d199..2d83992bfb 100644 --- a/stdlib/src/memory/unsafe.mojo +++ b/stdlib/src/memory/unsafe.mojo @@ -10,7 +10,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -"""Implements types that work with unsafe pointers. +"""Provides utility functions for unsafe manipulation of SIMD values. You can import these APIs from the `memory` package. For example: @@ -35,6 +35,23 @@ fn bitcast[ ](val: SIMD[type, width]) -> SIMD[new_type, new_width]: """Bitcasts a SIMD value to another SIMD value. + For a discussion of byte order, see + [Converting data: bitcasting and byte order](/mojo/manual/pointers/unsafe-pointers#converting-data-bitcasting-and-byte-order) + in the Mojo Manual. + + Examples: + + The following example uses `bitcast` to break a 32-bit integer into a vector + of four 8-bit integers: + + ```mojo + from memory import bitcast + + one = SIMD[DType.uint32, 1](4631) + many = bitcast[DType.uint8, 4](one) + print(one, many) # 4631 [23, 18, 0, 0] + ``` + Constraints: The bitwidth of the two types must be the same. @@ -84,7 +101,19 @@ fn pack_bits[ width: Int, //, new_type: DType = _uint(width), ](val: SIMD[DType.bool, width]) -> Scalar[new_type]: - """Packs a SIMD bool into an integer. + """Packs a SIMD vector of `bool` values into an integer. + + Examples: + + This example packs a vector of 8 `bool` values into a single 8-bit integer. + + ```mojo + from memory import pack_bits + + flags = SIMD[DType.bool, 8](1, 1, 0, 1, 0, 0, 0, 0) + i = pack_bits[DType.uint8](flags) + print(flags, i) # [True, True, False, True, False, False, False, False] 11 + ``` Constraints: The width of the bool vector must be the same as the bitwidth of the diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index e6e3809b64..f23ed25618 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -1141,13 +1141,14 @@ struct UnsafePointer[ This transfers the value out of `self` and into `dest` using at most one `__moveinit__()` call. - Safety: - * `self` must be non-null - * `self` must contain a valid, initialized instance of `T` - * `dst` must not be null - * The contents of `dst` should be uninitialized. If `dst` was - previously written with a valid value, that value will be be - overwritten and its destructor will NOT be run. + **Safety:** + + * `self` must be non-null + * `self` must contain a valid, initialized instance of `T` + * `dst` must not be null + * The contents of `dst` should be uninitialized. If `dst` was + previously written with a valid value, that value will be be + overwritten and its destructor will NOT be run. Parameters: T: The type the pointer points to, which must be `Movable`. From 7e7c07f5356efb73fbd57f311c6467e29fd58775 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Mon, 16 Dec 2024 12:28:57 -0800 Subject: [PATCH 103/509] [Docs] Changelog: update contributors. MODULAR_ORIG_COMMIT_REV_ID: 55530a200ea933447f39f035553f7350cf5be76a --- docs/changelog-released.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index a0d9609952..b420128e7e 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -799,7 +799,8 @@ Special thanks to our community contributors: [@rd4com](https://github.com/rd4com), [@fknfilewalker](https://github.com/fknfilewalker), [@gabrieldemarmiesse](https://github.com/gabrieldemarmiesse), -[@avitkauskas](https://github.com/avitkauskas) +[@avitkauskas](https://github.com/avitkauskas), and +[@martinvuyk](https://github.com/martinvuyk). ## v24.5 (2024-09-13) From 8427a10959c807aeab7a9cfa898e7a2a2c40c438 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Mon, 16 Dec 2024 15:13:22 -0600 Subject: [PATCH 104/509] [stdlib] polish: Rename `Pointer.is_mutable` to `mut` MODULAR_ORIG_COMMIT_REV_ID: 4c849e42e6d13da7d3909461a1dcf78db57aefff --- stdlib/src/memory/pointer.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 1ffd5528f8..dca95022cc 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -298,9 +298,9 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): @value @register_passable("trivial") struct Pointer[ - is_mutable: Bool, //, + mut: Bool, //, type: AnyType, - origin: Origin[is_mutable], + origin: Origin[mut], address_space: AddressSpace = AddressSpace.GENERIC, ](CollectionElementNew, Stringable): """Defines a non-nullable safe pointer. @@ -309,7 +309,7 @@ struct Pointer[ pointers](/mojo/manual/pointers/) in the Mojo Manual. Parameters: - is_mutable: Whether the pointee data may be mutated through this. + mut: Whether the pointee data may be mutated through this. type: Type of the underlying data. origin: The origin of the pointer. address_space: The address space of the pointee data. From 403234f6a3ee0ee57500ba56ef579723eed16411 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 17 Dec 2024 07:47:34 +1000 Subject: [PATCH 105/509] [stdlib] Simplify while loop in format_float Remove unnecessary loop to simplify code. MODULAR_ORIG_COMMIT_REV_ID: 3e93088be52fb4bc7a013fd9dc11acef1b9a98fe --- stdlib/src/builtin/_format_float.mojo | 24 +++++++++--------------- 1 file changed, 9 insertions(+), 15 deletions(-) diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index bbbc41f239..17f30d7de9 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -315,23 +315,17 @@ fn _to_decimal[ ](z_result.integer_part) var r = (z_result.integer_part - FP[type].big_divisor * sig) - while True: - if r < deltai: - # TODO(MSTDL-1064): Exclude the right endpoint if necessary - pass - elif r > deltai: - break - else: - # r == deltai, compare fractional parts - var x_result = _compute_mul_parity( - (two_fc - 1).cast[DType.uint64](), cache_index, beta - ) - if not (x_result.parity | x_result.is_integer): - break - # If no break conditions were met + if r < deltai: exp = minus_k + FP[type].kappa + 1 return _remove_trailing_zeros(sig, exp) - + # compare fractional parts if r == deltai + if r == deltai: + var x_result = _compute_mul_parity( + (two_fc - 1).cast[DType.uint64](), cache_index, beta + ) + if x_result.parity | x_result.is_integer: + exp = minus_k + FP[type].kappa + 1 + return _remove_trailing_zeros(sig, exp) ####################################################### # Step 3: Find the significand with the smaller divisor ####################################################### From 4a4f3d0545c66569670b6d0ed0725d115320a01e Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 16 Dec 2024 15:51:21 -0800 Subject: [PATCH 106/509] [stdlib] Update hash function type width calculation MODULAR_ORIG_COMMIT_REV_ID: d2746c545d3b29ca1ddd45fb0e450d21dfa2ff32 --- stdlib/src/hashlib/hash.mojo | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/hashlib/hash.mojo b/stdlib/src/hashlib/hash.mojo index 5eddfc21b8..6b5c028796 100644 --- a/stdlib/src/hashlib/hash.mojo +++ b/stdlib/src/hashlib/hash.mojo @@ -27,7 +27,7 @@ There are a few main tools in this module: import random from collections import InlineArray -from sys import bitwidthof, simdwidthof +from sys import bitwidthof, sizeof, simdwidthof from sys.ffi import _Global from builtin.dtype import _uint_type_of_width @@ -227,7 +227,7 @@ fn hash(bytes: UnsafePointer[UInt8], n: Int) -> UInt: hash collision statistical properties for common data structures. """ alias type = DType.uint64 - alias type_width = bitwidthof[type]() // bitwidthof[DType.int8]() + alias type_width = sizeof[type]() alias simd_width = simdwidthof[type]() # stride is the byte length of the whole SIMD vector alias stride = type_width * simd_width From 9e38ec35fea5439608564fe63a2a7f5dba03bf8e Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 16 Dec 2024 17:06:17 -0800 Subject: [PATCH 107/509] [******][GPU] Use str for WGMMA error constraints (#52958) Fixes MSTDL-1065 and uses str in the WGMMA constraints BEGIN_PUBLIC [******][GPU] Use str for WGMMA error constraints END_PubLIC MODULAR_ORIG_COMMIT_REV_ID: aa733aecbd66434a70082ded0337ca8f6c0c535e --- stdlib/src/utils/index.mojo | 62 ++++++++++++++----------------- stdlib/test/utils/test_index.mojo | 17 ++++++--- 2 files changed, 40 insertions(+), 39 deletions(-) diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 0ea7685fe4..337e195d3b 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -723,52 +723,46 @@ struct IndexList[ ) @no_inline - fn __str__(self) -> String: - """Get the tuple as a string. + fn write_to[W: Writer](self, mut writer: W): + """ + Formats this IndexList value to the provided Writer. - Returns: - A string representation. + Parameters: + W: A type conforming to the Writable trait. + + Args: + writer: The object to write to. """ - # Reserve space for opening and closing parentheses, plus each element - # and its trailing commas. - var buf = String._buffer_type() - var initial_buffer_size = 2 - for i in range(size): - initial_buffer_size += _calc_initial_buffer_size(self[i]) + 2 - buf.reserve(initial_buffer_size) - # Print an opening `(`. - buf.size += _snprintf["("](buf.data, 2) + writer.write("(") + for i in range(size): - # Print separators between each element. if i != 0: - buf.size += _snprintf[", "](buf.data + buf.size, 3) - buf.size += _snprintf[_get_dtype_printf_format[DType.index]()]( - buf.data + buf.size, _calc_initial_buffer_size(self[i]), self[i] - ) + writer.write(", ") + + var element = self[i] + + @parameter + if element_bitwidth == 32: + writer.write(Int32(element)) + else: + writer.write(Int64(element)) + # Single element tuples should be printed with a trailing comma. + @parameter if size == 1: - buf.size += _snprintf[","](buf.data + buf.size, 2) - # Print a closing `)`. - buf.size += _snprintf[")"](buf.data + buf.size, 2) + writer.write(",") - buf.size += 1 # for the null terminator. - return buf^ + writer.write(")") @no_inline - fn write_to[W: Writer](self, mut writer: W): - """ - Formats this int tuple to the provided Writer. - - Parameters: - W: A type conforming to the Writable trait. + fn __str__(self) -> String: + """Get the tuple as a string. - Args: - writer: The object to write to. + Returns: + A string representation. """ - - # TODO: Optimize this to avoid the intermediate String allocation. - writer.write(str(self)) + return String.write(self) @always_inline fn cast[ diff --git a/stdlib/test/utils/test_index.mojo b/stdlib/test/utils/test_index.mojo index d262c9fdbe..500fdcf259 100644 --- a/stdlib/test/utils/test_index.mojo +++ b/stdlib/test/utils/test_index.mojo @@ -25,6 +25,10 @@ def test_basics(): def test_cast(): + assert_equal( + str(IndexList[1](1)), + "(1,)", + ) assert_equal( str(IndexList[2](1, 2).cast[DType.int32]()), "(1, 2)", @@ -44,13 +48,16 @@ def test_cast(): "(1, -2)", ) assert_equal( - str( - IndexList[2, element_bitwidth=32](1, 2).cast[ - element_bitwidth=64, unsigned=True - ]() - ), + str(IndexList[2, element_bitwidth=32](1, 2)), "(1, 2)", ) + alias s = str( + IndexList[2, element_bitwidth=32](1, 2).cast[ + element_bitwidth=64, unsigned=True + ]() + ) + assert_equal(s, "(1, 2)") + assert_equal(StringLiteral.get[s](), "(1, 2)") def test_index(): From 2db5e3d445557eb964f199540d154e9f9f8d1517 Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 17 Dec 2024 16:40:19 +0000 Subject: [PATCH 108/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024121705 --- examples/life/magic.lock | 1181 ++++++++++++++++----------------- examples/magic.lock | 1077 +++++++++++++++--------------- examples/notebooks/magic.lock | 1077 +++++++++++++++--------------- examples/operators/magic.lock | 1077 +++++++++++++++--------------- magic.lock | 1077 +++++++++++++++--------------- 5 files changed, 2644 insertions(+), 2845 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index c9b212466d..5a7928b9c1 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -10,20 +10,20 @@ environments: - 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url: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.11-hd74edd7_1.conda - sha256: 7113618021cf6c80831a429b2ebb9d639f3c43cf7fe2257d235dc6ae0ab43289 - md5: 7e0125f8fb619620a0011dc9297e2493 + version: 1.0.12 + build: hb9d3cd8_0 + subdir: linux-64 + url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda + sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038 + md5: f6ebe2cb3f82ba6c057dde5d9debe4f7 depends: - - __osx >=11.0 + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 license: MIT license_family: MIT - size: 13515 - timestamp: 1727034783560 + size: 14780 + timestamp: 1734229004433 - kind: conda name: xorg-libxdmcp version: 1.1.5 From f3432eb9220fec4b12626db7d273ddca53178971 Mon Sep 17 00:00:00 2001 From: Ricardo Guzman Date: Wed, 18 Dec 2024 14:27:33 -0500 Subject: [PATCH 109/509] [docs] Fix typo (#3893) Signed-off-by: Ricardo Guzman --- docs/manual/get-started.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 469542567d..ccb7680952 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -665,7 +665,7 @@ In the case of `str()`, it requires a type to conform to either the `Stringable` or `StringableRaising` trait. Each trait requires a conforming type to implement a `__str__()` method that returns a `String` representation. The only difference between the two traits is that `Stringable` requires that the method *cannot* -raise and error, whereas `StringableRaising` indicates that the method *might* +raise an error, whereas `StringableRaising` indicates that the method *might* raise an error. (To learn more, read [The `Stringable`, `Representable`, and `Writable` traits](/mojo/manual/traits#the-stringable-representable-and-writable-traits).) From 832e9b9fd5526e3aa9e14d1ebd03412257a9c190 Mon Sep 17 00:00:00 2001 From: Chris Elrod Date: Tue, 17 Dec 2024 09:56:38 -0500 Subject: [PATCH 110/509] [stdlib] Add log2e to math/constants log2e, equal to `log2(e)`, is now a constant available in math/constants. MODULAR_ORIG_COMMIT_REV_ID: 78d9ac514699877f3f04964c4915804223fb4929 --- docs/manual/get-started.mdx | 2 +- stdlib/src/math/constants.mojo | 3 +++ stdlib/src/math/math.mojo | 7 +++---- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index ccb7680952..469542567d 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -665,7 +665,7 @@ In the case of `str()`, it requires a type to conform to either the `Stringable` or `StringableRaising` trait. Each trait requires a conforming type to implement a `__str__()` method that returns a `String` representation. The only difference between the two traits is that `Stringable` requires that the method *cannot* -raise an error, whereas `StringableRaising` indicates that the method *might* +raise and error, whereas `StringableRaising` indicates that the method *might* raise an error. (To learn more, read [The `Stringable`, `Representable`, and `Writable` traits](/mojo/manual/traits#the-stringable-representable-and-writable-traits).) diff --git a/stdlib/src/math/constants.mojo b/stdlib/src/math/constants.mojo index 49c814406a..d24c05e8f3 100644 --- a/stdlib/src/math/constants.mojo +++ b/stdlib/src/math/constants.mojo @@ -28,3 +28,6 @@ alias e = 2.7182818284590452353602874713526624977572470936999595749669676277 alias tau = 2 * pi """The mathematical constant τ = 6.283185.... Tau is a circumference of a circle (2π).""" + +alias log2e = 1.442695040888963407359924681001892137426646 +"""log2e = log2(e), where e is Euler's constant.""" diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 9d5f53e0e4..836a0b3c0b 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -42,6 +42,7 @@ from utils.index import IndexList from utils.numerics import FPUtils, isnan, nan from utils.static_tuple import StaticTuple +from .constants import log2e from .polynomial import polynomial_evaluate # ===----------------------------------------------------------------------=== # @@ -572,14 +573,13 @@ fn exp[ """ constrained[type.is_floating_point(), "must be a floating point value"]() alias neg_ln2 = -0.69314718055966295651160180568695068359375 - alias inv_lg2 = 1.442695040888963407359924681001892137426646 @parameter if is_nvidia_gpu(): @parameter if type in (DType.float16, DType.float32): - return exp2(x * inv_lg2) + return exp2(x * log2e) @parameter if type not in (DType.float32, DType.float64): @@ -597,7 +597,7 @@ fn exp[ max_val = 88.3762626647950 var xc = x.clamp(min_val, max_val) - var k = floor(xc.fma(inv_lg2, 0.5)) + var k = floor(xc.fma(log2e, 0.5)) var r = k.fma(neg_ln2, xc) return max(_ldexp_impl(_exp_taylor(r), k), xc) @@ -740,7 +740,6 @@ fn _log_base[ alias ln2 = 0.69314718055994530942 y = exp.fma(ln2, y) else: - alias log2e = 1.4426950408889634073599 y = y.fma(log2e, exp) return (x == 0).select(Scalar[type].MIN, (x > 0).select(y, nan[type]())) From 391edf50b45e8765a35a90bef813a54b0e9c73ce Mon Sep 17 00:00:00 2001 From: Owen Hilyard Date: Tue, 17 Dec 2024 10:47:03 -0600 Subject: [PATCH 111/509] [External] [docs] Update stdlib-insider-docs.md proposal status (#52988) [External] [docs] Update stdlib-insider-docs.md proposal status Update the status to reflect that the proposal has been accepted. Co-authored-by: Owen Hilyard Closes modularml/mojo#3891 MODULAR_ORIG_COMMIT_REV_ID: 9790a42e516d6b8c9409b89b95facce210efe9ca --- proposals/stdlib-insider-docs.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proposals/stdlib-insider-docs.md b/proposals/stdlib-insider-docs.md index 1bf0abfef2..a21099b151 100644 --- a/proposals/stdlib-insider-docs.md +++ b/proposals/stdlib-insider-docs.md @@ -2,7 +2,7 @@ Owen Hilyard, Created November 17, 2024 -**Status**: Initial Proposal +**Status**: Accepted ## Motivation From 181f899f2f39b5b358808c115c20df876aeff3d1 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Tue, 17 Dec 2024 13:47:31 -0600 Subject: [PATCH 112/509] [External] [stdlib] Refactor `String` from `StringSlice` constructor (#52967) MODULAR_ORIG_COMMIT_REV_ID: 652e47d7fcdbca8c982d9a935b35639d2ea4d994 --- stdlib/src/collections/string.mojo | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 06cd2afeb5..6ebf4d90ad 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -887,24 +887,19 @@ struct String( fn __init__(out self, str_slice: StringSlice): """Construct a string from a string slice. - This will allocate a new string that copies the string contents from - the provided string slice `str_slice`. - Args: str_slice: The string slice from which to construct this string. + + Notes: + This will allocate a new string that copies the string contents from + the provided string slice. """ - # Calculate length in bytes - var length: Int = len(str_slice.as_bytes()) - var buffer = Self._buffer_type() - # +1 for null terminator, initialized to 0 - buffer.resize(length + 1, 0) - memcpy( - dest=buffer.data, - src=str_slice.as_bytes().unsafe_ptr(), - count=length, - ) - self = Self(buffer^) + var length = str_slice.byte_length() + var ptr = UnsafePointer[Byte].alloc(length + 1) # null terminator + memcpy(ptr, str_slice.unsafe_ptr(), length) + ptr[length] = 0 + self = String(ptr=ptr, length=length + 1) @always_inline @implicit From 08b82aeb42df75b965571d5bbddf7da00551febd Mon Sep 17 00:00:00 2001 From: Scott Main Date: Tue, 17 Dec 2024 12:20:04 -0800 Subject: [PATCH 113/509] Fix link for moved Span type MODULAR_ORIG_COMMIT_REV_ID: 100df6d133f0b6d1d1dd4da796c0d8ae2146c928 --- docs/changelog-released.md | 2 +- docs/manual/values/lifetimes.mdx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index b420128e7e..d20de03317 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -2314,7 +2314,7 @@ Big themes for this release: ([PR 2587#](https://github.com/modularml/mojo/pull/2587)) ([PR #2703](https://github.com/modularml/mojo/pull/2703)) - - Added a new [`Span`](/mojo/stdlib/utils/span/Span) type for taking slices of + - Added a new [`Span`](/mojo/stdlib/memory/span/Span) type for taking slices of contiguous collections. ([PR #2595](https://github.com/modularml/mojo/pull/2595)) diff --git a/docs/manual/values/lifetimes.mdx b/docs/manual/values/lifetimes.mdx index baf5f1e860..3c1871c0d7 100644 --- a/docs/manual/values/lifetimes.mdx +++ b/docs/manual/values/lifetimes.mdx @@ -44,7 +44,7 @@ However, in some cases you'll need to interact with origins directly: * When working with types like [`Pointer`](/mojo/stdlib/memory/pointer/Pointer) or - [`Span`](/mojo/stdlib/utils/span/Span) which are parameterized on the + [`Span`](/mojo/stdlib/memory/span/Span) which are parameterized on the origin of the data they refer to. This section also covers [`ref` arguments](#ref-arguments) and From a4198cf0de4ed29d6a4db4a751e3151421b6d302 Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Tue, 17 Dec 2024 15:48:50 -0600 Subject: [PATCH 114/509] [stdlib] Change `Dict.hash` data member type to be `UInt64` The `Hashable` trait has `__hash__` function return a `UInt` hash value. So, `Dict` storing the hash value as an `Int` is incorrect after the change in the return type from `__hash__` when it used to be `Int`. In the near future, `__hash__` will be changed to return a `UInt64` (whcih also aligns with the new data-flow hashing in progress). So, go ahead and update the `Dict.hash` data member to prepare for this change (this is why we went straight to `UInt64` instead of `UInt`). MODULAR_ORIG_COMMIT_REV_ID: f6782bb4972e7b1c8ee2fc43fc7120105065e426 --- stdlib/src/collections/dict.mojo | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index f6b36900dc..3a2634814c 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -216,7 +216,7 @@ struct DictEntry[K: KeyElement, V: CollectionElement]( V: The value type of the dict. """ - var hash: Int + var hash: UInt64 """`key.__hash__()`, stored so hashing isn't re-computed during dict lookup.""" var key: K """The unique key for the entry.""" @@ -319,7 +319,7 @@ struct _DictIndex: fn __moveinit__(out self, owned existing: Self): self.data = existing.data - fn get_index(self, reserved: Int, slot: Int) -> Int: + fn get_index(self, reserved: Int, slot: UInt64) -> Int: if reserved <= 128: var data = self.data.bitcast[Int8]() return int(data.load(slot & (reserved - 1))) @@ -333,7 +333,7 @@ struct _DictIndex: var data = self.data.bitcast[Int64]() return int(data.load(slot & (reserved - 1))) - fn set_index(mut self, reserved: Int, slot: Int, value: Int): + fn set_index(mut self, reserved: Int, slot: UInt64, value: Int): if reserved <= 128: var data = self.data.bitcast[Int8]() return data.store(slot & (reserved - 1), value) @@ -774,7 +774,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( """ var hash = hash(key) var found: Bool - var slot: Int + var slot: UInt64 var index: Int found, slot, index = self._find_index(hash, key) if found: @@ -839,7 +839,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( """ var hash = hash(key) var found: Bool - var slot: Int + var slot: UInt64 var index: Int found, slot, index = self._find_index(hash, key) if found: @@ -972,7 +972,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( if not safe_context: self._maybe_resize() var found: Bool - var slot: Int + var slot: UInt64 var index: Int found, slot, index = self._find_index(entry.hash, entry.key) @@ -982,30 +982,30 @@ struct Dict[K: KeyElement, V: CollectionElement]( self.size += 1 self._n_entries += 1 - fn _get_index(self, slot: Int) -> Int: + fn _get_index(self, slot: UInt64) -> Int: return self._index.get_index(self._reserved(), slot) - fn _set_index(mut self, slot: Int, index: Int): + fn _set_index(mut self, slot: UInt64, index: Int): return self._index.set_index(self._reserved(), slot, index) - fn _next_index_slot(self, mut slot: Int, mut perturb: UInt64): + fn _next_index_slot(self, mut slot: UInt64, mut perturb: UInt64): alias PERTURB_SHIFT = 5 perturb >>= PERTURB_SHIFT slot = ((5 * slot) + int(perturb + 1)) & (self._reserved() - 1) - fn _find_empty_index(self, hash: Int) -> Int: + fn _find_empty_index(self, hash: UInt64) -> UInt64: var slot = hash & (self._reserved() - 1) - var perturb = bitcast[DType.uint64](Int64(hash)) + var perturb = hash while True: var index = self._get_index(slot) if index == Self.EMPTY: return slot self._next_index_slot(slot, perturb) - fn _find_index(self, hash: Int, key: K) -> (Bool, Int, Int): + fn _find_index(self, hash: UInt64, key: K) -> (Bool, UInt64, Int): # Return (found, slot, index) var slot = hash & (self._reserved() - 1) - var perturb = bitcast[DType.uint64](Int64(hash)) + var perturb = hash while True: var index = self._get_index(slot) if index == Self.EMPTY: From e62fe551d0cd0ff83f3e0590c0a0b12e5065d0ab Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Tue, 17 Dec 2024 15:52:02 -0800 Subject: [PATCH 115/509] [stdlib] Move `count` onto `StringSlice` Moving more immutable `String` methods onto `StringSlice`. MODULAR_ORIG_COMMIT_REV_ID: d3242c64a3cf8dd4b2141837b633bcbdaaf5844e --- stdlib/src/collections/string.mojo | 18 ++------------- stdlib/src/utils/string_slice.mojo | 29 ++++++++++++++++++++++++ stdlib/test/collections/test_string.mojo | 15 ------------ stdlib/test/utils/test_string_slice.mojo | 15 ++++++++++++ 4 files changed, 46 insertions(+), 31 deletions(-) diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index 6ebf4d90ad..b8073bd83d 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -1667,7 +1667,7 @@ struct String( self._buffer.capacity = 0 return ptr - fn count(self, substr: String) -> Int: + fn count(self, substr: StringSlice) -> Int: """Return the number of non-overlapping occurrences of substring `substr` in the string. @@ -1680,21 +1680,7 @@ struct String( Returns: The number of occurrences of `substr`. """ - if not substr: - return len(self) + 1 - - var res = 0 - var offset = 0 - - while True: - var pos = self.find(substr, offset) - if pos == -1: - break - res += 1 - - offset = pos + substr.byte_length() - - return res + return self.as_string_slice().count(substr) fn __contains__(self, substr: String) -> Bool: """Returns True if the substring is contained within the current string. diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index c465811f1b..9dc3cd00d5 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -1105,6 +1105,35 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( return output^ + fn count(self, substr: StringSlice) -> Int: + """Return the number of non-overlapping occurrences of substring + `substr` in the string. + + If sub is empty, returns the number of empty strings between characters + which is the length of the string plus one. + + Args: + substr: The substring to count. + + Returns: + The number of occurrences of `substr`. + """ + if not substr: + return len(self) + 1 + + var res = 0 + var offset = 0 + + while True: + var pos = self.find(substr, offset) + if pos == -1: + break + res += 1 + + offset = pos + substr.byte_length() + + return res + # ===-----------------------------------------------------------------------===# # Utils diff --git a/stdlib/test/collections/test_string.mojo b/stdlib/test/collections/test_string.mojo index 4d9151b279..a871adae4e 100644 --- a/stdlib/test/collections/test_string.mojo +++ b/stdlib/test/collections/test_string.mojo @@ -640,20 +640,6 @@ def test_find(): assert_equal(-1, String("abc").find("abcd")) -def test_count(): - var str = String("Hello world") - - assert_equal(12, str.count("")) - assert_equal(1, str.count("Hell")) - assert_equal(3, str.count("l")) - assert_equal(1, str.count("ll")) - assert_equal(1, str.count("ld")) - assert_equal(0, str.count("universe")) - - assert_equal(String("aaaaa").count("a"), 5) - assert_equal(String("aaaaaa").count("aa"), 3) - - def test_replace(): # Replace empty var s1 = String("abc") @@ -1617,7 +1603,6 @@ def main(): test_calc_initial_buffer_size_int64() test_contains() test_find() - test_count() test_replace() test_rfind() test_split() diff --git a/stdlib/test/utils/test_string_slice.mojo b/stdlib/test/utils/test_string_slice.mojo index f88ed32dbb..dd7269a956 100644 --- a/stdlib/test/utils/test_string_slice.mojo +++ b/stdlib/test/utils/test_string_slice.mojo @@ -624,6 +624,20 @@ def test_endswith(): assert_true(ab.endswith("ab")) +def test_count(): + var str = StringSlice("Hello world") + + assert_equal(12, str.count("")) + assert_equal(1, str.count("Hell")) + assert_equal(3, str.count("l")) + assert_equal(1, str.count("ll")) + assert_equal(1, str.count("ld")) + assert_equal(0, str.count("universe")) + + assert_equal(StringSlice("aaaaa").count("a"), 5) + assert_equal(StringSlice("aaaaaa").count("aa"), 3) + + def main(): test_string_literal_byte_span() test_string_byte_span() @@ -641,6 +655,7 @@ def main(): test_combination_10_good_utf8_sequences() test_combination_10_good_10_bad_utf8_sequences() test_count_utf8_continuation_bytes() + test_count() test_splitlines() test_rstrip() test_lstrip() From 74c9f3c128c05695ecf8aab7d021d7035266915c Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 17 Dec 2024 18:15:20 -0600 Subject: [PATCH 116/509] [stdlib] polish: Change `ExplicitlyCopyable` to require `T.copy()` instead of `T(other=self)` This is intended to improve readability "at a glance", making it clearer to readers where copies happen. There is a meaningful advantage to having a consistent, greppable "copy" keyword appear each place a copy is done. Seeing `Foo(value)` means a wider level of context is needed to know if a deep copy is being done vs "mere" object construction. In comparison, `value.copy()` is immediately clear and stands out, particularly in performance sensitive code. MODULAR_ORIG_COMMIT_REV_ID: 8fabcf2392e36b662751bf188cc160bd12deec74 --- docs/changelog.md | 9 +++++ stdlib/src/builtin/bool.mojo | 8 ++--- stdlib/src/builtin/builtin_slice.mojo | 9 ++--- stdlib/src/builtin/dtype.mojo | 8 ++--- stdlib/src/builtin/error.mojo | 8 ++--- stdlib/src/builtin/int.mojo | 9 ++--- stdlib/src/builtin/none.mojo | 8 ++--- stdlib/src/builtin/object.mojo | 34 ++++++------------- stdlib/src/builtin/simd.mojo | 10 ++++++ stdlib/src/builtin/string_literal.mojo | 8 ++--- stdlib/src/builtin/value.mojo | 19 ++++------- stdlib/src/collections/counter.mojo | 10 +++--- stdlib/src/collections/deque.mojo | 33 +++++++++--------- stdlib/src/collections/dict.mojo | 31 +++++++---------- stdlib/src/collections/inline_array.mojo | 16 +++++---- stdlib/src/collections/inline_list.mojo | 2 +- stdlib/src/collections/list.mojo | 19 ++++++----- stdlib/src/collections/optional.mojo | 14 +++++--- stdlib/src/collections/string.mojo | 8 ++--- stdlib/src/collections/vector.mojo | 27 ++++++++------- stdlib/src/memory/arc.mojo | 9 +++-- stdlib/src/memory/maybe_uninitialized.mojo | 12 ++++--- stdlib/src/memory/pointer.mojo | 8 ++--- stdlib/src/memory/span.mojo | 9 +++-- stdlib/src/memory/unsafe_pointer.mojo | 10 +++--- stdlib/src/pathlib/path.mojo | 8 ++--- stdlib/src/python/python_object.mojo | 8 ++--- stdlib/src/sys/ffi.mojo | 13 ++++--- stdlib/src/utils/format.mojo | 14 ++------ stdlib/src/utils/inline_string.mojo | 18 ++++------ stdlib/src/utils/string_slice.mojo | 8 ++--- stdlib/src/utils/stringref.mojo | 9 +++-- stdlib/src/utils/variant.mojo | 16 ++++----- stdlib/test/builtin/test_sort.mojo | 2 +- stdlib/test/collections/test_counter.mojo | 2 +- stdlib/test/collections/test_deque.mojo | 4 +-- stdlib/test/collections/test_dict.mojo | 8 ++--- .../test/collections/test_inline_array.mojo | 2 +- stdlib/test/collections/test_list.mojo | 8 ++--- stdlib/test/collections/test_optional.mojo | 2 +- stdlib/test/memory/test_arc.mojo | 6 ++-- stdlib/test/memory/test_reference.mojo | 2 +- stdlib/test/test_utils/types.mojo | 19 ++++++++--- stdlib/test/utils/test_variant.mojo | 2 +- 44 files changed, 250 insertions(+), 239 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 15a2b2a1e0..9071af42a6 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -62,6 +62,15 @@ what we publish. var ptr2 = list2.unsafe_ptr() ``` +- The `ExplicitlyCopyable` trait has changed to require a + `fn copy(self) -> Self` method. Previously, an initializer with the signature + `fn __init__(out self, *, other: Self)` had been required by + `ExplicitlyCopyable`. + + This improves the "greppability" and at-a-glance readability when a programmer + is looking for places in their code that may be performing copies + unnecessarily. + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index e48f1c9f5a..6c953bb0b5 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -121,13 +121,13 @@ struct Bool( self = False @always_inline("nodebug") - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicitly construct a deep copy of the provided value. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self.value = other.value + return self @doc_private @always_inline("nodebug") diff --git a/stdlib/src/builtin/builtin_slice.mojo b/stdlib/src/builtin/builtin_slice.mojo index b663b9350e..094db42e69 100644 --- a/stdlib/src/builtin/builtin_slice.mojo +++ b/stdlib/src/builtin/builtin_slice.mojo @@ -82,13 +82,13 @@ struct Slice( self.end = end self.step = step - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Creates a deep copy of the Slice. - Args: - other: The slice to copy. + Returns: + A copy of the value. """ - self = Self(start=other.start, end=other.end, step=other.step) + return self # ===-------------------------------------------------------------------===# # Trait implementations @@ -206,6 +206,7 @@ struct Slice( var start = self.start var end = self.end + var positive_step = step > 0 if not start: diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index b8a7dff5d6..48bfffaafd 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -147,13 +147,13 @@ struct DType( on the system.""" @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy this DType. - Args: - other: The DType to copy. + Returns: + A copy of the value. """ - self = other + return self @always_inline @implicit diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index e282d56ba0..1ddae435ff 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -111,13 +111,13 @@ struct Error( self.data = dest self.loaded_length = -length - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy the object. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self = other + return self fn __del__(owned self): """Releases memory if allocated.""" diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 92cb98be75..c0da32d8c5 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -307,6 +307,7 @@ struct Int( Roundable, IntLike, _HashableWithHasher, + ExplicitlyCopyable, ): """This type represents an integer value.""" @@ -329,13 +330,13 @@ struct Int( """Default constructor that produces zero.""" self.value = __mlir_op.`index.constant`[value = __mlir_attr.`0:index`]() - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicitly copy the provided value. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self = other + return self @doc_private @always_inline("nodebug") diff --git a/stdlib/src/builtin/none.mojo b/stdlib/src/builtin/none.mojo index 2e5dc6ef35..c50322306c 100644 --- a/stdlib/src/builtin/none.mojo +++ b/stdlib/src/builtin/none.mojo @@ -48,13 +48,13 @@ struct NoneType( self._value = value @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicit copy constructor. - Args: - other: Another `NoneType` instance to copy. + Returns: + A copy of the value. """ - self._value = None + return Self(None) @no_inline fn __str__(self) -> String: diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index c2337934a1..0165d05249 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -32,8 +32,8 @@ from utils import StringRef, Variant struct _NoneMarker(CollectionElementNew): """This is a trivial class to indicate that an object is `None`.""" - fn __init__(out self, *, other: Self): - pass + fn copy(self) -> Self: + return _NoneMarker {} @register_passable("trivial") @@ -55,8 +55,8 @@ struct _ImmutableString(CollectionElement, CollectionElementNew): self.length = length @always_inline - fn __init__(out self, *, other: Self): - self = other + fn copy(self) -> Self: + return self @always_inline fn string_compare(self, rhs: _ImmutableString) -> Int: @@ -94,10 +94,6 @@ struct _RefCountedListRef(CollectionElement, CollectionElementNew): __get_address_as_uninit_lvalue(ptr.address) = _RefCountedList() self.lst = ptr.bitcast[NoneType]() - @always_inline - fn __init__(out self, *, other: Self): - self.lst = other.lst - @always_inline fn copy(self) -> Self: _ = self.lst.bitcast[_RefCountedList]()[].impl @@ -188,10 +184,6 @@ struct _RefCountedAttrsDictRef(CollectionElement, CollectionElementNew): self.attrs = ptr.bitcast[Int8]() - @always_inline - fn __init__(out self, *, other: Self): - self = other - @always_inline fn copy(self) -> Self: _ = self.attrs.bitcast[_RefCountedAttrsDict]()[].impl @@ -217,8 +209,8 @@ struct _Function(CollectionElement, CollectionElementNew): self.value = f @always_inline - fn __init__(out self, *, other: Self): - self.value = other.value + fn copy(self) -> Self: + return self alias fn0 = fn () raises -> object """Nullary function type.""" @@ -348,15 +340,6 @@ struct _ObjectImpl( fn __init__(out self, value: _RefCountedAttrsDictRef): self.value = Self.type(value) - @always_inline - fn __init__(out self, *, other: Self): - """Copy the object. - - Args: - other: The value to copy. - """ - self = other.value - @always_inline fn __copyinit__(out self, existing: Self): self = existing.value @@ -367,6 +350,11 @@ struct _ObjectImpl( @always_inline fn copy(self) -> Self: + """Copy the object. + + Returns: + A copy of the value. + """ if self.is_str(): var str = self.get_as_string() var impl = _ImmutableString( diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 0f2508229e..7ac510fd42 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -238,6 +238,7 @@ struct SIMD[type: DType, size: Int]( CollectionElement, # FIXME(MOCO-1291): Can't implement this due to ambiguity. # CollectionElementNew, + ExplicitlyCopyable, Floatable, Floorable, Writable, @@ -306,6 +307,15 @@ struct SIMD[type: DType, size: Int]( # """ # self = other + @always_inline + fn copy(self) -> Self: + """Explicitly construct a deep copy of the provided value. + + Returns: + A copy of the value. + """ + return self + @always_inline("nodebug") @implicit fn __init__(out self, value: UInt): diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 581e21de5e..406a39dbda 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -75,13 +75,13 @@ struct StringLiteral( self.value = value @always_inline("nodebug") - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy constructor. - Args: - other: The string literal to copy. + Returns: + A copy of the value. """ - self = other + return self # TODO(MOCO-1460): This should be: fn __init__[*, value: String](out self): # but Mojo tries to bind the parameter in `StringLiteral["foo"]()` to the diff --git a/stdlib/src/builtin/value.mojo b/stdlib/src/builtin/value.mojo index 3dc291c522..af565bc129 100644 --- a/stdlib/src/builtin/value.mojo +++ b/stdlib/src/builtin/value.mojo @@ -112,7 +112,7 @@ trait ExplicitlyCopyable: a `read-only` argument of `Self`. Example implementing the `ExplicitlyCopyable` trait on `Foo` which requires - the `__init__(.., Self)` method: + the `fn(self) -> Self` method: ```mojo struct Foo(ExplicitlyCopyable): @@ -122,17 +122,16 @@ trait ExplicitlyCopyable: fn __init__(out self, s: String): self.s = s - @implicit - fn __init__(out self, copy: Self): + fn copy(self) -> Self: print("explicitly copying value") - self.s = copy.s + return Foo(self.s) ``` You can now copy objects inside a generic function: ```mojo fn copy_return[T: ExplicitlyCopyable](foo: T) -> T: - var copy = T(foo) + var copy = foo.copy() return copy var foo = Foo("test") @@ -144,15 +143,11 @@ trait ExplicitlyCopyable: ``` """ - # Note: - # `other` is a required named argument for the time being to minimize - # implicit conversion overload ambiguity errors, particularly - # with SIMD and Int. - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicitly construct a deep copy of the provided value. - Args: - other: The value to copy. + Returns: + A copy of the value. """ ... diff --git a/stdlib/src/collections/counter.mojo b/stdlib/src/collections/counter.mojo index 55baedf606..5992cf585f 100644 --- a/stdlib/src/collections/counter.mojo +++ b/stdlib/src/collections/counter.mojo @@ -70,13 +70,13 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): self._data[item] = self._data.get(item, 0) + 1 @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Create a new Counter by copying another Counter. - Args: - other: The Counter to copy. + Returns: + A copy of the value. """ - self._data = Dict[V, Int](other=other._data) + return Self(self._data.copy()) @staticmethod fn fromkeys(keys: List[V, *_], value: Int) -> Self: @@ -295,7 +295,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): A new Counter with the counts from the other Counter subtracted from this Counter. """ - var result = Counter[V](other=self) + var result = self.copy() result.subtract(other) diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index e8c1138071..0fb93ea179 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -168,24 +168,25 @@ struct Deque[ElementType: CollectionElement]( self._tail = args_length - @implicit - fn __init__(out self, other: Self): + fn copy(self) -> Self: """Creates a deepcopy of the given deque. - Args: - other: The deque to copy. + Returns: + A copy of the value. """ - self = Self( - capacity=other._capacity, - min_capacity=other._min_capacity, - maxlen=other._maxlen, - shrink=other._shrink, + var copy = Self( + capacity=self._capacity, + min_capacity=self._min_capacity, + maxlen=self._maxlen, + shrink=self._shrink, ) - for i in range(len(other)): - offset = other._physical_index(other._head + i) - (self._data + i).init_pointee_copy((other._data + offset)[]) + for i in range(len(self)): + offset = self._physical_index(self._head + i) + (copy._data + i).init_pointee_copy((self._data + offset)[]) + + copy._tail = len(self) - self._tail = len(other) + return copy^ fn __moveinit__(out self, owned existing: Self): """Moves data of an existing deque into a new one. @@ -221,7 +222,7 @@ struct Deque[ElementType: CollectionElement]( Returns: The newly created deque with the properties of `self`. """ - new = Self(other=self) + new = self.copy() for element in other: new.append(element[]) return new^ @@ -251,7 +252,7 @@ struct Deque[ElementType: CollectionElement]( maxlen=self._maxlen, shrink=self._shrink, ) - new = Self(other=self) + new = self.copy() for _ in range(n - 1): for element in self: new.append(element[]) @@ -267,7 +268,7 @@ struct Deque[ElementType: CollectionElement]( self.clear() return - orig = Self(other=self) + orig = self.copy() for _ in range(n - 1): for element in orig: self.append(element[]) diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 3a2634814c..214cc78d3f 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -234,15 +234,13 @@ struct DictEntry[K: KeyElement, V: CollectionElement]( self.key = key^ self.value = value^ - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy an existing entry. - Args: - other: The existing entry to copy. + Returns: + A copy of the value. """ - self.hash = other.hash - self.key = other.key - self.value = other.value + return self fn reap_value(owned self) -> V as out: """Take the value from an owned entry. @@ -517,16 +515,13 @@ struct Dict[K: KeyElement, V: CollectionElement]( return len(self._entries) @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy an existing dictiontary. - Args: - other: The existing dict. + Returns: + A copy of the value. """ - self.size = other.size - self._n_entries = other._n_entries - self._index = other._index.copy(other._reserved()) - self._entries = other._entries + return self @staticmethod fn fromkeys(keys: List[K, *_], value: V) -> Self: @@ -648,7 +643,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( Returns: The result of the merge. """ - var result = Dict(other=self) + var result = self.copy() result.update(other) return result^ @@ -1087,13 +1082,13 @@ struct OwnedKwargsDict[V: CollectionElement]( """Initialize an empty keyword dictionary.""" self._dict = Dict[Self.key_type, V]() - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy an existing keyword dictionary. - Args: - other: The existing keyword dictionary. + Returns: + A copy of the value. """ - self._dict = other._dict + return self fn __copyinit__(out self, existing: Self): """Copy an existing keyword dictionary. diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 6901ed1327..502104561c 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -199,18 +199,20 @@ struct InlineArray[ __get_mvalue_as_litref(storage) ) - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicitly copy the provided value. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self = Self(unsafe_uninitialized=True) + var copy = Self(unsafe_uninitialized=True) for idx in range(size): - var ptr = self.unsafe_ptr() + idx - ptr.init_pointee_copy(other[idx]) + var ptr = copy.unsafe_ptr() + idx + ptr.init_pointee_copy(self[idx]) + + return copy^ fn __copyinit__(out self, other: Self): """Copy construct the array. @@ -219,7 +221,7 @@ struct InlineArray[ other: The array to copy. """ - self = Self(other=other) + self = other.copy() fn __del__(owned self): """Deallocate the array.""" diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index f98e307940..798f71d5f8 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -120,7 +120,7 @@ struct InlineList[ElementType: CollectionElementNew, capacity: Int = 16](Sized): debug_assert(len(values) <= capacity, "List is full.") self = Self() for value in values: - self.append(ElementType(other=value[])) + self.append(value[].copy()) @always_inline fn __del__(owned self): diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index f0bc3d0c6a..5732bc8da5 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -114,15 +114,16 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( self.size = 0 self.capacity = 0 - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Creates a deep copy of the given list. - Args: - other: The list to copy. + Returns: + A copy of the value. """ - self = Self(capacity=other.capacity) - for e in other: - self.append(e[]) + var copy = Self(capacity=self.capacity) + for e in self: + copy.append(e[]) + return copy^ fn __init__(out self, *, capacity: Int): """Constructs a list with the given capacity. @@ -316,7 +317,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # avoid the copy since it would be cleared immediately anyways if x == 0: return Self() - var result = List(other=self) + var result = self.copy() result.__mul(x) return result^ @@ -337,7 +338,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Returns: The newly created list. """ - var result = List(other=self) + var result = self.copy() result.extend(other^) return result^ @@ -545,7 +546,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( if x == 0: self.clear() return - var orig = List(other=self) + var orig = self.copy() self.reserve(len(self) * x) for i in range(x - 1): self.extend(orig) diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index 0e318980e9..5d7eb7853a 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -42,6 +42,12 @@ struct _NoneType(CollectionElement, CollectionElementNew): fn __init__(out self, *, other: Self): pass + fn __copyinit__(out self, other: Self): + pass + + fn copy(self) -> Self: + return _NoneType() + # ===-----------------------------------------------------------------------===# # Optional @@ -124,13 +130,13 @@ struct Optional[T: CollectionElement]( """ self = Self() - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy construct an Optional. - Args: - other: The Optional to copy. + Returns: + A copy of the value. """ - self = other + return self # ===-------------------------------------------------------------------===# # Operator dunders diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string.mojo index b8073bd83d..4e5438303c 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string.mojo @@ -861,13 +861,13 @@ struct String( """ self._buffer = Self._buffer_type(capacity=capacity) - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicitly copy the provided value. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self = other # Just use the implicit copyinit. + return self # Just use the implicit copyinit. @implicit fn __init__(out self, str: StringRef): diff --git a/stdlib/src/collections/vector.mojo b/stdlib/src/collections/vector.mojo index 62196e6a15..556c343ea2 100644 --- a/stdlib/src/collections/vector.mojo +++ b/stdlib/src/collections/vector.mojo @@ -132,23 +132,26 @@ struct InlinedFixedVector[ self.capacity = capacity @always_inline - @implicit - fn __init__(out self, existing: Self): + fn copy(self) -> Self: """ Copy constructor. - Args: - existing: The `InlinedFixedVector` to copy. + Returns: + A copy of the value. """ - self.static_data = existing.static_data - self.dynamic_data = UnsafePointer[type]() - if existing.dynamic_data: - var ext_len = existing.capacity - size - self.dynamic_data = UnsafePointer[type].alloc(ext_len) - memcpy(self.dynamic_data, existing.dynamic_data, ext_len) + var copy = Self(capacity=self.capacity) - self.current_size = existing.current_size - self.capacity = existing.capacity + copy.static_data = self.static_data + copy.dynamic_data = UnsafePointer[type]() + if self.dynamic_data: + var ext_len = self.capacity - size + copy.dynamic_data = UnsafePointer[type].alloc(ext_len) + memcpy(copy.dynamic_data, self.dynamic_data, ext_len) + + copy.current_size = self.current_size + copy.capacity = self.capacity + + return copy^ @always_inline fn __moveinit__(out self, owned existing: Self): diff --git a/stdlib/src/memory/arc.mojo b/stdlib/src/memory/arc.mojo index 8680d06560..6178892617 100644 --- a/stdlib/src/memory/arc.mojo +++ b/stdlib/src/memory/arc.mojo @@ -104,14 +104,13 @@ struct ArcPointer[T: Movable]( value^ ) - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy the object. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - other._inner[].add_ref() - self._inner = other._inner + return self fn __copyinit__(out self, existing: Self): """Copy an existing reference. Increment the refcount to the object. diff --git a/stdlib/src/memory/maybe_uninitialized.mojo b/stdlib/src/memory/maybe_uninitialized.mojo index 8d224bdd88..53eeb8404e 100644 --- a/stdlib/src/memory/maybe_uninitialized.mojo +++ b/stdlib/src/memory/maybe_uninitialized.mojo @@ -43,20 +43,22 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): @doc_private @always_inline - fn __init__(out self, *, other: Self): - """It is not possible to call this method. + fn copy(self) -> Self: + """This method is not intended to be called. Trying to call this method will abort. + + Returns: + Nothing, this method always aborts. """ - abort( - "You should never call the explicit copy constructor of" + return abort[Self]( + "You should never call the copy() method of" " UnsafeMaybeUninitialized because it's ambiguous to copy" " possibly uninitialized memory. Use" " `UnsafeMaybeUninitialized.copy_from()` instead if you want to" " trigger an explicit copy of the content of" " UnsafeMaybeUninitialized. It has very specific semantics." ) - self = Self() @always_inline fn __init__[ diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index dca95022cc..2d1f5fce52 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -355,15 +355,15 @@ struct Pointer[ """ return Pointer(_mlir_value=__get_mvalue_as_litref(value)) - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Constructs a copy from another Pointer. Note that this does **not** copy the underlying data. - Args: - other: The `Pointer` to copy. + Returns: + A copy of the value. """ - self._value = other._value + return Self(_mlir_value=self._value) # ===------------------------------------------------------------------===# # Operator dunders diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 2a0c2d020b..5d5b780322 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -127,14 +127,13 @@ struct Span[ self._len = length @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicitly construct a copy of the provided `Span`. - Args: - other: The `Span` to copy. + Returns: + A copy of the `Span`. """ - self._data = other._data - self._len = other._len + return self @always_inline @implicit diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index f23ed25618..60e0762d3c 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -142,13 +142,13 @@ struct UnsafePointer[ ) @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy an existing pointer. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self.address = other.address + return UnsafePointer(self.address) # ===-------------------------------------------------------------------===# # Factory methods @@ -1121,7 +1121,7 @@ struct UnsafePointer[ value: The value to emplace. """ constrained[mut, _must_be_mut_err]() - __get_address_as_uninit_lvalue(self.address) = T(other=value) + __get_address_as_uninit_lvalue(self.address) = value.copy() @always_inline fn move_pointee_into[ diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index 5a0f285db1..b81c184e06 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -92,13 +92,13 @@ struct Path( """ self.path = path - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy the object. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self.path = String(other=other.path) + return Self(self.path) fn __truediv__(self, suffix: Self) -> Self: """Joins two paths using the system-defined path separator. diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index 5fb1847f8f..caeb85a53b 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -254,13 +254,13 @@ struct PythonObject( """Initialize the object with a `None` value.""" self = Self(None) - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy the object. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self = other + return self @implicit fn __init__(out self, ptr: PyObjectPtr): diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 2d6926568d..1c3299a196 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -145,13 +145,13 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): else: self.handle = OpaquePointer() - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy the object. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self = other + return self fn check_symbol(self, name: String) -> Bool: """Check that the symbol exists in the dynamic library. @@ -451,7 +451,10 @@ fn _get_global[ destroy_fn: fn (OpaquePointer) -> None, ](payload: OpaquePointer = OpaquePointer()) -> OpaquePointer: return external_call["KGEN_CompilerRT_GetGlobalOrCreate", OpaquePointer]( - StringRef(name), payload, init_fn, destroy_fn + StringRef(name), + payload, + init_fn, + destroy_fn, ) diff --git a/stdlib/src/utils/format.mojo b/stdlib/src/utils/format.mojo index 752a34898d..59d1885c58 100644 --- a/stdlib/src/utils/format.mojo +++ b/stdlib/src/utils/format.mojo @@ -65,17 +65,9 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): alias _args_t = VariadicPack[element_trait=_CurlyEntryFormattable, *_] """Args types that are formattable by curly entry.""" - fn __init__(out self, *, other: Self): - """Construct a format entry by copying another. - - Args: - other: The other format entry. - """ - self.first_curly = other.first_curly - self.last_curly = other.last_curly - self.conversion_flag = other.conversion_flag - self.field = Self._FieldVariantType(other=other.field) - self.format_spec = other.format_spec + fn copy(self) -> Self: + """Construct a format entry by copying another.""" + return self fn __init__( mut self, diff --git a/stdlib/src/utils/inline_string.mojo b/stdlib/src/utils/inline_string.mojo index 7ddb0df082..5fb4687089 100644 --- a/stdlib/src/utils/inline_string.mojo +++ b/stdlib/src/utils/inline_string.mojo @@ -91,13 +91,13 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): """ self._storage = Self.Layout(heap_string^) - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy the object. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self = other + return self # ===------------------------------------------------------------------=== # # Operator dunders @@ -333,13 +333,9 @@ struct _FixedString[CAP: Int]( self.buffer = InlineArray[UInt8, CAP](unsafe_uninitialized=True) self.size = 0 - fn __init__(out self, *, other: Self): - """Copy the object. - - Args: - other: The value to copy. - """ - self = other + fn copy(self) -> Self: + """Copy the object.""" + return self fn __init__(out self, literal: StringLiteral) raises: """Constructs a FixedString value given a string literal. diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/utils/string_slice.mojo index 9dc3cd00d5..d50447ab10 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/utils/string_slice.mojo @@ -334,13 +334,13 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( self._slice = Span[Byte, origin](ptr=ptr, length=length) @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Explicitly construct a deep copy of the provided `StringSlice`. - Args: - other: The `StringSlice` to copy. + Returns: + A copy of the value. """ - self._slice = other._slice + return Self(unsafe_from_utf8=self._slice) @implicit fn __init__[ diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 4e92e96848..0699acc424 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -77,14 +77,13 @@ struct StringRef( self = StringRef(UnsafePointer[UInt8](), 0) @always_inline - fn __init__(out self, *, other: Self): + fn copy(self) -> Self: """Copy the object. - Args: - other: The value to copy. + Returns: + A copy of the value. """ - self.data = other.data - self.length = other.length + return StringRef(self.data, self.length) @always_inline @implicit diff --git a/stdlib/src/utils/variant.mojo b/stdlib/src/utils/variant.mojo index 6936a3a003..8efd3ff63c 100644 --- a/stdlib/src/utils/variant.mojo +++ b/stdlib/src/utils/variant.mojo @@ -139,20 +139,20 @@ struct Variant[*Ts: CollectionElement]( self._get_discr() = idx self._get_ptr[T]().init_pointee_move(value^) - fn __init__(out self, *, other: Self): + fn copy(self, out copy: Self): """Explicitly creates a deep copy of an existing variant. - Args: - other: The value to copy from. + Returns: + A copy of the value. """ - self = Self(unsafe_uninitialized=()) - self._get_discr() = other._get_discr() + copy = Self(unsafe_uninitialized=()) + copy._get_discr() = self._get_discr() @parameter for i in range(len(VariadicList(Ts))): alias T = Ts[i] - if self._get_discr() == i: - self._get_ptr[T]().init_pointee_move(other._get_ptr[T]()[]) + if copy._get_discr() == i: + copy._get_ptr[T]().init_pointee_move(self._get_ptr[T]()[]) return fn __copyinit__(out self, other: Self): @@ -163,7 +163,7 @@ struct Variant[*Ts: CollectionElement]( """ # Delegate to explicit copy initializer. - self = Self(other=other) + self = other.copy() fn __moveinit__(out self, owned other: Self): """Move initializer for the variant. diff --git a/stdlib/test/builtin/test_sort.mojo b/stdlib/test/builtin/test_sort.mojo index 3c98be083f..de1a34e151 100644 --- a/stdlib/test/builtin/test_sort.mojo +++ b/stdlib/test/builtin/test_sort.mojo @@ -551,7 +551,7 @@ struct Person(ComparableCollectionElement): var age: Int fn __init__(out self, *, other: Self): - self.name = String(other=other.name) + self.name = other.name.copy() self.age = other.age fn __lt__(self, other: Self) -> Bool: diff --git a/stdlib/test/collections/test_counter.mojo b/stdlib/test/collections/test_counter.mojo index 27f5f3b9e6..a7c7b3e772 100644 --- a/stdlib/test/collections/test_counter.mojo +++ b/stdlib/test/collections/test_counter.mojo @@ -75,7 +75,7 @@ def test_copy(): c["a"] = 1 c["b"] = 2 - var copy = Counter[String](other=c) + var copy = c.copy() assert_equal(copy["a"], 1) assert_equal(copy["b"], 2) diff --git a/stdlib/test/collections/test_deque.mojo b/stdlib/test/collections/test_deque.mojo index fd2db54440..a5fdc772f9 100644 --- a/stdlib/test/collections/test_deque.mojo +++ b/stdlib/test/collections/test_deque.mojo @@ -690,7 +690,7 @@ fn test_init_variadic_list() raises: fn test_copy_trivial() raises: q = Deque(1, 2, 3) - p = Deque(q) + p = q.copy() assert_equal(p[0], q[0]) p[0] = 3 @@ -709,7 +709,7 @@ fn test_copy_list() raises: lst1[0] = 7 assert_equal(q[0], List(1, 2, 3)) - p = Deque(q) + p = q.copy() assert_equal(p[0], q[0]) p[0][0] = 7 diff --git a/stdlib/test/collections/test_dict.mojo b/stdlib/test/collections/test_dict.mojo index 4f8b1e965c..030b16a6a3 100644 --- a/stdlib/test/collections/test_dict.mojo +++ b/stdlib/test/collections/test_dict.mojo @@ -238,7 +238,7 @@ def test_dict_copy(): orig["a"] = 1 # test values copied to new Dict - var copy = Dict(other=orig) + var copy = orig.copy() assert_equal(1, copy["a"]) # test there are two copies of dict and @@ -253,7 +253,7 @@ def test_dict_copy_delete_original(): orig["a"] = 1 # test values copied to new Dict - var copy = Dict(other=orig) + var copy = orig.copy() # don't access the original dict, anymore, confirm that # deleting the original doesn't violate the integrity of the copy assert_equal(1, copy["a"]) @@ -264,7 +264,7 @@ def test_dict_copy_add_new_item(): orig["a"] = 1 # test values copied to new Dict - var copy = Dict(other=orig) + var copy = orig.copy() assert_equal(1, copy["a"]) # test there are two copies of dict and @@ -278,7 +278,7 @@ def test_dict_copy_calls_copy_constructor(): orig["a"] = CopyCounter() # test values copied to new Dict - var copy = Dict(other=orig) + var copy = orig.copy() assert_equal(0, orig["a"].copy_count) assert_equal(1, copy["a"].copy_count) assert_equal(0, orig._find_ref("a").copy_count) diff --git a/stdlib/test/collections/test_inline_array.mojo b/stdlib/test/collections/test_inline_array.mojo index 18a933c6be..29268017ba 100644 --- a/stdlib/test/collections/test_inline_array.mojo +++ b/stdlib/test/collections/test_inline_array.mojo @@ -175,7 +175,7 @@ def test_array_unsafe_assume_initialized_constructor_string(): assert_equal(initialized_arr2[2], "world") # trigger a copy - var initialized_arr3 = InlineArray(other=initialized_arr2) + var initialized_arr3 = initialized_arr2.copy() assert_equal(initialized_arr3[0], "hello") assert_equal(initialized_arr3[1], "mojo") diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index 56dab6510b..9f45d66f5a 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -531,7 +531,7 @@ def test_2d_dynamic_list(): def test_list_explicit_copy(): var list = List[CopyCounter]() list.append(CopyCounter()) - var list_copy = List(other=list) + var list_copy = list.copy() assert_equal(0, list[0].copy_count) assert_equal(1, list_copy[0].copy_count) @@ -539,7 +539,7 @@ def test_list_explicit_copy(): for i in range(10): l2.append(i) - var l2_copy = List(other=l2) + var l2_copy = l2.copy() assert_equal(len(l2), len(l2_copy)) for i in range(len(l2)): assert_equal(l2[i], l2_copy[i]) @@ -551,8 +551,8 @@ struct CopyCountedStruct(CollectionElement): var value: String fn __init__(out self, *, other: Self): - self.counter = CopyCounter(other=other.counter) - self.value = String(other=other.value) + self.counter = other.counter.copy() + self.value = other.value.copy() @implicit fn __init__(out self, value: String): diff --git a/stdlib/test/collections/test_optional.mojo b/stdlib/test/collections/test_optional.mojo index 70cca5de6f..d606a2b392 100644 --- a/stdlib/test/collections/test_optional.mojo +++ b/stdlib/test/collections/test_optional.mojo @@ -126,7 +126,7 @@ def test_optional_take_mutates(): def test_optional_explicit_copy(): var v1 = Optional[String](String("test")) - var v2 = Optional(other=v1) + var v2 = v1.copy() assert_equal(v1.value(), "test") assert_equal(v2.value(), "test") diff --git a/stdlib/test/memory/test_arc.mojo b/stdlib/test/memory/test_arc.mojo index 82b5342aea..83d0b4e4e4 100644 --- a/stdlib/test/memory/test_arc.mojo +++ b/stdlib/test/memory/test_arc.mojo @@ -54,12 +54,12 @@ def test_deleter_not_called_until_no_references(): def test_deleter_not_called_until_no_references_explicit_copy(): var deleted = False var p = ArcPointer(ObservableDel(UnsafePointer.address_of(deleted))) - var p2 = ArcPointer(other=p) + var p2 = p.copy() _ = p^ assert_false(deleted) var vec = List[ArcPointer[ObservableDel]]() - vec.append(ArcPointer(other=p2)^) + vec.append(p2.copy()) _ = p2^ assert_false(deleted) _ = vec^ @@ -68,7 +68,7 @@ def test_deleter_not_called_until_no_references_explicit_copy(): def test_count(): var a = ArcPointer(10) - var b = ArcPointer(other=a) + var b = a.copy() var c = a assert_equal(3, a.count()) _ = b^ diff --git a/stdlib/test/memory/test_reference.mojo b/stdlib/test/memory/test_reference.mojo index 44d448e791..6b84cbc900 100644 --- a/stdlib/test/memory/test_reference.mojo +++ b/stdlib/test/memory/test_reference.mojo @@ -18,7 +18,7 @@ def test_copy_reference_explicitly(): var a = List[Int](1, 2, 3) var b = Pointer.address_of(a) - var c = Pointer(other=b) + var c = b.copy() c[][0] = 4 assert_equal(a[0], 4) diff --git a/stdlib/test/test_utils/types.mojo b/stdlib/test/test_utils/types.mojo index 0e83d44dca..01870acebc 100644 --- a/stdlib/test/test_utils/types.mojo +++ b/stdlib/test/test_utils/types.mojo @@ -59,9 +59,9 @@ struct ExplicitCopyOnly(ExplicitlyCopyable): self.value = value self.copy_count = 0 - fn __init__(out self, *, other: Self): - self.value = other.value - self.copy_count = other.copy_count + 1 + fn copy(self, out copy: Self): + copy = Self(self.value) + copy.copy_count = self.copy_count + 1 # ===----------------------------------------------------------------------=== # @@ -105,6 +105,9 @@ struct CopyCounter(CollectionElement, ExplicitlyCopyable): fn __copyinit__(out self, existing: Self): self.copy_count = existing.copy_count + 1 + fn copy(self) -> Self: + return self + # ===----------------------------------------------------------------------=== # # MoveCounter @@ -135,7 +138,7 @@ struct MoveCounter[T: CollectionElementNew]( Args: other: The value to copy. """ - self.value = T(other=other.value) + self.value = other.value.copy() self.move_count = other.move_count fn __moveinit__(out self, owned existing: Self): @@ -146,9 +149,12 @@ struct MoveCounter[T: CollectionElementNew]( # CollectionElement at the moment. fn __copyinit__(out self, existing: Self): # print("ERROR: _MoveCounter copy constructor called unexpectedly!") - self.value = T(other=existing.value) + self.value = existing.value.copy() self.move_count = existing.move_count + fn copy(self) -> Self: + return self + # ===----------------------------------------------------------------------=== # # ValueDestructorRecorder @@ -167,6 +173,9 @@ struct ValueDestructorRecorder(ExplicitlyCopyable): fn __del__(owned self): self.destructor_counter[].append(self.value) + fn copy(self) -> Self: + return self + # ===----------------------------------------------------------------------=== # # ObservableDel diff --git a/stdlib/test/utils/test_variant.mojo b/stdlib/test/utils/test_variant.mojo index f07b3aa099..99dae52a54 100644 --- a/stdlib/test/utils/test_variant.mojo +++ b/stdlib/test/utils/test_variant.mojo @@ -116,7 +116,7 @@ def test_explicit_copy(): var v1 = TestVariant(TestCounter()) # Perform explicit copy - var v2 = TestVariant(other=v1) + var v2 = v1.copy() # Test copy counts assert_equal(v1[TestCounter].copied, 0) From 036a187bf1ce9d9eae9ca93b2472ece27cd30598 Mon Sep 17 00:00:00 2001 From: Ricardo Guzman Date: Wed, 18 Dec 2024 13:24:36 -0600 Subject: [PATCH 117/509] [External] [docs] Fix typo (#53093) [External] [docs] Fix typo "[...] requires that the method *cannot* raise and error, [...]" is grammatically incorrect (assumed typo) Co-authored-by: Ricardo Guzman Closes modularml/mojo#3893 MODULAR_ORIG_COMMIT_REV_ID: fa26fa527fa9a8eea719bdbc1e63835a8808db62 --- docs/manual/get-started.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 469542567d..ccb7680952 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -665,7 +665,7 @@ In the case of `str()`, it requires a type to conform to either the `Stringable` or `StringableRaising` trait. Each trait requires a conforming type to implement a `__str__()` method that returns a `String` representation. The only difference between the two traits is that `Stringable` requires that the method *cannot* -raise and error, whereas `StringableRaising` indicates that the method *might* +raise an error, whereas `StringableRaising` indicates that the method *might* raise an error. (To learn more, read [The `Stringable`, `Representable`, and `Writable` traits](/mojo/manual/traits#the-stringable-representable-and-writable-traits).) From 9dd7bd8d1576c355e126288a9fd668b67844d4a8 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Wed, 18 Dec 2024 13:29:07 -0800 Subject: [PATCH 118/509] [stdlib] Spin out `address_space_cast` and `origin_cast` from `bitcast`. Right now `bitcast` is kind of a grab bag that does a ton of things. Having more specialized methods makes intent clear. MODULAR_ORIG_COMMIT_REV_ID: 0336403b80737866c7e0ad63cc44cd799bb13e5f --- docs/changelog.md | 6 ++- stdlib/src/builtin/sort.mojo | 16 +++--- stdlib/src/builtin/string_literal.mojo | 2 +- stdlib/src/collections/dict.mojo | 2 +- stdlib/src/memory/memory.mojo | 4 +- stdlib/src/memory/unsafe_pointer.mojo | 59 +++++++++++++++++++--- stdlib/test/collections/test_string.mojo | 2 +- stdlib/test/memory/test_unsafepointer.mojo | 6 +-- 8 files changed, 73 insertions(+), 24 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 9071af42a6..f98b425f32 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -22,6 +22,10 @@ what we publish. ### Standard library changes +- `UnsafePointer`'s `bitcast` method has now been split into `bitcast` + for changing the type, `origin_cast` for changing mutability, + and `address_space_cast` for changing the address space. + - `UnsafePointer` is now parameterized on mutability. Previously, `UnsafePointer` could only represent mutable pointers. @@ -44,7 +48,7 @@ what we publish. ```mojo var local = 10 # Cast the mutable pointer to be immutable. - var ptr = UnsafePointer.address_of(local).bitcast[mut=False]() + var ptr = UnsafePointer.address_of(local).mut_cast[mut=False]() ``` - The `unsafe_ptr()` method on several standard library collection types have diff --git a/stdlib/src/builtin/sort.mojo b/stdlib/src/builtin/sort.mojo index f3f19ba9b2..00243d6f80 100644 --- a/stdlib/src/builtin/sort.mojo +++ b/stdlib/src/builtin/sort.mojo @@ -48,7 +48,7 @@ fn _insertion_sort[ cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): """Sort the array[start:end] slice""" - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var size = len(span) for i in range(1, size): @@ -72,7 +72,7 @@ fn _quicksort_partition_right[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]) -> Int: - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var size = len(span) var left = 1 @@ -101,7 +101,7 @@ fn _quicksort_partition_left[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]) -> Int: - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var size = len(span) var left = 1 @@ -127,7 +127,7 @@ fn _heap_sort_fix_down[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin], idx: Int): - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var size = len(span) var i = idx var j = i * 2 + 1 @@ -148,7 +148,7 @@ fn _heap_sort[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var size = len(span) # heapify for i in range(size // 2 - 1, -1, -1): @@ -177,7 +177,7 @@ fn _delegate_small_sort[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var size = len(span) if size == 2: _small_sort[2, type, cmp_fn](array) @@ -209,7 +209,7 @@ fn _quicksort[ origin: MutableOrigin, //, cmp_fn: fn (_SortWrapper[type], _SortWrapper[type]) capturing [_] -> Bool, ](span: Span[type, origin]): - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var size = len(span) if size == 0: return @@ -379,7 +379,7 @@ fn _partition[ if size <= 1: return 0 - var array = span.unsafe_ptr().bitcast[origin=MutableAnyOrigin]() + var array = span.unsafe_ptr().origin_cast[origin=MutableAnyOrigin]() var pivot = size // 2 var pivot_value = array[pivot] diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 406a39dbda..68bfc7a481 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -498,7 +498,7 @@ struct StringLiteral( # TODO(MSTDL-555): # Remove bitcast after changing pop.string.address # return type. - return ptr.bitcast[Byte, mut=False, origin=StaticConstantOrigin]() + return ptr.bitcast[Byte]().origin_cast[False, StaticConstantOrigin]() @always_inline fn unsafe_cstr_ptr( diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 214cc78d3f..3dd86b2d39 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -192,7 +192,7 @@ struct _DictValueIter[ # Cast through a pointer to grant additional mutability because # _DictEntryIter.next erases it. return Self.ref_type.address_of( - UnsafePointer.address_of(entry_ref[].value).bitcast[ + UnsafePointer.address_of(entry_ref[].value).origin_cast[ origin=dict_origin ]()[] ) diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 574c13af04..51abfb7b39 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -255,8 +255,8 @@ fn memcpy[ """ var n = count * sizeof[dest.type]() _memcpy_impl( - dest.bitcast[Byte, origin=MutableAnyOrigin](), - src.bitcast[Byte, origin=MutableAnyOrigin](), + dest.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), + src.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), n, ) diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 60e0762d3c..1c9dbf9b40 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -974,11 +974,7 @@ struct UnsafePointer[ fn bitcast[ T: AnyType = Self.type, /, - address_space: AddressSpace = Self.address_space, alignment: Int = Self.alignment, - *, - mut: Bool = Self.mut, - origin: Origin[mut] = Origin[mut].cast_from[Self.origin].result, ](self) -> UnsafePointer[ T, address_space=address_space, @@ -990,10 +986,7 @@ struct UnsafePointer[ Parameters: T: The target type. - address_space: The address space of the result. alignment: Alignment of the destination pointer. - mut: Whether the origin is mutable. - origin: Origin of the destination pointer. Returns: A new UnsafePointer object with the specified type and the same address, @@ -1005,6 +998,58 @@ struct UnsafePointer[ ]._mlir_type, ](self.address) + @always_inline("nodebug") + fn origin_cast[ + mut: Bool = Self.mut, + origin: Origin[mut] = Origin[mut].cast_from[Self.origin].result, + ](self) -> UnsafePointer[ + type, + address_space=address_space, + alignment=alignment, + mut=mut, + origin=origin, + ]: + """Changes the origin or mutability of a pointer. + + Parameters: + mut: Whether the origin is mutable. + origin: Origin of the destination pointer. + + Returns: + A new UnsafePointer object with the same type and the same address, + as the original UnsafePointer and the new specified mutability and origin. + """ + return __mlir_op.`pop.pointer.bitcast`[ + _type = UnsafePointer[ + type, address_space=address_space, alignment=alignment + ]._mlir_type, + ](self.address) + + @always_inline("nodebug") + fn address_space_cast[ + address_space: AddressSpace = Self.address_space, + ](self) -> UnsafePointer[ + type, + address_space=address_space, + alignment=alignment, + mut=mut, + origin=origin, + ]: + """Casts an UnsafePointer to a different address space. + + Parameters: + address_space: The address space of the result. + + Returns: + A new UnsafePointer object with the same type and the same address, + as the original UnsafePointer and the new address space. + """ + return __mlir_op.`pop.pointer.bitcast`[ + _type = UnsafePointer[ + type, address_space=address_space, alignment=alignment + ]._mlir_type, + ](self.address) + @always_inline fn destroy_pointee( self: UnsafePointer[type, address_space = AddressSpace.GENERIC, **_] diff --git a/stdlib/test/collections/test_string.mojo b/stdlib/test/collections/test_string.mojo index a871adae4e..bc76176772 100644 --- a/stdlib/test/collections/test_string.mojo +++ b/stdlib/test/collections/test_string.mojo @@ -1249,7 +1249,7 @@ def test_string_iter(): assert_equal(321, atol(concat)) for v in vs: - v.unsafe_ptr().bitcast[mut=True]()[] = ord("1") + v.unsafe_ptr().origin_cast[mut=True]()[] = ord("1") # Borrow immutably for v in vs: diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 168ffc7bdf..b57fc793cb 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -150,15 +150,15 @@ def test_unsafepointer_string(): def test_eq(): var local = 1 - var p1 = UnsafePointer.address_of(local).bitcast[mut=False]() + var p1 = UnsafePointer.address_of(local).origin_cast[mut=False]() var p2 = p1 assert_equal(p1, p2) var other_local = 2 - var p3 = UnsafePointer.address_of(other_local).bitcast[mut=False]() + var p3 = UnsafePointer.address_of(other_local).origin_cast[mut=False]() assert_not_equal(p1, p3) - var p4 = UnsafePointer.address_of(local).bitcast[mut=False]() + var p4 = UnsafePointer.address_of(local).origin_cast[mut=False]() assert_equal(p1, p4) _ = local _ = other_local From 98498edd830b4a3b476d6cf89c9f8f9bb599e989 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Wed, 18 Dec 2024 16:15:22 -0600 Subject: [PATCH 119/509] [External] [stdlib] Rename `bit_ceil` and `bit_floor` (#53092) [External] [stdlib] Rename `bit_ceil` and `bit_floor` Rename `bit_ceil` and `bit_floor`. Closes #3271 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3895 MODULAR_ORIG_COMMIT_REV_ID: 416856b02b0851cc5725b13fcb6febeee74b957c --- docs/changelog.md | 4 +- stdlib/benchmarks/collections/bench_dict.mojo | 2 +- stdlib/src/bit/__init__.mojo | 4 +- stdlib/src/bit/bit.mojo | 21 ++++-- stdlib/src/collections/deque.mojo | 10 +-- stdlib/test/bit/test_bit.mojo | 70 ++++++++++--------- 6 files changed, 64 insertions(+), 47 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index f98b425f32..4614d4c777 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -73,7 +73,9 @@ what we publish. This improves the "greppability" and at-a-glance readability when a programmer is looking for places in their code that may be performing copies - unnecessarily. + +- `bit_ceil` has been renamed to `next_power_of_two`, and `bit_floor` to + `prev_power_of_two`. This is to improve readability and clarity in their use. ### Tooling changes diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index e93406f837..23b5ad9f8c 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -21,7 +21,7 @@ from random import * from sys import sizeof from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run -from bit import bit_ceil +from bit import next_power_of_two # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/bit/__init__.mojo b/stdlib/src/bit/__init__.mojo index 75004d3618..891b8ea6d1 100644 --- a/stdlib/src/bit/__init__.mojo +++ b/stdlib/src/bit/__init__.mojo @@ -13,8 +13,8 @@ """Implements the bit package.""" from .bit import ( - bit_ceil, - bit_floor, + next_power_of_two, + prev_power_of_two, bit_not, bit_reverse, bit_width, diff --git a/stdlib/src/bit/bit.mojo b/stdlib/src/bit/bit.mojo index fb4170f825..e79dabaa98 100644 --- a/stdlib/src/bit/bit.mojo +++ b/stdlib/src/bit/bit.mojo @@ -412,16 +412,19 @@ fn is_power_of_two[ # ===-----------------------------------------------------------------------===# -# bit_ceil +# next_power_of_two # ===-----------------------------------------------------------------------===# # reference: https://en.cppreference.com/w/cpp/numeric/bit_ceil +# reference: https://doc.rust-lang.org/std/primitive.usize.html#method.next_power_of_two @always_inline -fn bit_ceil(val: Int) -> Int: +fn next_power_of_two(val: Int) -> Int: """Computes the smallest power of 2 that is greater than or equal to the input value. Any integral value less than or equal to 1 will be ceiled to 1. + This operation is called `bit_ceil()` in C++. + Args: val: The input value. @@ -438,13 +441,15 @@ fn bit_ceil(val: Int) -> Int: @always_inline -fn bit_ceil[ +fn next_power_of_two[ type: DType, width: Int, // ](val: SIMD[type, width]) -> SIMD[type, width]: """Computes the smallest power of 2 that is greater than or equal to the input value for each element of a SIMD vector. Any integral value less than or equal to 1 will be ceiled to 1. + This operation is called `bit_ceil()` in C++. + Parameters: type: `dtype` used for the computation. width: SIMD width used for the computation. @@ -468,16 +473,18 @@ fn bit_ceil[ # ===-----------------------------------------------------------------------===# -# bit_floor +# prev_power_of_two # ===-----------------------------------------------------------------------===# # reference: https://en.cppreference.com/w/cpp/numeric/bit_floor @always_inline -fn bit_floor(val: Int) -> Int: +fn prev_power_of_two(val: Int) -> Int: """Computes the largest power of 2 that is less than or equal to the input value. Any integral value less than or equal to 0 will be floored to 0. + This operation is called `bit_floor()` in C++. + Args: val: The input value. @@ -491,13 +498,15 @@ fn bit_floor(val: Int) -> Int: @always_inline -fn bit_floor[ +fn prev_power_of_two[ type: DType, width: Int, // ](val: SIMD[type, width]) -> SIMD[type, width]: """Computes the largest power of 2 that is less than or equal to the input value for each element of a SIMD vector. Any integral value less than or equal to 0 will be floored to 0. + This operation is called `bit_floor()` in C++. + Parameters: type: `dtype` used for the computation. width: SIMD width used for the computation. diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index 0fb93ea179..f55c345f79 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -23,7 +23,7 @@ from collections import Deque from collections import Optional -from bit import bit_ceil +from bit import next_power_of_two from memory import UnsafePointer # ===-----------------------------------------------------------------------===# @@ -105,18 +105,18 @@ struct Deque[ElementType: CollectionElement]( if capacity <= 0: deque_capacity = self.default_capacity else: - deque_capacity = bit_ceil(capacity) + deque_capacity = next_power_of_two(capacity) if min_capacity <= 0: min_deque_capacity = self.default_capacity else: - min_deque_capacity = bit_ceil(min_capacity) + min_deque_capacity = next_power_of_two(min_capacity) if maxlen <= 0: max_deque_len = -1 else: max_deque_len = maxlen - max_deque_capacity = bit_ceil(maxlen) + max_deque_capacity = next_power_of_two(maxlen) if max_deque_capacity == maxlen: max_deque_capacity <<= 1 deque_capacity = min(deque_capacity, max_deque_capacity) @@ -938,7 +938,7 @@ struct Deque[ElementType: CollectionElement]( n_total: The total number of elements the new buffer should support. n_retain: The number of existing elements to keep in the deque. """ - new_capacity = bit_ceil(n_total) + new_capacity = next_power_of_two(n_total) if new_capacity == n_total: new_capacity <<= 1 diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index 1f1063eee0..bedcc02021 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -13,8 +13,8 @@ # RUN: %bare-mojo %s from bit import ( - bit_ceil, - bit_floor, + next_power_of_two, + prev_power_of_two, bit_not, bit_reverse, bit_width, @@ -365,17 +365,17 @@ def test_bit_width_simd(): assert_equal(bit_width(var4), SIMD[int64_t, simd_width](27, 0, 22, 60)) -def test_bit_ceil(): - assert_equal(bit_ceil(-(2**59)), 1) - assert_equal(bit_ceil(-2), 1) - assert_equal(bit_ceil(1), 1) - assert_equal(bit_ceil(2), 2) - assert_equal(bit_ceil(4), 4) - assert_equal(bit_ceil(5), 8) - assert_equal(bit_ceil(2**59 - 3), 2**59) +def test_next_power_of_two(): + assert_equal(next_power_of_two(-(2**59)), 1) + assert_equal(next_power_of_two(-2), 1) + assert_equal(next_power_of_two(1), 1) + assert_equal(next_power_of_two(2), 2) + assert_equal(next_power_of_two(4), 4) + assert_equal(next_power_of_two(5), 8) + assert_equal(next_power_of_two(2**59 - 3), 2**59) -def test_bit_ceil_simd(): +def test_next_power_of_two_simd(): alias simd_width = 4 alias int8_t = DType.int8 alias int16_t = DType.int16 @@ -383,16 +383,20 @@ def test_bit_ceil_simd(): alias int64_t = DType.int64 alias var1 = SIMD[int8_t, simd_width](-114, 0, 2**7 - 3, 2**6) - assert_equal(bit_ceil(var1), SIMD[int8_t, simd_width](1, 1, 2**7, 2**6)) + assert_equal( + next_power_of_two(var1), SIMD[int8_t, simd_width](1, 1, 2**7, 2**6) + ) alias var2 = SIMD[int16_t, simd_width](-11444, 0, 2**12 - 3, 2**13) assert_equal( - bit_ceil(var2), SIMD[int16_t, simd_width](1, 1, 2**12, 2**13) + next_power_of_two(var2), + SIMD[int16_t, simd_width](1, 1, 2**12, 2**13), ) alias var3 = SIMD[int32_t, simd_width](-111444, 0, 2**14 - 3, 2**29) assert_equal( - bit_ceil(var3), SIMD[int32_t, simd_width](1, 1, 2**14, 2**29) + next_power_of_two(var3), + SIMD[int32_t, simd_width](1, 1, 2**14, 2**29), ) # TODO: use this line after #2882 is fixed @@ -401,22 +405,22 @@ def test_bit_ceil_simd(): -111444444, 1, 2**22 - 3, 576460752303423488 ) assert_equal( - bit_ceil(var4), + next_power_of_two(var4), SIMD[int64_t, simd_width](1, 1, 2**22, 2**59), ) -def test_bit_floor(): - assert_equal(bit_floor(-(2**59)), 0) - assert_equal(bit_floor(-2), 0) - assert_equal(bit_floor(1), 1) - assert_equal(bit_floor(2), 2) - assert_equal(bit_floor(4), 4) - assert_equal(bit_floor(5), 4) - assert_equal(bit_floor(2**59), 2**59) +def test_prev_power_of_two(): + assert_equal(prev_power_of_two(-(2**59)), 0) + assert_equal(prev_power_of_two(-2), 0) + assert_equal(prev_power_of_two(1), 1) + assert_equal(prev_power_of_two(2), 2) + assert_equal(prev_power_of_two(4), 4) + assert_equal(prev_power_of_two(5), 4) + assert_equal(prev_power_of_two(2**59), 2**59) -def test_bit_floor_simd(): +def test_prev_power_of_two_simd(): alias simd_width = 4 alias int8_t = DType.int8 alias int16_t = DType.int16 @@ -425,17 +429,19 @@ def test_bit_floor_simd(): alias var1 = SIMD[int8_t, simd_width](-114, 0, 2**5 + 3, 2**6) assert_equal( - bit_floor(var1), SIMD[int8_t, simd_width](0, 0, 2**5, 2**6) + prev_power_of_two(var1), SIMD[int8_t, simd_width](0, 0, 2**5, 2**6) ) alias var2 = SIMD[int16_t, simd_width](-11444, 0, 2**12 + 3, 2**13) assert_equal( - bit_floor(var2), SIMD[int16_t, simd_width](0, 0, 2**12, 2**13) + prev_power_of_two(var2), + SIMD[int16_t, simd_width](0, 0, 2**12, 2**13), ) alias var3 = SIMD[int32_t, simd_width](-111444, 0, 2**14 + 3, 2**29) assert_equal( - bit_floor(var3), SIMD[int32_t, simd_width](0, 0, 2**14, 2**29) + prev_power_of_two(var3), + SIMD[int32_t, simd_width](0, 0, 2**14, 2**29), ) # TODO: use this line after #2882 is fixed @@ -444,7 +450,7 @@ def test_bit_floor_simd(): -111444444, 1, 2**22 + 3, 576460752303423488 ) assert_equal( - bit_floor(var4), + prev_power_of_two(var4), SIMD[int64_t, simd_width](0, 1, 2**22, 2**59), ) @@ -526,10 +532,10 @@ def test_log2_floor(): def main(): test_rotate_bits_int() test_rotate_bits_simd() - test_bit_ceil() - test_bit_ceil_simd() - test_bit_floor() - test_bit_floor_simd() + test_next_power_of_two() + test_next_power_of_two_simd() + test_prev_power_of_two() + test_prev_power_of_two_simd() test_bit_width() test_bit_width_simd() test_is_power_of_two() From 8fae8597d733b22c75a6939545679efa546860c6 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 18 Dec 2024 16:16:40 -0600 Subject: [PATCH 120/509] [stdlib] cleanup: Use `_Global` and new `_OwnedDLHandle` to simplify `_get_dylib_function()` interface MODULAR_ORIG_COMMIT_REV_ID: 11f71a3c91de0eba9d09194cbc19afd0753eebba --- stdlib/src/sys/ffi.mojo | 60 ++++++++++++++++++++++++++++------------- 1 file changed, 42 insertions(+), 18 deletions(-) diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 1c3299a196..397bc099a3 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -113,6 +113,40 @@ struct RTLD: alias DEFAULT_RTLD = RTLD.NOW | RTLD.GLOBAL +struct _OwnedDLHandle: + """Represents an owned handle to a dynamically linked library that can be + loaded and unloaded. + + This type is intended to replace `DLHandle`, by incrementally introducing + ownership semantics to `DLHandle`. + """ + + var _handle: DLHandle + + # ===-------------------------------------------------------------------===# + # Life cycle methods + # ===-------------------------------------------------------------------===# + + @always_inline + fn __init__(out self, path: String, flags: Int = DEFAULT_RTLD): + self._handle = DLHandle(path, flags) + + fn __moveinit__(out self, owned other: Self): + self._handle = other._handle + + fn __del__(owned self): + """Delete the DLHandle object unloading the associated dynamic library. + """ + self._handle.close() + + # ===-------------------------------------------------------------------===# + # Methods + # ===-------------------------------------------------------------------===# + + fn handle(self) -> DLHandle: + return self._handle + + @value @register_passable("trivial") struct DLHandle(CollectionElement, CollectionElementNew, Boolable): @@ -367,35 +401,22 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): return self.get_function[fn (__type_of(v)) -> return_type](name)(v) -@always_inline -fn _get_dylib[ - name: StringLiteral, - init_fn: fn (OpaquePointer) -> OpaquePointer, - destroy_fn: fn (OpaquePointer) -> None, -](payload: OpaquePointer = OpaquePointer()) -> DLHandle: - var ptr = _get_global[name, init_fn, destroy_fn](payload).bitcast[ - DLHandle - ]() - return ptr[] - - @always_inline fn _get_dylib_function[ - name: StringLiteral, + dylib_global: _Global[_, _OwnedDLHandle, _], func_name: StringLiteral, - init_fn: fn (OpaquePointer) -> OpaquePointer, - destroy_fn: fn (OpaquePointer) -> None, result_type: AnyTrivialRegType, -](payload: OpaquePointer = OpaquePointer()) -> result_type: - alias func_cache_name = name + "/" + func_name +]() -> result_type: + alias func_cache_name = dylib_global.name + "/" + func_name var func_ptr = _get_global_or_null[func_cache_name]() if func_ptr: var result = UnsafePointer.address_of(func_ptr).bitcast[result_type]()[] _ = func_ptr return result - var dylib = _get_dylib[name, init_fn, destroy_fn](payload) + var dylib = dylib_global.get_or_create_ptr()[].handle() var new_func = dylib._get_function[func_name, result_type]() + external_call["KGEN_CompilerRT_InsertGlobal", NoneType]( StringRef(func_cache_name), UnsafePointer.address_of(new_func).bitcast[OpaquePointer]()[], @@ -414,6 +435,9 @@ struct _Global[ storage_type: Movable, init_fn: fn () -> storage_type, ]: + fn __init__(out self): + pass + @staticmethod fn _init_wrapper(payload: OpaquePointer) -> OpaquePointer: # Struct-based globals don't get to take arguments to their initializer. From 6eb0b8947e3560825fe89fd081d2bc97d40dbcf8 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Wed, 18 Dec 2024 16:19:36 -0600 Subject: [PATCH 121/509] [External] [stdlib] Add `List[Scalar[D]].extend()` from `SIMD` and `Span[Scalar[D]]` (#52584) [External] [stdlib] Add `List[Scalar[D]].extend()` from `SIMD` and `Span[Scalar[D]]` Add `List[Scalar[D]].extend()` from `SIMD` and `Span[Scalar[D]]` Split off from modularml/mojo#3814. This is needed to enable efficient appending of scalar value sequences to a `List` without having to resort to `UnsafePointer` manually. ORIGINAL_AUTHOR=martinvuyk <110240700+martinvuyk@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3854 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Co-authored-by: Connor Gray Closes modularml/mojo#3854 MODULAR_ORIG_COMMIT_REV_ID: 7d0c724497ba0671ae660f4de5758d6c4baad7bc --- docs/changelog.md | 5 ++ stdlib/src/base64/_b64encode.mojo | 28 +---------- stdlib/src/collections/list.mojo | 64 +++++++++++++++++++++++++- stdlib/src/testing/testing.mojo | 39 +++++++++++++++- stdlib/src/utils/inline_string.mojo | 25 +++------- stdlib/test/collections/test_list.mojo | 48 ++++++++++--------- 6 files changed, 140 insertions(+), 69 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 4614d4c777..ef7aec9af6 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -66,6 +66,11 @@ what we publish. var ptr2 = list2.unsafe_ptr() ``` +- Added new `List.extend()` overloads taking `SIMD` and `Span`. These enable + growing a `List[Scalar[..]]` by copying the elements of a `SIMD` vector or + `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware + functionality. + - The `ExplicitlyCopyable` trait has changed to require a `fn copy(self) -> Self` method. Previously, an initializer with the signature `fn __init__(out self, *, other: Self)` had been required by diff --git a/stdlib/src/base64/_b64encode.mojo b/stdlib/src/base64/_b64encode.mojo index 74b8c31501..35b7f4c5d9 100644 --- a/stdlib/src/base64/_b64encode.mojo +++ b/stdlib/src/base64/_b64encode.mojo @@ -195,21 +195,6 @@ fn load_incomplete_simd[ return result -fn store_incomplete_simd[ - simd_width: Int -]( - pointer: UnsafePointer[UInt8], - owned simd_vector: SIMD[DType.uint8, simd_width], - nb_of_elements_to_store: Int, -): - var tmp_buffer_pointer = UnsafePointer.address_of(simd_vector).bitcast[ - UInt8 - ]() - - memcpy(dest=pointer, src=tmp_buffer_pointer, count=nb_of_elements_to_store) - _ = simd_vector # We make it live long enough - - # TODO: Use Span instead of List as input when Span is easier to use @no_inline fn b64encode_with_buffers( @@ -229,11 +214,7 @@ fn b64encode_with_buffers( var input_vector = start_of_input_chunk.load[width=simd_width]() - result_vector = _to_b64_ascii(input_vector) - - (result.unsafe_ptr() + len(result)).store(result_vector) - - result.size += simd_width + result.extend(_to_b64_ascii(input_vector)) input_index += input_simd_width # We handle the last 0, 1 or 2 chunks @@ -268,12 +249,7 @@ fn b64encode_with_buffers( ]( nb_of_elements_to_load ) - store_incomplete_simd( - result.unsafe_ptr() + len(result), - result_vector_with_equals, - nb_of_elements_to_store, - ) - result.size += nb_of_elements_to_store + result.extend(result_vector_with_equals, count=nb_of_elements_to_store) input_index += input_simd_width diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 5732bc8da5..f537f7151f 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -497,9 +497,13 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Args: value: The value to append. + + Notes: + If there is no capacity left, resizes to twice the current capacity. + Except for 0 capacity where it sets 1. """ if self.size >= self.capacity: - self._realloc(max(1, self.capacity * 2)) + self._realloc(self.capacity * 2 | int(self.capacity == 0)) (self.data + self.size).init_pointee_move(value^) self.size += 1 @@ -591,6 +595,64 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # list. self.size = final_size + fn extend[ + D: DType, // + ](mut self: List[Scalar[D], *_, **_], value: SIMD[D, _]): + """Extends this list with the elements of a vector. + + Parameters: + D: The DType. + + Args: + value: The value to append. + + Notes: + If there is no capacity left, resizes to `len(self) + value.size`. + """ + self.reserve(self.size + value.size) + (self.data + self.size).store(value) + self.size += value.size + + fn extend[ + D: DType, // + ](mut self: List[Scalar[D], *_, **_], value: SIMD[D, _], *, count: Int): + """Extends this list with `count` number of elements from a vector. + + Parameters: + D: The DType. + + Args: + value: The value to append. + count: The ammount of items to append. Must be less than or equal to + `value.size`. + + Notes: + If there is no capacity left, resizes to `len(self) + count`. + """ + debug_assert(count <= value.size, "count must be <= value.size") + self.reserve(self.size + count) + var v_ptr = UnsafePointer.address_of(value).bitcast[Scalar[D]]() + memcpy(self.data + self.size, v_ptr, count) + self.size += count + + fn extend[ + D: DType, // + ](mut self: List[Scalar[D], *_, **_], value: Span[Scalar[D]]): + """Extends this list with the elements of a `Span`. + + Parameters: + D: The DType. + + Args: + value: The value to append. + + Notes: + If there is no capacity left, resizes to `len(self) + len(value)`. + """ + self.reserve(self.size + len(value)) + memcpy(self.data + self.size, value.unsafe_ptr(), len(value)) + self.size += len(value) + fn pop(mut self, i: Int = -1) -> T: """Pops a value from the list at the given index. diff --git a/stdlib/src/testing/testing.mojo b/stdlib/src/testing/testing.mojo index 20173be736..ef39769ff8 100644 --- a/stdlib/src/testing/testing.mojo +++ b/stdlib/src/testing/testing.mojo @@ -32,7 +32,7 @@ def main(): """ from collections import Optional from math import isclose - +from memory import memcmp from builtin._location import __call_location, _SourceLocation # ===----------------------------------------------------------------------=== # @@ -236,6 +236,43 @@ fn assert_equal[ ) +@always_inline +fn assert_equal[ + D: DType +]( + lhs: List[Scalar[D]], + rhs: List[Scalar[D]], + msg: String = "", + *, + location: Optional[_SourceLocation] = None, +) raises: + """Asserts that two lists are equal. + + Parameters: + D: A DType. + + Args: + lhs: The left-hand side list. + rhs: The right-hand side list. + msg: The message to be printed if the assertion fails. + location: The location of the error (default to the `__call_location`). + + Raises: + An Error with the provided message if assert fails and `None` otherwise. + """ + var length = len(lhs) + if ( + length != len(rhs) + or memcmp(lhs.unsafe_ptr(), rhs.unsafe_ptr(), length) != 0 + ): + raise _assert_cmp_error["`left == right` comparison"]( + lhs.__str__(), + rhs.__str__(), + msg=msg, + loc=location.or_else(__call_location()), + ) + + @always_inline fn assert_not_equal[ T: Testable diff --git a/stdlib/src/utils/inline_string.mojo b/stdlib/src/utils/inline_string.mojo index 5fb4687089..9be3326019 100644 --- a/stdlib/src/utils/inline_string.mojo +++ b/stdlib/src/utils/inline_string.mojo @@ -147,28 +147,15 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): # Begin by heap allocating enough space to store the combined # string. var buffer = List[UInt8](capacity=total_len) - # Copy the bytes from the current small string layout - memcpy( - dest=buffer.unsafe_ptr(), - src=self._storage[_FixedString[Self.SMALL_CAP]].unsafe_ptr(), - count=len(self), + var span_self = Span[Byte, __origin_of(self)]( + ptr=self._storage[_FixedString[Self.SMALL_CAP]].unsafe_ptr(), + length=len(self), ) - + buffer.extend(span_self) # Copy the bytes from the additional string. - memcpy( - dest=buffer.unsafe_ptr() + len(self), - src=str_slice.unsafe_ptr(), - count=str_slice.byte_length(), - ) - - # Record that we've initialized `total_len` count of elements - # in `buffer` - buffer.size = total_len - - # Add the NUL byte - buffer.append(0) - + buffer.extend(str_slice.as_bytes()) + buffer.append(0) # Add the NUL byte self._storage = Self.Layout(String(buffer^)) fn __add__(self, other: StringLiteral) -> Self: diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index 9f45d66f5a..c6b3e4fb1e 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -437,32 +437,35 @@ def test_list_index(): _ = test_list_b.index(20, start=4, stop=5) -def test_list_extend(): - # - # Test extending the list [1, 2, 3] with itself - # +def test_list_append(): + var items = List[UInt32]() + items.append(1) + items.append(2) + items.append(3) + assert_equal(items, List[UInt32](1, 2, 3)) - vec = List[Int]() - vec.append(1) - vec.append(2) - vec.append(3) - assert_equal(len(vec), 3) - assert_equal(vec[0], 1) - assert_equal(vec[1], 2) - assert_equal(vec[2], 3) +def test_list_extend(): + var items = List[UInt32](1, 2, 3) + var copy = items + items.extend(copy) + assert_equal(items, List[UInt32](1, 2, 3, 1, 2, 3)) + + items = List[UInt32](1, 2, 3) + copy = List[UInt32](1, 2, 3) - var copy = vec - vec.extend(copy) + # Extend with span + items.extend(Span(copy)) + assert_equal(items, List[UInt32](1, 2, 3, 1, 2, 3)) - # vec == [1, 2, 3, 1, 2, 3] - assert_equal(len(vec), 6) - assert_equal(vec[0], 1) - assert_equal(vec[1], 2) - assert_equal(vec[2], 3) - assert_equal(vec[3], 1) - assert_equal(vec[4], 2) - assert_equal(vec[5], 3) + # Extend with whole SIMD + items = List[UInt32](1, 2, 3) + items.extend(SIMD[DType.uint32, 4](1, 2, 3, 4)) + assert_equal(items, List[UInt32](1, 2, 3, 1, 2, 3, 4)) + # Extend with part of SIMD + items = List[UInt32](1, 2, 3) + items.extend(SIMD[DType.uint32, 4](1, 2, 3, 4), count=3) + assert_equal(items, List[UInt32](1, 2, 3, 1, 2, 3)) def test_list_extend_non_trivial(): @@ -952,6 +955,7 @@ def main(): test_list_reverse_move_count() test_list_insert() test_list_index() + test_list_append() test_list_extend() test_list_extend_non_trivial() test_list_explicit_copy() From f075bf263ba0e27b7874abd848848544d7b2bf7d Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Wed, 18 Dec 2024 18:34:59 -0600 Subject: [PATCH 122/509] [External] [stdlib] Doc improvements and cleanups around low-level `VariadicPack` use (#48996) [External] [stdlib] Doc improvements and cleanups around low-level VariadicPack use #48996 This includes changes from modularml/mojo#3632, minus parts which ended up conflicting with internal changes doing the same refactoring. Context: https://github.com/modularml/mojo/pull/3632#issuecomment-2462756696 Original commit description: [External] [stdlib] Refactor `external_call()` and inline `_LITRefPackHelper` into `VariadicPack` Refactor `external_call()` and inline most of `_LITRefPackHelper` into `VariadicPack`, leave `_LITRefPackHelper` itself intact since I found no way to inline the `AddressSpace` parameter into `VariadicPack`. ORIGINAL_AUTHOR=martinvuyk <110240700+martinvuyk@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3632 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3632 MODULAR_ORIG_COMMIT_REV_ID: 8a1c1a5a92c6910039158108ab47046583ae9586 --- stdlib/src/builtin/builtin_list.mojo | 26 ++++++------ stdlib/src/builtin/io.mojo | 15 ++++--- stdlib/src/sys/_assembly.mojo | 4 +- stdlib/src/sys/ffi.mojo | 63 +++++++++++++++++++--------- stdlib/src/sys/intrinsics.mojo | 21 +++++----- 5 files changed, 79 insertions(+), 50 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index e74b93cad3..50e63d02d2 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -605,29 +605,30 @@ struct VariadicPack[ # C Pack Utilities # ===-------------------------------------------------------------------===# - # This is the element_types list lowered to `variadic` type for kgen. alias _kgen_element_types = rebind[ __mlir_type.`!kgen.variadic` ](Self.element_types) - - # Use variadic_ptr_map to construct the type list of the !kgen.pack that the - # !lit.ref.pack will lower to. It exposes the pointers introduced by the - # references. + """This is the element_types list lowered to `variadic` type for kgen. + """ alias _variadic_pointer_types = __mlir_attr[ `#kgen.param.expr: !kgen.variadic`, ] - - # This is the !kgen.pack type with pointer elements. + """Use variadic_ptr_map to construct the type list of the !kgen.pack that + the !lit.ref.pack will lower to. It exposes the pointers introduced by the + references. + """ alias _kgen_pack_with_pointer_type = __mlir_type[ `!kgen.pack<:variadic `, Self._variadic_pointer_types, `>` ] + """This is the !kgen.pack type with pointer elements.""" - # This rebinds `in_pack` to the equivalent `!kgen.pack` with kgen pointers. @doc_private @always_inline("nodebug") fn get_as_kgen_pack(self) -> Self._kgen_pack_with_pointer_type: + """This rebinds `in_pack` to the equivalent `!kgen.pack` with kgen + pointers.""" return rebind[Self._kgen_pack_with_pointer_type](self._value) alias _variadic_with_pointers_removed = __mlir_attr[ @@ -635,15 +636,16 @@ struct VariadicPack[ Self._variadic_pointer_types, `>: !kgen.variadic`, ] - - # This is the `!kgen.pack` type that happens if one loads all the elements - # of the pack. alias _loaded_kgen_pack_type = __mlir_type[ `!kgen.pack<:variadic `, Self._variadic_with_pointers_removed, `>` ] + """This is the `!kgen.pack` type that happens if one loads all the elements + of the pack. + """ - # This returns the stored KGEN pack after loading all of the elements. @doc_private @always_inline("nodebug") fn get_loaded_kgen_pack(self) -> Self._loaded_kgen_pack_type: + """This returns the stored KGEN pack after loading all of the elements. + """ return __mlir_op.`kgen.pack.load`(self.get_as_kgen_pack()) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index fc363fc15c..a409c35c62 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -26,7 +26,7 @@ from sys import ( stdout, ) from sys._libc import dup, fclose, fdopen, fflush -from sys.ffi import OpaquePointer +from sys.ffi import OpaquePointer, c_char from builtin.dtype import _get_dtype_printf_format from builtin.file_descriptor import FileDescriptor @@ -165,11 +165,11 @@ fn _flush(file: FileDescriptor = stdout): @no_inline fn _printf[ fmt: StringLiteral, *types: AnyType -](*arguments: *types, file: FileDescriptor = stdout): +](*args: *types, file: FileDescriptor = stdout): # The argument pack will contain references for each value in the pack, # but we want to pass their values directly into the C printf call. Load # all the members of the pack. - var loaded_pack = arguments.get_loaded_kgen_pack() + var loaded_pack = args.get_loaded_kgen_pack() @parameter if is_nvidia_gpu(): @@ -181,6 +181,7 @@ fn _printf[ pass else: with _fdopen(file) as fd: + # FIXME: external_call should handle this _ = __mlir_op.`pop.external_call`[ func = "KGEN_CompilerRT_fprintf".value, variadicType = __mlir_attr[ @@ -201,7 +202,7 @@ fn _printf[ @no_inline fn _snprintf[ fmt: StringLiteral, *types: AnyType -](str: UnsafePointer[UInt8], size: Int, *arguments: *types) -> Int: +](str: UnsafePointer[UInt8], size: Int, *args: *types) -> Int: """Writes a format string into an output pointer. Parameters: @@ -211,16 +212,18 @@ fn _snprintf[ Args: str: A pointer into which the format string is written. size: At most, `size - 1` bytes are written into the output string. - arguments: Arguments interpolated into the format string. + args: Arguments interpolated into the format string. Returns: The number of bytes written into the output string. """ + # The argument pack will contain references for each value in the pack, # but we want to pass their values directly into the C snprintf call. Load # all the members of the pack. - var loaded_pack = arguments.get_loaded_kgen_pack() + var loaded_pack = args.get_loaded_kgen_pack() + # FIXME: external_call should handle this return int( __mlir_op.`pop.external_call`[ func = "snprintf".value, diff --git a/stdlib/src/sys/_assembly.mojo b/stdlib/src/sys/_assembly.mojo index 2074afde29..c48f518cd6 100644 --- a/stdlib/src/sys/_assembly.mojo +++ b/stdlib/src/sys/_assembly.mojo @@ -26,9 +26,9 @@ fn inlined_assembly[ *types: AnyType, constraints: StringLiteral, has_side_effect: Bool = True, -](*arguments: *types) -> result_type: +](*args: *types) -> result_type: """Generates assembly via inline assembly.""" - var loaded_pack = arguments.get_loaded_kgen_pack() + var loaded_pack = args.get_loaded_kgen_pack() @parameter if _mlirtype_is_eq[result_type, NoneType](): diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 397bc099a3..429dd1e0f7 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -496,37 +496,60 @@ fn _get_global_or_null[name: StringLiteral]() -> OpaquePointer: @always_inline("nodebug") fn external_call[ - callee: StringLiteral, type: AnyTrivialRegType, *types: AnyType -](*arguments: *types) -> type: + callee: StringLiteral, + return_type: AnyTrivialRegType, + *types: AnyType, +](*args: *types) -> return_type: """Calls an external function. Args: - arguments: The arguments to pass to the external function. + args: The arguments to pass to the external function. Parameters: - callee: The name of the external function. - type: The return type. - types: The argument types. + callee: The name of the external function. + return_type: The return type. + types: The argument types. Returns: - The external call result. + The external call result. + """ + return external_call[callee, return_type](args) + + +@always_inline("nodebug") +fn external_call[ + callee: StringLiteral, + return_type: AnyTrivialRegType, +](args: VariadicPack[element_trait=AnyType]) -> return_type: + """Calls an external function. + + Parameters: + callee: The name of the external function. + return_type: The return type. + + Args: + args: The arguments to pass to the external function. + + Returns: + The external call result. """ # The argument pack will contain references for each value in the pack, # but we want to pass their values directly into the C printf call. Load # all the members of the pack. - var loaded_pack = arguments.get_loaded_kgen_pack() + var loaded_pack = args.get_loaded_kgen_pack() @parameter - if _mlirtype_is_eq[type, NoneType](): + if _mlirtype_is_eq[return_type, NoneType](): __mlir_op.`pop.external_call`[func = callee.value, _type=None]( loaded_pack ) - return rebind[type](None) + return rebind[return_type](None) else: - return __mlir_op.`pop.external_call`[func = callee.value, _type=type]( - loaded_pack - ) + return __mlir_op.`pop.external_call`[ + func = callee.value, + _type=return_type, + ](loaded_pack) # ===-----------------------------------------------------------------------===# @@ -536,18 +559,20 @@ fn external_call[ @always_inline("nodebug") fn _external_call_const[ - callee: StringLiteral, type: AnyTrivialRegType, *types: AnyType -](*arguments: *types) -> type: + callee: StringLiteral, + return_type: AnyTrivialRegType, + *types: AnyType, +](*args: *types) -> return_type: """Mark the external function call as having no observable effects to the program state. This allows the compiler to optimize away successive calls to the same function. Args: - arguments: The arguments to pass to the external function. + args: The arguments to pass to the external function. Parameters: callee: The name of the external function. - type: The return type. + return_type: The return type. types: The argument types. Returns: @@ -557,7 +582,7 @@ fn _external_call_const[ # The argument pack will contain references for each value in the pack, # but we want to pass their values directly into the C printf call. Load # all the members of the pack. - var loaded_pack = arguments.get_loaded_kgen_pack() + var loaded_pack = args.get_loaded_kgen_pack() return __mlir_op.`pop.external_call`[ func = callee.value, @@ -568,5 +593,5 @@ fn _external_call_const[ `argMem = none, `, `inaccessibleMem = none>`, ], - _type=type, + _type=return_type, ](loaded_pack) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 3eb81a505a..1a7242cc63 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -37,25 +37,24 @@ fn llvm_intrinsic[ type: AnyTrivialRegType, *types: AnyType, has_side_effect: Bool = True, -](*arguments: *types) -> type: - """Calls an LLVM intrinsic with no arguments. - - Calls an LLVM intrinsic with the name intrin and return type type. +](*args: *types) -> type: + """Calls an LLVM intrinsic with the name `intrin` and return type `type`. Parameters: - intrin: The name of the llvm intrinsic. - type: The return type of the intrinsic. - types: The argument types for the function. - has_side_effect: If `True` the intrinsic will have side effects, otherwise its pure. + intrin: The name of the llvm intrinsic. + type: The return type of the intrinsic. + types: The argument types for the function. + has_side_effect: If `True` the intrinsic will have side effects, + otherwise its pure. Args: - arguments: The arguments to the function. + args: The arguments to the function. Returns: - The result of calling the llvm intrinsic with no arguments. + The result of calling the llvm intrinsic with no arguments. """ - var loaded_pack = arguments.get_loaded_kgen_pack() + var loaded_pack = args.get_loaded_kgen_pack() @parameter if _mlirtype_is_eq[type, NoneType](): From 7d8897a2fc8c8f2007c478bff0370060d3d4164e Mon Sep 17 00:00:00 2001 From: soraros Date: Thu, 19 Dec 2024 16:20:24 -0600 Subject: [PATCH 123/509] [External] [stdlib] Explicitly add the inferred parameter dtype in `min/max` (#53150) [External] [stdlib] Explicitly add the inferred parameter dtype in `min/max` This allows us to disambiguate and use them in higher-order functions like `f[min[dtype=...]]()`. Co-authored-by: soraros Closes modularml/mojo#3901 MODULAR_ORIG_COMMIT_REV_ID: 1148ff7987a426aef43e5198c23e0feca3e154b8 --- stdlib/src/builtin/math.mojo | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 428407f857..843b380e88 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -175,7 +175,7 @@ fn max(x: UInt, y: UInt, /) -> UInt: @always_inline("nodebug") -fn max(x: SIMD, y: __type_of(x), /) -> __type_of(x): +fn max[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): """Performs elementwise maximum of x and y. An element of the result SIMD vector will be the maximum of the @@ -184,6 +184,9 @@ fn max(x: SIMD, y: __type_of(x), /) -> __type_of(x): Constraints: The type of the inputs must be numeric or boolean. + Parameters: + dtype: The data type of the SIMD vector. + Args: x: First SIMD vector. y: Second SIMD vector. @@ -237,7 +240,7 @@ fn min(x: UInt, y: UInt, /) -> UInt: @always_inline("nodebug") -fn min(x: SIMD, y: __type_of(x), /) -> __type_of(x): +fn min[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): """Gets the elementwise minimum of x and y. An element of the result SIMD vector will be the minimum of the @@ -246,6 +249,9 @@ fn min(x: SIMD, y: __type_of(x), /) -> __type_of(x): Constraints: The type of the inputs must be numeric or boolean. + Parameters: + dtype: The data type of the SIMD vector. + Args: x: First SIMD vector. y: Second SIMD vector. From 8ba85b66d0c60408e68417cdb48d9b1fe8f9e3c8 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Thu, 19 Dec 2024 15:01:32 -0800 Subject: [PATCH 124/509] Fixed typo in @parameter documentation MODULAR_ORIG_COMMIT_REV_ID: 7d7f165ee7ad39beca388458632d90282aab0ca1 --- docs/manual/decorators/parameter.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/manual/decorators/parameter.md b/docs/manual/decorators/parameter.md index 2b9f27e9e0..c7a717e22c 100644 --- a/docs/manual/decorators/parameter.md +++ b/docs/manual/decorators/parameter.md @@ -5,10 +5,11 @@ codeTitle: true --- -You can add the `@parameter` decorator on an `if` statement or on a nested -function to run that code at compile time. +You can add the `@parameter` decorator on an `if` or `for` statement to run that +code at compile time, or on a nested function to create a [parametric +closure](#parametric-closure). -## Parametric if statement +## Parametric `if` statement You can add `@parameter` to any `if` condition that's based on a valid parameter expression (it's an expression that evaluates at compile time). This @@ -27,7 +28,7 @@ else: this will be included in the binary ``` -## Parametric for statement +## Parametric `for` statement You can add the `@parameter` decorator to a `for` loop to create a loop that's evaluated at compile time. The loop sequence and induction values must be @@ -39,7 +40,7 @@ This has the effect of "unrolling" the loop. ```mojo fn parameter_for[max: Int](): @parameter - for i in range(max) + for i in range(max): @parameter if i == 10: print("found 10!") @@ -61,8 +62,8 @@ differences when compared to the parametric `for` statement: (see below) and executes it a specified number of times. - The parametric `for` statement is more versatile, since you can do anything - you can do in a `for` statement: including using arbitrary sequences, - early-exiting from the loop, skipping iterations with `continue` and so on. + you can do in a `for` statement including: using arbitrary sequences, + early-exiting from the loop, skipping iterations with `continue`, and so on. By contrast, `unroll()` simply takes a function and a count, and executes the function the specified number of times. From 3988d06145095fd181bffac869985f857ebaa740 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Thu, 19 Dec 2024 18:10:20 -0600 Subject: [PATCH 125/509] [External] [stdlib] Create new string sub-package (#52916) [External] [stdlib] Create new string sub-package Closes https://github.com/modularml/mojo/issues/3871 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3880 MODULAR_ORIG_COMMIT_REV_ID: cd800d9888d1fe472fcb587c5b275781f94c0c87 --- .../benchmarks/collections/bench_string.mojo | 2 +- stdlib/src/builtin/int.mojo | 2 +- stdlib/src/builtin/simd.mojo | 3 ++- stdlib/src/builtin/string_literal.mojo | 9 +++---- stdlib/src/collections/string/__init__.mojo | 27 +++++++++++++++++++ .../string}/_unicode.mojo | 4 +-- .../string}/_unicode_lookups.mojo | 0 .../string}/_utf8_validation.mojo | 2 +- .../{utils => collections/string}/format.mojo | 2 +- .../string}/inline_string.mojo | 6 ++--- .../src/collections/{ => string}/string.mojo | 13 ++++----- .../string}/string_slice.mojo | 22 +++++++-------- stdlib/src/utils/__init__.mojo | 4 +-- stdlib/src/utils/index.mojo | 2 +- stdlib/src/utils/stringref.mojo | 4 +-- .../string}/test_inlined_string.mojo | 4 +-- .../collections/{ => string}/test_string.mojo | 18 ++++++------- .../string}/test_string_slice.mojo | 8 +++--- .../{test_format.mojo => test_write.mojo} | 2 +- ..._stdout.mojo => test_write_to_stdout.mojo} | 0 20 files changed, 79 insertions(+), 55 deletions(-) create mode 100644 stdlib/src/collections/string/__init__.mojo rename stdlib/src/{utils => collections/string}/_unicode.mojo (99%) rename stdlib/src/{utils => collections/string}/_unicode_lookups.mojo (100%) rename stdlib/src/{utils => collections/string}/_utf8_validation.mojo (100%) rename stdlib/src/{utils => collections/string}/format.mojo (99%) rename stdlib/src/{utils => collections/string}/inline_string.mojo (99%) rename stdlib/src/collections/{ => string}/string.mojo (99%) rename stdlib/src/{utils => collections/string}/string_slice.mojo (98%) rename stdlib/test/{utils => collections/string}/test_inlined_string.mojo (97%) rename stdlib/test/collections/{ => string}/test_string.mojo (99%) rename stdlib/test/{utils => collections/string}/test_string_slice.mojo (99%) rename stdlib/test/utils/{test_format.mojo => test_write.mojo} (97%) rename stdlib/test/utils/{test_format_to_stdout.mojo => test_write_to_stdout.mojo} (100%) diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index fc5dfc718e..8805ac9d06 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -22,7 +22,7 @@ from random import random_si64, seed from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run -from utils._utf8_validation import _is_valid_utf8 +from collections.string._utf8_validation import _is_valid_utf8 # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index c0da32d8c5..d90969f07f 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -16,7 +16,7 @@ These are Mojo built-ins, so you don't need to import them. """ from collections import KeyElement -from collections.string import ( +from collections.string.string import ( _calc_initial_buffer_size_int32, _calc_initial_buffer_size_int64, ) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 7ac510fd42..b720d4fb86 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -17,7 +17,8 @@ These are Mojo built-ins, so you don't need to import them. import math from collections import InlineArray -from collections.string import ( +from collections.string import StringSlice +from collections.string.string import ( _calc_format_buffer_size, _calc_initial_buffer_size, ) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 68bfc7a481..84f59e6abb 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -16,15 +16,14 @@ These are Mojo built-ins, so you don't need to import them. """ from collections import List +from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry +from collections.string.string_slice import _StringSliceIter, _to_string_list from hashlib._hasher import _HashableWithHasher, _Hasher -from sys.ffi import c_char - from memory import UnsafePointer, memcpy, Span - +from sys.ffi import c_char from utils import StaticString, StringRef, StringSlice, Writable, Writer from utils._visualizers import lldb_formatter_wrapping_type -from utils.format import _CurlyEntryFormattable, _FormatCurlyEntry -from utils.string_slice import _StringSliceIter, _to_string_list + # ===-----------------------------------------------------------------------===# # StringLiteral diff --git a/stdlib/src/collections/string/__init__.mojo b/stdlib/src/collections/string/__init__.mojo new file mode 100644 index 0000000000..97f9fdc563 --- /dev/null +++ b/stdlib/src/collections/string/__init__.mojo @@ -0,0 +1,27 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Implements the string package.""" + +from .string import ( + String, + ascii, + atof, + atol, + chr, + isdigit, + islower, + isprintable, + isupper, + ord, +) +from .string_slice import StringSlice, StaticString diff --git a/stdlib/src/utils/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo similarity index 99% rename from stdlib/src/utils/_unicode.mojo rename to stdlib/src/collections/string/_unicode.mojo index fb61a24cd9..69bb129418 100644 --- a/stdlib/src/utils/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -10,10 +10,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # + from bit import count_leading_zeros from memory import UnsafePointer, memcpy - -from ._unicode_lookups import * +from collections.string._unicode_lookups import * fn _uppercase_mapping_index(rune: Int) -> Int: diff --git a/stdlib/src/utils/_unicode_lookups.mojo b/stdlib/src/collections/string/_unicode_lookups.mojo similarity index 100% rename from stdlib/src/utils/_unicode_lookups.mojo rename to stdlib/src/collections/string/_unicode_lookups.mojo diff --git a/stdlib/src/utils/_utf8_validation.mojo b/stdlib/src/collections/string/_utf8_validation.mojo similarity index 100% rename from stdlib/src/utils/_utf8_validation.mojo rename to stdlib/src/collections/string/_utf8_validation.mojo index eb514733ca..31327dce89 100644 --- a/stdlib/src/utils/_utf8_validation.mojo +++ b/stdlib/src/collections/string/_utf8_validation.mojo @@ -26,9 +26,9 @@ https://github.com/simdutf/SimdUnicode/blob/main/src/UTF8.cs """ from base64._b64encode import _sub_with_saturation +from memory import UnsafePointer, Span from sys.intrinsics import llvm_intrinsic -from memory import UnsafePointer, Span alias TOO_SHORT: UInt8 = 1 << 0 alias TOO_LONG: UInt8 = 1 << 1 diff --git a/stdlib/src/utils/format.mojo b/stdlib/src/collections/string/format.mojo similarity index 99% rename from stdlib/src/utils/format.mojo rename to stdlib/src/collections/string/format.mojo index 59d1885c58..abaeb26c10 100644 --- a/stdlib/src/utils/format.mojo +++ b/stdlib/src/collections/string/format.mojo @@ -13,8 +13,8 @@ """Implements Formatting utilities.""" from collections import Optional - from memory import UnsafePointer +from utils import Variant # TODO: _FormatCurlyEntry and _FormatSpec should be public in the future for # people who want to write their own templating engines. This is not yet done diff --git a/stdlib/src/utils/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo similarity index 99% rename from stdlib/src/utils/inline_string.mojo rename to stdlib/src/collections/string/inline_string.mojo index 9be3326019..93703b8450 100644 --- a/stdlib/src/utils/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -16,12 +16,12 @@ """ from collections import InlineArray, Optional +from collections.string import StringSlice +from memory import UnsafePointer, memcpy, Span from os import abort from sys import sizeof +from utils import Variant, StringRef -from memory import UnsafePointer, memcpy, Span - -from utils import StringSlice, Variant # ===-----------------------------------------------------------------------===# # InlineString diff --git a/stdlib/src/collections/string.mojo b/stdlib/src/collections/string/string.mojo similarity index 99% rename from stdlib/src/collections/string.mojo rename to stdlib/src/collections/string/string.mojo index 4e5438303c..b31dbf6a49 100644 --- a/stdlib/src/collections/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -10,10 +10,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -"""Implements basic object methods for working with strings. - -These are Mojo built-ins, so you don't need to import them. -""" +"""Implements basic object methods for working with strings.""" from collections import KeyElement, List, Optional from collections._index_normalization import normalize_index @@ -30,20 +27,20 @@ from utils import ( IndexList, StaticString, StringRef, - StringSlice, Variant, Writable, Writer, write_args, ) -from utils._unicode import ( +from collections.string._unicode import ( is_lowercase, is_uppercase, to_lowercase, to_uppercase, ) -from utils.format import _CurlyEntryFormattable, _FormatCurlyEntry -from utils.string_slice import ( +from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry +from collections.string.string_slice import ( + StringSlice, _shift_unicode_to_utf8, _StringSliceIter, _to_string_list, diff --git a/stdlib/src/utils/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo similarity index 98% rename from stdlib/src/utils/string_slice.mojo rename to stdlib/src/collections/string/string_slice.mojo index d50447ab10..fb70f03a9a 100644 --- a/stdlib/src/utils/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -12,27 +12,25 @@ # ===----------------------------------------------------------------------=== # """Implements the StringSlice type. -You can import these APIs from the `utils.string_slice` module. +You can import these APIs from the `collections.string.string_slice` module. Examples: ```mojo -from utils import StringSlice +from collections.string import StringSlice ``` """ -from collections import List, Optional -from collections.string import _atof, _atol, _isspace -from sys import bitwidthof, simdwidthof -from sys.intrinsics import unlikely, likely - from bit import count_leading_zeros +from collections import List, Optional +from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry +from collections.string._utf8_validation import _is_valid_utf8 +from collections.string.string import _atof, _atol, _isspace from memory import UnsafePointer, memcmp, memcpy, Span from memory.memory import _memcmp_impl_unconstrained - -from utils.format import _CurlyEntryFormattable, _FormatCurlyEntry - -from ._utf8_validation import _is_valid_utf8 +from sys import bitwidthof, simdwidthof +from sys.intrinsics import unlikely, likely +from utils.stringref import StringRef, _memmem alias StaticString = StringSlice[StaticConstantOrigin] """An immutable static string slice.""" @@ -926,7 +924,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # is positive, and offset from the end if `start` is negative. var haystack_str = self._from_start(start) - var loc = stringref._memmem( + var loc = _memmem( haystack_str.unsafe_ptr(), haystack_str.byte_length(), substr.unsafe_ptr(), diff --git a/stdlib/src/utils/__init__.mojo b/stdlib/src/utils/__init__.mojo index fa6129cc1c..2e3a0ea21f 100644 --- a/stdlib/src/utils/__init__.mojo +++ b/stdlib/src/utils/__init__.mojo @@ -13,11 +13,11 @@ """Implements the utils package.""" from .index import Index, IndexList, product -from .inline_string import InlineString +from collections.string.inline_string import InlineString from .lock import BlockingScopedLock, BlockingSpinLock, SpinWaiter from .loop import unroll from .static_tuple import StaticTuple -from .string_slice import StaticString, StringSlice +from collections.string.string_slice import StaticString, StringSlice from .stringref import StringRef from .variant import Variant from .write import Writable, Writer, write_args, write_buffered diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 337e195d3b..91b36e464d 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -20,7 +20,7 @@ from utils import IndexList ``` """ -from collections.string import _calc_initial_buffer_size +from collections.string.string import _calc_initial_buffer_size from sys import bitwidthof from builtin.dtype import _int_type_of_width, _uint_type_of_width diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 0699acc424..0b6877c9a1 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -13,7 +13,8 @@ """Implements the StringRef class. """ -from collections.string import _atol, _isspace +from collections.string import StringSlice +from collections.string.string import _atol, _isspace from hashlib._hasher import _HashableWithHasher, _Hasher from sys import simdwidthof from sys.ffi import c_char @@ -23,7 +24,6 @@ from builtin.dtype import _uint_type_of_width from memory import UnsafePointer, memcmp, pack_bits, Span from memory.memory import _memcmp_impl_unconstrained -from utils import StringSlice # ===----------------------------------------------------------------------=== # # Utilities diff --git a/stdlib/test/utils/test_inlined_string.mojo b/stdlib/test/collections/string/test_inlined_string.mojo similarity index 97% rename from stdlib/test/utils/test_inlined_string.mojo rename to stdlib/test/collections/string/test_inlined_string.mojo index b2bc0d1956..83a191b0a7 100644 --- a/stdlib/test/utils/test_inlined_string.mojo +++ b/stdlib/test/collections/string/test_inlined_string.mojo @@ -17,8 +17,8 @@ from os import abort from testing import assert_equal, assert_true -from utils import InlineString -from utils.inline_string import _FixedString +from collections.string import InlineString +from collections.string.inline_string import _FixedString def main(): diff --git a/stdlib/test/collections/test_string.mojo b/stdlib/test/collections/string/test_string.mojo similarity index 99% rename from stdlib/test/collections/test_string.mojo rename to stdlib/test/collections/string/test_string.mojo index bc76176772..74ae492479 100644 --- a/stdlib/test/collections/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -12,14 +12,6 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from collections.string import ( - _calc_initial_buffer_size_int32, - _calc_initial_buffer_size_int64, - _isspace, -) - -from memory import UnsafePointer -from python import Python from testing import ( assert_equal, assert_false, @@ -28,7 +20,15 @@ from testing import ( assert_true, ) -from utils import StringRef, StringSlice +from collections.string import StringSlice +from collections.string.string import ( + _calc_initial_buffer_size_int32, + _calc_initial_buffer_size_int64, + _isspace, +) +from memory import UnsafePointer +from python import Python +from utils import StringRef @value diff --git a/stdlib/test/utils/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo similarity index 99% rename from stdlib/test/utils/test_string_slice.mojo rename to stdlib/test/collections/string/test_string_slice.mojo index dd7269a956..eb106c2e60 100644 --- a/stdlib/test/utils/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -14,10 +14,12 @@ from testing import assert_equal, assert_false, assert_true +from collections.string.string_slice import ( + StringSlice, + _count_utf8_continuation_bytes, +) +from collections.string._utf8_validation import _is_valid_utf8 from memory import Span -from utils import StringSlice -from utils._utf8_validation import _is_valid_utf8 -from utils.string_slice import _count_utf8_continuation_bytes fn test_string_literal_byte_span() raises: diff --git a/stdlib/test/utils/test_format.mojo b/stdlib/test/utils/test_write.mojo similarity index 97% rename from stdlib/test/utils/test_format.mojo rename to stdlib/test/utils/test_write.mojo index 975d26464b..579b573892 100644 --- a/stdlib/test/utils/test_format.mojo +++ b/stdlib/test/utils/test_write.mojo @@ -15,7 +15,7 @@ from testing import assert_equal from utils import Writable, Writer -from utils.inline_string import _FixedString +from collections.string.inline_string import _FixedString fn main() raises: diff --git a/stdlib/test/utils/test_format_to_stdout.mojo b/stdlib/test/utils/test_write_to_stdout.mojo similarity index 100% rename from stdlib/test/utils/test_format_to_stdout.mojo rename to stdlib/test/utils/test_write_to_stdout.mojo From 9576a00c4a96111562af99c76030e36f3ef181ea Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 19 Dec 2024 21:00:37 -0600 Subject: [PATCH 126/509] [stdlib] feat: Avoid `try/catch` in Dict.get() + add `Optional.copied()` This changes the implementation to `Dict.get()` so that `try/catch` is not used. `try/catch` are not currently supported as part of compile-time evaluations. * Add `Dict.get_ptr() -> Optional[Pointer[T]]` method. * Add `Optional.copied()` convenience method for copying an `Optional[Pointer[T]]`. Fixes MSTDL-1069 MODULAR_ORIG_COMMIT_REV_ID: f002e54b60b60f0f8d9b9a764816b841527596ef --- docs/changelog.md | 7 ++++ stdlib/src/collections/dict.mojo | 32 +++++++++++++---- stdlib/src/collections/optional.mojo | 42 ++++++++++++++++++++++ stdlib/test/collections/test_optional.mojo | 14 ++++++++ 4 files changed, 88 insertions(+), 7 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index ef7aec9af6..04ede5b915 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -66,6 +66,13 @@ what we publish. var ptr2 = list2.unsafe_ptr() ``` +- Added `Optional.copied()` for constructing an owned `Optional[T]` from an + `Optional[Pointer[T]]` by copying the pointee value. + +- Added `Dict.get_ptr()` which returns an `Optional[Pointer[V]]`. If the given + key is present in the dictionary, the optional will hold a pointer to the + value. Otherwise, an empty optional is returned. + - Added new `List.extend()` overloads taking `SIMD` and `Span`. These enable growing a `List[Scalar[..]]` by copying the elements of a `SIMD` vector or `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 3dd86b2d39..244f615bf7 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -749,12 +749,11 @@ struct Dict[K: KeyElement, V: CollectionElement]( An optional value containing a copy of the value if it was present, otherwise an empty Optional. """ - try: # TODO(MOCO-604): push usage through - return self._find_ref(key) - except: - return None - # TODO(MOCO-604): Return Optional[Pointer] instead of raising + # TODO(MOCO-604): push usage through + # TODO(MOCO-1522): Drop `[T=V]` after fixing param inference issue. + return self.get_ptr(key).copied[T=V]() + fn _find_ref( ref self, key: K ) raises -> ref [self._entries[0].value().value] Self.V: @@ -767,6 +766,24 @@ struct Dict[K: KeyElement, V: CollectionElement]( An optional value containing a reference to the value if it is present, otherwise an empty Optional. """ + if entry := self.get_ptr(key): + # SAFETY: We just checked that `entry` is populated. + return entry.unsafe_value()[] + else: + raise "KeyError" + + fn get_ptr( + ref self, key: K + ) -> Optional[Pointer[V, __origin_of(self._entries[0].value().value)]]: + """Get a pointer to a value in the dictionary by key. + + Args: + key: The key to search for in the dictionary. + + Returns: + An optional value containing a pointer to the value if it is + present, otherwise an empty Optional. + """ var hash = hash(key) var found: Bool var slot: UInt64 @@ -775,8 +792,9 @@ struct Dict[K: KeyElement, V: CollectionElement]( if found: var entry = Pointer.address_of(self._entries[index]) debug_assert(entry[].__bool__(), "entry in index must be full") - return entry[].value().value - raise "KeyError" + return Pointer.address_of(entry[].value().value) + + return None fn get(self, key: K) -> Optional[V]: """Get a value from the dictionary by key. diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index 5d7eb7853a..b96d3d694a 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -388,6 +388,48 @@ struct Optional[T: CollectionElement]( return self._value[T] return default + fn copied[ + mut: Bool, + origin: Origin[mut], //, + T: CollectionElement, + ](self: Optional[Pointer[T, origin]]) -> Optional[T]: + """Converts an Optional containing a Pointer to an Optional of an owned + value by copying. + + If `self` is an empty `Optional`, the returned `Optional` will be empty + as well. + + Parameters: + mut: Mutability of the pointee origin. + origin: Origin of the contained `Pointer`. + T: Type of the owned result value. + + Returns: + An Optional containing an owned copy of the pointee value. + + # Examples + + Copy the value of an `Optional[Pointer[_]]` + + ```mojo + from collections import Optional + + var data = String("foo") + + var opt = Optional(Pointer.address_of(data)) + + # TODO(MOCO-1522): Drop `[T=String]` after fixing param inference issue. + var opt_owned: Optional[String] = opt.copied[T=String]() + ``` + . + """ + if self: + # SAFETY: We just checked that `self` is populated. + # Perform an implicit copy + return self.unsafe_value()[] + else: + return None + # ===-----------------------------------------------------------------------===# # OptionalReg diff --git a/stdlib/test/collections/test_optional.mojo b/stdlib/test/collections/test_optional.mojo index d606a2b392..6fe264cafd 100644 --- a/stdlib/test/collections/test_optional.mojo +++ b/stdlib/test/collections/test_optional.mojo @@ -157,6 +157,19 @@ def test_optional_equality(): assert_true(n == None) +def test_optional_copied(): + var data = String("foo") + + var opt_ref: Optional[Pointer[String, __origin_of(data)]] = Optional( + Pointer.address_of(data) + ) + + # Copy the optional Pointer value. + var opt_owned: Optional[String] = opt_ref.copied[T=String]() + + assert_equal(opt_owned.value(), String("foo")) + + def main(): test_basic() test_optional_reg_basic() @@ -168,3 +181,4 @@ def main(): test_optional_explicit_copy() test_optional_str_repr() test_optional_equality() + test_optional_copied() From fa2bca6fdf1b2f3eab3df8499d32996ebc53218c Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 20 Dec 2024 20:07:08 +1100 Subject: [PATCH 127/509] [mojo-lang] Replace `InitSelf` convention with `ByRefResult` This replaces the use of the InitSelf convention with the existing ByRefResult convention, making inits static functions that return Self. This simplifies the compiler by removing a concept entirely. As a consequence of this, the 'self' argument to inits is passed as the last argument in the IR instead of the first. MODULAR_ORIG_COMMIT_REV_ID: 8d6124b11312eadace129cc1bc3344d4c13915ba --- docs/changelog.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 04ede5b915..3a7dcb5ae1 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -20,6 +20,13 @@ what we publish. ### Language changes +- Initializers are now treated as static methods that return an instance of + `Self`. This means the `out` argument of an initializer is now treated the + same as a any other function result or `out` argument. This is generally + invisible, except that patterns like `instance.__init__()` and + `x.__copyinit__(y)` no longer work. Simply replace them with `instance = T()` + and `x = y` respectively. + ### Standard library changes - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` From 5c54dca859b4e7beb06482b56354ff6e69b162d1 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Fri, 20 Dec 2024 07:49:15 -0800 Subject: [PATCH 128/509] [Stdlib] Add test case for compile time dict operations MODULAR_ORIG_COMMIT_REV_ID: cea44ab2a2ea8715b78580c9e8709af97899eeb5 --- stdlib/test/collections/test_dict.mojo | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/stdlib/test/collections/test_dict.mojo b/stdlib/test/collections/test_dict.mojo index 030b16a6a3..d9252d8f3d 100644 --- a/stdlib/test/collections/test_dict.mojo +++ b/stdlib/test/collections/test_dict.mojo @@ -602,6 +602,23 @@ fn test_dict_setdefault() raises: assert_equal(0, other_dict["b"].copy_count) +def test_compile_time_dict(): + alias N = 10 + + fn _get_dict() -> Dict[String, Int32]: + var res = Dict[String, Int32]() + for i in range(N): + res[str(i)] = i + return res + + alias my_dict = _get_dict() + + @parameter + for i in range(N): + alias val = my_dict.get(str(i)).value() + assert_equal(val, i) + + def main(): test_dict() test_dict_fromkeys() @@ -615,3 +632,4 @@ def main(): test_clear() test_init_initial_capacity() test_dict_setdefault() + test_compile_time_dict() From 104f66a19b96d945866b070f243505faf93dc395 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 20 Dec 2024 13:41:09 -0600 Subject: [PATCH 129/509] [stdlib] comment: Add standard comment heads to `Dict` MODULAR_ORIG_COMMIT_REV_ID: 1420859c42d336f259f27f1ab2ee59e08c77290b --- stdlib/src/collections/dict.mojo | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 244f615bf7..76a5a14007 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -453,11 +453,18 @@ struct Dict[K: KeyElement, V: CollectionElement]( # don't churn and compact on repeated insert/delete, and instead amortize # compaction cost to O(1) amortized cost. - # Fields + # ===-------------------------------------------------------------------===# + # Aliases + # ===-------------------------------------------------------------------===# + alias EMPTY = _EMPTY alias REMOVED = _REMOVED alias _initial_reservation = 8 + # ===-------------------------------------------------------------------===# + # Fields + # ===-------------------------------------------------------------------===# + var size: Int """The number of elements currently stored in the dict.""" var _n_entries: Int From f8025f252c456fc65327414657eae7a1b43d533b Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 20 Dec 2024 14:17:29 -0600 Subject: [PATCH 130/509] [stdlib] cleanup: Use `unsafe_value()` to avoid redundant `Optional` check MODULAR_ORIG_COMMIT_REV_ID: 429e0636e6a548e282cf1ff094b543579cd124b1 --- stdlib/src/collections/dict.mojo | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 76a5a14007..d84fc02797 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -799,7 +799,8 @@ struct Dict[K: KeyElement, V: CollectionElement]( if found: var entry = Pointer.address_of(self._entries[index]) debug_assert(entry[].__bool__(), "entry in index must be full") - return Pointer.address_of(entry[].value().value) + # SAFETY: We just checked that `entry` is present. + return Pointer.address_of(entry[].unsafe_value().value) return None From c33509ec9a4aa5e4bdee1641bab852fe9692a3f5 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Fri, 20 Dec 2024 14:23:02 -0800 Subject: [PATCH 131/509] [stdlib] Move UnsafePointer Alignment Casting To Its Own Method As a follow up from splitting up `bitcast`, alignment is the odd one out, so we should split that off too. MODULAR_ORIG_COMMIT_REV_ID: 19c2b3149e9f6f6c318f7765df74e0507b46b564 --- docs/changelog.md | 3 +- stdlib/src/memory/unsafe_pointer.mojo | 38 ++++++++++++++++++++-- stdlib/test/memory/test_unsafepointer.mojo | 4 ++- 3 files changed, 40 insertions(+), 5 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 3a7dcb5ae1..2101a9ff44 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -31,6 +31,7 @@ what we publish. - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` for changing the type, `origin_cast` for changing mutability, + `static_alignment_cast` for changing alignment, and `address_space_cast` for changing the address space. - `UnsafePointer` is now parameterized on mutability. Previously, @@ -55,7 +56,7 @@ what we publish. ```mojo var local = 10 # Cast the mutable pointer to be immutable. - var ptr = UnsafePointer.address_of(local).mut_cast[mut=False]() + var ptr = UnsafePointer.address_of(local).origin_cast[mut=False]() ``` - The `unsafe_ptr()` method on several standard library collection types have diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 1c9dbf9b40..efe6018cd9 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -973,8 +973,6 @@ struct UnsafePointer[ @always_inline("nodebug") fn bitcast[ T: AnyType = Self.type, - /, - alignment: Int = Self.alignment, ](self) -> UnsafePointer[ T, address_space=address_space, @@ -986,7 +984,6 @@ struct UnsafePointer[ Parameters: T: The target type. - alignment: Alignment of the destination pointer. Returns: A new UnsafePointer object with the specified type and the same address, @@ -998,6 +995,41 @@ struct UnsafePointer[ ]._mlir_type, ](self.address) + @always_inline("nodebug") + fn static_alignment_cast[ + alignment: Int = Self.alignment + ](self) -> UnsafePointer[ + type, + address_space=address_space, + alignment=alignment, + mut=mut, + origin=origin, + ]: + """Changes the `alignment` of an `UnsafePointer`. + + The static alignment of an UnsafePointer must be greater + or equal to the actual alignment of the runtime pointer + value. Casting an UnsafePointer to a static alignment greater + than its runtime alignment may cause undefined behavior". + + This only changes the compile-time alignment encoded in the type of + this pointer. This does not change the alignment of the pointer address + at runtime. + + + Parameters: + alignment: Alignment of the destination pointer. + + Returns: + A new UnsafePointer object with the same type, address_space, and address, + as the original UnsafePointer, and the new specified alignment. + """ + return __mlir_op.`pop.pointer.bitcast`[ + _type = UnsafePointer[ + type, address_space=address_space, alignment=alignment + ]._mlir_type, + ](self.address) + @always_inline("nodebug") fn origin_cast[ mut: Bool = Self.mut, diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index b57fc793cb..09b7d11f70 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -133,7 +133,9 @@ def test_bitcast(): assert_equal(int(ptr), int(aliased_ptr)) - assert_equal(ptr.bitcast[ptr.type, alignment=33]().alignment, 33) + assert_equal( + ptr.bitcast[ptr.type]().static_alignment_cast[33]().alignment, 33 + ) _ = local From 019e4dda1cc647b1f41eda9a929a1697ab91a59b Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 20 Dec 2024 20:51:09 -0600 Subject: [PATCH 132/509] [stdlib] feat: Add `StringSlice.from_utf8()` This adds a new convenience factory method to `StringSlice`, used for constructing a `StringSlice` to data containing valid UTF-8 encoded data. This method will raise an exception if the provided buffer does not contain valid UTF-8. This is part of pushing to do greater validating that `StringSlice` and `String` always contain valid UTF-8. All of the existing initializers for `StringSlice` are unsafe, and assume they've been given UTF-8 encoded data. MODULAR_ORIG_COMMIT_REV_ID: 2411144ce4399edaa71a31a27c60be691c9ca964 --- docs/changelog.md | 4 +++ .../src/collections/string/string_slice.mojo | 27 +++++++++++++++++++ .../collections/string/test_string_slice.mojo | 14 +++++++++- 3 files changed, 44 insertions(+), 1 deletion(-) diff --git a/docs/changelog.md b/docs/changelog.md index 2101a9ff44..df4f5fb50e 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -86,6 +86,10 @@ what we publish. `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware functionality. +- Added `StringSlice.from_utf()` factor method, for validated construction of + a `StringSlice` from a buffer containing UTF-8 encoded data. This method will + raise if the buffer contents are not valid UTF-8. + - The `ExplicitlyCopyable` trait has changed to require a `fn copy(self) -> Self` method. Previously, an initializer with the signature `fn __init__(out self, *, other: Self)` had been required by diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index fb70f03a9a..e4d8a60bdb 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -358,6 +358,33 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( ) self = StringSlice[O](unsafe_from_utf8=value.as_bytes()) + # ===-------------------------------------------------------------------===# + # Factory methods + # ===-------------------------------------------------------------------===# + + # TODO: Change to `__init__(out self, *, from_utf8: Span[..])` once ambiguity + # with existing `unsafe_from_utf8` overload is fixed. Would require + # signature comparision to take into account required named arguments. + @staticmethod + fn from_utf8(from_utf8: Span[Byte, origin]) raises -> StringSlice[origin]: + """Construct a new `StringSlice` from a buffer containing UTF-8 encoded + data. + + Args: + from_utf8: A span of bytes containing UTF-8 encoded data. + + Returns: + A new validated `StringSlice` pointing to the provided buffer. + + Raises: + An exception is raised if the provided buffer byte values do not + form valid UTF-8 encoded codepoints. + """ + if not _is_valid_utf8(from_utf8): + raise Error("StringSlice: buffer is not valid UTF-8") + + return StringSlice[origin](unsafe_from_utf8=from_utf8) + # ===------------------------------------------------------------------===# # Trait implementations # ===------------------------------------------------------------------===# diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index eb106c2e60..14e84ac80d 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -12,7 +12,7 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from testing import assert_equal, assert_false, assert_true +from testing import assert_equal, assert_false, assert_true, assert_raises from collections.string.string_slice import ( StringSlice, @@ -351,6 +351,17 @@ def test_bad_utf8_sequences(): assert_false(validate_utf8(sequence[])) +def test_stringslice_from_utf8(): + for sequence in GOOD_SEQUENCES: + var bytes = sequence[].as_bytes() + _ = StringSlice.from_utf8(bytes) + + for sequence in BAD_SEQUENCES: + with assert_raises(contains="buffer is not valid UTF-8"): + var bytes = sequence[].as_bytes() + _ = StringSlice.from_utf8(bytes) + + def test_combination_good_utf8_sequences(): # any combination of good sequences should be good for i in range(0, len(GOOD_SEQUENCES)): @@ -651,6 +662,7 @@ def main(): test_find() test_good_utf8_sequences() test_bad_utf8_sequences() + test_stringslice_from_utf8() test_combination_good_utf8_sequences() test_combination_bad_utf8_sequences() test_combination_good_bad_utf8_sequences() From f4b34fc038db51a4ec4ae9e8b08244d20300bd9e Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 21 Dec 2024 20:31:12 +0000 Subject: [PATCH 133/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024122105 --- examples/life/magic.lock | 418 ++++++++++++------------- examples/magic.lock | 418 ++++++++++++------------- examples/notebooks/magic.lock | 556 +++++++++++++++++----------------- examples/operators/magic.lock | 418 ++++++++++++------------- magic.lock | 436 +++++++++++++------------- 5 files changed, 1153 insertions(+), 1093 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 5a7928b9c1..f9b11ce876 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -9,7 +9,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.4.4-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.11.10-py312h178313f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.11.11-py312h178313f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.13-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda @@ -69,7 +69,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.5.0-py312h66e93f0_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.10.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.12.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-he02047a_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-he02047a_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda @@ -162,7 +162,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.49.2-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.5-h8d12d68_1.conda @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2024121705-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2024121705-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2024121705-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2024121705-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2024122105-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2024122105-release.conda + - 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kind: conda name: uvicorn version: 0.34.0 From 33a35d26f8fe38cef1035845e463e99e17d4b6ec Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 23 Dec 2024 19:14:55 +1100 Subject: [PATCH 135/509] [mojo-lang] Substantially rewrite named result slot handling. This aligns register results with normal result processing, among other things, this fixes https://github.com/modularml/mojo/issues/3540 Initializers are still not fully on the same model but will come shortly. MODULAR_ORIG_COMMIT_REV_ID: 0fea2d900644c2ac26cd1937cfe5c9a6b90eb769 --- docs/changelog.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index df4f5fb50e..9e2bf3509d 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -125,5 +125,8 @@ what we publish. - [Issue #3796](https://github.com/modularml/mojo/issues/3796) - Compiler crash handling for-else statement. +- [Issue #3540](https://github.com/modularml/mojo/issues/3540) - Using named + output slot breaks trait conformance + - The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. [Issue #3826](https://github.com/modularml/mojo/issues/3826). From c5f431fca7d1c558ff1489504ed889f27d0412ae Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 23 Dec 2024 04:29:22 -0800 Subject: [PATCH 136/509] [******][GPU] Make MatmulConfig hashable, NFC MODULAR_ORIG_COMMIT_REV_ID: 2cbc9ae5186444b89b34a6fe1d17f31ed3762cf8 --- stdlib/src/builtin/bool.mojo | 12 ++++++++++++ stdlib/src/utils/index.mojo | 15 +++++++++++++++ 2 files changed, 27 insertions(+) diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 6c953bb0b5..3b9c3a2569 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -19,6 +19,7 @@ from collections import List, Set from utils._select import _select_register_value from utils._visualizers import lldb_formatter_wrapping_type +from hashlib._hasher import _Hasher # ===----------------------------------------------------------------------=== # # Boolable @@ -505,6 +506,17 @@ struct Bool( """ return __mlir_op.`index.casts`[_type = __mlir_type.index](self.value) + fn __hash__[H: _Hasher](self, mut hasher: H): + """Updates hasher with the underlying bytes. + + Parameters: + H: The hasher type. + + Args: + hasher: The hasher instance. + """ + hasher._update_with_simd(Scalar[DType.bool](self)) + # ===----------------------------------------------------------------------=== # # bool diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 91b36e464d..1585123785 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -28,6 +28,7 @@ from builtin.io import _get_dtype_printf_format, _snprintf from . import unroll from .static_tuple import StaticTuple +from hashlib._hasher import _Hasher # ===-----------------------------------------------------------------------===# # Utilities @@ -828,6 +829,20 @@ struct IndexList[ self.cast[_type_of_width[element_bitwidth, unsigned]()]() ) + fn __hash__[H: _Hasher](self, mut hasher: H): + """Updates hasher with the underlying bytes. + + Parameters: + H: The hasher type. + + Args: + hasher: The hasher instance. + """ + + @parameter + for i in range(size): + hasher.update(self.data[i]) + # ===-----------------------------------------------------------------------===# # Factory functions for creating index. From c08eb1fb64a4b4bfe7388280d5df6c2c865a8135 Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 24 Dec 2024 07:20:27 +0000 Subject: [PATCH 137/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024122405 --- examples/life/magic.lock | 162 +++++++++++++++++----------------- examples/magic.lock | 162 +++++++++++++++++----------------- examples/notebooks/magic.lock | 160 ++++++++++++++++----------------- examples/operators/magic.lock | 162 +++++++++++++++++----------------- magic.lock | 162 +++++++++++++++++----------------- 5 files changed, 404 insertions(+), 404 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 1b8c6db189..ddd2ccc481 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - 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python license: LicenseRef-Modular-Proprietary - size: 22934 - timestamp: 1734931092588 + size: 22945 + timestamp: 1735017427768 - kind: conda name: multidict version: 6.1.0 From ea0d7ad4dbd35e8516ce8017180638b41bc3e5a9 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 24 Dec 2024 12:25:05 -0600 Subject: [PATCH 138/509] [stdlib] polish: Change `sys.argv()` to return `StringSlice`'s (was `StringRef`) This also adds tests and documents that `StringSlice` MUST have the same ABI as `llvm::StringRef`. This is a necessary part of replacing `StringRef` uses with `StringSlice` across the CompilerRT ABI boundary. This is a reasonable constraint to put on `StringSlice`, because `StringSlice` is only intended to be a basic pointer + length "view" type, same as the LLVM `StringRef` type, the Rust `&str` type, or the C++ `std::string_view` type. MODULAR_ORIG_COMMIT_REV_ID: f5bb8c177aec50a8799675089cc5fab5017a2a74 --- docs/changelog.md | 1 + .../src/collections/string/string_slice.mojo | 17 +++++++++++++ stdlib/src/sys/arg.mojo | 9 +++++-- .../collections/string/test_string_slice.mojo | 25 ++++++++++++++++++- 4 files changed, 49 insertions(+), 3 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 9e2bf3509d..c362404b7f 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -113,6 +113,7 @@ what we publish. ### ❌ Removed - `StringRef` is being deprecated. Use `StringSlice` instead. + - Changed `sys.argv()` to return list of `StringSlice`. - removed `StringRef.startswith()` and `StringRef.endswith()` ### 🛠️ Fixed diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index e4d8a60bdb..f632e806fd 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -31,6 +31,7 @@ from memory.memory import _memcmp_impl_unconstrained from sys import bitwidthof, simdwidthof from sys.intrinsics import unlikely, likely from utils.stringref import StringRef, _memmem +from os import PathLike alias StaticString = StringSlice[StaticConstantOrigin] """An immutable static string slice.""" @@ -239,9 +240,13 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( CollectionElement, CollectionElementNew, Hashable, + PathLike, ): """A non-owning view to encoded string data. + This type is guaranteed to have the same ABI (size, alignment, and field + layout) as the `llvm::StringRef` type. + Parameters: mut: Whether the slice is mutable. origin: The origin of the underlying string data. @@ -438,6 +443,18 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return hash(self._slice._data, self._slice._len) + fn __fspath__(self) -> String: + """Return the file system path representation of this string. + + Returns: + The file system path representation as a string. + """ + return String(self) + + # ===------------------------------------------------------------------===# + # Operator dunders + # ===------------------------------------------------------------------===# + # This decorator informs the compiler that indirect address spaces are not # dereferenced by the method. # TODO: replace with a safe model that checks the body of the method for diff --git a/stdlib/src/sys/arg.mojo b/stdlib/src/sys/arg.mojo index 69a0093630..df777a5adf 100644 --- a/stdlib/src/sys/arg.mojo +++ b/stdlib/src/sys/arg.mojo @@ -34,17 +34,22 @@ from sys import external_call from memory import UnsafePointer +from collections.string import StringSlice + from utils import StringRef # TODO: When we have global variables, this should be a global list. -fn argv() -> VariadicList[StringRef]: +fn argv() -> VariadicList[StringSlice[StaticConstantOrigin]]: """The list of command line arguments. Returns: The list of command line arguments provided when mojo was invoked. """ - var result = VariadicList[StringRef]("") + # SAFETY: + # It is valid to use `StringSlice` here because `StringSlice` is + # guaranteed to be ABI compatible with llvm::StringRef. + var result = VariadicList[StringSlice[StaticConstantOrigin]]("") external_call["KGEN_CompilerRT_GetArgV", NoneType]( Pointer.address_of(result) ) diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 14e84ac80d..ecdcf4ddf2 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -19,7 +19,29 @@ from collections.string.string_slice import ( _count_utf8_continuation_bytes, ) from collections.string._utf8_validation import _is_valid_utf8 -from memory import Span +from memory import Span, UnsafePointer + +from sys.info import sizeof, alignof + + +fn test_string_slice_layout() raises: + # Test that the layout of `StringSlice` is the same as `llvm::StringRef`. + # This is necessary for `StringSlice` to be validly bitcasted to and from + # `llvm::StringRef` + + # StringSlice should be two words in size. + assert_equal(sizeof[StringSlice[MutableAnyOrigin]](), 2 * sizeof[Int]()) + + var str_slice = StringSlice("") + + var base_ptr = int(UnsafePointer.address_of(str_slice)) + var first_word_ptr = int(UnsafePointer.address_of(str_slice._slice._data)) + var second_word_ptr = int(UnsafePointer.address_of(str_slice._slice._len)) + + # 1st field should be at 0-byte offset from base ptr + assert_equal(first_word_ptr - base_ptr, 0) + # 2nd field should at 1-word offset from base ptr + assert_equal(second_word_ptr - base_ptr, sizeof[Int]()) fn test_string_literal_byte_span() raises: @@ -652,6 +674,7 @@ def test_count(): def main(): + test_string_slice_layout() test_string_literal_byte_span() test_string_byte_span() test_heap_string_from_string_slice() From 134f9e52d50e6cd66dda9b1f11b310beafa1bde8 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Wed, 25 Dec 2024 13:19:46 +1100 Subject: [PATCH 139/509] [mojo-stdlib] Switch StaticTuple to use mark_initialized instead of kgen.unknown `#kgen.unknown` is a special attribute that is used in the Mojo frontend, not a generalized `undef` value. Using it in StaticTuple can break things at comptime because the interpreter ends up breaking invariants, e.g. something of SIMD type can only be used with SIMDAttr. MODULAR_ORIG_COMMIT_REV_ID: 645671840daa1e6f17343805ec54480aa0b9dac5 --- stdlib/src/utils/static_tuple.mojo | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index 0788b54b43..9ef6da0baf 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -129,10 +129,7 @@ struct StaticTuple[element_type: AnyTrivialRegType, size: Int](Sized): fn __init__(out self): """Constructs an empty (undefined) tuple.""" _static_tuple_construction_checks[size]() - self.array = __mlir_op.`kgen.param.constant`[ - _type = Self.type, - value = __mlir_attr[`#kgen.unknown : `, Self.type], - ]() + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) @always_inline @implicit From ea7e65acf6cc2fee10eb48e4e4b6855e04311345 Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 25 Dec 2024 07:12:18 +0000 Subject: [PATCH 140/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024122505 --- examples/life/magic.lock | 162 +++++++++++++++++----------------- examples/magic.lock | 162 +++++++++++++++++----------------- examples/notebooks/magic.lock | 160 ++++++++++++++++----------------- examples/operators/magic.lock | 162 +++++++++++++++++----------------- magic.lock | 162 +++++++++++++++++----------------- 5 files changed, 404 insertions(+), 404 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index ddd2ccc481..e2092c5f93 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - 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url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2024122405-release.conda - sha256: f94b572ac223871866d2e4fa6401f492d4396f1f6948676bdeba496136d8923c - md5: f79467d35eb2237032e802ac5ece411b + url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2024122505-release.conda + sha256: 596f48156650da6cc09b489ccdcd1532c982f37a61da16d5cea96b8c0b2b2a41 + md5: 3e9b1b3897e6044c6c3ce6a962818c12 depends: - - max-core ==25.1.0.dev2024122405 release + - max-core ==25.1.0.dev2024122505 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22945 - timestamp: 1735017427768 + size: 22942 + timestamp: 1735104162226 - kind: conda name: multidict version: 6.1.0 From 23e335b0dde0e2657e8c6ff52789ac39ca8ae447 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Wed, 25 Dec 2024 18:44:03 +1000 Subject: [PATCH 141/509] [mojo-lang] Warn about legacy arg convention syntax. This teaches the parser to warn about the old keyword syntax, which we'll do for a release before removing them. MODULAR_ORIG_COMMIT_REV_ID: 50f8e7d0bf00d19fd29bc0376e00f8748dbe9624 --- docs/changelog.md | 3 +++ stdlib/src/collections/dict.mojo | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index c362404b7f..74c5137d6e 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -27,6 +27,9 @@ what we publish. `x.__copyinit__(y)` no longer work. Simply replace them with `instance = T()` and `x = y` respectively. +- The legacy `borrowed`/`inout` keywords and `-> T as foo` syntax now generate + a warning. Please move to `read`/`mut`/`out` argument syntax instead. + ### Standard library changes - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index d84fc02797..9761dec244 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -242,13 +242,13 @@ struct DictEntry[K: KeyElement, V: CollectionElement]( """ return self - fn reap_value(owned self) -> V as out: + fn reap_value(owned self, out result: V): """Take the value from an owned entry. Returns: The value of the entry. """ - out = self.value^ + result = self.value^ __disable_del self From 2c8c0766ad99d3c75d910d17b90641db653bbf3c Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Thu, 26 Dec 2024 09:17:12 +1000 Subject: [PATCH 142/509] [mojo-lang][KGEN] Move interpreter off `#kgen.unknown` for uninit mem. (#53334) This patch introduces a dedicated `#interp.uninitmem` attribute to represent the use of uninitialized memory, making it easier to reason about where it is coming from, and enabling the `InterpreterState::writeAttributeToMemory` function to handle all stores of uninit memory consistently without a layering violation. It would be a layering violation for interp to know about kgen, because kgen depends on interp not the other way around. MODULAR_ORIG_COMMIT_REV_ID: 1c4db631bfdf703e97f62324159367b2d2ce7920 --- stdlib/src/builtin/simd.mojo | 27 ++++++---------------- stdlib/src/collections/inline_array.mojo | 26 ++++----------------- stdlib/src/memory/maybe_uninitialized.mojo | 5 +--- stdlib/src/utils/static_tuple.mojo | 9 ++------ stdlib/src/utils/variant.mojo | 6 ++--- 5 files changed, 18 insertions(+), 55 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index b720d4fb86..7f4a0e8576 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -461,15 +461,7 @@ struct SIMD[type: DType, size: Int]( ), ) - self = __mlir_op.`kgen.param.constant`[ - _type = __mlir_type[ - `!pop.simd<`, size.value, `, `, type.value, `>` - ], - value = __mlir_attr[ - `#kgen.unknown : `, - __mlir_type[`!pop.simd<`, size.value, `, `, type.value, `>`], - ], - ]() + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) @parameter for i in range(size): @@ -2022,17 +2014,12 @@ struct SIMD[type: DType, size: Int]( fn _convert_variadic_to_pop_array[ *mask: Int ]() -> __mlir_type[`!pop.array<`, output_size.value, `, `, Int, `>`]: - var array = __mlir_op.`kgen.param.constant`[ - _type = __mlir_type[ - `!pop.array<`, output_size.value, `, `, Int, `>` - ], - value = __mlir_attr[ - `#kgen.unknown : `, - __mlir_type[ - `!pop.array<`, output_size.value, `, `, Int, `>` - ], - ], - ]() + var array: __mlir_type[ + `!pop.array<`, output_size.value, `, `, Int, `>` + ] + __mlir_op.`lit.ownership.mark_initialized`( + __get_mvalue_as_litref(array) + ) var array_ptr = UnsafePointer.address_of(array) diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 502104561c..973406574b 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -80,10 +80,7 @@ struct InlineArray[ " 'unsafe_uninitialized'." ), ]() - self._array = __mlir_op.`kgen.param.constant`[ - _type = Self.type, - value = __mlir_attr[`#kgen.unknown : `, Self.type], - ]() + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) @always_inline fn __init__(out self, *, unsafe_uninitialized: Bool): @@ -106,10 +103,7 @@ struct InlineArray[ Always set to `True` (it's not actually used inside the constructor). """ _inline_array_construction_checks[size]() - self._array = __mlir_op.`kgen.param.constant`[ - _type = Self.type, - value = __mlir_attr[`#kgen.unknown : `, Self.type], - ]() + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) fn __init__( mut self, @@ -129,11 +123,7 @@ struct InlineArray[ unsafe_assume_initialized: The array of `UnsafeMaybeUninitialized` elements. """ - self._array = __mlir_op.`kgen.param.constant`[ - _type = Self.type, - value = __mlir_attr[`#kgen.unknown : `, Self.type], - ]() - + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) for i in range(Self.size): unsafe_assume_initialized[i].unsafe_ptr().move_pointee_into( self.unsafe_ptr() + i @@ -148,10 +138,7 @@ struct InlineArray[ fill: The element to fill each index. """ _inline_array_construction_checks[size]() - self._array = __mlir_op.`kgen.param.constant`[ - _type = Self.type, - value = __mlir_attr[`#kgen.unknown : `, Self.type], - ]() + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) @parameter for i in range(size): @@ -183,10 +170,7 @@ struct InlineArray[ debug_assert(len(storage) == size, "Elements must be of length size") _inline_array_construction_checks[size]() - self._array = __mlir_op.`kgen.param.constant`[ - _type = Self.type, - value = __mlir_attr[`#kgen.unknown : `, Self.type], - ]() + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) # Move each element into the array storage. @parameter diff --git a/stdlib/src/memory/maybe_uninitialized.mojo b/stdlib/src/memory/maybe_uninitialized.mojo index 53eeb8404e..9ebf803bcb 100644 --- a/stdlib/src/memory/maybe_uninitialized.mojo +++ b/stdlib/src/memory/maybe_uninitialized.mojo @@ -36,10 +36,7 @@ struct UnsafeMaybeUninitialized[ElementType: AnyType](CollectionElementNew): @always_inline fn __init__(out self): """The memory is now considered uninitialized.""" - self._array = __mlir_op.`kgen.param.constant`[ - _type = Self.type, - value = __mlir_attr[`#kgen.unknown : `, Self.type], - ]() + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) @doc_private @always_inline diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index 9ef6da0baf..0f118f6486 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -78,13 +78,8 @@ fn _create_array[ debug_assert(size == len(lst), "mismatch in the number of elements") - var array = __mlir_op.`kgen.param.constant`[ - _type = __mlir_type[`!pop.array<`, size.value, `, `, type, `>`], - value = __mlir_attr[ - `#kgen.unknown : `, - __mlir_type[`!pop.array<`, size.value, `, `, type, `>`], - ], - ]() + var array: __mlir_type[`!pop.array<`, size.value, `, `, type, `>`] + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(array)) @parameter for idx in range(size): diff --git a/stdlib/src/utils/variant.mojo b/stdlib/src/utils/variant.mojo index 8efd3ff63c..60d9279a30 100644 --- a/stdlib/src/utils/variant.mojo +++ b/stdlib/src/utils/variant.mojo @@ -121,7 +121,7 @@ struct Variant[*Ts: CollectionElement]( Args: unsafe_uninitialized: Marker argument indicating this initializer is unsafe. """ - self._impl = __mlir_attr[`#kgen.unknown : `, Self._mlir_type] + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) @implicit fn __init__[T: CollectionElement](mut self, owned value: T): @@ -134,7 +134,7 @@ struct Variant[*Ts: CollectionElement]( Args: value: The value to initialize the variant with. """ - self._impl = __mlir_attr[`#kgen.unknown : `, self._mlir_type] + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) alias idx = Self._check[T]() self._get_discr() = idx self._get_ptr[T]().init_pointee_move(value^) @@ -171,7 +171,7 @@ struct Variant[*Ts: CollectionElement]( Args: other: The variant to move. """ - self._impl = __mlir_attr[`#kgen.unknown : `, self._mlir_type] + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(self)) self._get_discr() = other._get_discr() @parameter From da8e0db396f46ed20b6f3ed12de95d5770adf048 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Thu, 26 Dec 2024 13:07:06 +1000 Subject: [PATCH 143/509] [mojo-lang] Convert trivial type inits to return by register. (#53331) This finishes the unifications of 'out' argument conventions with normal result types, across all declarations and all forms, making Mojo more consistent and simpler for users. This also reduces the amount of IR generated for trivial types which might be a small compile time improvement. MODULAR_ORIG_COMMIT_REV_ID: 68f5c91fe45b628fb75520a6dc6c48c6703e9cbf --- stdlib/src/utils/index.mojo | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 1585123785..8c87763619 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -514,7 +514,9 @@ struct IndexList[ The resulting index tuple. """ - @always_inline + # FIXME(#53331) - nodebug is required otherwise we crash in debug + # information generation. + @always_inline("nodebug") fn apply_fn[ type: DType ](a: Scalar[type], b: Scalar[type]) -> Scalar[type]: From d8ab58bced698f83d0aea13d2387cf4298196cd0 Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 26 Dec 2024 07:46:02 +0000 Subject: [PATCH 144/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024122605 --- examples/life/magic.lock | 180 +++++++++++++++++----------------- examples/magic.lock | 180 +++++++++++++++++----------------- examples/notebooks/magic.lock | 178 ++++++++++++++++----------------- examples/operators/magic.lock | 180 +++++++++++++++++----------------- magic.lock | 180 +++++++++++++++++----------------- 5 files changed, 449 insertions(+), 449 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index e2092c5f93..93547f7467 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - 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MODULAR_ORIG_COMMIT_REV_ID: 9a7491551a76491c4ad6b739c856bdac6515da10 --- stdlib/src/builtin/_stubs.mojo | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/stdlib/src/builtin/_stubs.mojo b/stdlib/src/builtin/_stubs.mojo index 3b5a12fdac..4879a225fb 100644 --- a/stdlib/src/builtin/_stubs.mojo +++ b/stdlib/src/builtin/_stubs.mojo @@ -95,12 +95,11 @@ fn parameter_for_generator[ fn _generator[ IteratorT: _IntIter ](it: IteratorT) -> _ParamForIterator[IteratorT]: - if it.__len__() == 0: - return _ParamForIterator[IteratorT]( - __mlir_attr[`#kgen.unknown : !kgen.paramref<`, IteratorT, `>`], - 0, - True, - ) - var next_it = it - var value = next_it.__next__() - return _ParamForIterator(next_it, value, False) + if it.__len__() != 0: + var next_it = it + var value = next_it.__next__() + return _ParamForIterator(next_it, value, False) + + var value: IteratorT + __mlir_op.`lit.ownership.mark_initialized`(__get_mvalue_as_litref(value)) + return _ParamForIterator(value^, 0, True) From f8e856dbff23b991f65bf454db8422326f0a9887 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 28 Dec 2024 07:42:53 +0000 Subject: [PATCH 147/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2024122805 --- examples/life/magic.lock | 162 ++++++++++++------------- examples/magic.lock | 162 ++++++++++++------------- examples/notebooks/magic.lock | 222 +++++++++++++++++----------------- examples/operators/magic.lock | 162 ++++++++++++------------- magic.lock | 162 ++++++++++++------------- 5 files changed, 435 insertions(+), 435 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 88ec51f6c7..621b2095cd 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - 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python license: LicenseRef-Modular-Proprietary - size: 22939 - timestamp: 1735276646127 + size: 22932 + timestamp: 1735363020605 - kind: conda name: multidict version: 6.1.0 From 739eae0647580b0ccde01e5ef10e3d262ee2dc41 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 3 Jan 2025 19:42:42 -0800 Subject: [PATCH 148/509] [mojo-lang] Fix memberwise init synthesis vs `!lit.ref` arguments. Memberwise initializer synthesize was using "flip" instead of "set" to determine if one was already present, and considering 'ref' arguments to be part of the equation. This fixes https://github.com/modularml/mojo/issues/3617 MODULAR_ORIG_COMMIT_REV_ID: 1b596feebdb324d885e582ee1be3174a82a114b6 --- docs/changelog.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 74c5137d6e..69c235f28c 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -132,5 +132,8 @@ what we publish. - [Issue #3540](https://github.com/modularml/mojo/issues/3540) - Using named output slot breaks trait conformance +- [Issue #3617](https://github.com/modularml/mojo/issues/3617) - Can't generate + the constructors for a type wrapping `!lit.ref` + - The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. [Issue #3826](https://github.com/modularml/mojo/issues/3826). From 9137d83e6eea1e8f374f746d4d2bc8c21b93e695 Mon Sep 17 00:00:00 2001 From: modularbot Date: Mon, 6 Jan 2025 20:59:34 +0000 Subject: [PATCH 149/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025010605 --- examples/life/magic.lock | 1450 ++++++++++++++++---------------- examples/magic.lock | 1395 ++++++++++++++++--------------- examples/notebooks/magic.lock | 1471 +++++++++++++++++---------------- examples/operators/magic.lock | 1395 ++++++++++++++++--------------- magic.lock | 1395 ++++++++++++++++--------------- 5 files changed, 3600 insertions(+), 3506 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 621b2095cd..3edbd9c9da 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -13,7 +13,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.13-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda - 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build_number: 1 - subdir: osx-arm64 - url: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2024.07.02-hcd0e937_1.conda - sha256: eebddde6cb10b146507810b701ef6df122d5309cd5151a39d0828aa44dc53725 - md5: 19e29f2ccc9168eb0a39dc40c04c0e21 + build: haa97905_2 + build_number: 2 + subdir: linux-aarch64 + url: https://conda.anaconda.org/conda-forge/linux-aarch64/re2-2024.07.02-haa97905_2.conda + sha256: 040848655df9119bae5a549fb5c8956a5537120859416c1d9d0712b7bac9f12e + md5: 1bf0135339b4a7419a198a795d2d4be0 depends: - - libre2-11 2024.07.02 h2348fd5_1 + - libre2-11 2024.07.02 h18dbdb1_2 license: BSD-3-Clause license_family: BSD - size: 26860 - timestamp: 1728779123653 + size: 26830 + timestamp: 1735540999398 - kind: conda name: readline version: '8.2' @@ -7988,12 +8005,12 @@ packages: timestamp: 1734415467047 - kind: conda name: safetensors - version: 0.4.5 + version: 0.5.0 build: py312h12e396e_0 subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/safetensors-0.4.5-py312h12e396e_0.conda - sha256: e44515f875c10efb5e041efcb250dfd18f2cb66ec3f268237549ead6284c6922 - md5: 3b87a00bcaab069172d6cef8124b7142 + url: https://conda.anaconda.org/conda-forge/linux-64/safetensors-0.5.0-py312h12e396e_0.conda + sha256: f9d0a4fbfbd2bcded116b81afd69d00aa5d5bac0d431dd47737cec1b1d24c2e2 + md5: 4b33e7c3e9bbc3936e08150fc9e17708 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=13 @@ -8003,16 +8020,16 @@ packages: - __glibc >=2.17 license: Apache-2.0 license_family: APACHE - size: 402547 - timestamp: 1725632183154 + size: 424699 + timestamp: 1735917812729 - kind: conda name: safetensors - version: 0.4.5 + version: 0.5.0 build: py312h8cbf658_0 subdir: linux-aarch64 - url: https://conda.anaconda.org/conda-forge/linux-aarch64/safetensors-0.4.5-py312h8cbf658_0.conda - sha256: e83ebeaba4a07bbe4a1d6c7eef0b4f7ae19901ef365bca043808d16e4c8faad8 - md5: 82ef253c37308b082a478fb92924cad6 + url: https://conda.anaconda.org/conda-forge/linux-aarch64/safetensors-0.5.0-py312h8cbf658_0.conda + sha256: aee581ce5c744f81ac4848b1810a88a750041ef91da646234f75092a8b4e06d1 + md5: 9c317f54bd3c95a830e98b7baee11ea9 depends: - libgcc >=13 - python >=3.12,<3.13.0a0 @@ -8022,16 +8039,16 @@ packages: - __glibc >=2.17 license: Apache-2.0 license_family: APACHE - size: 400284 - timestamp: 1725632278147 + size: 408792 + timestamp: 1735917879258 - kind: conda name: safetensors - version: 0.4.5 - build: py312he431725_0 + version: 0.5.0 + build: py312hcd83bfe_0 subdir: osx-arm64 - url: https://conda.anaconda.org/conda-forge/osx-arm64/safetensors-0.4.5-py312he431725_0.conda - sha256: 93a085d0d64237db7f4ff395c446f268c575dc2c324d8e3e5c5d7d836896295e - md5: ccb978cf1e3151c25a44c4ae65c1f20e + url: https://conda.anaconda.org/conda-forge/osx-arm64/safetensors-0.5.0-py312hcd83bfe_0.conda + sha256: 8992f78362793a465c3009be2fd80582c93e2151bc17d07ebd39ce7af757358a + md5: acbbbac2271653f4914cef842a84190f depends: - __osx >=11.0 - python >=3.12,<3.13.0a0 @@ -8041,8 +8058,8 @@ packages: - __osx >=11.0 license: Apache-2.0 license_family: APACHE - size: 353606 - timestamp: 1725632294079 + size: 377533 + timestamp: 1735918206452 - kind: conda name: shellingham version: 1.5.4 @@ -8153,6 +8170,7 @@ packages: - python >=3.9 - starlette >=0.41.3 license: BSD-3-Clause + license_family: BSD size: 15324 timestamp: 1735126414893 - kind: conda @@ -8337,18 +8355,19 @@ packages: - kind: conda name: tqdm version: 4.67.1 - build: pyhd8ed1ab_0 + build: pyhd8ed1ab_1 + build_number: 1 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.1-pyhd8ed1ab_0.conda - sha256: 5673b7104350a6998cb86cccf1d0058217d86950e8d6c927d8530606028edb1d - md5: 4085c9db273a148e149c03627350e22c + url: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.1-pyhd8ed1ab_1.conda + sha256: 11e2c85468ae9902d24a27137b6b39b4a78099806e551d390e394a8c34b48e40 + md5: 9efbfdc37242619130ea42b1cc4ed861 depends: - colorama - - python >=3.7 + - python >=3.9 license: MPL-2.0 or MIT - size: 89484 - timestamp: 1732497312317 + size: 89498 + timestamp: 1735661472632 - kind: conda name: traitlets version: 5.14.3 From 8412429b4c3706ca42bdc6ab4ad05efbde706dda Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 7 Jan 2025 05:51:09 +1000 Subject: [PATCH 150/509] [stdlib] Remove legacy `Int` SIMD ctors There were some legacy SIMD ctors still in `Int` that are now covered by the `Intable` trait and `SIMD.__int__`. MODULAR_ORIG_COMMIT_REV_ID: 5b0378be3f5df299f2abf014c86fe71f4e7fcc0e --- stdlib/benchmarks/collections/bench_dict.mojo | 4 +- stdlib/src/builtin/file.mojo | 11 ++--- stdlib/src/builtin/int.mojo | 45 ------------------- stdlib/src/builtin/object.mojo | 6 +-- 4 files changed, 11 insertions(+), 55 deletions(-) diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index 23b5ad9f8c..359d922c5e 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -30,7 +30,7 @@ from bit import next_power_of_two fn make_dict[size: Int]() -> Dict[Int, Int]: var d = Dict[Int, Int]() for i in range(0, size): - d[i] = random.random_si64(0, size).value + d[i] = int(random.random_si64(0, size)) return d @@ -62,7 +62,7 @@ fn bench_dict_insert[size: Int](mut b: Bencher) raises: @parameter fn call_fn() raises: for key in range(size, size + 100): - items[key] = random.random_si64(0, size).value + items[key] = int(random.random_si64(0, size)) b.iter[call_fn]() keep(bool(items)) diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index 1692a3458d..c622989871 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -472,11 +472,12 @@ struct FileHandle: return self^ fn _get_raw_fd(self) -> Int: - var i64_res = external_call[ - "KGEN_CompilerRT_IO_GetFD", - Int64, - ](self.handle) - return Int(i64_res.value) + return int( + external_call[ + "KGEN_CompilerRT_IO_GetFD", + Int64, + ](self.handle) + ) fn open[ diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index d90969f07f..698722f6f1 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -349,51 +349,6 @@ struct Int( """ self.value = value - @doc_private - @always_inline("nodebug") - @implicit - fn __init__(out self, value: __mlir_type.`!pop.scalar`): - """Construct Int from the given Int16 value. - - Args: - value: The init value. - """ - self = Self( - __mlir_op.`pop.cast`[_type = __mlir_type.`!pop.scalar`]( - value - ) - ) - - @doc_private - @always_inline("nodebug") - @implicit - fn __init__(out self, value: __mlir_type.`!pop.scalar`): - """Construct Int from the given Int32 value. - - Args: - value: The init value. - """ - self = Self( - __mlir_op.`pop.cast`[_type = __mlir_type.`!pop.scalar`]( - value - ) - ) - - @doc_private - @always_inline("nodebug") - @implicit - fn __init__(out self, value: __mlir_type.`!pop.scalar`): - """Construct Int from the given Int64 value. - - Args: - value: The init value. - """ - self = Self( - __mlir_op.`pop.cast`[_type = __mlir_type.`!pop.scalar`]( - value - ) - ) - @doc_private @always_inline("nodebug") @implicit diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index 0165d05249..cdd023ce36 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -1824,10 +1824,10 @@ struct object( @always_inline fn _convert_index_to_int(i: object) raises -> Int: if i._value.is_bool(): - return i._value.convert_bool_to_int().get_as_int().value + return int(i._value.convert_bool_to_int().get_as_int()) elif not i._value.is_int(): raise Error("TypeError: string indices must be integers") - return i._value.get_as_int().value + return int(i._value.get_as_int()) @always_inline fn __getitem__(self, i: object) raises -> object: @@ -1853,7 +1853,7 @@ struct object( var char = self._value.get_as_string().data[index] impl.data.init_pointee_move(char) return object(impl) - return self._value.get_list_element(i._value.get_as_int().value) + return self._value.get_list_element(int(i._value.get_as_int())) @always_inline fn __getitem__(self, *index: object) raises -> object: From 13296ee8d28ba92b0460675f164099c433ef3d34 Mon Sep 17 00:00:00 2001 From: "Ahmed S. Taei" Date: Mon, 6 Jan 2025 12:26:52 -0800 Subject: [PATCH 151/509] [stdlib] Remove FP8 SIMD constrains Otherwise we can't construct host data for FP8 tests. MODULAR_ORIG_COMMIT_REV_ID: 604d7e27bfdfc6e9c753119b07fb1b1e6281534e --- stdlib/src/builtin/simd.mojo | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 7f4a0e8576..0a56cdd820 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -184,17 +184,6 @@ fn _simd_construction_checks[type: DType, size: Int](): not (type is DType.bfloat16 and has_neon()), "bf16 is not supported for ARM architectures", ]() - constrained[ - not (type.is_float8() and not _has_native_f8_support()), - "f8 is not supported on non sm_89 and sm_90 architectures", - ]() - constrained[ - not ( - type in (DType.float8e4m3fnuz, DType.float8e5m2fnuz) - and not is_amd_gpu() - ), - "f8 fnuz variants is only supported for AMD GPU.", - ]() @always_inline("nodebug") From 3f23359edc03771ef5f11a22afd8a28aad5ed471 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Mon, 6 Jan 2025 16:01:06 -0600 Subject: [PATCH 152/509] [stdlib] polish: Take `StringSlice` instead of `String` in `atol()`/`atof()` (#53329) This changes several standard library functions to take a `StringSlice` instead of a `String`. Taking `String` requires that the passed in string value had been heap allocated, which may not need to be the case if the string was a literal, or if the calling code itself was passed only a `StringSlice`. MODULAR_ORIG_COMMIT_REV_ID: 452007e585132cb3dedf8f6698d31b6b21948cd6 --- docs/changelog.md | 7 ++ stdlib/src/collections/string/string.mojo | 118 ++++++++---------- .../src/collections/string/string_slice.mojo | 6 +- stdlib/src/utils/stringref.mojo | 4 +- .../test/collections/string/test_string.mojo | 74 +++++------ 5 files changed, 101 insertions(+), 108 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 69c235f28c..90b7c0ffc0 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -93,6 +93,13 @@ what we publish. a `StringSlice` from a buffer containing UTF-8 encoded data. This method will raise if the buffer contents are not valid UTF-8. +- Several standard library functions have been changed to take `StringSlice` + instead of `String`. This generalizes them to be used for any appropriately + encoded string in memory, without requiring that the string be heap allocated. + + - `atol()` + - `atof()` + - The `ExplicitlyCopyable` trait has changed to require a `fn copy(self) -> Self` method. Previously, an initializer with the signature `fn __init__(out self, *, other: Self)` had been required by diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index b31dbf6a49..0bbc5d09a4 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -229,11 +229,42 @@ fn ascii(value: String) -> String: # ===----------------------------------------------------------------------=== # -fn _atol(str_slice: StringSlice, base: Int = 10) raises -> Int: - """Implementation of `atol` for StringSlice inputs. +fn atol(str_slice: StringSlice, base: Int = 10) raises -> Int: + """Parses and returns the given string as an integer in the given base. + + If base is set to 0, the string is parsed as an Integer literal, with the + following considerations: + - '0b' or '0B' prefix indicates binary (base 2) + - '0o' or '0O' prefix indicates octal (base 8) + - '0x' or '0X' prefix indicates hexadecimal (base 16) + - Without a prefix, it's treated as decimal (base 10) + + Args: + str_slice: A string to be parsed as an integer in the given base. + base: Base used for conversion, value must be between 2 and 36, or 0. - Please see its docstring for details. + Returns: + An integer value that represents the string. + + Raises: + If the given string cannot be parsed as an integer value or if an + incorrect base is provided. + + Examples: + >>> atol("32") + 32 + >>> atol("FF", 16) + 255 + >>> atol("0xFF", 0) + 255 + >>> atol("0b1010", 0) + 10 + + Notes: + This follows [Python's integer literals]( + https://docs.python.org/3/reference/lexical_analysis.html#integers). """ + if (base != 0) and (base < 2 or base > 36): raise Error("Base must be >= 2 and <= 36, or 0.") if not str_slice: @@ -427,55 +458,28 @@ fn _identify_base(str_slice: StringSlice, start: Int) -> Tuple[Int, Int]: return 10, start -fn atol(str: String, base: Int = 10) raises -> Int: - """Parses and returns the given string as an integer in the given base. +fn _atof_error(str_ref: StringSlice) -> Error: + return Error("String is not convertible to float: '" + str(str_ref) + "'") - If base is set to 0, the string is parsed as an Integer literal, with the - following considerations: - - '0b' or '0B' prefix indicates binary (base 2) - - '0o' or '0O' prefix indicates octal (base 8) - - '0x' or '0X' prefix indicates hexadecimal (base 16) - - Without a prefix, it's treated as decimal (base 10) - Args: - str: A string to be parsed as an integer in the given base. - base: Base used for conversion, value must be between 2 and 36, or 0. +fn atof(str_slice: StringSlice) raises -> Float64: + """Parses the given string as a floating point and returns that value. - Returns: - An integer value that represents the string. + For example, `atof("2.25")` returns `2.25`. Raises: - If the given string cannot be parsed as an integer value or if an - incorrect base is provided. + If the given string cannot be parsed as an floating point value, for + example in `atof("hi")`. - Examples: - >>> atol("32") - 32 - >>> atol("FF", 16) - 255 - >>> atol("0xFF", 0) - 255 - >>> atol("0b1010", 0) - 10 + Args: + str_slice: A string to be parsed as a floating point. - Notes: - This follows [Python's integer literals]( - https://docs.python.org/3/reference/lexical_analysis.html#integers). + Returns: + An floating point value that represents the string, or otherwise raises. """ - return _atol(str.as_string_slice(), base) - -fn _atof_error(str_ref: StringSlice) -> Error: - return Error("String is not convertible to float: '" + str(str_ref) + "'") - - -fn _atof(str_ref: StringSlice) raises -> Float64: - """Implementation of `atof` for StringRef inputs. - - Please see its docstring for details. - """ - if not str_ref: - raise _atof_error(str_ref) + if not str_slice: + raise _atof_error(str_slice) var result: Float64 = 0.0 var exponent: Int = 0 @@ -492,9 +496,9 @@ fn _atof(str_ref: StringSlice) raises -> Float64: alias ord_E = UInt8(ord("E")) var start: Int = 0 - var str_ref_strip = str_ref.strip() - var str_len = len(str_ref_strip) - var buff = str_ref_strip.unsafe_ptr() + var str_slice_strip = str_slice.strip() + var str_len = len(str_slice_strip) + var buff = str_slice_strip.unsafe_ptr() # check sign, inf, nan if buff[start] == ord_plus: @@ -543,13 +547,13 @@ fn _atof(str_ref: StringSlice) raises -> Float64: start += 1 exponent += sign * shift if not has_number: - raise _atof_error(str_ref) + raise _atof_error(str_slice) # check for f/F at the end if buff[start] == ord_f or buff[start] == ord_F: start += 1 # check if string got fully parsed if start != str_len: - raise _atof_error(str_ref) + raise _atof_error(str_slice) # apply shift # NOTE: Instead of `var result *= 10.0 ** exponent`, we calculate a positive # integer factor as shift and multiply or divide by it based on the shift @@ -564,24 +568,6 @@ fn _atof(str_ref: StringSlice) raises -> Float64: return result * sign -fn atof(str: String) raises -> Float64: - """Parses the given string as a floating point and returns that value. - - For example, `atof("2.25")` returns `2.25`. - - Raises: - If the given string cannot be parsed as an floating point value, for - example in `atof("hi")`. - - Args: - str: A string to be parsed as a floating point. - - Returns: - An floating point value that represents the string, or otherwise raises. - """ - return _atof(str.as_string_slice()) - - # ===----------------------------------------------------------------------=== # # isdigit # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index f632e806fd..3bcaf35268 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -25,7 +25,7 @@ from bit import count_leading_zeros from collections import List, Optional from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string._utf8_validation import _is_valid_utf8 -from collections.string.string import _atof, _atol, _isspace +from collections.string.string import _isspace from memory import UnsafePointer, memcmp, memcpy, Span from memory.memory import _memcmp_impl_unconstrained from sys import bitwidthof, simdwidthof @@ -620,7 +620,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: An integer value that represents the string, or otherwise raises. """ - return _atol(self) + return atol(self) @always_inline fn __float__(self) raises -> Float64: @@ -630,7 +630,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: A float value that represents the string, or otherwise raises. """ - return _atof(self) + return atof(self) fn __mul__(self, n: Int) -> String: """Concatenates the string `n` times. diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 0b6877c9a1..bcf0a5669f 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -14,7 +14,7 @@ """ from collections.string import StringSlice -from collections.string.string import _atol, _isspace +from collections.string.string import _isspace from hashlib._hasher import _HashableWithHasher, _Hasher from sys import simdwidthof from sys.ffi import c_char @@ -380,7 +380,7 @@ struct StringRef( var str_slice = StringSlice[ImmutableAnyOrigin]( unsafe_from_utf8_strref=self ) - return _atol(str_slice) + return atol(str_slice) @always_inline fn __len__(self) -> Int: diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 74ae492479..ce7b2cf7d4 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -363,13 +363,13 @@ def test_string_indexing(): def test_atol(): # base 10 - assert_equal(375, atol(String("375"))) - assert_equal(1, atol(String("001"))) - assert_equal(5, atol(String(" 005"))) - assert_equal(13, atol(String(" 013 "))) - assert_equal(-89, atol(String("-89"))) - assert_equal(-52, atol(String(" -52"))) - assert_equal(-69, atol(String(" -69 "))) + assert_equal(375, atol("375")) + assert_equal(1, atol("001")) + assert_equal(5, atol(" 005")) + assert_equal(13, atol(" 013 ")) + assert_equal(-89, atol("-89")) + assert_equal(-52, atol(" -52")) + assert_equal(-69, atol(" -69 ")) assert_equal(1_100_200, atol(" 1_100_200")) # other bases @@ -393,12 +393,12 @@ def test_atol(): with assert_raises( contains="String is not convertible to integer with base 10: '9.03'" ): - _ = atol(String("9.03")) + _ = atol("9.03") with assert_raises( contains="String is not convertible to integer with base 10: ' 10 1'" ): - _ = atol(String(" 10 1")) + _ = atol(" 10 1") # start/end with underscore double underscores with assert_raises( @@ -455,12 +455,12 @@ def test_atol(): with assert_raises( contains="String is not convertible to integer with base 10: ''" ): - _ = atol(String("")) + _ = atol("") with assert_raises( contains="String expresses an integer too large to store in Int." ): - _ = atol(String("9223372036854775832")) + _ = atol("9223372036854775832") def test_atol_base_0(): @@ -521,63 +521,63 @@ def test_atol_base_0(): def test_atof(): - assert_equal(375.0, atof(String("375.f"))) - assert_equal(1.0, atof(String("001."))) - assert_equal(+5.0, atof(String(" +005."))) - assert_equal(13.0, atof(String(" 013.f "))) - assert_equal(-89, atof(String("-89"))) - assert_equal(-0.3, atof(String(" -0.3"))) - assert_equal(-69e3, atof(String(" -69E+3 "))) - assert_equal(123.2e1, atof(String(" 123.2E1 "))) - assert_equal(23e3, atof(String(" 23E3 "))) - assert_equal(989343e-13, atof(String(" 989343E-13 "))) - assert_equal(1.123, atof(String(" 1.123f"))) - assert_equal(0.78, atof(String(" .78 "))) - assert_equal(121234.0, atof(String(" 121234. "))) - assert_equal(985031234.0, atof(String(" 985031234.F "))) - assert_equal(FloatLiteral.negative_zero, atof(String("-0"))) - assert_equal(FloatLiteral.nan, atof(String(" nan"))) - assert_equal(FloatLiteral.infinity, atof(String(" inf "))) - assert_equal(FloatLiteral.negative_infinity, atof(String("-inf "))) + assert_equal(375.0, atof("375.f")) + assert_equal(1.0, atof("001.")) + assert_equal(+5.0, atof(" +005.")) + assert_equal(13.0, atof(" 013.f ")) + assert_equal(-89, atof("-89")) + assert_equal(-0.3, atof(" -0.3")) + assert_equal(-69e3, atof(" -69E+3 ")) + assert_equal(123.2e1, atof(" 123.2E1 ")) + assert_equal(23e3, atof(" 23E3 ")) + assert_equal(989343e-13, atof(" 989343E-13 ")) + assert_equal(1.123, atof(" 1.123f")) + assert_equal(0.78, atof(" .78 ")) + assert_equal(121234.0, atof(" 121234. ")) + assert_equal(985031234.0, atof(" 985031234.F ")) + assert_equal(FloatLiteral.negative_zero, atof("-0")) + assert_equal(FloatLiteral.nan, atof(" nan")) + assert_equal(FloatLiteral.infinity, atof(" inf ")) + assert_equal(FloatLiteral.negative_infinity, atof("-inf ")) # Negative cases with assert_raises(contains="String is not convertible to float: ''"): - _ = atof(String("")) + _ = atof("") with assert_raises( contains="String is not convertible to float: ' 123 asd'" ): - _ = atof(String(" 123 asd")) + _ = atof(" 123 asd") with assert_raises( contains="String is not convertible to float: ' f.9123 '" ): - _ = atof(String(" f.9123 ")) + _ = atof(" f.9123 ") with assert_raises( contains="String is not convertible to float: ' 989343E-1A3 '" ): - _ = atof(String(" 989343E-1A3 ")) + _ = atof(" 989343E-1A3 ") with assert_raises( contains="String is not convertible to float: ' 124124124_2134124124 '" ): - _ = atof(String(" 124124124_2134124124 ")) + _ = atof(" 124124124_2134124124 ") with assert_raises( contains="String is not convertible to float: ' 123.2E '" ): - _ = atof(String(" 123.2E ")) + _ = atof(" 123.2E ") with assert_raises( contains="String is not convertible to float: ' --958.23 '" ): - _ = atof(String(" --958.23 ")) + _ = atof(" --958.23 ") with assert_raises( contains="String is not convertible to float: ' ++94. '" ): - _ = atof(String(" ++94. ")) + _ = atof(" ++94. ") def test_calc_initial_buffer_size_int32(): From 4903cf9fbc979646aab2e6b7c181004bfa3623f3 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Mon, 6 Jan 2025 17:07:51 -0600 Subject: [PATCH 153/509] [stdlib] polish: Remove `@implicit` from `String.__init__({StringSlice, StringRef})` Also added `Path.__init__(StringSlice)` constructor. This allows the following pattern to continue to compile: var args = argv() var path = Path(args[index]) Previously, the `args[index]` `StringRef` result would implicitly coerce to the `String` argument taken by the `Path` initializer (implicitly performing an allocation that was immediately thrown away). With this change, the same code continues to compile, but without the temporary intermediate allocation. MODULAR_ORIG_COMMIT_REV_ID: 7f01194c5b05581b47645d12d824f7b4bf98750b --- docs/changelog.md | 10 ++++++++++ stdlib/src/builtin/string_literal.mojo | 12 ++++++------ stdlib/src/collections/string/inline_string.mojo | 4 ++-- stdlib/src/collections/string/string.mojo | 12 +++++------- stdlib/src/collections/string/string_slice.mojo | 11 ++++++++--- stdlib/src/os/os.mojo | 4 ++-- stdlib/src/os/path/path.mojo | 4 ++-- stdlib/src/pathlib/path.mojo | 10 ++++++++++ stdlib/test/builtin/test_string_literal.mojo | 6 +++--- stdlib/test/collections/string/test_string.mojo | 10 +++++----- 10 files changed, 53 insertions(+), 30 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 90b7c0ffc0..e0ea494658 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -100,6 +100,15 @@ what we publish. - `atol()` - `atof()` +- Removed `@implicit` decorator from some standard library initializer methods + that perform allocation. This reduces places where Mojo code could implicitly + allocate where the user may not be aware. + + Remove `@implicit` from: + + - `String.__init__(out self, StringRef)` + - `String.__init__(out self, StringSlice)` + - The `ExplicitlyCopyable` trait has changed to require a `fn copy(self) -> Self` method. Previously, an initializer with the signature `fn __init__(out self, *, other: Self)` had been required by @@ -124,6 +133,7 @@ what we publish. - `StringRef` is being deprecated. Use `StringSlice` instead. - Changed `sys.argv()` to return list of `StringSlice`. + - Added `Path` explicit constructor from `StringSlice`. - removed `StringRef.startswith()` and `StringRef.endswith()` ### 🛠️ Fixed diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 84f59e6abb..d20e8634d5 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -897,7 +897,7 @@ struct StringLiteral( Returns: A string with no leading or trailing whitespaces. """ - return self.lstrip().rstrip() + return String(self.lstrip().rstrip()) fn strip(self, chars: String) -> String: """Return a copy of the string literal with leading and trailing characters @@ -910,7 +910,7 @@ struct StringLiteral( A string with no leading or trailing characters. """ - return self.lstrip(chars).rstrip(chars) + return String(self.lstrip(chars).rstrip(chars)) fn rstrip(self, chars: String) -> String: """Return a copy of the string literal with trailing characters removed. @@ -921,7 +921,7 @@ struct StringLiteral( Returns: A string with no trailing characters. """ - return str(self).rstrip(chars) + return String(str(self).rstrip(chars)) fn rstrip(self) -> String: """Return a copy of the string with trailing whitespaces removed. This @@ -931,7 +931,7 @@ struct StringLiteral( Returns: A copy of the string with no trailing whitespaces. """ - return str(self).rstrip() + return String(str(self).rstrip()) fn lstrip(self, chars: String) -> String: """Return a copy of the string with leading characters removed. @@ -942,7 +942,7 @@ struct StringLiteral( Returns: A copy of the string with no leading characters. """ - return str(self).lstrip(chars) + return String(str(self).lstrip(chars)) fn lstrip(self) -> String: """Return a copy of the string with leading whitespaces removed. This @@ -952,4 +952,4 @@ struct StringLiteral( Returns: A copy of the string with no leading whitespaces. """ - return str(self).lstrip() + return String(str(self).lstrip()) diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index 93703b8450..783eafe207 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -109,7 +109,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): Args: literal: The string to append. """ - self.__iadd__(StringRef(literal)) + self.__iadd__(StringSlice(literal)) fn __iadd__(mut self, string: String): """Appends another string to this string. @@ -169,7 +169,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): """ var string = self - string += StringRef(other) + string += StringSlice(other) return string fn __add__(self, other: String) -> Self: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 0bbc5d09a4..62856c74ae 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -852,21 +852,19 @@ struct String( """ return self # Just use the implicit copyinit. - @implicit - fn __init__(out self, str: StringRef): + fn __init__(out self, strref: StringRef): """Construct a string from a StringRef object. Args: - str: The StringRef from which to construct this string object. + strref: The StringRef from which to construct this string object. """ - var length = len(str) + var length = len(strref) var buffer = Self._buffer_type() # +1 for null terminator, initialized to 0 buffer.resize(length + 1, 0) - memcpy(dest=buffer.data, src=str.data, count=length) + memcpy(dest=buffer.data, src=strref.data, count=length) self = Self(buffer^) - @implicit fn __init__(out self, str_slice: StringSlice): """Construct a string from a string slice. @@ -1087,7 +1085,7 @@ struct String( start, end, step = span.indices(self.byte_length()) var r = range(start, end, step) if step == 1: - return StringRef(self._buffer.data + start, len(r)) + return String(StringRef(self._buffer.data + start, len(r))) var buffer = Self._buffer_type() var result_len = len(r) diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 3bcaf35268..b0b0841260 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -358,9 +358,14 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( value: The string value. """ - debug_assert( - _is_valid_utf8(value.as_bytes()), "value is not valid utf8" - ) + # TODO(MOCO-1525): + # Support skipping UTF-8 during comptime evaluations, or support + # the necessary SIMD intrinsics to allow this to evaluate at compile + # time. + # debug_assert( + # _is_valid_utf8(value.as_bytes()), "value is not valid utf8" + # ) + self = StringSlice[O](unsafe_from_utf8=value.as_bytes()) # ===-------------------------------------------------------------------===# diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index 86c793fa6b..e80fb30e54 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -154,7 +154,7 @@ struct _DirHandle: ) if name_str == "." or name_str == "..": continue - res.append(name_str) + res.append(String(name_str)) _ = name^ return res @@ -180,7 +180,7 @@ struct _DirHandle: ) if name_str == "." or name_str == "..": continue - res.append(name_str) + res.append(String(name_str)) _ = name^ return res diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 4e65dfbf34..16083fa864 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -233,7 +233,7 @@ fn dirname[PathLike: os.PathLike, //](path: PathLike) -> String: var i = fspath.rfind(os.sep) + 1 var head = fspath[:i] if head and head != os.sep * len(head): - return head.rstrip(os.sep) + return String(head.rstrip(String(os.sep))) return head @@ -389,7 +389,7 @@ fn basename[PathLike: os.PathLike, //](path: PathLike) -> String: var i = fspath.rfind(os.sep) + 1 var head = fspath[i:] if head and head != os.sep * len(head): - return head.rstrip(os.sep) + return String(head.rstrip(String(os.sep))) return head diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index b81c184e06..8e2f5c5042 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -15,6 +15,7 @@ import os from collections import List +from collections.string import StringSlice from hashlib._hasher import _HashableWithHasher, _Hasher from os import PathLike, listdir, stat_result from sys import external_call, os_is_windows @@ -83,6 +84,15 @@ struct Path( """Initializes a path with the current directory.""" self = cwd() + # Note: Not @implicit so that allocation is not implicit. + fn __init__(out self, path: StringSlice): + """Initializes a path with the provided path. + + Args: + path: The file system path. + """ + self.path = String(path) + @implicit fn __init__(out self, path: String): """Initializes a path with the provided path. diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index 28ca91a1af..8db79b38c2 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -263,11 +263,11 @@ def test_iter(): var i = 0 for c in s: if i == 0: - assert_equal(c, "o") + assert_equal(String(c), "o") elif i == 1: - assert_equal(c, "n") + assert_equal(String(c), "n") elif i == 2: - assert_equal(c, "e") + assert_equal(String(c), "e") def test_layout(): diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index ce7b2cf7d4..0e2b3acfad 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -1262,19 +1262,19 @@ def test_string_iter(): var iterator = vs.__iter__() assert_equal(5, len(iterator)) var item = iterator.__next__() - assert_equal("m", item) + assert_equal(String("m"), String(item)) assert_equal(4, len(iterator)) item = iterator.__next__() - assert_equal("o", item) + assert_equal(String("o"), String(item)) assert_equal(3, len(iterator)) item = iterator.__next__() - assert_equal("j", item) + assert_equal(String("j"), String(item)) assert_equal(2, len(iterator)) item = iterator.__next__() - assert_equal("o", item) + assert_equal(String("o"), String(item)) assert_equal(1, len(iterator)) item = iterator.__next__() - assert_equal("🔥", item) + assert_equal(String("🔥"), String(item)) assert_equal(0, len(iterator)) var items = List[String]( From a802063167f91dde1530d2f92c9fbac6d0f016a8 Mon Sep 17 00:00:00 2001 From: Bernhard Merkle Date: Mon, 6 Jan 2025 17:30:01 -0800 Subject: [PATCH 154/509] Removed premature import of `random` in a step of the Intro to Mojo tutorial. MODULAR_ORIG_COMMIT_REV_ID: 0ea2a0a935a23dd0d0dd4d8159409afd84b43401 --- docs/manual/get-started.mdx | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index ccb7680952..cc9403b854 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -786,8 +786,6 @@ struct Grid(StringableRaising):

Click here to see the complete `gridv1.mojo` so far: ```mojo title="gridv1.mojo" -import random - @value struct Grid(StringableRaising): var rows: Int From ab7bfa581d7eab59afd6890db0cd42023988efa3 Mon Sep 17 00:00:00 2001 From: Manuel Saelices Date: Mon, 6 Jan 2025 18:52:29 -0800 Subject: [PATCH 155/509] [External] [stdlib] Implement `Int.from_bytes()` and `Int.as_bytes()` (#53303) [External] [stdlib] Implement `Int.from_bytes()` and `Int.as_bytes()` Similar to the Python's [int.from_bytes()](https://docs.python.org/3/library/stdtypes.html#int.from_bytes) and [int.to_bytes()](https://docs.python.org/3/library/stdtypes.html#int.to_bytes) one. Co-authored-by: Manuel Saelices Co-authored-by: Lukas Hermann <1734032+lsh@users.noreply.github.com> Closes modularml/mojo#3795 MODULAR_ORIG_COMMIT_REV_ID: 1e78cc9dd643009a98c7f3de77c4c250e7c5ea3f --- docs/changelog.md | 5 +++ stdlib/src/builtin/int.mojo | 33 +++++++++++++++- stdlib/src/builtin/simd.mojo | 60 ++++++++++++++++++++++++++++- stdlib/test/builtin/test_int.mojo | 62 +++++++++++++++++++++++++++++- stdlib/test/builtin/test_simd.mojo | 45 ++++++++++++++++++++++ 5 files changed, 200 insertions(+), 5 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index e0ea494658..1ad44a3195 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -18,6 +18,11 @@ what we publish. ### ✨ Highlights +- New `Int.from_bytes()`, `Int.as_bytes()`, `SIMD.from_bytes()` and `SIMD.as_bytes()` + functions to convert a list of bytes to an integer and vice versa, accepting the + endianess as an argument. Similar to Python `int.from_bytes()` and `int.to_bytes()` + functions. + ### Language changes - Initializers are now treated as static methods that return an instance of diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 698722f6f1..5c02ac8333 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -15,7 +15,7 @@ These are Mojo built-ins, so you don't need to import them. """ -from collections import KeyElement +from collections import InlineArray, KeyElement from collections.string.string import ( _calc_initial_buffer_size_int32, _calc_initial_buffer_size_int64, @@ -1168,6 +1168,37 @@ struct Int( writer.write(self) + @staticmethod + fn from_bytes[ + big_endian: Bool = False + ](bytes: InlineArray[Byte, DType.int64.sizeof()]) -> Self: + """Converts a byte array to an integer. + + Args: + bytes: The byte array to convert. + + Parameters: + big_endian: Whether the byte array is big-endian. + + Returns: + The integer value. + """ + return int(Scalar[DType.int64].from_bytes[big_endian](bytes)) + + fn as_bytes[ + big_endian: Bool = False + ](self) -> InlineArray[Byte, DType.int64.sizeof()]: + """Convert the integer to a byte array. + + Parameters: + big_endian: Whether the byte array should be big-endian. + + Returns: + The byte array. + """ + var value = Scalar[DType.int64](self) + return value.as_bytes[big_endian]() + @always_inline("nodebug") fn __mlir_index__(self) -> __mlir_type.index: """Convert to index. diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 0a56cdd820..c651891e07 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -34,6 +34,7 @@ from sys import ( bitwidthof, has_neon, is_amd_gpu, + is_big_endian, is_gpu, is_nvidia_gpu, is_x86, @@ -45,13 +46,13 @@ from sys import ( from sys._assembly import inlined_assembly from sys.info import _current_arch, _is_sm_8x, _is_sm_9x -from bit import pop_count +from bit import pop_count, byte_swap from builtin._format_float import _write_float from builtin.dtype import _uint_type_of_width from builtin.format_int import _try_write_int from builtin.io import _snprintf from documentation import doc_private -from memory import UnsafePointer, bitcast, Span +from memory import UnsafePointer, bitcast, memcpy, Span from utils import IndexList, StaticTuple from utils._visualizers import lldb_formatter_wrapping_type @@ -257,6 +258,7 @@ struct SIMD[type: DType, size: Int]( alias _Mask = SIMD[DType.bool, size] alias element_type = type + alias type_len = type.sizeof() var value: __mlir_type[`!pop.simd<`, size.value, `, `, type.value, `>`] """The underlying storage for the vector.""" @@ -1871,6 +1873,60 @@ struct SIMD[type: DType, size: Int]( return bitcast[_integral_type_of[type](), size](self).cast[int_dtype]() + @staticmethod + fn from_bytes[ + big_endian: Bool = False + ](bytes: InlineArray[Byte, Self.type_len]) -> Scalar[type]: + """Converts a byte array to an integer. + + Args: + bytes: The byte array to convert. + + Parameters: + big_endian: Whether the byte array is big-endian. + + Returns: + The integer value. + """ + var ptr: UnsafePointer[Scalar[type]] = bytes.unsafe_ptr().bitcast[ + Scalar[type] + ]() + var value = ptr[] + + @parameter + if is_big_endian() and not big_endian: + value = byte_swap(value) + elif not is_big_endian() and big_endian: + value = byte_swap(value) + return value + + fn as_bytes[ + big_endian: Bool = False + ](self) -> InlineArray[Byte, Self.type_len]: + """Convert the integer to a byte array. + + Parameters: + big_endian: Whether the byte array should be big-endian. + + Returns: + The byte array. + """ + var value = self + + @parameter + if is_big_endian() and not big_endian: + value = byte_swap(value) + elif not is_big_endian() and big_endian: + value = byte_swap(value) + + var ptr = UnsafePointer.address_of(value) + var array = InlineArray[Byte, Self.type_len](fill=0) + + # TODO: Maybe this can be a List.extend(ptr, count) method + memcpy(array.unsafe_ptr(), ptr.bitcast[Byte](), Self.type_len) + + return array^ + fn _floor_ceil_trunc_impl[intrinsic: StringLiteral](self) -> Self: constrained[ intrinsic == "llvm.floor" diff --git a/stdlib/test/builtin/test_int.mojo b/stdlib/test/builtin/test_int.mojo index 9919b2f636..8f1083c4cf 100644 --- a/stdlib/test/builtin/test_int.mojo +++ b/stdlib/test/builtin/test_int.mojo @@ -12,10 +12,10 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from sys.info import bitwidthof - +from collections import InlineArray from memory import UnsafePointer from python import PythonObject +from sys.info import bitwidthof from testing import assert_equal, assert_false, assert_raises, assert_true @@ -245,6 +245,63 @@ def test_conversion_from_python(): assert_equal(Int.try_from_python(PythonObject(-1)), -1) +def test_from_bytes_as_bytes(): + alias EightBytes = InlineArray[Byte, DType.int64.sizeof()] + + assert_equal( + Int.from_bytes[big_endian=True](EightBytes(0, 0, 0, 0, 0, 0, 0, 16)), 16 + ) + assert_equal( + Int.from_bytes[big_endian=False](EightBytes(0, 0, 0, 0, 0, 0, 1, 0)), + 281474976710656, + ) + assert_equal( + Int.from_bytes[big_endian=False](EightBytes(0, 16, 0, 0, 0, 0, 0, 0)), + 4096, + ) + assert_equal( + Int.from_bytes[big_endian=False](EightBytes(252, 0, 0, 0, 0, 0, 0, 0)), + 252, + ) + assert_equal( + Int.from_bytes[big_endian=True](EightBytes(102, 0, 0, 0, 0, 0, 0, 0)), + 7349874591868649472, + ) + assert_equal( + Int.from_bytes[big_endian=False](EightBytes(252, 0, 0, 0, 0, 0, 0, 0)), + 252, + ) + assert_equal( + Int.from_bytes[big_endian=False](EightBytes(0, 0, 0, 1, 0, 0, 0, 0)), + 16777216, + ) + assert_equal( + Int.from_bytes[big_endian=True](EightBytes(1, 0, 0, 0, 0, 0, 0, 0)), + 72057594037927936, + ) + assert_equal( + Int.from_bytes[big_endian=True](EightBytes(1, 0, 0, 1, 0, 0, 0, 0)), + 72057598332895232, + ) + assert_equal( + Int.from_bytes[big_endian=False](EightBytes(1, 0, 0, 1, 0, 0, 0, 0)), + 16777217, + ) + assert_equal( + Int.from_bytes[big_endian=True](EightBytes(255, 0, 0, 0, 0, 0, 0, 0)), + -72057594037927936, + ) + for x_ref in List[Int](10, 100, -12, 0, 1, -1, 1000, -1000): + x = x_ref[] + + @parameter + for b in range(2): + assert_equal( + Int.from_bytes[big_endian=b](Int(x).as_bytes[big_endian=b]()), + x, + ) + + def main(): test_properties() test_add() @@ -268,3 +325,4 @@ def main(): test_int_uint() test_float_conversion() test_conversion_from_python() + test_from_bytes_as_bytes() diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index d7bd8928b4..1bddab4501 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -1818,6 +1818,50 @@ def test_float_conversion(): assert_almost_equal(float(UInt64(36)), 36.0) +def test_from_bytes_as_bytes(): + alias TwoBytes = InlineArray[Byte, DType.int16.sizeof()] + alias TwoUBytes = InlineArray[Byte, DType.uint16.sizeof()] + alias FourBytes = InlineArray[Byte, DType.int32.sizeof()] + + assert_equal(Int16.from_bytes[big_endian=True](TwoBytes(0, 16)), 16) + assert_equal(Int16.from_bytes[big_endian=False](TwoBytes(0, 16)), 4096) + assert_equal(Int16.from_bytes[big_endian=True](TwoBytes(252, 0)), -1024) + assert_equal(UInt16.from_bytes[big_endian=True](TwoUBytes(252, 0)), 64512) + assert_equal(Int16.from_bytes[big_endian=False](TwoBytes(252, 0)), 252) + assert_equal(Int32.from_bytes[big_endian=True](FourBytes(0, 0, 0, 1)), 1) + assert_equal( + Int32.from_bytes[big_endian=False](FourBytes(0, 0, 0, 1)), + 16777216, + ) + assert_equal( + Int32.from_bytes[big_endian=True](FourBytes(1, 0, 0, 0)), + 16777216, + ) + assert_equal( + Int32.from_bytes[big_endian=True](FourBytes(1, 0, 0, 1)), + 16777217, + ) + assert_equal( + Int32.from_bytes[big_endian=False](FourBytes(1, 0, 0, 1)), + 16777217, + ) + assert_equal( + Int32.from_bytes[big_endian=True](FourBytes(255, 0, 0, 0)), + -16777216, + ) + for x_ref in List[Int16](10, 100, -12, 0, 1, -1, 1000, -1000): + x = x_ref[] + + @parameter + for b in range(2): + assert_equal( + Int16.from_bytes[big_endian=b]( + Int16(x).as_bytes[big_endian=b]() + ), + x, + ) + + def test_reversed(): fn test[D: DType]() raises: assert_equal(SIMD[D, 4](1, 2, 3, 4).reversed(), SIMD[D, 4](4, 3, 2, 1)) @@ -1847,6 +1891,7 @@ def main(): test_extract() test_floor() test_floordiv() + test_from_bytes_as_bytes() test_iadd() test_indexing() test_insert() From cdc60d0e6645dd6ef40c92578dcd4b221b3b7b63 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 6 Jan 2025 20:26:26 -0800 Subject: [PATCH 156/509] Revert "[External] [stdlib] Implement Int.from_bytes() and Int.as_bytes()" MODULAR_ORIG_COMMIT_REV_ID: e9f0a71cc6000e50355ac49dd45d34da348817d9 --- docs/changelog.md | 5 --- stdlib/src/builtin/int.mojo | 33 +--------------- stdlib/src/builtin/simd.mojo | 60 +---------------------------- stdlib/test/builtin/test_int.mojo | 62 +----------------------------- stdlib/test/builtin/test_simd.mojo | 45 ---------------------- 5 files changed, 5 insertions(+), 200 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 1ad44a3195..e0ea494658 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -18,11 +18,6 @@ what we publish. ### ✨ Highlights -- New `Int.from_bytes()`, `Int.as_bytes()`, `SIMD.from_bytes()` and `SIMD.as_bytes()` - functions to convert a list of bytes to an integer and vice versa, accepting the - endianess as an argument. Similar to Python `int.from_bytes()` and `int.to_bytes()` - functions. - ### Language changes - Initializers are now treated as static methods that return an instance of diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 5c02ac8333..698722f6f1 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -15,7 +15,7 @@ These are Mojo built-ins, so you don't need to import them. """ -from collections import InlineArray, KeyElement +from collections import KeyElement from collections.string.string import ( _calc_initial_buffer_size_int32, _calc_initial_buffer_size_int64, @@ -1168,37 +1168,6 @@ struct Int( writer.write(self) - @staticmethod - fn from_bytes[ - big_endian: Bool = False - ](bytes: InlineArray[Byte, DType.int64.sizeof()]) -> Self: - """Converts a byte array to an integer. - - Args: - bytes: The byte array to convert. - - Parameters: - big_endian: Whether the byte array is big-endian. - - Returns: - The integer value. - """ - return int(Scalar[DType.int64].from_bytes[big_endian](bytes)) - - fn as_bytes[ - big_endian: Bool = False - ](self) -> InlineArray[Byte, DType.int64.sizeof()]: - """Convert the integer to a byte array. - - Parameters: - big_endian: Whether the byte array should be big-endian. - - Returns: - The byte array. - """ - var value = Scalar[DType.int64](self) - return value.as_bytes[big_endian]() - @always_inline("nodebug") fn __mlir_index__(self) -> __mlir_type.index: """Convert to index. diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index c651891e07..0a56cdd820 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -34,7 +34,6 @@ from sys import ( bitwidthof, has_neon, is_amd_gpu, - is_big_endian, is_gpu, is_nvidia_gpu, is_x86, @@ -46,13 +45,13 @@ from sys import ( from sys._assembly import inlined_assembly from sys.info import _current_arch, _is_sm_8x, _is_sm_9x -from bit import pop_count, byte_swap +from bit import pop_count from builtin._format_float import _write_float from builtin.dtype import _uint_type_of_width from builtin.format_int import _try_write_int from builtin.io import _snprintf from documentation import doc_private -from memory import UnsafePointer, bitcast, memcpy, Span +from memory import UnsafePointer, bitcast, Span from utils import IndexList, StaticTuple from utils._visualizers import lldb_formatter_wrapping_type @@ -258,7 +257,6 @@ struct SIMD[type: DType, size: Int]( alias _Mask = SIMD[DType.bool, size] alias element_type = type - alias type_len = type.sizeof() var value: __mlir_type[`!pop.simd<`, size.value, `, `, type.value, `>`] """The underlying storage for the vector.""" @@ -1873,60 +1871,6 @@ struct SIMD[type: DType, size: Int]( return bitcast[_integral_type_of[type](), size](self).cast[int_dtype]() - @staticmethod - fn from_bytes[ - big_endian: Bool = False - ](bytes: InlineArray[Byte, Self.type_len]) -> Scalar[type]: - """Converts a byte array to an integer. - - Args: - bytes: The byte array to convert. - - Parameters: - big_endian: Whether the byte array is big-endian. - - Returns: - The integer value. - """ - var ptr: UnsafePointer[Scalar[type]] = bytes.unsafe_ptr().bitcast[ - Scalar[type] - ]() - var value = ptr[] - - @parameter - if is_big_endian() and not big_endian: - value = byte_swap(value) - elif not is_big_endian() and big_endian: - value = byte_swap(value) - return value - - fn as_bytes[ - big_endian: Bool = False - ](self) -> InlineArray[Byte, Self.type_len]: - """Convert the integer to a byte array. - - Parameters: - big_endian: Whether the byte array should be big-endian. - - Returns: - The byte array. - """ - var value = self - - @parameter - if is_big_endian() and not big_endian: - value = byte_swap(value) - elif not is_big_endian() and big_endian: - value = byte_swap(value) - - var ptr = UnsafePointer.address_of(value) - var array = InlineArray[Byte, Self.type_len](fill=0) - - # TODO: Maybe this can be a List.extend(ptr, count) method - memcpy(array.unsafe_ptr(), ptr.bitcast[Byte](), Self.type_len) - - return array^ - fn _floor_ceil_trunc_impl[intrinsic: StringLiteral](self) -> Self: constrained[ intrinsic == "llvm.floor" diff --git a/stdlib/test/builtin/test_int.mojo b/stdlib/test/builtin/test_int.mojo index 8f1083c4cf..9919b2f636 100644 --- a/stdlib/test/builtin/test_int.mojo +++ b/stdlib/test/builtin/test_int.mojo @@ -12,10 +12,10 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from collections import InlineArray +from sys.info import bitwidthof + from memory import UnsafePointer from python import PythonObject -from sys.info import bitwidthof from testing import assert_equal, assert_false, assert_raises, assert_true @@ -245,63 +245,6 @@ def test_conversion_from_python(): assert_equal(Int.try_from_python(PythonObject(-1)), -1) -def test_from_bytes_as_bytes(): - alias EightBytes = InlineArray[Byte, DType.int64.sizeof()] - - assert_equal( - Int.from_bytes[big_endian=True](EightBytes(0, 0, 0, 0, 0, 0, 0, 16)), 16 - ) - assert_equal( - Int.from_bytes[big_endian=False](EightBytes(0, 0, 0, 0, 0, 0, 1, 0)), - 281474976710656, - ) - assert_equal( - Int.from_bytes[big_endian=False](EightBytes(0, 16, 0, 0, 0, 0, 0, 0)), - 4096, - ) - assert_equal( - Int.from_bytes[big_endian=False](EightBytes(252, 0, 0, 0, 0, 0, 0, 0)), - 252, - ) - assert_equal( - Int.from_bytes[big_endian=True](EightBytes(102, 0, 0, 0, 0, 0, 0, 0)), - 7349874591868649472, - ) - assert_equal( - Int.from_bytes[big_endian=False](EightBytes(252, 0, 0, 0, 0, 0, 0, 0)), - 252, - ) - assert_equal( - Int.from_bytes[big_endian=False](EightBytes(0, 0, 0, 1, 0, 0, 0, 0)), - 16777216, - ) - assert_equal( - Int.from_bytes[big_endian=True](EightBytes(1, 0, 0, 0, 0, 0, 0, 0)), - 72057594037927936, - ) - assert_equal( - Int.from_bytes[big_endian=True](EightBytes(1, 0, 0, 1, 0, 0, 0, 0)), - 72057598332895232, - ) - assert_equal( - Int.from_bytes[big_endian=False](EightBytes(1, 0, 0, 1, 0, 0, 0, 0)), - 16777217, - ) - assert_equal( - Int.from_bytes[big_endian=True](EightBytes(255, 0, 0, 0, 0, 0, 0, 0)), - -72057594037927936, - ) - for x_ref in List[Int](10, 100, -12, 0, 1, -1, 1000, -1000): - x = x_ref[] - - @parameter - for b in range(2): - assert_equal( - Int.from_bytes[big_endian=b](Int(x).as_bytes[big_endian=b]()), - x, - ) - - def main(): test_properties() test_add() @@ -325,4 +268,3 @@ def main(): test_int_uint() test_float_conversion() test_conversion_from_python() - test_from_bytes_as_bytes() diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index 1bddab4501..d7bd8928b4 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -1818,50 +1818,6 @@ def test_float_conversion(): assert_almost_equal(float(UInt64(36)), 36.0) -def test_from_bytes_as_bytes(): - alias TwoBytes = InlineArray[Byte, DType.int16.sizeof()] - alias TwoUBytes = InlineArray[Byte, DType.uint16.sizeof()] - alias FourBytes = InlineArray[Byte, DType.int32.sizeof()] - - assert_equal(Int16.from_bytes[big_endian=True](TwoBytes(0, 16)), 16) - assert_equal(Int16.from_bytes[big_endian=False](TwoBytes(0, 16)), 4096) - assert_equal(Int16.from_bytes[big_endian=True](TwoBytes(252, 0)), -1024) - assert_equal(UInt16.from_bytes[big_endian=True](TwoUBytes(252, 0)), 64512) - assert_equal(Int16.from_bytes[big_endian=False](TwoBytes(252, 0)), 252) - assert_equal(Int32.from_bytes[big_endian=True](FourBytes(0, 0, 0, 1)), 1) - assert_equal( - Int32.from_bytes[big_endian=False](FourBytes(0, 0, 0, 1)), - 16777216, - ) - assert_equal( - Int32.from_bytes[big_endian=True](FourBytes(1, 0, 0, 0)), - 16777216, - ) - assert_equal( - Int32.from_bytes[big_endian=True](FourBytes(1, 0, 0, 1)), - 16777217, - ) - assert_equal( - Int32.from_bytes[big_endian=False](FourBytes(1, 0, 0, 1)), - 16777217, - ) - assert_equal( - Int32.from_bytes[big_endian=True](FourBytes(255, 0, 0, 0)), - -16777216, - ) - for x_ref in List[Int16](10, 100, -12, 0, 1, -1, 1000, -1000): - x = x_ref[] - - @parameter - for b in range(2): - assert_equal( - Int16.from_bytes[big_endian=b]( - Int16(x).as_bytes[big_endian=b]() - ), - x, - ) - - def test_reversed(): fn test[D: DType]() raises: assert_equal(SIMD[D, 4](1, 2, 3, 4).reversed(), SIMD[D, 4](4, 3, 2, 1)) @@ -1891,7 +1847,6 @@ def main(): test_extract() test_floor() test_floordiv() - test_from_bytes_as_bytes() test_iadd() test_indexing() test_insert() From 4af59dbd2207e9dddebb29ca24eb643ce9f8cbd1 Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 7 Jan 2025 17:45:45 +0000 Subject: [PATCH 157/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025010705 --- examples/life/magic.lock | 187 ++++++++++++------------- examples/magic.lock | 187 ++++++++++++------------- examples/notebooks/magic.lock | 252 ++++++++++++++++++---------------- examples/operators/magic.lock | 187 ++++++++++++------------- magic.lock | 187 ++++++++++++------------- 5 files changed, 510 insertions(+), 490 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 3edbd9c9da..2dbac0c094 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - 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conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025010605-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025010705-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.15-py312h98912ed_1.conda @@ -214,7 +214,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.27.2-py312h12e396e_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-settings-2.7.1-pyh3cfb1c2_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pygame-2.6.1-py312h4fcb14b_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda - 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kind: conda name: mojo-jupyter - version: 25.1.0.dev2025010605 + version: 25.1.0.dev2025010705 build: release subdir: noarch noarch: python - url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025010605-release.conda - sha256: 5d25a6cbeb7e23d0e751085fd4a8728224f4fb46667007046322109437fde8cc - md5: 0d7c538d4488f3d729c380af437148e3 + url: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025010705-release.conda + sha256: 5dca1179e4a37aa94b597fdb4c9d758b071f9ab3acc72ad525290c590db6b8c7 + md5: 6cc10303055ec07e344cb2a7bfdb493f depends: - - max-core ==25.1.0.dev2025010605 release + - max-core ==25.1.0.dev2025010705 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22938 - timestamp: 1736140643486 + size: 22937 + timestamp: 1736227019268 - kind: conda name: multidict version: 6.1.0 @@ -7283,20 +7286,18 @@ packages: timestamp: 1735650532951 - kind: conda name: pygments - version: 2.18.0 - build: pyhd8ed1ab_1 - build_number: 1 + version: 2.19.1 + build: pyhd8ed1ab_0 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_1.conda - sha256: 0d6133545f268b2b89c2617c196fc791f365b538d4057ecd636d658c3b1e885d - md5: b38dc0206e2a530e5c2cf11dc086b31a + url: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda + sha256: 28a3e3161390a9d23bc02b4419448f8d27679d9e2c250e29849e37749c8de86b + md5: 232fb4577b6687b2d503ef8e254270c9 depends: - python >=3.9 license: BSD-2-Clause - license_family: BSD - size: 876700 - timestamp: 1733221731178 + size: 888600 + timestamp: 1736243563082 - kind: conda name: pyinstrument version: 5.0.0 From 86ea8563dade98b0459590f37513572202f9fd8b Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Mon, 6 Jan 2025 21:21:52 -0800 Subject: [PATCH 158/509] [stdlib] Make Span's InlineArray constructor implicit (#53351) MODULAR_ORIG_COMMIT_REV_ID: bd8c2e30eaa734f63306eabfe15d1aea6a272d4f --- stdlib/src/memory/span.mojo | 1 + stdlib/test/memory/test_span.mojo | 13 +++++++++++++ 2 files changed, 14 insertions(+) diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 5d5b780322..5a19312929 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -147,6 +147,7 @@ struct Span[ self._len = len(list) @always_inline + @implicit fn __init__[ size: Int, // ](mut self, ref [origin]array: InlineArray[T, size]): diff --git a/stdlib/test/memory/test_span.mojo b/stdlib/test/memory/test_span.mojo index 4a3b6dd980..9b52577109 100644 --- a/stdlib/test/memory/test_span.mojo +++ b/stdlib/test/memory/test_span.mojo @@ -208,6 +208,19 @@ def test_reversed(): i += 1 +# We don't actually need to call this test +# but we want to make sure it compiles +def test_span_coerce(): + var l = List[Int](1, 2, 3) + var a = InlineArray[Int, 3](1, 2, 3) + + fn takes_span(s: Span[Int]): + pass + + takes_span(l) + takes_span(a) + + def main(): test_span_list_int() test_span_list_str() From e1e9f244c29df28b32a611b7ca0268fa0963a489 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 7 Jan 2025 12:24:14 -0600 Subject: [PATCH 159/509] [stdlib] polish: Remove `@implicit` from `List.__init__({*values: T, Span[T]})` This removes the implicitness of two `List` initializers that take, respectively, a `Span[T]` and a variadic list of `T`. Note that one subtle effect of this change is that you may no longer pass a "scalar" `T` to an argument expecting a `List[T]`. You must now allocate the list explicitly. MODULAR_ORIG_COMMIT_REV_ID: 69490345dce3f9e9a91fb28ec2693c52f561fedf --- docs/changelog.md | 2 ++ stdlib/src/collections/list.mojo | 2 -- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index e0ea494658..1ba3d70a26 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -108,6 +108,8 @@ what we publish. - `String.__init__(out self, StringRef)` - `String.__init__(out self, StringSlice)` + - `List.__init__(out self, owned *values: T)` + - `List.__init__(out self, span: Span[T])` - The `ExplicitlyCopyable` trait has changed to require a `fn copy(self) -> Self` method. Previously, an initializer with the signature diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index f537f7151f..c4d5bff5ca 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -135,7 +135,6 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( self.size = 0 self.capacity = capacity - @implicit fn __init__(out self, owned *values: T): """Constructs a list from the given values. @@ -167,7 +166,6 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( self.size = length - @implicit fn __init__(out self, span: Span[T]): """Constructs a list from the a Span of values. From f8b96d773a31fa14a67f96e457548ffffece23a8 Mon Sep 17 00:00:00 2001 From: Eric Hein Date: Tue, 7 Jan 2025 13:48:42 -0500 Subject: [PATCH 160/509] [stdlib] Disable printing from debug_assert on AMD GPU Until printf is supported on AMD GPU, we cannot print from debug_assert. MODULAR_ORIG_COMMIT_REV_ID: 652650d2013b8f1246489f7164b89f1041a177b5 --- stdlib/src/builtin/debug_assert.mojo | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index e66c968189..d20ea0444d 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -17,7 +17,7 @@ These are Mojo built-ins, so you don't need to import them. from os import abort -from sys import is_gpu, is_nvidia_gpu, llvm_intrinsic +from sys import is_gpu, is_nvidia_gpu, is_amd_gpu, llvm_intrinsic from sys._build import is_debug_build from sys.ffi import c_char, c_size_t, c_uint, external_call from sys.param_env import env_get_string @@ -241,7 +241,10 @@ fn _debug_assert_msg( var stdout = sys.stdout @parameter - if is_gpu(): + if is_amd_gpu(): + # FIXME: debug_assert printing is disabled on AMD GPU, see KERN-1448 + pass + elif is_nvidia_gpu(): # Count the total length of bytes to allocate only once var arg_bytes = _ArgBytes() arg_bytes.write( @@ -269,10 +272,6 @@ fn _debug_assert_msg( Span[Byte, ImmutableAnyOrigin](ptr=buffer.data, length=buffer.pos) ) - @parameter - if defined_mode != "warn": - abort() - else: var buffer = _WriteBufferStack[4096](stdout) buffer.write("At ", loc, ": ") @@ -286,9 +285,9 @@ fn _debug_assert_msg( write_args(buffer, messages, end="\n") buffer.flush() - @parameter - if defined_mode != "warn": - abort() + @parameter + if defined_mode != "warn": + abort() struct _ThreadContext(Writable): From a35a4ad2b916f45fced8c8c38c7d240c965859da Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 7 Jan 2025 13:48:55 -0600 Subject: [PATCH 161/509] [stdlib] feat: Implement `Representable` for `StringSlice` * Move implemention of `String.__repr__()` to `StringSlice`. MODULAR_ORIG_COMMIT_REV_ID: 6dfe815435dca6008366b883046dd7e931cc9c06 --- docs/changelog.md | 6 +++ stdlib/src/builtin/string_literal.mojo | 2 +- stdlib/src/collections/string/string.mojo | 29 +-------------- .../src/collections/string/string_slice.mojo | 37 +++++++++++++++++++ .../test/collections/string/test_string.mojo | 27 -------------- .../collections/string/test_string_slice.mojo | 29 +++++++++++++++ 6 files changed, 74 insertions(+), 56 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 1ba3d70a26..ea416dff84 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -100,6 +100,12 @@ what we publish. - `atol()` - `atof()` +- Various functionality has moved from `String` and `StringRef` to the more + general `StringSlice` type. + + - `StringSlice` now implements `Representable`, and that implementation is now + used by `String.__repr__()` and `StringRef.__repr__()`. + - Removed `@implicit` decorator from some standard library initializer methods that perform allocation. This reduces places where Mojo code could implicitly allocate where the user may not be aware. diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index d20e8634d5..bcc671ccce 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -402,7 +402,7 @@ struct StringLiteral( Returns: A new representation of the string. """ - return self.__str__().__repr__() + return repr(self.as_string_slice()) fn __hash__(self) -> UInt: """Hash the underlying buffer using builtin hash. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 62856c74ae..c59532861f 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -1389,34 +1389,7 @@ struct String( Returns: A new representation of the string. """ - var result = String() - var use_dquote = False - for s in self: - use_dquote = use_dquote or (s == "'") - - if s == "\\": - result += r"\\" - elif s == "\t": - result += r"\t" - elif s == "\n": - result += r"\n" - elif s == "\r": - result += r"\r" - else: - var codepoint = ord(s) - if isprintable(codepoint): - result += s - elif codepoint < 0x10: - result += hex(codepoint, prefix=r"\x0") - elif codepoint < 0x20 or codepoint == 0x7F: - result += hex(codepoint, prefix=r"\x") - else: # multi-byte character - result += s - - if use_dquote: - return '"' + result + '"' - else: - return "'" + result + "'" + return repr(StringSlice(self)) fn __fspath__(self) -> String: """Return the file system path representation (just the string itself). diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index b0b0841260..00d9f2849a 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -235,6 +235,7 @@ struct _StringSliceIter[ @register_passable("trivial") struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Stringable, + Representable, Sized, Writable, CollectionElement, @@ -408,6 +409,42 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return String(str_slice=self) + fn __repr__(self) -> String: + """Return a Mojo-compatible representation of this string slice. + + Returns: + Representation of this string slice as a Mojo string literal input + form syntax. + """ + var result = String() + var use_dquote = False + for s in self: + use_dquote = use_dquote or (s == "'") + + if s == "\\": + result += r"\\" + elif s == "\t": + result += r"\t" + elif s == "\n": + result += r"\n" + elif s == "\r": + result += r"\r" + else: + var codepoint = ord(s) + if isprintable(codepoint): + result += s + elif codepoint < 0x10: + result += hex(codepoint, prefix=r"\x0") + elif codepoint < 0x20 or codepoint == 0x7F: + result += hex(codepoint, prefix=r"\x") + else: # multi-byte character + result += s + + if use_dquote: + return '"' + result + '"' + else: + return "'" + result + "'" + fn __len__(self) -> Int: """Nominally returns the _length in Unicode codepoints_ (not bytes!). diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 0e2b3acfad..c9b9c411e1 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -44,32 +44,6 @@ def test_stringable(): assert_equal("a string", str(AString())) -def test_repr(): - # Standard single-byte characters - assert_equal(String.__repr__("hello"), "'hello'") - assert_equal(String.__repr__(str(0)), "'0'") - assert_equal(String.__repr__("A"), "'A'") - assert_equal(String.__repr__(" "), "' '") - assert_equal(String.__repr__("~"), "'~'") - - # Special single-byte characters - assert_equal(String.__repr__("\0"), r"'\x00'") - assert_equal(String.__repr__("\x06"), r"'\x06'") - assert_equal(String.__repr__("\x09"), r"'\t'") - assert_equal(String.__repr__("\n"), r"'\n'") - assert_equal(String.__repr__("\x0d"), r"'\r'") - assert_equal(String.__repr__("\x0e"), r"'\x0e'") - assert_equal(String.__repr__("\x1f"), r"'\x1f'") - assert_equal(String.__repr__("'"), '"\'"') - assert_equal(String.__repr__("\\"), r"'\\'") - assert_equal(String.__repr__("\x7f"), r"'\x7f'") - - # Multi-byte characters - assert_equal(String.__repr__("Örnsköldsvik"), "'Örnsköldsvik'") # 2-byte - assert_equal(String.__repr__("你好!"), "'你好!'") # 3-byte - assert_equal(String.__repr__("hello 🔥!"), "'hello 🔥!'") # 4-byte - - def test_constructors(): # Default construction assert_equal(0, len(String())) @@ -1587,7 +1561,6 @@ def main(): test_add() test_add_string_slice() test_stringable() - test_repr() test_string_join() test_string_literal_join() test_stringref() diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index ecdcf4ddf2..a82f43d4c1 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -202,6 +202,34 @@ fn test_slice_bool() raises: assert_true(not str2.as_string_slice().__bool__()) +def test_slice_repr(): + # Standard single-byte characters + assert_equal(StringSlice.__repr__("hello"), "'hello'") + assert_equal(StringSlice.__repr__(str(0)), "'0'") + assert_equal(StringSlice.__repr__("A"), "'A'") + assert_equal(StringSlice.__repr__(" "), "' '") + assert_equal(StringSlice.__repr__("~"), "'~'") + + # Special single-byte characters + assert_equal(StringSlice.__repr__("\0"), r"'\x00'") + assert_equal(StringSlice.__repr__("\x06"), r"'\x06'") + assert_equal(StringSlice.__repr__("\x09"), r"'\t'") + assert_equal(StringSlice.__repr__("\n"), r"'\n'") + assert_equal(StringSlice.__repr__("\x0d"), r"'\r'") + assert_equal(StringSlice.__repr__("\x0e"), r"'\x0e'") + assert_equal(StringSlice.__repr__("\x1f"), r"'\x1f'") + assert_equal(StringSlice.__repr__("'"), '"\'"') + assert_equal(StringSlice.__repr__("\\"), r"'\\'") + assert_equal(StringSlice.__repr__("\x7f"), r"'\x7f'") + + # Multi-byte characters + assert_equal( + StringSlice.__repr__("Örnsköldsvik"), "'Örnsköldsvik'" + ) # 2-byte + assert_equal(StringSlice.__repr__("你好!"), "'你好!'") # 3-byte + assert_equal(StringSlice.__repr__("hello 🔥!"), "'hello 🔥!'") # 4-byte + + fn test_utf8_validation() raises: var text = """Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam varius tellus quis tincidunt dictum. Donec eros orci, ultricies ac metus non @@ -681,6 +709,7 @@ def main(): test_slice_len() test_slice_eq() test_slice_bool() + test_slice_repr() test_utf8_validation() test_find() test_good_utf8_sequences() From 27f1361f15861677aadca90c1f815ae17e38f866 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Tue, 7 Jan 2025 12:45:44 -0800 Subject: [PATCH 162/509] [External] [stdlib] Remove `StringRef.strip()` (#53298) Remove `StringRef.strip()`. We already have this in `StringSlice`. ORIGINAL_AUTHOR=martinvuyk <110240700+martinvuyk@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3868 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3868 MODULAR_ORIG_COMMIT_REV_ID: 9a85ef521475c07ee7f9f537e42a9d2f5d3f8a78 --- docs/changelog.md | 1 + stdlib/src/utils/stringref.mojo | 18 ------ stdlib/test/builtin/test_string_literal.mojo | 12 ++++ .../test/collections/string/test_string.mojo | 58 ------------------- stdlib/test/utils/test_stringref.mojo | 39 ++++++++++++- 5 files changed, 51 insertions(+), 77 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index ea416dff84..242b60abd1 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -143,6 +143,7 @@ what we publish. - Changed `sys.argv()` to return list of `StringSlice`. - Added `Path` explicit constructor from `StringSlice`. - removed `StringRef.startswith()` and `StringRef.endswith()` + - removed `StringRef.strip()` ### 🛠️ Fixed diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index bcf0a5669f..ad78d89db9 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -596,24 +596,6 @@ struct StringRef( return StringRef(data, length) - fn strip(self) -> StringRef: - """Gets a StringRef with leading and trailing whitespaces removed. - This only takes C spaces into account: " \\t\\n\\v\\f\\r". - - For example, `" mojo "` returns `"mojo"`. - - Returns: - A StringRef with leading and trailing whitespaces removed. - """ - var start: Int = 0 - var end: Int = len(self) - var ptr = self.unsafe_ptr() - while start < end and _isspace(ptr[start]): - start += 1 - while end > start and _isspace(ptr[end - 1]): - end -= 1 - return StringRef(ptr + start, end - start) - fn split(self, delimiter: StringRef) raises -> List[StringRef]: """Split the StringRef by a delimiter. diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index 8db79b38c2..c549a9423c 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -232,6 +232,18 @@ def test_join(): assert_equal(sep.join(1, 2, 3), "1,2,3") assert_equal(sep.join(1, "abc", 3), "1,abc,3") + var s2 = ",".join(List[UInt8](1, 2, 3)) + assert_equal(s2, "1,2,3") + + var s3 = ",".join(List[UInt8](1, 2, 3, 4, 5, 6, 7, 8, 9)) + assert_equal(s3, "1,2,3,4,5,6,7,8,9") + + var s4 = ",".join(List[UInt8]()) + assert_equal(s4, "") + + var s5 = ",".join(List[UInt8](1)) + assert_equal(s5, "1") + def test_isdigit(): assert_true("123".isdigit()) diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index c9b9c411e1..53f9f6ace8 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -209,60 +209,6 @@ def test_string_join(): assert_equal(s6, "1,2,3") -def test_string_literal_join(): - var s2 = ",".join(List[UInt8](1, 2, 3)) - assert_equal(s2, "1,2,3") - - var s3 = ",".join(List[UInt8](1, 2, 3, 4, 5, 6, 7, 8, 9)) - assert_equal(s3, "1,2,3,4,5,6,7,8,9") - - var s4 = ",".join(List[UInt8]()) - assert_equal(s4, "") - - var s5 = ",".join(List[UInt8](1)) - assert_equal(s5, "1") - - -def test_stringref(): - var a = StringRef("AAA") - var b = StringRef("BBB") - var c = StringRef("AAA") - - assert_equal(3, len(a)) - assert_equal(3, len(b)) - assert_equal(3, len(c)) - assert_equal(4, len("ABBA")) - - # Equality operators - assert_not_equal(a, b) - assert_not_equal(b, a) - - # Self equality - assert_equal(a, a) - - # Value equality - assert_equal(a, c) - - -def test_stringref_from_dtypepointer(): - var a = StringRef("AAA") - var b = StringRef(ptr=a.data) - assert_equal(3, len(a)) - assert_equal(3, len(b)) - assert_equal(a, b) - - -def test_stringref_strip(): - var a = StringRef(" mojo rocks ") - var b = StringRef("mojo ") - var c = StringRef(" mojo") - var d = StringRef("") - assert_equal(a.strip(), "mojo rocks") - assert_equal(b.strip(), "mojo") - assert_equal(c.strip(), "mojo") - assert_equal(d.strip(), "") - - def test_ord(): # Regular ASCII assert_equal(ord("A"), 65) @@ -1562,10 +1508,6 @@ def main(): test_add_string_slice() test_stringable() test_string_join() - test_string_literal_join() - test_stringref() - test_stringref_from_dtypepointer() - test_stringref_strip() test_ord() test_chr() test_string_indexing() diff --git a/stdlib/test/utils/test_stringref.mojo b/stdlib/test/utils/test_stringref.mojo index 67965d22a4..18f319d4be 100644 --- a/stdlib/test/utils/test_stringref.mojo +++ b/stdlib/test/utils/test_stringref.mojo @@ -12,11 +12,46 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from testing import assert_equal, assert_false, assert_raises, assert_true +from testing import ( + assert_equal, + assert_false, + assert_raises, + assert_true, + assert_not_equal, +) from utils import StringRef +def test_stringref(): + var a = StringRef("AAA") + var b = StringRef("BBB") + var c = StringRef("AAA") + + assert_equal(3, len(a)) + assert_equal(3, len(b)) + assert_equal(3, len(c)) + assert_equal(4, len("ABBA")) + + # Equality operators + assert_not_equal(a, b) + assert_not_equal(b, a) + + # Self equality + assert_equal(a, a) + + # Value equality + assert_equal(a, c) + + +def test_stringref_from_pointer(): + var a = StringRef("AAA") + var b = StringRef(ptr=a.data) + assert_equal(3, len(a)) + assert_equal(3, len(b)) + assert_equal(a, b) + + def test_strref_from_start(): var str = StringRef("Hello") @@ -169,6 +204,8 @@ def test_str_and_ref(): def main(): + test_stringref() + test_stringref_from_pointer() test_strref_from_start() test_stringref_split() test_comparison_operators() From 88840a0492f945398a79769a05f5b80d458266d6 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Tue, 7 Jan 2025 14:40:19 -0800 Subject: [PATCH 163/509] [External] [stdlib] [NFC] Fix change `UnsafePointer` constructors to receive length as a `UInt` (#53492) [External] [stdlib] [NFC] Fix change `UnsafePointer` constructors to receive length as a `UInt` Fix change `UnsafePointer` constructors to receive length as a `UInt` ORIGINAL_AUTHOR=martinvuyk <110240700+martinvuyk@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3923 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3923 MODULAR_ORIG_COMMIT_REV_ID: 85aef9fef4e4ec76da0c1716a390698c3637e392 --- stdlib/src/builtin/string_literal.mojo | 4 ++-- stdlib/src/collections/list.mojo | 4 +++- stdlib/src/collections/string/string.mojo | 6 +++--- stdlib/src/collections/string/string_slice.mojo | 12 ++++++------ stdlib/src/memory/span.mojo | 2 +- 5 files changed, 15 insertions(+), 13 deletions(-) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index bcc671ccce..e680979614 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -440,7 +440,7 @@ struct StringLiteral( An iterator over the string. """ return _StringSliceIter[StaticConstantOrigin]( - unsafe_pointer=self.unsafe_ptr(), length=self.byte_length() + ptr=self.unsafe_ptr(), length=self.byte_length() ) fn __reversed__(self) -> _StringSliceIter[StaticConstantOrigin, False]: @@ -450,7 +450,7 @@ struct StringLiteral( A reversed iterator over the string. """ return _StringSliceIter[StaticConstantOrigin, False]( - unsafe_pointer=self.unsafe_ptr(), length=self.byte_length() + ptr=self.unsafe_ptr(), length=self.byte_length() ) fn __getitem__[IndexerType: Indexer](self, idx: IndexerType) -> String: diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index c4d5bff5ca..abe62df9ee 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -176,7 +176,9 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( for value in span: self.append(value[]) - fn __init__(mut self, *, ptr: UnsafePointer[T], length: Int, capacity: Int): + fn __init__( + out self, *, ptr: UnsafePointer[T], length: UInt, capacity: UInt + ): """Constructs a list from a pointer, its length, and its capacity. Args: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index c59532861f..6bf5cc24e1 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -893,7 +893,7 @@ struct String( self = literal.__str__() @always_inline - fn __init__(out self, *, ptr: UnsafePointer[Byte], length: Int): + fn __init__(out self, *, ptr: UnsafePointer[Byte], length: UInt): """Creates a string from the buffer. Note that the string now owns the buffer. @@ -1328,7 +1328,7 @@ struct String( An iterator of references to the string elements. """ return _StringSliceIter[__origin_of(self)]( - unsafe_pointer=self.unsafe_ptr(), length=self.byte_length() + ptr=self.unsafe_ptr(), length=self.byte_length() ) fn __reversed__(self) -> _StringSliceIter[__origin_of(self), False]: @@ -1338,7 +1338,7 @@ struct String( A reversed iterator of references to the string elements. """ return _StringSliceIter[__origin_of(self), forward=False]( - unsafe_pointer=self.unsafe_ptr(), length=self.byte_length() + ptr=self.unsafe_ptr(), length=self.byte_length() ) # ===------------------------------------------------------------------=== # diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 00d9f2849a..b4709c14fe 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -183,9 +183,9 @@ struct _StringSliceIter[ var ptr: UnsafePointer[Byte] var length: Int - fn __init__(mut self, *, unsafe_pointer: UnsafePointer[Byte], length: Int): + fn __init__(out self, *, ptr: UnsafePointer[Byte], length: UInt): self.index = 0 if forward else length - self.ptr = unsafe_pointer + self.ptr = ptr self.length = length fn __iter__(self) -> Self: @@ -321,7 +321,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( self = Self(unsafe_from_utf8=byte_slice) @always_inline - fn __init__(out self, *, ptr: UnsafePointer[Byte], length: Int): + fn __init__(out self, *, ptr: UnsafePointer[Byte], length: UInt): """Construct a `StringSlice` from a pointer to a sequence of UTF-8 encoded bytes and a length. @@ -335,7 +335,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( - `ptr` must point to data that is live for the duration of `origin`. """ - self._slice = Span[Byte, origin](ptr=ptr, length=length) + self = Self(unsafe_from_utf8=Span[Byte, origin](ptr=ptr, length=length)) @always_inline fn copy(self) -> Self: @@ -612,7 +612,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( An iterator of references to the string elements. """ return _StringSliceIter[origin]( - unsafe_pointer=self.unsafe_ptr(), length=self.byte_length() + ptr=self.unsafe_ptr(), length=self.byte_length() ) fn __reversed__(self) -> _StringSliceIter[origin, False]: @@ -622,7 +622,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( A reversed iterator of references to the string elements. """ return _StringSliceIter[origin, forward=False]( - unsafe_pointer=self.unsafe_ptr(), length=self.byte_length() + ptr=self.unsafe_ptr(), length=self.byte_length() ) fn __getitem__[IndexerType: Indexer](self, idx: IndexerType) -> String: diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 5a19312929..c0d919dff0 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -116,7 +116,7 @@ struct Span[ # ===------------------------------------------------------------------===# @always_inline - fn __init__(out self, *, ptr: UnsafePointer[T], length: Int): + fn __init__(out self, *, ptr: UnsafePointer[T], length: UInt): """Unsafe construction from a pointer and length. Args: From 1026f40c7f99ca7019a2c8d11fd237d1d1cfa35c Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 7 Jan 2025 17:41:13 -0600 Subject: [PATCH 164/509] [stdlib] polish: Take non-owning view args in `ord()`, `ascii()`, and `b64encode()` This removes a redundant `ord(String)` overload; changes `ascii()` to take a `StringSlice`; and changes `b64encode()` to take either `StringSlice` instead of a `String`, and a `Span` instead of a `List`. MODULAR_ORIG_COMMIT_REV_ID: 8711a35219b337ffa29f544b694f2b6b49c49fe9 --- docs/changelog.md | 5 +++++ stdlib/src/base64/_b64encode.mojo | 6 +++--- stdlib/src/base64/base64.mojo | 9 ++++----- stdlib/src/collections/string/string.mojo | 22 +++------------------- 4 files changed, 15 insertions(+), 27 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 242b60abd1..130d856dcb 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -99,6 +99,11 @@ what we publish. - `atol()` - `atof()` + - `ord()` + - `ascii()` + - `b64encode()` + - The `b64encode()` overload that previously took `List` has been changed to + take a `Span`. - Various functionality has moved from `String` and `StringRef` to the more general `StringSlice` type. diff --git a/stdlib/src/base64/_b64encode.mojo b/stdlib/src/base64/_b64encode.mojo index 35b7f4c5d9..9bd946b308 100644 --- a/stdlib/src/base64/_b64encode.mojo +++ b/stdlib/src/base64/_b64encode.mojo @@ -28,7 +28,7 @@ from collections import InlineArray from math.math import _compile_time_iota from sys import llvm_intrinsic -from memory import UnsafePointer, bitcast, memcpy +from memory import Span, UnsafePointer, bitcast, memcpy from utils import IndexList @@ -195,10 +195,10 @@ fn load_incomplete_simd[ return result -# TODO: Use Span instead of List as input when Span is easier to use @no_inline fn b64encode_with_buffers( - input_bytes: List[UInt8, _], mut result: List[UInt8, _] + input_bytes: Span[Byte, _], + mut result: List[UInt8, _], ): alias simd_width = sys.simdbytewidth() alias input_simd_width = simd_width * 3 // 4 diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index 6a9d585fb5..9a88b3fc14 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -20,6 +20,7 @@ from base64 import b64encode """ from collections import List +from memory import Span from sys import simdwidthof import bit @@ -64,8 +65,7 @@ fn _ascii_to_value(char: String) -> Int: # ===-----------------------------------------------------------------------===# -# TODO: Use Span instead of List as input when Span is easier to use -fn b64encode(input_bytes: List[UInt8, _], mut result: List[UInt8, _]): +fn b64encode(input_bytes: Span[Byte, _], mut result: List[Byte, _]): """Performs base64 encoding on the input string. Args: @@ -85,11 +85,10 @@ fn b64encode(input_string: String) -> String: Returns: The ASCII base64 encoded string. """ - # Slicing triggers a copy, but it should work with Span later on. - return b64encode(input_string._buffer[:-1]) + return b64encode(input_string.as_bytes()) -fn b64encode(input_bytes: List[UInt8, _]) -> String: +fn b64encode(input_bytes: Span[Byte, _]) -> String: """Performs base64 encoding on the input string. Args: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 6bf5cc24e1..1657f947d8 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -53,22 +53,6 @@ from collections.string.string_slice import ( # ===----------------------------------------------------------------------=== # -fn ord(s: String) -> Int: - """Returns an integer that represents the given one-character string. - - Given a string representing one character, return an integer - representing the code point of that character. For example, `ord("a")` - returns the integer `97`. This is the inverse of the `chr()` function. - - Args: - s: The input string slice, which must contain only a single character. - - Returns: - An integer representing the code point of the given character. - """ - return ord(s.as_string_slice()) - - fn ord(s: StringSlice) -> Int: """Returns an integer that represents the given one-character string. @@ -200,7 +184,7 @@ fn _repr_ascii(c: UInt8) -> String: @always_inline -fn ascii(value: String) -> String: +fn ascii(value: StringSlice) -> String: """Get the ASCII representation of the object. Args: @@ -213,8 +197,8 @@ fn ascii(value: String) -> String: var result = String() var use_dquote = False - for idx in range(len(value._buffer) - 1): - var char = value._buffer[idx] + for idx in range(len(value._slice)): + var char = value._slice[idx] result += _repr_ascii(char) use_dquote = use_dquote or (char == ord_squote) From ad50d45f8dc6a4496f69b3e88f788f9c246e8569 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Tue, 7 Jan 2025 16:14:36 -0800 Subject: [PATCH 165/509] [External] [stdlib] Remove `StringLiteral` `StringRef` dependencies (#53297) Remove `StringLiteral` `StringRef` dependencies Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3869 MODULAR_ORIG_COMMIT_REV_ID: b864aaf6dc01163588ad5775879912dea0544ea3 --- stdlib/src/builtin/string_literal.mojo | 19 ++++++++++++------- .../src/collections/string/string_slice.mojo | 15 +++++++-------- 2 files changed, 19 insertions(+), 15 deletions(-) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index e680979614..c5af9cef8d 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -17,11 +17,16 @@ These are Mojo built-ins, so you don't need to import them. from collections import List from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry -from collections.string.string_slice import _StringSliceIter, _to_string_list +from collections.string.string_slice import ( + StringSlice, + StaticString, + _StringSliceIter, + _to_string_list, +) from hashlib._hasher import _HashableWithHasher, _Hasher from memory import UnsafePointer, memcpy, Span from sys.ffi import c_char -from utils import StaticString, StringRef, StringSlice, Writable, Writer +from utils import Writable, Writer from utils._visualizers import lldb_formatter_wrapping_type @@ -240,7 +245,7 @@ struct StringLiteral( Returns: True if they are not equal. """ - return StringRef(self) != StringRef(rhs) + return self.as_string_slice() != rhs.as_string_slice() @always_inline("nodebug") fn __eq__(self, rhs: StringSlice) -> Bool: @@ -276,7 +281,7 @@ struct StringLiteral( Returns: True if this StringLiteral is strictly less than the RHS StringLiteral and False otherwise. """ - return StringRef(self) < StringRef(rhs) + return self.as_string_slice() < rhs.as_string_slice() @always_inline("nodebug") fn __le__(self, rhs: StringLiteral) -> Bool: @@ -323,7 +328,7 @@ struct StringLiteral( Returns: True if the string contains the substring. """ - return substr in StringRef(self) + return substr in self.as_string_slice() # ===-------------------------------------------------------------------===# # Trait implementations @@ -603,7 +608,7 @@ struct StringLiteral( Returns: The offset of `substr` relative to the beginning of the string. """ - return StringRef(self).find(substr, start=start) + return self.as_string_slice().find(substr, start=start) fn rfind(self, substr: StringLiteral, start: Int = 0) -> Int: """Finds the offset of the last occurrence of `substr` starting at @@ -616,7 +621,7 @@ struct StringLiteral( Returns: The offset of `substr` relative to the beginning of the string. """ - return StringRef(self).rfind(substr, start=start) + return self.as_string_slice().rfind(substr, start=start) fn replace(self, old: StringLiteral, new: StringLiteral) -> StringLiteral: """Return a copy of the string with all occurrences of substring `old` diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index b4709c14fe..f340747683 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -511,17 +511,16 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: If the `StringSlice` is equal to the input in length and contents. """ - if not self and not rhs: - return True - if len(self) != len(rhs): + + var s_len = self.byte_length() + var s_ptr = self.unsafe_ptr() + var rhs_ptr = rhs.unsafe_ptr() + if s_len != rhs.byte_length(): return False # same pointer and length, so equal - if self._slice.unsafe_ptr() == rhs._slice.unsafe_ptr(): + elif s_len == 0 or s_ptr == rhs_ptr: return True - for i in range(len(self)): - if self._slice[i] != rhs._slice.unsafe_ptr()[i]: - return False - return True + return memcmp(s_ptr, rhs_ptr, s_len) == 0 @always_inline fn __eq__(self, rhs: String) -> Bool: From 4c2c201e4eb6d2d5d35dad8171e8c06c75d39d21 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 7 Jan 2025 18:25:45 -0600 Subject: [PATCH 166/509] [stdlib] polish: Remove `@implicit` from `String` from `List` buffer constructor Constructing a String from a null-terminated buffer is subtle enough that it should not happen implicitly. For example, it will be incorrect and may error in the future if the buffer does not contain valid UTF-8 encoded data. * Additionally, remove redundant `String` constructor that is forced to always perform a copy. - Simplify the remaining constructor by introducing a `List._cast_hint_trivial_type()` method. MODULAR_ORIG_COMMIT_REV_ID: c0a7deeb40a2183dad77eb3567baabba24a5621b --- stdlib/src/base64/base64.mojo | 5 ++- stdlib/src/collections/list.mojo | 13 ++++++ stdlib/src/collections/string/string.mojo | 51 +++-------------------- stdlib/test/builtin/test_file.mojo | 2 +- 4 files changed, 22 insertions(+), 49 deletions(-) diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index 9a88b3fc14..0a48b8caf4 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -149,7 +149,8 @@ fn b64decode(str: String) -> String: p.append(((c & 0x03) << 6) | d) p.append(0) - return p + + return String(p^) # ===-----------------------------------------------------------------------===# @@ -231,4 +232,4 @@ fn b16decode(str: String) -> String: p.append(decode(hi) << 4 | decode(lo)) p.append(0) - return p + return String(p) diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index abe62df9ee..1f0d42bde8 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -1000,6 +1000,19 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( """ return self.data + fn _cast_hint_trivial_type[ + hint_trivial_type: Bool + ](owned self) -> List[T, hint_trivial_type]: + var size = self.size + var capacity = self.capacity + + # TODO: Why doesn't `__disable_del self` work here? + var data = self.steal_data() + + return List[T, hint_trivial_type]( + ptr=data, length=size, capacity=capacity + ) + fn _clip(value: Int, start: Int, end: Int) -> Int: return max(start, min(value, end)) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 1657f947d8..d08381f298 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -750,8 +750,7 @@ struct String( # ===------------------------------------------------------------------=== # @always_inline - @implicit - fn __init__(out self, owned impl: List[UInt8, *_]): + fn __init__(out self, owned buffer: List[UInt8, *_]): """Construct a string from a buffer of bytes without copying the allocated data. @@ -762,57 +761,17 @@ struct String( buf.append(ord('H')) buf.append(ord('i')) buf.append(0) - var hi = String(buf) - ``` - - Args: - impl: The buffer. - """ - debug_assert( - len(impl) > 0 and impl[-1] == 0, - "expected last element of String buffer to be null terminator", - ) - # We make a backup because steal_data() will clear size and capacity. - var size = impl.size - debug_assert( - impl[size - 1] == 0, - "expected last element of String buffer to be null terminator", - ) - var capacity = impl.capacity - self._buffer = Self._buffer_type( - ptr=impl.steal_data(), length=size, capacity=capacity - ) - - @always_inline - @implicit - fn __init__(out self, impl: Self._buffer_type): - """Construct a string from a buffer of bytes, copying the allocated - data. Use the transfer operator ^ to avoid the copy. - - The buffer must be terminated with a null byte: - - ```mojo - var buf = List[UInt8]() - buf.append(ord('H')) - buf.append(ord('i')) - buf.append(0) - var hi = String(buf) + var hi = String(buffer=buf) ``` Args: - impl: The buffer. + buffer: The buffer. """ debug_assert( - len(impl) > 0 and impl[-1] == 0, - "expected last element of String buffer to be null terminator", - ) - # We make a backup because steal_data() will clear size and capacity. - var size = impl.size - debug_assert( - impl[size - 1] == 0, + len(buffer) > 0 and buffer[-1] == 0, "expected last element of String buffer to be null terminator", ) - self._buffer = impl + self._buffer = buffer^._cast_hint_trivial_type[True]() @always_inline fn __init__(out self): diff --git a/stdlib/test/builtin/test_file.mojo b/stdlib/test/builtin/test_file.mojo index d30091958e..96043d9952 100644 --- a/stdlib/test/builtin/test_file.mojo +++ b/stdlib/test/builtin/test_file.mojo @@ -224,7 +224,7 @@ struct Word: word.append(self.fourth_letter) word.append(self.fith_letter) word.append(0) - return word + return String(word) def test_file_read_to_dtype_pointer(): From f444d8e53db56a9964fcd8b9d31dcb1b7e7bedb2 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Tue, 7 Jan 2025 17:12:29 -0800 Subject: [PATCH 167/509] [External] [stdlib] Add `assert_equal(List[StringSlice], List[StringSlice])` (#53253) Add `assert_equal(List[StringSlice], List[StringSlice])`. Ideally we should be able to generalize over this at some point, it still needs language features. Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Co-authored-by: Connor Gray Closes modularml/mojo#3898 MODULAR_ORIG_COMMIT_REV_ID: ffe60f2036972e3d7a64785246a7b9653a21f98f --- docs/changelog.md | 8 ++++ .../src/collections/string/string_slice.mojo | 31 +++++++++++++ stdlib/src/testing/testing.mojo | 45 +++++++++++++++++++ .../collections/string/test_string_slice.mojo | 32 +++++-------- stdlib/test/testing/test_assertion.mojo | 45 +++++++++++++++++-- 5 files changed, 137 insertions(+), 24 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 130d856dcb..527bb8b642 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -111,6 +111,14 @@ what we publish. - `StringSlice` now implements `Representable`, and that implementation is now used by `String.__repr__()` and `StringRef.__repr__()`. +- `StringSlice` now implements `EqualityComparable`. + + Up until now, `StringSlice` has implemented a more general `__eq__` and + `__ne__` comparision with `StringSlice` types that had arbitrary other + origins. However, to satisfy `EqualityComparable`, `StringSlice` now also + has narrower comparison methods that support comparing only with + `StringSlice`'s with the exact same origin. + - Removed `@implicit` decorator from some standard library initializer methods that perform allocation. This reduces places where Mojo code could implicitly allocate where the user may not be aware. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index f340747683..78268211da 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -240,6 +240,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Writable, CollectionElement, CollectionElementNew, + EqualityComparable, Hashable, PathLike, ): @@ -497,6 +498,23 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # Operator dunders # ===------------------------------------------------------------------===# + # This decorator informs the compiler that indirect address spaces are not + # dereferenced by the method. + # TODO: replace with a safe model that checks the body of the method for + # accesses to the origin. + @__unsafe_disable_nested_origin_exclusivity + fn __eq__(self, rhs_same: Self) -> Bool: + """Verify if a `StringSlice` is equal to another `StringSlice` with the + same origin. + + Args: + rhs_same: The `StringSlice` to compare against. + + Returns: + If the `StringSlice` is equal to the input in length and contents. + """ + return Self.__eq__(self, rhs=rhs_same) + # This decorator informs the compiler that indirect address spaces are not # dereferenced by the method. # TODO: replace with a safe model that checks the body of the method for @@ -546,6 +564,19 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return self == rhs.as_string_slice() + fn __ne__(self, rhs_same: Self) -> Bool: + """Verify if a `StringSlice` is not equal to another `StringSlice` with + the same origin. + + Args: + rhs_same: The `StringSlice` to compare against. + + Returns: + If the `StringSlice` is not equal to the input in length and + contents. + """ + return Self.__ne__(self, rhs=rhs_same) + @__unsafe_disable_nested_origin_exclusivity @always_inline fn __ne__(self, rhs: StringSlice) -> Bool: diff --git a/stdlib/src/testing/testing.mojo b/stdlib/src/testing/testing.mojo index ef39769ff8..e6235bc616 100644 --- a/stdlib/src/testing/testing.mojo +++ b/stdlib/src/testing/testing.mojo @@ -34,6 +34,7 @@ from collections import Optional from math import isclose from memory import memcmp from builtin._location import __call_location, _SourceLocation +from utils import StringSlice # ===----------------------------------------------------------------------=== # # Assertions @@ -236,6 +237,50 @@ fn assert_equal[ ) +# TODO(MSTDL-1071): +# Once Mojo supports parametric traits, implement EqualityComparable for +# StringSlice such that string slices with different origin types can be +# compared, then drop this overload. +@always_inline +fn assert_equal[ + O1: ImmutableOrigin, + O2: ImmutableOrigin, +]( + lhs: List[StringSlice[O1]], + rhs: List[StringSlice[O2]], + msg: String = "", + *, + location: Optional[_SourceLocation] = None, +) raises: + """Asserts that two lists are equal. + + Parameters: + O1: The origin of lhs. + O2: The origin of rhs. + + Args: + lhs: The left-hand side list. + rhs: The right-hand side list. + msg: The message to be printed if the assertion fails. + location: The location of the error (default to the `__call_location`). + + Raises: + An Error with the provided message if assert fails and `None` otherwise. + """ + + # Cast `rhs` to have the same origin as `lhs`, so that we can delegate to + # `List.__ne__`. + var rhs_origin_casted = rebind[List[StringSlice[O1]]](rhs) + + if lhs != rhs_origin_casted: + raise _assert_cmp_error["`left == right` comparison"]( + lhs.__str__(), + rhs.__str__(), + msg=msg, + loc=location.or_else(__call_location()), + ) + + @always_inline fn assert_equal[ D: DType diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index a82f43d4c1..32b6da2bf9 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -483,14 +483,6 @@ def test_splitlines(): alias S = StringSlice[StaticConstantOrigin] alias L = List[StringSlice[StaticConstantOrigin]] - # FIXME: remove once StringSlice conforms to TestableCollectionElement - fn _assert_equal[ - O1: ImmutableOrigin, O2: ImmutableOrigin - ](l1: List[StringSlice[O1]], l2: List[StringSlice[O2]]) raises: - assert_equal(len(l1), len(l2)) - for i in range(len(l1)): - assert_equal(str(l1[i]), str(l2[i])) - # FIXME: remove once StringSlice conforms to TestableCollectionElement fn _assert_equal[ O1: ImmutableOrigin @@ -500,36 +492,36 @@ def test_splitlines(): assert_equal(str(l1[i]), l2[i]) # Test with no line breaks - _assert_equal(S("hello world").splitlines(), L("hello world")) + assert_equal(S("hello world").splitlines(), L("hello world")) # Test with line breaks - _assert_equal(S("hello\nworld").splitlines(), L("hello", "world")) - _assert_equal(S("hello\rworld").splitlines(), L("hello", "world")) - _assert_equal(S("hello\r\nworld").splitlines(), L("hello", "world")) + assert_equal(S("hello\nworld").splitlines(), L("hello", "world")) + assert_equal(S("hello\rworld").splitlines(), L("hello", "world")) + assert_equal(S("hello\r\nworld").splitlines(), L("hello", "world")) # Test with multiple different line breaks s1 = S("hello\nworld\r\nmojo\rlanguage\r\n") hello_mojo = L("hello", "world", "mojo", "language") - _assert_equal(s1.splitlines(), hello_mojo) - _assert_equal( + assert_equal(s1.splitlines(), hello_mojo) + assert_equal( s1.splitlines(keepends=True), L("hello\n", "world\r\n", "mojo\r", "language\r\n"), ) # Test with an empty string - _assert_equal(S("").splitlines(), L()) + assert_equal(S("").splitlines(), L()) # test \v \f \x1c \x1d s2 = S("hello\vworld\fmojo\x1clanguage\x1d") - _assert_equal(s2.splitlines(), hello_mojo) - _assert_equal( + assert_equal(s2.splitlines(), hello_mojo) + assert_equal( s2.splitlines(keepends=True), L("hello\v", "world\f", "mojo\x1c", "language\x1d"), ) # test \x1c \x1d \x1e s3 = S("hello\x1cworld\x1dmojo\x1elanguage\x1e") - _assert_equal(s3.splitlines(), hello_mojo) - _assert_equal( + assert_equal(s3.splitlines(), hello_mojo) + assert_equal( s3.splitlines(keepends=True), L("hello\x1c", "world\x1d", "mojo\x1e", "language\x1e"), ) @@ -546,7 +538,7 @@ def test_splitlines(): u = i[] item = String("").join("hello", u, "world", u, "mojo", u, "language", u) s = StringSlice(item) - _assert_equal(s.splitlines(), hello_mojo) + assert_equal(s.splitlines(), hello_mojo) items = List("hello" + u, "world" + u, "mojo" + u, "language" + u) _assert_equal(s.splitlines(keepends=True), items) diff --git a/stdlib/test/testing/test_assertion.mojo b/stdlib/test/testing/test_assertion.mojo index 35283b7088..c9aaaf6d9d 100644 --- a/stdlib/test/testing/test_assertion.mojo +++ b/stdlib/test/testing/test_assertion.mojo @@ -26,28 +26,31 @@ from testing import ( ) from utils.numerics import inf, nan +from utils import StringSlice def test_assert_messages(): + assertion = "test_assertion.mojo:" + assertion_error = ": AssertionError:" try: assert_true(False) except e: - assert_true("test_assertion.mojo:33:20: AssertionError:" in str(e)) + assert_true(assertion in str(e) and assertion_error in str(e)) try: assert_false(True) except e: - assert_true("test_assertion.mojo:38:21: AssertionError:" in str(e)) + assert_true(assertion in str(e) and assertion_error in str(e)) try: assert_equal(1, 0) except e: - assert_true("test_assertion.mojo:43:21: AssertionError:" in str(e)) + assert_true(assertion in str(e) and assertion_error in str(e)) try: assert_not_equal(0, 0) except e: - assert_true("test_assertion.mojo:48:25: AssertionError:" in str(e)) + assert_true(assertion in str(e) and assertion_error in str(e)) @value @@ -237,6 +240,39 @@ def test_assert_custom_location(): assert_true("always_false" in str(e)) +def test_assert_equal_stringslice(): + str1 = "This is Mojo" + str2 = String("This is Mojo") + str3 = "This is mojo" + + fn _build( + value: StringLiteral, start: Int, end: Int + ) -> StringSlice[StaticConstantOrigin]: + return StringSlice[StaticConstantOrigin]( + ptr=value.unsafe_ptr() + start, length=end - start + ) + + fn _build( + read value: String, start: Int, end: Int + ) -> StringSlice[__origin_of(value)]: + return StringSlice[__origin_of(value)]( + ptr=value.unsafe_ptr() + start, length=end - start + ) + + l1 = List(_build(str1, 0, 4), _build(str1, 5, 7), _build(str1, 8, 12)) + l2 = List(_build(str2, 0, 4), _build(str2, 5, 7), _build(str2, 8, 12)) + l3 = List(_build(str3, 0, 4), _build(str3, 5, 7), _build(str3, 8, 12)) + assert_equal(l1, l1) + assert_equal(l2, l2) + assert_equal(l1, l2) + + with assert_raises(): + assert_equal(l1, l3) + + with assert_raises(): + assert_equal(l2, l3) + + def main(): test_assert_equal_is_generic() test_assert_not_equal_is_generic() @@ -248,3 +284,4 @@ def main(): test_assert_is() test_assert_is_not() test_assert_custom_location() + test_assert_equal_stringslice() From cbfcfd80cb04e0db86b64125544cd7f8d9a9878e Mon Sep 17 00:00:00 2001 From: Scott Main Date: Tue, 7 Jan 2025 17:38:05 -0800 Subject: [PATCH 168/509] Fix broken links MODULAR_ORIG_COMMIT_REV_ID: 7837532a5115d95418270a12ae5dbba876a41477 --- docs/manual/get-started.mdx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index cc9403b854..261a8b7fb4 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -175,9 +175,9 @@ Hello, World! Let's extend this basic program by prompting the user for their name and including that in the greeting printed. The built-in [`input()`](/mojo/stdlib/builtin/io/input) function accepts an optional -[`String`](/mojo/stdlib/collections/string/String) argument to use as a prompt, -and returns a `String` consisting of the characters the user entered (with the -newline character at the end stripped off). +[`String`](/mojo/stdlib/collections/string/string/String) argument to use as a +prompt, and returns a `String` consisting of the characters the user entered +(with the newline character at the end stripped off). So let's declare a variable, assign the return value from `input()` to it, and build a customized greeting. From aa14dcf87c83cd315839b62dff1a759247a91ba1 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Tue, 7 Jan 2025 17:46:23 -0800 Subject: [PATCH 169/509] [******][GPU] Remove the thread indexing in debug_assert (#53435) We do not need two implementations and the implementation was incorrect for amd. MODULAR_ORIG_COMMIT_REV_ID: 07dd468c7b6671fd4d40e3419c155f5547d855c1 --- stdlib/src/builtin/debug_assert.mojo | 4 ---- 1 file changed, 4 deletions(-) diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index d20ea0444d..52dde896aa 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -250,8 +250,6 @@ fn _debug_assert_msg( arg_bytes.write( "At ", loc, - ": ", - _ThreadContext(), " Assert ", "Warning: " if defined_mode == "warn" else " Error: ", ) @@ -261,8 +259,6 @@ fn _debug_assert_msg( buffer.write( "At ", loc, - ": ", - _ThreadContext(), " Assert ", "Warning: " if defined_mode == "warn" else "Error: ", ) From 497fdc0ec137f460fa8499ba09054150d8a8fcc3 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 7 Jan 2025 20:41:36 -0600 Subject: [PATCH 170/509] [stdlib] polish: Take `StringSlice` instead of `String` in b16/b64 encode/decode funcs MODULAR_ORIG_COMMIT_REV_ID: 0e9545201cb8d7cec6511f212e42ad3503f8332d --- docs/changelog.md | 6 +++++- stdlib/src/base64/base64.mojo | 21 +++++++++++---------- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 527bb8b642..86a8b15c41 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -102,8 +102,12 @@ what we publish. - `ord()` - `ascii()` - `b64encode()` - - The `b64encode()` overload that previously took `List` has been changed to + - Additionally, the `b64encode()` overload that previously took `List` has + been changed to take a `Span`. + - `b64decode()` + - `b16encode()` + - `b16decode()` - Various functionality has moved from `String` and `StringRef` to the more general `StringSlice` type. diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index 0a48b8caf4..b0f467f745 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -20,6 +20,7 @@ from base64 import b64encode """ from collections import List +from collections.string import StringSlice from memory import Span from sys import simdwidthof @@ -33,7 +34,7 @@ from ._b64encode import b64encode_with_buffers as _b64encode_with_buffers @always_inline -fn _ascii_to_value(char: String) -> Int: +fn _ascii_to_value(char: StringSlice) -> Int: """Converts an ASCII character to its integer value for base64 decoding. Args: @@ -76,7 +77,7 @@ fn b64encode(input_bytes: Span[Byte, _], mut result: List[Byte, _]): # For a nicer API, we provide those overloads: -fn b64encode(input_string: String) -> String: +fn b64encode(input_string: StringSlice) -> String: """Performs base64 encoding on the input string. Args: @@ -111,7 +112,7 @@ fn b64encode(input_bytes: Span[Byte, _]) -> String: @always_inline -fn b64decode(str: String) -> String: +fn b64decode(str: StringSlice) -> String: """Performs base64 decoding on the input string. Args: @@ -158,11 +159,11 @@ fn b64decode(str: String) -> String: # ===-----------------------------------------------------------------------===# -fn b16encode(str: String) -> String: - """Performs base16 encoding on the input string. +fn b16encode(str: StringSlice) -> String: + """Performs base16 encoding on the input string slice. Args: - str: The input string. + str: The input string slice. Returns: Base16 encoding of the input string. @@ -176,7 +177,7 @@ fn b16encode(str: String) -> String: @parameter @always_inline fn str_bytes(idx: UInt8) -> UInt8: - return str._buffer[int(idx)] + return str._slice[int(idx)] for i in range(length): var str_byte = str_bytes(i) @@ -196,7 +197,7 @@ fn b16encode(str: String) -> String: @always_inline -fn b16decode(str: String) -> String: +fn b16decode(str: StringSlice) -> String: """Performs base16 decoding on the input string. Args: @@ -209,7 +210,7 @@ fn b16decode(str: String) -> String: # TODO: Replace with dict literal when possible @parameter @always_inline - fn decode(c: String) -> Int: + fn decode(c: StringSlice) -> Int: var char_val = ord(c) if ord("A") <= char_val <= ord("Z"): @@ -232,4 +233,4 @@ fn b16decode(str: String) -> String: p.append(decode(hi) << 4 | decode(lo)) p.append(0) - return String(p) + return String(p^) From bf6db3fc7c1629f2e01011030331b5d8af717d47 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Tue, 7 Jan 2025 18:51:08 -0800 Subject: [PATCH 171/509] [External] [docs] Fix incorrect implicit type conversion output in variables.mdx documentation. (#53434) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [External] [docs] Fix incorrect implicit type conversion output in variables.mdx documentation. …ntation - The "Implicit type conversion" section shows the output of implicitly converting an Integer to Float64 as an Integer. It should have been a Float. Updated it to 1.0 instead of 1 - Also added a print statement in that code snippet. ORIGINAL_AUTHOR=Eldho Ittan George <7940237+el2e10@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3907 Co-authored-by: Eldho Ittan George <7940237+el2e10@users.noreply.github.com> Closes modularml/mojo#3907 MODULAR_ORIG_COMMIT_REV_ID: b4a85f2d75730556075d394e54b770134f0d1f3e --- docs/manual/variables.mdx | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/manual/variables.mdx b/docs/manual/variables.mdx index 9247ff5c9f..f9e1c5c249 100644 --- a/docs/manual/variables.mdx +++ b/docs/manual/variables.mdx @@ -184,10 +184,11 @@ floating-point type, it converts the value instead of giving a compiler error: ```mojo var number: Float64 = Int(1) +print(number) ``` ```output -1 +1.0 ``` As shown above, value assignment can be converted into a constructor call if the From 4de698b5cbbbb8c5b4261f414ea5cd957f4ba581 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 8 Jan 2025 10:49:42 -0600 Subject: [PATCH 172/509] [stdlib] cleanup: Remove redundant operator overloads for String/StringLiteral args The functionality of each of the removed methods is fulfilled by the remaining `StringSlice`-taking methods. For example, we don't need all three of: String.__iadd__(StringLiteral) String.__iadd__(String) String.__iadd__(StringSlice) StringLiteral and String can both implicitly coerce to StringSlice, so any callers currently calling one of those two overloads will gracefully fallback to simply calling the StringSlice-taking overload. MODULAR_ORIG_COMMIT_REV_ID: 24e87313c0d5a0df8764d98cb8828d32c0ee1a6a --- .../src/collections/string/inline_string.mojo | 52 +-------------- stdlib/src/collections/string/string.mojo | 66 ------------------- .../src/collections/string/string_slice.mojo | 50 -------------- 3 files changed, 3 insertions(+), 165 deletions(-) diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index 783eafe207..6cb7203e6d 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -103,22 +103,6 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): # Operator dunders # ===------------------------------------------------------------------=== # - fn __iadd__(mut self, literal: StringLiteral): - """Appends another string to this string. - - Args: - literal: The string to append. - """ - self.__iadd__(StringSlice(literal)) - - fn __iadd__(mut self, string: String): - """Appends another string to this string. - - Args: - string: The string to append. - """ - self.__iadd__(string.as_string_slice()) - fn __iadd__(mut self, str_slice: StringSlice): """Appends another string to this string. @@ -158,21 +142,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): buffer.append(0) # Add the NUL byte self._storage = Self.Layout(String(buffer^)) - fn __add__(self, other: StringLiteral) -> Self: - """Construct a string by appending another string at the end of this string. - - Args: - other: The string to append. - - Returns: - A new string containing the concatenation of `self` and `other`. - """ - - var string = self - string += StringSlice(other) - return string - - fn __add__(self, other: String) -> Self: + fn __add__(self, other: StringSlice) -> Self: """Construct a string by appending another string at the end of this string. Args: @@ -183,8 +153,8 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): """ var string = self - string += other.as_string_slice() - return string + string += other + return string^ fn __add__(self, other: InlineString) -> Self: """Construct a string by appending another string at the end of this string. @@ -378,22 +348,6 @@ struct _FixedString[CAP: Int]( # Operator dunders # ===------------------------------------------------------------------=== # - fn __iadd__(mut self, literal: StringLiteral) raises: - """Appends another string to this string. - - Args: - literal: The string to append. - """ - self.__iadd__(literal.as_string_slice()) - - fn __iadd__(mut self, string: String) raises: - """Appends another string to this string. - - Args: - string: The string to append. - """ - self.__iadd__(string.as_string_slice()) - @always_inline fn __iadd__(mut self, str_slice: StringSlice) raises: """Appends another string to this string. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index d08381f298..7cbbd2a884 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -1145,30 +1145,6 @@ struct String( ptr[sum_len] = 0 return Self(buffer^) - @always_inline - fn __add__(self, other: String) -> String: - """Creates a string by appending another string at the end. - - Args: - other: The string to append. - - Returns: - The new constructed string. - """ - return Self._add[True](self.as_bytes(), other.as_bytes()) - - @always_inline - fn __add__(self, other: StringLiteral) -> String: - """Creates a string by appending a string literal at the end. - - Args: - other: The string literal to append. - - Returns: - The new constructed string. - """ - return Self._add[False](self.as_bytes(), other.as_bytes()) - @always_inline fn __add__(self, other: StringSlice) -> String: """Creates a string by appending a string slice at the end. @@ -1181,30 +1157,6 @@ struct String( """ return Self._add[False](self.as_bytes(), other.as_bytes()) - @always_inline - fn __radd__(self, other: String) -> String: - """Creates a string by prepending another string to the start. - - Args: - other: The string to prepend. - - Returns: - The new constructed string. - """ - return Self._add[True](other.as_bytes(), self.as_bytes()) - - @always_inline - fn __radd__(self, other: StringLiteral) -> String: - """Creates a string by prepending another string literal to the start. - - Args: - other: The string to prepend. - - Returns: - The new constructed string. - """ - return Self._add[True](other.as_bytes(), self.as_bytes()) - @always_inline fn __radd__(self, other: StringSlice) -> String: """Creates a string by prepending another string slice to the start. @@ -1237,24 +1189,6 @@ struct String( if not has_null: s_ptr[sum_len] = 0 - @always_inline - fn __iadd__(mut self, other: String): - """Appends another string to this string. - - Args: - other: The string to append. - """ - self._iadd[True](other.as_bytes()) - - @always_inline - fn __iadd__(mut self, other: StringLiteral): - """Appends another string literal to this string. - - Args: - other: The string to append. - """ - self._iadd[False](other.as_bytes()) - @always_inline fn __iadd__(mut self, other: StringSlice): """Appends another string slice to this string. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 78268211da..977f5b7f76 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -540,30 +540,6 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( return True return memcmp(s_ptr, rhs_ptr, s_len) == 0 - @always_inline - fn __eq__(self, rhs: String) -> Bool: - """Verify if a `StringSlice` is equal to a string. - - Args: - rhs: The `String` to compare against. - - Returns: - If the `StringSlice` is equal to the input in length and contents. - """ - return self == rhs.as_string_slice() - - @always_inline - fn __eq__(self, rhs: StringLiteral) -> Bool: - """Verify if a `StringSlice` is equal to a literal. - - Args: - rhs: The `StringLiteral` to compare against. - - Returns: - If the `StringSlice` is equal to the input in length and contents. - """ - return self == rhs.as_string_slice() - fn __ne__(self, rhs_same: Self) -> Bool: """Verify if a `StringSlice` is not equal to another `StringSlice` with the same origin. @@ -591,32 +567,6 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return not self == rhs - @always_inline - fn __ne__(self, rhs: String) -> Bool: - """Verify if span is not equal to another `StringSlice`. - - Args: - rhs: The `StringSlice` to compare against. - - Returns: - If the `StringSlice` is not equal to the input in length and - contents. - """ - return not self == rhs - - @always_inline - fn __ne__(self, rhs: StringLiteral) -> Bool: - """Verify if span is not equal to a `StringLiteral`. - - Args: - rhs: The `StringLiteral` to compare against. - - Returns: - If the `StringSlice` is not equal to the input in length and - contents. - """ - return not self == rhs - @always_inline fn __lt__(self, rhs: StringSlice) -> Bool: """Verify if the `StringSlice` bytes are strictly less than the input in From 6708625aa7535a2fd76fd8484188567b9f790dad Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 8 Jan 2025 19:30:14 +0000 Subject: [PATCH 173/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025010817 --- examples/life/magic.lock | 284 +++++++++++++++++----------------- examples/magic.lock | 284 +++++++++++++++++----------------- examples/notebooks/magic.lock | 284 +++++++++++++++++----------------- examples/operators/magic.lock | 284 +++++++++++++++++----------------- magic.lock | 284 +++++++++++++++++----------------- 5 files changed, 711 insertions(+), 709 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 2dbac0c094..9d24a3a9a5 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -83,7 +83,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/httptools-0.6.4-py312h66e93f0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.26.5-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.27.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda @@ -146,7 +146,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-18.1.0-h081d1f1_7_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.44-hadc24fc_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.45-h943b412_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.3-h6128344_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2024.07.02-hbbce691_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025010705-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025010705-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025010705-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025010705-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025010817-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025010817-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025010817-3.12release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025010817-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025010705-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025010817-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.15-py312h98912ed_1.conda @@ -236,7 +236,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/rich-13.9.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/rich-toolkit-0.11.3-pyh29332c3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.10-hb5b8611_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/safetensors-0.5.0-py312h12e396e_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/safetensors-0.5.1-py312h12e396e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2-2.30.10-h63c27ac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2_image-2.8.2-h06ee604_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2_mixer-2.6.3-h8830914_1.conda @@ -359,7 +359,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/httptools-0.6.4-py312hb2c0f52_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.26.5-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.27.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/icu-75.1-hf9b3779_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda @@ -422,7 +422,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.28-pthreads_h9d3fd7e_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopus-1.3.1-hf897c2e_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libparquet-18.1.0-hfc78867_7_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.44-hc4a20ef_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.45-hec79eb8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libprotobuf-5.28.3-h44a3b7b_1.conda - 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- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsystemd0-256.9-ha536d29_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsystemd0-257.2-h27834fc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libthrift-0.21.0-h154c74f_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.7.0-h88f7998_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libutf8proc-2.9.0-h86ecc28_1.conda @@ -6982,15 +6982,13 @@ packages: timestamp: 1729089498541 - kind: conda name: libsystemd0 - version: '256.9' - build: h0b6a36f_2 - build_number: 2 - subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-256.9-h0b6a36f_2.conda - sha256: 28e1a3c4bd242e7eb3bd0bcd35e558680d186e7a1d61482d371dde2a0f1bfb42 - md5: 135bbeb376345b6847c065115be4221a + version: '257.2' + build: h27834fc_0 + subdir: linux-aarch64 + url: https://conda.anaconda.org/conda-forge/linux-aarch64/libsystemd0-257.2-h27834fc_0.conda + sha256: 7b95a42f507479c4e76855dbffe65a3b51fa5f1465649be00a1940dec6cdb9a8 + md5: 18fbe3093009820c6fb7d6f1c710fd66 depends: - - __glibc >=2.17,<3.0.a0 - libcap >=2.71,<2.72.0a0 - libgcc >=13 - libgcrypt-lib >=1.11.0,<2.0a0 @@ -6998,18 +6996,18 @@ packages: - lz4-c >=1.10.0,<1.11.0a0 - zstd >=1.5.6,<1.6.0a0 license: LGPL-2.1-or-later - size: 410566 - timestamp: 1733679350245 + size: 512091 + timestamp: 1736377189744 - kind: conda name: libsystemd0 - version: '256.9' - build: ha536d29_2 - build_number: 2 - subdir: linux-aarch64 - url: https://conda.anaconda.org/conda-forge/linux-aarch64/libsystemd0-256.9-ha536d29_2.conda - sha256: 2cf3b22760674612fa13ad9748164dffc993d58dd3fc05c343b646ce31375af7 - md5: 5ef220ea7aa46bd59f83ac7afe14910b + version: '257.2' + build: h3dc2cb9_0 + subdir: linux-64 + url: https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-257.2-h3dc2cb9_0.conda + sha256: 03f532cae9ca0417b29ead19490a9fa0fa5e6ad73f1bfc7ea0d4d3bd4c41156e + md5: 40c12fdd396297db83f789722027f5ed depends: + - __glibc >=2.17,<3.0.a0 - libcap >=2.71,<2.72.0a0 - libgcc >=13 - libgcrypt-lib >=1.11.0,<2.0a0 @@ -7017,8 +7015,8 @@ packages: - lz4-c >=1.10.0,<1.11.0a0 - zstd >=1.5.6,<1.6.0a0 license: LGPL-2.1-or-later - size: 432708 - timestamp: 1733679350109 + size: 487652 + timestamp: 1736377129372 - kind: conda name: libthrift version: 0.21.0 From ed424609486d908f3d83c40628b570de4d2fdc42 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Wed, 8 Jan 2025 11:35:36 -0800 Subject: [PATCH 175/509] [External] [stdlib] Improve `startswith()` and `endswith()` (#53548) Improve `startswith()` and `endswith()` This changes these methods to take `StringSlice`, and simplifies their implementation on `String` and `StringLiteral` to simply delegate to `StringSlice`'s implementation of the same operation. Closes https://github.com/modularml/mojo/issues/3903. Solves the current CI failure with `check_licences.mojo` passing a `StringSlice` to `String.startswith()`. Co-authored-by: Connor Gray Closes modularml/mojo#3922 MODULAR_ORIG_COMMIT_REV_ID: 8b078da9d23edc4e118dc9854763e0fb69887fba --- stdlib/src/builtin/string_literal.mojo | 36 +++++++++------- stdlib/src/collections/string/string.mojo | 42 +++++++------------ .../src/collections/string/string_slice.mojo | 26 +++++------- 3 files changed, 45 insertions(+), 59 deletions(-) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index c5af9cef8d..7fb34ade4e 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -832,33 +832,37 @@ struct StringLiteral( """ return str(self).center(width, fillchar) - fn startswith(self, prefix: String, start: Int = 0, end: Int = -1) -> Bool: - """Checks if the string literal starts with the specified prefix between start - and end positions. Returns True if found and False otherwise. + fn startswith( + self, prefix: StringSlice, start: Int = 0, end: Int = -1 + ) -> Bool: + """Checks if the string literal starts with the specified prefix between + start and end positions. Returns True if found and False otherwise. Args: - prefix: The prefix to check. - start: The start offset from which to check. - end: The end offset from which to check. + prefix: The prefix to check. + start: The start offset from which to check. + end: The end offset from which to check. Returns: - True if the self[start:end] is prefixed by the input prefix. + True if the `self[start:end]` is prefixed by the input prefix. """ - return str(self).startswith(prefix, start, end) + return self.as_string_slice().startswith(prefix, start, end) - fn endswith(self, suffix: String, start: Int = 0, end: Int = -1) -> Bool: - """Checks if the string literal end with the specified suffix between start - and end positions. Returns True if found and False otherwise. + fn endswith( + self, suffix: StringSlice, start: Int = 0, end: Int = -1 + ) -> Bool: + """Checks if the string literal end with the specified suffix between + start and end positions. Returns True if found and False otherwise. Args: - suffix: The suffix to check. - start: The start offset from which to check. - end: The end offset from which to check. + suffix: The suffix to check. + start: The start offset from which to check. + end: The end offset from which to check. Returns: - True if the self[start:end] is suffixed by the input suffix. + True if the `self[start:end]` is suffixed by the input suffix. """ - return str(self).endswith(suffix, start, end) + return self.as_string_slice().endswith(suffix, start, end) fn isdigit(self) -> Bool: """Returns True if all characters in the string literal are digits. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 7cbbd2a884..70e5ab003f 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -1888,50 +1888,36 @@ struct String( return to_uppercase(self) fn startswith( - ref self, prefix: String, start: Int = 0, end: Int = -1 + self, prefix: StringSlice, start: Int = 0, end: Int = -1 ) -> Bool: """Checks if the string starts with the specified prefix between start and end positions. Returns True if found and False otherwise. Args: - prefix: The prefix to check. - start: The start offset from which to check. - end: The end offset from which to check. + prefix: The prefix to check. + start: The start offset from which to check. + end: The end offset from which to check. Returns: - True if the self[start:end] is prefixed by the input prefix. + True if the `self[start:end]` is prefixed by the input prefix. """ - if end == -1: - return StringSlice[__origin_of(self)]( - ptr=self.unsafe_ptr() + start, - length=self.byte_length() - start, - ).startswith(prefix.as_string_slice()) + return self.as_string_slice().startswith(prefix, start, end) - return StringSlice[__origin_of(self)]( - ptr=self.unsafe_ptr() + start, length=end - start - ).startswith(prefix.as_string_slice()) - - fn endswith(self, suffix: String, start: Int = 0, end: Int = -1) -> Bool: + fn endswith( + self, suffix: StringSlice, start: Int = 0, end: Int = -1 + ) -> Bool: """Checks if the string end with the specified suffix between start and end positions. Returns True if found and False otherwise. Args: - suffix: The suffix to check. - start: The start offset from which to check. - end: The end offset from which to check. + suffix: The suffix to check. + start: The start offset from which to check. + end: The end offset from which to check. Returns: - True if the self[start:end] is suffixed by the input suffix. + True if the `self[start:end]` is suffixed by the input suffix. """ - if end == -1: - return StringSlice[__origin_of(self)]( - ptr=self.unsafe_ptr() + start, - length=self.byte_length() - start, - ).endswith(suffix.as_string_slice()) - - return StringSlice[__origin_of(self)]( - ptr=self.unsafe_ptr() + start, length=end - start - ).endswith(suffix.as_string_slice()) + return self.as_string_slice().endswith(suffix, start, end) fn removeprefix(self, prefix: String, /) -> String: """Returns a new string with the prefix removed if it was present. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 977f5b7f76..881b2df713 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -279,11 +279,6 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # StringLiteral is guaranteed to use UTF-8 encoding. # FIXME(MSTDL-160): # Ensure StringLiteral _actually_ always uses UTF-8 encoding. - # FIXME: this gets practically stuck at compile time - # debug_assert( - # _is_valid_utf8(lit.as_bytes()), - # "StringLiteral doesn't have valid UTF-8 encoding", - # ) self = StaticString(unsafe_from_utf8=lit.as_bytes()) @always_inline @@ -296,7 +291,14 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Safety: `unsafe_from_utf8` MUST be valid UTF-8 encoded data. """ - + # FIXME(#3706): can't run at compile time + # TODO(MOCO-1525): + # Support skipping UTF-8 during comptime evaluations, or support + # the necessary SIMD intrinsics to allow this to evaluate at compile + # time. + # debug_assert( + # _is_valid_utf8(value.as_bytes()), "value is not valid utf8" + # ) self._slice = unsafe_from_utf8 fn __init__(out self, *, unsafe_from_utf8_strref: StringRef): @@ -359,15 +361,6 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Args: value: The string value. """ - - # TODO(MOCO-1525): - # Support skipping UTF-8 during comptime evaluations, or support - # the necessary SIMD intrinsics to allow this to evaluate at compile - # time. - # debug_assert( - # _is_valid_utf8(value.as_bytes()), "value is not valid utf8" - # ) - self = StringSlice[O](unsafe_from_utf8=value.as_bytes()) # ===-------------------------------------------------------------------===# @@ -877,6 +870,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ if end == -1: return self.find(prefix, start) == start + # FIXME: use normalize_index return StringSlice[origin]( ptr=self.unsafe_ptr() + start, length=end - start ).startswith(prefix) @@ -899,6 +893,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( return False if end == -1: return self.rfind(suffix, start) + len(suffix) == len(self) + # FIXME: use normalize_index return StringSlice[origin]( ptr=self.unsafe_ptr() + start, length=end - start ).endswith(suffix) @@ -915,6 +910,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( A `StringSlice` borrowed from the current string containing the characters of the slice starting at start. """ + # FIXME: use normalize_index var self_len = self.byte_length() From dbd90f692459f12f35e7c26ff01bf7c4d9c8ce5d Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Wed, 8 Jan 2025 11:35:47 -0800 Subject: [PATCH 176/509] [External] [docs] update broken link to nightly branch (#53524) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [External] [docs] update broken link to nightly branch - The link to the example of implementing operators for a custom type was broken, as it pointed to the `main` branch. - Updated the URL to point to the correct `nightly` branch, ensuring the link now leads to the correct example: https://github.com/modularml/mojo/tree/nightly/examples/operators - The change was made on the page: https://docs.modular-dev.com/nightly/mojo/manual/operators/#an-example-of-implementing-operators-for-a-custom-type ORIGINAL_AUTHOR=Miguel Cárdenas <78029302+miguelcsx@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3885 Co-authored-by: Miguel Cárdenas <78029302+miguelcsx@users.noreply.github.com> Closes modularml/mojo#3885 MODULAR_ORIG_COMMIT_REV_ID: 24c0200789225bff5266771ede4f8994f3a94260 --- docs/manual/operators.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index a11cbd2cd8..b4ac20d127 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -1030,7 +1030,7 @@ operations like printing complex values. We'll also allow mixing `Complex` and This example builds our `Complex` struct incrementally. You can also find the [complete example in the public Mojo GitHub -repo](https://github.com/modularml/mojo/tree/main/examples/operators). +repo](https://github.com/modularml/mojo/tree/nightly/examples/operators). :::note From b19e2fdbbe14199bb7316fb911b46fe523883079 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 8 Jan 2025 16:56:54 -0600 Subject: [PATCH 177/509] [stdlib] cleanup: Remove `@implicit` from `Tuple` and `ListLiteral` variadic init These don't seem to be used widely, and can cause problems with overload resolution, since they can implicit convert from essentially anything. I specifically ran into the problem that: PythonObject("Foo") was ambiguous if you have both: PythonObject.__init__(ListLiteral[T]) PythonObject.__init__(StringSlice) because both are 1 implicit conversion step away from a StringLiteral. MODULAR_ORIG_COMMIT_REV_ID: c13da6ad20f728a044c7762508f66c213fcbdad8 --- stdlib/src/builtin/builtin_list.mojo | 1 - stdlib/src/builtin/tuple.mojo | 1 - 2 files changed, 2 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 50e63d02d2..c24ecb8d25 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -39,7 +39,6 @@ struct ListLiteral[*Ts: CollectionElement](Sized, CollectionElement): # ===-------------------------------------------------------------------===# @always_inline - @implicit fn __init__(out self, owned *args: *Ts): """Construct the list literal from the given values. diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index b18221e3b3..ea6ad3390c 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -48,7 +48,6 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): """The underlying storage for the tuple.""" @always_inline("nodebug") - @implicit fn __init__(out self, owned *args: *element_types): """Construct the tuple. From 7e6963370aca3b85c5d2b84d256fb0d6820fd6a7 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 8 Jan 2025 19:01:49 -0600 Subject: [PATCH 178/509] [stdlib] polish: Change almost all `read arg: String` to `StringSlice` This PR changes almost all uses of immutable `String` reference arguments to instead take a `StringSlice`. This gives more flexibility to callers, who may have string data not locally stored as a heap-allocated `String` (for example, a string literal or borrowed string data). Exceptions: * Functions that pass `String` data to C, and so depend on the NUL terminator character that `String` guarantees. Removed: * `FileHandle.__init__(path: String, mode: String)` - Redundant with existing StringSlice-taking initializer * `PythonObject.__init__(StringLiteral)` * `PythonObject.__init__(StringRef)` Misc: * string_literal.mojo: Change some uses of `str(self).foo()` to `self.as_string_slice().foo()` to avoid unnecessary allocations. * Change calls to `String.unsafe_ptr()` to `String.unsafe_cstr_ptr()`, to make it clear that these depend on the NUL terminator. This will act as a speedbump to anyone trying to change these instancdes of `String` to `StringSlice` who might unknowningly not realize `StringSlice` does NOT guarantee a NUL terminator. * Avoid unnecessary intermediate `String` allocations in code like: `string += foo + bar` where the RHS `foo + bar` could copy / reallocate the `foo` `String`. MODULAR_ORIG_COMMIT_REV_ID: 52a716fa25520a268e3aff37349d1f2adf1fe00d --- stdlib/src/builtin/dtype.mojo | 47 ++++++++++--------- stdlib/src/builtin/error.mojo | 1 + stdlib/src/builtin/file.mojo | 13 +---- stdlib/src/builtin/int.mojo | 3 +- stdlib/src/builtin/io.mojo | 3 +- stdlib/src/builtin/string_literal.mojo | 14 +++--- stdlib/src/collections/string/_unicode.mojo | 8 ++-- stdlib/src/collections/string/string.mojo | 26 +++++----- stdlib/src/os/env.mojo | 10 ++-- stdlib/src/os/os.mojo | 2 +- stdlib/src/os/path/path.mojo | 7 ++- stdlib/src/pathlib/path.mojo | 16 ++++--- stdlib/src/python/python.mojo | 4 +- stdlib/src/python/python_object.mojo | 28 +++++++---- stdlib/src/tempfile/tempfile.mojo | 3 +- .../collections/string/test_string_slice.mojo | 2 +- stdlib/test/python/test_python_object.mojo | 13 ----- 17 files changed, 101 insertions(+), 99 deletions(-) diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index 48bfffaafd..f46f28a89a 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -16,6 +16,7 @@ These are Mojo built-ins, so you don't need to import them. """ from collections import KeyElement +from collections.string import StringSlice from hashlib._hasher import _HashableWithHasher, _Hasher from sys import bitwidthof, os_is_windows, sizeof @@ -166,53 +167,53 @@ struct DType( self.value = value @staticmethod - fn _from_str(str: String) -> DType: + fn _from_str(str: StringSlice) -> DType: """Construct a DType from a string. Args: str: The name of the DType. """ - if str.startswith(String("DType.")): - return Self._from_str(str.removeprefix("DType.")) - elif str == String("bool"): + if str.startswith("DType."): + return Self._from_str(String(str).removeprefix("DType.")) + elif str == "bool": return DType.bool - elif str == String("int8"): + elif str == "int8": return DType.int8 - elif str == String("uint8"): + elif str == "uint8": return DType.uint8 - elif str == String("int16"): + elif str == "int16": return DType.int16 - elif str == String("uint16"): + elif str == "uint16": return DType.uint16 - elif str == String("int32"): + elif str == "int32": return DType.int32 - elif str == String("uint32"): + elif str == "uint32": return DType.uint32 - elif str == String("int64"): + elif str == "int64": return DType.int64 - elif str == String("uint64"): + elif str == "uint64": return DType.uint64 - elif str == String("index"): + elif str == "index": return DType.index - elif str == String("float8e5m2"): + elif str == "float8e5m2": return DType.float8e5m2 - elif str == String("float8e5m2fnuz"): + elif str == "float8e5m2fnuz": return DType.float8e5m2fnuz - elif str == String("float8e4m3"): + elif str == "float8e4m3": return DType.float8e4m3 - elif str == String("float8e4m3fnuz"): + elif str == "float8e4m3fnuz": return DType.float8e4m3fnuz - elif str == String("bfloat16"): + elif str == "bfloat16": return DType.bfloat16 - elif str == String("float16"): + elif str == "float16": return DType.float16 - elif str == String("float32"): + elif str == "float32": return DType.float32 - elif str == String("float64"): + elif str == "float64": return DType.float64 - elif str == String("tensor_float32"): + elif str == "tensor_float32": return DType.tensor_float32 - elif str == String("invalid"): + elif str == "invalid": return DType.invalid else: return DType.invalid diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index 1ddae435ff..6d78fa5191 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -20,6 +20,7 @@ from sys.ffi import c_char from memory import UnsafePointer, memcpy from memory.memory import _free +from collections.string import StringSlice from utils import StringRef diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index c622989871..c2bbdf295c 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -78,17 +78,8 @@ struct FileHandle: """Default constructor.""" self.handle = OpaquePointer() - fn __init__(out self, path: String, mode: String) raises: - """Construct the FileHandle using the file path and mode. - - Args: - path: The file path. - mode: The mode to open the file in (the mode can be "r" or "w" or "rw"). - """ - self = Self(path.as_string_slice(), mode.as_string_slice()) - fn __init__(out self, path: StringSlice, mode: StringSlice) raises: - """Construct the FileHandle using the file path and string. + """Construct the FileHandle using the file path and mode. Args: path: The file path. @@ -482,7 +473,7 @@ struct FileHandle: fn open[ PathLike: os.PathLike -](path: PathLike, mode: String) raises -> FileHandle: +](path: PathLike, mode: StringSlice) raises -> FileHandle: """Opens the file specified by path using the mode provided, returning a FileHandle. diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 698722f6f1..0f271cad42 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -16,6 +16,7 @@ These are Mojo built-ins, so you don't need to import them. """ from collections import KeyElement +from collections.string import StringSlice from collections.string.string import ( _calc_initial_buffer_size_int32, _calc_initial_buffer_size_int64, @@ -240,7 +241,7 @@ fn int[T: IntableRaising](value: T) raises -> Int: return value.__int__() -fn int(value: String, base: Int = 10) raises -> Int: +fn int(value: StringSlice, base: Int = 10) raises -> Int: """Parses and returns the given string as an integer in the given base. If base is set to 0, the string is parsed as an Integer literal, with the diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index a409c35c62..4e4c4afff6 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -16,6 +16,7 @@ These are Mojo built-ins, so you don't need to import them. """ from collections import InlineArray +from collections.string import StringSlice from sys import _libc as libc from sys import ( bitwidthof, @@ -94,7 +95,7 @@ struct _fdopen[mode: StringLiteral = "a"]: """ return self.read_until_delimiter("\n") - fn read_until_delimiter(self, delimiter: String) -> String: + fn read_until_delimiter(self, delimiter: StringSlice) -> String: """Reads an entire line from a stream, up to the `delimiter`. Does not include the delimiter in the result. diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 7fb34ade4e..a6fa37dd03 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -694,7 +694,7 @@ struct StringLiteral( elems.each[add_elt]() return result - fn split(self, sep: String, maxsplit: Int = -1) raises -> List[String]: + fn split(self, sep: StringSlice, maxsplit: Int = -1) raises -> List[String]: """Split the string literal by a separator. Args: @@ -761,7 +761,7 @@ struct StringLiteral( """ return _to_string_list(self.as_string_slice().splitlines(keepends)) - fn count(self, substr: String) -> Int: + fn count(self, substr: StringSlice) -> Int: """Return the number of non-overlapping occurrences of substring `substr` in the string literal. @@ -908,7 +908,7 @@ struct StringLiteral( """ return String(self.lstrip().rstrip()) - fn strip(self, chars: String) -> String: + fn strip(self, chars: StringSlice) -> String: """Return a copy of the string literal with leading and trailing characters removed. @@ -921,7 +921,7 @@ struct StringLiteral( return String(self.lstrip(chars).rstrip(chars)) - fn rstrip(self, chars: String) -> String: + fn rstrip(self, chars: StringSlice) -> String: """Return a copy of the string literal with trailing characters removed. Args: @@ -940,9 +940,9 @@ struct StringLiteral( Returns: A copy of the string with no trailing whitespaces. """ - return String(str(self).rstrip()) + return String(self.as_string_slice().rstrip()) - fn lstrip(self, chars: String) -> String: + fn lstrip(self, chars: StringSlice) -> String: """Return a copy of the string with leading characters removed. Args: @@ -951,7 +951,7 @@ struct StringLiteral( Returns: A copy of the string with no leading characters. """ - return String(str(self).lstrip(chars)) + return String(self.as_string_slice().lstrip(chars)) fn lstrip(self) -> String: """Return a copy of the string with leading whitespaces removed. This diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index 69bb129418..f9b26ca604 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -54,7 +54,7 @@ fn _to_index[lookup: List[UInt32, **_]](rune: Int) -> Int: return cursor if b.load(cursor) == x else -1 -fn is_uppercase(s: String) -> Bool: +fn is_uppercase(s: StringSlice) -> Bool: """Returns True if all characters in the string are uppercase, and there is at least one cased character. @@ -84,7 +84,7 @@ fn is_uppercase(s: String) -> Bool: return found -fn is_lowercase(s: String) -> Bool: +fn is_lowercase(s: StringSlice) -> Bool: """Returns True if all characters in the string are lowercase, and there is at least one cased character. @@ -161,7 +161,7 @@ fn _write_rune(rune: UInt32, p: UnsafePointer[UInt8]) -> Int: return num_bytes -fn to_lowercase(s: String) -> String: +fn to_lowercase(s: StringSlice) -> String: """Returns a new string with all characters converted to uppercase. Args: @@ -206,7 +206,7 @@ fn to_lowercase(s: String) -> String: return String(list) -fn to_uppercase(s: String) -> String: +fn to_uppercase(s: StringSlice) -> String: """Returns a new string with all characters converted to uppercase. Args: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 70e5ab003f..d82435643f 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -628,7 +628,7 @@ fn _is_ascii_lowercase(c: UInt8) -> Bool: # ===----------------------------------------------------------------------=== # -fn _isspace(c: String) -> Bool: +fn _isspace(c: StringSlice) -> Bool: """Determines whether the given character is a whitespace character. This only respects the default "C" locale, i.e. returns True only if the @@ -1513,7 +1513,7 @@ struct String( """ return self.as_string_slice().count(substr) - fn __contains__(self, substr: String) -> Bool: + fn __contains__(self, substr: StringSlice) -> Bool: """Returns True if the substring is contained within the current string. Args: @@ -1522,9 +1522,9 @@ struct String( Returns: True if the string contains the substring. """ - return substr.as_string_slice() in self.as_string_slice() + return substr in self.as_string_slice() - fn find(self, substr: String, start: Int = 0) -> Int: + fn find(self, substr: StringSlice, start: Int = 0) -> Int: """Finds the offset of the first occurrence of `substr` starting at `start`. If not found, returns -1. @@ -1536,9 +1536,9 @@ struct String( The offset of `substr` relative to the beginning of the string. """ - return self.as_string_slice().find(substr.as_string_slice(), start) + return self.as_string_slice().find(substr, start) - fn rfind(self, substr: String, start: Int = 0) -> Int: + fn rfind(self, substr: StringSlice, start: Int = 0) -> Int: """Finds the offset of the last occurrence of `substr` starting at `start`. If not found, returns -1. @@ -1550,9 +1550,7 @@ struct String( The offset of `substr` relative to the beginning of the string. """ - return self.as_string_slice().rfind( - substr.as_string_slice(), start=start - ) + return self.as_string_slice().rfind(substr, start=start) fn isspace(self) -> Bool: """Determines whether every character in the given String is a @@ -1567,7 +1565,7 @@ struct String( """ return self.as_string_slice().isspace() - fn split(self, sep: String, maxsplit: Int = -1) raises -> List[String]: + fn split(self, sep: StringSlice, maxsplit: Int = -1) raises -> List[String]: """Split the string by a separator. Args: @@ -1708,7 +1706,7 @@ struct String( """ return _to_string_list(self.as_string_slice().splitlines(keepends)) - fn replace(self, old: String, new: String) -> String: + fn replace(self, old: StringSlice, new: StringSlice) -> String: """Return a copy of the string with all occurrences of substring `old` if replaced by `new`. @@ -1853,7 +1851,7 @@ struct String( """ hasher._update_with_bytes(self.unsafe_ptr(), self.byte_length()) - fn _interleave(self, val: String) -> String: + fn _interleave(self, val: StringSlice) -> String: var res = Self._buffer_type() var val_ptr = val.unsafe_ptr() var self_ptr = self.unsafe_ptr() @@ -1919,7 +1917,7 @@ struct String( """ return self.as_string_slice().endswith(suffix, start, end) - fn removeprefix(self, prefix: String, /) -> String: + fn removeprefix(self, prefix: StringSlice, /) -> String: """Returns a new string with the prefix removed if it was present. For example: @@ -1942,7 +1940,7 @@ struct String( return self[prefix.byte_length() :] return self - fn removesuffix(self, suffix: String, /) -> String: + fn removesuffix(self, suffix: StringSlice, /) -> String: """Returns a new string with the suffix removed if it was present. For example: diff --git a/stdlib/src/os/env.mojo b/stdlib/src/os/env.mojo index 9ffe0eb2d8..b0403ca378 100644 --- a/stdlib/src/os/env.mojo +++ b/stdlib/src/os/env.mojo @@ -48,7 +48,9 @@ fn setenv(name: String, value: String, overwrite: Bool = True) -> Bool: return False var status = external_call["setenv", Int32]( - name.unsafe_ptr(), value.unsafe_ptr(), Int32(1 if overwrite else 0) + name.unsafe_cstr_ptr(), + value.unsafe_cstr_ptr(), + Int32(1 if overwrite else 0), ) return status == 0 @@ -66,7 +68,7 @@ fn unsetenv(name: String) -> Bool: not os_is_windows(), "operating system must be Linux or macOS" ]() - return external_call["unsetenv", c_int](name.unsafe_ptr()) == 0 + return external_call["unsetenv", c_int](name.unsafe_cstr_ptr()) == 0 fn getenv(name: String, default: String = "") -> String: @@ -89,7 +91,9 @@ fn getenv(name: String, default: String = "") -> String: if not os_is_supported: return default - var ptr = external_call["getenv", UnsafePointer[UInt8]](name.unsafe_ptr()) + var ptr = external_call["getenv", UnsafePointer[UInt8]]( + name.unsafe_cstr_ptr() + ) if not ptr: return default return String(StringRef(ptr=ptr)) diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index e80fb30e54..3a8baa7aee 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -110,7 +110,7 @@ struct _DirHandle: raise "the directory '" + path + "' does not exist" self._handle = external_call["opendir", OpaquePointer]( - path.unsafe_ptr() + path.unsafe_cstr_ptr() ) if not self._handle: diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 16083fa864..97a9737635 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -312,7 +312,10 @@ fn getsize[PathLike: os.PathLike, //](path: PathLike) raises -> Int: # ===----------------------------------------------------------------------=== # -fn join(path: String, *paths: String) -> String: +# TODO(MOCO-1532): +# Use StringSlice here once param inference bug for empty variadic +# list of parameterized types is fixed. +fn join(owned path: String, *paths: String) -> String: """Join two or more pathname components, inserting '/' as needed. If any component is an absolute path, all previous path components will be discarded. An empty last part will result in a path that @@ -335,7 +338,7 @@ fn join(path: String, *paths: String) -> String: else: joined_path += sep + cur_path[] - return joined_path + return joined_path^ # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index 8e2f5c5042..4cfbf1357a 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -119,9 +119,9 @@ struct Path( Returns: A new path with the suffix appended to the current path. """ - return self.__truediv__(suffix.path) + return self.__truediv__(StringSlice(suffix.path)) - fn __truediv__(self, suffix: String) -> Self: + fn __truediv__(self, suffix: StringSlice) -> Self: """Joins two paths using the system-defined path separator. Args: @@ -134,7 +134,7 @@ struct Path( res /= suffix return res - fn __itruediv__(mut self, suffix: String): + fn __itruediv__(mut self, suffix: StringSlice): """Joins two paths using the system-defined path separator. Args: @@ -143,7 +143,8 @@ struct Path( if self.path.endswith(DIR_SEPARATOR): self.path += suffix else: - self.path += DIR_SEPARATOR + suffix + self.path += DIR_SEPARATOR + self.path += suffix @no_inline fn __str__(self) -> String: @@ -204,7 +205,7 @@ struct Path( """ return str(self) == str(other) - fn __eq__(self, other: String) -> Bool: + fn __eq__(self, other: StringSlice) -> Bool: """Returns True if the two paths are equal. Args: @@ -213,7 +214,7 @@ struct Path( Returns: True if the String and Path are equal, and False otherwise. """ - return self.path == other + return self.path.as_string_slice() == other fn __ne__(self, other: Self) -> Bool: """Returns True if the two paths are not equal. @@ -358,6 +359,9 @@ struct Path( return "" + # TODO(MOCO-1532): + # Use StringSlice here once param inference bug for empty variadic + # list of parameterized types is fixed. fn joinpath(self, *pathsegments: String) -> Path: """Joins the Path using the pathsegments. diff --git a/stdlib/src/python/python.mojo b/stdlib/src/python/python.mojo index 8771e54c6f..ca1ccab3ab 100644 --- a/stdlib/src/python/python.mojo +++ b/stdlib/src/python/python.mojo @@ -20,6 +20,7 @@ from python import Python """ from collections import Dict +from collections.string import StringSlice from os import abort, getenv from sys import external_call, sizeof from sys.ffi import _Global @@ -172,7 +173,7 @@ struct Python: return PythonObject(result) @staticmethod - fn add_to_path(dir_path: String) raises: + fn add_to_path(dir_path: StringSlice) raises: """Adds a directory to the Python path. This might be necessary to import a Python module via `import_module()`. @@ -194,6 +195,7 @@ struct Python: var cpython = _get_global_python_itf().cpython() var sys = Python.import_module("sys") var directory: PythonObject = dir_path + _ = sys.path.append(directory) # ===-------------------------------------------------------------------===# diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index caeb85a53b..b1ff31ec2c 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -20,6 +20,7 @@ from python import PythonObject """ from collections import Dict +from collections.string import StringSlice from hashlib._hasher import _HashableWithHasher, _Hasher from sys.ffi import c_ssize_t from sys.intrinsics import _type_is_eq @@ -404,27 +405,26 @@ struct PythonObject( Args: value: The string value. """ - self = PythonObject(str(value)) + self = PythonObject(value.as_string_slice()) @implicit - fn __init__(out self, strref: StringRef): - """Initialize the object from a string reference. + fn __init__(out self, value: String): + """Initialize the object from a string. Args: - strref: The string reference. + value: The string value. """ - cpython = _get_global_python_itf().cpython() - self.py_object = cpython.PyUnicode_DecodeUTF8(strref) + self = PythonObject(value.as_string_slice()) @implicit - fn __init__(out self, string: String): + fn __init__(out self, string: StringSlice): """Initialize the object from a string. Args: string: The string value. """ cpython = _get_global_python_itf().cpython() - self.py_object = cpython.PyUnicode_DecodeUTF8(string.as_string_slice()) + self.py_object = cpython.PyUnicode_DecodeUTF8(string) @implicit fn __init__[*Ts: CollectionElement](mut self, value: ListLiteral[*Ts]): @@ -457,7 +457,11 @@ struct PythonObject( elif _type_is_eq[T, Bool](): obj = PythonObject(value.get[i, Bool]()) elif _type_is_eq[T, StringRef](): - obj = PythonObject(value.get[i, StringRef]()) + obj = PythonObject( + StringSlice[MutableAnyOrigin]( + unsafe_from_utf8_strref=value.get[i, StringRef]() + ) + ) elif _type_is_eq[T, StringLiteral](): obj = PythonObject(value.get[i, StringLiteral]()) else: @@ -501,7 +505,11 @@ struct PythonObject( elif _type_is_eq[T, Bool](): obj = PythonObject(value.get[i, Bool]()) elif _type_is_eq[T, StringRef](): - obj = PythonObject(value.get[i, StringRef]()) + obj = PythonObject( + StringSlice[MutableAnyOrigin]( + unsafe_from_utf8_strref=value.get[i, StringRef]() + ) + ) elif _type_is_eq[T, StringLiteral](): obj = PythonObject(value.get[i, StringLiteral]()) else: diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index 48ccb1ddcd..2ad3b91d60 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -22,6 +22,7 @@ from tempfile import gettempdir import os import sys from collections import List, Optional +from collections.string import StringSlice from pathlib import Path from memory import Span @@ -89,7 +90,7 @@ fn _get_default_tempdir() raises -> String: raise Error("No usable temporary directory found") -fn _try_to_create_file(dir: String) -> Bool: +fn _try_to_create_file(dir: StringSlice) -> Bool: for _ in range(TMP_MAX): var name = _get_random_name() # TODO use os.join when it exists diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 32b6da2bf9..8abe0fb5c1 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -387,7 +387,7 @@ alias BAD_SEQUENCES = List[String]( ) -fn validate_utf8(slice: String) -> Bool: +fn validate_utf8(slice: StringSlice) -> Bool: return _is_valid_utf8(slice.as_bytes()) diff --git a/stdlib/test/python/test_python_object.mojo b/stdlib/test/python/test_python_object.mojo index b1c6799157..50540361ef 100644 --- a/stdlib/test/python/test_python_object.mojo +++ b/stdlib/test/python/test_python_object.mojo @@ -273,18 +273,6 @@ fn test_string_conversions() raises -> None: except e: print("Error occurred") - fn test_string_ref() -> None: - try: - var mojo_str: StringLiteral = "mojo" - var mojo_strref = StringRef(mojo_str) - var py_str = PythonObject(mojo_strref) - var py_capitalized = py_str.capitalize() - var py = Python() - var mojo_capitalized = py.__str__(py_capitalized) - assert_equal(mojo_capitalized, "Mojo") - except e: - print("Error occurred") - fn test_string() -> None: try: var mo_str = String("mo") @@ -305,7 +293,6 @@ fn test_string_conversions() raises -> None: assert_equal(str(type_obj), "") test_string_literal() - test_string_ref() test_string() test_type_object() From 7a05c5cfbe7a54284f71421aa0ddffdd9c9a8f87 Mon Sep 17 00:00:00 2001 From: soraros Date: Thu, 9 Jan 2025 08:54:17 -0800 Subject: [PATCH 179/509] [External] [stdlib] Mark `assume` as `has_side_effect=False` (#49220) [External] [stdlib] Mark assume as has_side_effect=False To enable DCE when calling `assume` Co-authored-by: soraros Closes modularml/mojo#3658 MODULAR_ORIG_COMMIT_REV_ID: 6e6a12d09ecfa9e75c3c0bb75b383df01a205e62 --- stdlib/src/sys/intrinsics.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 1a7242cc63..818ff9bd17 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -859,4 +859,4 @@ fn assume(val: Bool): Args: val: The input value which is assumed to be `True`. """ - llvm_intrinsic["llvm.assume", NoneType](val) + llvm_intrinsic["llvm.assume", NoneType, has_side_effect=False](val) From f09bcce6b9b612241ad8c93b92c01dc5b21feea9 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 9 Jan 2025 15:15:24 -0600 Subject: [PATCH 180/509] [stdlib] feat: Add `Char` type for representing characters This adds a bare bones initial `Char` type, defined to store a single Unicode scalar value using a UInt32. At the moment all this type does is provide checked and unchecked constructor methods. There is much future work to do integrating this type throughout the standard library APIs. MODULAR_ORIG_COMMIT_REV_ID: f28059669f65e41fdbac247f1acb9f9fc6e168c4 --- docs/changelog.md | 2 + stdlib/src/builtin/char.mojo | 87 ++++++++++++++++++++++++++++++ stdlib/src/prelude/__init__.mojo | 1 + stdlib/test/builtin/test_char.mojo | 45 ++++++++++++++++ 4 files changed, 135 insertions(+) create mode 100644 stdlib/src/builtin/char.mojo create mode 100644 stdlib/test/builtin/test_char.mojo diff --git a/docs/changelog.md b/docs/changelog.md index 86a8b15c41..306a5a6bd5 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -89,6 +89,8 @@ what we publish. `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware functionality. +- Added `Char`, for representing and storing single Unicode characters. + - Added `StringSlice.from_utf()` factor method, for validated construction of a `StringSlice` from a buffer containing UTF-8 encoded data. This method will raise if the buffer contents are not valid UTF-8. diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo new file mode 100644 index 0000000000..3116e454a2 --- /dev/null +++ b/stdlib/src/builtin/char.mojo @@ -0,0 +1,87 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Implements the `Char` type for representing single characters.""" + +from collections import Optional + + +@always_inline +fn _is_unicode_scalar_value(codepoint: UInt32) -> Bool: + """Returns True if `codepoint` is a valid Unicode scalar value. + + Args: + codepoint: The codepoint integer value to check. + + Returns: + True if `codepoint` is a valid Unicode scalar value; False otherwise. + """ + return codepoint <= 0xD7FF or ( + codepoint >= 0xE000 and codepoint <= 0x10FFFF + ) + + +@value +struct Char(CollectionElement): + """A single textual character. + + This type represents a single textual character. Specifically, this type + stores a single Unicode [*scalar value*][1], typically encoding a single + user-recognizable character. + + All valid Unicode scalar values are in the range(s) 0 to 0xD7FF and + 0xE000 to 0x10FFFF, inclusive. This type guarantees that the stored integer + value falls in these ranges. + + [1]: https://www.unicode.org/glossary/#unicode_scalar_value + """ + + var _codepoint: UInt32 + """The Unicode scalar value represented by this type.""" + + fn __init__(out self, *, unsafe_unchecked_codepoint: UInt32): + """Construct a `Char` from a code point value without checking that it + falls in the valid range. + + Safety: + The provided codepoint value MUST be a valid Unicode scalar value. + Providing a value outside of the valid range could lead to undefined + behavior in algorithms that depend on the validity guarantees of + this type. + + Args: + unsafe_unchecked_codepoint: A valid Unicode scalar value code point. + """ + debug_assert( + _is_unicode_scalar_value(unsafe_unchecked_codepoint), + "codepoint is not a valid Unicode scalar value", + ) + + self._codepoint = unsafe_unchecked_codepoint + + @staticmethod + fn from_u32(codepoint: UInt32) -> Optional[Self]: + """Construct a `Char` from a code point value. Returns None if the + provided `codepoint` is not in the valid range. + + Args: + codepoint: An integer representing a Unicode scalar value. + + Returns: + A `Char` if `codepoint` falls in the valid range for Unicode scalar + values, otherwise None. + """ + + if _is_unicode_scalar_value(codepoint): + return Char(unsafe_unchecked_codepoint=codepoint) + else: + return None diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 3cb066f0d4..add29683cd 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -39,6 +39,7 @@ from builtin.builtin_list import ( VariadicPack, ) from builtin.builtin_slice import Slice, slice +from builtin.char import Char from builtin.comparable import Comparable from builtin.constrained import constrained from builtin.coroutine import AnyCoroutine, Coroutine, RaisingCoroutine diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo new file mode 100644 index 0000000000..97c2a56978 --- /dev/null +++ b/stdlib/test/builtin/test_char.mojo @@ -0,0 +1,45 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %mojo %s + +from testing import assert_true, assert_false, assert_equal + + +def test_char_validity(): + # Check that basic unchecked constructor behaves as expected. + var c1 = Char(unsafe_unchecked_codepoint=32) + assert_equal(c1._codepoint, 32) + + assert_true(Char.from_u32(0)) + + # For a visual intuition of what constitues a valid scalar value: + # https://connorgray.com/ephemera/project-log#2025-01-09 + + # Last valid code point in the smaller scalar value range. + assert_true(Char.from_u32(0xD7FF)) + + # First surrogate code point, not valid. + assert_false(Char.from_u32(0xD7FF + 1)) + + # Last surrogate code point, not valid + assert_false(Char.from_u32(0xDFFF)) + + # First valid code point in the larger scalar value range. + assert_true(Char.from_u32(0xE000)) + + # Beyond Unicode's committed range of code points. + assert_false(Char.from_u32(0x10FFFF + 1)) + + +def main(): + test_char_validity() From be605c7c7425cb39600b82e081e60303dbb5e29e Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 9 Jan 2025 15:24:04 -0600 Subject: [PATCH 181/509] [stdlib] Combine indexer traits and optimize uint indexing This sets up removing the Uint -> Int implicit conversion, the majority of which occur when indexing into collections, pointers, or creating n-dimensional index types. It removes the `__index__(self) -> Int` trait which was the same sig as `__int__(self) -> Int`, and instead uses a single `Indexer` trait for all indexing. This enables optimizing away the check to normalize a negative number when using a UInt, and using SIMD scalar integer types as an index across a wider variety of collections. The `Indexer` trait was also used to enable implicit conversions to `Int` on a few types such as `Bool` and `PythonObject`, this has been moved to `ImplicitlyIntable` to align with `ImplicitlyBoolable`. MODULAR_ORIG_COMMIT_REV_ID: 3d5968b3155b4751bd73aaa3d90709ebdfa43aa0 --- docs/changelog.md | 17 ++++ stdlib/src/builtin/bool.mojo | 18 +++- stdlib/src/builtin/builtin_list.mojo | 7 +- stdlib/src/builtin/format_int.mojo | 16 +-- stdlib/src/builtin/int.mojo | 98 +++++++++---------- stdlib/src/builtin/int_literal.mojo | 21 ++-- stdlib/src/builtin/range.mojo | 16 +-- stdlib/src/builtin/simd.mojo | 8 +- stdlib/src/builtin/uint.mojo | 25 +++-- stdlib/src/collections/inline_array.mojo | 50 +++++++--- stdlib/src/collections/inline_list.mojo | 18 ++-- stdlib/src/collections/list.mojo | 33 ++++--- stdlib/src/collections/string/string.mojo | 8 +- .../src/collections/string/string_slice.mojo | 4 +- stdlib/src/collections/vector.mojo | 44 ++++++--- stdlib/src/memory/pointer.mojo | 2 +- stdlib/src/memory/span.mojo | 9 +- stdlib/src/memory/unsafe_pointer.mojo | 48 ++++----- stdlib/src/prelude/__init__.mojo | 2 +- stdlib/src/python/python_object.mojo | 26 +++-- stdlib/src/utils/index.mojo | 17 ++-- stdlib/src/utils/static_tuple.mojo | 32 +++--- stdlib/src/utils/stringref.mojo | 7 +- stdlib/test/builtin/test_bool.mojo | 4 +- stdlib/test/builtin/test_format_int.mojo | 8 +- stdlib/test/builtin/test_int.mojo | 4 +- stdlib/test/builtin/test_int_literal.mojo | 4 +- stdlib/test/builtin/test_uint.mojo | 4 +- 28 files changed, 311 insertions(+), 239 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 306a5a6bd5..0e14258db3 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -147,6 +147,23 @@ what we publish. - `bit_ceil` has been renamed to `next_power_of_two`, and `bit_floor` to `prev_power_of_two`. This is to improve readability and clarity in their use. +- The `Indexer` and `IntLike` traits which were previously both used for + indexing have been combined. This enables SIMD scalar integer types and UInt + to be used for indexing into all of the collection types, as well as + optimizing away normalization checks for UInt indexing. + +- The `ImplicitlyIntable` trait has been added, allowing types to be implicitly + converted to an `Int` by implementing the `__as_int__` method: + + ```mojo + @value + struct Foo(ImplicitlyIntable): + var i: Int + + fn __as_int__(self) -> Int: + return self.i + ``` + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 3b9c3a2569..a0a65229dc 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -104,8 +104,8 @@ struct Bool( ComparableCollectionElement, Defaultable, ImplicitlyBoolable, + ImplicitlyIntable, Indexer, - Intable, Representable, Stringable, Writable, @@ -259,6 +259,16 @@ struct Bool( """ return _select_register_value(self, Int(1), Int(0)) + @always_inline("nodebug") + fn __as_int__(self) -> Int: + """Implicitly convert to an integral representation of the value, + wherever an `Int` is expected. + + Returns: + The integral representation of the value. + """ + return self.__int__() + @always_inline("nodebug") fn __float__(self) -> Float64: """Convert this Bool to a float. @@ -269,13 +279,13 @@ struct Bool( return _select_register_value(self, Float64(1.0), Float64(0.0)) @always_inline("nodebug") - fn __index__(self) -> Int: - """Convert this Bool to an integer for indexing purposes. + fn __index__(self) -> __mlir_type.index: + """Convert to index. Returns: 1 if the Bool is True, 0 otherwise. """ - return self.__int__() + return int(self).value @always_inline("nodebug") fn __eq__(self, rhs: Bool) -> Bool: diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index c24ecb8d25..516fdbe06c 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -195,16 +195,19 @@ struct VariadicList[type: AnyTrivialRegType](Sized): return __mlir_op.`pop.variadic.size`(self.value) @always_inline - fn __getitem__(self, idx: Int) -> type: + fn __getitem__[I: Indexer](self, idx: I) -> type: """Gets a single element on the variadic list. Args: idx: The index of the element to access on the list. + Parameters: + I: A type that can be used as an index. + Returns: The element on the list corresponding to the given index. """ - return __mlir_op.`pop.variadic.get`(self.value, idx.value) + return __mlir_op.`pop.variadic.get`(self.value, index(idx)) @always_inline fn __iter__(self) -> Self.IterType: diff --git a/stdlib/src/builtin/format_int.mojo b/stdlib/src/builtin/format_int.mojo index 0598bb816a..2f9bf016db 100644 --- a/stdlib/src/builtin/format_int.mojo +++ b/stdlib/src/builtin/format_int.mojo @@ -66,11 +66,11 @@ fn bin(b: Scalar[DType.bool], /, *, prefix: StaticString = "0b") -> String: return bin(b.cast[DType.int8](), prefix=prefix) -fn bin[T: Indexer, //](num: T, /, *, prefix: StaticString = "0b") -> String: +fn bin[T: Intable, //](num: T, /, *, prefix: StaticString = "0b") -> String: """Returns the binary representation of an indexer type. Parameters: - T: The Indexer type. + T: The Intable type. Args: num: An indexer value. @@ -79,7 +79,7 @@ fn bin[T: Indexer, //](num: T, /, *, prefix: StaticString = "0b") -> String: Returns: The binary string representation of num. """ - return bin(Scalar[DType.index](index(num)), prefix=prefix) + return bin(Scalar[DType.index](int(num)), prefix=prefix) # ===-----------------------------------------------------------------------===# @@ -105,7 +105,7 @@ fn hex(value: Scalar, /, *, prefix: StaticString = "0x") -> String: return _try_format_int(value, 16, prefix=prefix) -fn hex[T: Indexer, //](value: T, /, *, prefix: StaticString = "0x") -> String: +fn hex[T: Intable, //](value: T, /, *, prefix: StaticString = "0x") -> String: """Returns the hex string representation of the given integer. The hexadecimal representation is a base-16 encoding of the integer value. @@ -123,7 +123,7 @@ fn hex[T: Indexer, //](value: T, /, *, prefix: StaticString = "0x") -> String: Returns: A string containing the hex representation of the given integer. """ - return hex(Scalar[DType.index](index(value)), prefix=prefix) + return hex(Scalar[DType.index](int(value)), prefix=prefix) fn hex(value: Scalar[DType.bool], /, *, prefix: StaticString = "0x") -> String: @@ -167,7 +167,7 @@ fn oct(value: Scalar, /, *, prefix: StaticString = "0o") -> String: return _try_format_int(value, 8, prefix=prefix) -fn oct[T: Indexer, //](value: T, /, *, prefix: StaticString = "0o") -> String: +fn oct[T: Intable, //](value: T, /, *, prefix: StaticString = "0o") -> String: """Returns the octal string representation of the given integer. The octal representation is a base-8 encoding of the integer value. @@ -176,7 +176,7 @@ fn oct[T: Indexer, //](value: T, /, *, prefix: StaticString = "0o") -> String: subsequent digits are octal. Parameters: - T: The indexer type to represent in octal. + T: The intable type to represent in octal. Args: value: The integer value to format. @@ -185,7 +185,7 @@ fn oct[T: Indexer, //](value: T, /, *, prefix: StaticString = "0o") -> String: Returns: A string containing the octal representation of the given integer. """ - return oct(Scalar[DType.index](index(value)), prefix=prefix) + return oct(Scalar[DType.index](int(value)), prefix=prefix) fn oct(value: Scalar[DType.bool], /, *, prefix: StaticString = "0o") -> String: diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 0f271cad42..8205ad6886 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -40,24 +40,22 @@ from utils._visualizers import lldb_formatter_wrapping_type # ===----------------------------------------------------------------------=== # -trait Indexer: - """This trait denotes a type that can be used to index a container that - handles integral index values. - - This solves the issue of being able to index data structures such as `List` - with the various integral types without being too broad and allowing types - that are coercible to `Int` (e.g. floating point values that have `__int__` - method). In contrast to `Intable`, types conforming to `Indexer` must be - convertible to `Int` in a lossless way. - - Note that types conforming to `Indexer` are implicitly convertible to `Int`. +trait Indexer( + Intable, +): + """ + The `Indexer` trait is used for types that can index into a collection or + pointer. The type returned is the underlying __mlir_type.index, enabling + types like `UInt` to not have to be converted to an `Int` first. This type + is implicitly convertable to an `Int`, so can be used anywhere an `Int` can + e.g. for comparisons. """ - fn __index__(self) -> Int: - """Return the index value. + fn __index__(self) -> __mlir_type.index: + """Convert to index. Returns: - The index value of the object. + The corresponding __mlir_type.index value. """ ... @@ -68,7 +66,7 @@ trait Indexer: @always_inline("nodebug") -fn index[T: Indexer](idx: T, /) -> Int: +fn index[T: Indexer](idx: T, /) -> __mlir_type.index: """Returns the value of `__index__` for the given value. Parameters: @@ -78,7 +76,7 @@ fn index[T: Indexer](idx: T, /) -> Int: idx: The value. Returns: - An `Int` representing the index value. + An `__mlir_type` representing the index value. """ return idx.__index__() @@ -173,30 +171,38 @@ trait IntableRaising: ... -# ===----------------------------------------------------------------------=== # -# IntLike -# ===----------------------------------------------------------------------=== # +trait ImplicitlyIntable(Intable): + """The `ImplicitlyIntable` trait describes a type that can be converted to + an Int implicitly. + This trait requires the type to implement the `__as_int__()` method. For + example: -trait IntLike( - Absable, - Ceilable, - Floorable, - Writable, - Powable, - Stringable, - Truncable, -): - """ - The `IntLike` trait is a tag for `Int` or `UInt`. This allows writing - functions that works on either. + ```mojo + @value + struct Foo(ImplicitlyIntable): + var i: Int + + fn __as_int__(self) -> Int: + return self.i + ``` + + Now you can use `Foo` anywhere that an `Int` is expected, e.g. equality + checks: + + ```mojo + %# from testing import assert_equal + foo = Foo(42) + assert_equal(foo, 42) + ``` """ - fn __mlir_index__(self) -> __mlir_type.index: - """Convert to index. + fn __as_int__(self) -> Int: + """Implicitly convert to an integral representation of the value, + wherever an `Int` is expected. Returns: - The corresponding __mlir_type.index value. + The integral representation of the value. """ ... @@ -302,11 +308,9 @@ fn int(value: UInt) -> Int: struct Int( CeilDivable, Indexer, - Intable, ImplicitlyBoolable, KeyElement, Roundable, - IntLike, _HashableWithHasher, ExplicitlyCopyable, ): @@ -375,16 +379,16 @@ struct Int( @always_inline("nodebug") @implicit - fn __init__[IndexerTy: Indexer](mut self, value: IndexerTy): - """Construct Int from the given Indexer value. + fn __init__[I: ImplicitlyIntable](out self, value: I): + """Construct Int from implicitly convertable type. Parameters: - IndexerTy: A type conforming to Indexer. + I: The type that is implicitly convertable to an `Int`. Args: value: The init value. """ - self = value.__index__() + self = value.__as_int__() @always_inline("nodebug") @implicit @@ -988,18 +992,6 @@ struct Int( """ return self.__bool__() - @always_inline("nodebug") - fn __index__(self) -> Int: - """Return self converted to an integer, if self is suitable for use as - an index into a list. - - For Int type this is simply the value. - - Returns: - The corresponding Int value. - """ - return self - @always_inline("nodebug") fn __int__(self) -> Int: """Gets the integral value (this is an identity function for Int). @@ -1170,7 +1162,7 @@ struct Int( writer.write(self) @always_inline("nodebug") - fn __mlir_index__(self) -> __mlir_type.index: + fn __index__(self) -> __mlir_type.index: """Convert to index. Returns: diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 3d50918458..f7b72d9ea0 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -25,8 +25,8 @@ struct IntLiteral( Comparable, Floorable, ImplicitlyBoolable, + ImplicitlyIntable, Indexer, - Intable, Roundable, Stringable, Truncable, @@ -593,23 +593,22 @@ struct IntLiteral( return self.__bool__() @always_inline("nodebug") - fn __index__(self) -> Int: - """Return self converted to an integer, if self is suitable for use as - an index into a list. + fn __int__(self) -> Int: + """Convert from IntLiteral to Int. Returns: - The corresponding Int value. + The value as an integer of platform-specific width. """ - return self.__int__() + return self.__index__() @always_inline("nodebug") - fn __int__(self) -> Int: - """Convert from IntLiteral to Int. + fn __as_int__(self) -> Int: + """Implicitly convert to an Int. Returns: - The value as an integer of platform-specific width. + An integral value that represents this object. """ - return Int(self.__as_mlir_index()) + return self.__int__() @always_inline("nodebug") fn __uint__(self) -> UInt: @@ -731,7 +730,7 @@ struct IntLiteral( return __mlir_op.`kgen.int_literal.bit_width`(self.value) @always_inline("nodebug") - fn __as_mlir_index(self) -> __mlir_type.index: + fn __index__(self) -> __mlir_type.index: """Convert from IntLiteral to index. Returns: diff --git a/stdlib/src/builtin/range.mojo b/stdlib/src/builtin/range.mojo index 2af6fb942d..b011c350d5 100644 --- a/stdlib/src/builtin/range.mojo +++ b/stdlib/src/builtin/range.mojo @@ -74,8 +74,8 @@ struct _ZeroStartingRange(Sized, ReversibleRange, _IntIterable): return self.curr @always_inline - fn __getitem__(self, idx: Int) -> Int: - debug_assert(idx < self.__len__(), "index out of range") + fn __getitem__[I: Indexer](self, idx: I) -> Int: + debug_assert(int(idx) < self.__len__(), "index out of range") return index(idx) @always_inline @@ -108,8 +108,8 @@ struct _SequentialRange(Sized, ReversibleRange, _IntIterable): return max(0, self.end - self.start) @always_inline - fn __getitem__(self, idx: Int) -> Int: - debug_assert(idx < self.__len__(), "index out of range") + fn __getitem__[I: Indexer](self, idx: I) -> Int: + debug_assert(self.__len__() > index(idx), "index out of range") return self.start + index(idx) @always_inline @@ -189,8 +189,8 @@ struct _StridedRange(Sized, ReversibleRange, _StridedIterable): return ceildiv(select(cnd, 0, numerator), select(cnd, 1, denominator)) @always_inline - fn __getitem__(self, idx: Int) -> Int: - debug_assert(idx < self.__len__(), "index out of range") + fn __getitem__[I: Indexer](self, idx: I) -> Int: + debug_assert(self.__len__() > index(idx), "index out of range") return self.start + index(idx) * self.step @always_inline @@ -274,7 +274,7 @@ fn range[ @always_inline fn range[ - t0: Intable, t1: Intable, t2: Intable + t0: Indexer, t1: Indexer, t2: Indexer ](start: t0, end: t1, step: t2) -> _StridedRange: """Constructs a [start; end) Range with a given step. @@ -291,7 +291,7 @@ fn range[ Returns: The constructed range. """ - return _StridedRange(int(start), int(end), int(step)) + return _StridedRange(index(start), index(end), index(step)) @always_inline diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 0a56cdd820..a74d707205 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -235,7 +235,7 @@ struct SIMD[type: DType, size: Int]( Hashable, _HashableWithHasher, Intable, - IntLike, + Indexer, Representable, Roundable, Sized, @@ -650,7 +650,7 @@ struct SIMD[type: DType, size: Int]( """ return __mlir_op.`pop.simd.extractelement`[ _type = __mlir_type[`!pop.scalar<`, type.value, `>`] - ](self.value, index(idx).value) + ](self.value, idx.value) @always_inline("nodebug") fn __setitem__(mut self, idx: Int, val: Scalar[type]): @@ -661,7 +661,7 @@ struct SIMD[type: DType, size: Int]( val: The value to set. """ self.value = __mlir_op.`pop.simd.insertelement`( - self.value, val.value, index(idx).value + self.value, val.value, idx.value ) fn __contains__(self, value: Scalar[type]) -> Bool: @@ -1523,7 +1523,7 @@ struct SIMD[type: DType, size: Int]( ](rebind[Scalar[type]](self).value) @always_inline("nodebug") - fn __mlir_index__(self) -> __mlir_type.index: + fn __index__(self) -> __mlir_type.index: """Convert to index. Returns: diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index 9c3feb155f..7067e94d68 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -27,7 +27,7 @@ from utils._visualizers import lldb_formatter_wrapping_type @lldb_formatter_wrapping_type @value @register_passable("trivial") -struct UInt(IntLike, _HashableWithHasher): +struct UInt(Indexer, _HashableWithHasher): """This type represents an unsigned integer. An unsigned integer represents a positive integral number. @@ -105,7 +105,7 @@ struct UInt(IntLike, _HashableWithHasher): self = value.__uint__() @always_inline("nodebug") - fn __mlir_index__(self) -> __mlir_type.index: + fn __index__(self) -> __mlir_type.index: """Convert to index. Returns: @@ -142,6 +142,15 @@ struct UInt(IntLike, _HashableWithHasher): writer.write(UInt64(self)) + @always_inline("nodebug") + fn __int__(self) -> Int: + """Gets the integral value, wrapping to a negative number on overflow. + + Returns: + The value as an integer. + """ + return self.value + fn __repr__(self) -> String: """Convert this UInt to a string. @@ -728,18 +737,6 @@ struct UInt(IntLike, _HashableWithHasher): """ return self != 0 - @always_inline("nodebug") - fn __index__(self) -> UInt: - """Return self converted to an unsigned integer, if self is suitable for use as - an index into a list. - - For Int type this is simply the value. - - Returns: - The corresponding Int value. - """ - return self - @always_inline("nodebug") fn __ceil__(self) -> Self: """Return the ceiling of the UInt value, which is itself. diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 973406574b..f24d4dbe75 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -223,37 +223,54 @@ struct InlineArray[ # ===------------------------------------------------------------------===# @always_inline - fn __getitem__(ref self, idx: Int) -> ref [self] Self.ElementType: + fn __getitem__[I: Indexer](ref self, idx: I) -> ref [self] Self.ElementType: """Get a `Pointer` to the element at the given index. + Parameters: + I: A type that can be used as an index. + Args: idx: The index of the item. Returns: A reference to the item at the given index. """ - var normalized_index = normalize_index["InlineArray"](idx, self) - return self.unsafe_get(normalized_index) + + @parameter + if _type_is_eq[I, UInt](): + return self.unsafe_get(idx) + else: + var normalized_index = normalize_index["InlineArray"]( + int(idx), self + ) + return self.unsafe_get(normalized_index) @always_inline - fn __getitem__[idx: Int](ref self) -> ref [self] Self.ElementType: + fn __getitem__[ + I: Indexer, //, idx: I + ](ref self) -> ref [self] Self.ElementType: """Get a `Pointer` to the element at the given index. Parameters: + I: A type that can be used as an index. idx: The index of the item. Returns: A reference to the item at the given index. """ - constrained[-size <= idx < size, "Index must be within bounds."]() - - var normalized_idx = idx + constrained[-size <= int(idx) < size, "Index must be within bounds."]() @parameter - if idx < 0: - normalized_idx += size + if _type_is_eq[I, UInt](): + return self.unsafe_get(idx) + else: + var normalized_idx = int(idx) + + @parameter + if int(idx) < 0: + normalized_idx += size - return self.unsafe_get(normalized_idx) + return self.unsafe_get(normalized_idx) # ===------------------------------------------------------------------=== # # Trait implementations @@ -273,7 +290,7 @@ struct InlineArray[ # ===------------------------------------------------------------------===# @always_inline - fn unsafe_get(ref self, idx: Int) -> ref [self] Self.ElementType: + fn unsafe_get[I: Indexer](ref self, idx: I) -> ref [self] Self.ElementType: """Get a reference to an element of self without checking index bounds. Users should opt for `__getitem__` instead of this method as it is @@ -285,20 +302,23 @@ struct InlineArray[ Args: idx: The index of the element to get. + Parameters: + I: A type that can be used as an index. + Returns: A reference to the element at the given index. """ - var idx_as_int = index(idx) + var i = index(idx) debug_assert( - 0 <= idx_as_int < size, + 0 <= int(i) < size, " InlineArray.unsafe_get() index out of bounds: ", - idx_as_int, + int(idx), " should be less than: ", size, ) var ptr = __mlir_op.`pop.array.gep`( UnsafePointer.address_of(self._array).address, - idx_as_int.value, + i, ) return UnsafePointer(ptr)[] diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index 798f71d5f8..733fd917b3 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -133,25 +133,29 @@ struct InlineList[ElementType: CollectionElementNew, capacity: Int = 16](Sized): # ===-------------------------------------------------------------------===# @always_inline - fn __getitem__( - ref self, owned idx: Int - ) -> ref [self._array] Self.ElementType: + fn __getitem__[ + I: Indexer + ](ref self, idx: I) -> ref [self._array] Self.ElementType: """Get a `Pointer` to the element at the given index. + Parameters: + I: A type that can be used as an index. + Args: idx: The index of the item. Returns: A reference to the item at the given index. """ + var index = int(idx) debug_assert( - -self._size <= idx < self._size, "Index must be within bounds." + -self._size <= index < self._size, "Index must be within bounds." ) - if idx < 0: - idx += len(self) + if index < 0: + index += len(self) - return self._array[idx].assume_initialized() + return self._array[index].assume_initialized() # ===-------------------------------------------------------------------===# # Trait implementations diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 1f0d42bde8..561079a10e 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -22,6 +22,7 @@ from collections import List from os import abort from sys import sizeof +from sys.intrinsics import _type_is_eq from memory import Pointer, UnsafePointer, memcpy, Span @@ -850,29 +851,35 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( return res^ - fn __getitem__(ref self, idx: Int) -> ref [self] T: + fn __getitem__[I: Indexer](ref self, idx: I) -> ref [self] T: """Gets the list element at the given index. Args: idx: The index of the element. + Parameters: + I: A type that can be used as an index. + Returns: A reference to the element at the given index. """ - var normalized_idx = idx - - debug_assert( - -self.size <= normalized_idx < self.size, - "index: ", - normalized_idx, - " is out of bounds for `List` of size: ", - self.size, - ) - if normalized_idx < 0: - normalized_idx += len(self) + @parameter + if _type_is_eq[I, UInt](): + return (self.data + idx)[] + else: + var normalized_idx = int(idx) + debug_assert( + -self.size <= normalized_idx < self.size, + "index: ", + normalized_idx, + " is out of bounds for `List` of size: ", + self.size, + ) + if normalized_idx < 0: + normalized_idx += len(self) - return (self.data + normalized_idx)[] + return (self.data + normalized_idx)[] @always_inline fn unsafe_get(ref self, idx: Int) -> ref [self] Self.T: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index d82435643f..2ca4d03112 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -362,7 +362,7 @@ fn _trim_and_handle_sign(str_slice: StringSlice, str_len: Int) -> (Int, Bool): start += 1 var p: Bool = buff[start] == ord("+") var n: Bool = buff[start] == ord("-") - return start + (p or n), n + return start + (int(p) or int(n)), n @always_inline @@ -992,11 +992,11 @@ struct String( # Operator dunders # ===------------------------------------------------------------------=== # - fn __getitem__[IndexerType: Indexer](self, idx: IndexerType) -> String: + fn __getitem__[I: Indexer](self, idx: I) -> String: """Gets the character at the specified position. Parameters: - IndexerType: The inferred type of an indexer argument. + I: A type that can be used as an index. Args: idx: The index value. @@ -1005,7 +1005,7 @@ struct String( A new string containing the character at the specified position. """ # TODO(#933): implement this for unicode when we support llvm intrinsic evaluation at compile time - var normalized_idx = normalize_index["String"](idx, self) + var normalized_idx = normalize_index["String"](index(idx), self) var buf = Self._buffer_type(capacity=1) buf.append(self._buffer[normalized_idx]) buf.append(0) diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 881b2df713..614f4dd472 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -598,11 +598,11 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( ptr=self.unsafe_ptr(), length=self.byte_length() ) - fn __getitem__[IndexerType: Indexer](self, idx: IndexerType) -> String: + fn __getitem__[I: Indexer](self, idx: I) -> String: """Gets the character at the specified position. Parameters: - IndexerType: The inferred type of an indexer argument. + I: A type that can be used as an index. Args: idx: The index value. diff --git a/stdlib/src/collections/vector.mojo b/stdlib/src/collections/vector.mojo index 556c343ea2..1466c23f9d 100644 --- a/stdlib/src/collections/vector.mojo +++ b/stdlib/src/collections/vector.mojo @@ -20,6 +20,7 @@ from collections import InlinedFixedVector """ from sys import sizeof +from sys.intrinsics import _type_is_eq from memory import Pointer, UnsafePointer, memcpy @@ -204,49 +205,60 @@ struct InlinedFixedVector[ return self.current_size @always_inline - fn __getitem__(self, idx: Int) -> type: + fn __getitem__[I: Indexer](self, idx: I) -> type: """Gets a vector element at the given index. Args: idx: The index of the element. + Parameters: + I: A type that can be used as an index. + Returns: The element at the given index. """ - var normalized_idx = idx + var index = int(idx) debug_assert( - -self.current_size <= normalized_idx < self.current_size, + -self.current_size <= index < self.current_size, "index must be within bounds", ) - if normalized_idx < 0: - normalized_idx += len(self) + @parameter + if not _type_is_eq[I, UInt](): + if index < 0: + index += len(self) - if normalized_idx < Self.static_size: - return self.static_data[normalized_idx] + if index < Self.static_size: + return self.static_data[index] - return self.dynamic_data[normalized_idx - Self.static_size] + return self.dynamic_data[index - Self.static_size] @always_inline - fn __setitem__(mut self, idx: Int, value: type): + fn __setitem__[I: Indexer](mut self, idx: I, value: type): """Sets a vector element at the given index. + Parameters: + I: A type that can be used as an index. + Args: idx: The index of the element. value: The value to assign. """ - var normalized_idx = idx + var index = int(idx) debug_assert( - -self.current_size <= normalized_idx < self.current_size, + -self.current_size <= index < self.current_size, "index must be within bounds", ) - if normalized_idx < 0: - normalized_idx += len(self) - if normalized_idx < Self.static_size: - self.static_data[normalized_idx] = value + @parameter + if not _type_is_eq[I, UInt](): + if index < 0: + index += len(self) + + if index < Self.static_size: + self.static_data[index] = value else: - self.dynamic_data[normalized_idx - Self.static_size] = value + self.dynamic_data[index - Self.static_size] = value fn clear(mut self): """Clears the elements in the vector.""" diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 2d1f5fce52..280cc5fd3d 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -208,7 +208,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): return self._value @always_inline("nodebug") - fn __mlir_index__(self) -> __mlir_type.index: + fn __index__(self) -> __mlir_type.index: """Convert to index. Returns: diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index c0d919dff0..099c798bed 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -168,12 +168,15 @@ struct Span[ # ===------------------------------------------------------------------===# @always_inline - fn __getitem__(self, idx: Int) -> ref [origin] T: + fn __getitem__[I: Indexer](self, idx: I) -> ref [origin] T: """Get a reference to an element in the span. Args: idx: The index of the value to return. + Parameters: + I: A type that can be used as an index. + Returns: An element reference. """ @@ -181,8 +184,8 @@ struct Span[ debug_assert( -self._len <= int(idx) < self._len, "index must be within bounds" ) - - var offset = idx + # TODO(MSTDL-1086): optimize away SIMD/UInt normalization check + var offset = int(idx) if offset < 0: offset += len(self) return self._data[offset] diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index efe6018cd9..cf6c8d3cc6 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -218,11 +218,11 @@ struct UnsafePointer[ ) @always_inline - fn offset[T: IntLike, //](self, idx: T) -> Self: + fn offset[I: Indexer, //](self, idx: I) -> Self: """Returns a new pointer shifted by the specified offset. Parameters: - T: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: idx: The offset of the new pointer. @@ -230,16 +230,16 @@ struct UnsafePointer[ Returns: The new constructed UnsafePointer. """ - return __mlir_op.`pop.offset`(self.address, idx.__mlir_index__()) + return __mlir_op.`pop.offset`(self.address, index(idx)) @always_inline fn __getitem__[ - IntLike: IntLike, // - ](self, offset: IntLike) -> ref [origin, address_space] type: + I: Indexer, // + ](self, offset: I) -> ref [origin, address_space] type: """Return a reference to the underlying data, offset by the given index. Parameters: - IntLike: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: offset: The offset index. @@ -250,11 +250,11 @@ struct UnsafePointer[ return (self + offset)[] @always_inline - fn __add__[T: IntLike, //](self, offset: T) -> Self: + fn __add__[I: Indexer, //](self, offset: I) -> Self: """Return a pointer at an offset from the current one. Parameters: - T: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: offset: The offset index. @@ -265,11 +265,11 @@ struct UnsafePointer[ return self.offset(offset) @always_inline - fn __sub__[T: IntLike, //](self, offset: T) -> Self: + fn __sub__[I: Indexer, //](self, offset: I) -> Self: """Return a pointer at an offset from the current one. Parameters: - T: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: offset: The offset index. @@ -277,14 +277,14 @@ struct UnsafePointer[ Returns: An offset pointer. """ - return self + (-1 * Int(offset.__mlir_index__())) + return self + (-1 * index(offset)) @always_inline - fn __iadd__[T: IntLike, //](mut self, offset: T): + fn __iadd__[I: Indexer, //](mut self, offset: I): """Add an offset to this pointer. Parameters: - T: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: offset: The offset index. @@ -292,11 +292,11 @@ struct UnsafePointer[ self = self + offset @always_inline - fn __isub__[T: IntLike, //](mut self, offset: T): + fn __isub__[I: Indexer, //](mut self, offset: I): """Subtract an offset from this pointer. Parameters: - T: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: offset: The offset index. @@ -578,21 +578,21 @@ struct UnsafePointer[ @always_inline("nodebug") fn load[ - T: IntLike, + I: Indexer, type: DType, //, width: Int = 1, *, alignment: Int = _default_alignment[type, width](), volatile: Bool = False, invariant: Bool = False, - ](self: UnsafePointer[Scalar[type], **_], offset: T) -> SIMD[type, width]: + ](self: UnsafePointer[Scalar[type], **_], offset: I) -> SIMD[type, width]: """Loads the value the pointer points to with the given offset. Constraints: The width and alignment must be positive integer values. Parameters: - T: The type of offset, either `Int` or `UInt`. + I: A type that can be used as an index. type: The data type of SIMD vector elements. width: The size of the SIMD vector. alignment: The minimal alignment of the address. @@ -614,12 +614,12 @@ struct UnsafePointer[ @always_inline fn store[ - T: IntLike, + I: Indexer, type: DType, //, *, alignment: Int = _default_alignment[type](), volatile: Bool = False, - ](self: UnsafePointer[Scalar[type], **_], offset: T, val: Scalar[type],): + ](self: UnsafePointer[Scalar[type], **_], offset: I, val: Scalar[type],): """Stores a single element value at the given offset. Constraints: @@ -627,7 +627,7 @@ struct UnsafePointer[ The offset must be integer. Parameters: - T: The type of offset, either `Int` or `UInt`. + I: A type that can be used as an index. type: The data type of SIMD vector elements. alignment: The minimal alignment of the address. volatile: Whether the operation is volatile or not. @@ -641,7 +641,7 @@ struct UnsafePointer[ @always_inline fn store[ - T: IntLike, + I: Indexer, type: DType, width: Int, //, *, @@ -649,7 +649,7 @@ struct UnsafePointer[ volatile: Bool = False, ]( self: UnsafePointer[Scalar[type], **_], - offset: T, + offset: I, val: SIMD[type, width], ): """Stores a single element value at the given offset. @@ -659,7 +659,7 @@ struct UnsafePointer[ The offset must be integer. Parameters: - T: The type of offset, either `Int` or `UInt`. + I: A type that can be used as an index. type: The data type of SIMD vector elements. width: The size of the SIMD vector. alignment: The minimal alignment of the address. diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index add29683cd..50c1cb8d1b 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -57,8 +57,8 @@ from builtin.int import ( Indexer, Int, Intable, + ImplicitlyIntable, IntableRaising, - IntLike, index, int, ) diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index b1ff31ec2c..7492ce0c40 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -200,15 +200,17 @@ struct TypedPythonObject[type_hint: StringLiteral]( # 'Tuple' Operations # ===-------------------------------------------------------------------===# - fn __getitem__( - self: TypedPythonObject["Tuple"], - pos: Int, - ) raises -> PythonObject: + fn __getitem__[ + I: Indexer + ](self: TypedPythonObject["Tuple"], pos: I,) raises -> PythonObject: """Get an element from this tuple. Args: pos: The tuple element position to retrieve. + Parameters: + I: A type that can be used as an index. + Returns: The value of the tuple element at the specified position. """ @@ -216,7 +218,7 @@ struct TypedPythonObject[type_hint: StringLiteral]( var item: PyObjectPtr = cpython.PyTuple_GetItem( self.unsafe_as_py_object_ptr(), - pos, + index(pos), ) if item.is_null(): @@ -230,8 +232,8 @@ struct TypedPythonObject[type_hint: StringLiteral]( @register_passable struct PythonObject( ImplicitlyBoolable, + ImplicitlyIntable, Indexer, - Intable, KeyElement, SizedRaising, Stringable, @@ -1456,13 +1458,13 @@ struct PythonObject( debug_assert(result != -1, "object is not hashable") hasher.update(result) - fn __index__(self) -> Int: + fn __index__(self) -> __mlir_type.index: """Returns an index representation of the object. Returns: An index value that represents this object. """ - return self.__int__() + return self.__int__().value fn __int__(self) -> Int: """Returns an integral representation of the object. @@ -1473,6 +1475,14 @@ struct PythonObject( cpython = _get_global_python_itf().cpython() return cpython.PyLong_AsSsize_t(self.py_object) + fn __as_int__(self) -> Int: + """Implicitly convert to an Int. + + Returns: + An integral value that represents this object. + """ + return self.__int__() + fn __float__(self) -> Float64: """Returns a float representation of the object. diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 8c87763619..4e66f31f28 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -393,9 +393,12 @@ struct IndexList[ return int(self.data.__getitem__[idx]()) @always_inline("nodebug") - fn __getitem__(self, idx: Int) -> Int: + fn __getitem__[I: Indexer](self, idx: I) -> Int: """Gets an element from the tuple by index. + Parameters: + I: A type that can be used as an index. + Args: idx: The element index. @@ -405,28 +408,28 @@ struct IndexList[ return int(self.data[idx]) @always_inline("nodebug") - fn __setitem__[index: Int](mut self, val: Int): + fn __setitem__[idx: Int](mut self, val: Int): """Sets an element in the tuple at the given static index. Parameters: - index: The element index. + idx: The element index. Args: val: The value to store. """ - self.data.__setitem__[index](val) + self.data.__setitem__[idx](val) @always_inline("nodebug") - fn __setitem__[index: Int](mut self, val: Self._int_type): + fn __setitem__[idx: Int](mut self, val: Self._int_type): """Sets an element in the tuple at the given static index. Parameters: - index: The element index. + idx: The element index. Args: val: The value to store. """ - self.data.__setitem__[index](val) + self.data.__setitem__[idx](val) @always_inline("nodebug") fn __setitem__(mut self, idx: Int, val: Int): diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index 0f118f6486..3808906c12 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -193,13 +193,11 @@ struct StaticTuple[element_type: AnyTrivialRegType, size: Int](Sized): return val @always_inline("nodebug") - fn __getitem__[ - IntLike: IntLike, // - ](self, idx: IntLike) -> Self.element_type: + fn __getitem__[I: Indexer, //](self, idx: I) -> Self.element_type: """Returns the value of the tuple at the given dynamic index. Parameters: - IntLike: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: idx: The index into the tuple. @@ -207,46 +205,40 @@ struct StaticTuple[element_type: AnyTrivialRegType, size: Int](Sized): Returns: The value at the specified position. """ - debug_assert( - int(idx.__mlir_index__()) < size, "index must be within bounds" - ) + debug_assert(size > index(idx), "index must be within bounds") var ptr = __mlir_op.`pop.array.gep`( - UnsafePointer.address_of(self.array).address, idx.__mlir_index__() + UnsafePointer.address_of(self.array).address, index(idx) ) return UnsafePointer(ptr)[] @always_inline("nodebug") - fn __setitem__[ - IntLike: IntLike, // - ](mut self, idx: IntLike, val: Self.element_type): + fn __setitem__[I: Indexer, //](mut self, idx: I, val: Self.element_type): """Stores a single value into the tuple at the specified dynamic index. Parameters: - IntLike: The type of idx; either `Int` or `UInt`. + I: A type that can be used as an index. Args: idx: The index into the tuple. val: The value to store. """ - debug_assert( - int(idx.__mlir_index__()) < size, "index must be within bounds" - ) + debug_assert(size > index(idx), "index must be within bounds") var tmp = self var ptr = __mlir_op.`pop.array.gep`( - UnsafePointer.address_of(tmp.array).address, idx.__mlir_index__() + UnsafePointer.address_of(tmp.array).address, index(idx) ) UnsafePointer(ptr)[] = val self = tmp @always_inline("nodebug") - fn __setitem__[index: Int](mut self, val: Self.element_type): + fn __setitem__[idx: Int](mut self, val: Self.element_type): """Stores a single value into the tuple at the specified index. Parameters: - index: The index into the tuple. + idx: The index into the tuple. Args: val: The value to store. """ - constrained[index < size]() - _set_array_elem[index, size, Self.element_type](val, self.array) + constrained[idx < size]() + _set_array_elem[idx, size, Self.element_type](val, self.array) diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index ad78d89db9..a24d71c9bc 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -229,16 +229,19 @@ struct StringRef( # ===-------------------------------------------------------------------===# @always_inline("nodebug") - fn __getitem__(self, idx: Int) -> StringRef: + fn __getitem__[I: Indexer](self, idx: I) -> StringRef: """Get the string value at the specified position. Args: idx: The index position. + Parameters: + I: A type that can be used as an index. + Returns: The character at the specified position. """ - return StringRef {data: self.data + idx, length: 1} + return StringRef {data: self.data + int(idx), length: 1} @always_inline fn __eq__(self, rhs: StringRef) -> Bool: diff --git a/stdlib/test/builtin/test_bool.mojo b/stdlib/test/builtin/test_bool.mojo index bb3079a48e..ccde4536d2 100644 --- a/stdlib/test/builtin/test_bool.mojo +++ b/stdlib/test/builtin/test_bool.mojo @@ -113,8 +113,8 @@ def test_neg(): def test_indexer(): - assert_equal(1, Bool.__index__(True)) - assert_equal(0, Bool.__index__(False)) + assert_true(1 == index(Bool(True))) + assert_true(0 == index(Bool(False))) def test_comparisons(): diff --git a/stdlib/test/builtin/test_format_int.mojo b/stdlib/test/builtin/test_format_int.mojo index 58d8308c9b..990f0feb9d 100644 --- a/stdlib/test/builtin/test_format_int.mojo +++ b/stdlib/test/builtin/test_format_int.mojo @@ -78,8 +78,8 @@ fn test_hex() raises: @value -struct Ind(Indexer): - fn __index__(self) -> Int: +struct Ind(Intable): + fn __int__(self) -> Int: return 1 @@ -128,7 +128,7 @@ def test_oct_bool(): assert_equal(oct(False), "0o0") -def test_indexer(): +def test_intable(): assert_equal(bin(Ind()), "0b1") assert_equal(hex(Ind()), "0x1") assert_equal(oct(Ind()), "0o1") @@ -158,7 +158,7 @@ def main(): test_bin_scalar() test_bin_int() test_bin_bool() - test_indexer() + test_intable() test_oct_scalar() test_oct_bool() test_oct_int() diff --git a/stdlib/test/builtin/test_int.mojo b/stdlib/test/builtin/test_int.mojo index 9919b2f636..d6d306c391 100644 --- a/stdlib/test/builtin/test_int.mojo +++ b/stdlib/test/builtin/test_int.mojo @@ -153,8 +153,8 @@ def test_int_representation(): def test_indexer(): - assert_equal(5, Int(5).__index__()) - assert_equal(987, Int(987).__index__()) + assert_true(5 == index(Int(5))) + assert_true(987 == index(Int(987))) def test_bool(): diff --git a/stdlib/test/builtin/test_int_literal.mojo b/stdlib/test/builtin/test_int_literal.mojo index 9651e564b5..8bc148e4fd 100644 --- a/stdlib/test/builtin/test_int_literal.mojo +++ b/stdlib/test/builtin/test_int_literal.mojo @@ -92,8 +92,8 @@ def test_abs(): def test_indexer(): - assert_equal(1, IntLiteral.__index__(1)) - assert_equal(88, IntLiteral.__index__(88)) + assert_true(1 == index(1)) + assert_true(88 == index(88)) def test_divmod(): diff --git a/stdlib/test/builtin/test_uint.mojo b/stdlib/test/builtin/test_uint.mojo index 1af1d2f125..005c7ee43f 100644 --- a/stdlib/test/builtin/test_uint.mojo +++ b/stdlib/test/builtin/test_uint.mojo @@ -212,8 +212,8 @@ def test_int_representation(): def test_indexer(): - assert_equal(UInt(5), UInt(5).__index__()) - assert_equal(UInt(987), UInt(987).__index__()) + assert_true(5 == index(UInt(5))) + assert_true(987 == index(UInt(987))) def test_comparison(): From b505895a3bda7d04f013bfc96b6edffb86114d47 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 9 Jan 2025 15:27:48 -0600 Subject: [PATCH 182/509] [mojo] Add `ExplicitlyCopyable` support for `@value` and in MLIR types With this change, the `@value` annotation now will add an implementation of the `fn copy(self) -> Self` method required by the `ExplicitlyCopyable` trait. This change obviates the main ergonomics issue with the `ExplicitlyCopyable` trait today, which is that it has to be written out explicitly by type authors, even though its implementation is typically trivially `return self` for any type that also implements `__copyinit__()`. This will enable further changes to replace `Copyable` as a requirement of `CollectionElement` with `ExplicitlyCopyable`, advancing the cause of minimizing where implicit copies are done in core Mojo container implementations by making those copies explicit to authors and readers. MODULAR_ORIG_COMMIT_REV_ID: eb8b20da08b36588294e40d6e9031bcc463dabc3 --- docs/changelog.md | 4 ++++ stdlib/src/builtin/_stubs.mojo | 5 ++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/docs/changelog.md b/docs/changelog.md index 0e14258db3..c3780544a8 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -30,6 +30,10 @@ what we publish. - The legacy `borrowed`/`inout` keywords and `-> T as foo` syntax now generate a warning. Please move to `read`/`mut`/`out` argument syntax instead. +- The `@value` decorator now additionally derives an implementation of the + `ExplicitlyCopyable` trait. This will ease the transition to explicit + copyablility requirements by default in the Mojo collection types. + ### Standard library changes - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` diff --git a/stdlib/src/builtin/_stubs.mojo b/stdlib/src/builtin/_stubs.mojo index 4879a225fb..8a6bcaaf16 100644 --- a/stdlib/src/builtin/_stubs.mojo +++ b/stdlib/src/builtin/_stubs.mojo @@ -19,9 +19,12 @@ from builtin.range import _StridedRangeIterator, _UIntStridedRangeIterator @register_passable("trivial") -struct __MLIRType[T: AnyTrivialRegType](Movable, Copyable): +struct __MLIRType[T: AnyTrivialRegType](Movable, Copyable, ExplicitlyCopyable): var value: T + fn copy(self) -> Self: + return self + # ===-----------------------------------------------------------------------===# # parameter_for From 8bb74cdecd47da99557a2e35b63c2f8532680287 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Thu, 9 Jan 2025 15:13:21 -0800 Subject: [PATCH 183/509] [******] AMD print (#53623) MODULAR_ORIG_COMMIT_REV_ID: cfdbd432318f0e2a77f548d090411ffb102c19ea --- stdlib/src/os/atomic.mojo | 47 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/stdlib/src/os/atomic.mojo b/stdlib/src/os/atomic.mojo index 5eaac49644..5b34c70298 100644 --- a/stdlib/src/os/atomic.mojo +++ b/stdlib/src/os/atomic.mojo @@ -91,6 +91,53 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: rhs.value, ) + @staticmethod + @always_inline + fn _xchg( + ptr: UnsafePointer[Scalar[type], **_], value: Scalar[type] + ) -> Scalar[type]: + """Performs an atomic exchange. + The operation is a read-modify-write operation. Memory + is affected according to the value of order which is sequentially + consistent. + + Args: + ptr: The source pointer. + value: The to exchange. + + Returns: + The value of the value before the operation. + """ + return __mlir_op.`pop.atomic.rmw`[ + bin_op = __mlir_attr.`#pop`, + ordering = __mlir_attr.`#pop`, + _type = __mlir_type[`!pop.scalar<`, type.value, `>`], + ]( + ptr.bitcast[__mlir_type[`!pop.scalar<`, type.value, `>`]]().address, + value.value, + ) + + @staticmethod + @always_inline + fn store(ptr: UnsafePointer[Scalar[type], **_], value: Scalar[type]): + """Performs atomic store. + The operation is a read-modify-write operation. Memory + is affected according to the value of order which is sequentially + consistent. + + Args: + ptr: The source pointer. + value: The value to store. + """ + _ = __mlir_op.`pop.atomic.rmw`[ + bin_op = __mlir_attr.`#pop`, + ordering = __mlir_attr.`#pop`, + _type = __mlir_type[`!pop.scalar<`, type.value, `>`], + ]( + ptr.bitcast[__mlir_type[`!pop.scalar<`, type.value, `>`]]().address, + value.value, + ) + @always_inline fn fetch_add(mut self, rhs: Scalar[type]) -> Scalar[type]: """Performs atomic in-place add. From 50111d965dbf43669a511246df4e67859ecea9df Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Thu, 9 Jan 2025 17:05:04 -0800 Subject: [PATCH 184/509] [stdlib] Add several GPU intrinsics These intrinsics will be needed for follow up work. MODULAR_ORIG_COMMIT_REV_ID: 1d4ca2a5bb741dfdc56929ec53a64ac0019c70b6 --- stdlib/src/sys/intrinsics.mojo | 107 ++++++++++++++++++++++++++++++++- 1 file changed, 106 insertions(+), 1 deletion(-) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 818ff9bd17..3244052f14 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -22,9 +22,10 @@ from sys import PrefetchLocality import math from memory import AddressSpace, UnsafePointer +from memory.pointer import _GPUAddressSpace from ._assembly import inlined_assembly -from .info import is_nvidia_gpu, sizeof +from .info import is_nvidia_gpu, is_amd_gpu, sizeof # ===-----------------------------------------------------------------------===# # llvm_intrinsic @@ -860,3 +861,107 @@ fn assume(val: Bool): val: The input value which is assumed to be `True`. """ llvm_intrinsic["llvm.assume", NoneType, has_side_effect=False](val) + + +# ===-----------------------------------------------------------------------===# +# lane_id +# ===-----------------------------------------------------------------------===# + + +@always_inline("nodebug") +fn lane_id() -> UInt: + """Returns the lane ID of the current thread. + + Returns: + The lane ID of the the current thread. + """ + constrained[is_gpu(), "This function only applies to GPUs."]() + + @parameter + if is_nvidia_gpu(): + return UInt( + int( + llvm_intrinsic[ + "llvm.nvvm.read.ptx.sreg.laneid", + Int32, + has_side_effect=False, + ]().cast[DType.uint32]() + ) + ) + + else: + alias none = Scalar[DType.int32](-1) + alias zero = Scalar[DType.int32](0) + var t = llvm_intrinsic[ + "llvm.amdgcn.mbcnt.lo", Int32, has_side_effect=False + ](none, zero) + return UInt( + int( + llvm_intrinsic[ + "llvm.amdgcn.mbcnt.hi", Int32, has_side_effect=False + ](none, t).cast[DType.uint32]() + ) + ) + + +# ===-----------------------------------------------------------------------===# +# implicitarg_ptr +# ===-----------------------------------------------------------------------===# + + +@always_inline +fn implicitarg_ptr() -> ( + UnsafePointer[UInt8, address_space = _GPUAddressSpace.CONSTANT] +): + """ + Get a pointer to AMD's implicit arguments table. + + Returns: + A pointer to LLVM's implicit arguments table. + """ + constrained[is_amd_gpu(), "This intrinsic is only defined for AMD GPUs"]() + return llvm_intrinsic[ + "llvm.amdgcn.implicitarg.ptr", + UnsafePointer[UInt8, address_space=4], + ]() + + +# ===-----------------------------------------------------------------------===# +# readfirstlane +# ===-----------------------------------------------------------------------===# + + +@always_inline +fn readfirstlane(value: Int32) -> Int32: + """ + Get the lowest acitve lane of the input operand. + + Args: + value: The input thread. + + Returns: + The value in the lowest active lane of the input operand. + """ + constrained[is_amd_gpu(), "This intrinsic is only defined for AMD GPUs"]() + return llvm_intrinsic["llvm.amdgcn.readfirstlane.i32", Int32, Int32](value) + + +# ===-----------------------------------------------------------------------===# +# sendmsg +# ===-----------------------------------------------------------------------===# + + +@always_inline +fn sendmsg(opcode: Int32, msg: Int32): + """ + Send a message to fixed function hardware. + Refer to the specific ISA manual for the ops and messages. + + Args: + opcode: The operation to perform. + msg: The message to send. + """ + constrained[is_amd_gpu(), "This intrinsic is only defined for AMD GPUs"]() + _ = llvm_intrinsic["llvm.amdgcn.s.sendmsg", NoneType, Int32, Int32]( + opcode, msg + ) From 6254a8c052adae41b4ecc3bb61d926f5fe32800f Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 9 Jan 2025 19:17:53 -0600 Subject: [PATCH 185/509] [stdlib][NFC] cleanup: Implement `ExplicitlyCopyable` for a bunch of types As part of preparing to making `CollectionElement` require `ExplicitlyCopyable`, preemptively implement `ExplicitlyCopyable` for many types. MODULAR_ORIG_COMMIT_REV_ID: df2db3e3de2944b18664fb0597ffb5b0dc10e983 --- stdlib/src/builtin/builtin_list.mojo | 9 +++++++++ stdlib/src/builtin/object.mojo | 9 +++++++++ stdlib/src/builtin/simd.mojo | 4 ++-- stdlib/src/builtin/tuple.mojo | 9 +++++++++ stdlib/src/builtin/value.mojo | 4 ++-- stdlib/src/collections/counter.mojo | 8 ++++++++ stdlib/test/collections/test_list.mojo | 3 +++ stdlib/test/utils/test_variant.mojo | 7 +++++++ 8 files changed, 49 insertions(+), 4 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 516fdbe06c..535ccf206f 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -56,6 +56,15 @@ struct ListLiteral[*Ts: CollectionElement](Sized, CollectionElement): """ self.storage = existing.storage + @always_inline + fn copy(self) -> Self: + """Explicitly construct a copy of self. + + Returns: + A copy of this value. + """ + return self + fn __moveinit__(out self, owned existing: Self): """Move construct the list. diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index cdd023ce36..e307fac5f6 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -936,6 +936,15 @@ struct object( """ self._value = existing._value.copy() + @always_inline + fn copy(self) -> Self: + """Explicitly construct a copy of self. + + Returns: + A copy of this value. + """ + return self + @always_inline fn __del__(owned self): """Delete the object and release any owned memory.""" diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index a74d707205..d03a77c431 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -299,10 +299,10 @@ struct SIMD[type: DType, size: Int]( @always_inline fn copy(self) -> Self: - """Explicitly construct a deep copy of the provided value. + """Explicitly construct a copy of self. Returns: - A copy of the value. + A copy of this value. """ return self diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index ea6ad3390c..523e322ebc 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -110,6 +110,15 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): for i in range(Self.__len__()): UnsafePointer.address_of(self[i]).init_pointee_copy(existing[i]) + @always_inline + fn copy(self) -> Self: + """Explicitly construct a copy of self. + + Returns: + A copy of this value. + """ + return self + @always_inline("nodebug") fn __moveinit__(out self, owned existing: Self): """Move construct the tuple. diff --git a/stdlib/src/builtin/value.mojo b/stdlib/src/builtin/value.mojo index af565bc129..7376977778 100644 --- a/stdlib/src/builtin/value.mojo +++ b/stdlib/src/builtin/value.mojo @@ -144,10 +144,10 @@ trait ExplicitlyCopyable: """ fn copy(self) -> Self: - """Explicitly construct a deep copy of the provided value. + """Explicitly construct a copy of self. Returns: - A copy of the value. + A copy of this value. """ ... diff --git a/stdlib/src/collections/counter.mojo b/stdlib/src/collections/counter.mojo index 5992cf585f..abe33c2fb8 100644 --- a/stdlib/src/collections/counter.mojo +++ b/stdlib/src/collections/counter.mojo @@ -633,6 +633,14 @@ struct CountTuple[V: KeyElement]( self._value = other._value self._count = other._count + fn copy(self) -> Self: + """Explicitly construct a copy of self. + + Returns: + A copy of this value. + """ + return self + fn __moveinit__(out self, owned other: Self): """Create a new CountTuple by moving another CountTuple. diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index c6b3e4fb1e..6f69672a60 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -888,6 +888,9 @@ struct DtorCounter(CollectionElement): fn __copyinit__(out self, existing: Self, /): self.payload = existing.payload + fn copy(self) -> Self: + return self + fn __moveinit__(out self, owned existing: Self, /): self.payload = existing.payload existing.payload = 0 diff --git a/stdlib/test/utils/test_variant.mojo b/stdlib/test/utils/test_variant.mojo index 99dae52a54..264e1dd088 100644 --- a/stdlib/test/utils/test_variant.mojo +++ b/stdlib/test/utils/test_variant.mojo @@ -36,6 +36,9 @@ struct TestCounter(CollectionElement): self.copied = other.copied + 1 self.moved = other.moved + fn copy(self) -> Self: + return self + fn __moveinit__(out self, owned other: Self): self.copied = other.copied self.moved = other.moved + 1 @@ -68,6 +71,10 @@ struct Poison(CollectionElement): fn __copyinit__(out self, other: Self): _poison_ptr().init_pointee_move(True) + fn copy(self) -> Self: + # Invokes __copyinit__, which sets the poision value. + return self + fn __moveinit__(out self, owned other: Self): _poison_ptr().init_pointee_move(True) From 41a7e724dab42adc0f492865a45a191aa430ba13 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 11 Jan 2025 03:55:28 +0000 Subject: [PATCH 186/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011022 --- examples/life/magic.lock | 8771 ++++++++++++--------------------- examples/magic.lock | 6607 +++++++++---------------- examples/notebooks/magic.lock | 7326 +++++++++------------------ examples/operators/magic.lock | 6511 +++++++++--------------- magic.lock | 6519 +++++++++--------------- 5 files changed, 12490 insertions(+), 23244 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 3a66c8dfbe..e7b8af260a 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -1,4 +1,4 @@ -version: 5 +version: 6 environments: default: channels: @@ -170,12 +170,12 @@ environments: - 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Notably, this PR does _not_ implement `_printf` for AMD GPUs since we solve formatting with our type system rather than through C format strings. MODULAR_ORIG_COMMIT_REV_ID: edb67600cec2b10c9d04219894283b70f58075b0 --- stdlib/src/builtin/file_descriptor.mojo | 8 +- stdlib/src/builtin/io.mojo | 10 +- stdlib/src/sys/_amdgpu.mojo | 973 ++++++++++++++++++++++++ stdlib/src/sys/intrinsics.mojo | 29 + stdlib/src/utils/write.mojo | 4 +- 5 files changed, 1013 insertions(+), 11 deletions(-) create mode 100644 stdlib/src/sys/_amdgpu.mojo diff --git a/stdlib/src/builtin/file_descriptor.mojo b/stdlib/src/builtin/file_descriptor.mojo index fef115e471..beed667c63 100644 --- a/stdlib/src/builtin/file_descriptor.mojo +++ b/stdlib/src/builtin/file_descriptor.mojo @@ -24,7 +24,8 @@ f.close() """ from sys.ffi import external_call -from sys.info import is_gpu +from sys.info import is_nvidia_gpu, is_amd_gpu +from sys._amdgpu import printf_begin, printf_append_string_n from builtin.io import _printf from memory import UnsafePointer, Span @@ -67,8 +68,11 @@ struct FileDescriptor(Writer): var len_bytes = len(bytes) @parameter - if is_gpu(): + if is_nvidia_gpu(): _printf["%*s"](len_bytes, bytes.unsafe_ptr()) + elif is_amd_gpu(): + var msg = printf_begin() + _ = printf_append_string_n(msg, bytes, is_last=True) else: written = external_call["write", Int32]( self.value, bytes.unsafe_ptr(), len(bytes) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index 4e4c4afff6..fc77581cbd 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -178,7 +178,6 @@ fn _printf[ fmt.unsafe_cstr_ptr(), Pointer.address_of(loaded_pack) ) elif is_amd_gpu(): - # constrained[False, "_printf on AMDGPU is not implemented"]() pass else: with _fdopen(file) as fd: @@ -269,12 +268,9 @@ fn print[ file: The output stream. """ - # TODO(MSTDL-1027): Print on AMD GPUs is not implemented yet. - @parameter - if is_amd_gpu(): - return - - write_buffered[buffer_size=4096](file, values, sep=sep, end=end) + write_buffered[buffer_size = 512 if is_amd_gpu() else 4096]( + file, values, sep=sep, end=end + ) @parameter if not is_gpu(): diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo new file mode 100644 index 0000000000..351476d78a --- /dev/null +++ b/stdlib/src/sys/_amdgpu.mojo @@ -0,0 +1,973 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from memory import UnsafePointer, Span +from memory.pointer import _GPUAddressSpace +from os.atomic import Atomic, _compare_exchange_weak_integral_impl +from sys.intrinsics import ( + lane_id, + readfirstlane, + sendmsg, + implicitarg_ptr, + ballot, +) +from time import sleep +from collections import InlineArray +from utils import StaticTuple + +# NOTE: MOST OF THE CODE HERE IS ADAPTED FROM +# AMD'S `device-libs`. +# It is important that the ABI matches up! +# https://github.com/ROCm/llvm-project/tree/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs + +# ===-----------------------------------------------------------------------===# +# HSA Queue Ops +# ===-----------------------------------------------------------------------===# + +# Matches the ABI of: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/inc/amd_hsa_signal.h#L50 +alias amd_signal_kind64_t = Int64 + + +# Must match the ABI of: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/inc/amd_hsa_signal.h#L61 +@value +struct amd_signal_t: + var kind: amd_signal_kind64_t + var value: UInt64 + var event_mailbox_ptr: UInt64 + var event_id: UInt32 + var reserved1: UInt32 + var start_ts: UInt64 + var end_ts: UInt64 + var reserved2: UInt64 + var reserved3: InlineArray[UInt32, 2] + + +@always_inline +fn update_mbox(sig: UnsafePointer[amd_signal_t]): + var mb = UnsafePointer.address_of(sig[].event_mailbox_ptr).bitcast[ + UnsafePointer[UInt64] + ]()[] + if mb: + var id = sig[].event_id.cast[DType.uint64]() + Atomic.store(mb, id) + sendmsg(1 | (0 << 4), readfirstlane(id.cast[DType.int32]()) & 0xFF) + + +@always_inline +fn hsa_signal_add(sig: UInt64, value: UInt64): + var s = UnsafePointer.address_of(sig).bitcast[ + UnsafePointer[amd_signal_t] + ]()[] + _ = Atomic._fetch_add(UnsafePointer.address_of(s[].value), value) + update_mbox(s) + + +# ===-----------------------------------------------------------------------===# +# Services +# ===-----------------------------------------------------------------------===# + + +# Matches the values described in: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/src/services.cl#L21 +struct ServiceId: + alias reserved = 0 + alias function_call = 1 + alias printf = 2 + alias fprintf = Self.printf + alias devmem = 3 + alias sanitizer = 4 + + +# Matches the values described in: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/src/services.cl#L87 +struct DescriptorOffset: + alias flag_begin = 0 + alias flag_end = 1 + alias reserved0 = 2 + alias len = 5 + alias id = 8 + + +# Matches the values described in: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/src/services.cl#L95 +struct DescriptorWidth: + alias flag_begin = 1 + alias flag_end = 1 + alias reserved0 = 3 + alias len = 3 + alias id = 56 + + +@always_inline +fn msg_set_len(pd: UInt64, len: UInt32) -> UInt64: + var reset_mask = ~( + ((UInt64(1) << DescriptorWidth.len) - 1) << DescriptorOffset.len + ) + return (pd & reset_mask) | ( + len.cast[DType.uint64]() << DescriptorOffset.len + ) + + +@always_inline +fn msg_set_begin_flag(pd: UInt64) -> UInt64: + return pd | (UInt64(1) << DescriptorOffset.flag_begin) + + +@always_inline +fn msg_reset_begin_flag(pd: UInt64) -> UInt64: + return pd & (~(UInt64(1) << DescriptorOffset.flag_begin)) + + +@always_inline +fn msg_get_end_flag(pd: UInt64) -> UInt64: + return pd & (UInt64(1) << DescriptorOffset.flag_end) + + +@always_inline +fn msg_reset_end_flag(pd: UInt64) -> UInt64: + return pd & (~(UInt64(1) << DescriptorOffset.flag_end)) + + +@always_inline +fn msg_set_end_flag(pd: UInt64) -> UInt64: + return pd | (UInt64(1) << DescriptorOffset.flag_end) + + +fn append_bytes( + service_id: UInt32, + msg_desc: UInt64, + mut data: Span[UInt8], +) -> (UInt64, UInt64): + var msg_desc_ = msg_set_len(msg_desc, (len(data) + 7) // 8) + + @parameter + @always_inline + fn pack_uint64() -> UInt64: + var arg = UInt64(0) + if len(data) >= 8: + arg = ( + data[0].cast[DType.uint64]() + | (data[1].cast[DType.uint64]() << 8) + | (data[2].cast[DType.uint64]() << 16) + | (data[3].cast[DType.uint64]() << 24) + | (data[4].cast[DType.uint64]() << 32) + | (data[5].cast[DType.uint64]() << 40) + | (data[6].cast[DType.uint64]() << 48) + | (data[7].cast[DType.uint64]() << 56) + ) + data = data[8:] + else: + var ii = 0 + for byte in data: + arg |= byte[].cast[DType.uint64]() << (ii * 8) + ii += 1 + data = data[0:0] + return arg + + var arg1 = pack_uint64() + var arg2 = pack_uint64() + var arg3 = pack_uint64() + var arg4 = pack_uint64() + var arg5 = pack_uint64() + var arg6 = pack_uint64() + var arg7 = pack_uint64() + return hostcall( + service_id, + msg_desc_, + arg1, + arg2, + arg3, + arg4, + arg5, + arg6, + arg7, + ) + + +fn message_append_bytes( + service_id: UInt32, msg_desc: UInt64, data: Span[UInt8] +) -> (UInt64, UInt64): + """ + Append an array of bytes to a message. + + Args: + service_id: Identifier for the target host-side service. + msg_desc: Message descriptor for a new or existing message. + data: Span with an array of bytes. + + Returns: + Values depend on the state of the message. + + The function can transmit a byte array of arbitrary length, but + during transmission, the array is padded with zeroes until the + length is a multiple of eight bytes. Only the array contents are + transmitted, and not the length. + + If the END flag is set, the function returns two long values + received from the host message handler. Otherwise, the first + return value is the message descriptor to be used for a subsequent + message call, while the second return value is not defined. + + """ + var data_ = data + var end_flag = msg_get_end_flag(msg_desc) + var retval = (UInt64(0), UInt64(0)) + retval[0] = msg_reset_end_flag(msg_desc) + + while True: + var prev_len = len(data_) + # We can only send 7 packed UInt64s per message + # Therefore, if the length is greater than 56, + # we need to take a 56 byte == (7 * sizeof[UInt64]()) + # chunk to process. + if len(data_) > 56: + prev_len = 56 + else: + retval[0] |= end_flag + var d = data_[:prev_len] + retval = append_bytes(service_id, retval[0], d) + data_ = data_[prev_len:] + if not data_: + break + + return retval + + +@always_inline +fn message_append_args( + service_id: UInt32, + msg_desc: UInt64, + num_args: UInt32, + arg0: UInt64, + arg1: UInt64, + arg2: UInt64, + arg3: UInt64, + arg4: UInt64, + arg5: UInt64, + arg6: UInt64, +) -> (UInt64, UInt64): + """ + Append up to seven ulong values to a message. + + Args: + service_id: Identifier for the target host-side service. + msg_desc: Message descriptor for a new or existing message. + num_args: Number of arguments to be appended (maximum seven). + arg0: Argument to be appended. + arg1: Argument to be appended. + arg2: Argument to be appended. + arg3: Argument to be appended. + arg4: Argument to be appended. + arg5: Argument to be appended. + arg6: Argument to be appended. + + Returns: + Values depend on the state of the message. + + Only the first num_args arguments are appended to the + message. The remaining arguments are ignored. Behaviour is + undefined if num_args is greater then seven. + + If the END flag is set, the function returns two uint64_t values + received from the host message handler. Otherwise, the first + return value is the message descriptor to be used for a subsequent + message call, while the second return value is not defined. + """ + var msg_desc_ = msg_set_len(msg_desc, num_args) + + return hostcall( + service_id, + msg_desc_, + arg0, + arg1, + arg2, + arg3, + arg4, + arg5, + arg6, + ) + + +# ===-----------------------------------------------------------------------===# +# Serivces - printf +# ===-----------------------------------------------------------------------===# + + +# Matches the values described in: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/src/services.cl#L243 +@value +struct FprintfCtrl: + alias stdout = 0 + alias stderr = 1 + + +@always_inline +fn begin_fprintf(flags: UInt32) -> UInt64: + # The two standard output streams stderr and stdout are indicated + # using the lowest bits in the control qword. For now, all other + # bits are required to be zero. + var msg_desc = msg_set_begin_flag(0) + var control = flags.cast[DType.uint64]() + + var retval = message_append_args( + ServiceId.fprintf, + msg_desc, + 1, # num args + control, + 0, + 0, + 0, + 0, + 0, + 0, + ) + return retval[0] + + +@always_inline +fn fprintf_stdout_begin() -> UInt64: + """ + Begin a new fprintf message for stdout. + Returns: + Message descriptor for a new printf invocation. + """ + return begin_fprintf(0) + + +@always_inline +fn fprintf_stderr_begin() -> UInt64: + """ + Begin a new fprintf message for stderr. + + Returns: + Message descriptor for a new printf invocation. + """ + return begin_fprintf(FprintfCtrl.stderr) + + +@always_inline +fn fprintf_append_args( + msg_desc: UInt64, + num_args: UInt32, + value0: UInt64, + value1: UInt64, + value2: UInt64, + value3: UInt64, + value4: UInt64, + value5: UInt64, + value6: UInt64, + is_last: Int32, +) -> UInt64: + """ + Append up to seven arguments to the fprintf message. + + Args: + msg_desc: Message descriptor for the current fprintf. + num_args: Number of arguments to be appended (maximum seven). + value0: The argument values to be appended. + value1: The argument values to be appended. + value2: The argument values to be appended. + value3: The argument values to be appended. + value4: The argument values to be appended. + value5: The argument values to be appended. + value6: The argument values to be appended. + is_last: If non-zero, this causes the fprintf to be completed. + + Returns: + Value depends on is_last. + + Only the first num_args arguments are appended to the + message. The remaining arguments are ignored. Behaviour is + undefined if num_args is greater then seven. + + If is_last is zero, the function returns a message desciptor that + must be used by a subsequent call to any __ockl_fprintf* + function. If is_last is non-zero, the function causes the current + fprintf to be completed on the host-side, and returns the value + returned by that fprintf. + + """ + var msg_desc_ = msg_desc + if is_last: + msg_desc_ = msg_set_end_flag(msg_desc_) + + var retval = message_append_args( + ServiceId.fprintf, + msg_desc_, + num_args, + value0, + value1, + value2, + value3, + value4, + value5, + value6, + ) + return retval[0] + + +@always_inline +fn fprintf_append_string_n( + msg_desc: UInt64, data: Span[UInt8], is_last: Bool +) -> UInt64: + """ + Append a null-terminated string to the fprintf message. + + Args: + msg_desc: Message descriptor for the current fprintf. + data: Span with the bytes of the string, including null terminator. + is_last: If non-zero, this causes the fprintf to be completed. + + Returns: + Value depends on is_last. + + The function appends a single null-terminated string to a current + fprintf message, including the final null character. The host-side + can use the bytes as a null-terminated string in place, without + having to first copy the string and then append the null + terminator. + + length itself is not transmitted. Behaviour is undefined if + length does not include the final null character. data may + be a null pointer, in which case, length is ignored and a single + zero is transmitted. This makes the nullptr indistinguishable from + an empty string to the host-side receiver. + + The call to message_append_args() ensures that during + transmission, the string is null-padded to a multiple of eight. + + If is_last is zero, the function returns a message desciptor that + must be used by a subsequent call to any __ockl_fprintf* + function. If is_last is non-zero, the function causes the current + fprintf to be completed on the host-side, and returns the value + returned by that fprintf. + + """ + var retval = UInt64(0), UInt64(0) + var msg_desc_ = msg_desc + + if is_last: + msg_desc_ = msg_set_end_flag(msg_desc_) + + if not data: + retval = message_append_args( + ServiceId.fprintf, + msg_desc_, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ) + + return retval[0] + retval = message_append_bytes(ServiceId.fprintf, msg_desc_, data) + + return retval[0] + + +@always_inline +fn printf_begin() -> UInt64: + return fprintf_stdout_begin() + + +@always_inline +fn printf_append_args( + msg_desc: UInt64, + num_args: UInt32, + value0: UInt64 = 0, + value1: UInt64 = 0, + value2: UInt64 = 0, + value3: UInt64 = 0, + value4: UInt64 = 0, + value5: UInt64 = 0, + value6: UInt64 = 0, + is_last: Int32 = 0, +) -> UInt64: + return fprintf_append_args( + msg_desc, + num_args, + value0, + value1, + value2, + value3, + value4, + value5, + value6, + is_last, + ) + + +@always_inline +fn printf_append_string_n( + msg_desc: UInt64, data: Span[UInt8], is_last: Bool +) -> UInt64: + return fprintf_append_string_n(msg_desc, data, is_last) + + +# ===-----------------------------------------------------------------------===# +# Hostcall +# ===-----------------------------------------------------------------------===# + + +@value +@register_passable("trivial") +struct Header: + var _handle: UnsafePointer[ + header_t, address_space = _GPUAddressSpace.GLOBAL + ] + + fn fill_packet( + mut self, + mut payload: Payload, + service_id: UInt32, + arg0: UInt64, + arg1: UInt64, + arg2: UInt64, + arg3: UInt64, + arg4: UInt64, + arg5: UInt64, + arg6: UInt64, + arg7: UInt64, + me: UInt32, + low: UInt32, + ): + if me == low: + var control = set_ready_flag(0) + self._handle[].control = control + self._handle[].activemask = ballot[DType.int64](True).cast[ + DType.uint64 + ]() + self._handle[].service = service_id + + payload[int(me), 0] = arg0 + payload[int(me), 1] = arg1 + payload[int(me), 2] = arg2 + payload[int(me), 3] = arg3 + payload[int(me), 4] = arg4 + payload[int(me), 5] = arg5 + payload[int(me), 6] = arg6 + payload[int(me), 7] = arg7 + + fn get_return_value( + mut self, payload: Payload, me: UInt32, low: UInt32 + ) -> (UInt64, UInt64): + """ + Wait for the host response and return the first two ulong + entries per workitem. + + After the packet is submitted in READY state, the wave spins until + the host changes the state to DONE. Each workitem reads the first + two ulong elements in its slot and returns this. + """ + # The while loop needs to be executed by all active + # lanes. Otherwise, later reads from ptr are performed only by + # the first thread, while other threads reuse a value cached from + # previous operations. The use of readfirstlane in the while loop + # prevents this reordering. + # + # In the absence of the readfirstlane, only one thread has a + # sequenced-before relation from the atomic load on + # header->control to the ordinary loads on ptr. As a result, the + # compiler is free to reorder operations in such a way that the + # ordinary loads are performed only by the first thread. The use + # of readfirstlane provides a stronger code-motion barrier, and + # it effectively "spreads out" the sequenced-before relation to + # the ordinary stores in other threads too. + while True: + var ready_flag = UInt32(1) + if me == low: + var ptr = UnsafePointer.address_of(self._handle[].control) + var control = Atomic._fetch_add(ptr, 0) + ready_flag = get_ready_flag(control) + + ready_flag = readfirstlane(ready_flag.cast[DType.int32]()).cast[ + DType.uint32 + ]() + + if ready_flag == 0: + break + + sleep(UInt(1)) + + var ptr = payload._handle[].slots[int(me)] + var value0 = ptr[0] + var value1 = ptr[1] + return value0, value1 + + +# Must match the ABI of: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/src/hostcall_impl.cl#L30 +# but this is actually just conforming to the ABI of: +# https://github.com/ROCm/clr/blob/f5b2516f5d8a44b06ad1907594db1be25a9fe57b/rocclr/device/devhostcall.hpp#L104 +@value +@register_passable("trivial") +struct header_t: + var next: UInt64 + var activemask: UInt64 + var service: UInt32 + var control: UInt32 + + +@value +@register_passable("trivial") +struct Payload: + var _handle: UnsafePointer[ + payload_t, address_space = _GPUAddressSpace.GLOBAL + ] + + @always_inline + fn __setitem__(mut self, idx0: Int, idx1: Int, value: UInt64): + self._handle[].slots[idx0][idx1] = value + + +# Must match the ABI of: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/src/hostcall_impl.cl#L37 +# but this is actually just conforming to the ABI of: +# https://github.com/ROCm/clr/blob/f5b2516f5d8a44b06ad1907594db1be25a9fe57b/rocclr/device/devhostcall.hpp#L99 +@value +@register_passable("trivial") +struct payload_t: + var slots: StaticTuple[StaticTuple[UInt64, 8], 64] + + +@value +@register_passable("trivial") +struct Buffer: + var _handle: UnsafePointer[ + buffer_t, address_space = _GPUAddressSpace.GLOBAL + ] + + @always_inline + fn get_header(self, ptr: UInt64) -> Header: + return Header( + self._handle[].headers.offset(ptr & self._handle[].index_mask) + ) + + @always_inline + fn get_payload(self, ptr: UInt64) -> Payload: + return Payload( + self._handle[].payloads.offset(ptr & self._handle[].index_mask) + ) + + fn pop( + mut self, + mut top: UInt64, + ) -> UInt64: + var f = Atomic._fetch_add(UnsafePointer.address_of(top), 0) + # F is guaranteed to be non-zero, since there are at least as + # many packets as there are waves, and each wave can hold at most + # one packet. + while True: + var p = self.get_header(f) + var n = Atomic._fetch_add( + UnsafePointer.address_of(p._handle[].next), + 0, + ) + if _compare_exchange_weak_integral_impl[scope=""]( + UnsafePointer.address_of(top), f, n + ): + break + + sleep(UInt(1)) + return f + + fn pop_free_stack(mut self, me: UInt32, low: UInt32) -> UInt64: + """ + Use the first active lane to get a free packet and + broadcast to the whole wave. + """ + var packet_ptr = UInt64(0) + if me == low: + packet_ptr = Self.pop( + self, + self._handle[].free_stack, + ) + + var ptr_lo = packet_ptr + var ptr_hi = packet_ptr >> 32 + var ptr_lo_32 = readfirstlane(ptr_lo.cast[DType.int32]()) + var ptr_hi_32 = readfirstlane(ptr_hi.cast[DType.int32]()) + + return ( + ptr_hi_32.cast[DType.uint64]() << 32 + | ptr_lo_32.cast[DType.uint64]() + ) + + fn push(mut self, mut top: UInt64, ptr: UInt64): + var f = Atomic._fetch_add(UnsafePointer.address_of(top), 0) + var p = self.get_header(ptr) + while True: + p._handle[].next = f + if _compare_exchange_weak_integral_impl[scope=""]( + UnsafePointer.address_of(top), f, ptr + ): + break + sleep(UInt(1)) + + fn push_ready_stack(mut self, ptr: UInt64, me: UInt32, low: UInt32): + """ + Use the first active lane in a wave to submit a ready + packet and signal the host. + """ + if me == low: + self.push(self._handle[].ready_stack, ptr) + send_signal(self._handle[].doorbell) + + fn return_free_packet(mut self, ptr: UInt64, me: UInt32, low: UInt32): + """ + Return the packet after incrementing the ABA tag. + """ + if me == low: + var ptr = inc_ptr_tag(ptr, self._handle[].index_mask) + self.push(self._handle[].free_stack, ptr) + + +# Must match the ABI of: +# https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/amd/device-libs/ockl/src/hostcall_impl.cl#L45 +# but this is actually just conforming to the ABI of: +# https://github.com/ROCm/clr/blob/f5b2516f5d8a44b06ad1907594db1be25a9fe57b/rocclr/device/devhostcall.hpp#L144 +# +# AMD's note: Hostcall buffer struct defined here is not an exact +# match of runtime buffer layout but matches its prefix that +# this code tries to access. +@value +@register_passable("trivial") +struct buffer_t: + var headers: UnsafePointer[ + header_t, address_space = _GPUAddressSpace.GLOBAL + ] + var payloads: UnsafePointer[ + payload_t, address_space = _GPUAddressSpace.GLOBAL + ] + var doorbell: UInt64 + var free_stack: UInt64 + var ready_stack: UInt64 + var index_mask: UInt64 + + +@value +@register_passable("trivial") +struct ControlOffset: + var value: UInt32 + alias ready_flag = Self(0) + alias reserved0 = Self(1) + + @always_inline + fn __ne__(self, rhs: Self) -> Bool: + return self.value != rhs.value + + @always_inline + fn __eq__(self, rhs: Self) -> Bool: + return self.value == rhs.value + + +@value +@register_passable("trivial") +struct ControlWidth: + var value: UInt32 + alias ready_flag = Self(1) + alias reserved0 = Self(31) + + @always_inline + fn __ne__(self, rhs: Self) -> Bool: + return self.value != rhs.value + + @always_inline + fn __eq__(self, rhs: Self) -> Bool: + return self.value == rhs.value + + +@always_inline +fn get_control_mask(control: UInt32, offset: UInt32, width: UInt32) -> UInt32: + var value: UInt32 = (control >> offset) & ((1 << width) - 1) + return value + + +@always_inline +fn get_control_field( + control: UInt32, offset: ControlOffset, width: ControlWidth +) -> UInt32: + var value: UInt32 = (control >> offset.value) & ( + (UInt32(1) << width.value) - 1 + ) + return value + + +@always_inline +fn set_control_field( + control: UInt32, offset: ControlOffset, width: ControlWidth, value: UInt32 +) -> UInt32: + var mask: UInt32 = ~(((UInt32(1) << width.value) - 1) << offset.value) + return (control & mask) | (value << offset.value) + + +@always_inline +fn get_ready_flag(control: UInt32) -> UInt32: + return get_control_field( + control, ControlOffset.ready_flag, ControlWidth.ready_flag + ) + + +@always_inline +fn set_ready_flag(control: UInt32) -> UInt32: + return set_control_field( + control, ControlOffset.ready_flag, ControlWidth.ready_flag, 1 + ) + + +@always_inline +fn inc_ptr_tag(ptr: UInt64, index_mask: UInt64) -> UInt64: + var inc = index_mask + 1 + var ptr_ = ptr + inc + # Unit step for the tag. + # When the tag for index 0 wraps, increment the tag. + if ptr_ == 0: + return inc + return ptr_ + + +@always_inline +fn send_signal(signal: UInt64): + hsa_signal_add(signal, 1) + + +fn _hostcall( + mut buffer: Buffer, + service_id: UInt32, + arg0: UInt64, + arg1: UInt64, + arg2: UInt64, + arg3: UInt64, + arg4: UInt64, + arg5: UInt64, + arg6: UInt64, + arg7: UInt64, +) -> (UInt64, UInt64): + """ + The implementation that should be hidden behind an ABI + The transaction is a wave-wide operation, where the service_id + must be uniform, but the parameters are different for each + workitem. Parameters from all active lanes are written into a + hostcall packet. The hostcall blocks until the host processes the + request, and returns the response it receiveds. + + TODO: This function and everything above it should eventually move + to a separate library that is loaded by the language runtime. The + function itself will be exposed as an orindary function symbol to + be linked into kernel objects that are loaded after this library. + + *** INTERNAL USE ONLY *** + Internal function, not safe for direct use in user + code. Application kernels must only use __ockl_hostcall_preview() + defined elsewhere.""" + # all 0s + var me = lane_id() + # all 0s + var low = readfirstlane(me).cast[DType.uint32]() + + var packet_ptr = buffer.pop_free_stack(me, low) + + var header = buffer.get_header(packet_ptr) + var payload = buffer.get_payload(packet_ptr) + + header.fill_packet( + payload, + service_id, + arg0, + arg1, + arg2, + arg3, + arg4, + arg5, + arg6, + arg7, + me, + low, + ) + + buffer.push_ready_stack(packet_ptr, me, low) + + var retval = header.get_return_value(payload, me, low) + buffer.return_free_packet(packet_ptr, me, low) + return retval + + +fn hostcall( + service_id: UInt32, + arg0: UInt64, + arg1: UInt64, + arg2: UInt64, + arg3: UInt64, + arg4: UInt64, + arg5: UInt64, + arg6: UInt64, + arg7: UInt64, +) -> (UInt64, UInt64): + """ + Submit a wave-wide hostcall packet. + + Args: + service_id: The service to be invoked on the host. + arg0: A parameter. + arg1: A parameter. + arg2: A parameter. + arg3: A parameter. + arg4: A parameter. + arg5: A parameter. + arg6: A parameter. + arg7: A parameter. + + Returns: + Two 64-bit values. + + The hostcall is executed for all active threads in the + wave. service_id must be uniform across the active threads, + otherwise behaviour is undefined. The service parameters may be + different for each active thread, and correspondingly, the + returned values are also different. + + The contents of the input parameters and the return values are + defined by the service being invoked. + + *** PREVIEW FEATURE *** + This is a feature preview and considered alpha quality only; + behaviour may vary between ROCm releases. Device code that invokes + hostcall can be launched only on the ROCm release that it was + compiled for, otherwise behaviour is undefined. + """ + var buffer = Buffer( + implicitarg_ptr() + .bitcast[ + UnsafePointer[buffer_t, address_space = _GPUAddressSpace.GLOBAL] + ]() + .offset(10)[] + ) + + return _hostcall( + buffer, + service_id, + arg0, + arg1, + arg2, + arg3, + arg4, + arg5, + arg6, + arg7, + ) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 3244052f14..8541672871 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -965,3 +965,32 @@ fn sendmsg(opcode: Int32, msg: Int32): _ = llvm_intrinsic["llvm.amdgcn.s.sendmsg", NoneType, Int32, Int32]( opcode, msg ) + + +# ===-----------------------------------------------------------------------===# +# ballot +# ===-----------------------------------------------------------------------===# + + +@always_inline +fn ballot[dtype: DType](value: Bool) -> Scalar[dtype]: + """ + Returns a bitfield(Int32 or Int64) containing the result + of its Bool argument in all active lanes, and zero in all inactive lanes. + For example, ballot(True) returns EXEC mask. + + Parameters: + dtype: The DType of the return type. + + Args: + value: The value to place across the mask. + + Returns: + A bitfield(Int32 or Int64) containing the result of its Bool argument in all active lanes. + """ + constrained[is_amd_gpu(), "This intrinsic is only defined for AMD GPUs"]() + constrained[ + dtype == DType.int32 or dtype == DType.int64, + "This intrinsic is only defined for i32 or i64", + ]() + return llvm_intrinsic["llvm.amdgcn.ballot", Scalar[dtype]](value) diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index 6d68951b9b..f2a5172d2f 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -13,7 +13,7 @@ """Establishes the contract between `Writer` and `Writable` types.""" from collections import InlineArray -from sys.info import is_gpu +from sys.info import is_gpu, is_nvidia_gpu, is_amd_gpu from memory import UnsafePointer, memcpy, Span @@ -366,7 +366,7 @@ fn write_buffered[ """ @parameter - if is_gpu(): + if is_nvidia_gpu(): # Stack space is very small on GPU due to many threads, so use heap # Count the total length of bytes to allocate only once var arg_bytes = _ArgBytes() From 92b1f3ba951af8b1024ea6791347de00c9eb59fe Mon Sep 17 00:00:00 2001 From: Scott Main Date: Fri, 10 Jan 2025 15:21:18 -0800 Subject: [PATCH 188/509] Update use of the `GetMagic` component based on style changes Now that the include component is inlined instead of in a collapsible box, we can now use it effectively in procedures. MODULAR_ORIG_COMMIT_REV_ID: 8ebbea97854d1bbe4182896e0255bc3e6871dd5e --- docs/manual/get-started.mdx | 45 +++++++++++++++++++------------------ 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 261a8b7fb4..0495016925 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -32,36 +32,37 @@ GitHub](https://github.com/modularml/mojo/tree/nightly/examples/life). ## 1. Create a Mojo project with `magic` -We'll start by using the [`magic` CLI](/magic) to create a virtual environment +We'll start by using the `magic` CLI to create a virtual environment and generate our initial project directory. - +1. -In your terminal, go to the directory in which you want to create the project -and execute: +2. Navigate to the directory in which you want to create the project + and execute: -```bash -magic init life --format mojoproject -``` + ```bash + magic init life --format mojoproject + ``` -This creates a project directory named `life`. Let's go to the directory and -list its contents: + This creates a project directory named `life`. -```bash -cd life -``` +3. Let's go into the directory and list its contents: -```bash -ls -A -``` + ```bash + cd life + ``` -```output -.gitattributes -.gitignore -.magic -magic.lock -mojoproject.toml -``` + ```bash + ls -A + ``` + + ```output + .gitattributes + .gitignore + .magic + magic.lock + mojoproject.toml + ``` You should see that the project directory contains: From 4cecc66d94eafb3263a9a9e0330d11da2fac3a4f Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 10 Jan 2025 15:27:09 -0800 Subject: [PATCH 189/509] [KGEN] Rename `KGEN::ParamRefType` -> `KGEN::ParamType`, NFC. This renames a (very old) core kgen type to better align with that it does: it projects a TypedAttr into a Type. It is itself not a "reference" to a parameter decl like `ParamDeclRefAttr` so using that word is confusing, this is also more concise and consistent. MODULAR_ORIG_COMMIT_REV_ID: fa2b6ffbb099b8fcdab5ddd83d5990f348e8c7b0 --- stdlib/src/sys/info.mojo | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index ee149ca72a..5f6e10d9bf 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -624,7 +624,7 @@ fn sizeof[ Note: `align_of` is in same module. """ alias mlir_type = __mlir_attr[ - `#kgen.param.expr> : `, AnyType, @@ -677,7 +677,7 @@ fn alignof[ The alignment of the type in bytes. """ alias mlir_type = __mlir_attr[ - `#kgen.param.expr> : `, AnyType, From 4698d124f19aa576e07772980c86267417605bd5 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Fri, 10 Jan 2025 18:15:53 -0600 Subject: [PATCH 190/509] [stdlib] Move `int` functions to `Int` ctors Now that all the machinery is in place, `Int` can be constructed both implicitly and explictly from traits that other types implement. The `int` fucntion was a temporary workaround while we didn't have a way to distinguish between explicit and implicit ctors. MODULAR_ORIG_COMMIT_REV_ID: 57c009fce682e82e5398477108ac2dc13e0ec6dc --- docs/changelog.md | 5 + examples/life/gridv1.mojo | 2 +- examples/notebooks/RayTracing.ipynb | 8 +- stdlib/benchmarks/builtin/bench_sort.mojo | 2 +- stdlib/benchmarks/collections/bench_dict.mojo | 6 +- stdlib/benchmarks/utils/bench_memmem.mojo | 2 +- stdlib/src/base64/_b64encode.mojo | 4 +- stdlib/src/base64/base64.mojo | 8 +- stdlib/src/builtin/_format_float.mojo | 22 +-- stdlib/src/builtin/bool.mojo | 2 +- stdlib/src/builtin/dtype.mojo | 2 +- stdlib/src/builtin/file.mojo | 6 +- stdlib/src/builtin/format_int.mojo | 8 +- stdlib/src/builtin/int.mojo | 185 ++++++++---------- stdlib/src/builtin/io.mojo | 2 +- stdlib/src/builtin/object.mojo | 12 +- stdlib/src/builtin/range.mojo | 12 +- stdlib/src/builtin/simd.mojo | 10 +- stdlib/src/builtin/sort.mojo | 4 +- stdlib/src/builtin/string_literal.mojo | 2 +- stdlib/src/collections/dict.mojo | 10 +- stdlib/src/collections/inline_array.mojo | 12 +- stdlib/src/collections/inline_list.mojo | 4 +- stdlib/src/collections/list.mojo | 4 +- stdlib/src/collections/string/_unicode.mojo | 14 +- stdlib/src/collections/string/format.mojo | 8 +- stdlib/src/collections/string/string.mojo | 48 ++--- .../src/collections/string/string_slice.mojo | 22 +-- stdlib/src/collections/vector.mojo | 4 +- stdlib/src/hashlib/hash.mojo | 6 +- stdlib/src/math/math.mojo | 2 +- stdlib/src/memory/memory.mojo | 4 +- stdlib/src/memory/pointer.mojo | 2 +- stdlib/src/memory/span.mojo | 4 +- stdlib/src/memory/unsafe_pointer.mojo | 28 +-- stdlib/src/os/_linux_aarch64.mojo | 20 +- stdlib/src/os/_linux_x86.mojo | 20 +- stdlib/src/os/_macos.mojo | 22 +-- stdlib/src/os/os.mojo | 2 +- stdlib/src/os/path/path.mojo | 16 +- stdlib/src/prelude/__init__.mojo | 1 - stdlib/src/pwd/_linux.mojo | 4 +- stdlib/src/pwd/_macos.mojo | 4 +- stdlib/src/python/_cpython.mojo | 12 +- stdlib/src/python/python_object.mojo | 6 +- stdlib/src/random/random.mojo | 2 +- stdlib/src/stat/stat.mojo | 14 +- stdlib/src/sys/_amdgpu.mojo | 18 +- stdlib/src/sys/info.mojo | 6 +- stdlib/src/sys/intrinsics.mojo | 8 +- stdlib/src/sys/terminate.mojo | 2 +- stdlib/src/tempfile/tempfile.mojo | 2 +- stdlib/src/time/time.mojo | 12 +- stdlib/src/utils/index.mojo | 20 +- stdlib/src/utils/lock.mojo | 4 +- stdlib/src/utils/numerics.mojo | 6 +- stdlib/src/utils/stringref.mojo | 16 +- stdlib/test/bit/test_bit.mojo | 2 +- stdlib/test/builtin/test_bfloat16.mojo | 4 +- stdlib/test/builtin/test_bool.mojo | 4 +- stdlib/test/builtin/test_float_literal.mojo | 10 +- stdlib/test/builtin/test_int.mojo | 4 +- stdlib/test/builtin/test_math.mojo | 4 +- stdlib/test/builtin/test_range.mojo | 2 +- stdlib/test/builtin/test_simd.mojo | 30 +-- stdlib/test/builtin/test_sort.mojo | 12 +- stdlib/test/builtin/test_string_literal.mojo | 4 +- .../string/test_inlined_string.mojo | 4 +- .../test/collections/string/test_string.mojo | 8 +- .../collections/string/test_string_slice.mojo | 8 +- stdlib/test/collections/test_inline_list.mojo | 2 +- stdlib/test/collections/test_list.mojo | 2 +- stdlib/test/collections/test_vector.mojo | 2 +- stdlib/test/hashlib/test_ahash.mojo | 4 +- stdlib/test/hashlib/test_hash.mojo | 2 +- stdlib/test/memory/test_memory.mojo | 2 +- stdlib/test/memory/test_span.mojo | 2 +- stdlib/test/memory/test_unsafepointer.mojo | 20 +- stdlib/test/utils/issue_13632.mojo | 2 +- stdlib/test/utils/test_stringref.mojo | 6 +- 80 files changed, 403 insertions(+), 430 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index c3780544a8..dc8fe8995b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -36,6 +36,11 @@ what we publish. ### Standard library changes +- The `int` function to construct an `Int` has been removed, this was a + temporary workaround when Mojo didn't have a way to distinguish between + implicit and explicit constructors. You can do a search and replace for `int(` + to `Int(` to update your programs. + - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` for changing the type, `origin_cast` for changing mutability, `static_alignment_cast` for changing alignment, diff --git a/examples/life/gridv1.mojo b/examples/life/gridv1.mojo index 0eded6f24d..ec65b6ad74 100644 --- a/examples/life/gridv1.mojo +++ b/examples/life/gridv1.mojo @@ -87,7 +87,7 @@ struct Grid(StringableRaising): row_data = List[Int]() for col in range(cols): # Generate a random 0 or 1 and append it to the row. - row_data.append(int(random.random_si64(0, 1))) + row_data.append(Int(random.random_si64(0, 1))) data.append(row_data) return Self(rows, cols, data) diff --git a/examples/notebooks/RayTracing.ipynb b/examples/notebooks/RayTracing.ipynb index ea37cea474..ad3830bbe1 100644 --- a/examples/notebooks/RayTracing.ipynb +++ b/examples/notebooks/RayTracing.ipynb @@ -271,8 +271,8 @@ " var plt = Python.import_module(\"matplotlib.pyplot\")\n", "\n", " var np_image = plt.imread(fname)\n", - " var rows = int(np_image.shape[0])\n", - " var cols = int(np_image.shape[1])\n", + " var rows = Int(np_image.shape[0])\n", + " var cols = Int(np_image.shape[1])\n", " var image = Image(rows, cols)\n", "\n", " var in_pointer = np_image.__array_interface__[\"data\"][0].unsafe_get_as_pointer[DType.float32]()\n", @@ -926,8 +926,8 @@ " # Now map x from [-1,1] to [0,w-1] and do the same for y.\n", " var w = bg.width\n", " var h = bg.height\n", - " var col = int((1.0 + x) * 0.5 * (w - 1))\n", - " var row = int((1.0 + y) * 0.5 * (h - 1))\n", + " var col = Int((1.0 + x) * 0.5 * (w - 1))\n", + " var row = Int((1.0 + y) * 0.5 * (h - 1))\n", " return Material(bg.pixels[bg._pos_to_index(row, col)])\n", "\n", " var diffuse_light_intensity: Float32 = 0\n", diff --git a/stdlib/benchmarks/builtin/bench_sort.mojo b/stdlib/benchmarks/builtin/bench_sort.mojo index a44fc3a150..7c70e7ebb6 100644 --- a/stdlib/benchmarks/builtin/bench_sort.mojo +++ b/stdlib/benchmarks/builtin/bench_sort.mojo @@ -37,7 +37,7 @@ fn randomize_list[ ](mut list: List[Scalar[dt]], size: Int, max: Scalar[dt] = Scalar[dt].MAX): @parameter if dt.is_integral(): - randint(list.data, size, 0, int(max)) + randint(list.data, size, 0, Int(max)) else: for i in range(size): var res = random_float64() diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index 359d922c5e..a4ed39e99f 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -30,7 +30,7 @@ from bit import next_power_of_two fn make_dict[size: Int]() -> Dict[Int, Int]: var d = Dict[Int, Int]() for i in range(0, size): - d[i] = int(random.random_si64(0, size)) + d[i] = Int(random.random_si64(0, size)) return d @@ -62,7 +62,7 @@ fn bench_dict_insert[size: Int](mut b: Bencher) raises: @parameter fn call_fn() raises: for key in range(size, size + 100): - items[key] = int(random.random_si64(0, size)) + items[key] = Int(random.random_si64(0, size)) b.iter[call_fn]() keep(bool(items)) @@ -84,7 +84,7 @@ fn bench_dict_lookup[size: Int](mut b: Bencher) raises: @parameter if size < 100: for _ in range(closest_divisor): - for key in range(int(100 // closest_divisor)): + for key in range(Int(100 // closest_divisor)): var res = items[key] keep(res) else: diff --git a/stdlib/benchmarks/utils/bench_memmem.mojo b/stdlib/benchmarks/utils/bench_memmem.mojo index 777557784e..b3e09db5f5 100644 --- a/stdlib/benchmarks/utils/bench_memmem.mojo +++ b/stdlib/benchmarks/utils/bench_memmem.mojo @@ -171,7 +171,7 @@ fn _memmem_baseline[ var bool_mask = haystack.load[width=bool_mask_width](i) == first_needle var mask = pack_bits(bool_mask) while mask: - var offset = int(i + count_trailing_zeros(mask)) + var offset = Int(i + count_trailing_zeros(mask)) if memcmp(haystack + offset + 1, needle + 1, needle_len - 1) == 0: return haystack + offset mask = mask & (mask - 1) diff --git a/stdlib/src/base64/_b64encode.mojo b/stdlib/src/base64/_b64encode.mojo index 9bd946b308..e1a5301fa3 100644 --- a/stdlib/src/base64/_b64encode.mojo +++ b/stdlib/src/base64/_b64encode.mojo @@ -146,7 +146,7 @@ fn _get_number_of_bytes_to_store_from_number_of_bytes_to_load[ alias table = _get_table_number_of_bytes_to_store_from_number_of_bytes_to_load[ max_size ]() - return int(table[nb_of_elements_to_load]) + return Int(table[nb_of_elements_to_load]) fn _get_table_number_of_bytes_to_store_from_number_of_bytes_to_load_without_equal_sign[ @@ -181,7 +181,7 @@ fn _get_number_of_bytes_to_store_from_number_of_bytes_to_load_without_equal_sign alias table = _get_table_number_of_bytes_to_store_from_number_of_bytes_to_load_without_equal_sign[ max_size ]() - return int(table[nb_of_elements_to_load]) + return Int(table[nb_of_elements_to_load]) fn load_incomplete_simd[ diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index b0f467f745..750ee4cb78 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -99,7 +99,7 @@ fn b64encode(input_bytes: Span[Byte, _]) -> String: The ASCII base64 encoded string. """ # +1 for the null terminator and +1 to be sure - var result = List[UInt8, True](capacity=int(len(input_bytes) * (4 / 3)) + 2) + var result = List[UInt8, True](capacity=Int(len(input_bytes) * (4 / 3)) + 2) b64encode(input_bytes, result) # null-terminate the result result.append(0) @@ -177,14 +177,14 @@ fn b16encode(str: StringSlice) -> String: @parameter @always_inline fn str_bytes(idx: UInt8) -> UInt8: - return str._slice[int(idx)] + return str._slice[Int(idx)] for i in range(length): var str_byte = str_bytes(i) var hi = str_byte >> 4 var lo = str_byte & 0b1111 - out.append(b16chars[int(hi)]) - out.append(b16chars[int(lo)]) + out.append(b16chars[Int(hi)]) + out.append(b16chars[Int(lo)]) out.append(0) diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index 17f30d7de9..ad1a93b0da 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -106,16 +106,16 @@ fn _write_float[W: Writer, type: DType, //](mut writer: W, value: Scalar[type]): @parameter if type is DType.float8e5m2: - return writer.write(float8e5m2_to_str[int(bitcast[DType.uint8](value))]) + return writer.write(float8e5m2_to_str[Int(bitcast[DType.uint8](value))]) elif type is DType.float8e4m3: - return writer.write(float8e4m3_to_str[int(bitcast[DType.uint8](value))]) + return writer.write(float8e4m3_to_str[Int(bitcast[DType.uint8](value))]) elif type is DType.float8e5m2fnuz: return writer.write( - float8e5m2fnuz_to_str[int(bitcast[DType.uint8](value))] + float8e5m2fnuz_to_str[Int(bitcast[DType.uint8](value))] ) elif type is DType.float8e4m3fnuz: return writer.write( - float8e4m3fnuz_to_str[int(bitcast[DType.uint8](value))] + float8e4m3fnuz_to_str[Int(bitcast[DType.uint8](value))] ) else: # Upcast the float16 types to float32 @@ -493,25 +493,25 @@ fn _remove_trailing_zeros[ if CarrierDType is DType.uint64: var r = _rotr(sig * 28999941890838049, 8) var b = r < 184467440738 - var s = int(b) + var s = Int(b) if b: sig = r r = _rotr(sig * 182622766329724561, 4) b = r < 1844674407370956 - s = s * 2 + int(b) + s = s * 2 + Int(b) if b: sig = r r = _rotr(sig * 10330176681277348905, 2) b = r < 184467440737095517 - s = s * 2 + int(b) + s = s * 2 + Int(b) if b: sig = r r = _rotr(sig * 14757395258967641293, 1) b = r < 1844674407370955162 - s = s * 2 + int(b) + s = s * 2 + Int(b) if b: sig = r @@ -519,19 +519,19 @@ fn _remove_trailing_zeros[ else: var r = _rotr(sig * 184254097, 4) var b = r < 429497 - var s = int(b) + var s = Int(b) if b: sig = r r = _rotr(sig * 42949673, 2) b = r < 42949673 - s = s * 2 + int(b) + s = s * 2 + Int(b) if b: sig = r r = _rotr(sig * 1288490189, 1) b = r < 429496730 - s = s * 2 + int(b) + s = s * 2 + Int(b) if b: sig = r diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index a0a65229dc..213d080e21 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -285,7 +285,7 @@ struct Bool( Returns: 1 if the Bool is True, 0 otherwise. """ - return int(self).value + return Int(self).value @always_inline("nodebug") fn __eq__(self, rhs: Bool) -> Bool: diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index f46f28a89a..c0ab14b399 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -514,7 +514,7 @@ struct DType( """ if self._is_non_index_integral(): - return int( + return Int( UInt8( __mlir_op.`pop.shl`( UInt8(1).value, diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index c2bbdf295c..4aa4784f1b 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -193,7 +193,7 @@ struct FileHandle: if err_msg: raise err_msg^.consume_as_error() - return String(ptr=buf, length=int(size_copy) + 1) + return String(ptr=buf, length=Int(size_copy) + 1) fn read[ type: DType @@ -337,7 +337,7 @@ struct FileHandle: raise (err_msg^).consume_as_error() var list = List[UInt8]( - ptr=buf, length=int(size_copy), capacity=int(size_copy) + ptr=buf, length=Int(size_copy), capacity=Int(size_copy) ) return list @@ -463,7 +463,7 @@ struct FileHandle: return self^ fn _get_raw_fd(self) -> Int: - return int( + return Int( external_call[ "KGEN_CompilerRT_IO_GetFD", Int64, diff --git a/stdlib/src/builtin/format_int.mojo b/stdlib/src/builtin/format_int.mojo index 2f9bf016db..79726e1a99 100644 --- a/stdlib/src/builtin/format_int.mojo +++ b/stdlib/src/builtin/format_int.mojo @@ -79,7 +79,7 @@ fn bin[T: Intable, //](num: T, /, *, prefix: StaticString = "0b") -> String: Returns: The binary string representation of num. """ - return bin(Scalar[DType.index](int(num)), prefix=prefix) + return bin(Scalar[DType.index](Int(num)), prefix=prefix) # ===-----------------------------------------------------------------------===# @@ -123,7 +123,7 @@ fn hex[T: Intable, //](value: T, /, *, prefix: StaticString = "0x") -> String: Returns: A string containing the hex representation of the given integer. """ - return hex(Scalar[DType.index](int(value)), prefix=prefix) + return hex(Scalar[DType.index](Int(value)), prefix=prefix) fn hex(value: Scalar[DType.bool], /, *, prefix: StaticString = "0x") -> String: @@ -185,7 +185,7 @@ fn oct[T: Intable, //](value: T, /, *, prefix: StaticString = "0o") -> String: Returns: A string containing the octal representation of the given integer. """ - return oct(Scalar[DType.index](int(value)), prefix=prefix) + return oct(Scalar[DType.index](Int(value)), prefix=prefix) fn oct(value: Scalar[DType.bool], /, *, prefix: StaticString = "0o") -> String: @@ -366,7 +366,7 @@ fn _try_write_int[ # Write the char representing the value of the least significant # digit. buf.unsafe_ptr().offset(offset).init_pointee_copy( - digit_chars_array[int(digit_value)] + digit_chars_array[Int(digit_value)] ) # Position the offset to write the next digit. diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 8205ad6886..f21bb325fa 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -90,8 +90,8 @@ trait Intable(CollectionElement): """The `Intable` trait describes a type that can be converted to an Int. Any type that conforms to `Intable` or - [`IntableRaising`](/mojo/stdlib/builtin/int/IntableRaising) works with - the built-in [`int()`](/mojo/stdlib/builtin/int/int-function) function. + [`IntableRaising`](/mojo/stdlib/builtin/int/IntableRaising) can construct an + `Int`. This trait requires the type to implement the `__int__()` method. For example: @@ -105,13 +105,12 @@ trait Intable(CollectionElement): return self.i ``` - Now you can use the `int()` function to convert a `Foo` to an - `Int`: + Now you can construct an `Int`: ```mojo %# from testing import assert_equal foo = Foo(42) - assert_equal(int(foo), 42) + assert_equal(Int(foo), 42) ``` **Note:** If the `__int__()` method can raise an error, use the @@ -134,8 +133,7 @@ trait IntableRaising: the conversion might raise an error. Any type that conforms to [`Intable`](/mojo/stdlib/builtin/int/Intable) - or `IntableRaising` works with the built-in - [`int()`](/mojo/stdlib/builtin/int/int-function) function. + or `IntableRaising` can construct an `Int`. This trait requires the type to implement the `__int__()` method, which can raise an error. For example: @@ -149,13 +147,12 @@ trait IntableRaising: return self.i ``` - Now you can use the `int()` function to convert a `Foo` to an - `Int`: + Now you can construct an `Int`: ```mojo %# from testing import assert_equal foo = Foo(42) - assert_equal(int(foo), 42) + assert_equal(Int(foo), 42) ``` """ @@ -207,96 +204,6 @@ trait ImplicitlyIntable(Intable): ... -# ===----------------------------------------------------------------------=== # -# int -# ===----------------------------------------------------------------------=== # - - -@always_inline -fn int[T: Intable](value: T) -> Int: - """Get the Int representation of the value. - - Parameters: - T: The Intable type. - - Args: - value: The object to get the integral representation of. - - Returns: - The integral representation of the value. - """ - return value.__int__() - - -@always_inline -fn int[T: IntableRaising](value: T) raises -> Int: - """Get the Int representation of the value. - - Parameters: - T: The Intable type. - - Args: - value: The object to get the integral representation of. - - Returns: - The integral representation of the value. - - Raises: - If the type does not have an integral representation. - """ - return value.__int__() - - -fn int(value: StringSlice, base: Int = 10) raises -> Int: - """Parses and returns the given string as an integer in the given base. - - If base is set to 0, the string is parsed as an Integer literal, with the - following considerations: - - '0b' or '0B' prefix indicates binary (base 2) - - '0o' or '0O' prefix indicates octal (base 8) - - '0x' or '0X' prefix indicates hexadecimal (base 16) - - Without a prefix, it's treated as decimal (base 10) - - Args: - value: A string to be parsed as an integer in the given base. - base: Base used for conversion, value must be between 2 and 36, or 0. - - Returns: - An integer value that represents the string. - - Raises: - If the given string cannot be parsed as an integer value or if an - incorrect base is provided. - - Examples: - >>> int("32") - 32 - >>> int("FF", 16) - 255 - >>> int("0xFF", 0) - 255 - >>> int("0b1010", 0) - 10 - - Notes: - This follows [Python's integer literals]( - https://docs.python.org/3/reference/lexical_analysis.html#integers). - """ - return atol(value, base) - - -fn int(value: UInt) -> Int: - """Get the Int representation of the value. - - Args: - value: The object to get the integral representation of. - - Returns: - The integral representation of the value. - """ - return value.value - - # ===----------------------------------------------------------------------=== # # Int # ===----------------------------------------------------------------------=== # @@ -320,10 +227,10 @@ struct Int( var value: __mlir_type.index """The underlying storage for the integer value.""" - alias MAX = int(Scalar[DType.index].MAX) + alias MAX = Int(Scalar[DType.index].MAX) """Returns the maximum integer value.""" - alias MIN = int(Scalar[DType.index].MIN) + alias MIN = Int(Scalar[DType.index].MIN) """Returns the minimum value of type.""" # ===------------------------------------------------------------------=== # @@ -377,6 +284,43 @@ struct Int( """ self = value.__int__() + @always_inline("nodebug") + @implicit + fn __init__(out self, value: UInt): + """Construct Int from the given UInt value. + + Args: + value: The init value. + """ + self = Self(value.value) + + @always_inline("nodebug") + fn __init__[T: Intable](out self, value: T): + """Get the Int representation of the value. + + Parameters: + T: The Intable type. + + Args: + value: The object to get the integral representation of. + """ + self = value.__int__() + + @always_inline("nodebug") + fn __init__[T: IntableRaising](out self, value: T) raises: + """Get the Int representation of the value. + + Parameters: + T: The Intable type. + + Args: + value: The object to get the integral representation of. + + Raises: + If the type does not have an integral representation. + """ + self = value.__int__() + @always_inline("nodebug") @implicit fn __init__[I: ImplicitlyIntable](out self, value: I): @@ -391,14 +335,39 @@ struct Int( self = value.__as_int__() @always_inline("nodebug") - @implicit - fn __init__(out self, value: UInt): - """Construct Int from the given UInt value. + fn __init__(out self, value: StringSlice, base: UInt = 10) raises: + """Parses and returns the given string as an integer in the given base. + + If base is set to 0, the string is parsed as an Integer literal, with the + following considerations: + - '0b' or '0B' prefix indicates binary (base 2) + - '0o' or '0O' prefix indicates octal (base 8) + - '0x' or '0X' prefix indicates hexadecimal (base 16) + - Without a prefix, it's treated as decimal (base 10) Args: - value: The init value. - """ - self = Self(value.value) + value: A string to be parsed as an integer in the given base. + base: Base used for conversion, value must be between 2 and 36, or 0. + + Raises: + If the given string cannot be parsed as an integer value or if an + incorrect base is provided. + + Examples: + >>> Int("32") + 32 + >>> Int("FF", 16) + 255 + >>> Int("0xFF", 0) + 255 + >>> Int("0b1010", 0) + 10 + + Notes: + This follows [Python's integer literals]( + https://docs.python.org/3/reference/lexical_analysis.html#integers). + """ + self = atol(value, base) # ===------------------------------------------------------------------=== # # Operator dunders diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index fc77581cbd..465a4cefcf 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -224,7 +224,7 @@ fn _snprintf[ var loaded_pack = args.get_loaded_kgen_pack() # FIXME: external_call should handle this - return int( + return Int( __mlir_op.`pop.external_call`[ func = "snprintf".value, variadicType = __mlir_attr[ diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index e307fac5f6..a20b4b31c3 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -589,7 +589,7 @@ struct _ObjectImpl( return if self.is_func(): writer.write( - "Function at address " + hex(int(self.get_as_func().value)) + "Function at address " + hex(Int(self.get_as_func().value)) ) return if self.is_list(): @@ -986,10 +986,10 @@ struct object( return 1 if self._value.get_as_bool() else 0 if self._value.is_int(): - return int(self._value.get_as_int()) + return Int(self._value.get_as_int()) if self._value.is_float(): - return int(self._value.get_as_float()) + return Int(self._value.get_as_float()) raise "object type cannot be converted to an integer" @@ -1833,10 +1833,10 @@ struct object( @always_inline fn _convert_index_to_int(i: object) raises -> Int: if i._value.is_bool(): - return int(i._value.convert_bool_to_int().get_as_int()) + return Int(i._value.convert_bool_to_int().get_as_int()) elif not i._value.is_int(): raise Error("TypeError: string indices must be integers") - return int(i._value.get_as_int()) + return Int(i._value.get_as_int()) @always_inline fn __getitem__(self, i: object) raises -> object: @@ -1862,7 +1862,7 @@ struct object( var char = self._value.get_as_string().data[index] impl.data.init_pointee_move(char) return object(impl) - return self._value.get_list_element(int(i._value.get_as_int())) + return self._value.get_list_element(Int(i._value.get_as_int())) @always_inline fn __getitem__(self, *index: object) raises -> object: diff --git a/stdlib/src/builtin/range.mojo b/stdlib/src/builtin/range.mojo index b011c350d5..14fb9d979b 100644 --- a/stdlib/src/builtin/range.mojo +++ b/stdlib/src/builtin/range.mojo @@ -75,7 +75,7 @@ struct _ZeroStartingRange(Sized, ReversibleRange, _IntIterable): @always_inline fn __getitem__[I: Indexer](self, idx: I) -> Int: - debug_assert(int(idx) < self.__len__(), "index out of range") + debug_assert(Int(idx) < self.__len__(), "index out of range") return index(idx) @always_inline @@ -215,7 +215,7 @@ fn range[type: Intable](end: type) -> _ZeroStartingRange: Returns: The constructed range. """ - return _ZeroStartingRange(int(end)) + return _ZeroStartingRange(Int(end)) @always_inline @@ -231,7 +231,7 @@ fn range[type: IntableRaising](end: type) raises -> _ZeroStartingRange: Returns: The constructed range. """ - return _ZeroStartingRange(int(end)) + return _ZeroStartingRange(Int(end)) @always_inline @@ -249,7 +249,7 @@ fn range[t0: Intable, t1: Intable](start: t0, end: t1) -> _SequentialRange: Returns: The constructed range. """ - return _SequentialRange(int(start), int(end)) + return _SequentialRange(Int(start), Int(end)) @always_inline @@ -269,7 +269,7 @@ fn range[ Returns: The constructed range. """ - return _SequentialRange(int(start), int(end)) + return _SequentialRange(Int(start), Int(end)) @always_inline @@ -313,7 +313,7 @@ fn range[ Returns: The constructed range. """ - return _StridedRange(int(start), int(end), int(step)) + return _StridedRange(Int(start), Int(end), Int(step)) # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index d03a77c431..ec28c1de57 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -1532,7 +1532,7 @@ struct SIMD[type: DType, size: Int]( constrained[ type.is_integral(), "cannot index using a floating point type" ]() - return int(self).value + return Int(self).value @always_inline("nodebug") fn __float__(self) -> Float64: @@ -2165,7 +2165,7 @@ struct SIMD[type: DType, size: Int]( ``` result = SIMD[Self.type, mask_size]() for i in range(mask_size): - result[i] = self[int(mask[i])] + result[i] = self[Int(mask[i])] ``` Parameters: @@ -2223,7 +2223,7 @@ struct SIMD[type: DType, size: Int]( @parameter for i in range(0, mask_size): - result[i] = self[int(mask[i])] + result[i] = self[Int(mask[i])] return result @always_inline @@ -2717,12 +2717,12 @@ struct SIMD[type: DType, size: Int]( @parameter if type is DType.bool: - return int(self.cast[DType.uint8]().reduce_add()) + return Int(self.cast[DType.uint8]().reduce_add()) else: constrained[ type.is_integral(), "Expected either integral or bool type" ]() - return int(pop_count(self).reduce_add()) + return Int(pop_count(self).reduce_add()) # ===------------------------------------------------------------------=== # # select diff --git a/stdlib/src/builtin/sort.mojo b/stdlib/src/builtin/sort.mojo index 00243d6f80..6cce447b5c 100644 --- a/stdlib/src/builtin/sort.mojo +++ b/stdlib/src/builtin/sort.mojo @@ -163,12 +163,12 @@ fn _heap_sort[ @always_inline fn _estimate_initial_height(size: Int) -> Int: # Compute the log2 of the size rounded upward. - var log2 = int( + var log2 = Int( (bitwidthof[DType.index]() - 1) ^ count_leading_zeros(size | 1) ) # The number 1.3 was chosen by experimenting the max stack size for random # input. This also depends on insertion_sort_threshold - return max(2, int(ceil(1.3 * log2))) + return max(2, Int(ceil(1.3 * log2))) @always_inline diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index a6fa37dd03..72f1207149 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -363,7 +363,7 @@ struct StringLiteral( Returns: An integer value that represents the string, or otherwise raises. """ - return int(self.as_string_slice()) + return Int(self.as_string_slice()) @always_inline fn __float__(self) raises -> Float64: diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 9761dec244..31ea5bc0e4 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -320,16 +320,16 @@ struct _DictIndex: fn get_index(self, reserved: Int, slot: UInt64) -> Int: if reserved <= 128: var data = self.data.bitcast[Int8]() - return int(data.load(slot & (reserved - 1))) + return Int(data.load(slot & (reserved - 1))) elif reserved <= 2**16 - 2: var data = self.data.bitcast[Int16]() - return int(data.load(slot & (reserved - 1))) + return Int(data.load(slot & (reserved - 1))) elif reserved <= 2**32 - 2: var data = self.data.bitcast[Int32]() - return int(data.load(slot & (reserved - 1))) + return Int(data.load(slot & (reserved - 1))) else: var data = self.data.bitcast[Int64]() - return int(data.load(slot & (reserved - 1))) + return Int(data.load(slot & (reserved - 1))) fn set_index(mut self, reserved: Int, slot: UInt64, value: Int): if reserved <= 128: @@ -1012,7 +1012,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( fn _next_index_slot(self, mut slot: UInt64, mut perturb: UInt64): alias PERTURB_SHIFT = 5 perturb >>= PERTURB_SHIFT - slot = ((5 * slot) + int(perturb + 1)) & (self._reserved() - 1) + slot = ((5 * slot) + Int(perturb + 1)) & (self._reserved() - 1) fn _find_empty_index(self, hash: UInt64) -> UInt64: var slot = hash & (self._reserved() - 1) diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index f24d4dbe75..c016632e95 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -241,7 +241,7 @@ struct InlineArray[ return self.unsafe_get(idx) else: var normalized_index = normalize_index["InlineArray"]( - int(idx), self + Int(idx), self ) return self.unsafe_get(normalized_index) @@ -258,16 +258,16 @@ struct InlineArray[ Returns: A reference to the item at the given index. """ - constrained[-size <= int(idx) < size, "Index must be within bounds."]() + constrained[-size <= Int(idx) < size, "Index must be within bounds."]() @parameter if _type_is_eq[I, UInt](): return self.unsafe_get(idx) else: - var normalized_idx = int(idx) + var normalized_idx = Int(idx) @parameter - if int(idx) < 0: + if Int(idx) < 0: normalized_idx += size return self.unsafe_get(normalized_idx) @@ -310,9 +310,9 @@ struct InlineArray[ """ var i = index(idx) debug_assert( - 0 <= int(i) < size, + 0 <= Int(i) < size, " InlineArray.unsafe_get() index out of bounds: ", - int(idx), + Int(idx), " should be less than: ", size, ) diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index 733fd917b3..2d7b16d428 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -147,7 +147,7 @@ struct InlineList[ElementType: CollectionElementNew, capacity: Int = 16](Sized): Returns: A reference to the item at the given index. """ - var index = int(idx) + var index = Int(idx) debug_assert( -self._size <= index < self._size, "Index must be within bounds." ) @@ -244,7 +244,7 @@ struct InlineList[ElementType: CollectionElementNew, capacity: Int = 16](Sized): var count = 0 for e in self: - count += int(rebind[C](e[]) == value) + count += Int(rebind[C](e[]) == value) return count fn append(mut self, owned value: ElementType): diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 561079a10e..10456b1323 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -504,7 +504,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Except for 0 capacity where it sets 1. """ if self.size >= self.capacity: - self._realloc(self.capacity * 2 | int(self.capacity == 0)) + self._realloc(self.capacity * 2 | Int(self.capacity == 0)) (self.data + self.size).init_pointee_move(value^) self.size += 1 @@ -868,7 +868,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( if _type_is_eq[I, UInt](): return (self.data + idx)[] else: - var normalized_idx = int(idx) + var normalized_idx = Int(idx) debug_assert( -self.size <= normalized_idx < self.size, "index: ", diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index f9b26ca604..980211ae22 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -49,7 +49,7 @@ fn _to_index[lookup: List[UInt32, **_]](rune: Int) -> Int: while length > 1: var half = length >> 1 length -= half - cursor += int(b.load(cursor + half - 1) < x) * half + cursor += Int(b.load(cursor + half - 1) < x) * half return cursor if b.load(cursor) == x else -1 @@ -122,16 +122,16 @@ fn _ord(_p: UnsafePointer[UInt8]) -> (Int, Int): var p = _p var b1 = p[] if (b1 >> 7) == 0: # This is 1 byte ASCII char - return int(b1), 1 + return Int(b1), 1 var num_bytes = count_leading_zeros(~b1) - var shift = int((6 * (num_bytes - 1))) + var shift = Int((6 * (num_bytes - 1))) var b1_mask = 0b11111111 >> (num_bytes + 1) - var result = int(b1 & b1_mask) << shift + var result = Int(b1 & b1_mask) << shift for _ in range(1, num_bytes): p += 1 shift -= 6 - result |= int(p[] & 0b00111111) << shift - return result, int(num_bytes) + result |= Int(p[] & 0b00111111) << shift + return result, Int(num_bytes) fn _write_rune(rune: UInt32, p: UnsafePointer[UInt8]) -> Int: @@ -148,7 +148,7 @@ fn _write_rune(rune: UInt32, p: UnsafePointer[UInt8]) -> Int: ) var values = SIMD[DType.uint32, 4](val) var mask = values > sizes - return int(mask.cast[DType.uint8]().reduce_add()) + return Int(mask.cast[DType.uint8]().reduce_add()) var num_bytes = _utf8_len(rune) var shift = 6 * (num_bytes - 1) diff --git a/stdlib/src/collections/string/format.mojo b/stdlib/src/collections/string/format.mojo index abaeb26c10..1cd5cd968e 100644 --- a/stdlib/src/collections/string/format.mojo +++ b/stdlib/src/collections/string/format.mojo @@ -299,13 +299,13 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): else: new_idx += 1 - var extra = int(new_idx < field_len) + var extra = Int(new_idx < field_len) var fmt_field = _build_slice(field_ptr, new_idx + extra, field_len) self.format_spec = _FormatSpec.parse(fmt_field) - var w = int(self.format_spec.value().width) if self.format_spec else 0 + var w = Int(self.format_spec.value().width) if self.format_spec else 0 # fully guessing the byte width here to be at least 8 bytes per entry # minus the length of the whole format specification - total_estimated_entry_byte_width += 8 * int(w > 0) + w - (field_len + 2) + total_estimated_entry_byte_width += 8 * Int(w > 0) + w - (field_len + 2) if field.byte_length() == 0: # an empty field, so it's automatic indexing @@ -318,7 +318,7 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): # field is a number for manual indexing: # TODO: add support for "My name is {0.name}".format(Person(name="Fred")) # TODO: add support for "My name is {0[name]}".format({"name": "Fred"}) - var number = int(field) + var number = Int(field) self.field = number if number >= len_pos_args or number < 0: raised_manual_index = number diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 2ca4d03112..7f766c5897 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -75,24 +75,24 @@ fn ord(s: StringSlice) -> Int: var b1 = p[] if (b1 >> 7) == 0: # This is 1 byte ASCII char debug_assert(s.byte_length() == 1, "input string length must be 1") - return int(b1) + return Int(b1) var num_bytes = count_leading_zeros(~b1) debug_assert( - s.byte_length() == int(num_bytes), "input string must be one character" + s.byte_length() == Int(num_bytes), "input string must be one character" ) debug_assert( - 1 < int(num_bytes) < 5, "invalid UTF-8 byte ", b1, " at index 0" + 1 < Int(num_bytes) < 5, "invalid UTF-8 byte ", b1, " at index 0" ) - var shift = int((6 * (num_bytes - 1))) + var shift = Int((6 * (num_bytes - 1))) var b1_mask = 0b11111111 >> (num_bytes + 1) - var result = int(b1 & b1_mask) << shift + var result = Int(b1 & b1_mask) << shift for i in range(1, num_bytes): p += 1 debug_assert( p[] >> 6 == 0b00000010, "invalid UTF-8 byte ", b1, " at index ", i ) shift -= 6 - result |= int(p[] & 0b00111111) << shift + result |= Int(p[] & 0b00111111) << shift return result @@ -298,7 +298,7 @@ fn atol(str_slice: StringSlice, base: Int = 10) raises -> Int: # underscores under the conditions they have a prefix var was_last_digit_underscore = not (real_base in (2, 8, 16) and has_prefix) for pos in range(start, str_len): - var ord_current = int(buff[pos]) + var ord_current = Int(buff[pos]) if ord_current == ord_underscore: if was_last_digit_underscore: raise Error(_str_to_base_error(base, str_slice)) @@ -362,7 +362,7 @@ fn _trim_and_handle_sign(str_slice: StringSlice, str_len: Int) -> (Int, Bool): start += 1 var p: Bool = buff[start] == ord("+") var n: Bool = buff[start] == ord("-") - return start + (int(p) or int(n)), n + return start + (Int(p) or Int(n)), n @always_inline @@ -388,7 +388,7 @@ fn _handle_base_prefix( var start = pos var buff = str_slice.unsafe_ptr() if start + 1 < str_len: - var prefix_char = chr(int(buff[start + 1])) + var prefix_char = chr(Int(buff[start + 1])) if buff[start] == ord("0") and ( (base == 2 and (prefix_char == "b" or prefix_char == "B")) or (base == 8 and (prefix_char == "o" or prefix_char == "O")) @@ -498,7 +498,7 @@ fn atof(str_slice: StringSlice) raises -> Float64: # read before dot for pos in range(start, str_len): if ord_0 <= buff[pos] <= ord_9: - result = result * 10.0 + int(buff[pos] - ord_0) + result = result * 10.0 + Int(buff[pos] - ord_0) start += 1 else: break @@ -507,7 +507,7 @@ fn atof(str_slice: StringSlice) raises -> Float64: start += 1 for pos in range(start, str_len): if ord_0 <= buff[pos] <= ord_9: - result = result * 10.0 + int(buff[pos] - ord_0) + result = result * 10.0 + Int(buff[pos] - ord_0) exponent -= 1 else: break @@ -525,7 +525,7 @@ fn atof(str_slice: StringSlice) raises -> Float64: sign = -1 elif ord_0 <= buff[start] <= ord_9: has_number = True - shift = shift * 10 + int(buff[pos] - ord_0) + shift = shift * 10 + Int(buff[pos] - ord_0) else: break start += 1 @@ -568,7 +568,7 @@ fn isdigit(c: UInt8) -> Bool: """ alias ord_0 = ord("0") alias ord_9 = ord("9") - return ord_0 <= int(c) <= ord_9 + return ord_0 <= Int(c) <= ord_9 # ===----------------------------------------------------------------------=== # @@ -594,7 +594,7 @@ fn isupper(c: UInt8) -> Bool: fn _is_ascii_uppercase(c: UInt8) -> Bool: alias ord_a = ord("A") alias ord_z = ord("Z") - return ord_a <= int(c) <= ord_z + return ord_a <= Int(c) <= ord_z # ===----------------------------------------------------------------------=== # @@ -620,7 +620,7 @@ fn islower(c: UInt8) -> Bool: fn _is_ascii_lowercase(c: UInt8) -> Bool: alias ord_a = ord("a") alias ord_z = ord("z") - return ord_a <= int(c) <= ord_z + return ord_a <= Int(c) <= ord_z # ===----------------------------------------------------------------------=== # @@ -699,7 +699,7 @@ fn isprintable(c: UInt8) -> Bool: """ alias ord_space = ord(" ") alias ord_tilde = ord("~") - return ord_space <= int(c) <= ord_tilde + return ord_space <= Int(c) <= ord_tilde # ===----------------------------------------------------------------------=== # @@ -1137,7 +1137,7 @@ struct String( var buffer = Self._buffer_type(capacity=sum_len + 1) var ptr = buffer.unsafe_ptr() memcpy(ptr, lhs_ptr, lhs_len) - memcpy(ptr + lhs_len, rhs_ptr, rhs_len + int(rhs_has_null)) + memcpy(ptr + lhs_len, rhs_ptr, rhs_len + Int(rhs_has_null)) buffer.size = sum_len + 1 @parameter @@ -1182,7 +1182,7 @@ struct String( var sum_len = s_len + o_len self._buffer.reserve(sum_len + 1) var s_ptr = self.unsafe_ptr() - memcpy(s_ptr + s_len, o_ptr, o_len + int(has_null)) + memcpy(s_ptr + s_len, o_ptr, o_len + Int(has_null)) self._buffer.size = sum_len + 1 @parameter @@ -1483,7 +1483,7 @@ struct String( This does not include the trailing null terminator in the count. """ var length = len(self._buffer) - return length - int(length > 0) + return length - Int(length > 0) fn _steal_ptr(mut self) -> UnsafePointer[UInt8]: """Transfer ownership of pointer to the underlying memory. @@ -1652,7 +1652,7 @@ struct String( fn num_bytes(b: UInt8) -> Int: var flipped = ~b - return int(count_leading_zeros(flipped) + (flipped >> 7)) + return Int(count_leading_zeros(flipped) + (flipped >> 7)) var output = List[String]() var str_byte_len = self.byte_length() - 1 @@ -1736,7 +1736,7 @@ struct String( res.reserve(self_len + (old_len - new_len) * occurrences + 1) for _ in range(occurrences): - var curr_offset = int(self_ptr) - int(self_start) + var curr_offset = Int(self_ptr) - Int(self_start) var idx = self.find(old, curr_offset) @@ -2188,10 +2188,10 @@ fn _calc_initial_buffer_size_int32(n0: Int) -> Int: 42949672960, ) var n = UInt32(n0) - var log2 = int( + var log2 = Int( (bitwidthof[DType.uint32]() - 1) ^ count_leading_zeros(n | 1) ) - return (n0 + lookup_table[int(log2)]) >> 32 + return (n0 + lookup_table[Int(log2)]) >> 32 fn _calc_initial_buffer_size_int64(n0: UInt64) -> Int: @@ -2230,7 +2230,7 @@ fn _calc_initial_buffer_size[type: DType](n0: Scalar[type]) -> Int: @parameter if is_32bit_system or bitwidthof[type]() <= 32: - return sign + _calc_initial_buffer_size_int32(int(n)) + 1 + return sign + _calc_initial_buffer_size_int32(Int(n)) + 1 else: return ( sign diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 614f4dd472..129df13b88 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -54,11 +54,11 @@ fn _count_utf8_continuation_bytes(span: Span[Byte]) -> Int: for _ in range(rest // s): var vec = (ptr + processed).load[width=s]() var comp = (vec & 0b1100_0000) == 0b1000_0000 - amnt += int(comp.cast[DType.uint8]().reduce_add()) + amnt += Int(comp.cast[DType.uint8]().reduce_add()) processed += s for i in range(num_bytes - processed): - amnt += int((ptr[processed + i] & 0b1100_0000) == 0b1000_0000) + amnt += Int((ptr[processed + i] & 0b1100_0000) == 0b1000_0000) return amnt @@ -68,7 +68,7 @@ fn _unicode_codepoint_utf8_byte_length(c: Int) -> Int: 0 <= c <= 0x10FFFF, "Value: ", c, " is not a valid Unicode code point" ) alias sizes = SIMD[DType.int32, 4](0, 0b0111_1111, 0b0111_1111_1111, 0xFFFF) - return int((sizes < c).cast[DType.uint8]().reduce_add()) + return Int((sizes < c).cast[DType.uint8]().reduce_add()) @always_inline @@ -80,7 +80,7 @@ fn _utf8_first_byte_sequence_length(b: Byte) -> Int: (b & 0b1100_0000) != 0b1000_0000, "Function does not work correctly if given a continuation byte.", ) - return int(count_leading_zeros(~b)) + int(b < 0b1000_0000) + return Int(count_leading_zeros(~b)) + Int(b < 0b1000_0000) fn _shift_unicode_to_utf8(ptr: UnsafePointer[UInt8], c: Int, num_bytes: Int): @@ -574,7 +574,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ var len1 = len(self) var len2 = len(rhs) - return int(len1 < len2) > _memcmp_impl_unconstrained( + return Int(len1 < len2) > _memcmp_impl_unconstrained( self.unsafe_ptr(), rhs.unsafe_ptr(), min(len1, len2) ) @@ -996,7 +996,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( if not loc: return -1 - return int(loc) - int(self.unsafe_ptr()) + return Int(loc) - Int(self.unsafe_ptr()) fn rfind(self, substr: StringSlice, start: Int = 0) -> Int: """Finds the offset of the last occurrence of `substr` starting at @@ -1029,7 +1029,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( if not loc: return -1 - return int(loc) - int(self.unsafe_ptr()) + return Int(loc) - Int(self.unsafe_ptr()) fn isspace(self) -> Bool: """Determines whether every character in the given StringSlice is a @@ -1148,17 +1148,17 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( var isnewline = unlikely( _is_newline_char(ptr, eol_start, b0, char_len) ) - var char_end = int(isnewline) * (eol_start + char_len) - var next_idx = char_end * int(char_end < length) + var char_end = Int(isnewline) * (eol_start + char_len) + var next_idx = char_end * Int(char_end < length) var is_r_n = b0 == `\r` and next_idx != 0 and ptr[ next_idx ] == `\n` - eol_length = int(isnewline) * char_len + int(is_r_n) + eol_length = Int(isnewline) * char_len + Int(is_r_n) if isnewline: break eol_start += char_len - var str_len = eol_start - offset + int(keepends) * eol_length + var str_len = eol_start - offset + Int(keepends) * eol_length var s = StringSlice[O](ptr=ptr + offset, length=str_len) output.append(s) offset = eol_start + eol_length diff --git a/stdlib/src/collections/vector.mojo b/stdlib/src/collections/vector.mojo index 1466c23f9d..6beb0ef3b6 100644 --- a/stdlib/src/collections/vector.mojo +++ b/stdlib/src/collections/vector.mojo @@ -217,7 +217,7 @@ struct InlinedFixedVector[ Returns: The element at the given index. """ - var index = int(idx) + var index = Int(idx) debug_assert( -self.current_size <= index < self.current_size, "index must be within bounds", @@ -244,7 +244,7 @@ struct InlinedFixedVector[ idx: The index of the element. value: The value to assign. """ - var index = int(idx) + var index = Int(idx) debug_assert( -self.current_size <= index < self.current_size, "index must be within bounds", diff --git a/stdlib/src/hashlib/hash.mojo b/stdlib/src/hashlib/hash.mojo index 6b5c028796..315cc056bb 100644 --- a/stdlib/src/hashlib/hash.mojo +++ b/stdlib/src/hashlib/hash.mojo @@ -41,11 +41,11 @@ from memory import UnsafePointer, bitcast, memcpy, memset_zero, stack_allocation # Doing so can help prevent DDOS attacks on data structures relying on these # hash functions. See `hash(bytes, n)` documentation for more details. # TODO(27659): This is always 0 right now -# var HASH_SECRET = int(random.random_ui64(0, UInt64.MAX) +# var HASH_SECRET = Int(random.random_ui64(0, UInt64.MAX) fn _init_hash_secret() -> Int: - return int(random.random_ui64(0, UInt64.MAX)) + return Int(random.random_ui64(0, UInt64.MAX)) alias _HASH_SECRET_VALUE = _Global["HASH_SECRET", Int, _init_hash_secret] @@ -173,7 +173,7 @@ fn _hash_simd[type: DType, size: Int](data: SIMD[type, size]) -> UInt: bitcast[int_type, 1](hash_data[i]).cast[DType.uint64](), ) - return int(final_data) + return Int(final_data) fn hash(bytes: UnsafePointer[UInt8], n: Int) -> UInt: diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 836a0b3c0b..81d53289cf 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -243,7 +243,7 @@ fn sqrt[ @parameter for i in range(simd_width): - res[i] = sqrt(int(x[i])) + res[i] = sqrt(Int(x[i])) return res elif is_nvidia_gpu(): diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 51abfb7b39..0cbf1e0d65 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -79,7 +79,7 @@ fn _memcmp_impl_unconstrained[ var s2i = s2.load[width=simd_width](i) var diff = s1i != s2i if any(diff): - var index = int( + var index = Int( diff.select( iota, SIMD[DType.uint8, simd_width](255) ).reduce_min() @@ -90,7 +90,7 @@ fn _memcmp_impl_unconstrained[ var s2i = s2.load[width=simd_width](last) var diff = s1i != s2i if any(diff): - var index = int( + var index = Int( diff.select(iota, SIMD[DType.uint8, simd_width](255)).reduce_min() ) return -1 if s1i[index] < s2i[index] else 1 diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 280cc5fd3d..1cd5f70dc4 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -187,7 +187,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): Args: value: The address space value. """ - self._value = int(value) + self._value = Int(value) @always_inline("nodebug") fn value(self) -> Int: diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 099c798bed..af08b48728 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -182,10 +182,10 @@ struct Span[ """ # TODO: Simplify this with a UInt type. debug_assert( - -self._len <= int(idx) < self._len, "index must be within bounds" + -self._len <= Int(idx) < self._len, "index must be within bounds" ) # TODO(MSTDL-1086): optimize away SIMD/UInt normalization check - var offset = int(idx) + var offset = Int(idx) if offset < 0: offset += len(self) return self._data[offset] diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index cf6c8d3cc6..c2aefe847e 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -318,7 +318,7 @@ struct UnsafePointer[ Returns: True if the two pointers are equal and False otherwise. """ - return int(self) == int(rhs) + return Int(self) == Int(rhs) @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") @@ -344,7 +344,7 @@ struct UnsafePointer[ Returns: True if this pointer represents a lower address and False otherwise. """ - return int(self) < int(rhs) + return Int(self) < Int(rhs) @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") @@ -358,7 +358,7 @@ struct UnsafePointer[ Returns: True if this pointer represents a lower address and False otherwise. """ - return int(self) <= int(rhs) + return Int(self) <= Int(rhs) @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") @@ -372,7 +372,7 @@ struct UnsafePointer[ True if this pointer represents a higher than or equal address and False otherwise. """ - return int(self) > int(rhs) + return Int(self) > Int(rhs) @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") @@ -387,7 +387,7 @@ struct UnsafePointer[ True if this pointer represents a higher than or equal address and False otherwise. """ - return int(self) >= int(rhs) + return Int(self) >= Int(rhs) # ===-------------------------------------------------------------------===# # Trait implementations @@ -400,7 +400,7 @@ struct UnsafePointer[ Returns: Whether the pointer is null. """ - return int(self) != 0 + return Int(self) != 0 @always_inline fn __as_bool__(self) -> Bool: @@ -427,7 +427,7 @@ struct UnsafePointer[ Returns: The string representation of the pointer. """ - return hex(int(self)) + return hex(Int(self)) @no_inline fn write_to[W: Writer](self, mut writer: W): @@ -569,7 +569,7 @@ struct UnsafePointer[ The loaded value. """ constrained[offset.type.is_integral(), "offset must be integer"]() - return self.offset(int(offset)).load[ + return self.offset(Int(offset)).load[ width=width, alignment=alignment, volatile=volatile, @@ -701,7 +701,7 @@ struct UnsafePointer[ """ constrained[mut, _must_be_mut_err]() constrained[offset_type.is_integral(), "offset must be integer"]() - self.offset(int(offset))._store[alignment=alignment, volatile=volatile]( + self.offset(Int(offset))._store[alignment=alignment, volatile=volatile]( val ) @@ -736,7 +736,7 @@ struct UnsafePointer[ """ constrained[mut, _must_be_mut_err]() constrained[offset_type.is_integral(), "offset must be integer"]() - self.offset(int(offset))._store[alignment=alignment, volatile=volatile]( + self.offset(Int(offset))._store[alignment=alignment, volatile=volatile]( val ) @@ -829,7 +829,7 @@ struct UnsafePointer[ Returns: A vector which is stride loaded. """ - return strided_load(self, int(stride), SIMD[DType.bool, width](True)) + return strided_load(self, Int(stride), SIMD[DType.bool, width](True)) @always_inline("nodebug") fn strided_store[ @@ -853,7 +853,7 @@ struct UnsafePointer[ stride: The stride between stores. """ constrained[mut, _must_be_mut_err]() - strided_store(val, self, int(stride), True) + strided_store(val, self, Int(stride), True) @always_inline("nodebug") fn gather[ @@ -907,7 +907,7 @@ struct UnsafePointer[ "alignment must be a power of two integer value", ]() - var base = offset.cast[DType.index]().fma(sizeof[type](), int(self)) + var base = offset.cast[DType.index]().fma(sizeof[type](), Int(self)) return gather(base, mask, default, alignment) @always_inline("nodebug") @@ -962,7 +962,7 @@ struct UnsafePointer[ "alignment must be a power of two integer value", ]() - var base = offset.cast[DType.index]().fma(sizeof[type](), int(self)) + var base = offset.cast[DType.index]().fma(sizeof[type](), Int(self)) scatter(val, base, mask, alignment) @always_inline diff --git a/stdlib/src/os/_linux_aarch64.mojo b/stdlib/src/os/_linux_aarch64.mojo index 9647be1218..f6184d56ab 100644 --- a/stdlib/src/os/_linux_aarch64.mojo +++ b/stdlib/src/os/_linux_aarch64.mojo @@ -88,20 +88,20 @@ struct _c_stat(Stringable): fn _to_stat_result(self) -> stat_result: return stat_result( - st_dev=int(self.st_dev), - st_mode=int(self.st_mode), - st_nlink=int(self.st_nlink), - st_ino=int(self.st_ino), - st_uid=int(self.st_uid), - st_gid=int(self.st_gid), - st_rdev=int(self.st_rdev), + st_dev=Int(self.st_dev), + st_mode=Int(self.st_mode), + st_nlink=Int(self.st_nlink), + st_ino=Int(self.st_ino), + st_uid=Int(self.st_uid), + st_gid=Int(self.st_gid), + st_rdev=Int(self.st_rdev), st_atimespec=self.st_atimespec, st_ctimespec=self.st_ctimespec, st_mtimespec=self.st_mtimespec, st_birthtimespec=self.st_birthtimespec, - st_size=int(self.st_size), - st_blocks=int(self.st_blocks), - st_blksize=int(self.st_blksize), + st_size=Int(self.st_size), + st_blocks=Int(self.st_blocks), + st_blksize=Int(self.st_blksize), st_flags=0, ) diff --git a/stdlib/src/os/_linux_x86.mojo b/stdlib/src/os/_linux_x86.mojo index 7fec00bfdf..76c1816e81 100644 --- a/stdlib/src/os/_linux_x86.mojo +++ b/stdlib/src/os/_linux_x86.mojo @@ -86,20 +86,20 @@ struct _c_stat(Stringable): fn _to_stat_result(self) -> stat_result: return stat_result( - st_dev=int(self.st_dev), - st_mode=int(self.st_mode), - st_nlink=int(self.st_nlink), - st_ino=int(self.st_ino), - st_uid=int(self.st_uid), - st_gid=int(self.st_gid), - st_rdev=int(self.st_rdev), + st_dev=Int(self.st_dev), + st_mode=Int(self.st_mode), + st_nlink=Int(self.st_nlink), + st_ino=Int(self.st_ino), + st_uid=Int(self.st_uid), + st_gid=Int(self.st_gid), + st_rdev=Int(self.st_rdev), st_atimespec=self.st_atimespec, st_ctimespec=self.st_ctimespec, st_mtimespec=self.st_mtimespec, st_birthtimespec=self.st_birthtimespec, - st_size=int(self.st_size), - st_blocks=int(self.st_blocks), - st_blksize=int(self.st_blksize), + st_size=Int(self.st_size), + st_blocks=Int(self.st_blocks), + st_blksize=Int(self.st_blksize), st_flags=0, ) diff --git a/stdlib/src/os/_macos.mojo b/stdlib/src/os/_macos.mojo index 1d1a7d11d3..b23a5f713d 100644 --- a/stdlib/src/os/_macos.mojo +++ b/stdlib/src/os/_macos.mojo @@ -93,21 +93,21 @@ struct _c_stat(Stringable): fn _to_stat_result(self) -> stat_result: return stat_result( - st_dev=int(self.st_dev), - st_mode=int(self.st_mode), - st_nlink=int(self.st_nlink), - st_ino=int(self.st_ino), - st_uid=int(self.st_uid), - st_gid=int(self.st_gid), - st_rdev=int(self.st_rdev), + st_dev=Int(self.st_dev), + st_mode=Int(self.st_mode), + st_nlink=Int(self.st_nlink), + st_ino=Int(self.st_ino), + st_uid=Int(self.st_uid), + st_gid=Int(self.st_gid), + st_rdev=Int(self.st_rdev), st_atimespec=self.st_atimespec, st_ctimespec=self.st_ctimespec, st_mtimespec=self.st_mtimespec, st_birthtimespec=self.st_birthtimespec, - st_size=int(self.st_size), - st_blocks=int(self.st_blocks), - st_blksize=int(self.st_blksize), - st_flags=int(self.st_flags), + st_size=Int(self.st_size), + st_blocks=Int(self.st_blocks), + st_blksize=Int(self.st_blksize), + st_flags=Int(self.st_flags), ) diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index 3a8baa7aee..cf44c53814 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -201,7 +201,7 @@ fn getuid() -> Int: constrained[ not os_is_windows(), "operating system must be Linux or macOS" ]() - return int(external_call["getuid", UInt32]()) + return Int(external_call["getuid", UInt32]()) # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 97a9737635..60ea06e96b 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -52,22 +52,22 @@ fn _constrain_unix(): fn _get_stat_st_mode(path: String) raises -> Int: @parameter if os_is_macos(): - return int(_stat_macos(path).st_mode) + return Int(_stat_macos(path).st_mode) elif has_neon(): - return int(_stat_linux_arm(path).st_mode) + return Int(_stat_linux_arm(path).st_mode) else: - return int(_stat_linux_x86(path).st_mode) + return Int(_stat_linux_x86(path).st_mode) @always_inline fn _get_lstat_st_mode(path: String) raises -> Int: @parameter if os_is_macos(): - return int(_lstat_macos(path).st_mode) + return Int(_lstat_macos(path).st_mode) elif has_neon(): - return int(_lstat_linux_arm(path).st_mode) + return Int(_lstat_linux_arm(path).st_mode) else: - return int(_lstat_linux_x86(path).st_mode) + return Int(_lstat_linux_x86(path).st_mode) # ===----------------------------------------------------------------------=== # @@ -489,7 +489,7 @@ fn _is_shell_special_variable(byte: Byte) -> Bool: ord("8"), ord("9"), ) - return int(byte) in shell_variables + return Int(byte) in shell_variables fn _is_alphanumeric(byte: Byte) -> Bool: @@ -501,7 +501,7 @@ fn _is_alphanumeric(byte: Byte) -> Bool: Returns: True if the byte is an ASCII letter, number, or underscore and False otherwise. """ - var b = int(byte) + var b = Int(byte) return ( b == ord("_") or ord("0") <= b diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 50c1cb8d1b..75b88c8700 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -60,7 +60,6 @@ from builtin.int import ( ImplicitlyIntable, IntableRaising, index, - int, ) from builtin.int_literal import IntLiteral from builtin.io import input, print diff --git a/stdlib/src/pwd/_linux.mojo b/stdlib/src/pwd/_linux.mojo index 2c485aa5e7..3f1d3b4460 100644 --- a/stdlib/src/pwd/_linux.mojo +++ b/stdlib/src/pwd/_linux.mojo @@ -37,8 +37,8 @@ fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: return Passwd( pw_name=String(c_pwuid.pw_name), pw_passwd=String(c_pwuid.pw_passwd), - pw_uid=int(c_pwuid.pw_uid), - pw_gid=int(c_pwuid.pw_gid), + pw_uid=Int(c_pwuid.pw_uid), + pw_gid=Int(c_pwuid.pw_gid), pw_gecos=String(c_pwuid.pw_gecos), pw_dir=String(c_pwuid.pw_dir), pw_shell=String(c_pwuid.pw_shell), diff --git a/stdlib/src/pwd/_macos.mojo b/stdlib/src/pwd/_macos.mojo index 5958ab1a58..348a4b1377 100644 --- a/stdlib/src/pwd/_macos.mojo +++ b/stdlib/src/pwd/_macos.mojo @@ -41,8 +41,8 @@ fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: var passwd = Passwd( pw_name=String(c_pwuid.pw_name), pw_passwd=String(c_pwuid.pw_passwd), - pw_uid=int(c_pwuid.pw_uid), - pw_gid=int(c_pwuid.pw_gid), + pw_uid=Int(c_pwuid.pw_uid), + pw_gid=Int(c_pwuid.pw_gid), pw_gecos=String(c_pwuid.pw_gecos), pw_dir=String(c_pwuid.pw_dir), pw_shell=String(c_pwuid.pw_shell), diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index d047c84b3d..e247533f6f 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -175,7 +175,7 @@ struct PyObjectPtr: Returns: Bool: True if the pointers are equal, False otherwise. """ - return int(self.unsized_obj_ptr) == int(rhs.unsized_obj_ptr) + return Int(self.unsized_obj_ptr) == Int(rhs.unsized_obj_ptr) fn __ne__(self, rhs: PyObjectPtr) -> Bool: """Compare two PyObjectPtr for inequality. @@ -233,16 +233,16 @@ struct PyObjectPtr: Returns: Bool: True if the pointer is null, False otherwise. """ - return int(self.unsized_obj_ptr) == 0 + return Int(self.unsized_obj_ptr) == 0 - # TODO: Consider removing this and inlining int(p.value) into callers + # TODO: Consider removing this and inlining Int(p.value) into callers fn _get_ptr_as_int(self) -> Int: """Get the pointer value as an integer. Returns: Int: The integer representation of the pointer. """ - return int(self.unsized_obj_ptr) + return Int(self.unsized_obj_ptr) @value @@ -1469,13 +1469,13 @@ struct CPython: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_Length). """ - return int(self.lib.call["PyObject_Length", Int](obj)) + return Int(self.lib.call["PyObject_Length", Int](obj)) fn PyObject_Hash(mut self, obj: PyObjectPtr) -> Int: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_Hash). """ - return int(self.lib.call["PyObject_Hash", Int](obj)) + return Int(self.lib.call["PyObject_Hash", Int](obj)) fn PyObject_GetIter( mut self, traversable_py_object: PyObjectPtr diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index 7492ce0c40..4abc013786 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -364,7 +364,7 @@ struct PythonObject( value: The boolean value. """ cpython = _get_global_python_itf().cpython() - self.py_object = cpython.PyBool_FromLong(int(value)) + self.py_object = cpython.PyBool_FromLong(Int(value)) @implicit fn __init__(out self, integer: Int): @@ -392,7 +392,7 @@ struct PythonObject( @parameter if dt is DType.bool: - self.py_object = cpython.PyBool_FromLong(int(value)) + self.py_object = cpython.PyBool_FromLong(Int(value)) elif dt.is_integral(): int_val = value.cast[DType.index]().value self.py_object = cpython.PyLong_FromSsize_t(int_val) @@ -1573,7 +1573,7 @@ struct PythonObject( Returns: An `UnsafePointer` for the underlying Python data. """ - var tmp = int(self) + var tmp = Int(self) var result = UnsafePointer.address_of(tmp).bitcast[ UnsafePointer[Scalar[type]] ]()[] diff --git a/stdlib/src/random/random.mojo b/stdlib/src/random/random.mojo index 03d2f57275..96c6fdb6dd 100644 --- a/stdlib/src/random/random.mojo +++ b/stdlib/src/random/random.mojo @@ -236,5 +236,5 @@ fn shuffle[T: CollectionElement, //](mut list: List[T]): T: The type of element in the List. """ for i in reversed(range(len(list))): - var j = int(random_ui64(0, i)) + var j = Int(random_ui64(0, i)) list.swap_elements(i, j) diff --git a/stdlib/src/stat/stat.mojo b/stdlib/src/stat/stat.mojo index 3f17248e9a..c9ffba910b 100644 --- a/stdlib/src/stat/stat.mojo +++ b/stdlib/src/stat/stat.mojo @@ -50,7 +50,7 @@ fn S_ISLNK[intable: Intable](mode: intable) -> Bool: Returns: True if the mode is a symlink and False otherwise. """ - return (int(mode) & S_IFMT) == S_IFLNK + return (Int(mode) & S_IFMT) == S_IFLNK fn S_ISREG[intable: Intable](mode: intable) -> Bool: @@ -66,7 +66,7 @@ fn S_ISREG[intable: Intable](mode: intable) -> Bool: Returns: True if the mode is a regular file and False otherwise. """ - return (int(mode) & S_IFMT) == S_IFREG + return (Int(mode) & S_IFMT) == S_IFREG fn S_ISDIR[intable: Intable](mode: intable) -> Bool: @@ -82,7 +82,7 @@ fn S_ISDIR[intable: Intable](mode: intable) -> Bool: Returns: True if the mode is a directory and False otherwise. """ - return (int(mode) & S_IFMT) == S_IFDIR + return (Int(mode) & S_IFMT) == S_IFDIR fn S_ISCHR[intable: Intable](mode: intable) -> Bool: @@ -98,7 +98,7 @@ fn S_ISCHR[intable: Intable](mode: intable) -> Bool: Returns: True if the mode is a character device and False otherwise. """ - return (int(mode) & S_IFMT) == S_IFCHR + return (Int(mode) & S_IFMT) == S_IFCHR fn S_ISBLK[intable: Intable](mode: intable) -> Bool: @@ -114,7 +114,7 @@ fn S_ISBLK[intable: Intable](mode: intable) -> Bool: Returns: True if the mode is a block device and False otherwise. """ - return (int(mode) & S_IFMT) == S_IFBLK + return (Int(mode) & S_IFMT) == S_IFBLK fn S_ISFIFO[intable: Intable](mode: intable) -> Bool: @@ -130,7 +130,7 @@ fn S_ISFIFO[intable: Intable](mode: intable) -> Bool: Returns: True if the mode is a fifo and False otherwise. """ - return (int(mode) & S_IFMT) == S_IFIFO + return (Int(mode) & S_IFMT) == S_IFIFO fn S_ISSOCK[intable: Intable](mode: intable) -> Bool: @@ -146,4 +146,4 @@ fn S_ISSOCK[intable: Intable](mode: intable) -> Bool: Returns: True if the mode is a socket and False otherwise. """ - return (int(mode) & S_IFMT) == S_IFSOCK + return (Int(mode) & S_IFMT) == S_IFSOCK diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo index 351476d78a..7e3f2ca06a 100644 --- a/stdlib/src/sys/_amdgpu.mojo +++ b/stdlib/src/sys/_amdgpu.mojo @@ -555,14 +555,14 @@ struct Header: ]() self._handle[].service = service_id - payload[int(me), 0] = arg0 - payload[int(me), 1] = arg1 - payload[int(me), 2] = arg2 - payload[int(me), 3] = arg3 - payload[int(me), 4] = arg4 - payload[int(me), 5] = arg5 - payload[int(me), 6] = arg6 - payload[int(me), 7] = arg7 + payload[Int(me), 0] = arg0 + payload[Int(me), 1] = arg1 + payload[Int(me), 2] = arg2 + payload[Int(me), 3] = arg3 + payload[Int(me), 4] = arg4 + payload[Int(me), 5] = arg5 + payload[Int(me), 6] = arg6 + payload[Int(me), 7] = arg7 fn get_return_value( mut self, payload: Payload, me: UInt32, low: UInt32 @@ -605,7 +605,7 @@ struct Header: sleep(UInt(1)) - var ptr = payload._handle[].slots[int(me)] + var ptr = payload._handle[].slots[Int(me)] var value0 = ptr[0] var value1 = ptr[1] return value0, value1 diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index 5f6e10d9bf..b25fd15142 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -851,15 +851,15 @@ fn _macos_version() raises -> Tuple[Int, Int, Int]: var patch = 0 if "." in osver: - major = int(osver[: osver.find(".")]) + major = Int(osver[: osver.find(".")]) osver = osver[osver.find(".") + 1 :] if "." in osver: - minor = int(osver[: osver.find(".")]) + minor = Int(osver[: osver.find(".")]) osver = osver[osver.find(".") + 1 :] if "." in osver: - patch = int(osver[: osver.find(".")]) + patch = Int(osver[: osver.find(".")]) return (major, minor, patch) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 8541672871..cdcf3a19ac 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -661,7 +661,7 @@ fn strided_load[ if simd_width == 1: return addr.load() if mask else Scalar[type]() - var offset = int(addr) + stride * sizeof[type]() * math.iota[ + var offset = Int(addr) + stride * sizeof[type]() * math.iota[ DType.index, simd_width ]() var passthrough = SIMD[type, simd_width]() @@ -702,7 +702,7 @@ fn strided_store[ addr.store(value[0]) return - var offset = int(addr) + stride * sizeof[type]() * math.iota[ + var offset = Int(addr) + stride * sizeof[type]() * math.iota[ DType.index, simd_width ]() scatter(value, offset, mask) @@ -880,7 +880,7 @@ fn lane_id() -> UInt: @parameter if is_nvidia_gpu(): return UInt( - int( + Int( llvm_intrinsic[ "llvm.nvvm.read.ptx.sreg.laneid", Int32, @@ -896,7 +896,7 @@ fn lane_id() -> UInt: "llvm.amdgcn.mbcnt.lo", Int32, has_side_effect=False ](none, zero) return UInt( - int( + Int( llvm_intrinsic[ "llvm.amdgcn.mbcnt.hi", Int32, has_side_effect=False ](none, t).cast[DType.uint32]() diff --git a/stdlib/src/sys/terminate.mojo b/stdlib/src/sys/terminate.mojo index 69fae08ca4..fb78b0e59c 100644 --- a/stdlib/src/sys/terminate.mojo +++ b/stdlib/src/sys/terminate.mojo @@ -34,4 +34,4 @@ fn exit[intable: Intable](code: intable): Args: code: The exit code. """ - libc.exit(c_int(int(code))) + libc.exit(c_int(Int(code))) diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index 2ad3b91d60..18b783cffe 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -35,7 +35,7 @@ fn _get_random_name(size: Int = 8) -> String: alias characters = String("abcdefghijklmnopqrstuvwxyz0123456789_") var name_list = List[UInt8](capacity=size + 1) for _ in range(size): - var rand_index = int( + var rand_index = Int( random.random_ui64(0, characters.byte_length() - 1) ) name_list.append(ord(characters[rand_index])) diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index f6221590ac..5ace9e42ce 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -101,7 +101,7 @@ struct _FILETIME: ) + self.dw_low_date_time.cast[ DType.uint64 ]() - windows_to_unix_epoch_offset_ns - return int(interval_count * 100) + return Int(interval_count * 100) @always_inline @@ -123,7 +123,7 @@ fn _gettime_as_nsec_unix(clockid: Int) -> UInt: var ts = _clock_gettime(clockid) return ts.as_nanoseconds() else: - return int( + return Int( external_call["clock_gettime_nsec_np", Int64](Int32(clockid)) ) @@ -132,7 +132,7 @@ fn _gettime_as_nsec_unix(clockid: Int) -> UInt: fn _gpu_clock() -> UInt: """Returns a 64-bit unsigned cycle counter.""" alias asm = "llvm.nvvm.read.ptx.sreg.clock64" if is_nvidia_gpu() else "llvm.amdgcn.s.memtime" - return int(llvm_intrinsic[asm, Int64]()) + return Int(llvm_intrinsic[asm, Int64]()) @always_inline @@ -271,7 +271,7 @@ fn _time_function_windows[ # Note: Windows performance counter resolution is in µs. var elapsed_time_in_ns = (elapsed_ticks * 1_000_000_000) // ticks_per_sec - return int(elapsed_time_in_ns) + return Int(elapsed_time_in_ns) @always_inline @@ -340,8 +340,8 @@ fn sleep(sec: Float64): alias NANOSECONDS_IN_SECOND = 1_000_000_000 var total_secs = floor(sec) var tv_spec = _CTimeSpec( - int(total_secs), - int((sec - total_secs) * NANOSECONDS_IN_SECOND), + Int(total_secs), + Int((sec - total_secs) * NANOSECONDS_IN_SECOND), ) var req = UnsafePointer[_CTimeSpec].address_of(tv_spec) var rem = UnsafePointer[_CTimeSpec]() diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 4e66f31f28..27dc95bbb3 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -390,7 +390,7 @@ struct IndexList[ Returns: The tuple element value. """ - return int(self.data.__getitem__[idx]()) + return Int(self.data.__getitem__[idx]()) @always_inline("nodebug") fn __getitem__[I: Indexer](self, idx: I) -> Int: @@ -405,7 +405,7 @@ struct IndexList[ Returns: The tuple element value. """ - return int(self.data[idx]) + return Int(self.data[idx]) @always_inline("nodebug") fn __setitem__[idx: Int](mut self, val: Int): @@ -452,7 +452,7 @@ struct IndexList[ @parameter for i in range(size): - res[i] = int(self.__getitem__[i]()) + res[i] = Int(self.__getitem__[i]()) return res @always_inline("nodebug") @@ -877,7 +877,7 @@ fn Index[ Returns: The constructed IndexList. """ - return __type_of(result)(int(x)) + return __type_of(result)(Int(x)) @always_inline @@ -901,7 +901,7 @@ fn Index[ Returns: The constructed IndexList. """ - return __type_of(result)(int(x)) + return __type_of(result)(Int(x)) @always_inline @@ -933,7 +933,7 @@ fn Index[ Returns: The constructed IndexList. """ - return __type_of(result)(int(x), int(y)) + return __type_of(result)(Int(x), Int(y)) @always_inline @@ -959,7 +959,7 @@ fn Index[ Returns: The constructed IndexList. """ - return __type_of(result)(int(x), int(y)) + return __type_of(result)(Int(x), Int(y)) @always_inline @@ -995,7 +995,7 @@ fn Index[ Returns: The constructed IndexList. """ - return __type_of(result)(int(x), int(y), int(z)) + return __type_of(result)(Int(x), Int(y), Int(z)) @always_inline @@ -1035,7 +1035,7 @@ fn Index[ Returns: The constructed IndexList. """ - return __type_of(result)(int(x), int(y), int(z), int(w)) + return __type_of(result)(Int(x), Int(y), Int(z), Int(w)) @always_inline @@ -1079,7 +1079,7 @@ fn Index[ Returns: The constructed IndexList. """ - return __type_of(result)(int(x), int(y), int(z), int(w), int(v)) + return __type_of(result)(Int(x), Int(y), Int(z), Int(w), Int(v)) # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/utils/lock.mojo b/stdlib/src/utils/lock.mojo index 6459952b4e..4648552302 100644 --- a/stdlib/src/utils/lock.mojo +++ b/stdlib/src/utils/lock.mojo @@ -136,11 +136,11 @@ struct BlockingScopedLock: """Acquire the lock on entry. This is done by setting the owner of the lock to own address.""" var address = UnsafePointer[Self].address_of(self) - self.lock[].lock(int(address)) + self.lock[].lock(Int(address)) @no_inline fn __exit__(mut self): """Release the lock on exit. Reset the address on the underlying lock.""" var address = UnsafePointer[Self].address_of(self) - _ = self.lock[].unlock(int(address)) + _ = self.lock[].unlock(Int(address)) diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index e1ec498345..4d01169865 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -160,7 +160,7 @@ struct FPUtils[ The sign mask. """ # convert to `Int` first to bypass overflow check - return 1 << int(Self.exponent_width() + Self.mantissa_width()) + return 1 << Int(Self.exponent_width() + Self.mantissa_width()) @staticmethod @always_inline @@ -216,7 +216,7 @@ struct FPUtils[ Returns: An integer representation of the floating-point value. """ - return int(bitcast[Self.integral_type, 1](value)) + return Int(bitcast[Self.integral_type, 1](value)) @staticmethod @always_inline @@ -303,7 +303,7 @@ struct FPUtils[ Returns: The biased exponent as an Int. """ - return int( + return Int( Self.bitcast_to_uint(value) >> Self.mantissa_width() & ((1 << Self.exponent_width()) - 1) ) diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index a24d71c9bc..5bdb71d0b2 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -241,7 +241,7 @@ struct StringRef( Returns: The character at the specified position. """ - return StringRef {data: self.data + int(idx), length: 1} + return StringRef {data: self.data + Int(idx), length: 1} @always_inline fn __eq__(self, rhs: StringRef) -> Bool: @@ -293,7 +293,7 @@ struct StringRef( """ var len1 = len(self) var len2 = len(rhs) - return int(len1 < len2) > _memcmp_impl_unconstrained( + return Int(len1 < len2) > _memcmp_impl_unconstrained( self.data, rhs.data, min(len1, len2) ) @@ -373,8 +373,8 @@ struct StringRef( fn __int__(self) raises -> Int: """Parses the given string as a base-10 integer and returns that value. - For example, `int("19")` returns `19`. If the given string cannot be parsed - as an integer value, an error is raised. For example, `int("hi")` raises an + For example, `Int("19")` returns `19`. If the given string cannot be parsed + as an integer value, an error is raised. For example, `Int("hi")` raises an error. Returns: @@ -523,7 +523,7 @@ struct StringRef( if not loc: return -1 - return int(loc) - int(self.unsafe_ptr()) + return Int(loc) - Int(self.unsafe_ptr()) fn rfind(self, substr: StringRef, start: Int = 0) -> Int: """Finds the offset of the last occurrence of `substr` starting at @@ -556,7 +556,7 @@ struct StringRef( if not loc: return -1 - return int(loc) - int(self.unsafe_ptr()) + return Int(loc) - Int(self.unsafe_ptr()) fn _from_start(self, start: Int) -> StringRef: """Gets the StringRef pointing to the substring after the specified slice start position. @@ -656,7 +656,7 @@ fn _memchr[ var bool_mask = source.load[width=bool_mask_width](i) == first_needle var mask = pack_bits(bool_mask) if mask: - return source + int(i + count_trailing_zeros(mask)) + return source + Int(i + count_trailing_zeros(mask)) for i in range(vectorized_end, len): if source[i] == char: @@ -701,7 +701,7 @@ fn _memmem[ var mask = pack_bits(bool_mask) while mask: - var offset = int(i + count_trailing_zeros(mask)) + var offset = Int(i + count_trailing_zeros(mask)) if memcmp(haystack + offset + 1, needle + 1, needle_len - 1) == 0: return haystack + offset mask = mask & (mask - 1) diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index bedcc02021..489976fdeb 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -506,7 +506,7 @@ def test_rotate_bits_simd(): fn _log2_floor(n: Int) -> Int: - return int(floor(log2(float(n)))) + return Int(floor(log2(float(n)))) def test_log2_floor(): diff --git a/stdlib/test/builtin/test_bfloat16.mojo b/stdlib/test/builtin/test_bfloat16.mojo index 399c2deb27..1b01473741 100644 --- a/stdlib/test/builtin/test_bfloat16.mojo +++ b/stdlib/test/builtin/test_bfloat16.mojo @@ -23,8 +23,8 @@ def test_methods(): assert_equal(BFloat16(4.4) * 0.5, 2.2) assert_equal(BFloat16(4.4) / 0.5, 8.8) - assert_equal(int(BFloat16(3.0)), 3) - assert_equal(int(BFloat16(3.5)), 3) + assert_equal(Int(BFloat16(3.0)), 3) + assert_equal(Int(BFloat16(3.5)), 3) assert_almost_equal(BFloat16(4.4).cast[DType.float32](), 4.40625) assert_equal(BFloat16(3.0).cast[DType.float32](), 3) diff --git a/stdlib/test/builtin/test_bool.mojo b/stdlib/test/builtin/test_bool.mojo index ccde4536d2..2225b795bf 100644 --- a/stdlib/test/builtin/test_bool.mojo +++ b/stdlib/test/builtin/test_bool.mojo @@ -23,8 +23,8 @@ def test_bool_cast_to_int(): assert_equal(False.__int__(), 0) assert_equal(True.__int__(), 1) - assert_equal(int(False), 0) - assert_equal(int(True), 1) + assert_equal(Int(False), 0) + assert_equal(Int(True), 1) def test_bool_none(): diff --git a/stdlib/test/builtin/test_float_literal.mojo b/stdlib/test/builtin/test_float_literal.mojo index 6746b140b6..1723e8cd9c 100644 --- a/stdlib/test/builtin/test_float_literal.mojo +++ b/stdlib/test/builtin/test_float_literal.mojo @@ -158,11 +158,11 @@ def test_div_mod(): def test_int_conversion(): - assert_equal(int(-4.0), -4) - assert_equal(int(-4.5), -4) - assert_equal(int(-4.3), -4) - assert_equal(int(4.5), 4) - assert_equal(int(4.0), 4) + assert_equal(Int(-4.0), -4) + assert_equal(Int(-4.5), -4) + assert_equal(Int(-4.3), -4) + assert_equal(Int(4.5), 4) + assert_equal(Int(4.0), 4) def test_bool(): diff --git a/stdlib/test/builtin/test_int.mojo b/stdlib/test/builtin/test_int.mojo index d6d306c391..ad487d7241 100644 --- a/stdlib/test/builtin/test_int.mojo +++ b/stdlib/test/builtin/test_int.mojo @@ -191,10 +191,10 @@ def test_decimal_digit_count(): def test_int_uint(): var u1 = UInt(42) - assert_equal(42, int(u1)) + assert_equal(42, Int(u1)) var u2 = UInt(0) - assert_equal(0, int(u2)) + assert_equal(0, Int(u2)) def test_comparison(): diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index 4f79fe6da5..430d0ffde9 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -107,9 +107,9 @@ def test_pow(): alias F = SIMD[DType.float32, 4] var base = F(0.0, 1.0, 2.0, 3.0) assert_equal(pow(base, 2.0), F(0.0, 1.0, 4.0, 9.0)) - assert_equal(pow(base, int(2)), F(0.0, 1.0, 4.0, 9.0)) + assert_equal(pow(base, Int(2)), F(0.0, 1.0, 4.0, 9.0)) alias I = SIMD[DType.int32, 4] - assert_equal(pow(I(0, 1, 2, 3), int(2)), I(0, 1, 4, 9)) + assert_equal(pow(I(0, 1, 2, 3), Int(2)), I(0, 1, 4, 9)) def main(): diff --git a/stdlib/test/builtin/test_range.mojo b/stdlib/test/builtin/test_range.mojo index 051656d27d..f9535b288e 100644 --- a/stdlib/test/builtin/test_range.mojo +++ b/stdlib/test/builtin/test_range.mojo @@ -167,7 +167,7 @@ def test_range_reversed(): def test_indexing(): var r = range(10) assert_equal(r[True], 1) - assert_equal(r[int(4)], 4) + assert_equal(r[Int(4)], 4) assert_equal(r[3], 3) diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index d7bd8928b4..4871fcaca2 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -51,10 +51,10 @@ def test_cast(): ) var b: UInt16 = 128 - assert_equal(int(b.cast[DType.uint8]()), 128) - assert_equal(int(b.cast[DType.uint16]()), 128) - assert_equal(int(b.cast[DType.int8]()), -128) - assert_equal(int(b.cast[DType.int16]()), 128) + assert_equal(Int(b.cast[DType.uint8]()), 128) + assert_equal(Int(b.cast[DType.uint16]()), 128) + assert_equal(Int(b.cast[DType.int8]()), -128) + assert_equal(Int(b.cast[DType.int16]()), 128) @parameter if not has_neon(): @@ -1114,7 +1114,7 @@ def test_clamp(): def test_indexing(): var s = SIMD[DType.int32, 4](1, 2, 3, 4) assert_equal(s[False], 1) - assert_equal(s[int(2)], 3) + assert_equal(s[Int(2)], 3) assert_equal(s[3], 4) @@ -1136,7 +1136,7 @@ def test_reduce(): x8 = X8(0, 1, 2, 3, 4, 5, 6, 7) x4 = X4(4, 6, 8, 10) x2 = X2(12, 16) - x1 = X1(int(28)) # TODO: fix MOCO-697 and use X1(28) instead + x1 = X1(Int(28)) # TODO: fix MOCO-697 and use X1(28) instead assert_equal(x8.reduce_add(), x1) assert_equal(x4.reduce_add(), x1) assert_equal(x2.reduce_add(), x1) @@ -1153,7 +1153,7 @@ def test_reduce(): x8 = X8(0, 1, 2, 3, 4, 5, 6, 7) x4 = X4(0, 5, 12, 21) x2 = X2(0, 105) - x1 = X1(int(0)) # TODO: fix MOCO-697 and use X1(0) instead + x1 = X1(Int(0)) # TODO: fix MOCO-697 and use X1(0) instead assert_equal(x8.reduce_mul(), x1) assert_equal(x4.reduce_mul(), x1) assert_equal(x2.reduce_mul(), x1) @@ -1170,7 +1170,7 @@ def test_reduce(): x8 = X8(0, 1, 2, 3, 4, 5, 6, 7) x4 = X4(0, 1, 2, 3) x2 = X2(0, 1) - x1 = X1(int(0)) # TODO: fix MOCO-697 and use X1(0) instead + x1 = X1(Int(0)) # TODO: fix MOCO-697 and use X1(0) instead assert_equal(x8.reduce_min(), x1) assert_equal(x4.reduce_min(), x1) assert_equal(x2.reduce_min(), x1) @@ -1187,7 +1187,7 @@ def test_reduce(): x8 = X8(0, 1, 2, 3, 4, 5, 6, 7) x4 = X4(4, 5, 6, 7) x2 = X2(6, 7) - x1 = X1(int(7)) # TODO: fix MOCO-697 and use X1(7) instead + x1 = X1(Int(7)) # TODO: fix MOCO-697 and use X1(7) instead assert_equal(x8.reduce_max(), x1) assert_equal(x4.reduce_max(), x1) assert_equal(x2.reduce_max(), x1) @@ -1206,7 +1206,7 @@ def test_reduce(): x8 = X8(0, -1, 2, -3, 4, -5, 6, -7) x4 = X4(4, -6, 8, -10) x2 = X2(12, -16) - x1 = X1(int(-4)) # TODO: fix MOCO-697 and use X1(-4) instead + x1 = X1(Int(-4)) # TODO: fix MOCO-697 and use X1(-4) instead assert_equal(x8.reduce_add(), x1) assert_equal(x4.reduce_add(), x1) assert_equal(x2.reduce_add(), x1) @@ -1223,7 +1223,7 @@ def test_reduce(): x8 = X8(0, -1, 2, -3, 4, -5, 6, -7) x4 = X4(0, 5, 12, 21) x2 = X2(0, 105) - x1 = X1(int(0)) # TODO: fix MOCO-697 and use X1(0) instead + x1 = X1(Int(0)) # TODO: fix MOCO-697 and use X1(0) instead assert_equal(x8.reduce_mul(), x1) assert_equal(x4.reduce_mul(), x1) assert_equal(x2.reduce_mul(), x1) @@ -1240,7 +1240,7 @@ def test_reduce(): x8 = X8(0, -1, 2, -3, 4, -5, 6, -7) x4 = X4(0, -5, 2, -7) x2 = X2(0, -7) - x1 = X1(int(-7)) # TODO: fix MOCO-697 and use X1(-7) instead + x1 = X1(Int(-7)) # TODO: fix MOCO-697 and use X1(-7) instead assert_equal(x8.reduce_min(), x1) assert_equal(x4.reduce_min(), x1) assert_equal(x2.reduce_min(), x1) @@ -1257,7 +1257,7 @@ def test_reduce(): x8 = X8(0, -1, 2, -3, 4, -5, 6, -7) x4 = X4(4, -1, 6, -3) x2 = X2(6, -1) - x1 = X1(int(6)) # TODO: fix MOCO-697 and use X1(6) instead + x1 = X1(Int(6)) # TODO: fix MOCO-697 and use X1(6) instead assert_equal(x8.reduce_max(), x1) assert_equal(x4.reduce_max(), x1) assert_equal(x2.reduce_max(), x1) @@ -1316,7 +1316,7 @@ def test_reduce(): x8 = X8(0, 1, 2, 3, 4, 5, 6, 7) x4 = X4(0, 1, 2, 3) x2 = X2(0, 1) - x1 = X1(int(0)) # TODO: fix MOCO-697 and use X1(0) instead + x1 = X1(Int(0)) # TODO: fix MOCO-697 and use X1(0) instead assert_equal(x8.reduce_and(), x1) assert_equal(x4.reduce_and(), x1) assert_equal(x2.reduce_and(), x1) @@ -1333,7 +1333,7 @@ def test_reduce(): x8 = X8(0, 1, 2, 3, 4, 5, 6, 7) x4 = X4(4, 5, 6, 7) x2 = X2(6, 7) - x1 = X1(int(7)) # TODO: fix MOCO-697 and use X1(7) instead + x1 = X1(Int(7)) # TODO: fix MOCO-697 and use X1(7) instead assert_equal(x8.reduce_or(), x1) assert_equal(x4.reduce_or(), x1) assert_equal(x2.reduce_or(), x1) diff --git a/stdlib/test/builtin/test_sort.mojo b/stdlib/test/builtin/test_sort.mojo index de1a34e151..5c9e9a253d 100644 --- a/stdlib/test/builtin/test_sort.mojo +++ b/stdlib/test/builtin/test_sort.mojo @@ -460,7 +460,7 @@ fn test_sort_stress() raises: ](length: Int) raises: var list = List[Int](capacity=length) for _ in range(length): - list.append(int(random_si64(-length, length))) + list.append(Int(random_si64(-length, length))) _quicksort[cmp_fn](list) @@ -522,7 +522,7 @@ def test_sort_string_small_list(): var list = random_numbers[DType.int32](10) var string_list = List[String]() for n in list: - string_list.append(str(int(n[]))) + string_list.append(str(Int(n[]))) sort(string_list) assert_sorted_string(string_list) @@ -531,7 +531,7 @@ def test_sort_string_big_list(): var list = random_numbers[DType.int32](1000) var string_list = List[String]() for n in list: - string_list.append(str(int(n[]))) + string_list.append(str(Int(n[]))) sort(string_list) assert_sorted_string(string_list) @@ -586,8 +586,8 @@ def test_sort_comparamble_elements_list(): var ages = random_numbers[DType.uint8](count) var names = List[String]("Maxim", "Max", "Alex", "Bob", "Joe") for age in ages: - var name = names[int(age[]) % len(names)] - list.append(Person(name, int(age[]))) + var name = names[Int(age[]) % len(names)] + list.append(Person(name, Int(age[]))) gen_list(10) sort(list) @@ -627,7 +627,7 @@ def test_stable_sort_stress(): var list = List[IntPair](capacity=length) for i in range(length): # make the range smaller so we can get more repeats - list.append(IntPair(int(random_si64(0, 100)), i)) + list.append(IntPair(Int(random_si64(0, 100)), i)) sort[cmp_fn, stable=True](list) diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index c549a9423c..b0782481f3 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -208,7 +208,7 @@ def test_hash(): def test_indexing(): var s = "hello" assert_equal(s[False], "h") - assert_equal(s[int(1)], "e") + assert_equal(s[Int(1)], "e") assert_equal(s[2], "l") @@ -286,7 +286,7 @@ def test_layout(): # Test empty StringLiteral contents var empty = "".unsafe_ptr() # An empty string literal is stored as just the NUL terminator. - assert_true(int(empty) != 0) + assert_true(Int(empty) != 0) # TODO(MSTDL-596): This seems to hang? # assert_equal(empty[0], 0) diff --git a/stdlib/test/collections/string/test_inlined_string.mojo b/stdlib/test/collections/string/test_inlined_string.mojo index 83a191b0a7..027ef84fd2 100644 --- a/stdlib/test/collections/string/test_inlined_string.mojo +++ b/stdlib/test/collections/string/test_inlined_string.mojo @@ -102,14 +102,14 @@ def test_small_string_construction(): # ================================== var heap_s1 = String("hello") - var heap_s1_addr = int(heap_s1.unsafe_ptr()) + var heap_s1_addr = Int(heap_s1.unsafe_ptr()) var s3 = InlineString(heap_s1^) # Test that a InlineString constructed from a String uses the same # allocation as the original String (even if the String size is small # enough to fit inline). - assert_equal(int(s3.unsafe_ptr()), heap_s1_addr) + assert_equal(Int(s3.unsafe_ptr()), heap_s1_addr) def test_small_string_iadd(): diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 53f9f6ace8..aa4c130ddc 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -1131,11 +1131,11 @@ def test_removesuffix(): def test_intable(): - assert_equal(int(String("123")), 123) - assert_equal(int(String("10"), base=8), 8) + assert_equal(Int(String("123")), 123) + assert_equal(Int(String("10"), base=8), 8) with assert_raises(): - _ = int(String("hi")) + _ = Int(String("hi")) def test_string_mul(): @@ -1147,7 +1147,7 @@ def test_string_mul(): def test_indexing(): a = String("abc") assert_equal(a[False], "a") - assert_equal(a[int(1)], "b") + assert_equal(a[Int(1)], "b") assert_equal(a[2], "c") diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 8abe0fb5c1..4b4618bd11 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -34,9 +34,9 @@ fn test_string_slice_layout() raises: var str_slice = StringSlice("") - var base_ptr = int(UnsafePointer.address_of(str_slice)) - var first_word_ptr = int(UnsafePointer.address_of(str_slice._slice._data)) - var second_word_ptr = int(UnsafePointer.address_of(str_slice._slice._len)) + var base_ptr = Int(UnsafePointer.address_of(str_slice)) + var first_word_ptr = Int(UnsafePointer.address_of(str_slice._slice._data)) + var second_word_ptr = Int(UnsafePointer.address_of(str_slice._slice._len)) # 1st field should be at 0-byte offset from base ptr assert_equal(first_word_ptr - base_ptr, 0) @@ -113,7 +113,7 @@ fn test_string_byte_span() raises: assert_equal(len(sub5), 0) # Empty slices still have a pointer value - assert_equal(int(sub5.unsafe_ptr()) - int(sub4.unsafe_ptr()), 2) + assert_equal(Int(sub5.unsafe_ptr()) - Int(sub4.unsafe_ptr()), 2) # ---------------------------------- # Test invalid slicing diff --git a/stdlib/test/collections/test_inline_list.mojo b/stdlib/test/collections/test_inline_list.mojo index 52adea853c..8ca491f11c 100644 --- a/stdlib/test/collections/test_inline_list.mojo +++ b/stdlib/test/collections/test_inline_list.mojo @@ -155,7 +155,7 @@ def test_indexing(): list.append(i) assert_equal(list[True], 1) - assert_equal(list[int(4)], 4) + assert_equal(list[Int(4)], 4) assert_equal(list[0], 0) diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index 6f69672a60..2ed9a71306 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -863,7 +863,7 @@ def test_list_init_span(): def test_indexing(): var l = List[Int](1, 2, 3) - assert_equal(l[int(1)], 2) + assert_equal(l[Int(1)], 2) assert_equal(l[False], 1) assert_equal(l[True], 2) assert_equal(l[2], 3) diff --git a/stdlib/test/collections/test_vector.mojo b/stdlib/test/collections/test_vector.mojo index 2a5454db6c..3ed126d148 100644 --- a/stdlib/test/collections/test_vector.mojo +++ b/stdlib/test/collections/test_vector.mojo @@ -125,7 +125,7 @@ def test_indexing(): var vector = InlinedFixedVector[Int](10) for i in range(5): vector.append(i) - assert_equal(0, vector[int(0)]) + assert_equal(0, vector[Int(0)]) assert_equal(1, vector[True]) assert_equal(2, vector[2]) diff --git a/stdlib/test/hashlib/test_ahash.mojo b/stdlib/test/hashlib/test_ahash.mojo index c9f01c3a14..9a58a753b2 100644 --- a/stdlib/test/hashlib/test_ahash.mojo +++ b/stdlib/test/hashlib/test_ahash.mojo @@ -587,7 +587,7 @@ fn gen_word_pairs[words: String = words_en]() -> List[String]: def dif_bits(i1: UInt64, i2: UInt64) -> Int: - return int(pop_count(i1 ^ i2)) + return Int(pop_count(i1 ^ i2)) @always_inline @@ -696,7 +696,7 @@ def assert_fill_factor[ var buckets = List[Int](0) * num_buckets for w in words: var h = hash[HasherType=hasher0](w[]) - buckets[int(h) % num_buckets] += 1 + buckets[Int(h) % num_buckets] += 1 var unfilled = 0 for v in buckets: if v[] == 0: diff --git a/stdlib/test/hashlib/test_hash.mojo b/stdlib/test/hashlib/test_hash.mojo index 56f475f55d..39a992d51d 100644 --- a/stdlib/test/hashlib/test_hash.mojo +++ b/stdlib/test/hashlib/test_hash.mojo @@ -25,7 +25,7 @@ from testing import assert_equal, assert_not_equal, assert_true def same_low_bits(i1: Int, i2: Int, bits: Int = 5) -> UInt8: var mask = (1 << bits) - 1 - return int(not (i1 ^ i2) & mask) + return Int(not (i1 ^ i2) & mask) def test_hash_byte_array(): diff --git a/stdlib/test/memory/test_memory.mojo b/stdlib/test/memory/test_memory.mojo index b9f754c97d..26d12e1d03 100644 --- a/stdlib/test/memory/test_memory.mojo +++ b/stdlib/test/memory/test_memory.mojo @@ -490,7 +490,7 @@ def test_indexing(): for i in range(4): ptr[i] = i - assert_equal(ptr[int(2)], 2) + assert_equal(ptr[Int(2)], 2) assert_equal(ptr[1], 1) diff --git a/stdlib/test/memory/test_span.mojo b/stdlib/test/memory/test_span.mojo index 9b52577109..9ccd0d76de 100644 --- a/stdlib/test/memory/test_span.mojo +++ b/stdlib/test/memory/test_span.mojo @@ -115,7 +115,7 @@ def test_indexing(): var l = InlineArray[Int, 7](1, 2, 3, 4, 5, 6, 7) var s = Span[Int](array=l) assert_equal(s[True], 2) - assert_equal(s[int(0)], 1) + assert_equal(s[Int(0)], 1) assert_equal(s[3], 4) diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 09b7d11f70..5e8fefc1f5 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -112,7 +112,7 @@ def test_refitem_offset(): def test_address_of(): var local = 1 - assert_not_equal(0, int(UnsafePointer[Int].address_of(local))) + assert_not_equal(0, Int(UnsafePointer[Int].address_of(local))) _ = local @@ -120,7 +120,7 @@ def test_explicit_copy_of_pointer_address(): var local = 1 var ptr = UnsafePointer[Int].address_of(local) var copy = UnsafePointer(other=ptr) - assert_equal(int(ptr), int(copy)) + assert_equal(Int(ptr), Int(copy)) _ = local @@ -129,9 +129,9 @@ def test_bitcast(): var ptr = UnsafePointer[Int].address_of(local) var aliased_ptr = ptr.bitcast[SIMD[DType.uint8, 4]]() - assert_equal(int(ptr), int(ptr.bitcast[Int]())) + assert_equal(Int(ptr), Int(ptr.bitcast[Int]())) - assert_equal(int(ptr), int(aliased_ptr)) + assert_equal(Int(ptr), Int(aliased_ptr)) assert_equal( ptr.bitcast[ptr.type]().static_alignment_cast[33]().alignment, 33 @@ -190,19 +190,19 @@ def test_unsafepointer_address_space(): def test_unsafepointer_aligned_alloc(): alias alignment_1 = 32 var ptr = UnsafePointer[UInt8, alignment=alignment_1].alloc(1) - var ptr_uint64 = UInt64(int(ptr)) + var ptr_uint64 = UInt64(Int(ptr)) ptr.free() assert_equal(ptr_uint64 % alignment_1, 0) alias alignment_2 = 64 var ptr_2 = UnsafePointer[UInt8, alignment=alignment_2].alloc(1) - var ptr_uint64_2 = UInt64(int(ptr_2)) + var ptr_uint64_2 = UInt64(Int(ptr_2)) ptr_2.free() assert_equal(ptr_uint64_2 % alignment_2, 0) alias alignment_3 = 128 var ptr_3 = UnsafePointer[UInt8, alignment=alignment_3].alloc(1) - var ptr_uint64_3 = UInt64(int(ptr_3)) + var ptr_uint64_3 = UInt64(Int(ptr_3)) ptr_3.free() assert_equal(ptr_uint64_3 % alignment_3, 0) @@ -225,7 +225,7 @@ def test_indexing(): for i in range(4): ptr[i] = i - assert_equal(ptr[int(1)], 1) + assert_equal(ptr[Int(1)], 1) assert_equal(ptr[3], 3) @@ -266,11 +266,11 @@ def test_bool(): def test_alignment(): var ptr = UnsafePointer[Int64, alignment=64].alloc(8) - assert_equal(int(ptr) % 64, 0) + assert_equal(Int(ptr) % 64, 0) ptr.free() var ptr_2 = UnsafePointer[UInt8, alignment=32].alloc(32) - assert_equal(int(ptr_2) % 32, 0) + assert_equal(Int(ptr_2) % 32, 0) ptr_2.free() diff --git a/stdlib/test/utils/issue_13632.mojo b/stdlib/test/utils/issue_13632.mojo index d9d0655d19..0bdfbff6ce 100644 --- a/stdlib/test/utils/issue_13632.mojo +++ b/stdlib/test/utils/issue_13632.mojo @@ -20,7 +20,7 @@ from testing import assert_equal fn sum_items(data: List[Int8]) -> Int: var sum: Int = 0 for i in range(len(data)): - sum += int(data[i]) + sum += Int(data[i]) return sum diff --git a/stdlib/test/utils/test_stringref.mojo b/stdlib/test/utils/test_stringref.mojo index 18f319d4be..6a368f29a7 100644 --- a/stdlib/test/utils/test_stringref.mojo +++ b/stdlib/test/utils/test_stringref.mojo @@ -104,16 +104,16 @@ fn test_comparison_operators() raises: def test_intable(): - assert_equal(int(StringRef("123")), 123) + assert_equal(Int(StringRef("123")), 123) with assert_raises(): - _ = int(StringRef("hi")) + _ = Int(StringRef("hi")) def test_indexing(): a = StringRef("abc") assert_equal(a[False], "a") - assert_equal(a[int(1)], "b") + assert_equal(a[Int(1)], "b") assert_equal(a[0], "a") From 8d4edb07a99b3b8b0807ba047e836ea57610b042 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Fri, 10 Jan 2025 20:04:34 -0600 Subject: [PATCH 191/509] [docs] update `int()` -> `Int()` in docs Missed some docs updates after moving `int` function to `Int` ctors. MODULAR_ORIG_COMMIT_REV_ID: 5ab8a9be0c5c7e0d33ee9177e7e30e15a84cbdc7 --- docs/manual/decorators/implicit.md | 2 +- docs/roadmap.md | 8 +++----- proposals/improved-hash-module.md | 2 +- 3 files changed, 5 insertions(+), 7 deletions(-) diff --git a/docs/manual/decorators/implicit.md b/docs/manual/decorators/implicit.md index 1e9cd07e0b..e8b1131b55 100644 --- a/docs/manual/decorators/implicit.md +++ b/docs/manual/decorators/implicit.md @@ -19,7 +19,7 @@ struct MyInt: self.value = value fn __init__(out self, value: Float64): - self.value = int(value) + self.value = Int(value) ``` diff --git a/docs/roadmap.md b/docs/roadmap.md index 52a20d257b..46df347afd 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -374,11 +374,9 @@ print(One()) # prints '1' ``` Mojo currently supports similar functionality through the -[`Writable`](/mojo/stdlib/utils/write/Writable) trait, so that -`print()` works on all `Writable` types. Similar support exists for the -[`int()`](/mojo/stdlib/builtin/int/int-function) and -[`len()`](/mojo/stdlib/builtin/len/len) functions. We'll continue to -add traits support to the standard library to enable common use cases like this. +[`Writable`](/mojo/stdlib/utils/write/Writable) trait, so that `print()` works +on all `Writable` types. We'll continue to add traits support to the standard +library to enable common use cases like this. ### The standard library has limited exceptions use diff --git a/proposals/improved-hash-module.md b/proposals/improved-hash-module.md index ee2bd138f5..1e4dd64b13 100644 --- a/proposals/improved-hash-module.md +++ b/proposals/improved-hash-module.md @@ -221,7 +221,7 @@ fn _DJBX33A_SECRET() -> UInt64: """Example how secret and seed can be stored and retrieved.""" try: var secret_string = getenv("DJBX33A_SECRET", "") - return bitcast[DType.uint64](Int64(int(secret_string))) + return bitcast[DType.uint64](Int64(Int(secret_string))) except: var value = random_si64(Int64.MIN, Int64.MAX) _ = setenv("DJBX33A_SECRET", str(value)) From 557bd7b01a41f11361e38813a9e542bdc661f4f0 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 11 Jan 2025 06:34:16 +0000 Subject: [PATCH 192/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011105 --- examples/life/magic.lock | 142 +++++++++++++++++----------------- examples/magic.lock | 142 +++++++++++++++++----------------- examples/notebooks/magic.lock | 142 +++++++++++++++++----------------- examples/operators/magic.lock | 142 +++++++++++++++++----------------- magic.lock | 142 +++++++++++++++++----------------- 5 files changed, 355 insertions(+), 355 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index e7b8af260a..fda145688e 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011022-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025011022-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025011022-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011022-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011105-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025011105-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025011105-3.12release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011105-release.conda - 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sha256: 0c619f3bff0c0fa6cdc1a314bcc34b450058779c41676b6673acec10030c5fea - md5: 12e9bb285b21a65333d0031a9225e0f0 + sha256: 05af473b2b4983829660ba70eb0cb6607f019c4f4b8d107350ea491c6bd8d1cd + md5: 036f5ddfbcce66b3f9972cebf1cec107 depends: - - max-core ==25.1.0.dev2025011022 release + - max-core ==25.1.0.dev2025011105 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22934 - timestamp: 1736547478660 + size: 22937 + timestamp: 1736572603499 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From 4ae7f172172b34039a3d65a28fda7f192d42b327 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Fri, 10 Jan 2025 21:45:30 -0800 Subject: [PATCH 193/509] [Stdlib] Add convenience methods to check if pointer is None MODULAR_ORIG_COMMIT_REV_ID: feac24ac6fed4bb2c1ca4e0462af3f92aa760725 --- docs/changelog.md | 4 ++ stdlib/src/memory/unsafe_pointer.mojo | 48 ++++++++++++++++++++++ stdlib/test/memory/test_unsafepointer.mojo | 11 +++++ 3 files changed, 63 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index dc8fe8995b..11c680f851 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -156,6 +156,10 @@ what we publish. - `bit_ceil` has been renamed to `next_power_of_two`, and `bit_floor` to `prev_power_of_two`. This is to improve readability and clarity in their use. +- The `UnsafePointer` type now is comparable against the `None` type. This + enables one to write code like `if ptr is not None:` to check if the value + of the pointer is not a null ptr. + - The `Indexer` and `IntLike` traits which were previously both used for indexing have been combined. This enables SIMD scalar integer types and UInt to be used for indexing into all of the collection types, as well as diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index c2aefe847e..eb430aaea5 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -320,6 +320,18 @@ struct UnsafePointer[ """ return Int(self) == Int(rhs) + @always_inline("nodebug") + fn __eq__(self, none: NoneType) -> Bool: + """Returns True if the pointer is null. + + Args: + none: The NoneType value. + + Returns: + True if the pointer is null and False otherwise. + """ + return not self + @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") fn __ne__(self, rhs: Self) -> Bool: @@ -333,6 +345,18 @@ struct UnsafePointer[ """ return not (self == rhs) + @always_inline("nodebug") + fn __ne__(self, none: NoneType) -> Bool: + """Returns True if the pointer is not null. + + Args: + none: The NoneType value. + + Returns: + True if the pointer is null and False otherwise. + """ + return self + @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") fn __lt__(self, rhs: Self) -> Bool: @@ -389,6 +413,30 @@ struct UnsafePointer[ """ return Int(self) >= Int(rhs) + @always_inline("nodebug") + fn __is__(self, none: NoneType) -> Bool: + """Returns True if the pointer is null. + + Args: + none: The NoneType value. + + Returns: + True if the pointer is null and False otherwise. + """ + return self == None + + @always_inline("nodebug") + fn __isnot__(self, none: NoneType) -> Bool: + """Returns True if the pointer is not null. + + Args: + none: The NoneType value. + + Returns: + True if the pointer is not null and False otherwise. + """ + return self != None + # ===-------------------------------------------------------------------===# # Trait implementations # ===-------------------------------------------------------------------===# diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 5e8fefc1f5..34ce70e5b1 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -162,6 +162,17 @@ def test_eq(): var p4 = UnsafePointer.address_of(local).origin_cast[mut=False]() assert_equal(p1, p4) + + var p5 = UnsafePointer[Int]() + assert_true(p5 == None) + assert_true(p5 is None) + assert_false(p5 != None) + assert_false(p5 is not None) + assert_false(p1 == None) + assert_false(p1 is None) + assert_true(p1 != None) + assert_true(p1 is not None) + _ = local _ = other_local From 40a1091f74343dcf105c1af6d572687fb5cfb7b2 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 12:00:26 -0600 Subject: [PATCH 194/509] feat: Add `Char.utf8_byte_length()` + `Intable` impl MODULAR_ORIG_COMMIT_REV_ID: d66ad7a3ce557b718029359aed89201951b38948 --- stdlib/src/builtin/char.mojo | 53 ++++++++++++++++++- stdlib/src/collections/string/string.mojo | 4 +- .../src/collections/string/string_slice.mojo | 8 --- stdlib/test/builtin/test_char.mojo | 31 ++++++++++- 4 files changed, 83 insertions(+), 13 deletions(-) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 3116e454a2..dc3eb0129d 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -45,9 +45,27 @@ struct Char(CollectionElement): [1]: https://www.unicode.org/glossary/#unicode_scalar_value """ - var _codepoint: UInt32 + var _scalar_value: UInt32 """The Unicode scalar value represented by this type.""" + # ===-------------------------------------------------------------------===# + # Trait implementations + # ===-------------------------------------------------------------------===# + + @always_inline + fn __int__(self) -> Int: + """Returns the numeric value of this scalar value as an integer. + + Returns: + The numeric value of this scalar value as an integer. + """ + return Int(self._scalar_value) + + # ===-------------------------------------------------------------------===# + # Life cycle methods + # ===-------------------------------------------------------------------===# + + @always_inline fn __init__(out self, *, unsafe_unchecked_codepoint: UInt32): """Construct a `Char` from a code point value without checking that it falls in the valid range. @@ -66,7 +84,11 @@ struct Char(CollectionElement): "codepoint is not a valid Unicode scalar value", ) - self._codepoint = unsafe_unchecked_codepoint + self._scalar_value = unsafe_unchecked_codepoint + + # ===-------------------------------------------------------------------===# + # Factory methods + # ===-------------------------------------------------------------------===# @staticmethod fn from_u32(codepoint: UInt32) -> Optional[Self]: @@ -85,3 +107,30 @@ struct Char(CollectionElement): return Char(unsafe_unchecked_codepoint=codepoint) else: return None + + # ===-------------------------------------------------------------------===# + # Methods + # ===-------------------------------------------------------------------===# + + @always_inline + fn utf8_byte_length(self) -> UInt: + """Returns the number of UTF-8 bytes required to encode this character. + + The returned value is always between 1 and 4 bytes. + + Returns: + Byte count of UTF-8 bytes required to encode this character. + """ + + # Minimum codepoint values (respectively) that can fit in a 1, 2, 3, + # and 4 byte encoded UTF-8 sequence. + alias sizes = SIMD[DType.int32, 4]( + 0, + 2**7, + 2**11, + 2**16, + ) + + # Count how many of the minimums this codepoint exceeds, which is equal + # to the number of bytes needed to encode it. + return UInt(Int((sizes <= Int(self)).cast[DType.uint8]().reduce_add())) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 7f766c5897..68c676ad1a 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -44,7 +44,6 @@ from collections.string.string_slice import ( _shift_unicode_to_utf8, _StringSliceIter, _to_string_list, - _unicode_codepoint_utf8_byte_length, _utf8_byte_type, ) @@ -122,7 +121,8 @@ fn chr(c: Int) -> String: if c < 0b1000_0000: # 1 byte ASCII char return String(String._buffer_type(c, 0)) - var num_bytes = _unicode_codepoint_utf8_byte_length(c) + # FIXME: Validate that this is a valid scalar value + var num_bytes = Char(unsafe_unchecked_codepoint=c).utf8_byte_length() var p = UnsafePointer[UInt8].alloc(num_bytes + 1) _shift_unicode_to_utf8(p, c, num_bytes) # TODO: decide whether to use replacement char (�) or raise ValueError diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 129df13b88..6d17128f84 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -63,14 +63,6 @@ fn _count_utf8_continuation_bytes(span: Span[Byte]) -> Int: return amnt -fn _unicode_codepoint_utf8_byte_length(c: Int) -> Int: - debug_assert( - 0 <= c <= 0x10FFFF, "Value: ", c, " is not a valid Unicode code point" - ) - alias sizes = SIMD[DType.int32, 4](0, 0b0111_1111, 0b0111_1111_1111, 0xFFFF) - return Int((sizes < c).cast[DType.uint8]().reduce_add()) - - @always_inline fn _utf8_first_byte_sequence_length(b: Byte) -> Int: """Get the length of the sequence starting with given byte. Do note that diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index 97c2a56978..e220506c2d 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -18,7 +18,7 @@ from testing import assert_true, assert_false, assert_equal def test_char_validity(): # Check that basic unchecked constructor behaves as expected. var c1 = Char(unsafe_unchecked_codepoint=32) - assert_equal(c1._codepoint, 32) + assert_equal(c1._scalar_value, 32) assert_true(Char.from_u32(0)) @@ -41,5 +41,34 @@ def test_char_validity(): assert_false(Char.from_u32(0x10FFFF + 1)) +def test_char_utf8_byte_length(): + fn codepoint_len(cp: UInt32) -> Int: + return Char.from_u32(cp).value().utf8_byte_length() + + # 1-byte (ASCII) codepoints + assert_equal(codepoint_len(0), 1) + assert_equal(codepoint_len(32), 1) + assert_equal(codepoint_len(127), 1) + + # 2-byte codepoints -- 0b110x_xxxx 0b10xx_xxxx (11 x's) + # Smallest 2-byte codepoint + assert_equal(codepoint_len(128), 2) + # Largest 2-byte codepoint + assert_equal(codepoint_len(2**11 - 1), 2) # 2^11 - 1 == 2047 + + # 3-byte codepoints -- 0b1110_xxxx 0b10xx_xxxx 0b10xx_xxxx (16 x's) + # Smallest 3-byte codepoint + assert_equal(codepoint_len(2**11), 3) # 2^11 == 2048 + # Largest 3-byte codepoint + assert_equal(codepoint_len(2**16 - 1), 3) # 2^16 - 1 == 65535 == 0xFFFF + + # 4-byte codepoints 0b1111_0xxx 0b10xx_xxxx 0b10xx_xxxx 0b10xx_xxxx (21 x's) + # Smallest 4-byte codepoint + assert_equal(codepoint_len(2**16), 4) + # Largest 4-byte codepoint + assert_equal(codepoint_len(0x10FFFF), 4) # Maximum Unicode codepoint + + def main(): test_char_validity() + test_char_utf8_byte_length() From 7f1b2d048a7258ee03d9438089e8d5a2f89add7f Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 13:02:03 -0600 Subject: [PATCH 195/509] [stdlib] feat: Add `Char.is_ascii()` This function returns True if a given character is part of the ASCII character set. MODULAR_ORIG_COMMIT_REV_ID: bcde46c67d0e45dd20e19f0db0d1bd749b948759 --- stdlib/src/builtin/char.mojo | 12 ++++++++++++ stdlib/test/builtin/test_char.mojo | 11 +++++++++++ 2 files changed, 23 insertions(+) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index dc3eb0129d..dfe0954742 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -112,6 +112,18 @@ struct Char(CollectionElement): # Methods # ===-------------------------------------------------------------------===# + @always_inline + fn is_ascii(self) -> Bool: + """Returns True if this `Char` is an ASCII character. + + All ASCII characters are less than or equal to codepoint value 127, and + take exactly 1 byte to encode in UTF-8. + + Returns: + A boolean indicating if this `Char` is an ASCII character. + """ + return self._scalar_value <= 0b0111_1111 + @always_inline fn utf8_byte_length(self) -> UInt: """Returns the number of UTF-8 bytes required to encode this character. diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index e220506c2d..0b4b9ad915 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -41,6 +41,16 @@ def test_char_validity(): assert_false(Char.from_u32(0x10FFFF + 1)) +def test_char_properties(): + assert_true(Char.from_u32(0).value().is_ascii()) + # Last ASCII codepoint. + assert_true(Char.from_u32(0b0111_1111).value().is_ascii()) # ASCII 127 0x7F + + # First non-ASCII codepoint. + assert_false(Char.from_u32(0b1000_0000).value().is_ascii()) + assert_false(Char.from_u32(0b1111_1111).value().is_ascii()) + + def test_char_utf8_byte_length(): fn codepoint_len(cp: UInt32) -> Int: return Char.from_u32(cp).value().utf8_byte_length() @@ -71,4 +81,5 @@ def test_char_utf8_byte_length(): def main(): test_char_validity() + test_char_properties() test_char_utf8_byte_length() From f7ae5f27e046ce3240a60069b4c58776406ca4c2 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 14:35:58 -0600 Subject: [PATCH 196/509] [stdlib] cleanup: Consolidate UTF-8 encoding in `Char.unsafe_write_utf8()` There were at least two places in the standard library where UTF-8 encoding logic had been duplicated. This is now fixed by consolidating this logic as part of the new `Char` type. Also added basic smoke tests for the encoding of various significant codepoint values. MODULAR_ORIG_COMMIT_REV_ID: fe709bf3db52f9092747dc7c5d4ffe70059c84fd --- docs/changelog.md | 3 + stdlib/src/builtin/char.mojo | 60 ++++++++++++++++- stdlib/src/collections/string/_unicode.mojo | 23 +------ stdlib/src/collections/string/string.mojo | 28 ++++---- .../src/collections/string/string_slice.mojo | 28 -------- stdlib/test/builtin/test_char.mojo | 66 +++++++++++++++++++ 6 files changed, 145 insertions(+), 63 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 11c680f851..23979ca0ff 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -100,6 +100,9 @@ what we publish. - Added `Char`, for representing and storing single Unicode characters. +- `chr(Int)` will now abort if given a codepoint value that is not a valid + `Char`. + - Added `StringSlice.from_utf()` factor method, for validated construction of a `StringSlice` from a buffer containing UTF-8 encoded data. This method will raise if the buffer contents are not valid UTF-8. diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index dfe0954742..149ea82b96 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -14,6 +14,8 @@ from collections import Optional +from memory import UnsafePointer + @always_inline fn _is_unicode_scalar_value(codepoint: UInt32) -> Bool: @@ -124,6 +126,54 @@ struct Char(CollectionElement): """ return self._scalar_value <= 0b0111_1111 + @always_inline + fn unsafe_write_utf8(self, ptr: UnsafePointer[Byte]) -> UInt: + """Shift unicode to utf8 representation. + + Safety: + `ptr` MUST point to at least `self.utf8_byte_length()` allocated + bytes or else an out-of-bounds write will occur, which is undefined + behavior. + + Args: + ptr: Pointer value to write the encoded UTF-8 bytes. Must validly + point to a sufficient number of bytes (1-4) to hold the encoded + data. + + Returns: + Returns the number of bytes written. + + ### Unicode (represented as UInt32 BE) to UTF-8 conversion: + - 1: 00000000 00000000 00000000 0aaaaaaa -> 0aaaaaaa + - a + - 2: 00000000 00000000 00000aaa aabbbbbb -> 110aaaaa 10bbbbbb + - (a >> 6) | 0b11000000, b | 0b10000000 + - 3: 00000000 00000000 aaaabbbb bbcccccc -> 1110aaaa 10bbbbbb 10cccccc + - (a >> 12) | 0b11100000, (b >> 6) | 0b10000000, c | 0b10000000 + - 4: 00000000 000aaabb bbbbcccc ccdddddd -> 11110aaa 10bbbbbb 10cccccc + 10dddddd + - (a >> 18) | 0b11110000, (b >> 12) | 0b10000000, (c >> 6) | 0b10000000, + d | 0b10000000 + . + """ + var c = Int(self) + + var num_bytes = self.utf8_byte_length() + + if num_bytes == 1: + ptr[0] = UInt8(c) + return 1 + + var shift = 6 * (num_bytes - 1) + var mask = UInt8(0xFF) >> (num_bytes + 1) + var num_bytes_marker = UInt8(0xFF) << (8 - num_bytes) + ptr[0] = ((c >> shift) & mask) | num_bytes_marker + for i in range(1, num_bytes): + shift -= 6 + ptr[i] = ((c >> shift) & 0b0011_1111) | 0b1000_0000 + + return num_bytes + @always_inline fn utf8_byte_length(self) -> UInt: """Returns the number of UTF-8 bytes required to encode this character. @@ -145,4 +195,12 @@ struct Char(CollectionElement): # Count how many of the minimums this codepoint exceeds, which is equal # to the number of bytes needed to encode it. - return UInt(Int((sizes <= Int(self)).cast[DType.uint8]().reduce_add())) + var lt = (sizes <= Int(self)).cast[DType.uint8]() + + # TODO(MOCO-1537): Support `reduce_add()` at compile time. + # var count = Int(lt.reduce_add()) + var count = 0 + for i in range(len(lt)): + count += Int(lt[i]) + + return UInt(count) diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index 980211ae22..8f340923e9 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -137,28 +137,7 @@ fn _ord(_p: UnsafePointer[UInt8]) -> (Int, Int): fn _write_rune(rune: UInt32, p: UnsafePointer[UInt8]) -> Int: """Write rune as UTF-8 into provided pointer. Return number of added bytes. """ - if (rune >> 7) == 0: # This is 1 byte ASCII char - p[0] = rune.cast[DType.uint8]() - return 1 - - @always_inline - fn _utf8_len(val: UInt32) -> Int: - alias sizes = SIMD[DType.uint32, 4]( - 0, 0b1111_111, 0b1111_1111_111, 0b1111_1111_1111_1111 - ) - var values = SIMD[DType.uint32, 4](val) - var mask = values > sizes - return Int(mask.cast[DType.uint8]().reduce_add()) - - var num_bytes = _utf8_len(rune) - var shift = 6 * (num_bytes - 1) - var mask = UInt32(0xFF) >> (num_bytes + 1) - var num_bytes_marker = UInt32(0xFF) << (8 - num_bytes) - p[0] = (((rune >> shift) & mask) | num_bytes_marker).cast[DType.uint8]() - for i in range(1, num_bytes): - shift -= 6 - p[i] = (((rune >> shift) & 0b00111111) | 0b10000000).cast[DType.uint8]() - return num_bytes + return Char(unsafe_unchecked_codepoint=rune).unsafe_write_utf8(p) fn to_lowercase(s: StringSlice) -> String: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 68c676ad1a..f52992d86a 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -15,6 +15,7 @@ from collections import KeyElement, List, Optional from collections._index_normalization import normalize_index from hashlib._hasher import _HashableWithHasher, _Hasher +from os import abort from sys import bitwidthof, llvm_intrinsic from sys.ffi import c_char from sys.intrinsics import _type_is_eq @@ -41,7 +42,6 @@ from collections.string._unicode import ( from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( StringSlice, - _shift_unicode_to_utf8, _StringSliceIter, _to_string_list, _utf8_byte_type, @@ -121,17 +121,21 @@ fn chr(c: Int) -> String: if c < 0b1000_0000: # 1 byte ASCII char return String(String._buffer_type(c, 0)) - # FIXME: Validate that this is a valid scalar value - var num_bytes = Char(unsafe_unchecked_codepoint=c).utf8_byte_length() - var p = UnsafePointer[UInt8].alloc(num_bytes + 1) - _shift_unicode_to_utf8(p, c, num_bytes) - # TODO: decide whether to use replacement char (�) or raise ValueError - # if not _is_valid_utf8(p, num_bytes): - # debug_assert(False, "Invalid Unicode code point") - # p.free() - # return chr(0xFFFD) - p[num_bytes] = 0 - return String(ptr=p, length=num_bytes + 1) + var char_opt = Char.from_u32(c) + if not char_opt: + # TODO: Raise ValueError instead. + return abort[String]( + String.write("chr(", c, ") is not a valid Unicode codepoint") + ) + + # SAFETY: We just checked that `char` is present. + var char = char_opt.unsafe_value() + + var num_bytes = char.utf8_byte_length() + var ptr = UnsafePointer[UInt8].alloc(num_bytes + 1) + _ = char.unsafe_write_utf8(ptr) + ptr[num_bytes] = 0 + return String(ptr=ptr, length=num_bytes + 1) # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 6d17128f84..33cd07cf76 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -75,34 +75,6 @@ fn _utf8_first_byte_sequence_length(b: Byte) -> Int: return Int(count_leading_zeros(~b)) + Int(b < 0b1000_0000) -fn _shift_unicode_to_utf8(ptr: UnsafePointer[UInt8], c: Int, num_bytes: Int): - """Shift unicode to utf8 representation. - - ### Unicode (represented as UInt32 BE) to UTF-8 conversion: - - 1: 00000000 00000000 00000000 0aaaaaaa -> 0aaaaaaa - - a - - 2: 00000000 00000000 00000aaa aabbbbbb -> 110aaaaa 10bbbbbb - - (a >> 6) | 0b11000000, b | 0b10000000 - - 3: 00000000 00000000 aaaabbbb bbcccccc -> 1110aaaa 10bbbbbb 10cccccc - - (a >> 12) | 0b11100000, (b >> 6) | 0b10000000, c | 0b10000000 - - 4: 00000000 000aaabb bbbbcccc ccdddddd -> 11110aaa 10bbbbbb 10cccccc - 10dddddd - - (a >> 18) | 0b11110000, (b >> 12) | 0b10000000, (c >> 6) | 0b10000000, - d | 0b10000000 - """ - if num_bytes == 1: - ptr[0] = UInt8(c) - return - - var shift = 6 * (num_bytes - 1) - var mask = UInt8(0xFF) >> (num_bytes + 1) - var num_bytes_marker = UInt8(0xFF) << (8 - num_bytes) - ptr[0] = ((c >> shift) & mask) | num_bytes_marker - for i in range(1, num_bytes): - shift -= 6 - ptr[i] = ((c >> shift) & 0b0011_1111) | 0b1000_0000 - - fn _utf8_byte_type(b: SIMD[DType.uint8, _], /) -> __type_of(b): """UTF-8 byte type. diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index 0b4b9ad915..dce651b41c 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -51,6 +51,62 @@ def test_char_properties(): assert_false(Char.from_u32(0b1111_1111).value().is_ascii()) +fn assert_utf8_bytes(codepoint: UInt32, owned expected: List[Byte]) raises: + var char_opt = Char.from_u32(codepoint) + var char = char_opt.value() + + # Allocate a length-4 buffer to write to. + var buffer = List[Byte](0, 0, 0, 0) + var written = char.unsafe_write_utf8(buffer.unsafe_ptr()) + + # Check that the number of bytes written was as expected. + assert_equal( + written, + len(expected), + "wrong byte count written encoding codepoint: {}".format(codepoint), + ) + + # Normalize `expected` to length 4 so we can compare the written byte + # values with `buffer`. + for _ in range(4 - len(expected)): + expected.append(0) + + assert_equal( + buffer, + expected, + "wrong byte values written encoding codepoint: {}".format(codepoint), + ) + + +def test_char_utf8_encoding(): + assert_utf8_bytes(0, List[Byte](0)) + assert_utf8_bytes(1, List[Byte](1)) + assert_utf8_bytes(127, List[Byte](127)) + + # Smallest 2-byte codepoint + assert_utf8_bytes(128, List[Byte](0b1100_0010, 0b1000_0000)) + # Largest 2-byte codepoint + assert_utf8_bytes(2**11 - 1, List[Byte](0b1101_1111, 0b1011_1111)) + + # Smallest 3-byte codepoint -- 2^11 == 2048 + assert_utf8_bytes( + 2**11, List[Byte](0b1110_0000, 0b1010_0000, 0b1000_0000) + ) + # Largest 3-byte codepoint -- 2^16 - 1 == 65535 == 0xFFFF + assert_utf8_bytes( + 2**16 - 1, List[Byte](0b1110_1111, 0b1011_1111, 0b1011_1111) + ) + + # Smallest 4-byte codepoint + assert_utf8_bytes( + 2**16, List[Byte](0b1111_0000, 0b1001_0000, 0b1000_0000, 0b1000_0000) + ) + # Largest 4-byte codepoint -- Maximum Unicode codepoint + assert_utf8_bytes( + 0x10FFFF, List[Byte](0b1111_0100, 0b1000_1111, 0b1011_1111, 0b1011_1111) + ) + + def test_char_utf8_byte_length(): fn codepoint_len(cp: UInt32) -> Int: return Char.from_u32(cp).value().utf8_byte_length() @@ -79,7 +135,17 @@ def test_char_utf8_byte_length(): assert_equal(codepoint_len(0x10FFFF), 4) # Maximum Unicode codepoint +def test_char_comptime(): + alias c1 = Char.from_u32(32).value() + + # Test that `utf8_byte_length()` works at compile time. + alias c1_bytes = c1.utf8_byte_length() + assert_equal(c1_bytes, 1) + + def main(): test_char_validity() test_char_properties() + test_char_utf8_encoding() test_char_utf8_byte_length() + test_char_comptime() From c2a2301c68f7ce49236149b9ecfc47c85533c96c Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 15:22:25 -0600 Subject: [PATCH 197/509] [stdlib] feat: Add `Char` from `UInt8` constructor MODULAR_ORIG_COMMIT_REV_ID: 509bc070e8484f7daf3dc5b6ec37093c9aad898c --- stdlib/src/builtin/char.mojo | 12 ++++++++++++ stdlib/test/builtin/test_char.mojo | 10 ++++++++++ 2 files changed, 22 insertions(+) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 149ea82b96..ef55d7211d 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -88,6 +88,18 @@ struct Char(CollectionElement): self._scalar_value = unsafe_unchecked_codepoint + @always_inline + fn __init__(out self, codepoint: UInt8): + """Construct a `Char` from a single byte value. + + This constructor cannot fail because non-negative 8-bit integers are + valid Unicode scalar values. + + Args: + codepoint: The 8-bit codepoint value to convert to a `Char`. + """ + self._scalar_value = UInt32(Int(codepoint)) + # ===-------------------------------------------------------------------===# # Factory methods # ===-------------------------------------------------------------------===# diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index dce651b41c..d8700000a3 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -41,6 +41,15 @@ def test_char_validity(): assert_false(Char.from_u32(0x10FFFF + 1)) +def test_char_from_u8(): + var c1 = Char(UInt8(0)) + assert_true(c1.is_ascii()) + + # All non-negative 8-bit integers are codepoints, but not all are ASCII. + var c2 = Char(UInt8(255)) + assert_false(c2.is_ascii()) + + def test_char_properties(): assert_true(Char.from_u32(0).value().is_ascii()) # Last ASCII codepoint. @@ -145,6 +154,7 @@ def test_char_comptime(): def main(): test_char_validity() + test_char_from_u8() test_char_properties() test_char_utf8_encoding() test_char_utf8_byte_length() From 9dadc46264a4a9eec3bf2d4fff3bd089ade0eb16 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 16:13:04 -0600 Subject: [PATCH 198/509] [stdlib] feat: Add `Char.unsafe_decode_utf8_char()` This function decodes a single UTF-8 encoded character and returns it as a `Char` value, along with returning the number of bytes read as well. This consolidates two places in the standard library where UTF-8 decoding logic had become duplicated. MODULAR_ORIG_COMMIT_REV_ID: e2670855e4dc2eceb2980b24268360d627c4aa96 --- stdlib/src/builtin/char.mojo | 65 +++++++++++++++++++++ stdlib/src/collections/string/_unicode.mojo | 31 ++-------- stdlib/src/collections/string/string.mojo | 32 ++-------- 3 files changed, 75 insertions(+), 53 deletions(-) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index ef55d7211d..a2ac9bf650 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -14,6 +14,8 @@ from collections import Optional +from bit import count_leading_zeros + from memory import UnsafePointer @@ -122,6 +124,69 @@ struct Char(CollectionElement): else: return None + @staticmethod + fn unsafe_decode_utf8_char(_ptr: UnsafePointer[Byte]) -> (Char, Int): + """Decodes a single `Char` and number of bytes read from a given UTF-8 + string pointer. + + Safety: + `_ptr` MUST point to the first byte in a **known-valid** UTF-8 + character sequence. This function MUST NOT be used on unvalidated + input. + + Args: + _ptr: Pointer to UTF-8 encoded data containing at least one valid + encoded codepoint. + + Returns: + The decoded codepoint `Char`, as well as the number of bytes read. + + """ + # UTF-8 to Unicode conversion: (represented as UInt32 BE) + # 1: 0aaaaaaa -> 00000000 00000000 00000000 0aaaaaaa a + # 2: 110aaaaa 10bbbbbb -> 00000000 00000000 00000aaa aabbbbbb a << 6 | b + # 3: 1110aaaa 10bbbbbb 10cccccc -> 00000000 00000000 aaaabbbb bbcccccc a << 12 | b << 6 | c + # 4: 11110aaa 10bbbbbb 10cccccc 10dddddd -> 00000000 000aaabb bbbbcccc ccdddddd a << 18 | b << 12 | c << 6 | d + var ptr = _ptr + + var b1 = ptr[] + if (b1 >> 7) == 0: # This is 1 byte ASCII char + return Char(b1), 1 + + # TODO: Use _utf8_first_byte_sequence_length() here instead for + # consistency. + var num_bytes = count_leading_zeros(~b1) + debug_assert( + 1 < Int(num_bytes) < 5, "invalid UTF-8 byte ", b1, " at index 0" + ) + + var shift = Int((6 * (num_bytes - 1))) + var b1_mask = 0b11111111 >> (num_bytes + 1) + var result = Int(b1 & b1_mask) << shift + for i in range(1, num_bytes): + ptr += 1 + # Assert that this is a continuation byte + debug_assert( + ptr[] >> 6 == 0b00000010, + "invalid UTF-8 byte ", + ptr[], + " at index ", + i, + ) + shift -= 6 + result |= Int(ptr[] & 0b00111111) << shift + + # SAFETY: Safe because the input bytes are required to be valid UTF-8, + # and valid UTF-8 will never decode to an out of bounds codepoint + # using the above algorithm. + # FIXME: + # UTF-8 encoding algorithms that do not properly exclude surrogate + # pair code points are actually relatively common (as I understand + # it); the algorithm above does not check for that. + var char = Char(unsafe_unchecked_codepoint=result) + + return char, Int(num_bytes) + # ===-------------------------------------------------------------------===# # Methods # ===-------------------------------------------------------------------===# diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index 8f340923e9..7c41c0f08a 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -11,7 +11,6 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # -from bit import count_leading_zeros from memory import UnsafePointer, memcpy from collections.string._unicode_lookups import * @@ -116,24 +115,6 @@ fn is_lowercase(s: StringSlice) -> Bool: return found -fn _ord(_p: UnsafePointer[UInt8]) -> (Int, Int): - """Return the rune and number of bytes to be consumed, for given UTF-8 string pointer - """ - var p = _p - var b1 = p[] - if (b1 >> 7) == 0: # This is 1 byte ASCII char - return Int(b1), 1 - var num_bytes = count_leading_zeros(~b1) - var shift = Int((6 * (num_bytes - 1))) - var b1_mask = 0b11111111 >> (num_bytes + 1) - var result = Int(b1 & b1_mask) << shift - for _ in range(1, num_bytes): - p += 1 - shift -= 6 - result |= Int(p[] & 0b00111111) << shift - return result, Int(num_bytes) - - fn _write_rune(rune: UInt32, p: UnsafePointer[UInt8]) -> Int: """Write rune as UTF-8 into provided pointer. Return number of added bytes. """ @@ -155,8 +136,8 @@ fn to_lowercase(s: StringSlice) -> String: var input_offset = 0 var output_offset = 0 while input_offset < s.byte_length(): - var rune_and_size = _ord(input + input_offset) - var index = _lowercase_mapping_index(rune_and_size[0]) + var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) + var index = _lowercase_mapping_index(Int(rune_and_size[0])) if index == -1: memcpy( output + output_offset, input + input_offset, rune_and_size[1] @@ -200,13 +181,13 @@ fn to_uppercase(s: StringSlice) -> String: var input_offset = 0 var output_offset = 0 while input_offset < s.byte_length(): - var rune_and_size = _ord(input + input_offset) - var index = _uppercase_mapping_index(rune_and_size[0]) + var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) + var index = _uppercase_mapping_index(Int(rune_and_size[0])) var index2 = _uppercase_mapping2_index( - rune_and_size[0] + Int(rune_and_size[0]) ) if index == -1 else -1 var index3 = _uppercase_mapping3_index( - rune_and_size[0] + Int(rune_and_size[0]) ) if index == -1 and index2 == -1 else -1 if index != -1: output_offset += _write_rune( diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index f52992d86a..58b6a3058d 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -65,34 +65,10 @@ fn ord(s: StringSlice) -> Int: Returns: An integer representing the code point of the given character. """ - # UTF-8 to Unicode conversion: (represented as UInt32 BE) - # 1: 0aaaaaaa -> 00000000 00000000 00000000 0aaaaaaa a - # 2: 110aaaaa 10bbbbbb -> 00000000 00000000 00000aaa aabbbbbb a << 6 | b - # 3: 1110aaaa 10bbbbbb 10cccccc -> 00000000 00000000 aaaabbbb bbcccccc a << 12 | b << 6 | c - # 4: 11110aaa 10bbbbbb 10cccccc 10dddddd -> 00000000 000aaabb bbbbcccc ccdddddd a << 18 | b << 12 | c << 6 | d - var p = s.unsafe_ptr() - var b1 = p[] - if (b1 >> 7) == 0: # This is 1 byte ASCII char - debug_assert(s.byte_length() == 1, "input string length must be 1") - return Int(b1) - var num_bytes = count_leading_zeros(~b1) - debug_assert( - s.byte_length() == Int(num_bytes), "input string must be one character" - ) - debug_assert( - 1 < Int(num_bytes) < 5, "invalid UTF-8 byte ", b1, " at index 0" - ) - var shift = Int((6 * (num_bytes - 1))) - var b1_mask = 0b11111111 >> (num_bytes + 1) - var result = Int(b1 & b1_mask) << shift - for i in range(1, num_bytes): - p += 1 - debug_assert( - p[] >> 6 == 0b00000010, "invalid UTF-8 byte ", b1, " at index ", i - ) - shift -= 6 - result |= Int(p[] & 0b00111111) << shift - return result + # SAFETY: + # This is safe because `s.unsafe_ptr()` is guaranteed to be valid UTF-8. + char, _ = Char.unsafe_decode_utf8_char(s.unsafe_ptr()) + return Int(char) # ===----------------------------------------------------------------------=== # From 40fd3f4cf2b8f575b579118c8b0ac727f86a24d2 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 18:05:08 -0600 Subject: [PATCH 199/509] [stdlib] feat: Add `Char.ord()` Unlike the exising `ord()` free function, which (for the time being) returns an `Int`, this function returns a `Char`, which can be useful when wanting to further query properties of the resulting character, which is not easily possible with the raw `Int` returned by `ord()`. MODULAR_ORIG_COMMIT_REV_ID: 70c9ea8afa023f97b7ec918c86a333be225ce932 --- stdlib/src/builtin/char.mojo | 31 +++++++++++++++++++++++ stdlib/src/collections/string/string.mojo | 5 +--- 2 files changed, 32 insertions(+), 4 deletions(-) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index a2ac9bf650..d1e17910b9 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -13,6 +13,7 @@ """Implements the `Char` type for representing single characters.""" from collections import Optional +from collections.string import StringSlice from bit import count_leading_zeros @@ -124,6 +125,36 @@ struct Char(CollectionElement): else: return None + @staticmethod + fn ord(string: StringSlice) -> Char: + """Returns the `Char` that represents the given one-character string. + + Given a string representing one character, return a `Char` + representing the codepoint of that character. For example, `Char.ord("a")` + returns the codepoint `97`. This is the inverse of the `chr()` function. + + This function is similar to the `ord()` free function, except that it + returns a `Char` instead of an `Int`. + + Args: + string: The input string, which must contain only a single character. + + Returns: + A `Char` representing the codepoint of the given character. + """ + + # SAFETY: + # This is safe because `StringSlice` is guaranteed to point to valid + # UTF-8. + char, num_bytes = Char.unsafe_decode_utf8_char(string.unsafe_ptr()) + + debug_assert( + string.byte_length() == Int(num_bytes), + "input string must be one character", + ) + + return char + @staticmethod fn unsafe_decode_utf8_char(_ptr: UnsafePointer[Byte]) -> (Char, Int): """Decodes a single `Char` and number of bytes read from a given UTF-8 diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 58b6a3058d..bad55ae433 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -65,10 +65,7 @@ fn ord(s: StringSlice) -> Int: Returns: An integer representing the code point of the given character. """ - # SAFETY: - # This is safe because `s.unsafe_ptr()` is guaranteed to be valid UTF-8. - char, _ = Char.unsafe_decode_utf8_char(s.unsafe_ptr()) - return Int(char) + return Int(Char.ord(s)) # ===----------------------------------------------------------------------=== # From af984469f084c133ee5dcf6b56490a9a4e032342 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 18:37:59 -0600 Subject: [PATCH 200/509] [stdlib] feat: Add `Char.is_posix_space()`, replacing `_isspace()` MODULAR_ORIG_COMMIT_REV_ID: e145e05030e3f046e243f8fbbe09b7ef19f65427 --- stdlib/src/builtin/char.mojo | 46 +++++++++++++ stdlib/src/collections/string/string.mojo | 68 ++----------------- .../src/collections/string/string_slice.mojo | 10 +-- stdlib/src/utils/stringref.mojo | 1 - stdlib/test/builtin/test_char.mojo | 23 +++++++ .../test/collections/string/test_string.mojo | 21 ------ 6 files changed, 79 insertions(+), 90 deletions(-) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index d1e17910b9..3e07032117 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -234,6 +234,52 @@ struct Char(CollectionElement): """ return self._scalar_value <= 0b0111_1111 + fn is_posix_space(self) -> Bool: + """Returns True if this `Char` is a **space** character according to the + [POSIX locale][1]. + + The POSIX locale is also known as the C locale. + + [1]: https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap07.html#tag_07_03_01 + + This only respects the default "C" locale, i.e. returns True only if the + character specified is one of " \\t\\n\\v\\f\\r". For semantics similar + to Python, use `String.isspace()`. + + Returns: + True iff the character is one of the whitespace characters listed + above. + """ + if not self.is_ascii(): + return False + + # ASCII char + var c = UInt8(Int(self)) + + # NOTE: a global LUT doesn't work at compile time so we can't use it here. + alias ` ` = UInt8(ord(" ")) + alias `\t` = UInt8(ord("\t")) + alias `\n` = UInt8(ord("\n")) + alias `\r` = UInt8(ord("\r")) + alias `\f` = UInt8(ord("\f")) + alias `\v` = UInt8(ord("\v")) + alias `\x1c` = UInt8(ord("\x1c")) + alias `\x1d` = UInt8(ord("\x1d")) + alias `\x1e` = UInt8(ord("\x1e")) + + # This compiles to something very clever that's even faster than a LUT. + return ( + c == ` ` + or c == `\t` + or c == `\n` + or c == `\r` + or c == `\f` + or c == `\v` + or c == `\x1c` + or c == `\x1d` + or c == `\x1e` + ) + @always_inline fn unsafe_write_utf8(self, ptr: UnsafePointer[Byte]) -> UInt: """Shift unicode to utf8 representation. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index bad55ae433..984637758f 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -293,13 +293,13 @@ fn atol(str_slice: StringSlice, base: Int = 10) raises -> Int: elif ord_letter_min[1] <= ord_current <= ord_letter_max[1]: result += ord_current - ord_letter_min[1] + 10 found_valid_chars_after_start = True - elif _isspace(ord_current): + elif Char(UInt8(ord_current)).is_posix_space(): has_space_after_number = True start = pos + 1 break else: raise Error(_str_to_base_error(base, str_slice)) - if pos + 1 < str_len and not _isspace(buff[pos + 1]): + if pos + 1 < str_len and not Char(buff[pos + 1]).is_posix_space(): var nextresult = result * real_base if nextresult < result: raise Error( @@ -313,7 +313,7 @@ fn atol(str_slice: StringSlice, base: Int = 10) raises -> Int: if has_space_after_number: for pos in range(start, str_len): - if not _isspace(buff[pos]): + if not Char(buff[pos]).is_posix_space(): raise Error(_str_to_base_error(base, str_slice)) if is_negative: result = -result @@ -335,7 +335,7 @@ fn _trim_and_handle_sign(str_slice: StringSlice, str_len: Int) -> (Int, Bool): """ var buff = str_slice.unsafe_ptr() var start: Int = 0 - while start < str_len and _isspace(buff[start]): + while start < str_len and Char(buff[start]).is_posix_space(): start += 1 var p: Bool = buff[start] == ord("+") var n: Bool = buff[start] == ord("-") @@ -600,66 +600,6 @@ fn _is_ascii_lowercase(c: UInt8) -> Bool: return ord_a <= Int(c) <= ord_z -# ===----------------------------------------------------------------------=== # -# _isspace -# ===----------------------------------------------------------------------=== # - - -fn _isspace(c: StringSlice) -> Bool: - """Determines whether the given character is a whitespace character. - - This only respects the default "C" locale, i.e. returns True only if the - character specified is one of " \\t\\n\\v\\f\\r". For semantics similar - to Python, use `String.isspace()`. - - Args: - c: The character to check. - - Returns: - True iff the character is one of the whitespace characters listed above. - """ - return _isspace(ord(c)) - - -fn _isspace(c: UInt8) -> Bool: - """Determines whether the given character is a whitespace character. - - This only respects the default "C" locale, i.e. returns True only if the - character specified is one of " \\t\\n\\v\\f\\r". For semantics similar - to Python, use `String.isspace()`. - - Args: - c: The character to check. - - Returns: - True iff the character is one of the whitespace characters listed above. - """ - - # NOTE: a global LUT doesn't work at compile time so we can't use it here. - alias ` ` = UInt8(ord(" ")) - alias `\t` = UInt8(ord("\t")) - alias `\n` = UInt8(ord("\n")) - alias `\r` = UInt8(ord("\r")) - alias `\f` = UInt8(ord("\f")) - alias `\v` = UInt8(ord("\v")) - alias `\x1c` = UInt8(ord("\x1c")) - alias `\x1d` = UInt8(ord("\x1d")) - alias `\x1e` = UInt8(ord("\x1e")) - - # This compiles to something very clever that's even faster than a LUT. - return ( - c == ` ` - or c == `\t` - or c == `\n` - or c == `\r` - or c == `\f` - or c == `\v` - or c == `\x1c` - or c == `\x1d` - or c == `\x1e` - ) - - # ===----------------------------------------------------------------------=== # # isprintable # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 33cd07cf76..06ed5b66d7 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -25,7 +25,6 @@ from bit import count_leading_zeros from collections import List, Optional from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string._utf8_validation import _is_valid_utf8 -from collections.string.string import _isspace from memory import UnsafePointer, memcmp, memcpy, Span from memory.memory import _memcmp_impl_unconstrained from sys import bitwidthof, simdwidthof @@ -720,7 +719,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # if not s.isspace(): # break # r_idx -= 1 - while r_idx > 0 and _isspace(self.as_bytes()[r_idx - 1]): + while r_idx > 0 and Char(self.as_bytes()[r_idx - 1]).is_posix_space(): r_idx -= 1 return Self(unsafe_from_utf8=self.as_bytes()[:r_idx]) @@ -770,7 +769,10 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # if not s.isspace(): # break # l_idx += 1 - while l_idx < self.byte_length() and _isspace(self.as_bytes()[l_idx]): + while ( + l_idx < self.byte_length() + and Char(self.as_bytes()[l_idx]).is_posix_space() + ): l_idx += 1 return Self(unsafe_from_utf8=self.as_bytes()[l_idx:]) @@ -1022,7 +1024,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( for s in self: var no_null_len = s.byte_length() var ptr = s.unsafe_ptr() - if no_null_len == 1 and _isspace(ptr[0]): + if no_null_len == 1 and Char(ptr[0]).is_posix_space(): continue elif ( no_null_len == 2 and memcmp(ptr, next_line.unsafe_ptr(), 2) == 0 diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 5bdb71d0b2..7d41867971 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -14,7 +14,6 @@ """ from collections.string import StringSlice -from collections.string.string import _isspace from hashlib._hasher import _HashableWithHasher, _Hasher from sys import simdwidthof from sys.ffi import c_char diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index d8700000a3..70bcf8d706 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -60,6 +60,28 @@ def test_char_properties(): assert_false(Char.from_u32(0b1111_1111).value().is_ascii()) +def test_char_is_posix_space(): + # checking true cases + assert_true(Char.ord(" ").is_posix_space()) + assert_true(Char.ord("\n").is_posix_space()) + assert_true(Char.ord("\n").is_posix_space()) + assert_true(Char.ord("\t").is_posix_space()) + assert_true(Char.ord("\r").is_posix_space()) + assert_true(Char.ord("\v").is_posix_space()) + assert_true(Char.ord("\f").is_posix_space()) + + # Checking false cases + assert_false(Char.ord("a").is_posix_space()) + assert_false(Char.ord("a").is_posix_space()) + assert_false(Char.ord("u").is_posix_space()) + assert_false(Char.ord("s").is_posix_space()) + assert_false(Char.ord("t").is_posix_space()) + assert_false(Char.ord("i").is_posix_space()) + assert_false(Char.ord("n").is_posix_space()) + assert_false(Char.ord("z").is_posix_space()) + assert_false(Char.ord(".").is_posix_space()) + + fn assert_utf8_bytes(codepoint: UInt32, owned expected: List[Byte]) raises: var char_opt = Char.from_u32(codepoint) var char = char_opt.value() @@ -156,6 +178,7 @@ def main(): test_char_validity() test_char_from_u8() test_char_properties() + test_char_is_posix_space() test_char_utf8_encoding() test_char_utf8_byte_length() test_char_comptime() diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index aa4c130ddc..12ccb20871 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -24,7 +24,6 @@ from collections.string import StringSlice from collections.string.string import ( _calc_initial_buffer_size_int32, _calc_initial_buffer_size_int64, - _isspace, ) from memory import UnsafePointer from python import Python @@ -872,26 +871,6 @@ def test_upper(): def test_isspace(): - # checking true cases - assert_true(_isspace(ord(" "))) - assert_true(_isspace(ord("\n"))) - assert_true(_isspace("\n")) - assert_true(_isspace(ord("\t"))) - assert_true(_isspace(ord("\r"))) - assert_true(_isspace(ord("\v"))) - assert_true(_isspace(ord("\f"))) - - # Checking false cases - assert_false(_isspace(ord("a"))) - assert_false(_isspace("a")) - assert_false(_isspace(ord("u"))) - assert_false(_isspace(ord("s"))) - assert_false(_isspace(ord("t"))) - assert_false(_isspace(ord("i"))) - assert_false(_isspace(ord("n"))) - assert_false(_isspace(ord("z"))) - assert_false(_isspace(ord("."))) - # test all utf8 and unicode separators # 0 is to build a String with null terminator alias next_line = List[UInt8](0xC2, 0x85, 0) From f8fae244ced17b6a9de4872ec1af6349b79a5992 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sat, 11 Jan 2025 19:07:54 -0600 Subject: [PATCH 201/509] [stdlib] cleanup: Factor out `List._binary_search_index()` helper MODULAR_ORIG_COMMIT_REV_ID: 282b985c8f6eb1b599662442be62f3bf3c0617d8 --- stdlib/src/collections/list.mojo | 25 +++++++++++++++++++++ stdlib/src/collections/string/_unicode.mojo | 17 ++++++-------- 2 files changed, 32 insertions(+), 10 deletions(-) diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 10456b1323..2350d21209 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -808,6 +808,31 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( return i raise "ValueError: Given element is not in list" + fn _binary_search_index[ + dtype: DType, //, + ](self: List[Scalar[dtype], **_], needle: Scalar[dtype]) -> Optional[UInt]: + """Finds the index of `needle` with binary search. + + This function will return an unspecified index if `self` is not + sorted in ascending order. + + Args: + needle: The value to binary search for. + + Returns: + Returns None if `needle` is not present, or if `self` was not + sorted. + """ + var cursor = UInt(0) + var b = self.data + var length = len(self) + while length > 1: + var half = length >> 1 + length -= half + cursor += Int(b[cursor + half - 1] < needle) * half + + return Optional(cursor) if b[cursor] == needle else None + fn clear(mut self): """Clears the elements in the list.""" for i in range(self.size): diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index 7c41c0f08a..55fd84018a 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -41,16 +41,13 @@ fn _lowercase_mapping_index(rune: Int) -> Int: fn _to_index[lookup: List[UInt32, **_]](rune: Int) -> Int: """Find index of rune in lookup with binary search. Returns -1 if not found.""" - var cursor = 0 - var x = UInt32(rune) - var b = lookup.data - var length = len(lookup) - while length > 1: - var half = length >> 1 - length -= half - cursor += Int(b.load(cursor + half - 1) < x) * half - - return cursor if b.load(cursor) == x else -1 + + var result = lookup._binary_search_index(UInt32(rune)) + + if result: + return result.unsafe_value() + else: + return -1 fn is_uppercase(s: StringSlice) -> Bool: From a0208de27f7861be2ef237db2134b984d7d3dd51 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sun, 12 Jan 2025 06:53:13 +0000 Subject: [PATCH 202/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011205 --- examples/life/magic.lock | 573 +++++++++++++++++----------------- examples/magic.lock | 573 +++++++++++++++++----------------- examples/notebooks/magic.lock | 573 +++++++++++++++++----------------- examples/operators/magic.lock | 573 +++++++++++++++++----------------- magic.lock | 573 +++++++++++++++++----------------- 5 files changed, 1440 insertions(+), 1425 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index fda145688e..5aa437248b 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -17,7 +17,7 @@ environments: - 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- max-core ==25.1.0.dev2025011105 release + - max-core ==25.1.0.dev2025011205 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22937 - timestamp: 1736572603499 + size: 22934 + timestamp: 1736659018398 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a @@ -6816,6 +6816,7 @@ packages: arch: x86_64 platform: linux license: MIT + license_family: MIT size: 410192 timestamp: 1736550568524 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/watchfiles-1.0.4-py312h8cbf658_0.conda @@ -6832,6 +6833,7 @@ packages: arch: aarch64 platform: linux license: MIT + license_family: MIT size: 403791 timestamp: 1736550743174 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/watchfiles-1.0.4-py312hcd83bfe_0.conda @@ -6848,6 +6850,7 @@ packages: arch: arm64 platform: osx license: MIT + license_family: MIT size: 363822 timestamp: 1736550859472 - conda: https://conda.anaconda.org/conda-forge/linux-64/websockets-14.1-py312h66e93f0_0.conda From 6926e253c4c35b1e801bb221681e57d02e1cc9b4 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sun, 12 Jan 2025 15:18:24 -0600 Subject: [PATCH 203/509] [stdlib] feat: Add `String` constructor from `Char` MODULAR_ORIG_COMMIT_REV_ID: 487aff5c32e10728930468c3876f75002e316667 --- docs/changelog.md | 1 + stdlib/src/collections/string/string.mojo | 51 ++++++++++++------- .../test/collections/string/test_string.mojo | 5 ++ 3 files changed, 38 insertions(+), 19 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 23979ca0ff..b0951f6618 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -99,6 +99,7 @@ what we publish. functionality. - Added `Char`, for representing and storing single Unicode characters. + - Added `String` constructor from `Char` - `chr(Int)` will now abort if given a codepoint value that is not a valid `Char`. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 984637758f..cca7e2c210 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -104,11 +104,7 @@ fn chr(c: Int) -> String: # SAFETY: We just checked that `char` is present. var char = char_opt.unsafe_value() - var num_bytes = char.utf8_byte_length() - var ptr = UnsafePointer[UInt8].alloc(num_bytes + 1) - _ = char.unsafe_write_utf8(ptr) - ptr[num_bytes] = 0 - return String(ptr=ptr, length=num_bytes + 1) + return String(char) # ===----------------------------------------------------------------------=== # @@ -666,6 +662,20 @@ struct String( # Life cycle methods # ===------------------------------------------------------------------=== # + @always_inline + fn __init__(out self): + """Construct an uninitialized string.""" + self._buffer = Self._buffer_type() + + @always_inline + fn __init__(out self, *, capacity: Int): + """Construct an uninitialized string with the given capacity. + + Args: + capacity: The capacity of the string. + """ + self._buffer = Self._buffer_type(capacity=capacity) + @always_inline fn __init__(out self, owned buffer: List[UInt8, *_]): """Construct a string from a buffer of bytes without copying the @@ -690,20 +700,6 @@ struct String( ) self._buffer = buffer^._cast_hint_trivial_type[True]() - @always_inline - fn __init__(out self): - """Construct an uninitialized string.""" - self._buffer = Self._buffer_type() - - @always_inline - fn __init__(out self, *, capacity: Int): - """Construct an uninitialized string with the given capacity. - - Args: - capacity: The capacity of the string. - """ - self._buffer = Self._buffer_type(capacity=capacity) - fn copy(self) -> Self: """Explicitly copy the provided value. @@ -712,6 +708,23 @@ struct String( """ return self # Just use the implicit copyinit. + fn __init__(out self, char: Char): + """Construct a string from a character. + + Args: + char: The character to construct this string from. + + Notes: + This will allocate a new string holding the UTF-8 encoded + representation of `char`. + """ + var char_len = char.utf8_byte_length() + var buffer = List[Byte](capacity=char_len + 1) + _ = char.unsafe_write_utf8(buffer.unsafe_ptr()) + buffer.unsafe_ptr()[char_len] = 0 + buffer.size = char_len + 1 + self = String(buffer^) + fn __init__(out self, strref: StringRef): """Construct a string from a StringRef object. diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 12ccb20871..7828ba1de7 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -75,6 +75,11 @@ def test_constructors(): var s4 = String(capacity=1) assert_equal(s4._buffer.capacity, 1) + # Construction from Char + var s5 = String(Char(65)) + assert_equal(s4._buffer.capacity, 1) + assert_equal(s5, "A") + def test_copy(): var s0 = String("find") From 61f5224eb18082cfaab566ce7ae9f6d0d9ea3baf Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sun, 12 Jan 2025 16:30:12 -0600 Subject: [PATCH 204/509] =?UTF-8?q?[stdlib]=20feat:=20Impl=20`EqualityComp?= =?UTF-8?q?arable`=20and=20`Stringable`=20for=20`Char`=20(Part=208=20?= =?UTF-8?q?=C2=BE=20=F0=9F=A7=99/X)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit MODULAR_ORIG_COMMIT_REV_ID: 1ff1e6c411e76eae09c14f1b4674349ca997ad70 --- docs/changelog.md | 2 + stdlib/src/builtin/char.mojo | 66 +++++++++++++++++++++++------- stdlib/test/builtin/test_char.mojo | 17 +++++++- 3 files changed, 70 insertions(+), 15 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index b0951f6618..12d44a0173 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -99,6 +99,8 @@ what we publish. functionality. - Added `Char`, for representing and storing single Unicode characters. + - `Char` implements `CollectionElement`, `EqualityComparable`, `Intable`, and + `Stringable`. - Added `String` constructor from `Char` - `chr(Int)` will now abort if given a codepoint value that is not a valid diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 3e07032117..bf7bcf7b5e 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -36,7 +36,7 @@ fn _is_unicode_scalar_value(codepoint: UInt32) -> Bool: @value -struct Char(CollectionElement): +struct Char(CollectionElement, EqualityComparable, Intable, Stringable): """A single textual character. This type represents a single textual character. Specifically, this type @@ -53,19 +53,6 @@ struct Char(CollectionElement): var _scalar_value: UInt32 """The Unicode scalar value represented by this type.""" - # ===-------------------------------------------------------------------===# - # Trait implementations - # ===-------------------------------------------------------------------===# - - @always_inline - fn __int__(self) -> Int: - """Returns the numeric value of this scalar value as an integer. - - Returns: - The numeric value of this scalar value as an integer. - """ - return Int(self._scalar_value) - # ===-------------------------------------------------------------------===# # Life cycle methods # ===-------------------------------------------------------------------===# @@ -218,6 +205,57 @@ struct Char(CollectionElement): return char, Int(num_bytes) + # ===-------------------------------------------------------------------===# + # Operator dunders + # ===-------------------------------------------------------------------===# + + fn __eq__(self, other: Self) -> Bool: + """Return True if this character has the same codepoint value as `other`. + + Args: + other: The codepoint value to compare against. + + Returns: + True if this character and `other` have the same codepoint value; + False otherwise. + """ + return Int(self) == Int(other) + + fn __ne__(self, other: Self) -> Bool: + """Return True if this character has a different codepoint value from + `other`. + + Args: + other: The codepoint value to compare against. + + Returns: + True if this character and `other` have different codepoint values; + False otherwise. + """ + return Int(self) != Int(other) + + # ===-------------------------------------------------------------------===# + # Trait implementations + # ===-------------------------------------------------------------------===# + + @always_inline + fn __int__(self) -> Int: + """Returns the numeric value of this scalar value as an integer. + + Returns: + The numeric value of this scalar value as an integer. + """ + return Int(self._scalar_value) + + @always_inline + fn __str__(self) -> String: + """Formats this `Char` as a single-character string. + + Returns: + A string containing this single character. + """ + return String(self) + # ===-------------------------------------------------------------------===# # Methods # ===-------------------------------------------------------------------===# diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index 70bcf8d706..346467d3e2 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -12,7 +12,7 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from testing import assert_true, assert_false, assert_equal +from testing import assert_true, assert_false, assert_equal, assert_not_equal def test_char_validity(): @@ -50,6 +50,19 @@ def test_char_from_u8(): assert_false(c2.is_ascii()) +def test_char_comparison(): + assert_equal(Char(0), Char(0)) + assert_not_equal(Char(0), Char(1)) + + +def test_char_formatting(): + assert_equal(str(Char(0)), "\0") + assert_equal(str(Char(32)), " ") + assert_equal(str(Char(97)), "a") + assert_equal(str(Char.from_u32(0x00BE).value()), "¾") + assert_equal(str(Char.from_u32(0x1F642).value()), "🙂") + + def test_char_properties(): assert_true(Char.from_u32(0).value().is_ascii()) # Last ASCII codepoint. @@ -177,6 +190,8 @@ def test_char_comptime(): def main(): test_char_validity() test_char_from_u8() + test_char_comparison() + test_char_formatting() test_char_properties() test_char_is_posix_space() test_char_utf8_encoding() From 94298c84730c508bf586ba69443ced52a2253415 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sun, 12 Jan 2025 17:04:45 -0600 Subject: [PATCH 205/509] [stdlib] refactor: Use `Char` in Unicode casing logic cleanup + add `Char.to_u32()` MODULAR_ORIG_COMMIT_REV_ID: 20261f437d464f55ad3d7c55ec643265c5cdde03 --- docs/changelog.md | 1 + stdlib/src/builtin/char.mojo | 15 ++- stdlib/src/collections/string/_unicode.mojo | 118 ++++++++++++------ .../test/collections/string/test_unicode.mojo | 47 +++++++ 4 files changed, 142 insertions(+), 39 deletions(-) create mode 100644 stdlib/test/collections/string/test_unicode.mojo diff --git a/docs/changelog.md b/docs/changelog.md index 12d44a0173..2fea5208c5 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -102,6 +102,7 @@ what we publish. - `Char` implements `CollectionElement`, `EqualityComparable`, `Intable`, and `Stringable`. - Added `String` constructor from `Char` + - `Char` can be converted to `UInt32` via `Char.to_u32()`. - `chr(Int)` will now abort if given a codepoint value that is not a valid `Char`. diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index bf7bcf7b5e..863a046442 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -219,7 +219,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): True if this character and `other` have the same codepoint value; False otherwise. """ - return Int(self) == Int(other) + return self.to_u32() == other.to_u32() fn __ne__(self, other: Self) -> Bool: """Return True if this character has a different codepoint value from @@ -232,7 +232,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): True if this character and `other` have different codepoint values; False otherwise. """ - return Int(self) != Int(other) + return self.to_u32() != other.to_u32() # ===-------------------------------------------------------------------===# # Trait implementations @@ -318,6 +318,17 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): or c == `\x1e` ) + @always_inline + fn to_u32(self) -> UInt32: + """Returns the numeric value of this scalar value as an unsigned 32-bit + integer. + + Returns: + The numeric value of this scalar value as an unsigned 32-bit + integer. + """ + return self._scalar_value + @always_inline fn unsafe_write_utf8(self, ptr: UnsafePointer[Byte]) -> UInt: """Shift unicode to utf8 representation. diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index 55fd84018a..a066930c7f 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -12,37 +12,38 @@ # ===----------------------------------------------------------------------=== # from memory import UnsafePointer, memcpy +from memory import Span from collections.string._unicode_lookups import * -fn _uppercase_mapping_index(rune: Int) -> Int: +fn _uppercase_mapping_index(rune: Char) -> Int: """Return index for upper case mapping or -1 if no mapping is given.""" return _to_index[has_uppercase_mapping](rune) -fn _uppercase_mapping2_index(rune: Int) -> Int: +fn _uppercase_mapping2_index(rune: Char) -> Int: """Return index for upper case mapping converting the rune to 2 runes, or -1 if no mapping is given. """ return _to_index[has_uppercase_mapping2](rune) -fn _uppercase_mapping3_index(rune: Int) -> Int: +fn _uppercase_mapping3_index(rune: Char) -> Int: """Return index for upper case mapping converting the rune to 3 runes, or -1 if no mapping is given. """ return _to_index[has_uppercase_mapping3](rune) -fn _lowercase_mapping_index(rune: Int) -> Int: +fn _lowercase_mapping_index(rune: Char) -> Int: """Return index for lower case mapping or -1 if no mapping is given.""" return _to_index[has_lowercase_mapping](rune) @always_inline -fn _to_index[lookup: List[UInt32, **_]](rune: Int) -> Int: +fn _to_index[lookup: List[UInt32, **_]](rune: Char) -> Int: """Find index of rune in lookup with binary search. Returns -1 if not found.""" - var result = lookup._binary_search_index(UInt32(rune)) + var result = lookup._binary_search_index(rune.to_u32()) if result: return result.unsafe_value() @@ -50,6 +51,60 @@ fn _to_index[lookup: List[UInt32, **_]](rune: Int) -> Int: return -1 +# TODO: +# Refactor this to return a Span[Char, StaticConstantOrigin], so that the +# return `UInt` count and fixed-size `InlineArray` are not necessary. +fn _get_uppercase_mapping( + char: Char, +) -> Optional[Tuple[UInt, InlineArray[Char, 3]]]: + """Returns the 1, 2, or 3 character sequence that is the uppercase form of + `char`. + + Returns None if `char` does not have an uppercase equivalent. + """ + var array = InlineArray[Char, 3](fill=Char(0)) + + var index1 = _uppercase_mapping_index(char) + if index1 != -1: + var rune = uppercase_mapping[index1] + array[0] = Char(unsafe_unchecked_codepoint=rune) + return Tuple(UInt(1), array) + + var index2 = _uppercase_mapping2_index(char) + if index2 != -1: + var runes = uppercase_mapping2[index2] + array[0] = Char(unsafe_unchecked_codepoint=runes[0]) + array[1] = Char(unsafe_unchecked_codepoint=runes[1]) + return Tuple(UInt(2), array) + + var index3 = _uppercase_mapping3_index(char) + if index3 != -1: + var runes = uppercase_mapping3[index3] + array[0] = Char(unsafe_unchecked_codepoint=runes[0]) + array[1] = Char(unsafe_unchecked_codepoint=runes[1]) + array[2] = Char(unsafe_unchecked_codepoint=runes[2]) + return Tuple(UInt(3), array) + + return None + + +fn _get_lowercase_mapping(char: Char) -> Optional[Char]: + var index: Optional[UInt] = has_lowercase_mapping._binary_search_index( + char.to_u32() + ) + + if index: + # SAFETY: We just checked that `result` is present. + var codepoint = lowercase_mapping[index.unsafe_value()] + + # SAFETY: + # We know this is a valid `Char` because the mapping data tables + # contain only valid codepoints. + return Char(unsafe_unchecked_codepoint=codepoint) + else: + return None + + fn is_uppercase(s: StringSlice) -> Bool: """Returns True if all characters in the string are uppercase, and there is at least one cased character. @@ -63,7 +118,7 @@ fn is_uppercase(s: StringSlice) -> Bool: """ var found = False for c in s: - var rune = ord(c) + var rune = Char.ord(c) var index = _lowercase_mapping_index(rune) if index != -1: found = True @@ -93,7 +148,7 @@ fn is_lowercase(s: StringSlice) -> Bool: """ var found = False for c in s: - var rune = ord(c) + var rune = Char.ord(c) var index = _uppercase_mapping_index(rune) if index != -1: found = True @@ -112,12 +167,6 @@ fn is_lowercase(s: StringSlice) -> Bool: return found -fn _write_rune(rune: UInt32, p: UnsafePointer[UInt8]) -> Int: - """Write rune as UTF-8 into provided pointer. Return number of added bytes. - """ - return Char(unsafe_unchecked_codepoint=rune).unsafe_write_utf8(p) - - fn to_lowercase(s: StringSlice) -> String: """Returns a new string with all characters converted to uppercase. @@ -134,15 +183,16 @@ fn to_lowercase(s: StringSlice) -> String: var output_offset = 0 while input_offset < s.byte_length(): var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) - var index = _lowercase_mapping_index(Int(rune_and_size[0])) - if index == -1: + var lowercase_char_opt = _get_lowercase_mapping(rune_and_size[0]) + if lowercase_char_opt is None: memcpy( output + output_offset, input + input_offset, rune_and_size[1] ) output_offset += rune_and_size[1] else: - output_offset += _write_rune( - lowercase_mapping[index], output + output_offset + var lower_char: Char = lowercase_char_opt.unsafe_value() + output_offset += lower_char.unsafe_write_utf8( + output + output_offset ) input_offset += rune_and_size[1] @@ -179,26 +229,20 @@ fn to_uppercase(s: StringSlice) -> String: var output_offset = 0 while input_offset < s.byte_length(): var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) - var index = _uppercase_mapping_index(Int(rune_and_size[0])) - var index2 = _uppercase_mapping2_index( - Int(rune_and_size[0]) - ) if index == -1 else -1 - var index3 = _uppercase_mapping3_index( - Int(rune_and_size[0]) - ) if index == -1 and index2 == -1 else -1 - if index != -1: - output_offset += _write_rune( - uppercase_mapping[index], output + output_offset + + var uppercase_replacement_opt = _get_uppercase_mapping(rune_and_size[0]) + + if uppercase_replacement_opt: + # A given character can be replaced with a sequence of characters + # up to 3 characters in length. A fixed size `Char` array is + # returned, along with a `count` (1, 2, or 3) of how many + # replacement characters are in the uppercase replacement sequence. + count, uppercase_replacement_chars = ( + uppercase_replacement_opt.unsafe_value() ) - elif index2 != -1: - var runes = uppercase_mapping2[index2] - output_offset += _write_rune(runes[0], output + output_offset) - output_offset += _write_rune(runes[1], output + output_offset) - elif index3 != -1: - var runes = uppercase_mapping3[index3] - output_offset += _write_rune(runes[0], output + output_offset) - output_offset += _write_rune(runes[1], output + output_offset) - output_offset += _write_rune(runes[2], output + output_offset) + for char_idx in range(count): + var char: Char = uppercase_replacement_chars[char_idx] + output_offset += char.unsafe_write_utf8(output + output_offset) else: memcpy( output + output_offset, input + input_offset, rune_and_size[1] diff --git a/stdlib/test/collections/string/test_unicode.mojo b/stdlib/test/collections/string/test_unicode.mojo new file mode 100644 index 0000000000..142962118c --- /dev/null +++ b/stdlib/test/collections/string/test_unicode.mojo @@ -0,0 +1,47 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %mojo %s + +from collections import InlineArray +from collections.string._unicode import _get_uppercase_mapping + +from testing import assert_equal + + +def test_uppercase_conversion(): + # a -> A + count1, chars1 = _get_uppercase_mapping(Char(97)).value() + assert_equal(count1, 1) + assert_equal(chars1[0], Char(65)) + assert_equal(chars1[1], Char(0)) + assert_equal(chars1[2], Char(0)) + + # ß -> SS + count2, chars2 = _get_uppercase_mapping(Char.from_u32(0xDF).value()).value() + assert_equal(count2, 2) + assert_equal(chars2[0], Char.from_u32(0x53).value()) + assert_equal(chars2[1], Char.from_u32(0x53).value()) + assert_equal(chars2[2], Char(0)) + + # ΐ -> Ϊ́ + count3, chars3 = _get_uppercase_mapping( + Char.from_u32(0x390).value() + ).value() + assert_equal(count3, 3) + assert_equal(chars3[0], Char.from_u32(0x0399).value()) + assert_equal(chars3[1], Char.from_u32(0x0308).value()) + assert_equal(chars3[2], Char.from_u32(0x0301).value()) + + +def main(): + test_uppercase_conversion() From 2c230c1fd425f17b058bdcb9079d5a20f98df7d5 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Sun, 12 Jan 2025 20:07:40 -0600 Subject: [PATCH 206/509] [stdlib] cleanup: Refactor Unicode casing logic to use List This logic was juggling a data pointer, an offset (the size), and a capacity. That's exactly what `List` manages, so rewrite this to use `List`, improving the readability and letting us reuse functionality like `List.reserve()`. MODULAR_ORIG_COMMIT_REV_ID: 2026b46bb96bdfa76d54035bc8d4ed7e06fe088c --- stdlib/src/collections/list.mojo | 42 ++++++++++- stdlib/src/collections/string/_unicode.mojo | 81 ++++++++++----------- 2 files changed, 78 insertions(+), 45 deletions(-) diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 2350d21209..3c488d3388 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -505,7 +505,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( """ if self.size >= self.capacity: self._realloc(self.capacity * 2 | Int(self.capacity == 0)) - (self.data + self.size).init_pointee_move(value^) + self._unsafe_next_uninit_ptr().init_pointee_move(value^) self.size += 1 fn insert(mut self, i: Int, owned value: T): @@ -611,7 +611,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( If there is no capacity left, resizes to `len(self) + value.size`. """ self.reserve(self.size + value.size) - (self.data + self.size).store(value) + self._unsafe_next_uninit_ptr().store(value) self.size += value.size fn extend[ @@ -633,7 +633,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( debug_assert(count <= value.size, "count must be <= value.size") self.reserve(self.size + count) var v_ptr = UnsafePointer.address_of(value).bitcast[Scalar[D]]() - memcpy(self.data + self.size, v_ptr, count) + memcpy(self._unsafe_next_uninit_ptr(), v_ptr, count) self.size += count fn extend[ @@ -651,7 +651,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( If there is no capacity left, resizes to `len(self) + len(value)`. """ self.reserve(self.size + len(value)) - memcpy(self.data + self.size, value.unsafe_ptr(), len(value)) + memcpy(self._unsafe_next_uninit_ptr(), value.unsafe_ptr(), len(value)) self.size += len(value) fn pop(mut self, i: Int = -1) -> T: @@ -1032,6 +1032,40 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( """ return self.data + @always_inline + fn _unsafe_next_uninit_ptr( + ref self, + ) -> UnsafePointer[ + T, + mut = Origin(__origin_of(self)).is_mutable, + origin = __origin_of(self), + ]: + """Retrieves a pointer to the next uninitialized element position. + + This returns a pointer that points to the element position immediately + after the last initialized element. + + This is equivalent to `list.unsafe_ptr() + len(list)`. + + # Safety + + - This pointer MUST not be used to read or write memory beyond the + allocated capacity of this list. + - This pointer may not be used to initialize non-contiguous elements. + - Ensure that `List.size` is updated to reflect the new number of + initialized elements, otherwise elements may be unexpectedly + overwritten or not destroyed correctly. + """ + debug_assert( + self.capacity > 0 and self.capacity > self.size, + ( + "safety violation: Insufficient capacity to retrieve pointer to" + " next uninitialized element" + ), + ) + + return self.data + self.size + fn _cast_hint_trivial_type[ hint_trivial_type: Bool ](owned self) -> List[T, hint_trivial_type]: diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index a066930c7f..e96dff1260 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -177,40 +177,36 @@ fn to_lowercase(s: StringSlice) -> String: A new string where cased letters have been converted to lowercase. """ var input = s.unsafe_ptr() - var capacity = (s.byte_length() >> 1) * 3 + 1 - var output = UnsafePointer[UInt8].alloc(capacity) + var output = List[Byte](capacity=_estimate_needed_size(s.byte_length())) var input_offset = 0 - var output_offset = 0 while input_offset < s.byte_length(): var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) var lowercase_char_opt = _get_lowercase_mapping(rune_and_size[0]) if lowercase_char_opt is None: memcpy( - output + output_offset, input + input_offset, rune_and_size[1] + output._unsafe_next_uninit_ptr(), + input + input_offset, + rune_and_size[1], ) - output_offset += rune_and_size[1] + output.size += rune_and_size[1] else: var lower_char: Char = lowercase_char_opt.unsafe_value() - output_offset += lower_char.unsafe_write_utf8( - output + output_offset + output.size += lower_char.unsafe_write_utf8( + output._unsafe_next_uninit_ptr() ) input_offset += rune_and_size[1] - if output_offset >= ( - capacity - 5 - ): # check if we need to resize the ouput - capacity += ((s.byte_length() - input_offset) >> 1) * 3 + 1 - var new_output = UnsafePointer[UInt8].alloc(capacity) - memcpy(new_output, output, output_offset) - output.free() - output = new_output - - output[output_offset] = 0 - var list = List[UInt8]( - ptr=output, length=(output_offset + 1), capacity=capacity - ) - return String(list) + # Check if we need to reserve additional capacity. + if output.size >= output.capacity - 5: + output.reserve( + output.capacity + + _estimate_needed_size(s.byte_length() - input_offset) + ) + + # Add NUL terminator + output.append(0) + return String(output^) fn to_uppercase(s: StringSlice) -> String: @@ -223,10 +219,8 @@ fn to_uppercase(s: StringSlice) -> String: A new string where cased letters have been converted to uppercase. """ var input = s.unsafe_ptr() - var capacity = (s.byte_length() >> 1) * 3 + 1 - var output = UnsafePointer[UInt8].alloc(capacity) + var output = List[Byte](capacity=_estimate_needed_size(s.byte_length())) var input_offset = 0 - var output_offset = 0 while input_offset < s.byte_length(): var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) @@ -242,26 +236,31 @@ fn to_uppercase(s: StringSlice) -> String: ) for char_idx in range(count): var char: Char = uppercase_replacement_chars[char_idx] - output_offset += char.unsafe_write_utf8(output + output_offset) + output.size += char.unsafe_write_utf8( + output._unsafe_next_uninit_ptr() + ) else: memcpy( - output + output_offset, input + input_offset, rune_and_size[1] + output._unsafe_next_uninit_ptr(), + input + input_offset, + rune_and_size[1], ) - output_offset += rune_and_size[1] + output.size += rune_and_size[1] input_offset += rune_and_size[1] - if output_offset >= ( - capacity - 5 - ): # check if we need to resize the ouput - capacity += ((s.byte_length() - input_offset) >> 1) * 3 + 1 - var new_output = UnsafePointer[UInt8].alloc(capacity) - memcpy(new_output, output, output_offset) - output.free() - output = new_output - - output[output_offset] = 0 - var list = List[UInt8]( - ptr=output, length=(output_offset + 1), capacity=capacity - ) - return String(list) + # Check if we need to reserve additional capacity. + if output.size >= output.capacity - 5: + output.reserve( + output.capacity + + _estimate_needed_size(s.byte_length() - input_offset) + ) + + # Add NUL terminator + output.append(0) + return String(output^) + + +@always_inline +fn _estimate_needed_size(byte_len: Int) -> Int: + return 3 * (byte_len >> 1) + 1 From 35b373a192e9519bb7d1050cedaf3a41f0647489 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Mon, 13 Jan 2025 14:18:56 +1100 Subject: [PATCH 207/509] [stdlib] Enable SIMD ctor from other SIMD values Previously you couldn't construct a SIMD type from another due to ambiguity of initializers, this has recently been resolved. This enables casting of SIMD types of the same size, and casting a scalar to a SIMD type of a larger size: ```mojo print(UInt64(UInt8(42))) # 42 print(SIMD[DType.uint64, 4](UInt8(42))) # [42, 42, 42, 42] ``` Also move `from_bits` to a static method, this was conflicting with the signature. MODULAR_ORIG_COMMIT_REV_ID: 7139455af2703db8f6b0ef33b97389993d18c9ae --- stdlib/src/builtin/simd.mojo | 102 +++++++++++++++++++++++++---- stdlib/src/math/math.mojo | 8 +-- stdlib/test/builtin/test_simd.mojo | 37 +++++++++++ 3 files changed, 129 insertions(+), 18 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index ec28c1de57..e1f8f186d7 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -287,15 +287,55 @@ struct SIMD[type: DType, size: Int]( _simd_construction_checks[type, size]() self = _unchecked_zero[type, size]() - # FIXME(MOCO-1291): Can't implement this due to ambiguity. - # @always_inline("nodebug") - # fn __init__(out self, *, other: SIMD[type, size]): - # """Explicitly copy the provided value. + @always_inline("nodebug") + fn __init__[other_type: DType, //](out self, value: SIMD[other_type, size]): + """Initialize from another SIMD of the same size. If the value + passed is a scalar, you can initialize a SIMD vector with more elements. + + Parameters: + other_type: The type of the value that is being cast from. + + Args: + value: The value to cast from. + + Example: + + ```mojo + print(UInt64(UInt8(42))) # 42 + print(SIMD[DType.uint64, 4](UInt8(42))) # [42, 42, 42, 42] + ``` + + Casting behavior: + + ```mojo + # Basic casting preserves value within range + Int8(UInt8(127)) == Int8(127) + + # Numbers above signed max wrap to negative using two's complement + Int8(UInt8(128)) == Int8(-128) + Int8(UInt8(129)) == Int8(-127) + Int8(UInt8(256)) == Int8(0) + + # Negative signed cast to unsigned using two's complement + UInt8(Int8(-128)) == UInt8(128) + UInt8(Int8(-127)) == UInt8(129) + UInt8(Int8(-1)) == UInt8(255) + + # Truncate precision after downcast and upcast + Float64(Float32(Float64(123456789.123456789))) == Float64(123456792.0) + + # Rightmost bits of significand become 0's on upcast + Float64(Float32(0.3)) == Float64(0.30000001192092896) - # Args: - # other: The value to copy. - # """ - # self = other + # Numbers equal after truncation of float literal and cast truncation + Float32(Float64(123456789.123456789)) == Float32(123456789.123456789) + + # Float to int/uint floors + Int64(Float64(42.2)) == Int64(42) + ``` + . + """ + self = value.cast[type]() @always_inline fn copy(self) -> Self: @@ -620,19 +660,23 @@ struct SIMD[type: DType, size: Int]( ) ) - fn __init__[ + @staticmethod + fn from_bits[ int_type: DType, // - ](mut self, *, from_bits: SIMD[int_type, size]): + ](value: SIMD[int_type, size]) -> SIMD[type, size]: """Initializes the SIMD vector from the bits of an integral SIMD vector. Parameters: int_type: The integral type of the input SIMD vector. Args: - from_bits: The SIMD vector to copy the bits from. + value: The SIMD vector to copy the bits from. + + Returns: + The bitcast SIMD vector. """ constrained[int_type.is_integral(), "the SIMD type must be integral"]() - self = bitcast[type, size](from_bits) + return bitcast[type, size](value) # ===-------------------------------------------------------------------===# # Operator dunders @@ -1637,7 +1681,7 @@ struct SIMD[type: DType, size: Int]( # FIXME: This should be an alias var mask = FPUtils[type].exponent_mantissa_mask() - return Self(from_bits=self.to_bits() & mask) + return Self.from_bits(self.to_bits() & mask) else: return (self < 0).select(-self, self) @@ -1719,6 +1763,36 @@ struct SIMD[type: DType, size: Int]( Returns: A new SIMD vector whose elements have been casted to the target element type. + + Casting behavior: + + ```mojo + # Basic casting preserves value within range + Int8(UInt8(127)) == Int8(127) + + # Numbers above signed max wrap to negative using two's complement + Int8(UInt8(128)) == Int8(-128) + Int8(UInt8(129)) == Int8(-127) + Int8(UInt8(256)) == Int8(0) + + # Negative signed cast to unsigned using two's complement + UInt8(Int8(-128)) == UInt8(128) + UInt8(Int8(-127)) == UInt8(129) + UInt8(Int8(-1)) == UInt8(255) + + # Truncate precision after downcast and upcast + Float64(Float32(Float64(123456789.123456789))) == Float64(123456792.0) + + # Rightmost bits of significand become 0's on upcast + Float64(Float32(0.3)) == Float64(0.30000001192092896) + + # Numbers equal after truncation of float literal and cast truncation + Float32(Float64(123456789.123456789)) == Float32(123456789.123456789) + + # Float to int/uint floors + Int64(Float64(42.2)) == Int64(42) + ``` + . """ @parameter @@ -3444,7 +3518,7 @@ fn _floor(x: SIMD) -> __type_of(x): bits & ~((1 << (shift_factor - e)) - 1), bits, ) - return __type_of(x)(from_bits=bits) + return __type_of(x).from_bits(bits) fn _write_scalar[ diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 81d53289cf..ec2461fe65 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -421,8 +421,8 @@ fn exp2[ 1.33336498402e-3, ), ](xc) - return __type_of(r)( - from_bits=(r.to_bits() + (m << FPUtils[type].mantissa_width())) + return __type_of(r).from_bits( + (r.to_bits() + (m << FPUtils[type].mantissa_width())) ) @@ -489,7 +489,7 @@ fn _ldexp_impl[ alias integral_type = FPUtils[type].integral_type var m = exp.cast[integral_type]() + FPUtils[type].exponent_bias() - return x * __type_of(x)(from_bits=m << FPUtils[type].mantissa_width()) + return x * __type_of(x).from_bits(m << FPUtils[type].mantissa_width()) @always_inline @@ -676,7 +676,7 @@ fn frexp[ (((mask1 & x_int) >> mantissa_width) - exponent_bias).cast[type](), zero, ) - var frac = selector.select(T(from_bits=x_int & ~mask1 | mask2), zero) + var frac = selector.select(T.from_bits(x_int & ~mask1 | mask2), zero) return StaticTuple[size=2](frac, exp) diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index 4871fcaca2..2e85459f3a 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -69,6 +69,42 @@ def test_cast(): ) +def test_cast_init(): + # Basic casting preserves value within range + assert_equal(Int8(UInt8(127)), Int8(127)) + + # Numbers above signed max wrap to negative using two's complement + assert_equal(Int8(UInt8(128)), Int8(-128)) + assert_equal(Int8(UInt8(129)), Int8(-127)) + assert_equal(Int8(UInt8(256)), Int8(0)) + + # Negative signed convert to unsigned using two's complement + assert_equal(UInt8(Int8(-128)), UInt8(128)) + assert_equal(UInt8(Int8(-127)), UInt8(129)) + assert_equal(UInt8(Int8(-1)), UInt8(255)) + + # Truncate precision after downcast and upcast + assert_equal( + Float64(Float32(Float64(123456789.123456789))), Float64(123456792.0) + ) + + # Rightmost bits of significand become 0's on upcast + assert_equal(Float64(Float32(0.3)), Float64(0.30000001192092896)) + + # Numbers equal after truncation of float literal and cast truncation + assert_equal( + Float32(Float64(123456789.123456789)), Float32(123456789.123456789) + ) + + # Float to int/uint floors + assert_equal(Int64(Float64(42.2)), Int64(42)) + + # Pass a scalar to initialize a SIMD vector with more elements + assert_equal( + SIMD[DType.float64, 4](Float32(21.5)), SIMD[DType.float64, 4](21.5) + ) + + def test_simd_variadic(): assert_equal(str(SIMD[DType.index, 4](52, 12, 43, 5)), "[52, 12, 43, 5]") @@ -1839,6 +1875,7 @@ def main(): test_abs() test_add() test_cast() + test_cast_init() test_ceil() test_convert_simd_to_string() test_simd_repr() From 8e5e14084957460994878c1f957216902721ac8d Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Mon, 13 Jan 2025 14:19:20 +1100 Subject: [PATCH 208/509] [docs] Move from `int` to `Int` in mdx docs Missed some changes throughout the mdx docs for the change from an `int` function to an `Int` constructor. MODULAR_ORIG_COMMIT_REV_ID: 150254606d95405955bcd9f9fe0b93a7322b3744 --- docs/manual/functions.mdx | 2 +- docs/manual/get-started.mdx | 4 +--- docs/manual/parameters/index.mdx | 4 ++-- docs/manual/pointers/unsafe-pointers.mdx | 4 ++-- docs/manual/python/types.mdx | 12 ++++++------ docs/manual/traits.mdx | 2 +- 6 files changed, 13 insertions(+), 15 deletions(-) diff --git a/docs/manual/functions.mdx b/docs/manual/functions.mdx index 46cd3f5aaa..b7e34a14ff 100644 --- a/docs/manual/functions.mdx +++ b/docs/manual/functions.mdx @@ -331,7 +331,7 @@ fn count_many_things[*ArgTypes: Intable](*args: *ArgTypes) -> Int: @parameter fn add[Type: Intable](value: Type): - total += int(value) + total += Int(value) args.each[add]() return total diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 0495016925..9f27b5d5cc 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -850,7 +850,7 @@ struct Grid(StringableRaising): row_data = List[Int]() for col in range(cols): # Generate a random 0 or 1 and append it to the row. - row_data.append(int(random.random_si64(0, 1))) + row_data.append(Int(random.random_si64(0, 1))) data.append(row_data) return Self(rows, cols, data) @@ -877,8 +877,6 @@ signed 64-bit integer value. As described in [Numeric types](/mojo/manual/types#numeric-types), this is *not* the same as the `Int` type whose precision is dependent on the native word size of the system. Therefore we're passing this value to the built-in -[`int()`](/mojo/stdlib/builtin/int/int-function/) function, which explicitly -converts a numeric value to an `Int`. The return type of the method is `Self`, which is an alias for the type of the struct. This is a convenient shortcut if the actual name of the struct is long diff --git a/docs/manual/parameters/index.mdx b/docs/manual/parameters/index.mdx index fae5e5f72c..c602ce9522 100644 --- a/docs/manual/parameters/index.mdx +++ b/docs/manual/parameters/index.mdx @@ -808,9 +808,9 @@ fn slice[ty: DType, new_size: Int, size: Int]( fn reduce_add[ty: DType, size: Int](x: SIMD[ty, size]) -> Int: @parameter if size == 1: - return int(x[0]) + return Int(x[0]) elif size == 2: - return int(x[0]) + int(x[1]) + return Int(x[0]) + Int(x[1]) # Extract the top/bottom halves, add them, sum the elements. alias half_size = size // 2 diff --git a/docs/manual/pointers/unsafe-pointers.mdx b/docs/manual/pointers/unsafe-pointers.mdx index a2a5f04acd..9bfb18a23a 100644 --- a/docs/manual/pointers/unsafe-pointers.mdx +++ b/docs/manual/pointers/unsafe-pointers.mdx @@ -403,7 +403,7 @@ in the chunk. def read_chunks(owned ptr: UnsafePointer[UInt8]) -> List[List[UInt32]]: chunks = List[List[UInt32]]() # A chunk size of 0 indicates the end of the data - chunk_size = int(ptr[]) + chunk_size = Int(ptr[]) while (chunk_size > 0): # Skip the 1 byte chunk_size and get a pointer to the first # UInt32 in the chunk @@ -415,7 +415,7 @@ def read_chunks(owned ptr: UnsafePointer[UInt8]) -> List[List[UInt32]]: # Move our pointer to the next byte after the current chunk ptr += (1 + 4 * chunk_size) # Read the size of the next chunk - chunk_size = int(ptr[]) + chunk_size = Int(ptr[]) return chunks ``` diff --git a/docs/manual/python/types.mdx b/docs/manual/python/types.mdx index 4ef8bee6aa..90f75fb866 100644 --- a/docs/manual/python/types.mdx +++ b/docs/manual/python/types.mdx @@ -148,18 +148,18 @@ Currently `PythonObject` conforms to the [`Intable`](/mojo/stdlib/builtin/int/Intable), [`Stringable`](/mojo/stdlib/builtin/str/Stringable), and [`Boolable`](/mojo/stdlib/builtin/bool/Boolable) traits, which means you can -convert Python values to Mojo `Int`, `String`, and `Bool` types using the -built-in [`int()`](/mojo/stdlib/builtin/int/int-function), -[`str()`](/mojo/stdlib/builtin/str/str), and -[`bool()`](/mojo/stdlib/builtin/bool/bool-function) functions, and print Python -values using the built-in [`print()`](/mojo/stdlib/builtin/io/print) function. +convert Python values to Mojo `String`, and `Bool` types using the +[`str()`](/mojo/stdlib/builtin/str/str) and +[`bool()`](/mojo/stdlib/builtin/bool/bool-function) functions, construct an +`Int` using `Int(python_object)`, and print Python values using the built-in +[`print()`](/mojo/stdlib/builtin/io/print) function. `PythonObject` also provides the [`to_float64()`](/mojo/stdlib/python/python_object/PythonObject#to_float64) for converting to a Mojo floating point value. ```mojo -var i: Int = int(py_int) +var i: Int = Int(py_int) var s: String = str(py_string) var b: Bool = bool(py_bool) var f: Float64 = py_float.to_float64() diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index 4c33a10578..a779396a65 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -375,7 +375,7 @@ struct Foo(Intable): return self.i var foo = Foo(42) -print(int(foo) == 42) +print(Int(foo) == 42) ``` ```output From 41a3e66397d680d6f43bb7f5477fb86b4d87e9ad Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Mon, 13 Jan 2025 15:08:48 +1100 Subject: [PATCH 209/509] [docs] Add changelog entry for SIMD ctor casting Add changelog entry. MODULAR_ORIG_COMMIT_REV_ID: 1aeb9f04478844fc42327085928fbd317f549c94 --- docs/changelog.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 2fea5208c5..af03864c57 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -184,6 +184,31 @@ what we publish. return self.i ``` +- You can now cast SIMD types using constructors: + + ```mojo + var val = Int8(42) + var cast = Int32(val) + ``` + + It also works when passing a scalar type to larger vector size: + + ```mojo + var vector = SIMD[DType.int64, 4](cast) # [42, 42, 42, 42] + ``` + + For values other than scalars the size of the SIMD vector needs to be equal: + + ```mojo + var float_vector = SIMD[DType.float64, 4](vector) + ``` + + `SIMD.cast` still exists to infer the size of new vector: + + ```mojo + var inferred_size = float_vector.cast[DType.uint64]() # [42, 42, 42, 42] + ``` + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents From 4d0e8db07205d761eef28cb7f080cacf4e0928cf Mon Sep 17 00:00:00 2001 From: modularbot Date: Mon, 13 Jan 2025 06:35:58 +0000 Subject: [PATCH 210/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011305 --- examples/life/magic.lock | 222 +++++++++++++++++----------------- examples/magic.lock | 182 ++++++++++++++-------------- examples/notebooks/magic.lock | 182 ++++++++++++++-------------- examples/operators/magic.lock | 182 ++++++++++++++-------------- magic.lock | 182 ++++++++++++++-------------- 5 files changed, 476 insertions(+), 474 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 5aa437248b..594e9f6057 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -78,7 +78,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.14.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_1.conda - 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__osx >=11.0 arch: arm64 platform: osx license: X11 AND BSD-3-Clause - size: 802321 - timestamp: 1724658775723 + size: 796754 + timestamp: 1736683572099 - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda sha256: fe3459c75cf84dcef6ef14efcc4adb0ade66038ddd27cadb894f34f4797687d8 md5: d8285bea2a350f63fab23bf460221f3f From 365be3655ca6bbb01141636dece69a18eb315875 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 12 Jan 2025 22:56:09 -0800 Subject: [PATCH 211/509] [mojo-stdlib] Use prettier syntax to get trait metatypes. Trait metatypes are directly accessible through `__type_of` now that all the plumbing is wired together correctly (and probably was before as well). Use `__type_of` instead of low-level MLIR nonsense. MODULAR_ORIG_COMMIT_REV_ID: 573b3620f974852d09886c0f33c3ae844313fbc5 --- stdlib/src/builtin/builtin_list.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 535ccf206f..2540db3735 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -457,7 +457,7 @@ struct VariadicListMem[ # ===-----------------------------------------------------------------------===# -alias _AnyTypeMetaType = __mlir_type[`!lit.anytrait<`, AnyType, `>`] +alias _AnyTypeMetaType = __type_of(AnyType) @register_passable From bcd638a882b9cb10abac12c404b47ce6e0b43e84 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 12 Jan 2025 23:29:29 -0800 Subject: [PATCH 212/509] [mojo-lang] Fix indexing of homogenous tuples. This fixes indexing of homogenous Tuple values to know that the element type is always consistent, even if indexed with a comptime-dynamic parameter value. This eliminates the need for rebind-like operations with obviously-homogenous tuples. This finishes up the work and fixes [Internal link] MODULAR_ORIG_COMMIT_REV_ID: 879a851adf9bc3763457f3c842bb9ba75fbddfde --- docs/changelog.md | 8 ++++++++ stdlib/benchmarks/collections/bench_dict.mojo | 4 ++-- stdlib/benchmarks/collections/bench_string.mojo | 8 ++++---- stdlib/src/collections/string/string_slice.mojo | 2 +- stdlib/test/builtin/test_simd.mojo | 4 ++-- 5 files changed, 17 insertions(+), 9 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index af03864c57..6bcf9a5976 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -34,6 +34,14 @@ what we publish. `ExplicitlyCopyable` trait. This will ease the transition to explicit copyablility requirements by default in the Mojo collection types. +- Indexing into a homogenous tuple now produces the consistent element type + without needing a rebind: + + ```mojo + var x = (1, 2, 3, 3, 4) + var y : Int = x[idx] # Just works! + ``` + ### Standard library changes - The `int` function to construct an `Int` has been removed, this was a diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index a4ed39e99f..e5d4ba2e37 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -132,7 +132,7 @@ def main(): @parameter for i in range(len(sizes)): - alias size = sizes.get[i, Int]() + alias size = sizes[i] m.bench_function[bench_dict_insert[size]]( BenchId("bench_dict_insert[" + str(size) + "]") ) @@ -144,7 +144,7 @@ def main(): @parameter for i in range(len(sizes)): - alias size = sizes.get[i, Int]() + alias size = sizes[i] var mem_s = total_bytes_used(make_dict[size]()) print( '"bench_dict_memory_size[' + str(size) + ']",' + str(mem_s) + ",0" diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index 8805ac9d06..ee7647989a 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -255,13 +255,13 @@ def main(): @parameter for i in range(len(lengths)): - alias length = lengths.get[i, Int]() + alias length = lengths[i] @parameter for j in range(len(filenames)): - alias fname = filenames.get[j, StringLiteral]() - alias old = old_chars.get[j, StringLiteral]() - alias new = new_chars.get[j, StringLiteral]() + alias fname = filenames[j] + alias old = old_chars[j] + alias new = new_chars[j] suffix = "[" + str(length) + "]" # "(" + fname + ")" m.bench_function[bench_string_count[length, fname, old]]( BenchId("bench_string_count" + suffix) diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 06ed5b66d7..974f2a4e6e 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -45,7 +45,7 @@ fn _count_utf8_continuation_bytes(span: Span[Byte]) -> Int: @parameter for i in range(len(sizes)): - alias s = sizes.get[i, Int]() + alias s = sizes[i] @parameter if simdwidthof[DType.uint8]() >= s: diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index 2e85459f3a..c9c4a9ed53 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -305,7 +305,7 @@ def test_truthy(): @parameter fn test_dtype_unrolled[i: Int]() raises: - alias type = dtypes.get[i, DType]() + alias type = dtypes[i] test_dtype[type]() unroll[test_dtype_unrolled, dtypes.__len__()]() @@ -1837,7 +1837,7 @@ def test_comparison(): @parameter fn test_dtype_unrolled[i: Int]() raises: - alias type = dtypes.get[i, DType]() + alias type = dtypes[i] test_dtype[type]() unroll[test_dtype_unrolled, dtypes.__len__()]() From 44ac88cf220a2c678030f3040b8772b1c1f5c267 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 13 Jan 2025 05:56:21 -0800 Subject: [PATCH 213/509] Revert "[Stdlib] Add convenience methods to check if pointer is None" MODULAR_ORIG_COMMIT_REV_ID: 043ce040493b8eef320dd1acb414e6c30ee7c32d --- docs/changelog.md | 4 -- stdlib/src/memory/unsafe_pointer.mojo | 48 ---------------------- stdlib/test/memory/test_unsafepointer.mojo | 11 ----- 3 files changed, 63 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 6bcf9a5976..0077d2f04b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -171,10 +171,6 @@ what we publish. - `bit_ceil` has been renamed to `next_power_of_two`, and `bit_floor` to `prev_power_of_two`. This is to improve readability and clarity in their use. -- The `UnsafePointer` type now is comparable against the `None` type. This - enables one to write code like `if ptr is not None:` to check if the value - of the pointer is not a null ptr. - - The `Indexer` and `IntLike` traits which were previously both used for indexing have been combined. This enables SIMD scalar integer types and UInt to be used for indexing into all of the collection types, as well as diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index eb430aaea5..c2aefe847e 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -320,18 +320,6 @@ struct UnsafePointer[ """ return Int(self) == Int(rhs) - @always_inline("nodebug") - fn __eq__(self, none: NoneType) -> Bool: - """Returns True if the pointer is null. - - Args: - none: The NoneType value. - - Returns: - True if the pointer is null and False otherwise. - """ - return not self - @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") fn __ne__(self, rhs: Self) -> Bool: @@ -345,18 +333,6 @@ struct UnsafePointer[ """ return not (self == rhs) - @always_inline("nodebug") - fn __ne__(self, none: NoneType) -> Bool: - """Returns True if the pointer is not null. - - Args: - none: The NoneType value. - - Returns: - True if the pointer is null and False otherwise. - """ - return self - @__unsafe_disable_nested_origin_exclusivity @always_inline("nodebug") fn __lt__(self, rhs: Self) -> Bool: @@ -413,30 +389,6 @@ struct UnsafePointer[ """ return Int(self) >= Int(rhs) - @always_inline("nodebug") - fn __is__(self, none: NoneType) -> Bool: - """Returns True if the pointer is null. - - Args: - none: The NoneType value. - - Returns: - True if the pointer is null and False otherwise. - """ - return self == None - - @always_inline("nodebug") - fn __isnot__(self, none: NoneType) -> Bool: - """Returns True if the pointer is not null. - - Args: - none: The NoneType value. - - Returns: - True if the pointer is not null and False otherwise. - """ - return self != None - # ===-------------------------------------------------------------------===# # Trait implementations # ===-------------------------------------------------------------------===# diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 34ce70e5b1..5e8fefc1f5 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -162,17 +162,6 @@ def test_eq(): var p4 = UnsafePointer.address_of(local).origin_cast[mut=False]() assert_equal(p1, p4) - - var p5 = UnsafePointer[Int]() - assert_true(p5 == None) - assert_true(p5 is None) - assert_false(p5 != None) - assert_false(p5 is not None) - assert_false(p1 == None) - assert_false(p1 is None) - assert_true(p1 != None) - assert_true(p1 is not None) - _ = local _ = other_local From 38127618f296e91a8ef97f8812220f40db46ee55 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 13 Jan 2025 09:35:20 -0800 Subject: [PATCH 214/509] [mojo-stdlib] Remove the `Tuple.get` method. This old method serves as a combination of an "index + rebind" operation. remove it and expand the relevant users to remove a footgun. We should also fix `ListLiteral` to follow. This fixes https://github.com/modularml/mojo/issues/3935 MODULAR_ORIG_COMMIT_REV_ID: ec2396f2626f01ea29bb9e9c1a72a52161702f5b --- docs/changelog.md | 5 +++++ stdlib/src/builtin/builtin_list.mojo | 3 ++- stdlib/src/builtin/tuple.mojo | 17 +---------------- stdlib/src/python/python_object.mojo | 12 ++++++------ 4 files changed, 14 insertions(+), 23 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 0077d2f04b..ab4491d523 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -229,6 +229,8 @@ what we publish. - Added `Path` explicit constructor from `StringSlice`. - removed `StringRef.startswith()` and `StringRef.endswith()` - removed `StringRef.strip()` +- The `Tuple.get[i, T]()` method has been removed. Please use `tup[i]` or + `rebind[T](tup[i])` as needed instead. ### 🛠️ Fixed @@ -248,3 +250,6 @@ what we publish. - The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. [Issue #3826](https://github.com/modularml/mojo/issues/3826). + +- [Issue #3935](https://github.com/modularml/mojo/issues/3935) - Confusing OOM + error when using Tuple.get incorrectly. diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 2540db3735..b89b9db1c6 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -91,6 +91,7 @@ struct ListLiteral[*Ts: CollectionElement](Sized, CollectionElement): # Methods # ===-------------------------------------------------------------------===# + # FIXME: This should have a getitem like Tuple does, not a "get" method. @always_inline fn get[i: Int, T: CollectionElement](self) -> ref [self.storage] T: """Get a list element at the given index. @@ -102,7 +103,7 @@ struct ListLiteral[*Ts: CollectionElement](Sized, CollectionElement): Returns: The element at the given index. """ - return self.storage.get[i, T]() + return rebind[T](self.storage[i]) # ===-------------------------------------------------------------------===# # Operator dunders diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index 523e322ebc..57a7950944 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -186,21 +186,6 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): # Use an immortal mut reference, which converts to self's origin. return UnsafePointer(elt_kgen_ptr)[] - # TODO(#38268): Remove this method when references and parameter expressions - # cooperate better. We can't handle the use in test_simd without this. - @always_inline("nodebug") - fn get[i: Int, T: CollectionElement](ref self) -> ref [self] T: - """Get a tuple element and rebind to the specified type. - - Parameters: - i: The element index. - T: The element type. - - Returns: - The tuple element at the requested index. - """ - return rebind[T](self[i]) - @always_inline("nodebug") fn __contains__[ T: EqualityComparableCollectionElement @@ -230,7 +215,7 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): @parameter if _type_is_eq[element_types[i], T](): - if self.get[i, T]() == value: + if rebind[T](self[i]) == value: return True return False diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index 4abc013786..e247ec033b 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -499,21 +499,21 @@ struct PythonObject( @parameter if _type_is_eq[T, PythonObject](): - obj = value.get[i, PythonObject]() + obj = rebind[PythonObject](value[i]) elif _type_is_eq[T, Int](): - obj = PythonObject(value.get[i, Int]()) + obj = PythonObject(rebind[Int](value[i])) elif _type_is_eq[T, Float64](): - obj = PythonObject(value.get[i, Float64]()) + obj = PythonObject(rebind[Float64](value[i])) elif _type_is_eq[T, Bool](): - obj = PythonObject(value.get[i, Bool]()) + obj = PythonObject(rebind[Bool](value[i])) elif _type_is_eq[T, StringRef](): obj = PythonObject( StringSlice[MutableAnyOrigin]( - unsafe_from_utf8_strref=value.get[i, StringRef]() + unsafe_from_utf8_strref=rebind[StringRef](value[i]) ) ) elif _type_is_eq[T, StringLiteral](): - obj = PythonObject(value.get[i, StringLiteral]()) + obj = PythonObject(rebind[StringLiteral](value[i])) else: obj = PythonObject(0) constrained[ From 9a887af12a73bab2b13487d4cb8ffd80ce31fcbe Mon Sep 17 00:00:00 2001 From: Billy Zhu Date: Mon, 13 Jan 2025 10:18:31 -0800 Subject: [PATCH 215/509] [stdlib] Resolve FIXME(#53331) Fixed a crash in debuginfo emission to resolve FIXME(#53331). MODULAR_ORIG_COMMIT_REV_ID: 62d1d6c2c03d8b81e63eefcec30526991b48be50 --- stdlib/src/utils/index.mojo | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 27dc95bbb3..5a1d0c1aed 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -517,9 +517,7 @@ struct IndexList[ The resulting index tuple. """ - # FIXME(#53331) - nodebug is required otherwise we crash in debug - # information generation. - @always_inline("nodebug") + @always_inline fn apply_fn[ type: DType ](a: Scalar[type], b: Scalar[type]) -> Scalar[type]: From b9238f5f95facbc2a90ba25a0269966d3322464e Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 13 Jan 2025 10:48:46 -0800 Subject: [PATCH 216/509] [Stdlib] Split the comparable trait into its constituents, NFC MODULAR_ORIG_COMMIT_REV_ID: 1faad81c86f61961fc99e8d936c4f15e0aac8f3e --- stdlib/src/builtin/comparable.mojo | 41 ++++++++++++++++++++++++------ 1 file changed, 33 insertions(+), 8 deletions(-) diff --git a/stdlib/src/builtin/comparable.mojo b/stdlib/src/builtin/comparable.mojo index 7d74b062fd..5f503487d9 100644 --- a/stdlib/src/builtin/comparable.mojo +++ b/stdlib/src/builtin/comparable.mojo @@ -12,8 +12,8 @@ # ===----------------------------------------------------------------------=== # -trait Comparable(EqualityComparable): - """A type which can be compared with other instances of itself.""" +trait LessThanComparable: + """A type which can be less than compared with other instances of itself.""" fn __lt__(self, rhs: Self) -> Bool: """Define whether `self` is less than `rhs`. @@ -26,28 +26,43 @@ trait Comparable(EqualityComparable): """ ... - fn __le__(self, rhs: Self) -> Bool: - """Define whether `self` is less than or equal to `rhs`. + +trait GreaterThanComparable: + """A type which can be greater than compared with other instances of itself. + """ + + fn __gt__(self, rhs: Self) -> Bool: + """Define whether `self` is greater than `rhs`. Args: rhs: The right hand side of the comparison. Returns: - True if `self` is less than or equal to `rhs`. + True if `self` is greater than `rhs`. """ ... - fn __gt__(self, rhs: Self) -> Bool: - """Define whether `self` is greater than `rhs`. + +trait LessThanOrEqualComparable: + """A type which can be less than or equal to compared with other instances of itself. + """ + + fn __le__(self, rhs: Self) -> Bool: + """Define whether `self` is less than or equal to `rhs`. Args: rhs: The right hand side of the comparison. Returns: - True if `self` is greater than `rhs`. + True if `self` is less than or equal to `rhs`. """ ... + +trait GreaterThanOrEqualComparable: + """A type which can be greater than or equal to compared with other instances of itself. + """ + fn __ge__(self, rhs: Self) -> Bool: """Define whether `self` is greater than or equal to `rhs`. @@ -58,3 +73,13 @@ trait Comparable(EqualityComparable): True if `self` is greater than or equal to `rhs`. """ ... + + +trait Comparable( + EqualityComparable, + LessThanComparable, + GreaterThanComparable, + LessThanOrEqualComparable, + GreaterThanOrEqualComparable, +): + """A type which can be compared with other instances of itself.""" From 81beca28ef162961da82acbf2a69939c4c9eda7f Mon Sep 17 00:00:00 2001 From: Roberto Torrez Date: Mon, 13 Jan 2025 13:30:24 -0800 Subject: [PATCH 217/509] [External] [examples] Add missing import for UnsafePointer (#53821) [External] [examples] Add missing import for UnsafePointer Add missing import for UnsafePointer on MandelBrot notebook example Co-authored-by: Roberto Torrez Closes modularml/mojo#3940 MODULAR_ORIG_COMMIT_REV_ID: 421b8fa873208e0a964b6a3fb5a09eeafa107fd3 --- examples/notebooks/Mandelbrot.ipynb | 765 ++++++++++++++-------------- 1 file changed, 383 insertions(+), 382 deletions(-) diff --git a/examples/notebooks/Mandelbrot.ipynb b/examples/notebooks/Mandelbrot.ipynb index 4dcec04c6a..348d0013ff 100644 --- a/examples/notebooks/Mandelbrot.ipynb +++ b/examples/notebooks/Mandelbrot.ipynb @@ -1,384 +1,385 @@ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: Mandelbrot in Mojo with Python plots\n", - "description: Learn how to write high-performance Mojo code and import Python packages.\n", - "website:\n", - " open-graph:\n", - " image: /static/images/mojo-social-card.png\n", - " twitter-card:\n", - " image: /static/images/mojo-social-card.png\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "# Mandelbrot in Mojo with Python plots\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Not only is Mojo great for writing high-performance code, but it also allows us to leverage the huge Python ecosystem of libraries and tools. With seamless Python interoperability, Mojo can use Python for what it's good at, especially GUIs, without sacrificing performance in critical code. Let's take the classic Mandelbrot set algorithm and implement it in Mojo.\n", - "\n", - "This tutorial shows two aspects of Mojo. First, it shows that Mojo can be used to develop fast programs for irregular applications. It also shows how we can leverage Python for visualizing the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#|code-fold: true\n", - "import benchmark\n", - "from math import iota\n", - "from sys import num_physical_cores, simdwidthof\n", - "from algorithm import parallelize, vectorize\n", - "from complex import ComplexFloat64, ComplexSIMD\n", - "from python import Python\n", - "\n", - "alias float_type = DType.float32\n", - "alias int_type = DType.int32\n", - "alias simd_width = 2 * simdwidthof[float_type]()\n", - "alias unit = benchmark.Unit.ms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First set some parameters, you can try changing these to see different results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "alias width = 960\n", - "alias height = 960\n", - "alias MAX_ITERS = 200\n", - "\n", - "alias min_x = -2.0\n", - "alias max_x = 0.6\n", - "alias min_y = -1.5\n", - "alias max_y = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define a simple `Matrix` struct:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@value\n", - "struct Matrix[type: DType, rows: Int, cols: Int]:\n", - " var data: UnsafePointer[Scalar[type]]\n", - "\n", - " fn __init__(out self):\n", - " self.data = UnsafePointer[Scalar[type]].alloc(rows * cols)\n", - "\n", - " fn __getitem__(self, row: Int, col: Int) -> Scalar[type]:\n", - " return self.data.load(row * cols + col)\n", - "\n", - " fn store[width: Int = 1](self, row: Int, col: Int, val: SIMD[type, width]):\n", - " self.data.store(row * cols + col, val)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The core [Mandelbrot](https://en.wikipedia.org/wiki/Mandelbrot_set) algorithm involves computing an iterative complex function for each pixel until it \"escapes\" the complex circle of radius 2, counting the number of iterations to escape:\n", - "\n", - "$$z_{i+1} = z_i^2 + c$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the number of steps to escape.\n", - "def mandelbrot_kernel(c: ComplexFloat64) -> Int:\n", - " z = c\n", - " for i in range(MAX_ITERS):\n", - " z = z * z + c\n", - " if z.squared_norm() > 4:\n", - " return i\n", - " return MAX_ITERS\n", - "\n", - "\n", - "def compute_mandelbrot() -> Matrix[float_type, height, width]:\n", - " # create a matrix. Each element of the matrix corresponds to a pixel\n", - " matrix = Matrix[float_type, height, width]()\n", - "\n", - " dx = (max_x - min_x) / width\n", - " dy = (max_y - min_y) / height\n", - "\n", - " y = min_y\n", - " for row in range(height):\n", - " x = min_x\n", - " for col in range(width):\n", - " matrix.store(row, col, mandelbrot_kernel(ComplexFloat64(x, y)))\n", - " x += dx\n", - " y += dy\n", - " return matrix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the number of iterations to escape with some color gives us the canonical Mandelbrot set plot. To render it we can directly leverage Python's `matplotlib` right from Mojo!\n", - "\n", - "First install the required libraries:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%python\n", - "from importlib.util import find_spec\n", - "import shutil\n", - "import subprocess\n", - "\n", - "fix = \"\"\"\n", - "-------------------------------------------------------------------------\n", - "fix following the steps here:\n", - " https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719\n", - "-------------------------------------------------------------------------\n", - "\"\"\"\n", - "\n", - "def install_if_missing(name: str):\n", - " if find_spec(name):\n", - " return\n", - " print(\"missing\", name)\n", - " print(f\"{name} not found, installing...\")\n", - " try:\n", - " if shutil.which('python3'): python = \"python3\"\n", - " elif shutil.which('python'): python = \"python\"\n", - " else: raise (\"python not on path\" + fix)\n", - " subprocess.check_call([python, \"-m\", \"pip\", \"install\", name])\n", - " except:\n", - " raise ImportError(f\"{name} not found\" + fix)\n", - "\n", - "install_if_missing(\"numpy\")\n", - "install_if_missing(\"matplotlib\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def show_plot[type: DType](matrix: Matrix[type, height, width]):\n", - " alias scale = 10\n", - " alias dpi = 64\n", - "\n", - " np = Python.import_module(\"numpy\")\n", - " plt = Python.import_module(\"matplotlib.pyplot\")\n", - " colors = Python.import_module(\"matplotlib.colors\")\n", - "\n", - " numpy_array = np.zeros((height, width), np.float64)\n", - "\n", - " for row in range(height):\n", - " for col in range(width):\n", - " numpy_array.itemset((row, col), matrix[row, col])\n", - "\n", - " fig = plt.figure(1, [scale, scale * height // width], dpi)\n", - " ax = fig.add_axes([0.0, 0.0, 1.0, 1.0], False, 1)\n", - " light = colors.LightSource(315, 10, 0, 1, 1, 0)\n", - "\n", - " image = light.shade(numpy_array, plt.cm.hot, colors.PowerNorm(0.3), \"hsv\", 0, 0, 1.5)\n", - " plt.imshow(image)\n", - " plt.axis(\"off\")\n", - " plt.show()\n", - "\n", - "show_plot(compute_mandelbrot())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Vectorizing Mandelbrot\n", - "We showed a naive implementation of the Mandelbrot algorithm, but there are two things we can do to speed it up. We can early-stop the loop iteration when a pixel is known to have escaped, and we can leverage Mojo's access to hardware by vectorizing the loop, computing multiple pixels simultaneously. To do that we will use the `vectorize` higher order generator.\n", - "\n", - "We start by defining our main iteration loop in a vectorized fashion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn mandelbrot_kernel_SIMD[\n", - " simd_width: Int\n", - "](c: ComplexSIMD[float_type, simd_width]) -> SIMD[int_type, simd_width]:\n", - " \"\"\"A vectorized implementation of the inner mandelbrot computation.\"\"\"\n", - " var cx = c.re\n", - " var cy = c.im\n", - " var x = SIMD[float_type, simd_width](0)\n", - " var y = SIMD[float_type, simd_width](0)\n", - " var y2 = SIMD[float_type, simd_width](0)\n", - " var iters = SIMD[int_type, simd_width](0)\n", - "\n", - " var t: SIMD[DType.bool, simd_width] = True\n", - " for _ in range(MAX_ITERS):\n", - " if not any(t):\n", - " break\n", - " y2 = y * y\n", - " y = x.fma(y + y, cy)\n", - " t = x.fma(x, y2) <= 4\n", - " x = x.fma(x, cx - y2)\n", - " iters = t.select(iters + 1, iters)\n", - " return iters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above function is parameterized on the `simd_width` and processes simd_width pixels. It only escapes once all pixels within the vector lane are done. We can use the same iteration loop as above, but this time we vectorize within each row instead. We use the `vectorize` generator to make this a simple function call. The benchmark can run in parallel or just vectorized." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn run_mandelbrot(parallel: Bool) raises -> Float64:\n", - " var matrix = Matrix[int_type, height, width]()\n", - "\n", - " @parameter\n", - " fn worker(row: Int):\n", - " alias scale_x = (max_x - min_x) / width\n", - " alias scale_y = (max_y - min_y) / height\n", - "\n", - " @parameter\n", - " fn compute_vector[simd_width: Int](col: Int):\n", - " \"\"\"Each time we operate on a `simd_width` vector of pixels.\"\"\"\n", - " var cx = min_x + (col + iota[float_type, simd_width]()) * scale_x\n", - " var cy = min_y + row * SIMD[float_type, simd_width](scale_y)\n", - " var c = ComplexSIMD[float_type, simd_width](cx, cy)\n", - " matrix.store(row, col, mandelbrot_kernel_SIMD[simd_width](c))\n", - "\n", - " # Vectorize the call to compute_vector where call gets a chunk of pixels.\n", - " vectorize[compute_vector, simd_width](width)\n", - "\n", - " @parameter\n", - " fn bench():\n", - " for row in range(height):\n", - " worker(row)\n", - "\n", - " @parameter\n", - " fn bench_parallel():\n", - " parallelize[worker](height, height)\n", - "\n", - " var time: Float64 = 0\n", - " if parallel:\n", - " time = benchmark.run[bench_parallel](max_runtime_secs=0.5).mean(unit)\n", - " else:\n", - " time = benchmark.run[bench](max_runtime_secs=0.5).mean(unit)\n", - "\n", - " show_plot(matrix)\n", - " matrix.data.free()\n", - " return time\n", - "\n", - "vectorized = run_mandelbrot(parallel=False)\n", - "print(\"Vectorized:\", vectorized, unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parallelizing Mandelbrot\n", - "While the vectorized implementation above is efficient, we can get better performance by parallelizing on the cols. This again is simple in Mojo using the `parallelize` higher order function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parallelized = run_mandelbrot(parallel=True)\n", - "print(\"Parallelized:\", parallelized, unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Benchmarking\n", - "\n", - "In this section we compare the vectorized speed to the parallelized speed " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Number of physical cores:\", num_physical_cores())\n", - "print(\"Vectorized:\", vectorized, \"ms\")\n", - "print(\"Parallelized:\", parallelized, \"ms\")\n", - "#| CHECK: speedup\n", - "print(\"Parallel speedup:\", vectorized / parallelized)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Mojo (nightly)", - "language": "mojo", - "name": "mojo-nightly-jupyter-kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "mojo" - }, - "file_extension": ".mojo", - "mimetype": "text/x-mojo", - "name": "mojo" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "title: Mandelbrot in Mojo with Python plots\n", + "description: Learn how to write high-performance Mojo code and import Python packages.\n", + "website:\n", + " open-graph:\n", + " image: /static/images/mojo-social-card.png\n", + " twitter-card:\n", + " image: /static/images/mojo-social-card.png\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[//]: # REMOVE_FOR_WEBSITE\n", + "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[//]: # REMOVE_FOR_WEBSITE\n", + "# Mandelbrot in Mojo with Python plots\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not only is Mojo great for writing high-performance code, but it also allows us to leverage the huge Python ecosystem of libraries and tools. With seamless Python interoperability, Mojo can use Python for what it's good at, especially GUIs, without sacrificing performance in critical code. Let's take the classic Mandelbrot set algorithm and implement it in Mojo.\n", + "\n", + "This tutorial shows two aspects of Mojo. First, it shows that Mojo can be used to develop fast programs for irregular applications. It also shows how we can leverage Python for visualizing the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#|code-fold: true\n", + "import benchmark\n", + "from math import iota\n", + "from sys import num_physical_cores, simdwidthof\n", + "from algorithm import parallelize, vectorize\n", + "from complex import ComplexFloat64, ComplexSIMD\n", + "from python import Python\n", + "from memory import UnsafePointer\n", + "\n", + "alias float_type = DType.float32\n", + "alias int_type = DType.int32\n", + "alias simd_width = 2 * simdwidthof[float_type]()\n", + "alias unit = benchmark.Unit.ms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First set some parameters, you can try changing these to see different results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alias width = 960\n", + "alias height = 960\n", + "alias MAX_ITERS = 200\n", + "\n", + "alias min_x = -2.0\n", + "alias max_x = 0.6\n", + "alias min_y = -1.5\n", + "alias max_y = 1.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we define a simple `Matrix` struct:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@value\n", + "struct Matrix[type: DType, rows: Int, cols: Int]:\n", + " var data: UnsafePointer[Scalar[type]]\n", + "\n", + " fn __init__(out self):\n", + " self.data = UnsafePointer[Scalar[type]].alloc(rows * cols)\n", + "\n", + " fn __getitem__(self, row: Int, col: Int) -> Scalar[type]:\n", + " return self.data.load(row * cols + col)\n", + "\n", + " fn store[width: Int = 1](self, row: Int, col: Int, val: SIMD[type, width]):\n", + " self.data.store(row * cols + col, val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The core [Mandelbrot](https://en.wikipedia.org/wiki/Mandelbrot_set) algorithm involves computing an iterative complex function for each pixel until it \"escapes\" the complex circle of radius 2, counting the number of iterations to escape:\n", + "\n", + "$$z_{i+1} = z_i^2 + c$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the number of steps to escape.\n", + "def mandelbrot_kernel(c: ComplexFloat64) -> Int:\n", + " z = c\n", + " for i in range(MAX_ITERS):\n", + " z = z * z + c\n", + " if z.squared_norm() > 4:\n", + " return i\n", + " return MAX_ITERS\n", + "\n", + "\n", + "def compute_mandelbrot() -> Matrix[float_type, height, width]:\n", + " # create a matrix. Each element of the matrix corresponds to a pixel\n", + " matrix = Matrix[float_type, height, width]()\n", + "\n", + " dx = (max_x - min_x) / width\n", + " dy = (max_y - min_y) / height\n", + "\n", + " y = min_y\n", + " for row in range(height):\n", + " x = min_x\n", + " for col in range(width):\n", + " matrix.store(row, col, mandelbrot_kernel(ComplexFloat64(x, y)))\n", + " x += dx\n", + " y += dy\n", + " return matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the number of iterations to escape with some color gives us the canonical Mandelbrot set plot. To render it we can directly leverage Python's `matplotlib` right from Mojo!\n", + "\n", + "First install the required libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%python\n", + "from importlib.util import find_spec\n", + "import shutil\n", + "import subprocess\n", + "\n", + "fix = \"\"\"\n", + "-------------------------------------------------------------------------\n", + "fix following the steps here:\n", + " https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719\n", + "-------------------------------------------------------------------------\n", + "\"\"\"\n", + "\n", + "def install_if_missing(name: str):\n", + " if find_spec(name):\n", + " return\n", + " print(\"missing\", name)\n", + " print(f\"{name} not found, installing...\")\n", + " try:\n", + " if shutil.which('python3'): python = \"python3\"\n", + " elif shutil.which('python'): python = \"python\"\n", + " else: raise (\"python not on path\" + fix)\n", + " subprocess.check_call([python, \"-m\", \"pip\", \"install\", name])\n", + " except:\n", + " raise ImportError(f\"{name} not found\" + fix)\n", + "\n", + "install_if_missing(\"numpy\")\n", + "install_if_missing(\"matplotlib\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def show_plot[type: DType](matrix: Matrix[type, height, width]):\n", + " alias scale = 10\n", + " alias dpi = 64\n", + "\n", + " np = Python.import_module(\"numpy\")\n", + " plt = Python.import_module(\"matplotlib.pyplot\")\n", + " colors = Python.import_module(\"matplotlib.colors\")\n", + "\n", + " numpy_array = np.zeros((height, width), np.float64)\n", + "\n", + " for row in range(height):\n", + " for col in range(width):\n", + " numpy_array.itemset((row, col), matrix[row, col])\n", + "\n", + " fig = plt.figure(1, [scale, scale * height // width], dpi)\n", + " ax = fig.add_axes([0.0, 0.0, 1.0, 1.0], False, 1)\n", + " light = colors.LightSource(315, 10, 0, 1, 1, 0)\n", + "\n", + " image = light.shade(numpy_array, plt.cm.hot, colors.PowerNorm(0.3), \"hsv\", 0, 0, 1.5)\n", + " plt.imshow(image)\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + "show_plot(compute_mandelbrot())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vectorizing Mandelbrot\n", + "We showed a naive implementation of the Mandelbrot algorithm, but there are two things we can do to speed it up. We can early-stop the loop iteration when a pixel is known to have escaped, and we can leverage Mojo's access to hardware by vectorizing the loop, computing multiple pixels simultaneously. To do that we will use the `vectorize` higher order generator.\n", + "\n", + "We start by defining our main iteration loop in a vectorized fashion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fn mandelbrot_kernel_SIMD[\n", + " simd_width: Int\n", + "](c: ComplexSIMD[float_type, simd_width]) -> SIMD[int_type, simd_width]:\n", + " \"\"\"A vectorized implementation of the inner mandelbrot computation.\"\"\"\n", + " var cx = c.re\n", + " var cy = c.im\n", + " var x = SIMD[float_type, simd_width](0)\n", + " var y = SIMD[float_type, simd_width](0)\n", + " var y2 = SIMD[float_type, simd_width](0)\n", + " var iters = SIMD[int_type, simd_width](0)\n", + "\n", + " var t: SIMD[DType.bool, simd_width] = True\n", + " for _ in range(MAX_ITERS):\n", + " if not any(t):\n", + " break\n", + " y2 = y * y\n", + " y = x.fma(y + y, cy)\n", + " t = x.fma(x, y2) <= 4\n", + " x = x.fma(x, cx - y2)\n", + " iters = t.select(iters + 1, iters)\n", + " return iters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above function is parameterized on the `simd_width` and processes simd_width pixels. It only escapes once all pixels within the vector lane are done. We can use the same iteration loop as above, but this time we vectorize within each row instead. We use the `vectorize` generator to make this a simple function call. The benchmark can run in parallel or just vectorized." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fn run_mandelbrot(parallel: Bool) raises -> Float64:\n", + " var matrix = Matrix[int_type, height, width]()\n", + "\n", + " @parameter\n", + " fn worker(row: Int):\n", + " alias scale_x = (max_x - min_x) / width\n", + " alias scale_y = (max_y - min_y) / height\n", + "\n", + " @parameter\n", + " fn compute_vector[simd_width: Int](col: Int):\n", + " \"\"\"Each time we operate on a `simd_width` vector of pixels.\"\"\"\n", + " var cx = min_x + (col + iota[float_type, simd_width]()) * scale_x\n", + " var cy = min_y + row * SIMD[float_type, simd_width](scale_y)\n", + " var c = ComplexSIMD[float_type, simd_width](cx, cy)\n", + " matrix.store(row, col, mandelbrot_kernel_SIMD[simd_width](c))\n", + "\n", + " # Vectorize the call to compute_vector where call gets a chunk of pixels.\n", + " vectorize[compute_vector, simd_width](width)\n", + "\n", + " @parameter\n", + " fn bench():\n", + " for row in range(height):\n", + " worker(row)\n", + "\n", + " @parameter\n", + " fn bench_parallel():\n", + " parallelize[worker](height, height)\n", + "\n", + " var time: Float64 = 0\n", + " if parallel:\n", + " time = benchmark.run[bench_parallel](max_runtime_secs=0.5).mean(unit)\n", + " else:\n", + " time = benchmark.run[bench](max_runtime_secs=0.5).mean(unit)\n", + "\n", + " show_plot(matrix)\n", + " matrix.data.free()\n", + " return time\n", + "\n", + "vectorized = run_mandelbrot(parallel=False)\n", + "print(\"Vectorized:\", vectorized, unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parallelizing Mandelbrot\n", + "While the vectorized implementation above is efficient, we can get better performance by parallelizing on the cols. This again is simple in Mojo using the `parallelize` higher order function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parallelized = run_mandelbrot(parallel=True)\n", + "print(\"Parallelized:\", parallelized, unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmarking\n", + "\n", + "In this section we compare the vectorized speed to the parallelized speed " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Number of physical cores:\", num_physical_cores())\n", + "print(\"Vectorized:\", vectorized, \"ms\")\n", + "print(\"Parallelized:\", parallelized, \"ms\")\n", + "#| CHECK: speedup\n", + "print(\"Parallel speedup:\", vectorized / parallelized)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Mojo (nightly)", + "language": "mojo", + "name": "mojo-nightly-jupyter-kernel" + }, + "language_info": { + "codemirror_mode": { + "name": "mojo" + }, + "file_extension": ".mojo", + "mimetype": "text/x-mojo", + "name": "mojo" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 } From df5423ca642912c9055f1097db1a96f5353a4034 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 13 Jan 2025 17:05:04 -0800 Subject: [PATCH 218/509] [Stdlib] Add variadic max/min operation MODULAR_ORIG_COMMIT_REV_ID: e81afc4674019503713890af465d16f2ff811b03 --- docs/changelog.md | 2 ++ stdlib/src/builtin/math.mojo | 50 ++++++++++++++++++++++++++++++ stdlib/src/prelude/__init__.mojo | 8 ++++- stdlib/test/builtin/test_math.mojo | 8 +++++ 4 files changed, 67 insertions(+), 1 deletion(-) diff --git a/docs/changelog.md b/docs/changelog.md index ab4491d523..b1e027026d 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -213,6 +213,8 @@ what we publish. var inferred_size = float_vector.cast[DType.uint64]() # [42, 42, 42, 42] ``` +- You can now use `max()` and `min()` with variadic number of arguments. + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 843b380e88..28e3c72833 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -206,6 +206,31 @@ fn max[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): return __mlir_op.`pop.max`(x.value, y.value) +trait _CopyableGreaterThanComparable(Copyable, GreaterThanComparable): + ... + + +@always_inline +fn max[T: _CopyableGreaterThanComparable](x: T, *ys: T) -> T: + """Gets the maximum value from a sequence of values. + + Parameters: + T: A type that is both copyable and comparable with greater than. + + Args: + x: The first value to compare. + ys: Zero or more additional values to compare. + + Returns: + The maximum value from the input sequence. + """ + var res = x + for y in ys: + if y[] > res: + res = y[] + return res + + # ===----------------------------------------------------------------------=== # # min # ===----------------------------------------------------------------------=== # @@ -271,6 +296,31 @@ fn min[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): return __mlir_op.`pop.min`(x.value, y.value) +trait _CopyableLessThanComparable(Copyable, LessThanComparable): + ... + + +@always_inline +fn min[T: _CopyableLessThanComparable](x: T, *ys: T) -> T: + """Gets the minimum value from a sequence of values. + + Parameters: + T: A type that is both copyable and comparable with less than. + + Args: + x: The first value to compare. + ys: Zero or more additional values to compare. + + Returns: + The minimum value from the input sequence. + """ + var res = x + for y in ys: + if y[] < res: + res = y[] + return res + + # ===----------------------------------------------------------------------=== # # pow # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 75b88c8700..a73a129610 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -39,8 +39,14 @@ from builtin.builtin_list import ( VariadicPack, ) from builtin.builtin_slice import Slice, slice +from builtin.comparable import ( + LessThanComparable, + GreaterThanComparable, + LessThanOrEqualComparable, + GreaterThanOrEqualComparable, + Comparable, +) from builtin.char import Char -from builtin.comparable import Comparable from builtin.constrained import constrained from builtin.coroutine import AnyCoroutine, Coroutine, RaisingCoroutine from builtin.debug_assert import debug_assert diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index 430d0ffde9..ec1304b926 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -59,6 +59,10 @@ def test_min(): assert_equal(min(i, I(-9, -6, -4, 11)), I(-10, -6, -4, 10)) assert_equal(min(i, -4), I(-10, -5, -4, -4)) + assert_equal(min(1), 1) + assert_equal(min(1, 2, 3, 4), 1) + assert_equal(min(500, 1, 2, 3, 4), 1) + def test_max(): assert_equal(-1, max(-2, -1)) @@ -79,6 +83,10 @@ def test_max(): assert_equal(max(i, I(-9, -6, -4, 11)), I(-9, -5, 5, 11)) assert_equal(max(i, -4), I(-4, -4, 5, 10)) + assert_equal(max(1), 1) + assert_equal(max(1, 2, 3, 4), 4) + assert_equal(max(-10, 2, 3, 4, -10), 4) + def test_round(): assert_equal(0, round(0.0)) From c4261ca853dcd1134fddc51f5515f4034bd88b25 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Mon, 13 Jan 2025 19:01:14 -0800 Subject: [PATCH 219/509] Added links to API references and fixed whitespace in code samples MODULAR_ORIG_COMMIT_REV_ID: 08f97ad1651fd8940c9d1e8bc6681ffb1460e007 --- docs/manual/get-started.mdx | 10 ++++++---- docs/manual/python/types.mdx | 30 ++++++++++++++++-------------- 2 files changed, 22 insertions(+), 18 deletions(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 9f27b5d5cc..8540761f2e 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -344,7 +344,7 @@ def main(): ## 5. Create and use a function to print the grid Now let's create a function to generate a string representation of the game grid -that we can print it to the terminal. +that we can print to the terminal. There are actually two different keywords that we can use to define functions in Mojo: `def` and `fn`. Using `fn` gives us finer level control over the function @@ -362,7 +362,7 @@ def grid_str(rows: Int, cols: Int, grid: List[List[Int]]) -> String: # Iterate through rows 0 through rows-1 for row in range(rows): - # Iterate through columns 0 through cols-1 + # Iterate through columns 0 through cols-1 for col in range(cols): if grid[row][col] == 1: str += "*" # If cell is populated, append an asterisk @@ -540,7 +540,7 @@ def grid_str(grid: Grid) -> String: # Iterate through rows 0 through rows-1 for row in range(grid.rows): - # Iterate through columns 0 through cols-1 + # Iterate through columns 0 through cols-1 for col in range(grid.cols): if grid.data[row][col] == 1: str += "*" # If cell is populated, append an asterisk @@ -876,7 +876,9 @@ respectively. This function actually returns a value of type `Int64`, which is a signed 64-bit integer value. As described in [Numeric types](/mojo/manual/types#numeric-types), this is *not* the same as the `Int` type whose precision is dependent on the native word size of the system. -Therefore we're passing this value to the built-in +Therefore we're passing this value to the +[`Int()`](/mojo/stdlib/builtin/int/Int/#__init__) constructor, which explicitly +converts a numeric value to an `Int`. The return type of the method is `Self`, which is an alias for the type of the struct. This is a convenient shortcut if the actual name of the struct is long diff --git a/docs/manual/python/types.mdx b/docs/manual/python/types.mdx index 90f75fb866..4aab0ace57 100644 --- a/docs/manual/python/types.mdx +++ b/docs/manual/python/types.mdx @@ -145,24 +145,26 @@ Some Mojo APIs handle `PythonObject` just fine, but sometimes you'll need to explicitly convert a Python value into a native Mojo value. Currently `PythonObject` conforms to the -[`Intable`](/mojo/stdlib/builtin/int/Intable), -[`Stringable`](/mojo/stdlib/builtin/str/Stringable), and -[`Boolable`](/mojo/stdlib/builtin/bool/Boolable) traits, which means you can -convert Python values to Mojo `String`, and `Bool` types using the -[`str()`](/mojo/stdlib/builtin/str/str) and -[`bool()`](/mojo/stdlib/builtin/bool/bool-function) functions, construct an -`Int` using `Int(python_object)`, and print Python values using the built-in -[`print()`](/mojo/stdlib/builtin/io/print) function. - -`PythonObject` also provides the -[`to_float64()`](/mojo/stdlib/python/python_object/PythonObject#to_float64) for -converting to a Mojo floating point value. +[`Stringable`](/mojo/stdlib/builtin/str/Stringable), +[`Boolable`](/mojo/stdlib/builtin/bool/Boolable), +[`Floatable`](/mojo/stdlib/builtin/floatable/Floatable/), and +[`Intable`](/mojo/stdlib/builtin/int/Intable) traits, which means you can +convert Python values to Mojo `String`, `Bool`, and `Float64` types using the +[`str()`](/mojo/stdlib/builtin/str/str), +[`bool()`](/mojo/stdlib/builtin/bool/bool-function) and +[`float()`](/mojo/stdlib/builtin/floatable/float/) built-in functions, and +construct an `Int` using the [`Int()`](/mojo/stdlib/builtin/int/Int/#__init__) +constructor. `PythonObject` also conforms to the +[`Writable`](/mojo/stdlib/utils/write/Writable) trait so that you can print +Python values using the built-in [`print()`](/mojo/stdlib/builtin/io/print) +function. ```mojo -var i: Int = Int(py_int) var s: String = str(py_string) var b: Bool = bool(py_bool) -var f: Float64 = py_float.to_float64() +var f: Float64 = float(py_float) +var i: Int = Int(py_int) +print(py_obj) ``` ### Comparing Python types in Mojo From ab8734fad5f5a943e5a90009400be3992994c5cb Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 14 Jan 2025 06:37:23 +0000 Subject: [PATCH 220/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011405 --- examples/life/magic.lock | 216 +++++++++++++++++++--------------- examples/magic.lock | 215 ++++++++++++++++++--------------- examples/notebooks/magic.lock | 176 +++++++++++++-------------- examples/operators/magic.lock | 215 ++++++++++++++++++--------------- magic.lock | 215 ++++++++++++++++++--------------- 5 files changed, 569 insertions(+), 468 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 594e9f6057..49b4b05dfd 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - 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size: 64783 - timestamp: 1732523806 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.17.0-py312hea69d52_0.conda - sha256: 0fb35c3d1642f9f47db87bdb33148f88ef19a3af1eb0ee99b5491551c57269c7 - md5: 73414acdb779a8694a14527865b4357a + size: 64616 + timestamp: 1736757911835 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.17.1-py312hea69d52_0.conda + sha256: 213a18609ccfdde862a8fcf9d96b9133cc6ca0abf5953ed148b35a9c8eb27a9c + md5: e3a62bb92c2e71e95f0e3be67a5ff351 depends: - __osx >=11.0 - python >=3.12,<3.13.0a0 @@ -6935,8 +6960,8 @@ packages: platform: osx license: BSD-2-Clause license_family: BSD - size: 61043 - timestamp: 1732523852129 + size: 61365 + timestamp: 1736758030632 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038 md5: f6ebe2cb3f82ba6c057dde5d9debe4f7 From b1cfc195a31f2ea96007457bee67c6de51c5be35 Mon Sep 17 00:00:00 2001 From: Rasool Sharifi Date: Tue, 14 Jan 2025 12:08:04 -0500 Subject: [PATCH 221/509] [stdlib] Add fp32 to fp8 conversions Add fp32 to fp8 conversions MODULAR_ORIG_COMMIT_REV_ID: 49f0d7d4a19daf3bf54d10bbe68b84100cbc818f --- stdlib/src/builtin/simd.mojo | 184 +++++++++++++++++++++++++++++++++++ 1 file changed, 184 insertions(+) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index e1f8f186d7..8debd181dc 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -57,6 +57,7 @@ from utils import IndexList, StaticTuple from utils._visualizers import lldb_formatter_wrapping_type from utils.numerics import FPUtils from utils.numerics import isnan as _isnan +from utils.numerics import isinf as _isinf from utils.numerics import max_finite as _max_finite from utils.numerics import max_or_inf as _max_or_inf from utils.numerics import min_finite as _min_finite @@ -1831,6 +1832,15 @@ struct SIMD[type: DType, size: Int]( # use the optimizations defined above. return self.cast[DType.float32]().cast[target]() + @parameter + if type is DType.float32 and target in ( + DType.float8e4m3, + DType.float8e5m2, + ): + return _convert_f32_to_float8[size=size, target=target]( + rebind[SIMD[DType.float32, size]](self) + ) + @parameter if type in (DType.float8e4m3, DType.float8e5m2): constrained[ @@ -3284,6 +3294,180 @@ fn _convert_float8_to_f16[ ]() +@always_inline +fn _convert_f32_to_float8[ + type: DType, + target: DType, + size: Int, +](val: SIMD[type, size],) -> SIMD[target, size]: + @parameter + if is_nvidia_gpu() and _is_sm_9x(): + alias asm_prefix = "cvt.rn.satfinite.e4m3x2.f32" if target is DType.float8e4m3 else "cvt.rn.satfinite.e5m2x2.f32" + + @parameter + if size > 1: + var res = SIMD[target, size]() + + @parameter + for i in range(0, size, 2): + var f8x2_f32x2 = inlined_assembly[ + asm_prefix + " $0, $1, $2;", + Scalar[DType.uint16], + constraints="=h,f,f", + has_side_effect=False, + ](val[i + 1], val[i]) + var ui8x2 = bitcast[target, 2](f8x2_f32x2) + res = res.insert[offset=i](ui8x2) + return res + else: + var f8x2_f32x2 = inlined_assembly[ + asm_prefix + " $0, $1, $2;", + Scalar[DType.uint16], + constraints="=h,f,f", + has_side_effect=False, + ](Float32(0.0), val[0]) + var ui8x2 = bitcast[target, 2](f8x2_f32x2) + return ui8x2[0] + else: + + @always_inline + @parameter + fn wrapper_fn[ + input_type: DType, result_type: DType + ](val: Scalar[input_type]) capturing -> Scalar[result_type]: + return rebind[Scalar[result_type]]( + _convert_f32_to_float8_scaler[type, result_type]( + rebind[Scalar[type]](val) + ) + ) + + return _simd_apply[wrapper_fn, target, size](val) + + +@always_inline +fn _convert_f32_to_float8_scaler[ + type: DType, + target: DType, +](x: Scalar[type]) -> Scalar[target]: + # software implementation rounds toward nearest even + + alias IS_E4M3 = target is DType.float8e4m3 + alias FP8_NUM_MANTISSA_BITS = FPUtils[target].mantissa_width() + alias FP8_NUM_EXPONENT_BITS = FPUtils[target].exponent_width() + alias FP32_NUM_BITS = bitwidthof[type]() + alias FP8_EXPONENT_MASK: UInt8 = (1 << FP8_NUM_EXPONENT_BITS) - 1 + alias FP8_MANTISSA_MASK: UInt8 = (1 << FP8_NUM_MANTISSA_BITS) - 1 + alias FP8_MAX_EXPONENT = FPUtils[target].exponent_bias() + var FP8_MIN_EXPONENT = -6 if IS_E4M3 else -14 + alias FP8_EXPONENT_BIAS = FPUtils[target].exponent_bias() + alias FP32_EXPONENT_BIAS = FPUtils[type].exponent_bias() + alias FP32_NUM_MANTISSA_BITS = FPUtils[type].mantissa_width() + alias FP8_MAX_FLT: UInt8 = 0x7E if IS_E4M3 else 0x7B + + # Extract the bits in the FP32 type + var sign: UInt8 = 0x80 if FPUtils[type].get_sign(x) else 0x00 + var exp = Int32(FPUtils[type].get_exponent_biased(x)) - FP32_EXPONENT_BIAS + var mantissa = Int32(FPUtils[type].get_mantissa(x)) + var u: UInt8 = 0 + + var kF8_NaN: UInt8 = 0x7F + + # NaN => NaN + if _isnan(x): + return bitcast[target](kF8_NaN) + + # Inf => MAX_FLT (satfinite) + if _isinf(x): + return bitcast[target](sign | FP8_MAX_FLT) + + # Special handling + if exp == -128: + # int8 range is from -128 to 127 + # So 255(inf) - 127(bias) = 128 - will show up as -128 + + # satfinite + return bitcast[target](sign | FP8_MAX_FLT) + + var sticky_bit: Int32 = 0 + + var skip_sign = False + var may_be_nan = False + + if exp >= FP8_MIN_EXPONENT and exp <= FP8_MAX_EXPONENT: + # normal fp32 to normal fp8 + exp += FP8_EXPONENT_BIAS + u = ( + ( + (exp).cast[DType.uint32]() + & FP8_EXPONENT_MASK.cast[DType.uint32]() + ) + << FP8_NUM_MANTISSA_BITS + ).cast[DType.uint8]() + u = ( + u + | ( + mantissa >> (FP32_NUM_MANTISSA_BITS - FP8_NUM_MANTISSA_BITS) + ).cast[DType.uint8]() + ) + elif exp < FP8_MIN_EXPONENT: + # normal single-precision to subnormal float8-precision representation + var rshift: Int32 = FP8_MIN_EXPONENT - exp + if rshift < FP32_NUM_BITS: + mantissa |= 1 << FP32_NUM_MANTISSA_BITS + sticky_bit = ((mantissa & ((1 << rshift) - 1)) != 0).cast[ + DType.int32 + ]() + mantissa = mantissa >> rshift + u = ( + mantissa >> (FP32_NUM_MANTISSA_BITS - FP8_NUM_MANTISSA_BITS) + ).cast[DType.uint8]() & FP8_MANTISSA_MASK + else: + mantissa = 0 + u = 0 + # Exponent > FP8_MAX_EXPONENT - this is a special case done to match HW + # 0x4380_0000 to 0x43e0_0000 - maps from 256 to 448, and does not saturate / inf. + else: + if exp == (FP8_MAX_EXPONENT + 1): + var mantissa_tmp: UInt8 = ( + mantissa >> (FP32_NUM_MANTISSA_BITS - FP8_NUM_MANTISSA_BITS) + ).cast[DType.uint8]() + if mantissa_tmp < FP8_MANTISSA_MASK: + exp = exp + FP8_EXPONENT_BIAS + u = ((exp).cast[DType.uint32]() << FP8_NUM_MANTISSA_BITS).cast[ + DType.uint8 + ]() | mantissa_tmp + may_be_nan = mantissa_tmp == (FP8_MANTISSA_MASK - 1) + else: + # satfinite + return bitcast[target](sign | FP8_MAX_FLT) + else: + # satfinite + return bitcast[target](sign | FP8_MAX_FLT) + + # round to nearest even + var NUM_BITS_SHIFT: Int32 = FP32_NUM_MANTISSA_BITS - ( + FP8_NUM_MANTISSA_BITS + 1 + ) + var round_bit: Int32 = ((mantissa >> NUM_BITS_SHIFT) & 1) + sticky_bit |= ((mantissa & ((1 << NUM_BITS_SHIFT) - 1)) != 0).cast[ + DType.int32 + ]() + + if (round_bit and sticky_bit) or (round_bit and (u & 1)): + u = (u + 1).cast[DType.uint8]() + if may_be_nan: + skip_sign = True + + if u > FP8_MAX_FLT: + # satfinite + u = sign | FP8_MAX_FLT + + if not skip_sign: + u |= sign + + return bitcast[target](u) + + # ===----------------------------------------------------------------------=== # # bfloat16 # ===----------------------------------------------------------------------=== # From eb7eb3d7ea9b1b81aee80c6d0e00879dee7ef7f7 Mon Sep 17 00:00:00 2001 From: Owen Hilyard Date: Tue, 14 Jan 2025 09:46:42 -0800 Subject: [PATCH 222/509] [External] [stdlib] Make atomics sharable (#53867) [External] [stdlib] Make atomics sharable Prior to this, most atomic operations required a mutable reference, which meant you had statically guarenteed mutual exclusion, which means you don't need atomics. This fixes those operations to only require an immutable reference, allowing them to be used in multiple threads. Co-authored-by: Owen Hilyard Closes modularml/mojo#3943 MODULAR_ORIG_COMMIT_REV_ID: ba2460225e7243f65d80ea397c97c9c0a77f26f6 --- stdlib/src/os/atomic.mojo | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/stdlib/src/os/atomic.mojo b/stdlib/src/os/atomic.mojo index 5b34c70298..3b1088fcce 100644 --- a/stdlib/src/os/atomic.mojo +++ b/stdlib/src/os/atomic.mojo @@ -53,7 +53,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: self.value = value @always_inline - fn load(mut self) -> Scalar[type]: + fn load(self) -> Scalar[type]: """Loads the current value from the atomic. Returns: @@ -139,7 +139,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: ) @always_inline - fn fetch_add(mut self, rhs: Scalar[type]) -> Scalar[type]: + fn fetch_add(self, rhs: Scalar[type]) -> Scalar[type]: """Performs atomic in-place add. Atomically replaces the current value with the result of arithmetic @@ -158,7 +158,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: return Self._fetch_add(value_addr, rhs) @always_inline - fn __iadd__(mut self, rhs: Scalar[type]): + fn __iadd__(self, rhs: Scalar[type]): """Performs atomic in-place add. Atomically replaces the current value with the result of arithmetic @@ -173,7 +173,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: _ = self.fetch_add(rhs) @always_inline - fn fetch_sub(mut self, rhs: Scalar[type]) -> Scalar[type]: + fn fetch_sub(self, rhs: Scalar[type]) -> Scalar[type]: """Performs atomic in-place sub. Atomically replaces the current value with the result of arithmetic @@ -197,7 +197,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: ](value_addr.address, rhs.value) @always_inline - fn __isub__(mut self, rhs: Scalar[type]): + fn __isub__(self, rhs: Scalar[type]): """Performs atomic in-place sub. Atomically replaces the current value with the result of arithmetic @@ -213,7 +213,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: @always_inline fn compare_exchange_weak( - mut self, mut expected: Scalar[type], desired: Scalar[type] + self, mut expected: Scalar[type], desired: Scalar[type] ) -> Bool: """Atomically compares the self value with that of the expected value. If the values are equal, then the self value is replaced with the @@ -271,7 +271,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: _max_impl[scope=scope](ptr, rhs) @always_inline - fn max(mut self, rhs: Scalar[type]): + fn max(self, rhs: Scalar[type]): """Performs atomic in-place max. Atomically replaces the current value with the result of max of the @@ -311,7 +311,7 @@ struct Atomic[type: DType, *, scope: StringLiteral = ""]: _min_impl[scope=scope](ptr, rhs) @always_inline - fn min(mut self, rhs: Scalar[type]): + fn min(self, rhs: Scalar[type]): """Performs atomic in-place min. Atomically replaces the current value with the result of min of the From 0b42ea7188faf47e538ef456aee17019d1ca8a92 Mon Sep 17 00:00:00 2001 From: Nikolay Panchenko Date: Tue, 14 Jan 2025 14:28:05 -0500 Subject: [PATCH 223/509] [stdlib] Limit size of the SIMD to 2^15 Matching LLVM's expectations of a vector size allowed in Clang and CodeGen. After fix of the https://github.com/llvm/llvm-project/issues/122571 limit can be increased if needed. MODULAR_ORIG_COMMIT_REV_ID: 1e1f830425adfad2b1460cfb6c8fd9dee731d987 --- stdlib/src/builtin/simd.mojo | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 8debd181dc..e97cb3c9bf 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -174,7 +174,7 @@ fn _simd_construction_checks[type: DType, size: Int](): Parameters: type: The data type of SIMD vector elements. - size: The number of elements in the SIMD vector. + size: The number of elements in the SIMD vector. The size must not be greater than 2**15. """ constrained[ type is not DType.invalid, "simd type cannot be DType.invalid" @@ -185,6 +185,16 @@ fn _simd_construction_checks[type: DType, size: Int](): not (type is DType.bfloat16 and has_neon()), "bf16 is not supported for ARM architectures", ]() + # MOCO-1388: Until LLVM's issue #122571 is fixed, LLVM's SelectionDAG has + # a limit of 2^15 for the number of operands of the instruction. + # NOTE: Even after the limit increases in LLVM, compile time might be 3x + # slower than with GCC, therefore until we have a real use case for large + # SIMD, we better to keep limit at 2^15. + # NOTE: Might need to revisit the limit for targets that use GlobalISel + # as it does have smaller limit now. + constrained[ + size <= 2**15, "simd size is too large and must be less than 2^15" + ]() @always_inline("nodebug") From 5887a9187a65a43cbf5e8a73ef57d86338cec28b Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 14 Jan 2025 13:36:17 -0600 Subject: [PATCH 224/509] [stdlib][NFC] cleanup: Factor out codepoint test data MODULAR_ORIG_COMMIT_REV_ID: ebb0b89772d7462416c97559b41e4f4cd198a74f --- stdlib/test/builtin/test_char.mojo | 93 +++++++++++++++--------------- 1 file changed, 45 insertions(+), 48 deletions(-) diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index 346467d3e2..c8c083a82a 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -95,6 +95,42 @@ def test_char_is_posix_space(): assert_false(Char.ord(".").is_posix_space()) +alias SIGNIFICANT_CODEPOINTS = List[Tuple[Int, List[Byte]]]( + # -------------------------- + # 1-byte (ASCII) codepoints + # -------------------------- + # Smallest 1-byte codepoint value + (0, List[Byte](0)), + (1, List[Byte](1)), + (32, List[Byte](32)), # First non-control character + (0b0111_1111, List[Byte](127)), # 127 + # ------------------ + # 2-byte codepoints -- 0b110x_xxxx 0b10xx_xxxx (11 x's) + # ------------------ + # Smallest 2-byte codepoint + (128, List[Byte](0b1100_0010, 0b1000_0000)), + # Largest 2-byte codepoint -- 2^11 - 1 == 2047 + (2**11 - 1, List[Byte](0b1101_1111, 0b1011_1111)), + # ------------------ + # 3-byte codepoints -- 0b1110_xxxx 0b10xx_xxxx 0b10xx_xxxx (16 x's) + # ------------------ + # Smallest 3-byte codepoint -- 2^11 == 2048 + (2**11, List[Byte](0b1110_0000, 0b1010_0000, 0b1000_0000)), + # Largest 3-byte codepoint -- 2^16 - 1 == 65535 == 0xFFFF + (2**16 - 1, List[Byte](0b1110_1111, 0b1011_1111, 0b1011_1111)), + # ------------------ + # 4-byte codepoints 0b1111_0xxx 0b10xx_xxxx 0b10xx_xxxx 0b10xx_xxxx (21 x's) + # ------------------ + # Smallest 4-byte codepoint + (2**16, List[Byte](0b1111_0000, 0b1001_0000, 0b1000_0000, 0b1000_0000)), + # Largest 4-byte codepoint -- Maximum Unicode codepoint + ( + 0x10FFFF, + List[Byte](0b1111_0100, 0b1000_1111, 0b1011_1111, 0b1011_1111), + ), +) + + fn assert_utf8_bytes(codepoint: UInt32, owned expected: List[Byte]) raises: var char_opt = Char.from_u32(codepoint) var char = char_opt.value() @@ -123,60 +159,21 @@ fn assert_utf8_bytes(codepoint: UInt32, owned expected: List[Byte]) raises: def test_char_utf8_encoding(): - assert_utf8_bytes(0, List[Byte](0)) - assert_utf8_bytes(1, List[Byte](1)) - assert_utf8_bytes(127, List[Byte](127)) - - # Smallest 2-byte codepoint - assert_utf8_bytes(128, List[Byte](0b1100_0010, 0b1000_0000)) - # Largest 2-byte codepoint - assert_utf8_bytes(2**11 - 1, List[Byte](0b1101_1111, 0b1011_1111)) - - # Smallest 3-byte codepoint -- 2^11 == 2048 - assert_utf8_bytes( - 2**11, List[Byte](0b1110_0000, 0b1010_0000, 0b1000_0000) - ) - # Largest 3-byte codepoint -- 2^16 - 1 == 65535 == 0xFFFF - assert_utf8_bytes( - 2**16 - 1, List[Byte](0b1110_1111, 0b1011_1111, 0b1011_1111) - ) + for entry in SIGNIFICANT_CODEPOINTS: + var codepoint = entry[][0] + var expected_utf8 = entry[][1] - # Smallest 4-byte codepoint - assert_utf8_bytes( - 2**16, List[Byte](0b1111_0000, 0b1001_0000, 0b1000_0000, 0b1000_0000) - ) - # Largest 4-byte codepoint -- Maximum Unicode codepoint - assert_utf8_bytes( - 0x10FFFF, List[Byte](0b1111_0100, 0b1000_1111, 0b1011_1111, 0b1011_1111) - ) + assert_utf8_bytes(codepoint, expected_utf8) def test_char_utf8_byte_length(): - fn codepoint_len(cp: UInt32) -> Int: - return Char.from_u32(cp).value().utf8_byte_length() + for entry in SIGNIFICANT_CODEPOINTS: + var codepoint = entry[][0] + var expected_utf8 = entry[][1] - # 1-byte (ASCII) codepoints - assert_equal(codepoint_len(0), 1) - assert_equal(codepoint_len(32), 1) - assert_equal(codepoint_len(127), 1) + var computed_len = Char.from_u32(codepoint).value().utf8_byte_length() - # 2-byte codepoints -- 0b110x_xxxx 0b10xx_xxxx (11 x's) - # Smallest 2-byte codepoint - assert_equal(codepoint_len(128), 2) - # Largest 2-byte codepoint - assert_equal(codepoint_len(2**11 - 1), 2) # 2^11 - 1 == 2047 - - # 3-byte codepoints -- 0b1110_xxxx 0b10xx_xxxx 0b10xx_xxxx (16 x's) - # Smallest 3-byte codepoint - assert_equal(codepoint_len(2**11), 3) # 2^11 == 2048 - # Largest 3-byte codepoint - assert_equal(codepoint_len(2**16 - 1), 3) # 2^16 - 1 == 65535 == 0xFFFF - - # 4-byte codepoints 0b1111_0xxx 0b10xx_xxxx 0b10xx_xxxx 0b10xx_xxxx (21 x's) - # Smallest 4-byte codepoint - assert_equal(codepoint_len(2**16), 4) - # Largest 4-byte codepoint - assert_equal(codepoint_len(0x10FFFF), 4) # Maximum Unicode codepoint + assert_equal(computed_len, len(expected_utf8)) def test_char_comptime(): From 32cd7fa6f75568b49c787a577c9cc4da6f295651 Mon Sep 17 00:00:00 2001 From: Rasool Sharifi Date: Tue, 14 Jan 2025 15:30:44 -0500 Subject: [PATCH 225/509] [******][GPU] Add conversion from all floating point types to fp8 Add conversion from all floating point types to fp8. MODULAR_ORIG_COMMIT_REV_ID: 6c8a09290a6a9b67ea2e95167ceb8e9673125ded --- stdlib/src/builtin/simd.mojo | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index e97cb3c9bf..c01370d2cb 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -1843,12 +1843,14 @@ struct SIMD[type: DType, size: Int]( return self.cast[DType.float32]().cast[target]() @parameter - if type is DType.float32 and target in ( - DType.float8e4m3, - DType.float8e5m2, - ): - return _convert_f32_to_float8[size=size, target=target]( - rebind[SIMD[DType.float32, size]](self) + if target in (DType.float8e4m3, DType.float8e5m2): + # TODO(KERN-1488): use gpu (H100) instruction to convert from fp16 to fp8 + return rebind[SIMD[target, size]]( + _convert_f32_to_float8[size=size, target=target]( + rebind[SIMD[DType.float32, size]]( + self.cast[DType.float32]() + ) + ) ) @parameter From f7a3561b0075294361681f1b384c3ed65da9e3f0 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 14 Jan 2025 15:38:12 -0600 Subject: [PATCH 226/509] [stdlib] feat: Add `StringSlice.char_length()` + use `StringSlice` in low-level utils This is part of preparing the way to change `StringSlice.__len__()` to return the string length in bytes instead of codepoints. MODULAR_ORIG_COMMIT_REV_ID: 05955b1974e7682b8d9da1214049e850a2278e21 --- docs/changelog.md | 9 ++++ .../src/collections/string/string_slice.mojo | 48 +++++++++++++------ .../collections/string/test_string_slice.mojo | 28 +++++++++-- 3 files changed, 67 insertions(+), 18 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index b1e027026d..fc57571237 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -149,6 +149,15 @@ what we publish. has narrower comparison methods that support comparing only with `StringSlice`'s with the exact same origin. +- Added `StringSlice.char_length()` method, to pair with the existing + `StringSlice.byte_length()` method. + + In a future version of Mojo, `StringSlice.__len__()` may be changed to return + the length in bytes, matching the convention of string length methods in + languages like C++ and Rust. Callers that know they need the length in + Unicode codepoints should update to calling `StringSlice.char_length()` + instead. + - Removed `@implicit` decorator from some standard library initializer methods that perform allocation. This reduces places where Mojo code could implicitly allocate where the user may not be aware. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 974f2a4e6e..fd1eeae585 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -36,10 +36,10 @@ alias StaticString = StringSlice[StaticConstantOrigin] """An immutable static string slice.""" -fn _count_utf8_continuation_bytes(span: Span[Byte]) -> Int: +fn _count_utf8_continuation_bytes(str_slice: StringSlice) -> Int: alias sizes = (256, 128, 64, 32, 16, 8) - var ptr = span.unsafe_ptr() - var num_bytes = len(span) + var ptr = str_slice.unsafe_ptr() + var num_bytes = str_slice.byte_length() var amnt: Int = 0 var processed = 0 @@ -181,17 +181,16 @@ struct _StringSliceIter[ fn __len__(self) -> Int: @parameter if forward: - remaining = self.length - self.index - cont = _count_utf8_continuation_bytes( - Span[Byte, ImmutableAnyOrigin]( - ptr=self.ptr + self.index, length=remaining - ) + var remaining = self.length - self.index + var span = Span[Byte, ImmutableAnyOrigin]( + ptr=self.ptr + self.index, length=remaining ) - return remaining - cont + return StringSlice(unsafe_from_utf8=span).char_length() else: - return self.index - _count_utf8_continuation_bytes( - Span[Byte, ImmutableAnyOrigin](ptr=self.ptr, length=self.index) + var span = Span[Byte, ImmutableAnyOrigin]( + ptr=self.ptr, length=self.index ) + return StringSlice(unsafe_from_utf8=span).char_length() @value @@ -408,10 +407,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: The length in Unicode codepoints. """ - var b_len = self.byte_length() - alias S = Span[Byte, StaticConstantOrigin] - var s = S(ptr=self.unsafe_ptr(), length=b_len) - return b_len - _count_utf8_continuation_bytes(s) + return self.char_length() fn write_to[W: Writer](self, mut writer: W): """Formats this string slice to the provided `Writer`. @@ -806,6 +802,28 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( return len(self.as_bytes()) + fn char_length(self) -> UInt: + """Returns the length in Unicode codepoints. + + This returns the number of `Char` codepoint values encoded in the UTF-8 + representation of this string. + + Note: To get the length in bytes, use `StringSlice.byte_length()`. + + Returns: + The length in Unicode codepoints. + """ + # Every codepoint is encoded as one leading byte + 0 to 3 continuation + # bytes. + # The total number of codepoints is equal the number of leading bytes. + # So we can compute the number of leading bytes (and thereby codepoints) + # by subtracting the number of continuation bytes length from the + # overall length in bytes. + # For a visual explanation of how this UTF-8 codepoint counting works: + # https://connorgray.com/ephemera/project-log#2025-01-13 + var continuation_count = _count_utf8_continuation_bytes(self) + return self.byte_length() - continuation_count + fn get_immutable( self, ) -> StringSlice[ImmutableOrigin.cast_from[origin].result]: diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 4b4618bd11..556128a19e 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -170,6 +170,26 @@ fn test_slice_len() raises: assert_equal(1, len(slice5)) +fn test_slice_char_length() raises: + var s0 = StringSlice("") + assert_equal(s0.byte_length(), 0) + assert_equal(s0.char_length(), 0) + + var s1 = StringSlice("foo") + assert_equal(s1.byte_length(), 3) + assert_equal(s1.char_length(), 3) + + # This string contains 1-, 2-, 3-, and 4-byte codepoint sequences. + var s2 = StringSlice("߷കൈ🔄!") + assert_equal(s2.byte_length(), 13) + assert_equal(s2.char_length(), 5) + + # Just a bit of Zalgo text. + var s3 = StringSlice("H̵͙̖̼̬̬̲̱͊̇̅͂̍͐͌͘͜͝") + assert_equal(s3.byte_length(), 37) + assert_equal(s3.char_length(), 19) + + fn test_slice_eq() raises: var str1: String = "12345" var str2: String = "12345" @@ -460,9 +480,10 @@ def test_count_utf8_continuation_bytes(): alias b4 = UInt8(0b1111_0000) def _test(amnt: Int, items: List[UInt8]): - p = items.unsafe_ptr() - span = Span[Byte, StaticConstantOrigin](ptr=p, length=len(items)) - assert_equal(amnt, _count_utf8_continuation_bytes(span)) + var p = items.unsafe_ptr() + var span = Span[Byte, StaticConstantOrigin](ptr=p, length=len(items)) + var str_slice = StringSlice(unsafe_from_utf8=span) + assert_equal(amnt, _count_utf8_continuation_bytes(str_slice)) _test(5, List[UInt8](c, c, c, c, c)) _test(2, List[UInt8](b2, c, b2, c, b1)) @@ -699,6 +720,7 @@ def main(): test_string_byte_span() test_heap_string_from_string_slice() test_slice_len() + test_slice_char_length() test_slice_eq() test_slice_bool() test_slice_repr() From 803ab28ca18836383d79358a4d30469f3f56d8ef Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Tue, 14 Jan 2025 13:42:28 -0800 Subject: [PATCH 227/509] [stdlib] Get print working on multithreads `StaticTuple` has some issues that `InlineArray` fixes. By switching to `InlineArray` the payload copies behave properly. One side effect of this change is that we have to get rid of the `address_space` tag on the `payload_t` pointers, since non-trivial types (`InlineArray`) can't be copied across address spaces. MODULAR_ORIG_COMMIT_REV_ID: f48eedfc43db45c3465508390aa9ae2421c52bdd --- stdlib/src/sys/_amdgpu.mojo | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo index 7e3f2ca06a..169ddcd9f5 100644 --- a/stdlib/src/sys/_amdgpu.mojo +++ b/stdlib/src/sys/_amdgpu.mojo @@ -23,7 +23,6 @@ from sys.intrinsics import ( ) from time import sleep from collections import InlineArray -from utils import StaticTuple # NOTE: MOST OF THE CODE HERE IS ADAPTED FROM # AMD'S `device-libs`. @@ -547,12 +546,11 @@ struct Header: me: UInt32, low: UInt32, ): + var active = ballot[DType.int64](True).cast[DType.uint64]() if me == low: var control = set_ready_flag(0) self._handle[].control = control - self._handle[].activemask = ballot[DType.int64](True).cast[ - DType.uint64 - ]() + self._handle[].activemask = active self._handle[].service = service_id payload[Int(me), 0] = arg0 @@ -605,7 +603,7 @@ struct Header: sleep(UInt(1)) - var ptr = payload._handle[].slots[Int(me)] + var ptr = payload._handle[].slots[Int(me) * 8] var value0 = ptr[0] var value1 = ptr[1] return value0, value1 @@ -627,9 +625,7 @@ struct header_t: @value @register_passable("trivial") struct Payload: - var _handle: UnsafePointer[ - payload_t, address_space = _GPUAddressSpace.GLOBAL - ] + var _handle: UnsafePointer[payload_t] @always_inline fn __setitem__(mut self, idx0: Int, idx1: Int, value: UInt64): @@ -641,9 +637,8 @@ struct Payload: # but this is actually just conforming to the ABI of: # https://github.com/ROCm/clr/blob/f5b2516f5d8a44b06ad1907594db1be25a9fe57b/rocclr/device/devhostcall.hpp#L99 @value -@register_passable("trivial") struct payload_t: - var slots: StaticTuple[StaticTuple[UInt64, 8], 64] + var slots: InlineArray[InlineArray[UInt64, 8], 64] @value @@ -752,9 +747,7 @@ struct buffer_t: var headers: UnsafePointer[ header_t, address_space = _GPUAddressSpace.GLOBAL ] - var payloads: UnsafePointer[ - payload_t, address_space = _GPUAddressSpace.GLOBAL - ] + var payloads: UnsafePointer[payload_t] var doorbell: UInt64 var free_stack: UInt64 var ready_stack: UInt64 From d5dfbe8d6a8e238fbbd970a0cd0eb9fdbe289155 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 14 Jan 2025 17:06:33 -0600 Subject: [PATCH 228/509] [stdlib] feat: Add `String.chars()` and `CharsIter` iterator type In many cases, code that currently iterates over `String` using it's `__iter__()` method that return single-character substring slice as `StringSlice` elements, would instead be more readable if it was written instead using a `Char` iterator. This will enable us to do that refactoring. MODULAR_ORIG_COMMIT_REV_ID: 1f9a40abee881a7438707b4e6159fac7b42206f9 --- docs/changelog.md | 4 + .../src/collections/string/string_slice.mojo | 183 ++++++++++++++++++ .../collections/string/test_string_slice.mojo | 74 +++++++ 3 files changed, 261 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index fc57571237..0e24712deb 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -119,6 +119,10 @@ what we publish. a `StringSlice` from a buffer containing UTF-8 encoded data. This method will raise if the buffer contents are not valid UTF-8. +- Added `StringSlice.chars()` which returns an iterator over `Char`s. This is a + compliant UTF-8 decoder that returns each Unicode codepoint encoded in the + string. + - Several standard library functions have been changed to take `StringSlice` instead of `String`. This generalizes them to be used for any appropriately encoded string in memory, without requiring that the string be heap allocated. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index fd1eeae585..910fa02883 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -193,6 +193,144 @@ struct _StringSliceIter[ return StringSlice(unsafe_from_utf8=span).char_length() +@value +struct CharsIter[mut: Bool, //, origin: Origin[mut]]: + """Iterator over the `Char`s in a string slice, constructed by + `StringSlice.chars()`. + + Parameters: + mut: Mutability of the underlying string data. + origin: Origin of the underlying string data. + """ + + var _slice: StringSlice[origin] + """String slice containing the bytes that have not been read yet. + + When this iterator advances, the pointer in `_slice` is advanced by the + byte length of each read character, and the slice length is decremented by + the same amount. + """ + + # Note: + # Marked private since `StringSlice.chars()` is the intended public way to + # construct this type. + @doc_private + fn __init__(out self, str_slice: StringSlice[origin]): + self._slice = str_slice + + # ===-------------------------------------------------------------------===# + # Trait implementations + # ===-------------------------------------------------------------------===# + + @doc_private + fn __iter__(self) -> Self: + return self + + fn __next__(mut self) -> Char: + """Get the next character in the underlying string slice. + + This returns the next `Char` encoded in the underlying string, and + advances the iterator state. + + This function will abort if this iterator has been exhausted. + + Returns: + The next character in the string. + """ + + return self.next().value() + + @always_inline + fn __has_next__(self) -> Bool: + """Returns True if there are still elements in this iterator. + + Returns: + A boolean indicating if there are still elements in this iterator. + """ + return bool(self.peek_next()) + + @always_inline + fn __len__(self) -> Int: + """Returns the remaining length of this iterator in `Char`s. + + The value returned from this method indicates the number of subsequent + calls to `next()` that will return a value. + + Returns: + Number of codepoints remaining in this iterator. + """ + return self._slice.char_length() + + # ===-------------------------------------------------------------------===# + # Methods + # ===-------------------------------------------------------------------===# + + fn peek_next(self) -> Optional[Char]: + """Check what the next character in this iterator is, without advancing + the iterator state. + + Repeated calls to this method will return the same value. + + Returns: + The next character in the underlying string, or None if the string + is empty. + + # Examples + + `peek_next()` does not advance the iterator, so repeated calls will + return the same value: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + var input = StringSlice("123") + var iter = input.chars() + + assert_equal(iter.peek_next().value(), Char.ord("1")) + assert_equal(iter.peek_next().value(), Char.ord("1")) + assert_equal(iter.peek_next().value(), Char.ord("1")) + + # A call to `next()` return the same value as `peek_next()` had, + # but also advance the iterator. + assert_equal(iter.next().value(), Char.ord("1")) + + # Later `peek_next()` calls will return the _new_ next character: + assert_equal(iter.peek_next().value(), Char.ord("2")) + ``` + . + """ + if len(self._slice) > 0: + # SAFETY: Will not read out of bounds because `_slice` is guaranteed + # to contain valid UTF-8. + char, _ = Char.unsafe_decode_utf8_char(self._slice.unsafe_ptr()) + return char + else: + return None + + fn next(mut self) -> Optional[Char]: + """Get the next character in the underlying string slice, or None if + the iterator is empty. + + This returns the next `Char` encoded in the underlying string, and + advances the iterator state. + + Returns: + A character if the string is not empty, otherwise None. + """ + var result: Optional[Char] = self.peek_next() + + if result: + # SAFETY: We just checked that `result` holds a value + var char_len = result.unsafe_value().utf8_byte_length() + # Advance the pointer in _slice. + self._slice._slice._data += char_len + # Decrement the byte-length of _slice. + self._slice._slice._len -= char_len + + return result + + @value @register_passable("trivial") struct StringSlice[mut: Bool, //, origin: Origin[mut]]( @@ -772,6 +910,51 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( l_idx += 1 return Self(unsafe_from_utf8=self.as_bytes()[l_idx:]) + @always_inline + fn chars(self) -> CharsIter[origin]: + """Returns an iterator over the `Char`s encoded in this string slice. + + Returns: + An iterator type that returns successive `Char` values stored in + this string slice. + + # Examples + + Print the characters in a string: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + var s = StringSlice("abc") + var iter = s.chars() + assert_equal(iter.__next__(), Char.ord("a")) + assert_equal(iter.__next__(), Char.ord("b")) + assert_equal(iter.__next__(), Char.ord("c")) + assert_equal(iter.__has_next__(), False) + ``` + + `chars()` iterates over Unicode codepoints, and supports multibyte + codepoints: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + # A visual character composed of a combining sequence of 2 codepoints. + var s = StringSlice("á") + assert_equal(s.byte_length(), 3) + + var iter = s.chars() + assert_equal(iter.__next__(), Char.ord("a")) + # U+0301 Combining Acute Accent + assert_equal(iter.__next__().to_u32(), 0x0301) + assert_equal(iter.__has_next__(), False) + ``` + . + """ + return CharsIter(self) + @always_inline fn as_bytes(self) -> Span[Byte, origin]: """Get the sequence of encoded bytes of the underlying string. diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 556128a19e..8a94cb024f 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -714,6 +714,79 @@ def test_count(): assert_equal(StringSlice("aaaaaa").count("aa"), 3) +def test_chars_iter(): + # Test `for` loop iteration support + for char in StringSlice("abc").chars(): + assert_true(char in (Char.ord("a"), Char.ord("b"), Char.ord("c"))) + + # Test empty string chars + var s0 = StringSlice("") + var s0_iter = s0.chars() + + assert_false(s0_iter.__has_next__()) + assert_true(s0_iter.peek_next() is None) + assert_true(s0_iter.next() is None) + + # Test simple ASCII string chars + var s1 = StringSlice("abc") + var s1_iter = s1.chars() + + assert_equal(s1_iter.next().value(), Char.ord("a")) + assert_equal(s1_iter.next().value(), Char.ord("b")) + assert_equal(s1_iter.next().value(), Char.ord("c")) + assert_true(s1_iter.next() is None) + + # Multibyte character decoding: A visual character composed of a combining + # sequence of 2 codepoints. + var s2 = StringSlice("á") + assert_equal(s2.byte_length(), 3) + assert_equal(s2.char_length(), 2) + + var iter = s2.chars() + assert_equal(iter.__next__(), Char.ord("a")) + # U+0301 Combining Acute Accent + assert_equal(iter.__next__().to_u32(), 0x0301) + assert_equal(iter.__has_next__(), False) + + # A piece of text containing, 1-byte, 2-byte, 3-byte, and 4-byte codepoint + # sequences. + # For a visualization of this sequence, see: + # https://connorgray.com/ephemera/project-log#2025-01-13 + var s3 = StringSlice("߷കൈ🔄!") + assert_equal(s3.byte_length(), 13) + assert_equal(s3.char_length(), 5) + var s3_iter = s3.chars() + + # Iterator __len__ returns length in codepoints, not bytes. + assert_equal(s3_iter.__len__(), 5) + assert_equal(s3_iter._slice.byte_length(), 13) + assert_equal(s3_iter.__has_next__(), True) + assert_equal(s3_iter.__next__(), Char.ord("߷")) + + assert_equal(s3_iter.__len__(), 4) + assert_equal(s3_iter._slice.byte_length(), 11) + assert_equal(s3_iter.__next__(), Char.ord("ക")) + + # Combining character, visually comes first, but codepoint-wise comes + # after the character it combines with. + assert_equal(s3_iter.__len__(), 3) + assert_equal(s3_iter._slice.byte_length(), 8) + assert_equal(s3_iter.__next__(), Char.ord("ൈ")) + + assert_equal(s3_iter.__len__(), 2) + assert_equal(s3_iter._slice.byte_length(), 5) + assert_equal(s3_iter.__next__(), Char.ord("🔄")) + + assert_equal(s3_iter.__len__(), 1) + assert_equal(s3_iter._slice.byte_length(), 1) + assert_equal(s3_iter.__has_next__(), True) + assert_equal(s3_iter.__next__(), Char.ord("!")) + + assert_equal(s3_iter.__len__(), 0) + assert_equal(s3_iter._slice.byte_length(), 0) + assert_equal(s3_iter.__has_next__(), False) + + def main(): test_string_slice_layout() test_string_literal_byte_span() @@ -742,3 +815,4 @@ def main(): test_strip() test_startswith() test_endswith() + test_chars_iter() From 6660b42dc166150fc43080e0539fb947467fca23 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 14 Jan 2025 18:37:20 -0500 Subject: [PATCH 229/509] [stdlib] Add deprecation warnings to `int` function Phase in removal of `int` with a deprecation warning. MODULAR_ORIG_COMMIT_REV_ID: 8d6dee6333592145c3a7945a1a6f8624fc32326b --- docs/changelog.md | 7 +- stdlib/src/builtin/int.mojo | 103 ++++++++++++++++++ stdlib/src/prelude/__init__.mojo | 1 + .../builtin/test_deprecation_warnings.mojo | 19 ++++ 4 files changed, 127 insertions(+), 3 deletions(-) create mode 100644 stdlib/test/builtin/test_deprecation_warnings.mojo diff --git a/docs/changelog.md b/docs/changelog.md index 0e24712deb..c62f9a5adc 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -44,10 +44,11 @@ what we publish. ### Standard library changes -- The `int` function to construct an `Int` has been removed, this was a +- The `int` function to construct an `Int` has been deprecated, this was a temporary workaround when Mojo didn't have a way to distinguish between - implicit and explicit constructors. You can do a search and replace for `int(` - to `Int(` to update your programs. + implicit and explicit constructors. You can do a search and replace from + `int(` to `Int(` to update your programs, the `int` function will be removed + in the next release. - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` for changing the type, `origin_cast` for changing mutability, diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index f21bb325fa..abc536abb9 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -204,6 +204,109 @@ trait ImplicitlyIntable(Intable): ... +# ===----------------------------------------------------------------------=== # +# int +# ===----------------------------------------------------------------------=== # + + +# FIXME(25.1): Move `int` deprecation warnings to a compiler error +@deprecated( + "the `int` function is deprecated, use the `Int` constructor instead." +) +@always_inline +fn int[T: Intable](value: T) -> Int: + """Get the Int representation of the value. + + Parameters: + T: The Intable type. + + Args: + value: The object to get the integral representation of. + + Returns: + The integral representation of the value. + """ + return value.__int__() + + +@deprecated( + "the `int` function is deprecated, use the `Int` constructor instead." +) +@always_inline +fn int[T: IntableRaising](value: T) raises -> Int: + """Get the Int representation of the value. + + Parameters: + T: The Intable type. + + Args: + value: The object to get the integral representation of. + + Returns: + The integral representation of the value. + + Raises: + If the type does not have an integral representation. + """ + return value.__int__() + + +@deprecated( + "the `int` function is deprecated, use the `Int` constructor instead." +) +fn int(value: StringSlice, base: Int = 10) raises -> Int: + """Parses and returns the given string as an integer in the given base. + + If base is set to 0, the string is parsed as an Integer literal, with the + following considerations: + - '0b' or '0B' prefix indicates binary (base 2) + - '0o' or '0O' prefix indicates octal (base 8) + - '0x' or '0X' prefix indicates hexadecimal (base 16) + - Without a prefix, it's treated as decimal (base 10) + + Args: + value: A string to be parsed as an integer in the given base. + base: Base used for conversion, value must be between 2 and 36, or 0. + + Returns: + An integer value that represents the string. + + Raises: + If the given string cannot be parsed as an integer value or if an + incorrect base is provided. + + Examples: + >>> int("32") + 32 + >>> int("FF", 16) + 255 + >>> int("0xFF", 0) + 255 + >>> int("0b1010", 0) + 10 + + Notes: + This follows [Python's integer literals]( + https://docs.python.org/3/reference/lexical_analysis.html#integers). + """ + return atol(value, base) + + +@deprecated( + "the `int` function is deprecated, use the `Int` constructor instead." +) +fn int(value: UInt) -> Int: + """Get the Int representation of the value. + + Args: + value: The object to get the integral representation of. + + Returns: + The integral representation of the value. + """ + return value.value + + # ===----------------------------------------------------------------------=== # # Int # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index a73a129610..572ff016ad 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -66,6 +66,7 @@ from builtin.int import ( ImplicitlyIntable, IntableRaising, index, + int, ) from builtin.int_literal import IntLiteral from builtin.io import input, print diff --git a/stdlib/test/builtin/test_deprecation_warnings.mojo b/stdlib/test/builtin/test_deprecation_warnings.mojo new file mode 100644 index 0000000000..d7a36cdecb --- /dev/null +++ b/stdlib/test/builtin/test_deprecation_warnings.mojo @@ -0,0 +1,19 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %mojo %s 2>&1 1>/dev/null | FileCheck %s --check-prefix=CHECK-STDERR + + +fn main(): + # FIXME(25.1): Move `int` deprecation warnings to a compiler error + # CHECK-STDERR: warning: the `int` function is deprecated, use the `Int` constructor instead + _ = int(42) From 090bc9f498cb0c2e9ed9b4447d97afac676fd4f4 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Tue, 14 Jan 2025 15:58:42 -0800 Subject: [PATCH 230/509] [stdlib] Fix GPU Indexing bug Accidentally multiplying by chunk size. MODULAR_ORIG_COMMIT_REV_ID: 26a6cb4c7a2546794ef3f63728a71097326a2b50 --- stdlib/src/sys/_amdgpu.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo index 169ddcd9f5..b8cbb9a876 100644 --- a/stdlib/src/sys/_amdgpu.mojo +++ b/stdlib/src/sys/_amdgpu.mojo @@ -603,7 +603,7 @@ struct Header: sleep(UInt(1)) - var ptr = payload._handle[].slots[Int(me) * 8] + var ptr = payload._handle[].slots[Int(me)] var value0 = ptr[0] var value1 = ptr[1] return value0, value1 From 3e4a57b70da1eb0ac3c9c21a99bfcd7e9f1b2af7 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 14 Jan 2025 18:44:45 -0600 Subject: [PATCH 231/509] [stdlib] feat: Add `Char.is_python_space()` + cleanup `StringSlice.isspace()` This adds a new `Char.is_python_space()` utility function, and cleans up `StringSlice.isspace()` to use the it, along with the recently added character iterator. MODULAR_ORIG_COMMIT_REV_ID: 0739c7a540cedefec7e31ced1550310b5a6a56e2 --- docs/changelog.md | 2 + stdlib/src/builtin/char.mojo | 42 +++++++++++++++ .../src/collections/string/string_slice.mojo | 54 +++++++++---------- .../test/collections/string/test_string.mojo | 2 + 4 files changed, 72 insertions(+), 28 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index c62f9a5adc..b1bfb3c91a 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -112,6 +112,8 @@ what we publish. `Stringable`. - Added `String` constructor from `Char` - `Char` can be converted to `UInt32` via `Char.to_u32()`. + - `Char` provides methods for categorizing character types, including: + `Char.is_ascii()`, `Char.is_posix_space()`, `Char.is_python_space()`. - `chr(Int)` will now abort if given a codepoint value that is not a valid `Char`. diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 863a046442..0a48f7cdc6 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -272,6 +272,48 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): """ return self._scalar_value <= 0b0111_1111 + @always_inline + fn is_python_space(self) -> Bool: + """Determines whether this character is a Python whitespace string. + + This corresponds to Python's [universal separators]( + https://docs.python.org/3/library/stdtypes.html#str.splitlines): + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e\\x85\\u2028\\u2029"`. + + Returns: + True if this character is one of the whitespace characters listed + above, otherwise False. + + # Examples + + Check if a string contains only whitespace: + + ```mojo + from testing import assert_true, assert_false + + # ASCII space characters + assert_true(Char.ord(" ").is_python_space()) + assert_true(Char.ord("\t").is_python_space()) + + # Unicode paragraph separator: + assert_true(Char.from_u32(0x2029).value().is_python_space()) + + # Letters are not space characters + assert_fales(Char.ord("a").is_python_space()) + ``` + . + """ + + alias next_line = Char.from_u32(0x85).value() + alias unicode_line_sep = Char.from_u32(0x2028).value() + alias unicode_paragraph_sep = Char.from_u32(0x2029).value() + + return self.is_posix_space() or self in ( + next_line, + unicode_line_sep, + unicode_paragraph_sep, + ) + fn is_posix_space(self) -> Bool: """Returns True if this `Char` is a **space** character according to the [POSIX locale][1]. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 910fa02883..5a48b47b5e 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -1201,44 +1201,42 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( fn isspace(self) -> Bool: """Determines whether every character in the given StringSlice is a python whitespace String. This corresponds to Python's - [universal separators:]( - https://docs.python.org/3/library/stdtypes.html#str.splitlines) - `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e\\x85\\u2028\\u2029"`. + [universal separators]( + https://docs.python.org/3/library/stdtypes.html#str.splitlines): + `" \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e\\x85\\u2028\\u2029"`. Returns: True if the whole StringSlice is made up of whitespace characters listed above, otherwise False. + + Examples: + + Check if a string contains only whitespace: + + ```mojo + from collections.string import StringSlice + from testing import assert_true, assert_false + + # An empty string is not considered to contain only whitespace chars: + assert_false(StringSlice("").isspace()) + + # ASCII space characters + assert_true(StringSlice(" ").isspace()) + assert_true(StringSlice("\t").isspace()) + + # Contains non-space characters + assert_false(StringSlice(" abc ").isspace()) + ``` + . """ if self.byte_length() == 0: return False - # TODO add line and paragraph separator as stringliteral - # once Unicode escape sequences are accepted - var next_line = List[UInt8](0xC2, 0x85) - """TODO: \\x85""" - var unicode_line_sep = List[UInt8](0xE2, 0x80, 0xA8) - """TODO: \\u2028""" - var unicode_paragraph_sep = List[UInt8](0xE2, 0x80, 0xA9) - """TODO: \\u2029""" - - for s in self: - var no_null_len = s.byte_length() - var ptr = s.unsafe_ptr() - if no_null_len == 1 and Char(ptr[0]).is_posix_space(): - continue - elif ( - no_null_len == 2 and memcmp(ptr, next_line.unsafe_ptr(), 2) == 0 - ): - continue - elif no_null_len == 3 and ( - memcmp(ptr, unicode_line_sep.unsafe_ptr(), 3) == 0 - or memcmp(ptr, unicode_paragraph_sep.unsafe_ptr(), 3) == 0 - ): - continue - else: + for s in self.chars(): + if not s.is_python_space(): return False - _ = next_line, unicode_line_sep, unicode_paragraph_sep + return True fn isnewline[single_character: Bool = False](self) -> Bool: diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 7828ba1de7..d938ab0fd0 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -876,6 +876,8 @@ def test_upper(): def test_isspace(): + assert_false(String("").isspace()) + # test all utf8 and unicode separators # 0 is to build a String with null terminator alias next_line = List[UInt8](0xC2, 0x85, 0) From 9e71e8eda36ab156e2cff991716303cfbf563810 Mon Sep 17 00:00:00 2001 From: Judy Heflin Date: Tue, 14 Jan 2025 18:51:48 -0800 Subject: [PATCH 232/509] Add Mojo manual links about Mojo and Python dictionaries to the Dict API. MODULAR_ORIG_COMMIT_REV_ID: bcc8fb2803ffa8e234a27467ad18c422c5990aa1 --- stdlib/src/collections/dict.mojo | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 31ea5bc0e4..27b7f42a3f 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -22,6 +22,11 @@ Its implementation closely mirrors Python's `dict` implementation: - Insertion order is implicitly preserved. Iteration over keys, values, and items have a deterministic order based on insertion. +- For more information on the Mojo `Dict` type, see the + [Mojo `Dict` manual](/mojo/manual/types/#dict). To learn more about using + Python dictionaries from Mojo, see + [Python types in Mojo](/mojo/manual/python/types/#python-types-in-mojo). + Key elements must implement the `KeyElement` trait, which encompasses Movable, Hashable, and EqualityComparable. It also includes CollectionElement and Copyable until we push references through the standard library types. @@ -380,6 +385,11 @@ struct Dict[K: KeyElement, V: CollectionElement]( K: The type of the dictionary key. Must be Hashable and EqualityComparable so we can find the key in the map. V: The value type of the dictionary. Currently must be CollectionElement. + + For more information on the Mojo `Dict` type, see the + [Mojo `Dict` manual](/mojo/manual/types/#dict). To learn more about using + Python dictionaries from Mojo, see + [Python types in Mojo](/mojo/manual/python/types/#python-types-in-mojo). """ # Implementation: From 1a8af7f17776bec8958c0c9e03d0bae65cc7084a Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 15 Jan 2025 17:02:29 +0000 Subject: [PATCH 233/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011505 --- examples/life/magic.lock | 191 +++++++++++++++--------------- examples/magic.lock | 191 +++++++++++++++--------------- examples/notebooks/magic.lock | 216 +++++++++++++++++----------------- examples/operators/magic.lock | 191 +++++++++++++++--------------- magic.lock | 207 ++++++++++++++++---------------- 5 files changed, 500 insertions(+), 496 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 49b4b05dfd..dd001a3484 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011405-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025011405-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025011405-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011405-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011505-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025011505-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025011505-3.12release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011505-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025011405-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025011505-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.15-py312h98912ed_1.conda @@ -266,7 +266,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/uvloop-0.21.0-py312h66e93f0_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/watchfiles-1.0.4-py312h12e396e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/websockets-14.1-py312h66e93f0_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.17.1-py312h66e93f0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.17.2-py312h66e93f0_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.5-he73a12e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.10-h4f16b4b_1.conda @@ -446,12 +446,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-c-1.10.0-h5ad3122_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/markupsafe-3.0.2-py312h74ce7d3_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011405-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-core-25.1.0.dev2025011405-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.1.0.dev2025011405-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011405-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011505-release.conda + - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-core-25.1.0.dev2025011505-release.conda + - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.1.0.dev2025011505-3.12release.conda + - 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size: 64616 - timestamp: 1736757911835 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.17.1-py312hea69d52_0.conda - sha256: 213a18609ccfdde862a8fcf9d96b9133cc6ca0abf5953ed148b35a9c8eb27a9c - md5: e3a62bb92c2e71e95f0e3be67a5ff351 + size: 63967 + timestamp: 1736869675870 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.17.2-py312hea69d52_0.conda + sha256: 6a3e68b57de29802e8703d1791dcacb7613bfdc17bbb087c6b2ea2796e6893ef + md5: e49608c832fcf438f70cbcae09c3adc5 depends: - __osx >=11.0 - python >=3.12,<3.13.0a0 @@ -6960,8 +6961,8 @@ packages: platform: osx license: BSD-2-Clause license_family: BSD - size: 61365 - timestamp: 1736758030632 + size: 61198 + timestamp: 1736869673767 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038 md5: f6ebe2cb3f82ba6c057dde5d9debe4f7 From add55a57f60842b888ecacea2d110b13b8d52b7a Mon Sep 17 00:00:00 2001 From: bgreni <42788181+bgreni@users.noreply.github.com> Date: Wed, 15 Jan 2025 07:30:32 -0800 Subject: [PATCH 234/509] [External] [stdlib] Use named output for _ListIter __next__() method (#53928) [External] [stdlib] Use named output for _ListIter __next__() method Add a trivial optimization to use a named output for the `__next__()` method in `_ListIter` to save a subtraction operation on every iteration Co-authored-by: bgreni <42788181+bgreni@users.noreply.github.com> Closes modularml/mojo#3941 MODULAR_ORIG_COMMIT_REV_ID: 426ada8f07f91a0d14ffe0c6e77ae074405e52c2 --- stdlib/src/collections/list.mojo | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 3c488d3388..cc688f34af 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -60,16 +60,14 @@ struct _ListIter[ fn __iter__(self) -> Self: return self - fn __next__( - mut self, - ) -> Pointer[T, list_origin]: + fn __next__(mut self, out p: Pointer[T, list_origin]): @parameter if forward: + p = Pointer.address_of(self.src[][self.index]) self.index += 1 - return Pointer.address_of(self.src[][self.index - 1]) else: self.index -= 1 - return Pointer.address_of(self.src[][self.index]) + p = Pointer.address_of(self.src[][self.index]) @always_inline fn __has_next__(self) -> Bool: From 9aec341ee28b5829a3a951ea40ff8214bb4d505c Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Wed, 15 Jan 2025 10:31:27 -0500 Subject: [PATCH 235/509] [stdlib] Remove `bencher` docs Now that we have nightly docs, people can see the documentation for otherwise closed-source things in the standard library, such as the `benchmark` module. So, remove the temporary markdown docs we provided as an interim thing for users and contributors of the standard library. They can be read in a more up-to-date form at https://docs.modular.com/nightly/mojo/stdlib/benchmark. MODULAR_ORIG_COMMIT_REV_ID: f5dfec06fc337ca87349888e14079971d09e75b9 --- stdlib/docs/bencher/Bench.md | 174 --------------------------- stdlib/docs/bencher/BenchConfig.md | 78 ------------ stdlib/docs/bencher/BenchId.md | 62 ---------- stdlib/docs/bencher/Bencher.md | 117 ------------------ stdlib/docs/bencher/BenchmarkInfo.md | 51 -------- stdlib/docs/bencher/Mode.md | 52 -------- stdlib/docs/bencher/index.md | 39 ------ 7 files changed, 573 deletions(-) delete mode 100644 stdlib/docs/bencher/Bench.md delete mode 100644 stdlib/docs/bencher/BenchConfig.md delete mode 100644 stdlib/docs/bencher/BenchId.md delete mode 100644 stdlib/docs/bencher/Bencher.md delete mode 100644 stdlib/docs/bencher/BenchmarkInfo.md delete mode 100644 stdlib/docs/bencher/Mode.md delete mode 100644 stdlib/docs/bencher/index.md diff --git a/stdlib/docs/bencher/Bench.md b/stdlib/docs/bencher/Bench.md deleted file mode 100644 index 19133b572f..0000000000 --- a/stdlib/docs/bencher/Bench.md +++ /dev/null @@ -1,174 +0,0 @@ ---- -title: Bench -version: 0.0.0 -slug: Bench -type: struct -namespace: benchmark.bencher ---- - -
- -Defines the main Benchmark struct which executes a Benchmark and print result. - -## Fields - -- ​config (`BenchConfig`): Constructs a Benchmark object based on specific - configuration and mode. -- ​mode (`Mode`): Benchmark mode object representing benchmark or test - mode. -- ​info_vec (`List[BenchmarkInfo]`): A list containing the bencmark info. - -## Implemented traits - -`AnyType`, -`Copyable`, -`Movable` - -## Methods - -### `__init__` - -
- -
- -```mojo -__init__(out self: Self, config: Optional[BenchConfig] = #kgen.none, mode: Mode = 0) -``` - -
- -Constructs a Benchmark object based on specific configuration and mode. - -**Args:** - -- ​config (`Optional[BenchConfig]`): Benchmark configuration object to - control length and frequency of benchmarks. -- ​mode (`Mode`): Benchmark mode object representing benchmark or test - mode. - -
- -### `bench_with_input` - -
- -
- -```mojo -bench_with_input[T: AnyType, bench_fn: fn(mut Bencher, $0) capturing -> None](mut self: Self, bench_id: BenchId, input: T, throughput_elems: Optional[Int] = #kgen.none) -``` - -
- -Benchmarks an input function with input args of type AnyType. - -**Parameters:** - -- ​T (`AnyType`): Benchmark function input type. -- ​bench_fn (`fn(mut Bencher, $0) capturing -> None`): The function to - be benchmarked. - -**Args:** - -- ​bench_id (`BenchId`): The benchmark Id object used for identification. -- ​input (`T`): Represents the target function's input arguments. -- ​throughput_elems (`Optional[Int]`): Optional argument representing - algorithmic throughput. - -
- -
- -
- -```mojo -bench_with_input[T: AnyTrivialRegType, bench_fn: fn(mut Bencher, $0) capturing -> None](mut self: Self, bench_id: BenchId, input: T, throughput_elems: Optional[Int] = #kgen.none) -``` - -
- -Benchmarks an input function with input args of type AnyTrivialRegType. - -**Parameters:** - -- ​T (`AnyTrivialRegType`): Benchmark function input type. -- ​bench_fn (`fn(mut Bencher, $0) capturing -> None`): The function to - be benchmarked. - -**Args:** - -- ​bench_id (`BenchId`): The benchmark Id object used for identification. -- ​input (`T`): Represents the target function's input arguments. -- ​throughput_elems (`Optional[Int]`): Optional argument representing - algorithmic throughput. - -
- -### `bench_function` - -
- -
- -```mojo -bench_function[bench_fn: fn(mut Bencher) capturing -> None](mut self: Self, bench_id: BenchId, throughput_elems: Optional[Int] = #kgen.none) -``` - -
- -Benchmarks or Tests an input function. - -**Parameters:** - -- ​bench_fn (`fn(mut Bencher) capturing -> None`): The function to be - benchmarked. - -**Args:** - -- ​bench_id (`BenchId`): The benchmark Id object used for identification. -- ​throughput_elems (`Optional[Int]`): Optional argument representing - algorithmic throughput. - -
- -
- -
- -```mojo -bench_function[bench_fn: fn(mut Bencher) raises capturing -> None](mut self: Self, bench_id: BenchId, throughput_elems: Optional[Int] = #kgen.none) -``` - -
- -Benchmarks or Tests an input function. - -**Parameters:** - -- ​bench_fn (`fn(mut Bencher) raises capturing -> None`): The function - to be benchmarked. - -**Args:** - -- ​bench_id (`BenchId`): The benchmark Id object used for identification. -- ​throughput_elems (`Optional[Int]`): Optional argument representing - algorithmic throughput. - -
- -### `dump_report` - -
- -
- -`dump_report(self: Self)` - -
- -Prints out the report from a Benchmark execution. - -
- -
diff --git a/stdlib/docs/bencher/BenchConfig.md b/stdlib/docs/bencher/BenchConfig.md deleted file mode 100644 index 8a83984d5e..0000000000 --- a/stdlib/docs/bencher/BenchConfig.md +++ /dev/null @@ -1,78 +0,0 @@ ---- -title: BenchConfig -version: 0.0.0 -slug: BenchConfig -type: struct -namespace: benchmark.bencher ---- - -
- -Defines a benchmark configuration struct to control execution times and -frequency. - -## Fields - -- ​out_file (`Optional[Path]`): Output file to write results to. -- ​min_runtime_secs (`SIMD[float64, 1]`): Lower bound on benchmarking time - in secs. -- ​max_runtime_secs (`SIMD[float64, 1]`): Upper bound on benchmarking time - in secs. -- ​min_warmuptime_secs (`SIMD[float64, 1]`): Lower bound on the warmup time - in secs. -- ​max_batch_size (`Int`): The maximum number of iterations to perform per - time measurement. -- ​max_iters (`Int`): Max number of iterations to run. -- ​num_repetitions (`Int`): Number of times the benchmark has to be - repeated. -- ​flush_denormals (`Bool`): Whether or not the denormal values are - flushed. -- ​show_progress (`Bool`): Whether or not to show the progress of each - benchmark. -- ​tabular_view (`Bool`): Whether to print results in csv readable/tabular - format. - -## Implemented traits - -`AnyType`, -`CollectionElement`, -`Copyable`, -`Movable` - -## Methods - -### `__init__` - -
- -
- -```mojo -__init__(out self: out_file: Optional[Path] = None, min_runtime_secs: SIMD[float64, 1] = 1.0, max_runtime_secs: SIMD[float64, 1] = 2.0, min_warmuptime_secs: SIMD[float64, 1] = 1.0, max_batch_size: Int = 0, max_iters: Int = 1000000000, num_repetitions: Int = 1, flush_denormals: Bool = True) -``` - -
- -Constructs and initializes Benchmark config object with default and inputted values. - -**Args:** - -- ​out_file (`Optional[Path]`): Output file to write results to. -- ​min_runtime_secs (`SIMD[float64, 1]`): Upper bound on benchmarking time - in secs (default `1.0`). -- ​max_runtime_secs (`SIMD[float64, 1]`): Lower bound on benchmarking time - in secs (default `2.0`). -- ​min_warmuptime_secs (`SIMD[float64, 1]`): Lower bound on the warmup time - in secs (default `1.0`). -- ​max_batch_size (`Int`): The maximum number of iterations to perform per - time measurement. -- ​max_iters (`Int`): Max number of iterations to run (default - `1_000_000_000`). -- ​num_repetitions (`Int`): Number of times the benchmark has to be - repeated. -- ​flush_denormals (`Bool`): Whether or not the denormal values are - flushed. - -
- -
diff --git a/stdlib/docs/bencher/BenchId.md b/stdlib/docs/bencher/BenchId.md deleted file mode 100644 index 0d215bd972..0000000000 --- a/stdlib/docs/bencher/BenchId.md +++ /dev/null @@ -1,62 +0,0 @@ ---- -title: BenchId -version: 0.0.0 -slug: BenchId -type: struct -namespace: benchmark.bencher ---- - -
- -Defines a benchmark ID struct to identify and represent a particular benchmark -execution. - -## Fields - -- ​func_name (`String`): The target function name. -- ​input_id (`Optional[String]`): The target function input ID phrase. - -## Implemented traits - -`AnyType`, -`Copyable`, -`Movable` - -## Methods - -### `__init__` - -
- -
- -`__init__(out self: Self, func_name: String, input_id: String)` - -
- -Constructs a Benchmark Id object from input function name and Id phrase. - -**Args:** - -- ​func_name (`String`): The target function name. -- ​input_id (`String`): The target function input id phrase. - -
- -
- -
- -`__init__(out self: Self, func_name: String)` - -
- -Constructs a Benchmark Id object from input function name. - -**Args:** - -- ​func_name (`String`): The target function name. - -
- -
diff --git a/stdlib/docs/bencher/Bencher.md b/stdlib/docs/bencher/Bencher.md deleted file mode 100644 index 91d68e80d8..0000000000 --- a/stdlib/docs/bencher/Bencher.md +++ /dev/null @@ -1,117 +0,0 @@ ---- -title: Bencher -version: 0.0.0 -slug: Bencher -type: struct -namespace: benchmark.bencher ---- - -
- -Defines a Bencher struct which facilitates the timing of a target function. - -## Fields - -- ​num_iters (`Int`): Number of iterations to run the target function. -- ​elapsed (`Int`): The total time elpased when running the target - function. - -## Implemented traits - -`AnyType`, -`Copyable`, -`Movable` - -## Methods - -### `__init__` - -
- -
- -`__init__(out self: Self, num_iters: Int)` - -
- -Constructs a Bencher object to run and time a function. - -**Args:** - -- ​num_iters (`Int`): Number of times to run the target function. - -
- -### `iter` - -
- -
- -`iter[iter_fn: fn() capturing -> None](mut self: Self)` - -
- -Returns the total elapsed time by running a target function a particular number -of times. - -**Parameters:** - -- ​iter_fn (`fn() capturing -> None`): The target function to benchmark. - -
- -
- -
- -`iter[iter_fn: fn() raises capturing -> None](mut self: Self)` - -
- -Returns the total elapsed time by running a target function a particular number -of times. - -**Parameters:** - -- ​iter_fn (`fn() raises capturing -> None`): The target function to - benchmark. - -
- -### `iter_custom` - -
- -
- -`iter_custom[iter_fn: fn(Int) capturing -> Int](mut self: Self)` - -
- -Times a target function with custom number of iterations. - -**Parameters:** - -- ​iter_fn (`fn(Int) capturing -> Int`): The target function to benchmark. - -
- -
- -
- -`iter_custom[iter_fn: fn(Int) raises capturing -> Int](mut self: Self)` - -
- -Times a target function with custom number of iterations. - -**Parameters:** - -- ​iter_fn (`fn(Int) raises capturing -> Int`): The target function to - benchmark. - -
- -
diff --git a/stdlib/docs/bencher/BenchmarkInfo.md b/stdlib/docs/bencher/BenchmarkInfo.md deleted file mode 100644 index 52b2922fa0..0000000000 --- a/stdlib/docs/bencher/BenchmarkInfo.md +++ /dev/null @@ -1,51 +0,0 @@ ---- -title: BenchmarkInfo -version: 0.0.0 -slug: BenchmarkInfo -type: struct -namespace: benchmark.bencher ---- - -
- -Defines a Benchmark Info struct to record execution Statistics. - -## Fields - -- ​name (`String`): The name of the benchmark. -- ​result (`Report`): The output report after executing a benchmark. -- ​elems (`Optional[Int]`): Optional arg used to represent a specific - metric like throughput. - -## Implemented traits - -`AnyType`, -`CollectionElement`, -`Copyable`, -`Movable`, -`Stringable` - -## Methods - -### `__init__` - -
- -
- -`__init__(out self: Self, name: String, result: Report, elems: Optional[Int])` - -
- -Constructs a Benchmark Info object to return Benchmark report and Stats. - -**Args:** - -- ​name (`String`): The name of the benchmark. -- ​result (`Report`): The output report after executing a benchmark. -- ​elems (`Optional[Int]`): Optional arg used to represent a specific - metric like throughput. - -
- -
diff --git a/stdlib/docs/bencher/Mode.md b/stdlib/docs/bencher/Mode.md deleted file mode 100644 index 74efd79cc0..0000000000 --- a/stdlib/docs/bencher/Mode.md +++ /dev/null @@ -1,52 +0,0 @@ ---- -title: Mode -version: 0.0.0 -slug: Mode -type: struct -namespace: benchmark.bencher ---- - -
- -Defines a Benchmark Mode to distinguish between test runs and actual benchmarks. - -## Aliases - -- `Benchmark = 0`: -- `Test = 1`: - -## Fields - -- ​value (`Int`): Represents the mode type. - -## Implemented traits - -`AnyType`, -`Copyable`, -`Movable` - -## Methods - -### `__eq__` - -
- -
- -`__eq__(self: Self, other: Self) -> Bool` - -
- -Check if its Benchmark mode or test mode. - -**Args:** - -- ​other (`Self`): The mode to be compared against. - -**Returns:** - -If its a test mode or benchmark mode. - -
- -
diff --git a/stdlib/docs/bencher/index.md b/stdlib/docs/bencher/index.md deleted file mode 100644 index 0372bc6dfb..0000000000 --- a/stdlib/docs/bencher/index.md +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: bencher -version: 0.0.0 -type: module -namespace: benchmark ---- - -
- -
- -This is preview documentation for the `bencher` module, available in nightly -builds now. This documentation will move to -[docs.modular.com](https://docs.modular.com/mojo/stdlib/benchmark/) soon. - -You can import these APIs from the `benchmark` package. For example: - -```mojo -from benchmark import Bencher -``` - -
- -## Structs - -- [​`BenchConfig`](./BenchConfig): Defines a benchmark configuration struct to - control execution times and frequency. -- [​`BenchId`](./BenchId): Defines a benchmark Id struct to identify and - represent a particular benchmark execution. -- [​`BenchmarkInfo`](./BenchmarkInfo): Defines a Benchmark Info struct to record - execution Statistics. -- [​`Mode`](./Mode): Defines a Benchmark Mode to distinguish between test runs - and actual benchmarks. -- [​`Bench`](./Bench): Defines the main Benchmark struct which executes a - Benchmark and print result. -- [​`Bencher`](./Bencher): Defines a Bencher struct which facilitates the timing - of a target function. - -
From 4ef6859e166927e3774cde0eb372ef4d0cfbc9ef Mon Sep 17 00:00:00 2001 From: mahiro21h Date: Wed, 15 Jan 2025 07:32:56 -0800 Subject: [PATCH 236/509] [External] [stdlib] Fix `input()` segfaults on EOF (#53925) [External] [stdlib] Fix `input()` segfaults on EOF pressing `ctrl-d` with no input when `input()` is called causes mojo to crash because `read_until_delimiter()` doesn't check the return value of the C function `getdelim()`. it assumes `getdelim()` always succeeds and so, in the case of an error, it blindly creates a `StringRef` with its length set to the return value - 1 (so the length is -2 in this case). this `StringRef` is then passed to `String()` which in turn passes the `StringRef` to `memcpy()` with a count of -2 and ultimately crashing mojo. this pr adds a check in `read_until_delimiter()` to check if `getdelim()` failed and raise an error if it does, along with a test to ensure `read_until_delimiter()` continues to behave as it should. Fixes https://github.com/modularml/mojo/issues/3908 Co-authored-by: mahiro21h Closes modularml/mojo#3919 MODULAR_ORIG_COMMIT_REV_ID: c3457f3377bfcfe0379e31fbd31e72ec53fe7516 --- stdlib/src/builtin/io.mojo | 13 ++++++++--- stdlib/test/builtin/test_issue_3908.mojo | 28 ++++++++++++++++++++++++ 2 files changed, 38 insertions(+), 3 deletions(-) create mode 100644 stdlib/test/builtin/test_issue_3908.mojo diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index 465a4cefcf..b4971adf71 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -68,7 +68,7 @@ struct _fdopen[mode: StringLiteral = "a"]: """Closes the file handle.""" _ = fclose(self.handle) - fn readline(self) -> String: + fn readline(self) raises -> String: """Reads an entire line from stdin or until EOF. Lines are delimited by a newline character. Returns: @@ -95,7 +95,7 @@ struct _fdopen[mode: StringLiteral = "a"]: """ return self.read_until_delimiter("\n") - fn read_until_delimiter(self, delimiter: StringSlice) -> String: + fn read_until_delimiter(self, delimiter: StringSlice) raises -> String: """Reads an entire line from a stream, up to the `delimiter`. Does not include the delimiter in the result. @@ -140,6 +140,13 @@ struct _fdopen[mode: StringLiteral = "a"]: ord(delimiter), self.handle, ) + # Per man getdelim(3), getdelim will return -1 if an error occurs + # (or the user sends EOF without providing any input). We must + # raise an error in this case because otherwise, String() will crash mojo + # if the user sends EOF with no input. + # TODO: check errno to ensure we haven't encountered EINVAL or ENOMEM instead + if bytes_read == -1: + raise Error("EOF") # Copy the buffer (excluding the delimiter itself) into a Mojo String. var s = String(StringRef(buffer, bytes_read - 1)) # Explicitly free the buffer using free() instead of the Mojo allocator. @@ -283,7 +290,7 @@ fn print[ # ===----------------------------------------------------------------------=== # -fn input(prompt: String = "") -> String: +fn input(prompt: String = "") raises -> String: """Reads a line of input from the user. Reads a line from standard input, converts it to a string, and returns that string. diff --git a/stdlib/test/builtin/test_issue_3908.mojo b/stdlib/test/builtin/test_issue_3908.mojo new file mode 100644 index 0000000000..3e468a428f --- /dev/null +++ b/stdlib/test/builtin/test_issue_3908.mojo @@ -0,0 +1,28 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: echo -n | %mojo %s + +from builtin.io import _fdopen +from testing import testing + + +fn test_read_until_delimiter_raises_eof() raises: + var stdin = _fdopen["r"](0) + with testing.assert_raises(contains="EOF"): + # Assign to a variable to silence a warning about unused String value + # if an error wasn't raised. + var unused = stdin.read_until_delimiter("\n") + + +fn main() raises: + test_read_until_delimiter_raises_eof() From 16024b1e8005717bf41eb7c27d3dd6f0d987d7d0 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 15 Jan 2025 12:08:59 -0800 Subject: [PATCH 237/509] [Stdlib] Add basic linked list implementation MODULAR_ORIG_COMMIT_REV_ID: 639aca98351b3616d041da634ec24c456cb44f4d --- docs/changelog.md | 2 + stdlib/src/collections/__init__.mojo | 1 + stdlib/src/collections/linked_list.mojo | 326 ++++++++++++++++++ stdlib/test/collections/test_linked_list.mojo | 122 +++++++ 4 files changed, 451 insertions(+) create mode 100644 stdlib/src/collections/linked_list.mojo create mode 100644 stdlib/test/collections/test_linked_list.mojo diff --git a/docs/changelog.md b/docs/changelog.md index b1bfb3c91a..a0456c42dd 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -231,6 +231,8 @@ what we publish. - You can now use `max()` and `min()` with variadic number of arguments. +- A new `LinkedList` type has been added to the standard library. + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents diff --git a/stdlib/src/collections/__init__.mojo b/stdlib/src/collections/__init__.mojo index 97f58c9c88..123ed4db6b 100644 --- a/stdlib/src/collections/__init__.mojo +++ b/stdlib/src/collections/__init__.mojo @@ -21,3 +21,4 @@ from .list import List from .optional import Optional, OptionalReg from .set import Set from .vector import InlinedFixedVector +from .linked_list import LinkedList diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo new file mode 100644 index 0000000000..afa3c0b624 --- /dev/null +++ b/stdlib/src/collections/linked_list.mojo @@ -0,0 +1,326 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from memory import UnsafePointer +from collections import Optional +from collections._index_normalization import normalize_index + + +trait WritableCollectionElement(CollectionElement, Writable): + """A trait that combines CollectionElement and Writable traits. + + This trait requires types to implement both CollectionElement and Writable + interfaces, allowing them to be used in collections and written to output. + """ + + pass + + +@value +struct Node[ElementType: WritableCollectionElement]: + """A node in a linked list data structure. + + Parameters: + ElementType: The type of element stored in the node. + """ + + var value: ElementType + """The value stored in this node.""" + var prev: UnsafePointer[Node[ElementType]] + """The previous node in the list.""" + var next: UnsafePointer[Node[ElementType]] + """The next node in the list.""" + + fn __init__( + out self, + owned value: ElementType, + prev: Optional[UnsafePointer[Node[ElementType]]], + next: Optional[UnsafePointer[Node[ElementType]]], + ): + """Initialize a new Node with the given value and optional prev/next + pointers. + + Args: + value: The value to store in this node. + prev: Optional pointer to the previous node. + next: Optional pointer to the next node. + """ + self.value = value^ + self.prev = prev.value() if prev else __type_of(self.prev)() + self.next = next.value() if next else __type_of(self.next)() + + fn __str__(self) -> String: + """Convert this node's value to a string representation. + + Returns: + String representation of the node's value. + """ + return String.write(self) + + @no_inline + fn write_to[W: Writer](self, mut writer: W): + """Write this node's value to the given writer. + + Parameters: + W: The type of writer to write the value to. + + Args: + writer: The writer to write the value to. + """ + writer.write(self.value) + + +struct LinkedList[ElementType: WritableCollectionElement]: + """A doubly-linked list implementation. + + A doubly-linked list is a data structure where each element points to both + the next and previous elements, allowing for efficient insertion and deletion + at any position. + + Parameters: + ElementType: The type of elements stored in the list. Must implement + WritableCollectionElement. + """ + + var _head: UnsafePointer[Node[ElementType]] + """The first node in the list.""" + var _tail: UnsafePointer[Node[ElementType]] + """The last node in the list.""" + var _size: Int + """The number of elements in the list.""" + + fn __init__(out self): + """Initialize an empty linked list.""" + self._head = __type_of(self._head)() + self._tail = __type_of(self._tail)() + self._size = 0 + + fn __init__(mut self, owned *elements: ElementType): + """Initialize a linked list with the given elements. + + Args: + elements: Variable number of elements to initialize the list with. + """ + self = Self(elements=elements^) + + fn __init__(out self, *, owned elements: VariadicListMem[ElementType, _]): + """Initialize a linked list with the given elements. + + Args: + elements: Variable number of elements to initialize the list with. + """ + self = Self() + + for elem in elements: + self.append(elem[]) + + # Do not destroy the elements when their backing storage goes away. + __mlir_op.`lit.ownership.mark_destroyed`( + __get_mvalue_as_litref(elements) + ) + + fn __copyinit__(mut self, read other: Self): + """Initialize this list as a copy of another list. + + Args: + other: The list to copy from. + """ + self._head = other._head + self._tail = other._tail + self._size = other._size + + fn __moveinit__(mut self, owned other: Self): + """Initialize this list by moving elements from another list. + + Args: + other: The list to move elements from. + """ + self._head = other._head + self._tail = other._tail + self._size = other._size + other._head = __type_of(other._head)() + other._tail = __type_of(other._tail)() + other._size = 0 + + fn __del__(owned self): + """Clean up the list by freeing all nodes.""" + var curr = self._head + while curr: + var next = curr[].next + curr[].prev = __type_of(self._head)() + curr[].next = __type_of(self._tail)() + curr.destroy_pointee() + curr.free() + curr = next + + fn append(mut self, owned value: ElementType): + """Add an element to the end of the list. + + Args: + value: The value to append. + """ + var node = Node[ElementType](value^, self._tail, None) + var addr = UnsafePointer[__type_of(node)].alloc(1) + addr.init_pointee_move(node) + if self: + self._tail[].next = addr + self._tail = addr + else: + self._head = addr + self._tail = addr + self._size += 1 + + fn prepend(mut self, owned value: ElementType): + """Add an element to the beginning of the list. + + Args: + value: The value to prepend. + """ + var node = Node[ElementType](value^, None, self._head) + var addr = UnsafePointer[__type_of(node)].alloc(1) + addr.init_pointee_move(node) + if self: + self._head[].prev = addr + self._head = addr + else: + self._head = addr + self._tail = addr + self._size += 1 + + fn reverse(mut self): + """Reverse the order of elements in the list.""" + var prev = __type_of(self._head)() + var curr = self._head + while curr: + var next = curr[].next + curr[].next = prev + prev = curr + curr = next + self._head = prev + self._tail = self._head[].prev + + fn pop(mut self) -> ElementType: + """Remove and return the first element of the list. + + Returns: + The first element in the list. + """ + var elem = self._tail + var value = elem[].value + self._tail = elem[].prev + self._size -= 1 + return value^ + + fn copy(self) -> Self: + """Create a deep copy of the list. + + Returns: + A new list containing copies of all elements. + """ + var new = Self() + var curr = self._head + while curr: + new.append(curr[].value) + curr = curr[].next + return new^ + + fn __getitem__(ref self, index: Int) -> ref [self] ElementType: + """Get the element at the specified index. + + Args: + index: The index of the element to get. + + Returns: + The element at the specified index. + """ + var curr = self._head + for _ in range( + normalize_index[container_name="LinkedList"](index, self) + ): + curr = curr[].next + return curr[].value + + fn __setitem__(mut self, index: Int, owned value: ElementType): + """Set the element at the specified index. + + Args: + index: The index of the element to set. + value: The new value to set. + """ + var curr = self._head + for _ in range( + normalize_index[container_name="LinkedList"](index, self) + ): + curr = curr[].next + curr[].value = value^ + + fn __len__(self) -> Int: + """Get the number of elements in the list. + + Returns: + The number of elements in the list. + """ + return self._size + + fn __bool__(self) -> Bool: + """Check if the list is non-empty. + + Returns: + True if the list has elements, False otherwise. + """ + return len(self) != 0 + + fn __str__(self) -> String: + """Convert the list to its string representation. + + Returns: + String representation of the list. + """ + return String.write(self) + + fn __repr__(self) -> String: + """Convert the list to its string representation. + + Returns: + String representation of the list. + """ + var writer = String() + self._write(writer, prefix="LinkedList(", suffix=")") + return writer + + fn write_to[W: Writer](self, mut writer: W): + """Write the list to the given writer. + + Parameters: + W: The type of writer to write the list to. + + Args: + writer: The writer to write the list to. + """ + self._write(writer) + + @no_inline + fn _write[ + W: Writer + ](self, mut writer: W, *, prefix: String = "[", suffix: String = "]"): + if not self: + return writer.write(prefix, suffix) + + var curr = self._head + writer.write(prefix) + for i in range(len(self)): + if i: + writer.write(", ") + writer.write(curr[]) + curr = curr[].next + writer.write(suffix) diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo new file mode 100644 index 0000000000..fa8b12b85d --- /dev/null +++ b/stdlib/test/collections/test_linked_list.mojo @@ -0,0 +1,122 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %mojo-no-debug %s + +from collections import LinkedList +from testing import assert_equal + + +def test_construction(): + var l1 = LinkedList[Int]() + assert_equal(len(l1), 0) + + var l2 = LinkedList[Int](1, 2, 3) + assert_equal(len(l2), 3) + assert_equal(l2[0], 1) + assert_equal(l2[1], 2) + assert_equal(l2[2], 3) + + +def test_append(): + var l1 = LinkedList[Int]() + l1.append(1) + l1.append(2) + l1.append(3) + assert_equal(len(l1), 3) + assert_equal(l1[0], 1) + assert_equal(l1[1], 2) + assert_equal(l1[2], 3) + + +def test_prepend(): + var l1 = LinkedList[Int]() + l1.prepend(1) + l1.prepend(2) + l1.prepend(3) + assert_equal(len(l1), 3) + assert_equal(l1[0], 3) + assert_equal(l1[1], 2) + assert_equal(l1[2], 1) + + +def test_copy(): + var l1 = LinkedList[Int](1, 2, 3) + var l2 = l1.copy() + assert_equal(len(l2), 3) + assert_equal(l2[0], 1) + assert_equal(l2[1], 2) + assert_equal(l2[2], 3) + + +def test_reverse(): + var l1 = LinkedList[Int](1, 2, 3) + l1.reverse() + assert_equal(len(l1), 3) + assert_equal(l1[0], 3) + assert_equal(l1[1], 2) + assert_equal(l1[2], 1) + + +def test_pop(): + var l1 = LinkedList[Int](1, 2, 3) + assert_equal(l1.pop(), 3) + assert_equal(len(l1), 2) + assert_equal(l1[0], 1) + assert_equal(l1[1], 2) + + +def test_getitem(): + var l1 = LinkedList[Int](1, 2, 3) + assert_equal(l1[0], 1) + assert_equal(l1[1], 2) + assert_equal(l1[2], 3) + + assert_equal(l1[-1], 3) + assert_equal(l1[-2], 2) + assert_equal(l1[-3], 1) + + +def test_setitem(): + var l1 = LinkedList[Int](1, 2, 3) + l1[0] = 4 + assert_equal(l1[0], 4) + assert_equal(l1[1], 2) + assert_equal(l1[2], 3) + + l1[-1] = 5 + assert_equal(l1[0], 4) + assert_equal(l1[1], 2) + assert_equal(l1[2], 5) + + +def test_str(): + var l1 = LinkedList[Int](1, 2, 3) + assert_equal(str(l1), "[1, 2, 3]") + + +def test_repr(): + var l1 = LinkedList[Int](1, 2, 3) + assert_equal(repr(l1), "LinkedList(1, 2, 3)") + + +def main(): + test_construction() + test_append() + test_prepend() + test_copy() + test_reverse() + test_pop() + test_getitem() + test_setitem() + test_str() + test_repr() From 4de6a061313b03bbd63556b01041b2b274f41d29 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 15 Jan 2025 14:36:28 -0600 Subject: [PATCH 238/509] [stdlib] polish: Move ASCII free function predicates like `isdigit()` to be `Char` methods MODULAR_ORIG_COMMIT_REV_ID: ea353f81276ccc4a700ec8373ee150b38f0bb17c --- docs/changelog.md | 4 +- stdlib/src/builtin/char.mojo | 46 ++++++++ stdlib/src/collections/string/__init__.mojo | 4 - stdlib/src/collections/string/string.mojo | 100 +----------------- .../src/collections/string/string_slice.mojo | 8 +- stdlib/src/prelude/__init__.mojo | 4 - stdlib/test/builtin/test_char.mojo | 47 ++++++++ .../test/collections/string/test_string.mojo | 29 ----- 8 files changed, 105 insertions(+), 137 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index a0456c42dd..1d8ca2919e 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -113,7 +113,9 @@ what we publish. - Added `String` constructor from `Char` - `Char` can be converted to `UInt32` via `Char.to_u32()`. - `Char` provides methods for categorizing character types, including: - `Char.is_ascii()`, `Char.is_posix_space()`, `Char.is_python_space()`. + `Char.is_ascii()`, `Char.is_posix_space()`, `Char.is_python_space()`, + `Char.is_ascii_digit()`, `Char.is_ascii_upper()`, `Char.is_ascii_lower()`, + `Char.is_ascii_printable()`. - `chr(Int)` will now abort if given a codepoint value that is not a valid `Char`. diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 0a48f7cdc6..261fc2ffeb 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -272,6 +272,52 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): """ return self._scalar_value <= 0b0111_1111 + fn is_ascii_digit(self) -> Bool: + """Determines whether the given character is a digit [0-9]. + + Returns: + True if the character is a digit. + """ + alias ord_0 = UInt32(ord("0")) + alias ord_9 = UInt32(ord("9")) + return ord_0 <= self.to_u32() <= ord_9 + + fn is_ascii_upper(self) -> Bool: + """Determines whether the given character is an uppercase character. + + This currently only respects the default "C" locale, i.e. returns True + iff the character specified is one of "ABCDEFGHIJKLMNOPQRSTUVWXYZ". + + Returns: + True if the character is uppercase. + """ + alias ord_a = UInt32(ord("A")) + alias ord_z = UInt32(ord("Z")) + return ord_a <= self.to_u32() <= ord_z + + fn is_ascii_lower(self) -> Bool: + """Determines whether the given character is an lowercase character. + + This currently only respects the default "C" locale, i.e. returns True + iff the character specified is one of "abcdefghijklmnopqrstuvwxyz". + + Returns: + True if the character is lowercase. + """ + alias ord_a = UInt32(ord("a")) + alias ord_z = UInt32(ord("z")) + return ord_a <= self.to_u32() <= ord_z + + fn is_ascii_printable(self) -> Bool: + """Determines whether the given character is a printable character. + + Returns: + True if the character is a printable character, otherwise False. + """ + alias ord_space = UInt32(ord(" ")) + alias ord_tilde = UInt32(ord("~")) + return ord_space <= self.to_u32() <= ord_tilde + @always_inline fn is_python_space(self) -> Bool: """Determines whether this character is a Python whitespace string. diff --git a/stdlib/src/collections/string/__init__.mojo b/stdlib/src/collections/string/__init__.mojo index 97f9fdc563..0b7a981d4c 100644 --- a/stdlib/src/collections/string/__init__.mojo +++ b/stdlib/src/collections/string/__init__.mojo @@ -18,10 +18,6 @@ from .string import ( atof, atol, chr, - isdigit, - islower, - isprintable, - isupper, ord, ) from .string_slice import StringSlice, StaticString diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index cca7e2c210..db6afcb928 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -140,7 +140,7 @@ fn _repr_ascii(c: UInt8) -> String: if c == ord_back_slash: return r"\\" - elif isprintable(c): + elif Char(c).is_ascii_printable(): return _chr_ascii(c) elif c == ord_tab: return r"\t" @@ -525,96 +525,6 @@ fn atof(str_slice: StringSlice) raises -> Float64: return result * sign -# ===----------------------------------------------------------------------=== # -# isdigit -# ===----------------------------------------------------------------------=== # - - -fn isdigit(c: UInt8) -> Bool: - """Determines whether the given character is a digit [0-9]. - - Args: - c: The character to check. - - Returns: - True if the character is a digit. - """ - alias ord_0 = ord("0") - alias ord_9 = ord("9") - return ord_0 <= Int(c) <= ord_9 - - -# ===----------------------------------------------------------------------=== # -# isupper -# ===----------------------------------------------------------------------=== # - - -fn isupper(c: UInt8) -> Bool: - """Determines whether the given character is an uppercase character. - - This currently only respects the default "C" locale, i.e. returns True iff - the character specified is one of "ABCDEFGHIJKLMNOPQRSTUVWXYZ". - - Args: - c: The character to check. - - Returns: - True if the character is uppercase. - """ - return _is_ascii_uppercase(c) - - -fn _is_ascii_uppercase(c: UInt8) -> Bool: - alias ord_a = ord("A") - alias ord_z = ord("Z") - return ord_a <= Int(c) <= ord_z - - -# ===----------------------------------------------------------------------=== # -# islower -# ===----------------------------------------------------------------------=== # - - -fn islower(c: UInt8) -> Bool: - """Determines whether the given character is an lowercase character. - - This currently only respects the default "C" locale, i.e. returns True iff - the character specified is one of "abcdefghijklmnopqrstuvwxyz". - - Args: - c: The character to check. - - Returns: - True if the character is lowercase. - """ - return _is_ascii_lowercase(c) - - -fn _is_ascii_lowercase(c: UInt8) -> Bool: - alias ord_a = ord("a") - alias ord_z = ord("z") - return ord_a <= Int(c) <= ord_z - - -# ===----------------------------------------------------------------------=== # -# isprintable -# ===----------------------------------------------------------------------=== # - - -fn isprintable(c: UInt8) -> Bool: - """Determines whether the given character is a printable character. - - Args: - c: The character to check. - - Returns: - True if the character is a printable character, otherwise False. - """ - alias ord_space = ord(" ") - alias ord_tilde = ord("~") - return ord_space <= Int(c) <= ord_tilde - - # ===----------------------------------------------------------------------=== # # String # ===----------------------------------------------------------------------=== # @@ -1961,8 +1871,8 @@ struct String( """ if not self: return False - for c in self: - if not isdigit(ord(c)): + for char in self.as_string_slice().chars(): + if not char.is_ascii_digit(): return False return True @@ -1994,8 +1904,8 @@ struct String( Returns: True if all characters are printable else False. """ - for c in self: - if not isprintable(ord(c)): + for char in self.as_string_slice().chars(): + if not char.is_ascii_printable(): return False return True diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 5a48b47b5e..74c471f491 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -524,12 +524,12 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( elif s == "\r": result += r"\r" else: - var codepoint = ord(s) - if isprintable(codepoint): + var codepoint = Char.ord(s) + if codepoint.is_ascii_printable(): result += s - elif codepoint < 0x10: + elif codepoint.to_u32() < 0x10: result += hex(codepoint, prefix=r"\x0") - elif codepoint < 0x20 or codepoint == 0x7F: + elif codepoint.to_u32() < 0x20 or codepoint.to_u32() == 0x7F: result += hex(codepoint, prefix=r"\x") else: # multi-byte character result += s diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 572ff016ad..dffb5725b5 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -21,10 +21,6 @@ from collections.string import ( atof, atol, chr, - isdigit, - islower, - isprintable, - isupper, ord, ) from hashlib.hash import Hashable, hash diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index c8c083a82a..b38a855051 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -95,6 +95,49 @@ def test_char_is_posix_space(): assert_false(Char.ord(".").is_posix_space()) +def test_char_is_lower(): + assert_true(Char.ord("a").is_ascii_lower()) + assert_true(Char.ord("b").is_ascii_lower()) + assert_true(Char.ord("y").is_ascii_lower()) + assert_true(Char.ord("z").is_ascii_lower()) + + assert_false(Char.from_u32(ord("a") - 1).value().is_ascii_lower()) + assert_false(Char.from_u32(ord("z") + 1).value().is_ascii_lower()) + + assert_false(Char.ord("!").is_ascii_lower()) + assert_false(Char.ord("0").is_ascii_lower()) + + +def test_char_is_upper(): + assert_true(Char.ord("A").is_ascii_upper()) + assert_true(Char.ord("B").is_ascii_upper()) + assert_true(Char.ord("Y").is_ascii_upper()) + assert_true(Char.ord("Z").is_ascii_upper()) + + assert_false(Char.from_u32(ord("A") - 1).value().is_ascii_upper()) + assert_false(Char.from_u32(ord("Z") + 1).value().is_ascii_upper()) + + assert_false(Char.ord("!").is_ascii_upper()) + assert_false(Char.ord("0").is_ascii_upper()) + + +def test_char_is_digit(): + assert_true(Char.ord("1").is_ascii_digit()) + assert_false(Char.ord("g").is_ascii_digit()) + + # Devanagari Digit 6 — non-ASCII digits are not "ascii digit". + assert_false(Char.ord("६").is_ascii_digit()) + + +def test_char_is_printable(): + assert_true(Char.ord("a").is_ascii_printable()) + assert_false(Char.ord("\n").is_ascii_printable()) + assert_false(Char.ord("\t").is_ascii_printable()) + + # Non-ASCII characters are not considered "ascii printable". + assert_false(Char.ord("स").is_ascii_printable()) + + alias SIGNIFICANT_CODEPOINTS = List[Tuple[Int, List[Byte]]]( # -------------------------- # 1-byte (ASCII) codepoints @@ -191,6 +234,10 @@ def main(): test_char_formatting() test_char_properties() test_char_is_posix_space() + test_char_is_lower() + test_char_is_upper() + test_char_is_digit() + test_char_is_printable() test_char_utf8_encoding() test_char_utf8_byte_length() test_char_comptime() diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index d938ab0fd0..ea971afab7 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -814,17 +814,6 @@ def test_splitlines(): def test_isupper(): - assert_true(isupper(ord("A"))) - assert_true(isupper(ord("B"))) - assert_true(isupper(ord("Y"))) - assert_true(isupper(ord("Z"))) - - assert_false(isupper(ord("A") - 1)) - assert_false(isupper(ord("Z") + 1)) - - assert_false(isupper(ord("!"))) - assert_false(isupper(ord("0"))) - assert_true(String("ASDG").isupper()) assert_false(String("AsDG").isupper()) assert_true(String("ABC123").isupper()) @@ -834,17 +823,6 @@ def test_isupper(): def test_islower(): - assert_true(islower(ord("a"))) - assert_true(islower(ord("b"))) - assert_true(islower(ord("y"))) - assert_true(islower(ord("z"))) - - assert_false(islower(ord("a") - 1)) - assert_false(islower(ord("z") + 1)) - - assert_false(islower(ord("!"))) - assert_false(islower(ord("0"))) - assert_true(String("asdfg").islower()) assert_false(String("asdFDg").islower()) assert_true(String("abc123").islower()) @@ -1425,9 +1403,6 @@ def test_format_conversion_flags(): def test_isdigit(): - assert_true(isdigit(ord("1"))) - assert_false(isdigit(ord("g"))) - assert_false(String("").isdigit()) assert_true(String("123").isdigit()) assert_false(String("asdg").isdigit()) @@ -1435,10 +1410,6 @@ def test_isdigit(): def test_isprintable(): - assert_true(isprintable(ord("a"))) - assert_false(isprintable(ord("\n"))) - assert_false(isprintable(ord("\t"))) - assert_true(String("aasdg").isprintable()) assert_false(String("aa\nae").isprintable()) assert_false(String("aa\tae").isprintable()) From b89734cf6075cfd12509f3533d764aac21720f75 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Wed, 15 Jan 2025 12:48:15 -0800 Subject: [PATCH 239/509] Add image metadata to support rendering in card stacks MODULAR_ORIG_COMMIT_REV_ID: fd1b1dafcf04683dae78ad61f7d5086db50ebd6b --- docs/manual/get-started.mdx | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 8540761f2e..67bd86d592 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -3,6 +3,7 @@ title: "Get started with Mojo" sidebar_label: "Tutorial: Get started with Mojo" description: "Install Mojo and learn the language basics by building a complete Mojo program" github_url: https://github.com/modularml/mojo/tree/nightly/examples/life +images: /images/artwork/mojo-get-started.jpg --- import GetMagic from '@site/src/includes/get_magic.mdx'; From 35b10b5d284b4bdfd054ead0884663c2551faeb8 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Wed, 15 Jan 2025 13:45:44 -0800 Subject: [PATCH 240/509] Revert adding image front matter due to build issue MODULAR_ORIG_COMMIT_REV_ID: 73f546472958e8f645c868ffa41db6b3e331ede6 --- docs/manual/get-started.mdx | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 67bd86d592..8540761f2e 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -3,7 +3,6 @@ title: "Get started with Mojo" sidebar_label: "Tutorial: Get started with Mojo" description: "Install Mojo and learn the language basics by building a complete Mojo program" github_url: https://github.com/modularml/mojo/tree/nightly/examples/life -images: /images/artwork/mojo-get-started.jpg --- import GetMagic from '@site/src/includes/get_magic.mdx'; From a9f9e0ac245c977cb8bd5a2cdf7153bdbcbc646f Mon Sep 17 00:00:00 2001 From: Scott Main Date: Wed, 15 Jan 2025 14:37:07 -0800 Subject: [PATCH 241/509] Add image metadata to support rendering in card stacks MODULAR_ORIG_COMMIT_REV_ID: 6df31c8fe894ab751471259dae63af76a6ace222 --- docs/manual/get-started.mdx | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 8540761f2e..9a3aaeff9b 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -3,6 +3,7 @@ title: "Get started with Mojo" sidebar_label: "Tutorial: Get started with Mojo" description: "Install Mojo and learn the language basics by building a complete Mojo program" github_url: https://github.com/modularml/mojo/tree/nightly/examples/life +image: /images/artwork/mojo-get-started.jpg --- import GetMagic from '@site/src/includes/get_magic.mdx'; From f486ba276ccbebd91fe4dc75741266372bc29bd0 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Wed, 15 Jan 2025 15:19:40 -0800 Subject: [PATCH 242/509] Adding an example to the public repo of using the Mojo testing framework MODULAR_ORIG_COMMIT_REV_ID: 111e67f7d270db6368c3b592ff5437d24c4a464c --- docs/tools/testing.mdx | 167 +++++++++++--------- examples/testing/.gitattributes | 2 + examples/testing/.gitignore | 6 + examples/testing/README.md | 68 ++++++++ examples/testing/mojoproject.toml | 16 ++ examples/testing/src/example.mojo | 26 +++ examples/testing/src/my_math/__init__.mojo | 51 ++++++ examples/testing/src/my_math/utils.mojo | 94 +++++++++++ examples/testing/test/my_math/test_dec.mojo | 28 ++++ examples/testing/test/my_math/test_inc.mojo | 28 ++++ 10 files changed, 413 insertions(+), 73 deletions(-) create mode 100644 examples/testing/.gitattributes create mode 100644 examples/testing/.gitignore create mode 100644 examples/testing/README.md create mode 100644 examples/testing/mojoproject.toml create mode 100644 examples/testing/src/example.mojo create mode 100644 examples/testing/src/my_math/__init__.mojo create mode 100644 examples/testing/src/my_math/utils.mojo create mode 100644 examples/testing/test/my_math/test_dec.mojo create mode 100644 examples/testing/test/my_math/test_inc.mojo diff --git a/docs/tools/testing.mdx b/docs/tools/testing.mdx index 287e40c690..bd67e80ec3 100644 --- a/docs/tools/testing.mdx +++ b/docs/tools/testing.mdx @@ -2,6 +2,7 @@ title: Testing sidebar_position: 2 description: Testing Mojo programs. +github_url: https://github.com/modularml/mojo/tree/nightly/examples/testing --- Mojo includes a framework for developing and executing unit tests. The framework @@ -66,7 +67,7 @@ mojo test test_quickstart.mojo You should see output similar to this (note that this example elides the full filesystem paths from the output shown): -``` +```output Testing Time: 1.193s Total Discovered Tests: 2 @@ -92,15 +93,20 @@ its error message. ### Next steps -* [The `testing` module](#the-testing-module) describes the assertion +- [The `testing` module](#the-testing-module) describes the assertion functions available to help implement tests. -* [Writing unit tests](#writing-unit-tests) shows how to write unit tests and +- [Writing unit tests](#writing-unit-tests) shows how to write unit tests and organize them into test files. -* [The `mojo test` command](#the-mojo-test-command) describes how to execute +- [The `mojo test` command](#the-mojo-test-command) describes how to execute and collect lists of tests. -* [Writing API documentation tests](#writing-api-documentation-tests) +- [Writing API documentation tests](#writing-api-documentation-tests) discusses how to use the Mojo testing framework to test code examples in your API documentation. +- The public [GitHub repo](https://github.com/modularml/mojo/tree/nightly) + contains an [example + project](https://github.com/modularml/mojo/tree/nightly/examples/testing) to + demonstrate both unit testing and docstring testing. Several of the examples + shown later are based on this project. ## The `testing` module @@ -108,15 +114,15 @@ The Mojo standard library includes a [`testing`](/mojo/stdlib/testing/testing/) module that defines several assertion functions for implementing tests. Each assertion returns `None` if its condition is met or raises an error if it isn't. -* [`assert_true()`](/mojo/stdlib/testing/testing/assert_true): +- [`assert_true()`](/mojo/stdlib/testing/testing/assert_true): Asserts that the input value is `True`. -* [`assert_false()`](/mojo/stdlib/testing/testing/assert_false): +- [`assert_false()`](/mojo/stdlib/testing/testing/assert_false): Asserts that the input value is `False`. -* [`assert_equal()`](/mojo/stdlib/testing/testing/assert_equal): +- [`assert_equal()`](/mojo/stdlib/testing/testing/assert_equal): Asserts that the input values are equal. -* [`assert_not_equal()`](/mojo/stdlib/testing/testing/assert_not_equal): +- [`assert_not_equal()`](/mojo/stdlib/testing/testing/assert_not_equal): Asserts that the input values are not equal. -* [`assert_almost_equal()`](/mojo/stdlib/testing/testing/assert_almost_equal): +- [`assert_almost_equal()`](/mojo/stdlib/testing/testing/assert_almost_equal): Asserts that the input values are equal up to a tolerance. The boolean assertions report a basic error message when they fail. @@ -191,8 +197,7 @@ Error: AssertionError: Didn't raise at Expression [4] wrapper:18:23 The example above assigns the return value from `inc()` to a [*discard pattern*](/mojo/manual/lifecycle/death#explicit-lifetimes). -Without it, the Mojo compiler detects that the return value is unused and -optimizes the code to eliminate the function call. +Without it, the Mojo compiler reports a warning that the return value is unused. ::: @@ -222,11 +227,11 @@ Error: invalid value A Mojo unit test is simply a function that fulfills all of these requirements: -* Has a name that starts with `test_`. -* Accepts no arguments. -* Returns either `None` or a value of type `object`. -* Raises an error to indicate test failure. -* Is defined at the module scope, not as a Mojo struct method. +- Has a name that starts with `test_`. +- Accepts no arguments. +- Returns either `None` or a value of type `object`. +- Raises an error to indicate test failure. +- Is defined at the module scope, not as a Mojo struct method. You can use either `def` or `fn` to define a test function. Because a test function always raises an error to indicate failure, any test function defined @@ -269,9 +274,9 @@ The unique identity of a unit test consists of the path of the test file and the name of the test function, separated by `::`. So the test IDs from the example above are: -* `test_my_target_module.mojo::test_validate_input()` -* `test_my_target_module.mojo::test_convert_input()` -* `test_my_target_module.mojo::test_convert_error()` +- `test_my_target_module.mojo::test_validate_input()` +- `test_my_target_module.mojo::test_convert_input()` +- `test_my_target_module.mojo::test_convert_error()` ## The `mojo test` command @@ -283,18 +288,19 @@ command for running tests or collecting a list of tests. By default, the `mojo test` command runs the tests that you specify using one of the following: -* A single test ID with either an absolute or relative file path, to run only +- A single test ID with either an absolute or relative file path, to run only that test. -* A single absolute or relative file path, to run all tests in that file. -* A single absolute or relative directory path, to recurse through that +- A single absolute or relative file path, to run all tests in that file. +- A single absolute or relative directory path, to recurse through that directory hierarchy and run all tests found. If needed, you can optionally use the `-I` option one or more times to append additional paths to the list of directories searched to import Mojo modules and -packages. For example, consider a project with the following directory -structure: +packages. Consider the [example testing +project](https://github.com/modularml/mojo/tree/nightly/examples/testing) in +GitHub, which has the following directory structure: -``` +```output . ├── src │   ├── example.mojo @@ -307,11 +313,14 @@ structure: └── test_inc.mojo ``` -From the project root directory, you could execute all of the tests in the -`test` directory like this: +From the project root directory, you can execute all of the tests in the `test` +directory like this: +```bash +mojo test -I src test ``` -$ mojo test -I src test + +```output Testing Time: 3.433s Total Discovered Tests: 4 @@ -321,10 +330,13 @@ Failed : 0 (0.00%) Skipped: 0 (0.00%) ``` -You could run the tests contained in only the `test_dec.mojo` file like this: +You can run the tests contained in only the `test_dec.mojo` file like this: +```bash +mojo test -I src test/my_math/test_dec.mojo ``` -$ mojo test -I src test/my_math/test_dec.mojo + +```output Testing Time: 1.175s Total Discovered Tests: 2 @@ -334,11 +346,14 @@ Failed : 0 (0.00%) Skipped: 0 (0.00%) ``` -And you could run a single test from a file by providing its fully qualified +And you can run a single test from a file by providing its fully qualified ID like this: +```bash +mojo test -I src 'test/my_math/test_dec.mojo::test_dec_valid()' ``` -$ mojo test -I src 'test/my_math/test_dec.mojo::test_dec_valid()' + +```output Testing Time: 0.66s Total Discovered Tests: 1 @@ -353,9 +368,11 @@ Skipped: 0 (0.00%) By including the `--collect-only` or `--co` option, you can use `mojo test` to discover and print a list of tests. -As an example, consider the project structure shown in the -[Running tests](#running-tests) section. The following command produces a list -of all of the tests defined in the `test` directory hierarchy. +Consider the [example testing +project](https://github.com/modularml/mojo/tree/nightly/examples/testing) +directory structure shown in the [Running tests](#running-tests) section. The +following command produces a list of all of the tests defined in the `test` +directory hierarchy. ```bash mojo test --co test @@ -364,7 +381,7 @@ mojo test --co test The output shows the hierarchy of directories, test files, and individual tests (note that this example elides the full filesystem paths from the output shown): -``` +```output @@ -380,7 +397,7 @@ By default `mojo test` produces concise, human-readable output. Alternatively you can produce JSON formatted output more suitable for input to other tools by including the `--diagnostic-format json` option. -For example, you could run the tests in the `test_quickstart.mojo` file shown +For example, you can run the tests in the `test_quickstart.mojo` file shown in the [Get started](#get-started) section with JSON formatted output using this command: @@ -392,7 +409,7 @@ The output shows the detailed results for each individual test and summary results (note that this example elides the full filesystem paths from the output shown): -``` +```json { "children": [ { @@ -421,19 +438,21 @@ output shown): } ``` -You can also produce JSON output for test collection as well. As an example, -consider the project structure shown in the [Running tests](#running-tests) -section. The following command collects a list in JSON format of all of the -tests defined in the `test` directory hierarchy: +You can also produce JSON output for test collection as well. Consider the +[example testing +project](https://github.com/modularml/mojo/tree/nightly/examples/testing) +directory structure shown in the [Running tests](#running-tests) section. The +following command collects a list in JSON format of all of the tests defined in +the `test` directory hierarchy: ```bash mojo test --diagnostic-format json --co test ``` -The output would appear as follows (note that this example elides the full +The output will appear as follows (note that this example elides the full filesystem paths from the output shown): -``` +```json { "children": [ { @@ -442,18 +461,18 @@ filesystem paths from the output shown): "id": "ROOT_DIR/test/my_math/test_dec.mojo::test_dec_valid()", "location": { "endColumn": 5, - "endLine": 5, + "endLine": 19, "startColumn": 5, - "startLine": 5 + "startLine": 19 } }, { "id": "ROOT_DIR/test/my_math/test_dec.mojo::test_dec_min()", "location": { "endColumn": 5, - "endLine": 9, + "endLine": 24, "startColumn": 5, - "startLine": 9 + "startLine": 24 } } ], @@ -465,18 +484,18 @@ filesystem paths from the output shown): "id": "ROOT_DIR/test/my_math/test_inc.mojo::test_inc_valid()", "location": { "endColumn": 5, - "endLine": 5, + "endLine": 19, "startColumn": 5, - "startLine": 5 + "startLine": 19 } }, { "id": "ROOT_DIR/test/my_math/test_inc.mojo::test_inc_max()", "location": { "endColumn": 5, - "endLine": 9, + "endLine": 24, "startColumn": 5, - "startLine": 9 + "startLine": 24 } } ], @@ -539,7 +558,7 @@ assert_equals(b, 2) Sometimes you might want to execute a line of code as part of the test but *not* display that line in the API documentation. To achieve this, prefix the line of -code with `%#`. For example, you could use this technique to omit `import` +code with `%#`. For example, you can use this technique to omit `import` statements and assertion functions from the documentation. ```` @@ -560,7 +579,7 @@ print(c) ### Documentation test suites and scoping The Mojo testing framework treats each docstring as a separate *test suite*. -In other words, a single test suite could correspond to the docstring for an +In other words, a single test suite can correspond to the docstring for an individual package, module, function, struct, struct method, etc. Each executable code block within a given docstring is a single test of the same @@ -606,43 +625,45 @@ suite within the same module. ### Documentation test identifiers The format of a documentation test identifier is `@::`. -This is best explained by an example. Consider the project structure shown in -the [Running tests](#running-tests) section. The source files in the `src` -directory might contain docstrings for the `my_math` package, the `utils.mojo` -module, and the individual functions within that module. You could collect the -full list of tests by executing: +This is best explained by an example. Consider the [example testing +project](https://github.com/modularml/mojo/tree/nightly/examples/testing) +directory structure shown in the [Running tests](#running-tests) section. The +source files in the `src` directory might contain docstrings for the `my_math` +package, the `utils.mojo` module, and the individual functions within that +module. You can collect the full list of tests by executing: -``` +```bash mojo test --co src ``` The output shows the hierarchy of directories, test files, and individual tests (note that this example elides the full filesystem paths from the output shown): -``` +```output + - - - - - - + + + + + + ``` Several different test suites appear in this result: -| Test suite scope | File | Test suite name | -| ---------------- | --------------------------- | ---------------------------------------------------- | -| Package | `src/my_math/__init__.mojo` | `__doc__` | -| Module | `src/my_math/utils.mojo` | `__doc__` | -| Function | `src/my_math/utils.mojo` | `inc(stdlib\3A\3Abuiltin\3A\3Aint\3A\3AInt).__doc__` | +| Test suite scope | File | Test suite name | +| ---------------- | --------------------------- | ------------------------ | +| Package | `src/my_math/__init__.mojo` | `__doc__` | +| Module | `src/my_math/utils.mojo` | `__doc__` | +| Function | `src/my_math/utils.mojo` | `inc(\3A\3AInt).__doc__` | Then within a specific test suite, tests are numbered sequentially in the order they appear in the docstring, starting with 0. diff --git a/examples/testing/.gitattributes b/examples/testing/.gitattributes new file mode 100644 index 0000000000..8f61a8e774 --- /dev/null +++ b/examples/testing/.gitattributes @@ -0,0 +1,2 @@ +# SCM syntax highlighting +pixi.lock linguist-language=YAML linguist-generated=true diff --git a/examples/testing/.gitignore b/examples/testing/.gitignore new file mode 100644 index 0000000000..4edde08eef --- /dev/null +++ b/examples/testing/.gitignore @@ -0,0 +1,6 @@ +# pixi environments +.pixi +*.egg-info + +# Magic environments +.magic diff --git a/examples/testing/README.md b/examples/testing/README.md new file mode 100644 index 0000000000..849554de04 --- /dev/null +++ b/examples/testing/README.md @@ -0,0 +1,68 @@ +# Modular testing framework examples + +This directory contains examples of using the Mojo testing framework. It +demonstrates using the testing framework for both unit testing and testing code +examples in the [documentation +strings](https://docs.modular.com/mojo/manual/basics#code-comments) (also known +as *docstrings*) of Mojo API documentation. See the +[Testing](https://docs.modular.com/mojo/tools/testing) section of the [Mojo +manual](https://docs.modular.com/mojo/manual/) for a complete discussion of how +to use the Mojo testing framework. + +## Files + +This directory contains the following files: + +- `src/my_math/__init__.mojo`: a Mojo package file with package-level docstrings + containing code examples to test + +- `src/my_math/utils.mojo`: a Mojo module source file with both module-level and + function-level docstrings containing code examples to test + +- `src/example.mojo`: a simple Mojo program that uses the functions from the + `my_math` package + +- `test/my_math/test_*.mojo`: Mojo test files containing unit tests for + functions defined in the `my_math` package + +- `mojoproject.toml`: a [Magic](https://docs.modular.com/magic/) project file + containing the project dependencies and task definitions. + +## Run the code + +This example project uses the [Magic](https://docs.modular.com/magic/) package +and virtual environment manager. To run the code in this project, you should +follow the instructions in [Install +Magic](https://docs.modular.com/nightly/magic/#install-magic) first. + +Once you have installed Magic, activate the project's virtual environment by +navigating to the project's root directory and executing: + +```bash +magic shell +``` + +Run the unit tests contained in the `test` directory by executing: + +```bash +mojo test -I src test +``` + +Run the docstring tests for the API documentation contained in the `src` +directory by executing: + +```bash +mojo test src +``` + +If desired, you can run the example program by executing: + +```bash +mojo src/example.mojo +``` + +Once you're done, deactivate the project's virtual environment by executing: + +```bash +exit +``` diff --git a/examples/testing/mojoproject.toml b/examples/testing/mojoproject.toml new file mode 100644 index 0000000000..487fdcfa48 --- /dev/null +++ b/examples/testing/mojoproject.toml @@ -0,0 +1,16 @@ +[project] +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly"] +description = "An example of using the Mojo testing framework" +name = "testing" +platforms = ["osx-arm64", "linux-64", "linux-aarch64"] +version = "0.1.0" + +[tasks] +main = "mojo run src/example.mojo" +doc-tests = "mojo test src" +unit-tests = "mojo test -I src test" +tests = { depends-on = ["doc-tests", "unit-tests"] } + +[dependencies] +max = "*" diff --git a/examples/testing/src/example.mojo b/examples/testing/src/example.mojo new file mode 100644 index 0000000000..28807bbccb --- /dev/null +++ b/examples/testing/src/example.mojo @@ -0,0 +1,26 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from my_math import dec, inc + + +def main(): + i = 0 + print("Incrementing") + while i < 5: + i = inc(i) + print(i) + print("Decrementing") + while i > 0: + print(i) + i = dec(i) diff --git a/examples/testing/src/my_math/__init__.mojo b/examples/testing/src/my_math/__init__.mojo new file mode 100644 index 0000000000..968532820f --- /dev/null +++ b/examples/testing/src/my_math/__init__.mojo @@ -0,0 +1,51 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# ===----------------------------------------------------------------------=== # +# Package file +# ===----------------------------------------------------------------------=== # +"""Basic mathematical utilities. + +This package defines a collection of utility functions for manipulating +integer values. + +You can import these APIs from the `my_math` package. For example: + +```mojo +from my_math import dec, inc +``` + +The `inc()` function performs a simple increment: + +```mojo +%# from testing import assert_equal +from my_math import inc +a = 1 +b = inc(a) # b = 2 +%# assert_equal(b, 2) +``` + +However, `inc()` raises an error if it would result in integer overflow: + +```mojo +c = 0 +try: + c = inc(Int.MAX) +except e: + print(e) +%# assert_equal("inc overflow", str(e)) +``` + +""" + +from .utils import dec, inc diff --git a/examples/testing/src/my_math/utils.mojo b/examples/testing/src/my_math/utils.mojo new file mode 100644 index 0000000000..8eb0d42577 --- /dev/null +++ b/examples/testing/src/my_math/utils.mojo @@ -0,0 +1,94 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Implements various mathematical utilities. + +You can import these APIs from the `my_math` package. For example: + +```mojo +from my_math import inc +``` +""" + + +def inc(n: Int) -> Int: + """Returns an incremented integer value. + + ```mojo + %# from testing import assert_equal + from my_math import inc + i = 7 + j = inc(i) # j = 8 + %# assert_equal(j, 8) + ``` + + However, `inc()` raises an error if it would result in integer overflow: + + ```mojo + k = 0 + try: + k = inc(Int.MAX) + except e: + print(e) # inc overflow + %# assert_equal("inc overflow", str(e)) + ``` + + Args: + n: The integer value to increment. + + Returns: + The input value plus one. + + Raises: + An error if the incremented value exceeds `Int.MAX`. + """ + if n == Int.MAX: + raise Error("inc overflow") + return n + 1 + + +def dec(n: Int) -> Int: + """Returns a decremented integer value. + + ```mojo + %# from testing import assert_equal + from my_math import dec + i = 7 + j = dec(i) # j = 6 + %# assert_equal(j, 6) + ``` + + However, `dec()` raises an error if it would result in integer overflow: + + ```mojo + k = 0 + try: + k = dec(Int.MIN) + except e: + print(e) # inc overflow + %# assert_equal("dec overflow", str(e)) + ``` + + Args: + n: The integer value to decrement. + + Returns: + The input value minus one. + + Raises: + An error if the decremented value is less than `Int.MIN`. + + """ + if n == Int.MIN: + raise Error("dec overflow") + return n - 1 diff --git a/examples/testing/test/my_math/test_dec.mojo b/examples/testing/test/my_math/test_dec.mojo new file mode 100644 index 0000000000..7484124d6b --- /dev/null +++ b/examples/testing/test/my_math/test_dec.mojo @@ -0,0 +1,28 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from my_math import dec + +from testing import assert_equal, assert_raises + + +def test_dec_valid(): + assert_equal(dec(1), 0) + assert_equal(dec(0), -1) + + +def test_dec_min(): + with assert_raises(): + # Assign the return value to the discard pattern to prevent the Mojo + # compiler from warning that it is unused. + _ = dec(Int.MIN) diff --git a/examples/testing/test/my_math/test_inc.mojo b/examples/testing/test/my_math/test_inc.mojo new file mode 100644 index 0000000000..8b18919c75 --- /dev/null +++ b/examples/testing/test/my_math/test_inc.mojo @@ -0,0 +1,28 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from my_math import inc + +from testing import assert_equal, assert_raises + + +def test_inc_valid(): + assert_equal(inc(0), 1) + assert_equal(inc(1), 2) + + +def test_inc_max(): + with assert_raises(): + # Assign the return value to the discard pattern to prevent the Mojo + # compiler from warning that it is unused. + _ = inc(Int.MAX) From f53a6246819331a23a3fe0e0f6561314cf9174cb Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 15 Jan 2025 17:54:42 -0600 Subject: [PATCH 243/509] [stdlib] polish: Add `StringSlice.char_slices()` + deprecate `__iter__()` method MODULAR_ORIG_COMMIT_REV_ID: a50d7efa09350c65b20877feec2d99217c8da65b --- stdlib/src/collections/string/_unicode.mojo | 22 +++++++++---------- .../src/collections/string/string_slice.mojo | 19 +++++++++++----- 2 files changed, 24 insertions(+), 17 deletions(-) diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index e96dff1260..152ec38d54 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -117,19 +117,18 @@ fn is_uppercase(s: StringSlice) -> Bool: there is at least one cased character, False otherwise. """ var found = False - for c in s: - var rune = Char.ord(c) - var index = _lowercase_mapping_index(rune) + for char in s.chars(): + var index = _lowercase_mapping_index(char) if index != -1: found = True continue - index = _uppercase_mapping_index(rune) + index = _uppercase_mapping_index(char) if index != -1: return False - index = _uppercase_mapping2_index(rune) + index = _uppercase_mapping2_index(char) if index != -1: return False - index = _uppercase_mapping3_index(rune) + index = _uppercase_mapping3_index(char) if index != -1: return False return found @@ -147,21 +146,20 @@ fn is_lowercase(s: StringSlice) -> Bool: there is at least one cased character, False otherwise. """ var found = False - for c in s: - var rune = Char.ord(c) - var index = _uppercase_mapping_index(rune) + for char in s.chars(): + var index = _uppercase_mapping_index(char) if index != -1: found = True continue - index = _uppercase_mapping2_index(rune) + index = _uppercase_mapping2_index(char) if index != -1: found = True continue - index = _uppercase_mapping3_index(rune) + index = _uppercase_mapping3_index(char) if index != -1: found = True continue - index = _lowercase_mapping_index(rune) + index = _lowercase_mapping_index(char) if index != -1: return False return found diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 74c471f491..4104962de4 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -512,7 +512,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ var result = String() var use_dquote = False - for s in self: + for s in self.char_slices(): use_dquote = use_dquote or (s == "'") if s == "\\": @@ -675,15 +675,14 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( self.unsafe_ptr(), rhs.unsafe_ptr(), min(len1, len2) ) + @deprecated("Use `str.chars()` or `str.char_slices()` instead.") fn __iter__(self) -> _StringSliceIter[origin]: """Iterate over the string, returning immutable references. Returns: An iterator of references to the string elements. """ - return _StringSliceIter[origin]( - ptr=self.unsafe_ptr(), length=self.byte_length() - ) + return self.char_slices() fn __reversed__(self) -> _StringSliceIter[origin, False]: """Iterate backwards over the string, returning immutable references. @@ -955,6 +954,16 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return CharsIter(self) + fn char_slices(self) -> _StringSliceIter[origin]: + """Iterate over the string, returning immutable references. + + Returns: + An iterator of references to the string elements. + """ + return _StringSliceIter[origin]( + ptr=self.unsafe_ptr(), length=self.byte_length() + ) + @always_inline fn as_bytes(self) -> Span[Byte, origin]: """Get the sequence of encoded bytes of the underlying string. @@ -1265,7 +1274,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( ) else: var offset = 0 - for s in self: + for s in self.char_slices(): var b_len = s.byte_length() if not _is_newline_char(ptr, offset, ptr[offset], b_len): return False From 33fda10e040fc77a2205eeb18bda28785249bd3b Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 15 Jan 2025 19:26:53 -0500 Subject: [PATCH 244/509] [mojo-lang] Allow keyword-only argument overloading This allows overloading keyword-only argument names, and overloading a positional arg with a keyword-only argument: ```mojo struct OverloadedKwArgs: var val: Int fn __init__(out self, single: Int): self.val = single fn __init__(out self, *, double: Int): self.val = double * 2 fn __init__(out self, *, triple: Int): self.val = triple * 3 fn main(): OverloadedKwArgs(1) # val=1 OverloadedKwArgs(double=1) # val=2 OverloadedKwArgs(triple=2) # val=3 ``` It also enables indexing overloading: ```mojo struct OverloadedKwArgs: var vals: List[Int] fn __init__(out self): self.vals = List[Int](0, 1, 2, 3, 4) fn __getitem__(self, idx: Int) -> Int: return self.vals[idx] fn __getitem__(self, *, idx2: Int) -> Int: return self.vals[idx2 * 2] fn __setitem__(mut self, idx: Int, val: Int): self.vals[idx] = val fn __setitem__(mut self, val: Int, *, idx2: Int): self.vals[idx2 * 2] = val fn main(): var x = OverloadedKwArgs() print(x[1]) # 1 print(x[idx2=1]) # 2 x[1] = 42 x[idx2=1] = 84 print(x[1]) # 42 print(x[idx2=1]) # 84 ``` MODULAR_ORIG_COMMIT_REV_ID: 3a2fbcd84ea3ecb83e30ae21d34006759d61743c --- docs/changelog.md | 56 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 1d8ca2919e..bb34b332c0 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -42,6 +42,62 @@ what we publish. var y : Int = x[idx] # Just works! ``` +- You can now overload positional arguments with a keyword-only argument, and + keyword-only arguments with different names: + + ```mojo + struct OverloadedKwArgs: + var val: Int + + fn __init__(out self, single: Int): + self.val = single + + fn __init__(out self, *, double: Int): + self.val = double * 2 + + fn __init__(out self, *, triple: Int): + self.val = triple * 3 + + fn main(): + OverloadedKwArgs(1) # val=1 + OverloadedKwArgs(double=1) # val=2 + OverloadedKwArgs(triple=2) # val=3 + ``` + + This also works with indexing operations: + + ```mojo + struct OverloadedKwArgs: + var vals: List[Int] + + fn __init__(out self): + self.vals = List[Int](0, 1, 2, 3, 4) + + fn __getitem__(self, idx: Int) -> Int: + return self.vals[idx] + + fn __getitem__(self, *, idx2: Int) -> Int: + return self.vals[idx2 * 2] + + fn __setitem__(mut self, idx: Int, val: Int): + self.vals[idx] = val + + fn __setitem__(mut self, val: Int, *, idx2: Int): + self.vals[idx2 * 2] = val + + + fn main(): + var x = OverloadedKwArgs() + print(x[1]) # 1 + print(x[idx2=1]) # 2 + + x[1] = 42 + x[idx2=1] = 84 + + print(x[1]) # 42 + print(x[idx2=1]) # 84 + ``` + ### Standard library changes - The `int` function to construct an `Int` has been deprecated, this was a From 3407d2f7c408a2958f8277331ddceb8c2897870a Mon Sep 17 00:00:00 2001 From: soraros Date: Wed, 15 Jan 2025 18:36:59 -0800 Subject: [PATCH 245/509] [External] [stdlib] Make examples compile in `math` (#53924) [External] [stdlib] Make examples compile in `math` - This is needed because of https://github.com/modularml/mojo/issues/3828. Co-authored-by: soraros Closes modularml/mojo#3912 MODULAR_ORIG_COMMIT_REV_ID: 1b67d641b8fc389d43fab99ebf6785fc9c906d55 --- stdlib/src/math/math.mojo | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index ec2461fe65..d2acffd50a 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -2588,7 +2588,7 @@ trait CeilDivable: var x: Float64 fn __ceildiv__(self, denominator: Self) -> Self: - return -(self.x // -denominator.x) + return Self(self.x // denominator.x) ``` """ @@ -2621,7 +2621,7 @@ trait CeilDivableRaising: var x: object fn __ceildiv__(self, denominator: Self) raises -> Self: - return -(self.x // -denominator.x) + return Self(self.x // denominator.x) ``` """ From 0e14e6424c4eae7a2acc1e1af80d7f2c6037a088 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 15 Jan 2025 18:56:04 -0800 Subject: [PATCH 246/509] [Stdlib] Simplify code for append/prepend in linked list, NFC MODULAR_ORIG_COMMIT_REV_ID: e80f95fab01d0a24aeabdfa7438e94131b3b2493 --- stdlib/src/collections/linked_list.mojo | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index afa3c0b624..9591374a33 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -174,10 +174,9 @@ struct LinkedList[ElementType: WritableCollectionElement]: addr.init_pointee_move(node) if self: self._tail[].next = addr - self._tail = addr else: self._head = addr - self._tail = addr + self._tail = addr self._size += 1 fn prepend(mut self, owned value: ElementType): @@ -191,10 +190,9 @@ struct LinkedList[ElementType: WritableCollectionElement]: addr.init_pointee_move(node) if self: self._head[].prev = addr - self._head = addr else: - self._head = addr self._tail = addr + self._head = addr self._size += 1 fn reverse(mut self): From 22bd7af24faf154b7f28350c6359f8be575309ef Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 15 Jan 2025 22:10:29 -0500 Subject: [PATCH 247/509] [stdlib] Move `str` functions to `String` ctors And add deprecation warnings to the `str` functions, to be phased out to a compiler error in the release after next. MODULAR_ORIG_COMMIT_REV_ID: 54247b2e5fd77efc455237f0b372fa7c983c8cda --- docs/changelog.md | 11 ++- docs/manual/errors.mdx | 12 +-- docs/manual/functions.mdx | 4 +- docs/manual/get-started.mdx | 14 +-- docs/manual/operators.mdx | 7 +- docs/manual/parameters/index.mdx | 28 +++--- docs/manual/pointers/unsafe-pointers.mdx | 2 +- docs/manual/python/types.mdx | 13 +-- docs/manual/traits.mdx | 6 +- docs/manual/types.mdx | 29 ++++-- docs/manual/values/ownership.mdx | 6 +- docs/roadmap.md | 2 +- examples/life/test/test_gridv1.mojo | 2 +- examples/life/test/test_gridv2.mojo | 2 +- examples/matmul.mojo | 4 +- examples/operators/main.mojo | 4 +- examples/operators/test_my_complex.mojo | 4 +- examples/testing/src/my_math/__init__.mojo | 2 +- examples/testing/src/my_math/utils.mojo | 4 +- proposals/improved-hash-module.md | 2 +- stdlib/benchmarks/builtin/bench_int.mojo | 2 +- stdlib/benchmarks/builtin/bench_sort.mojo | 22 +++-- stdlib/benchmarks/collections/bench_dict.mojo | 10 +- .../benchmarks/collections/bench_string.mojo | 2 +- stdlib/benchmarks/hashlib/bench_hash.mojo | 2 +- stdlib/docs/development.md | 2 +- stdlib/docs/style-guide.md | 2 +- stdlib/src/builtin/_location.mojo | 2 +- stdlib/src/builtin/bool.mojo | 2 +- stdlib/src/builtin/char.mojo | 7 +- stdlib/src/builtin/debug_assert.mojo | 8 +- stdlib/src/builtin/float_literal.mojo | 2 +- stdlib/src/builtin/format_int.mojo | 2 +- stdlib/src/builtin/int.mojo | 4 +- stdlib/src/builtin/int_literal.mojo | 2 +- stdlib/src/builtin/object.mojo | 14 +-- stdlib/src/builtin/repr.mojo | 2 +- stdlib/src/builtin/str.mojo | 33 +++---- stdlib/src/builtin/string_literal.mojo | 38 +++---- stdlib/src/builtin/uint.mojo | 4 +- stdlib/src/collections/deque.mojo | 2 +- stdlib/src/collections/dict.mojo | 6 +- stdlib/src/collections/list.mojo | 2 +- stdlib/src/collections/string/format.mojo | 22 +++-- .../src/collections/string/inline_string.mojo | 14 +-- stdlib/src/collections/string/string.mojo | 99 +++++++++---------- .../src/collections/string/string_slice.mojo | 16 ++- stdlib/src/memory/pointer.mojo | 2 +- stdlib/src/memory/unsafe_pointer.mojo | 2 +- stdlib/src/os/_linux_aarch64.mojo | 28 +++--- stdlib/src/os/_linux_x86.mojo | 28 +++--- stdlib/src/os/_macos.mojo | 32 +++--- stdlib/src/os/fstat.mojo | 10 +- stdlib/src/os/os.mojo | 4 +- stdlib/src/os/path/path.mojo | 4 +- stdlib/src/pathlib/path.mojo | 12 +-- stdlib/src/pwd/_linux.mojo | 16 +-- stdlib/src/pwd/_macos.mojo | 16 +-- stdlib/src/python/_cpython.mojo | 6 +- stdlib/src/python/python.mojo | 2 +- stdlib/src/python/python_object.mojo | 4 +- stdlib/src/tempfile/tempfile.mojo | 8 +- stdlib/src/testing/testing.mojo | 40 +++++--- stdlib/src/time/time.mojo | 2 +- stdlib/src/utils/_serialize.mojo | 2 +- stdlib/src/utils/stringref.mojo | 11 ++- stdlib/src/utils/variant.mojo | 4 +- stdlib/src/utils/write.mojo | 4 +- stdlib/test/bit/test_bit.mojo | 2 +- stdlib/test/builtin/test_bfloat16.mojo | 2 +- stdlib/test/builtin/test_bool.mojo | 8 +- stdlib/test/builtin/test_char.mojo | 10 +- stdlib/test/builtin/test_dtype.mojo | 4 +- stdlib/test/builtin/test_error.mojo | 6 +- stdlib/test/builtin/test_file.mojo | 6 +- stdlib/test/builtin/test_format_float.mojo | 6 +- stdlib/test/builtin/test_issue_1004.mojo | 2 +- stdlib/test/builtin/test_location.mojo | 2 +- stdlib/test/builtin/test_object.mojo | 48 ++++----- stdlib/test/builtin/test_print.mojo | 2 +- stdlib/test/builtin/test_simd.mojo | 35 +++---- stdlib/test/builtin/test_slice.mojo | 2 +- stdlib/test/builtin/test_sort.mojo | 11 ++- stdlib/test/builtin/test_str.mojo | 2 +- stdlib/test/builtin/test_string_literal.mojo | 2 +- stdlib/test/builtin/test_uint.mojo | 8 +- .../string/test_inlined_string.mojo | 18 ++-- .../test/collections/string/test_string.mojo | 22 ++--- .../collections/string/test_string_slice.mojo | 50 +++++----- stdlib/test/collections/test_dict.mojo | 16 +-- .../test/collections/test_inline_array.mojo | 4 +- stdlib/test/collections/test_linked_list.mojo | 2 +- stdlib/test/hashlib/test_ahash.mojo | 12 +-- stdlib/test/hashlib/test_hasher.mojo | 2 +- stdlib/test/memory/test_memory.mojo | 40 ++++---- stdlib/test/memory/test_reference.mojo | 2 +- stdlib/test/memory/test_unsafepointer.mojo | 6 +- stdlib/test/os/path/test_isdir.mojo | 4 +- stdlib/test/os/path/test_islink.mojo | 2 +- stdlib/test/os/test_mkdir_and_rmdir.mojo | 6 +- stdlib/test/os/test_remove.mojo | 4 +- stdlib/test/os/test_stat.mojo | 2 +- stdlib/test/pathlib/test_pathlib.mojo | 20 ++-- stdlib/test/python/test_ownership.mojo | 10 +- .../python/test_python_error_handling.mojo | 6 +- stdlib/test/python/test_python_interop.mojo | 22 ++--- stdlib/test/python/test_python_object.mojo | 98 +++++++++--------- stdlib/test/python/test_python_to_mojo.mojo | 4 +- stdlib/test/random/test_random.mojo | 18 ++-- stdlib/test/tempfile/test_tempfile.mojo | 28 +++--- stdlib/test/testing/test_assert_raises.mojo | 8 +- stdlib/test/testing/test_assertion.mojo | 12 +-- stdlib/test/utils/test_index.mojo | 22 ++--- stdlib/test/utils/test_tuple.mojo | 14 +-- stdlib/test/utils/test_write.mojo | 4 +- 115 files changed, 693 insertions(+), 641 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index bb34b332c0..0dde1eb064 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -100,11 +100,12 @@ what we publish. ### Standard library changes -- The `int` function to construct an `Int` has been deprecated, this was a - temporary workaround when Mojo didn't have a way to distinguish between - implicit and explicit constructors. You can do a search and replace from - `int(` to `Int(` to update your programs, the `int` function will be removed - in the next release. +- These functions have been deprecated: `int`, `str` in favor of constructors: + `Int`, `String`. The functions were a temporary workaround for when Mojo + didn't have a way to distinguish between implicit and explicit constructors. + You can do a search and replace e.g. from `int(` to `Int(` to update your + programs. This release will show a deprecation warning for the old functions, + and they'll be removed in the next release. - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` for changing the type, `origin_cast` for changing mutability, diff --git a/docs/manual/errors.mdx b/docs/manual/errors.mdx index 47e1a23711..ff7512798a 100644 --- a/docs/manual/errors.mdx +++ b/docs/manual/errors.mdx @@ -103,12 +103,12 @@ recovery that's appropriate for your application. If you provide the name of a variable after the `except` keyword, then the `Error` instance is bound to the variable if an error occurs. The `Error` type -implements the [`Writable`](/mojo/stdlib/utils/write/Writable) trait, so -you can pass it as an argument to the [`print()`](/mojo/stdlib/builtin/io/print) +implements the [`Writable`](/mojo/stdlib/utils/write/Writable) trait, so you can +pass it as an argument to the [`print()`](/mojo/stdlib/builtin/io/print) function if you'd like to print its error message to the console. It also implements the [`Stringable`](/mojo/stdlib/builtin/str/Stringable) trait, so you -can pass it to the [`str()`](/mojo/stdlib/builtin/str/str) function if you want -to extract the error message as a `String` for further processing. +can construct a `String` with `String(error)` if you want to extract the error +message as a `String` for further processing. If desired, you can re-raise an error condition from your `except` clause simply by executing a `raise` statement from within its code block. This can be either @@ -152,7 +152,7 @@ def main(): print("try =>", value[]) if value[] == 1: continue - result = str("{} incremented is {}").format(value[], incr(value[])) + result = String("{} incremented is {}").format(value[], incr(value[])) except e: print("except =>", e) else: @@ -384,7 +384,7 @@ struct ConditionalTimer: print("Elapsed time:", elapsed_time_ms, "milliseconds") fn __exit__(mut self, e: Error) raises -> Bool: - if str(e) == "just a warning": + if String(e) == "just a warning": print("Suppressing error:", e) self.__exit__() return True diff --git a/docs/manual/functions.mdx b/docs/manual/functions.mdx index b7e34a14ff..c6f51aeaf9 100644 --- a/docs/manual/functions.mdx +++ b/docs/manual/functions.mdx @@ -364,12 +364,12 @@ fn print_string(s: String): print(s, end="") fn print_many[T: Stringable, *Ts: Stringable](first: T, *rest: *Ts): - print_string(str(first)) + print_string(String(first)) @parameter fn print_elt[T: Stringable](a: T): print_string(" ") - print_string(str(a)) + print_string(String(a)) rest.each[print_elt]() print_many("Bob") ``` diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 9a3aaeff9b..08e6f575e0 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -647,11 +647,11 @@ produces the same output. You can verify that by running the program again. ## 9. Implement support for the `StringableRaising` trait -You can pass most Mojo types to the built-in `str()` function to produce a +You can convert most Mojo types to `String` using `String(my_val)` to produce a `String` representation of that instance. But you'll get an error if you try to do that with our current implementation of `Grid`. So let's fix that. -Because the Mojo compiler performs static type checking, a function like `str()` +Because the Mojo compiler performs static type checking, a `String` constructor can accept a value only if its type implements some required behavior—in this case, it only accepts types that can generate a `String` representation. @@ -663,7 +663,7 @@ any type that conform to a specified trait. (This type of function is sometimes referred to as a [*generic* function](/mojo/manual/parameters/#parameters-and-generics).) -In the case of `str()`, it requires a type to conform to either the `Stringable` +In the case of `String()`, it requires a type to conform to either the `Stringable` or `StringableRaising` trait. Each trait requires a conforming type to implement a `__str__()` method that returns a `String` representation. The only difference between the two traits is that `Stringable` requires that the method *cannot* @@ -692,13 +692,13 @@ struct Grid(StringableRaising): ... ``` -Now let's verify that `str()` works with an instance of `Grid`. +Now let's verify that `String()` works with an instance of `Grid`. ```mojo title="life.mojo" def main(): ... start = Grid(8, 8, glider) - print(str(start)) + print(String(start)) ``` If you run the program again, you should still see the same glider pattern as before. @@ -896,7 +896,7 @@ and print it. def main(): start = Grid.random(8, 16) - print(str(start)) + print(String(start)) ``` Run the program a few times to verify that it generates a different grid each @@ -1009,7 +1009,7 @@ from gridv1 import Grid def run_display(owned grid: Grid) -> None: while True: - print(str(grid)) + print(String(grid)) print() if input("Enter 'q' to quit or press to continue: ") == "q": break diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index b4ac20d127..3ae0802151 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -1100,7 +1100,7 @@ c2: Real: 3.1415899999999999; Imaginary: 0.0 To make it simpler to print `Complex` values, let's implement the [Writable](/mojo/stdlib/utils/write/Writable) trait. While we're at it, let's also implement the [`Stringable`](/mojo/stdlib/builtin/str/Stringable) trait so -that we can use the `str()` function to generate a `String` representation of a +that we can use the `String()` constructor to generate a `String` representation of a `Complex` value. You can find out more about these traits and their associated methods in [The `Stringable`, `Representable`, and `Writable` traits](/mojo/manual/traits#the-stringable-representable-and-writable-traits). @@ -1137,13 +1137,14 @@ between defining functions with `def` and `fn`. ::: Now we can print a `Complex` value directly, and we can explicitly generate a -`String` representation by passing a `Complex` value to `str()`. +`String` representation by passing a `Complex` value to `String.write()` which +constructs a new `String` from all the arguments passed to it. ```mojo c3 = Complex(3.14159, -2.71828) print("c3 =", c3) -var msg: String = "The value is: " + str(c3) +var msg = String.write("The value is: ", c3) print(msg) ``` diff --git a/docs/manual/parameters/index.mdx b/docs/manual/parameters/index.mdx index c602ce9522..084aba0ca8 100644 --- a/docs/manual/parameters/index.mdx +++ b/docs/manual/parameters/index.mdx @@ -103,7 +103,7 @@ conforms to the [`Stringable`](/mojo/stdlib/builtin/str/Stringable) trait: fn repeat[MsgType: Stringable, count: Int](msg: MsgType): @parameter for i in range(count): - print(str(msg)) + print(String(msg)) # Must use keyword parameter for `count` repeat[count=2](42) @@ -125,7 +125,7 @@ by the argument. Now you can pass the following parameter `count` positionally: fn repeat[MsgType: Stringable, //, count: Int](msg: MsgType): @parameter for i in range(count): - print(str(msg)) + print(String(msg)) # MsgType is always inferred, so first positional keyword `2` is passed to `count` repeat[2](42) @@ -252,7 +252,7 @@ struct Container[ElementType: CollectionElement]: def __str__[StrElementType: StringableCollectionElement, //]( self: Container[StrElementType]) -> String: - return str(self.element) + return String(self.element) def use_container(): float_container = Container(5) @@ -282,7 +282,7 @@ This trait must be a superset of `ElementType`'s original trait: for example, all of requirements of the original trait. Note that the `use_container()` function calls the `__str__()` method directly, -rather than calling `str(float_container)`. One current limitation of +rather than calling `String(float_container)`. One current limitation of conditional conformance is that Mojo can't recognize the struct `Container[Int]` as conforming to `Stringable`, even though the `__str__()` method is implemented for any `ElementType` that's also `Stringable`. @@ -545,11 +545,11 @@ struct Two[Type: StringableCollectionElement]: fn __init__(out self, one: One[Type], another: One[Type]): self.val1 = one.value self.val2 = another.value - print(str(self.val1), str(self.val2)) + print(String(self.val1), String(self.val2)) @staticmethod fn fire(thing1: One[Type], thing2: One[Type]): - print("🔥", str(thing1.value), str(thing2.value)) + print("🔥", String(thing1.value), String(thing2.value)) def use_two(): s3 = Two(One("infer"), One("me")) @@ -1163,9 +1163,7 @@ For example, suppose we have a `Fudge` struct with three parameters: @value struct Fudge[sugar: Int, cream: Int, chocolate: Int = 7](Stringable): fn __str__(self) -> String: - return str("Fudge (") + str(sugar) + "," + - str(cream) + "," + str(chocolate) + ")" - + return String.write("Fudge (", sugar, ",", cream, ",", chocolate, ")") ``` We can write a function that takes a `Fudge` argument with just one bound @@ -1173,7 +1171,7 @@ parameter (it's *partially bound*): ```mojo fn eat(f: Fudge[5, *_]): - print("Ate " + str(f)) + print("Ate " + String(f)) ``` The `eat()` function takes a `Fudge` struct with the first parameter (`sugar`) @@ -1185,7 +1183,7 @@ on the `eat` function. In practice, this is roughly equivalent to writing: ```mojo fn eat[cr: Int, ch: Int](f: Fudge[5, cr, ch]): - print("Ate", str(f)) + print("Ate", String(f)) ``` In both cases, we can call the function by passing in an instance with the @@ -1218,7 +1216,7 @@ parameter value with a single underscore (`_`): ```mojo fn devour(f: Fudge[_, 6, _]): - print("Devoured", str(f)) + print("Devoured", String(f)) ``` Again, the unbound parameters (`sugar` and `chocolate`) are added as implicit @@ -1228,7 +1226,7 @@ parameters, `su` and `ch`: ```mojo fn devour[su: Int, ch: Int](f: Fudge[su, 6, ch]): - print("Devoured", str(f)) + print("Devoured", String(f)) ``` You can also specify parameters by keyword, or mix positional and keyword @@ -1239,7 +1237,7 @@ And `cream` is explicitly bound to the value 6: ```mojo fn devour(f: Fudge[_, chocolate=_, cream=6]): - print("Devoured", str(f)) + print("Devoured", String(f)) ``` All three versions of the `devour()` function work with the following calls: @@ -1261,7 +1259,7 @@ for example: ```mojo fn nibble(f: Fudge[5]): - print("Ate", str(f)) + print("Ate", String(f)) nibble(Fudge[5, 4, 7]()) diff --git a/docs/manual/pointers/unsafe-pointers.mdx b/docs/manual/pointers/unsafe-pointers.mdx index 9bfb18a23a..69acb084b0 100644 --- a/docs/manual/pointers/unsafe-pointers.mdx +++ b/docs/manual/pointers/unsafe-pointers.mdx @@ -168,7 +168,7 @@ type](/mojo/manual/types#register-passable-memory-only-and-trivial-types) (like `Int`) or a newly-constructed, "owned" value: ```mojo -str_ptr.init_pointee_move(str("Owned string")) +str_ptr.init_pointee_move(String("Owned string")) ``` Alternately, you can get a pointer to an existing value using the static diff --git a/docs/manual/python/types.mdx b/docs/manual/python/types.mdx index 4aab0ace57..ee0f0d17a5 100644 --- a/docs/manual/python/types.mdx +++ b/docs/manual/python/types.mdx @@ -149,18 +149,19 @@ Currently `PythonObject` conforms to the [`Boolable`](/mojo/stdlib/builtin/bool/Boolable), [`Floatable`](/mojo/stdlib/builtin/floatable/Floatable/), and [`Intable`](/mojo/stdlib/builtin/int/Intable) traits, which means you can -convert Python values to Mojo `String`, `Bool`, and `Float64` types using the -[`str()`](/mojo/stdlib/builtin/str/str), +convert Python values to Mojo `Bool` and `Float64` types using the [`bool()`](/mojo/stdlib/builtin/bool/bool-function) and -[`float()`](/mojo/stdlib/builtin/floatable/float/) built-in functions, and -construct an `Int` using the [`Int()`](/mojo/stdlib/builtin/int/Int/#__init__) -constructor. `PythonObject` also conforms to the +[`float()`](/mojo/stdlib/builtin/floatable/float/) built-in functions, construct +an `Int` using the [`Int()`](/mojo/stdlib/builtin/int/Int/#__init__) +constructor, and construct a `String` using the +[`String()`](/mojo/stdlib/collections/string/String/#__init__) constructor. +`PythonObject` also conforms to the [`Writable`](/mojo/stdlib/utils/write/Writable) trait so that you can print Python values using the built-in [`print()`](/mojo/stdlib/builtin/io/print) function. ```mojo -var s: String = str(py_string) +var s: String = String(py_string) var b: Bool = bool(py_bool) var f: Float64 = float(py_float) var i: Int = Int(py_int) diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index a779396a65..c4d62de147 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -385,13 +385,11 @@ True ### The `Stringable`, `Representable`, and `Writable` traits The [`Stringable`](/mojo/stdlib/builtin/str/Stringable) trait identifies a type -that can be implicitly converted to +that can be explicitly converted to [`String`](/mojo/stdlib/collections/string/String). The [`StringableRaising`](/mojo/stdlib/builtin/str/StringableRaising) trait describes a type that can be converted to a `String`, but the conversion might -raise an error. Any type that conforms to `Stringable` or `StringableRaising` -also works with the built-in [`str()`](/mojo/stdlib/builtin/str/str) function to -explicitly return a `String`. These traits also mean that the type can support +raise an error. These traits also mean that the type can support both the `{!s}` and `{}` format specifiers of the `String` class's [`format()`](/mojo/stdlib/collections/string/String#format) method. These traits require the type to define the diff --git a/docs/manual/types.mdx b/docs/manual/types.mdx index ee6ddcf586..6f46013f86 100644 --- a/docs/manual/types.mdx +++ b/docs/manual/types.mdx @@ -322,11 +322,23 @@ Testing Mojo strings Most standard library types conform to the [`Stringable`](/mojo/stdlib/builtin/str/Stringable) trait, which represents -a type that can be converted to a string. Use `str(value)` to +a type that can be converted to a string. Use `String(value)` to explicitly convert a value to a string: ```mojo -var s = str("Items in list: ") + str(5) +var s = String("Items in list: ") + String(5) +print(s) +``` + +```output +Items in list: 5 +``` + +Or use `String.write` to take variadic `Stringable` types, so you don't have to +call `String()` on each value: + +```mojo +var s = String.write("Items in list: ", 5) print(s) ``` @@ -362,9 +374,8 @@ Note that the triple double quote form is also used for API documentation strings. Unlike `IntLiteral` and `FloatLiteral`, `StringLiteral` doesn't automatically -materialize to a runtime type. In some cases, you may need to manually convert -`StringLiteral` values to `String` using the built-in -[`str()`](/mojo/stdlib/builtin/str/str) method. +materialize to a runtime type. In some cases, you may need to explicitly convert +`StringLiteral` values to `String`. ```mojo # Variable is type `StringLiteral` @@ -374,7 +385,7 @@ var s1 = "Example" var s2: String = "Example" # Variable is type `String` -var s3 = str("Example") +var s3 = String("Example") ``` ## Booleans @@ -428,7 +439,7 @@ same tuple from the previous example with implicit typing instead of explicit, we must also convert `"Example"` from type `StringLiteral` to type `String`. ```mojo -example_tuple = (1, str("Example")) +example_tuple = (1, String("Example")) s = example_tuple.get[1, String]() print(s) ``` @@ -672,7 +683,7 @@ results in undefined behavior, so you should always guard a call to `value()` inside a conditional that checks whether a value exists. ```mojo -var opt: Optional[String] = str("Testing") +var opt: Optional[String] = String("Testing") if opt: var value_ref = opt.value() print(value_ref) @@ -689,7 +700,7 @@ value if there is one, or a user-specified default value otherwise: var custom_greeting: Optional[String] = None print(custom_greeting.or_else("Hello")) -custom_greeting = str("Hi") +custom_greeting = String("Hi") print(custom_greeting.or_else("Hello")) ``` diff --git a/docs/manual/values/ownership.mdx b/docs/manual/values/ownership.mdx index e8d201e8b6..817f43ef4c 100644 --- a/docs/manual/values/ownership.mdx +++ b/docs/manual/values/ownership.mdx @@ -256,7 +256,7 @@ fn append_twice(mut s: String, other: String): s += other fn invalid_access(): - var my_string = str("o") + var my_string = String("o") # error: passing `my_string` mut is invalid since it is also passed # read. @@ -274,8 +274,8 @@ reference (or need to pass the same value to two arguments) is to make a copy: ```mojo fn valid_access(): - var my_string = str("o") - var other_string = str(my_string) + var my_string = String("o") + var other_string = String(my_string) append_twice(my_string, other_string) print(my_string) ``` diff --git a/docs/roadmap.md b/docs/roadmap.md index 46df347afd..8ffd0fa4f8 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -472,5 +472,5 @@ converting string literals to `String` values. For example: ```mojo var h: String = "hello" # or -print(g or str("hello")) +print(g or String("hello")) ``` diff --git a/examples/life/test/test_gridv1.mojo b/examples/life/test/test_gridv1.mojo index bded4cc2f5..19e0d4c8a0 100644 --- a/examples/life/test/test_gridv1.mojo +++ b/examples/life/test/test_gridv1.mojo @@ -44,7 +44,7 @@ def test_gridv1_index(): def test_gridv1_str(): grid = Grid(4, 4, data4x4) - grid_str = str(grid) + grid_str = String(grid) assert_equal(str4x4, grid_str) diff --git a/examples/life/test/test_gridv2.mojo b/examples/life/test/test_gridv2.mojo index 1987272743..cc251a9d6b 100644 --- a/examples/life/test/test_gridv2.mojo +++ b/examples/life/test/test_gridv2.mojo @@ -53,7 +53,7 @@ def test_gridv2_index(): def test_gridv2_str(): grid = grid4x4() - grid_str = str(grid) + grid_str = String(grid) assert_equal(str4x4, grid_str) diff --git a/examples/matmul.mojo b/examples/matmul.mojo index ad81185519..44638daf34 100644 --- a/examples/matmul.mojo +++ b/examples/matmul.mojo @@ -84,7 +84,7 @@ def run_matmul_python() -> Float64: var pymatmul: PythonObject = Python.import_module("pymatmul") var py = Python.import_module("builtins") - var gflops = pymatmul.benchmark_matmul_python(128, 128, 128).to_float64() + var gflops = float(pymatmul.benchmark_matmul_python(128, 128, 128)) py.print(py.str("{:<18}{:>8.3f} GFLOPS").format("Python:", gflops)) return gflops @@ -94,7 +94,7 @@ def run_matmul_numpy() -> Float64: var pymatmul: PythonObject = Python.import_module("pymatmul") var py = Python.import_module("builtins") - var gflops = pymatmul.benchmark_matmul_numpy(M, N, K).to_float64() + var gflops = float(pymatmul.benchmark_matmul_numpy(M, N, K)) py.print(py.str("{:<18}{:>8.3f} GFLOPS").format("Numpy:", gflops)) return gflops diff --git a/examples/operators/main.mojo b/examples/operators/main.mojo index 413b68cd10..1cffa1cbbc 100644 --- a/examples/operators/main.mojo +++ b/examples/operators/main.mojo @@ -24,10 +24,10 @@ def main(): print() - # Examples of using Complex values with str(), repr(), and print() + # Examples of using Complex values with String(), repr(), and print() c3 = Complex(3.14159, -2.71828) print("c3 =", c3) - var msg: String = "The value is: " + str(c3) + var msg: String = "The value is: " + String(c3) print(msg) print(String("{!r}").format(c3)) diff --git a/examples/operators/test_my_complex.mojo b/examples/operators/test_my_complex.mojo index 1e6481aef7..2846f01bea 100644 --- a/examples/operators/test_my_complex.mojo +++ b/examples/operators/test_my_complex.mojo @@ -35,7 +35,7 @@ def test_str(): str1 = "({} + {}i)".format(re1, im1) out_str1 = String() c1 = Complex(re1, im1) - assert_equal(str1, str(c1)) + assert_equal(str1, String(c1)) c1.write_to(out_str1) assert_equal(str1, out_str1) @@ -45,7 +45,7 @@ def test_str(): str2 = "({} - {}i)".format(re2, abs(im2)) out_str2 = String() c2 = Complex(re2, im2) - assert_equal(str2, str(c2)) + assert_equal(str2, String(c2)) c2.write_to(out_str2) assert_equal(str2, out_str2) diff --git a/examples/testing/src/my_math/__init__.mojo b/examples/testing/src/my_math/__init__.mojo index 968532820f..79dd92b79d 100644 --- a/examples/testing/src/my_math/__init__.mojo +++ b/examples/testing/src/my_math/__init__.mojo @@ -43,7 +43,7 @@ try: c = inc(Int.MAX) except e: print(e) -%# assert_equal("inc overflow", str(e)) +%# assert_equal("inc overflow", String(e)) ``` """ diff --git a/examples/testing/src/my_math/utils.mojo b/examples/testing/src/my_math/utils.mojo index 8eb0d42577..000fa023cd 100644 --- a/examples/testing/src/my_math/utils.mojo +++ b/examples/testing/src/my_math/utils.mojo @@ -40,7 +40,7 @@ def inc(n: Int) -> Int: k = inc(Int.MAX) except e: print(e) # inc overflow - %# assert_equal("inc overflow", str(e)) + %# assert_equal("inc overflow", String(e)) ``` Args: @@ -76,7 +76,7 @@ def dec(n: Int) -> Int: k = dec(Int.MIN) except e: print(e) # inc overflow - %# assert_equal("dec overflow", str(e)) + %# assert_equal("dec overflow", String(e)) ``` Args: diff --git a/proposals/improved-hash-module.md b/proposals/improved-hash-module.md index 1e4dd64b13..22643e7a7a 100644 --- a/proposals/improved-hash-module.md +++ b/proposals/improved-hash-module.md @@ -224,7 +224,7 @@ fn _DJBX33A_SECRET() -> UInt64: return bitcast[DType.uint64](Int64(Int(secret_string))) except: var value = random_si64(Int64.MIN, Int64.MAX) - _ = setenv("DJBX33A_SECRET", str(value)) + _ = setenv("DJBX33A_SECRET", String(value)) return bitcast[DType.uint64](value) struct DJBX33A_Hasher[custom_secret: UInt64 = 0](Hasher): diff --git a/stdlib/benchmarks/builtin/bench_int.mojo b/stdlib/benchmarks/builtin/bench_int.mojo index 2d65c690c3..efc4b171a5 100644 --- a/stdlib/benchmarks/builtin/bench_int.mojo +++ b/stdlib/benchmarks/builtin/bench_int.mojo @@ -26,7 +26,7 @@ fn bench_stringify_small_integers(mut b: Bencher) raises: @parameter fn call_fn(): for i in range(1_000): - var a = str(i) + var a = String(i) benchmark.keep(bool(a)) b.iter[call_fn]() diff --git a/stdlib/benchmarks/builtin/bench_sort.mojo b/stdlib/benchmarks/builtin/bench_sort.mojo index 7c70e7ebb6..addf6c200c 100644 --- a/stdlib/benchmarks/builtin/bench_sort.mojo +++ b/stdlib/benchmarks/builtin/bench_sort.mojo @@ -147,13 +147,13 @@ fn bench_tiny_list_sort[type: DType](mut m: Bench) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId("std_sort_random_" + str(count) + "_" + str(type)) + BenchId("std_sort_random_" + String(count) + "_" + String(type)) ) m.bench_function[bench_small_sort]( - BenchId("sml_sort_random_" + str(count) + "_" + str(type)) + BenchId("sml_sort_random_" + String(count) + "_" + String(type)) ) m.bench_function[bench_insertion_sort]( - BenchId("ins_sort_random_" + str(count) + "_" + str(type)) + BenchId("ins_sort_random_" + String(count) + "_" + String(type)) ) @@ -202,10 +202,10 @@ fn bench_small_list_sort[type: DType](mut m: Bench, count: Int) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId("std_sort_random_" + str(count) + "_" + str(type)) + BenchId("std_sort_random_" + String(count) + "_" + String(type)) ) m.bench_function[bench_insertion_sort]( - BenchId("ins_sort_random_" + str(count) + "_" + str(type)) + BenchId("ins_sort_random_" + String(count) + "_" + String(type)) ) @@ -254,11 +254,11 @@ fn bench_large_list_sort[type: DType](mut m: Bench, count: Int) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId("std_sort_random_" + str(count) + "_" + str(type)) + BenchId("std_sort_random_" + String(count) + "_" + String(type)) ) m.bench_function[bench_heap_sort]( - BenchId("heap_sort_random_" + str(count) + "_" + str(type)) + BenchId("heap_sort_random_" + String(count) + "_" + String(type)) ) @@ -307,10 +307,14 @@ fn bench_low_cardinality_list_sort(mut m: Bench, count: Int, delta: Int) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId("std_sort_low_card_" + str(count) + "_delta_" + str(delta)) + BenchId( + "std_sort_low_card_" + String(count) + "_delta_" + String(delta) + ) ) m.bench_function[bench_heap_sort]( - BenchId("heap_sort_low_card_" + str(count) + "_delta_" + str(delta)) + BenchId( + "heap_sort_low_card_" + String(count) + "_delta_" + String(delta) + ) ) diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index e5d4ba2e37..3a740b96f9 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -134,10 +134,10 @@ def main(): for i in range(len(sizes)): alias size = sizes[i] m.bench_function[bench_dict_insert[size]]( - BenchId("bench_dict_insert[" + str(size) + "]") + BenchId("bench_dict_insert[" + String(size) + "]") ) m.bench_function[bench_dict_lookup[size]]( - BenchId("bench_dict_lookup[" + str(size) + "]") + BenchId("bench_dict_lookup[" + String(size) + "]") ) m.dump_report() @@ -147,5 +147,9 @@ def main(): alias size = sizes[i] var mem_s = total_bytes_used(make_dict[size]()) print( - '"bench_dict_memory_size[' + str(size) + ']",' + str(mem_s) + ",0" + '"bench_dict_memory_size[' + + String(size) + + ']",' + + String(mem_s) + + ",0" ) diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index ee7647989a..bcbf29a0c0 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -262,7 +262,7 @@ def main(): alias fname = filenames[j] alias old = old_chars[j] alias new = new_chars[j] - suffix = "[" + str(length) + "]" # "(" + fname + ")" + suffix = "[" + String(length) + "]" # "(" + fname + ")" m.bench_function[bench_string_count[length, fname, old]]( BenchId("bench_string_count" + suffix) ) diff --git a/stdlib/benchmarks/hashlib/bench_hash.mojo b/stdlib/benchmarks/hashlib/bench_hash.mojo index 6d2ba54044..0d95cc9afe 100644 --- a/stdlib/benchmarks/hashlib/bench_hash.mojo +++ b/stdlib/benchmarks/hashlib/bench_hash.mojo @@ -586,7 +586,7 @@ fn gen_word_pairs[words: String = words_en]() -> List[String]: try: var list = words.split(",") for w in list: - var w1 = str(w[].strip()) + var w1 = String(w[].strip()) for w in list: var w2 = w[].strip() result.append(w1 + " " + w2) diff --git a/stdlib/docs/development.md b/stdlib/docs/development.md index 024dc87b6c..ef21e6fb81 100644 --- a/stdlib/docs/development.md +++ b/stdlib/docs/development.md @@ -239,7 +239,7 @@ fn get_cwd_and_paths() raises -> List[String]: result = List[String]() result.append(pathlib.get_cwd_message()) for path in cwd().listdir(): - result.append(str(path[])) + result.append(String(path[])) ``` This won't work because it's importing `pathlib` from the `stdlib.mojopkg` that diff --git a/stdlib/docs/style-guide.md b/stdlib/docs/style-guide.md index 82002c152c..e2298d1b66 100644 --- a/stdlib/docs/style-guide.md +++ b/stdlib/docs/style-guide.md @@ -371,7 +371,7 @@ a closure as a parameter that only runs when assertions are enabled: tensor = Tensor[DType.uint8, 1](TensorShape(1), cpu_device()) fn _test_cpu() capturing -> Bool: - return "cpu" in str(tensor._device) + return "cpu" in String(tensor._device) debug_assert[_test_cpu]("This code is only runnable on CPU") ``` diff --git a/stdlib/src/builtin/_location.mojo b/stdlib/src/builtin/_location.mojo index 463cba7b08..410d332e66 100644 --- a/stdlib/src/builtin/_location.mojo +++ b/stdlib/src/builtin/_location.mojo @@ -40,7 +40,7 @@ struct _SourceLocation(Writable, Stringable): Args: msg: The message to attach the prefix to. """ - return "At " + str(self) + ": " + str(msg) + return "At " + String(self) + ": " + String(msg) fn write_to[W: Writer](self, mut writer: W): """ diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 213d080e21..3ca5f39680 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -248,7 +248,7 @@ struct Bool( Returns: A string representation. """ - return str(self) + return String(self) @always_inline("nodebug") fn __int__(self) -> Int: diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 261fc2ffeb..51e7769704 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -254,7 +254,12 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): Returns: A string containing this single character. """ - return String(self) + var char_len = self.utf8_byte_length() + var buffer = List[Byte](capacity=char_len + 1) + _ = self.unsafe_write_utf8(buffer.unsafe_ptr()) + buffer.unsafe_ptr()[char_len] = 0 + buffer.size = char_len + 1 + return String(buffer^) # ===-------------------------------------------------------------------===# # Methods diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index 52dde896aa..896368dba2 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -113,7 +113,7 @@ fn debug_assert[ ```mojo person = "name: john, age: 50" name = "john" - debug_assert(str("name: ") + name == person, "unexpected name") + debug_assert(String("name: ") + name == person, "unexpected name") ``` This will have a runtime penality due to allocating a `String` in the @@ -122,7 +122,7 @@ fn debug_assert[ ```mojo fn check_name() capturing -> Bool: - return str("name: ") + name == person + return String("name: ") + name == person debug_assert[check_name]("unexpected name") ``` @@ -196,7 +196,7 @@ fn debug_assert[ ```mojo person = "name: john, age: 50" name = "john" - debug_assert(str("name: ") + name == person, "unexpected name") + debug_assert(String("name: ") + name == person, "unexpected name") ``` This will have a runtime penality due to allocating a `String` in the @@ -205,7 +205,7 @@ fn debug_assert[ ```mojo fn check_name() capturing -> Bool: - return str("name: ") + name == person + return String("name: ") + name == person debug_assert[check_name]("unexpected name") ``` diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 6cee98bafa..9ddc2de605 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -122,7 +122,7 @@ struct FloatLiteral( Returns: A string representation. """ - return str(Float64(self)) + return String(Float64(self)) @always_inline("nodebug") fn __int_literal__(self) -> IntLiteral: diff --git a/stdlib/src/builtin/format_int.mojo b/stdlib/src/builtin/format_int.mojo index 79726e1a99..c54b602d19 100644 --- a/stdlib/src/builtin/format_int.mojo +++ b/stdlib/src/builtin/format_int.mojo @@ -225,7 +225,7 @@ fn _try_format_int( # incompatible radix and custom digit chars, which we aren't doing # above. return abort[String]( - "unexpected exception formatting value as hexadecimal: " + str(e) + "unexpected exception formatting value as hexadecimal: " + String(e) ) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index abc536abb9..662ee68de0 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -209,7 +209,7 @@ trait ImplicitlyIntable(Intable): # ===----------------------------------------------------------------------=== # -# FIXME(25.1): Move `int` deprecation warnings to a compiler error +# FIXME(25.2): Move `int` deprecation warnings to a compiler error @deprecated( "the `int` function is deprecated, use the `Int` constructor instead." ) @@ -1149,7 +1149,7 @@ struct Int( Returns: A string representation. """ - return str(self) + return String(self) fn __hash__(self) -> UInt: """Hash the int using builtin hash. diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index f7b72d9ea0..c114104ea6 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -701,7 +701,7 @@ struct IntLiteral( Returns: The value as a string. """ - return str(Int(self)) + return String(Int(self)) @always_inline fn __ceildiv__(self, denominator: Self) -> Self: diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index a20b4b31c3..94ee27873b 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -568,18 +568,18 @@ struct _ObjectImpl( writer.write("None") return if self.is_bool(): - writer.write(str(self.get_as_bool())) + writer.write(String(self.get_as_bool())) return if self.is_int(): - writer.write(str(self.get_as_int())) + writer.write(String(self.get_as_int())) return if self.is_float(): - writer.write(str(self.get_as_float())) + writer.write(String(self.get_as_float())) return if self.is_str(): writer.write( "'" - + str( + + String( StringRef( self.get_as_string().data, self.get_as_string().length ) @@ -597,7 +597,7 @@ struct _ObjectImpl( for j in range(self.get_list_length()): if j != 0: writer.write(", ") - writer.write(str(object(self.get_list_element(j)))) + writer.write(String(object(self.get_list_element(j)))) writer.write("]") return @@ -609,9 +609,9 @@ struct _ObjectImpl( writer.write(", ") writer.write( "'" - + str(entry[].key) + + String(entry[].key) + "' = " - + str(object(entry[].value.copy())) + + String(object(entry[].value.copy())) ) print_sep = True writer.write("}") diff --git a/stdlib/src/builtin/repr.mojo b/stdlib/src/builtin/repr.mojo index 5ea538abce..9fd07cdc43 100644 --- a/stdlib/src/builtin/repr.mojo +++ b/stdlib/src/builtin/repr.mojo @@ -50,7 +50,7 @@ trait Representable: This is typically used for debugging, so it is important that the representation is information-rich and unambiguous. Note that when computing the string representation of a collection (`Dict`, `List`, `Set`, etc...), - the `repr` function is called on each element, not the `str()` function. + the `repr` function is called on each element, not the `String()` function. """ fn __repr__(self) -> String: diff --git a/stdlib/src/builtin/str.mojo b/stdlib/src/builtin/str.mojo index 93f1482cf2..f32bf25ee1 100644 --- a/stdlib/src/builtin/str.mojo +++ b/stdlib/src/builtin/str.mojo @@ -28,7 +28,7 @@ trait Stringable: Any type that conforms to `Stringable` or [`StringableRaising`](/mojo/stdlib/builtin/str/StringableRaising) works with the built-in [`print()`](/mojo/stdlib/builtin/io/print) and - [`str()`](/mojo/stdlib/builtin/str/str) functions. + [`String()`](/mojo/stdlib/builtin/str/str) functions. The `Stringable` trait requires the type to define the `__str__()` method. For example: @@ -42,12 +42,12 @@ trait Stringable: return self.s ``` - Now you can pass an instance of `Foo` to the `str()` function to get back a + Now you can pass an instance of `Foo` to the `String()` function to get back a `String`: ```mojo var foo = Foo("test") - print(str(foo) == "test") + print(String(foo) == "test") ``` ```plaintext @@ -83,7 +83,7 @@ trait StringableRaising: [`Stringable`](/mojo/stdlib/builtin/str/Stringable) or `StringableRaising` works with the built-in [`print()`](/mojo/stdlib/builtin/io/print) and - [`str()`](/mojo/stdlib/builtin/str/str) functions. + [`String()`](/mojo/stdlib/builtin/str/str) functions. The `StringableRaising` trait requires the type to define the `__str__()` method, which can raise an error. For example: @@ -99,13 +99,13 @@ trait StringableRaising: return self.s ``` - Now you can pass an instance of `Foo` to the `str()` function to get back a + Now you can pass an instance of `Foo` to the `String()` function to get back a `String`: ```mojo fn main() raises: var foo = Foo("test") - print(str(foo) == "test") + print(String(foo) == "test") ``` ```plaintext @@ -130,6 +130,10 @@ trait StringableRaising: # ===----------------------------------------------------------------------=== # +# FIXME(25.2): Move `str` deprecation warnings to a compiler error +@deprecated( + "the `str` function is deprecated, use the `String` constructor instead." +) @no_inline fn str[T: Stringable](value: T) -> String: """Get the string representation of a value. @@ -146,19 +150,10 @@ fn str[T: Stringable](value: T) -> String: return value.__str__() -@no_inline -fn str(value: None) -> String: - """Get the string representation of the `None` type. - - Args: - value: The object to get the string representation of. - - Returns: - The string representation of the object. - """ - return "None" - - +# FIXME(25.2): Move `str` deprecation warnings to a compiler error +@deprecated( + "the `str` function is deprecated, use the `String` constructor instead." +) @no_inline fn str[T: StringableRaising](value: T) raises -> String: """Get the string representation of a value. diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 72f1207149..deaf26f17f 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -135,7 +135,7 @@ struct StringLiteral( Returns: The string value as a StringLiteral. """ - return Self._from_string[str(value)]() + return Self._from_string[String(value)]() # ===-------------------------------------------------------------------===# # Operator dunders @@ -470,7 +470,7 @@ struct StringLiteral( Returns: A new string containing the character at the specified position. """ - return str(self)[idx] + return String(self)[idx] # ===-------------------------------------------------------------------===# # Methods @@ -648,7 +648,7 @@ struct StringLiteral( Returns: The joined string. """ - return str(self).join(elems) + return String(self).join(elems) fn join(self, *elems: Int) -> String: """Joins the elements from the tuple using the current string literal as a @@ -662,9 +662,9 @@ struct StringLiteral( """ if len(elems) == 0: return "" - var curr = str(elems[0]) + var curr = String(elems[0]) for i in range(1, len(elems)): - curr += self + str(elems[i]) + curr += self + String(elems[i]) return curr fn join[*Types: Stringable](self, *elems: *Types) -> String: @@ -689,7 +689,7 @@ struct StringLiteral( is_first = False else: result += self - result += str(a) + result += String(a) elems.each[add_elt]() return result @@ -717,7 +717,7 @@ struct StringLiteral( ``` . """ - return str(self).split(sep, maxsplit) + return String(self).split(sep, maxsplit) fn split(self, sep: NoneType = None, maxsplit: Int = -1) -> List[String]: """Split the string literal by every whitespace separator. @@ -745,7 +745,7 @@ struct StringLiteral( ``` . """ - return str(self).split(sep, maxsplit) + return String(self).split(sep, maxsplit) fn splitlines(self, keepends: Bool = False) -> List[String]: """Split the string literal at line boundaries. This corresponds to Python's @@ -774,7 +774,7 @@ struct StringLiteral( Returns: The number of occurrences of `substr`. """ - return str(self).count(substr) + return String(self).count(substr) fn lower(self) -> String: """Returns a copy of the string literal with all cased characters @@ -784,7 +784,7 @@ struct StringLiteral( A new string where cased letters have been converted to lowercase. """ - return str(self).lower() + return String(self).lower() fn upper(self) -> String: """Returns a copy of the string literal with all cased characters @@ -794,7 +794,7 @@ struct StringLiteral( A new string where cased letters have been converted to uppercase. """ - return str(self).upper() + return String(self).upper() fn rjust(self, width: Int, fillchar: StringLiteral = " ") -> String: """Returns the string right justified in a string literal of specified width. @@ -806,7 +806,7 @@ struct StringLiteral( Returns: Returns right justified string, or self if width is not bigger than self length. """ - return str(self).rjust(width, fillchar) + return String(self).rjust(width, fillchar) fn ljust(self, width: Int, fillchar: StringLiteral = " ") -> String: """Returns the string left justified in a string literal of specified width. @@ -818,7 +818,7 @@ struct StringLiteral( Returns: Returns left justified string, or self if width is not bigger than self length. """ - return str(self).ljust(width, fillchar) + return String(self).ljust(width, fillchar) fn center(self, width: Int, fillchar: StringLiteral = " ") -> String: """Returns the string center justified in a string literal of specified width. @@ -830,7 +830,7 @@ struct StringLiteral( Returns: Returns center justified string, or self if width is not bigger than self length. """ - return str(self).center(width, fillchar) + return String(self).center(width, fillchar) fn startswith( self, prefix: StringSlice, start: Int = 0, end: Int = -1 @@ -872,7 +872,7 @@ struct StringLiteral( Returns: True if all characters are digits else False. """ - return str(self).isdigit() + return String(self).isdigit() fn isupper(self) -> Bool: """Returns True if all cased characters in the string literal are @@ -884,7 +884,7 @@ struct StringLiteral( True if all cased characters in the string literal are uppercase and there is at least one cased character, False otherwise. """ - return str(self).isupper() + return String(self).isupper() fn islower(self) -> Bool: """Returns True if all cased characters in the string literal @@ -896,7 +896,7 @@ struct StringLiteral( True if all cased characters in the string literal are lowercase and there is at least one cased character, False otherwise. """ - return str(self).islower() + return String(self).islower() fn strip(self) -> String: """Return a copy of the string literal with leading and trailing @@ -930,7 +930,7 @@ struct StringLiteral( Returns: A string with no trailing characters. """ - return String(str(self).rstrip(chars)) + return String(String(self).rstrip(chars)) fn rstrip(self) -> String: """Return a copy of the string with trailing whitespaces removed. This @@ -961,4 +961,4 @@ struct StringLiteral( Returns: A copy of the string with no leading whitespaces. """ - return String(str(self).lstrip()) + return String(String(self).lstrip()) diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index 7067e94d68..81c04d48e9 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -121,7 +121,7 @@ struct UInt(Indexer, _HashableWithHasher): ```mojo %# from testing import assert_equal x = UInt(50) - assert_equal(str(x), "50") + assert_equal(String(x), "50") ``` Returns: @@ -164,7 +164,7 @@ struct UInt(Indexer, _HashableWithHasher): Returns: The string representation of this UInt. """ - return String.write("UInt(", str(self), ")") + return String.write("UInt(", String(self), ")") fn __hash__(self) -> UInt: """Hash the UInt using builtin hash. diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index f55c345f79..a0b8bfa587 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -444,7 +444,7 @@ struct Deque[ElementType: CollectionElement]( print(my_deque.__str__()) ``` - When the compiler supports conditional methods, then a simple `str(my_deque)` will + When the compiler supports conditional methods, then a simple `String(my_deque)` will be enough. The elements' type must implement the `__repr__()` method for this to work. diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 27b7f42a3f..f593677437 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -712,7 +712,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( # prints "{1: 1.1, 2: 2.2}" ``` - When the compiler supports conditional methods, then a simple `str(my_dict)` will + When the compiler supports conditional methods, then a simple `String(my_dict)` will be enough. Note that both they keys and values' types must implement the `__repr__()` method @@ -748,11 +748,11 @@ struct Dict[K: KeyElement, V: CollectionElement]( fn _minimum_size_of_string_representation(self) -> Int: # we do a rough estimation of the minimum number of chars that we'll see - # in the string representation, we assume that str(key) and str(value) + # in the string representation, we assume that String(key) and String(value) # will be both at least one char. return ( 2 # '{' and '}' - + len(self) * 6 # str(key), str(value) ": " and ", " + + len(self) * 6 # String(key), String(value) ": " and ", " - 2 # remove the last ", " ) diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index cc688f34af..aedb0f4612 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -401,7 +401,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( print(my_list.__str__()) ``` - When the compiler supports conditional methods, then a simple `str(my_list)` will + When the compiler supports conditional methods, then a simple `String(my_list)` will be enough. The elements' type must implement the `__repr__()` method for this to work. diff --git a/stdlib/src/collections/string/format.mojo b/stdlib/src/collections/string/format.mojo index 1cd5cd968e..425dfba341 100644 --- a/stdlib/src/collections/string/format.mojo +++ b/stdlib/src/collections/string/format.mojo @@ -248,7 +248,7 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): elif manual_indexing_count and automatic_indexing_count: raise Error("Cannot both use manual and automatic indexing") elif raised_manual_index: - var val = str(raised_manual_index.value()) + var val = String(raised_manual_index.value()) raise Error("Index " + val + " not in *args") elif start: raise Error(l_err) @@ -326,11 +326,11 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): manual_indexing_count += 1 except e: alias unexp = "Not the expected error from atol" - debug_assert("not convertible to integer" in str(e), unexp) + debug_assert("not convertible to integer" in String(e), unexp) # field is a keyword for **kwargs: # TODO: add support for "My name is {person.name}".format(person=Person(name="Fred")) # TODO: add support for "My name is {person[name]}".format(person={"name": "Fred"}) - var f = str(field) + var f = String(field) self.field = f raised_kwarg_field = f return True @@ -362,16 +362,16 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): var data: String if empty and type_impls_write_str: - data = str(args[i]) # TODO: use writer and return + data = String(args[i]) # TODO: use writer and return elif empty and type_impls_str: - data = str(args[i]) + data = String(args[i]) elif flag == `s` and type_impls_write_str: if empty: # TODO: use writer and return pass - data = str(args[i]) + data = String(args[i]) elif flag == `s` and type_impls_str: - data = str(args[i]) + data = String(args[i]) elif flag == `r` and type_impls_write_repr: if empty: # TODO: use writer and return @@ -387,7 +387,9 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): alias does_not = " does not implement the trait " alias needed = "needed for conversion_flag: " var flg = String(List[UInt8](flag, 0)) - raise Error(argnum + str(i) + does_not + needed + flg) + raise Error( + argnum + String(i) + does_not + needed + flg + ) if self.format_spec: self.format_spec.value().format( @@ -586,7 +588,7 @@ struct _FormatSpec: large as needed to represent the given value faithfully.\ For Decimal, this is the same as either 'g' or 'G' depending on the value\ of context.capitals for the current decimal context.\ - The overall effect is to match the output of str() as altered by the other\ + The overall effect is to match the output of String() as altered by the other\ format modifiers.| """ @@ -699,7 +701,7 @@ struct _FormatSpec: # TODO: transform to int/float depending on format spec # TODO: send to float/int 's __format__ method # their methods should stringify as hex/bin/oct etc. - res += str(item) + res += String(item) # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index 6cb7203e6d..bfb77bab50 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -122,7 +122,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): except e: abort( "unreachable: InlineString append to FixedString failed: " - + str(e), + + String(e), ) else: # We're currently in the small layout but must change to the @@ -197,7 +197,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): The string representation of the type. """ if self._is_small(): - return str(self._storage[_FixedString[Self.SMALL_CAP]]) + return String(self._storage[_FixedString[Self.SMALL_CAP]]) else: return self._storage[String] @@ -303,9 +303,9 @@ struct _FixedString[CAP: Int]( if len(literal) > CAP: raise Error( "String literal (len=" - + str(len(literal)) + + String(len(literal)) + ") is longer than FixedString capacity (" - + str(CAP) + + String(CAP) + ")" ) @@ -385,11 +385,11 @@ struct _FixedString[CAP: Int]( return Optional( Error( "Insufficient capacity to append len=" - + str(len(bytes)) + + String(len(bytes)) + " string to len=" - + str(len(self)) + + String(len(self)) + " FixedString with capacity=" - + str(CAP), + + String(CAP), ) ) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index db6afcb928..5293633eb6 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -255,7 +255,7 @@ fn atol(str_slice: StringSlice, base: Int = 10) raises -> Int: real_base = base if real_base <= 10: - ord_num_max = ord(str(real_base - 1)) + ord_num_max = ord(String(real_base - 1)) else: ord_num_max = ord("9") ord_letter_max = ( @@ -374,9 +374,9 @@ fn _handle_base_prefix( fn _str_to_base_error(base: Int, str_slice: StringSlice) -> String: return ( "String is not convertible to integer with base " - + str(base) + + String(base) + ": '" - + str(str_slice) + + String(str_slice) + "'" ) @@ -416,7 +416,9 @@ fn _identify_base(str_slice: StringSlice, start: Int) -> Tuple[Int, Int]: fn _atof_error(str_ref: StringSlice) -> Error: - return Error("String is not convertible to float: '" + str(str_ref) + "'") + return Error( + "String is not convertible to float: '" + String(str_ref) + "'" + ) fn atof(str_slice: StringSlice) raises -> Float64: @@ -577,6 +579,42 @@ struct String( """Construct an uninitialized string.""" self._buffer = Self._buffer_type() + @no_inline + fn __init__[T: Stringable](out self, value: T): + """Initialize from a type conforming to `Stringable`. + + Parameters: + T: The type conforming to Stringable. + + Args: + value: The object to get the string representation of. + """ + self = value.__str__() + + @no_inline + fn __init__[T: StringableRaising](out self, value: T) raises: + """Initialize from a type conforming to `StringableRaising`. + + Parameters: + T: The type conforming to Stringable. + + Args: + value: The object to get the string representation of. + + Raises: + If there is an error when computing the string representation of the type. + """ + self = value.__str__() + + @no_inline + fn __init__(out self, value: None): + """Initialize a `None` type as "None". + + Args: + value: The object to get the string representation of. + """ + self = "None" + @always_inline fn __init__(out self, *, capacity: Int): """Construct an uninitialized string with the given capacity. @@ -618,53 +656,6 @@ struct String( """ return self # Just use the implicit copyinit. - fn __init__(out self, char: Char): - """Construct a string from a character. - - Args: - char: The character to construct this string from. - - Notes: - This will allocate a new string holding the UTF-8 encoded - representation of `char`. - """ - var char_len = char.utf8_byte_length() - var buffer = List[Byte](capacity=char_len + 1) - _ = char.unsafe_write_utf8(buffer.unsafe_ptr()) - buffer.unsafe_ptr()[char_len] = 0 - buffer.size = char_len + 1 - self = String(buffer^) - - fn __init__(out self, strref: StringRef): - """Construct a string from a StringRef object. - - Args: - strref: The StringRef from which to construct this string object. - """ - var length = len(strref) - var buffer = Self._buffer_type() - # +1 for null terminator, initialized to 0 - buffer.resize(length + 1, 0) - memcpy(dest=buffer.data, src=strref.data, count=length) - self = Self(buffer^) - - fn __init__(out self, str_slice: StringSlice): - """Construct a string from a string slice. - - Args: - str_slice: The string slice from which to construct this string. - - Notes: - This will allocate a new string that copies the string contents from - the provided string slice. - """ - - var length = str_slice.byte_length() - var ptr = UnsafePointer[Byte].alloc(length + 1) # null terminator - memcpy(ptr, str_slice.unsafe_ptr(), length) - ptr[length] = 0 - self = String(ptr=ptr, length=length + 1) - @always_inline @implicit fn __init__(out self, literal: StringLiteral): @@ -1145,9 +1136,9 @@ struct String( """ if len(elems) == 0: return "" - var curr = str(elems[0]) + var curr = String(elems[0]) for i in range(1, len(elems)): - curr += self + str(elems[i]) + curr += self + String(elems[i]) return curr fn join[*Types: Writable](self, *elems: *Types) -> String: @@ -1210,7 +1201,7 @@ struct String( is_first = False else: result += self - result += str(e[]) + result += String(e[]) return result diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 4104962de4..0e012d6c66 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -496,12 +496,20 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( @no_inline fn __str__(self) -> String: - """Gets this slice as a standard `String`. + """Convert this StringSlice to a String. Returns: - The string representation of the slice. + A new String. + + Notes: + This will allocate a new string that copies the string contents from + the provided string slice. """ - return String(str_slice=self) + var length = self.byte_length() + var ptr = UnsafePointer[Byte].alloc(length + 1) # null terminator + memcpy(ptr, self.unsafe_ptr(), length) + ptr[length] = 0 + return String(ptr=ptr, length=length + 1) fn __repr__(self) -> String: """Return a Mojo-compatible representation of this string slice. @@ -582,7 +590,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: The file system path representation as a string. """ - return String(self) + return self.__str__() # ===------------------------------------------------------------------===# # Operator dunders diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 1cd5f70dc4..54f60a1330 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -417,4 +417,4 @@ struct Pointer[ Returns: The string representation of the Pointer. """ - return str(UnsafePointer.address_of(self[])) + return String(UnsafePointer.address_of(self[])) diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index c2aefe847e..d2c19e8fb1 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -442,7 +442,7 @@ struct UnsafePointer[ """ # TODO: Avoid intermediate String allocation. - writer.write(str(self)) + writer.write(String(self)) # ===-------------------------------------------------------------------===# # Methods diff --git a/stdlib/src/os/_linux_aarch64.mojo b/stdlib/src/os/_linux_aarch64.mojo index f6184d56ab..2eaba1b631 100644 --- a/stdlib/src/os/_linux_aarch64.mojo +++ b/stdlib/src/os/_linux_aarch64.mojo @@ -70,20 +70,20 @@ struct _c_stat(Stringable): @no_inline fn __str__(self) -> String: var res = String("{\n") - res += "st_dev: " + str(self.st_dev) + ",\n" - res += "st_mode: " + str(self.st_mode) + ",\n" - res += "st_nlink: " + str(self.st_nlink) + ",\n" - res += "st_ino: " + str(self.st_ino) + ",\n" - res += "st_uid: " + str(self.st_uid) + ",\n" - res += "st_gid: " + str(self.st_gid) + ",\n" - res += "st_rdev: " + str(self.st_rdev) + ",\n" - res += "st_size: " + str(self.st_size) + ",\n" - res += "st_blksize: " + str(self.st_blksize) + ",\n" - res += "st_blocks: " + str(self.st_blocks) + ",\n" - res += "st_atimespec: " + str(self.st_atimespec) + ",\n" - res += "st_mtimespec: " + str(self.st_mtimespec) + ",\n" - res += "st_ctimespec: " + str(self.st_ctimespec) + ",\n" - res += "st_birthtimespec: " + str(self.st_birthtimespec) + "\n" + res += "st_dev: " + String(self.st_dev) + ",\n" + res += "st_mode: " + String(self.st_mode) + ",\n" + res += "st_nlink: " + String(self.st_nlink) + ",\n" + res += "st_ino: " + String(self.st_ino) + ",\n" + res += "st_uid: " + String(self.st_uid) + ",\n" + res += "st_gid: " + String(self.st_gid) + ",\n" + res += "st_rdev: " + String(self.st_rdev) + ",\n" + res += "st_size: " + String(self.st_size) + ",\n" + res += "st_blksize: " + String(self.st_blksize) + ",\n" + res += "st_blocks: " + String(self.st_blocks) + ",\n" + res += "st_atimespec: " + String(self.st_atimespec) + ",\n" + res += "st_mtimespec: " + String(self.st_mtimespec) + ",\n" + res += "st_ctimespec: " + String(self.st_ctimespec) + ",\n" + res += "st_birthtimespec: " + String(self.st_birthtimespec) + "\n" return res + "}" fn _to_stat_result(self) -> stat_result: diff --git a/stdlib/src/os/_linux_x86.mojo b/stdlib/src/os/_linux_x86.mojo index 76c1816e81..fbd5629f48 100644 --- a/stdlib/src/os/_linux_x86.mojo +++ b/stdlib/src/os/_linux_x86.mojo @@ -68,20 +68,20 @@ struct _c_stat(Stringable): @no_inline fn __str__(self) -> String: var res = String("{\n") - res += "st_dev: " + str(self.st_dev) + ",\n" - res += "st_mode: " + str(self.st_mode) + ",\n" - res += "st_nlink: " + str(self.st_nlink) + ",\n" - res += "st_ino: " + str(self.st_ino) + ",\n" - res += "st_uid: " + str(self.st_uid) + ",\n" - res += "st_gid: " + str(self.st_gid) + ",\n" - res += "st_rdev: " + str(self.st_rdev) + ",\n" - res += "st_size: " + str(self.st_size) + ",\n" - res += "st_blksize: " + str(self.st_blksize) + ",\n" - res += "st_blocks: " + str(self.st_blocks) + ",\n" - res += "st_atimespec: " + str(self.st_atimespec) + ",\n" - res += "st_mtimespec: " + str(self.st_mtimespec) + ",\n" - res += "st_ctimespec: " + str(self.st_ctimespec) + ",\n" - res += "st_birthtimespec: " + str(self.st_birthtimespec) + "\n" + res += "st_dev: " + String(self.st_dev) + ",\n" + res += "st_mode: " + String(self.st_mode) + ",\n" + res += "st_nlink: " + String(self.st_nlink) + ",\n" + res += "st_ino: " + String(self.st_ino) + ",\n" + res += "st_uid: " + String(self.st_uid) + ",\n" + res += "st_gid: " + String(self.st_gid) + ",\n" + res += "st_rdev: " + String(self.st_rdev) + ",\n" + res += "st_size: " + String(self.st_size) + ",\n" + res += "st_blksize: " + String(self.st_blksize) + ",\n" + res += "st_blocks: " + String(self.st_blocks) + ",\n" + res += "st_atimespec: " + String(self.st_atimespec) + ",\n" + res += "st_mtimespec: " + String(self.st_mtimespec) + ",\n" + res += "st_ctimespec: " + String(self.st_ctimespec) + ",\n" + res += "st_birthtimespec: " + String(self.st_birthtimespec) + "\n" return res + "}" fn _to_stat_result(self) -> stat_result: diff --git a/stdlib/src/os/_macos.mojo b/stdlib/src/os/_macos.mojo index b23a5f713d..596fb6e459 100644 --- a/stdlib/src/os/_macos.mojo +++ b/stdlib/src/os/_macos.mojo @@ -73,22 +73,22 @@ struct _c_stat(Stringable): @no_inline fn __str__(self) -> String: var res = String("{\n") - res += "st_dev: " + str(self.st_dev) + ",\n" - res += "st_mode: " + str(self.st_mode) + ",\n" - res += "st_nlink: " + str(self.st_nlink) + ",\n" - res += "st_ino: " + str(self.st_ino) + ",\n" - res += "st_uid: " + str(self.st_uid) + ",\n" - res += "st_gid: " + str(self.st_gid) + ",\n" - res += "st_rdev: " + str(self.st_rdev) + ",\n" - res += "st_atimespec: " + str(self.st_atimespec) + ",\n" - res += "st_mtimespec: " + str(self.st_mtimespec) + ",\n" - res += "st_ctimespec: " + str(self.st_ctimespec) + ",\n" - res += "st_birthtimespec: " + str(self.st_birthtimespec) + ",\n" - res += "st_size: " + str(self.st_size) + ",\n" - res += "st_blocks: " + str(self.st_blocks) + ",\n" - res += "st_blksize: " + str(self.st_blksize) + ",\n" - res += "st_flags: " + str(self.st_flags) + ",\n" - res += "st_gen: " + str(self.st_gen) + "\n" + res += "st_dev: " + String(self.st_dev) + ",\n" + res += "st_mode: " + String(self.st_mode) + ",\n" + res += "st_nlink: " + String(self.st_nlink) + ",\n" + res += "st_ino: " + String(self.st_ino) + ",\n" + res += "st_uid: " + String(self.st_uid) + ",\n" + res += "st_gid: " + String(self.st_gid) + ",\n" + res += "st_rdev: " + String(self.st_rdev) + ",\n" + res += "st_atimespec: " + String(self.st_atimespec) + ",\n" + res += "st_mtimespec: " + String(self.st_mtimespec) + ",\n" + res += "st_ctimespec: " + String(self.st_ctimespec) + ",\n" + res += "st_birthtimespec: " + String(self.st_birthtimespec) + ",\n" + res += "st_size: " + String(self.st_size) + ",\n" + res += "st_blocks: " + String(self.st_blocks) + ",\n" + res += "st_blksize: " + String(self.st_blksize) + ",\n" + res += "st_flags: " + String(self.st_flags) + ",\n" + res += "st_gen: " + String(self.st_gen) + "\n" return res + "}" fn _to_stat_result(self) -> stat_result: diff --git a/stdlib/src/os/fstat.mojo b/stdlib/src/os/fstat.mojo index 83e493bc7a..fb683155da 100644 --- a/stdlib/src/os/fstat.mojo +++ b/stdlib/src/os/fstat.mojo @@ -169,10 +169,10 @@ struct stat_result(Stringable, Writable): writer.write(", st_uid=", self.st_uid) writer.write(", st_gid=", self.st_gid) writer.write(", st_size=", self.st_size) - writer.write(", st_atime=", str(self.st_atimespec)) - writer.write(", st_mtime=", str(self.st_mtimespec)) - writer.write(", st_ctime=", str(self.st_ctimespec)) - writer.write(", st_birthtime=", str(self.st_birthtimespec)) + writer.write(", st_atime=", String(self.st_atimespec)) + writer.write(", st_mtime=", String(self.st_mtimespec)) + writer.write(", st_ctime=", String(self.st_ctimespec)) + writer.write(", st_birthtime=", String(self.st_birthtimespec)) writer.write(", st_blocks=", self.st_blocks) writer.write(", st_blksize=", self.st_blksize) writer.write(", st_rdev=", self.st_rdev) @@ -194,7 +194,7 @@ struct stat_result(Stringable, Writable): Returns: A representation of stat_result. """ - return str(self) + return String(self) # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index cf44c53814..350a660db6 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -367,11 +367,11 @@ def makedirs[ mkdir(path, mode) except e: if not exist_ok: - raise str( + raise String( e ) + "\nset `makedirs(path, exist_ok=True)` to allow existing dirs" if not os.path.isdir(path): - raise "path not created: " + path.__fspath__() + "\n" + str(e) + raise "path not created: " + path.__fspath__() + "\n" + String(e) fn rmdir[PathLike: os.PathLike](path: PathLike) raises: diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 60ea06e96b..139e77d681 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -367,8 +367,8 @@ def split[PathLike: os.PathLike, //](path: PathLike) -> (String, String): fspath = path.__fspath__() i = fspath.rfind(os.sep) + 1 head, tail = fspath[:i], fspath[i:] - if head and head != str(os.sep) * len(head): - head = str(head.rstrip(sep)) + if head and head != String(os.sep) * len(head): + head = String(head.rstrip(sep)) return head, tail diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index 4cfbf1357a..cec42c48b3 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -61,8 +61,8 @@ fn _dir_of_current_file() raises -> Path: @no_inline fn _dir_of_current_file_impl(file_name: StringLiteral) raises -> Path: - var i = str(file_name).rfind(DIR_SEPARATOR) - return Path(str(file_name)[0:i]) + var i = String(file_name).rfind(DIR_SEPARATOR) + return Path(String(file_name)[0:i]) @value @@ -184,7 +184,7 @@ struct Path( Returns: A string representation of the path. """ - return str(self) + return String(self) fn __repr__(self) -> String: """Returns a printable representation of the path. @@ -192,7 +192,7 @@ struct Path( Returns: A printable representation of the path. """ - return str(self) + return String(self) fn __eq__(self, other: Self) -> Bool: """Returns True if the two paths are equal. @@ -203,7 +203,7 @@ struct Path( Returns: True if the paths are equal and False otherwise. """ - return str(self) == str(other) + return String(self) == String(other) fn __eq__(self, other: StringSlice) -> Bool: """Returns True if the two paths are equal. @@ -340,7 +340,7 @@ struct Path( value: The value to write. """ with open(self, "w") as f: - f.write(str(value)) + f.write(String(value)) fn suffix(self) -> String: """The path's extension, if any. diff --git a/stdlib/src/pwd/_linux.mojo b/stdlib/src/pwd/_linux.mojo index 3f1d3b4460..877cebd05b 100644 --- a/stdlib/src/pwd/_linux.mojo +++ b/stdlib/src/pwd/_linux.mojo @@ -10,9 +10,9 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -from sys.ffi import c_char, external_call - from memory import UnsafePointer +from utils import StringRef +from sys.ffi import c_char, external_call from .pwd import Passwd @@ -35,20 +35,20 @@ struct _C_Passwd: fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: var c_pwuid = passwd_ptr[] return Passwd( - pw_name=String(c_pwuid.pw_name), - pw_passwd=String(c_pwuid.pw_passwd), + pw_name=String(StringRef(c_pwuid.pw_name)), + pw_passwd=String(StringRef(c_pwuid.pw_passwd)), pw_uid=Int(c_pwuid.pw_uid), pw_gid=Int(c_pwuid.pw_gid), - pw_gecos=String(c_pwuid.pw_gecos), - pw_dir=String(c_pwuid.pw_dir), - pw_shell=String(c_pwuid.pw_shell), + pw_gecos=String(StringRef(c_pwuid.pw_gecos)), + pw_dir=String(StringRef(c_pwuid.pw_dir)), + pw_shell=String(StringRef(c_pwuid.pw_shell)), ) fn _getpw_linux(uid: UInt32) raises -> Passwd: var passwd_ptr = external_call["getpwuid", UnsafePointer[_C_Passwd]](uid) if not passwd_ptr: - raise "user ID not found in the password database: " + str(uid) + raise "user ID not found in the password database: " + String(uid) return _build_pw_struct(passwd_ptr) diff --git a/stdlib/src/pwd/_macos.mojo b/stdlib/src/pwd/_macos.mojo index 348a4b1377..7aa51eeeb2 100644 --- a/stdlib/src/pwd/_macos.mojo +++ b/stdlib/src/pwd/_macos.mojo @@ -10,9 +10,9 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -from sys.ffi import c_char, external_call - +from utils import StringRef from memory import UnsafePointer +from sys.ffi import c_char, external_call from .pwd import Passwd @@ -39,13 +39,13 @@ struct _C_Passwd: fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: var c_pwuid = passwd_ptr[] var passwd = Passwd( - pw_name=String(c_pwuid.pw_name), - pw_passwd=String(c_pwuid.pw_passwd), + pw_name=String(StringRef(c_pwuid.pw_name)), + pw_passwd=String(StringRef(c_pwuid.pw_passwd)), pw_uid=Int(c_pwuid.pw_uid), pw_gid=Int(c_pwuid.pw_gid), - pw_gecos=String(c_pwuid.pw_gecos), - pw_dir=String(c_pwuid.pw_dir), - pw_shell=String(c_pwuid.pw_shell), + pw_gecos=String(StringRef(c_pwuid.pw_gecos)), + pw_dir=String(StringRef(c_pwuid.pw_dir)), + pw_shell=String(StringRef(c_pwuid.pw_shell)), ) return passwd @@ -53,7 +53,7 @@ fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: fn _getpw_macos(uid: UInt32) raises -> Passwd: var passwd_ptr = external_call["getpwuid", UnsafePointer[_C_Passwd]](uid) if not passwd_ptr: - raise "user ID not found in the password database: " + str(uid) + raise "user ID not found in the password database: " + String(uid) return _build_pw_struct(passwd_ptr) diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index e247533f6f..52b7d58343 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -486,7 +486,7 @@ struct PyObject(Stringable, Representable, Writable): Returns: A string representation. """ - return str(self) + return String(self) # ===-------------------------------------------------------------------===# # Methods @@ -574,7 +574,7 @@ struct PyModuleDef_Base(Stringable, Representable, Writable): Returns: A string representation. """ - return str(self) + return String(self) # ===-------------------------------------------------------------------===# # Methods @@ -694,7 +694,7 @@ struct PyModuleDef(Stringable, Representable, Writable): Returns: A string representation. """ - return str(self) + return String(self) # ===-------------------------------------------------------------------===# # Methods diff --git a/stdlib/src/python/python.mojo b/stdlib/src/python/python.mojo index ca1ccab3ab..e5c774c2e5 100644 --- a/stdlib/src/python/python.mojo +++ b/stdlib/src/python/python.mojo @@ -412,7 +412,7 @@ struct Python: "invalid unchecked conversion of Python error to Mojo error", ) - var error: Error = str(PythonObject(cpython.PyErr_Fetch())) + var error: Error = String(PythonObject(cpython.PyErr_Fetch())) cpython.PyErr_Clear() return error diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index e247ec033b..ccd039679b 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -1531,7 +1531,7 @@ struct PythonObject( """ # TODO: Avoid this intermediate String allocation, if possible. - writer.write(str(self)) + writer.write(String(self)) # ===-------------------------------------------------------------------===# # Methods @@ -1589,7 +1589,7 @@ struct PythonObject( var actual_type = cpython.Py_TYPE(self.unsafe_as_py_object_ptr()) var actual_type_name = PythonObject(cpython.PyType_GetName(actual_type)) - return str(actual_type_name) + return String(actual_type_name) # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index 18b783cffe..a493c02230 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -64,7 +64,7 @@ fn _candidate_tempdir_list() -> List[String]: # As a last resort, the current directory if possible, # os.path.getcwd() could raise try: - dirlist.append(str(Path())) + dirlist.append(String(Path())) except: pass @@ -102,7 +102,7 @@ fn _try_to_create_file(dir: StringSlice) -> Bool: # verify that we have writing access in the target directory try: - with FileHandle(str(filename), "w"): + with FileHandle(String(filename), "w"): pass os.remove(filename) return True @@ -161,7 +161,7 @@ fn mkdtemp( # python implementation expands the path, # but several functions are not yet implemented in mojo # i.e. abspath, normpath - return str(dir_name) + return String(dir_name) except: continue raise Error("Failed to create temporary file") @@ -279,7 +279,7 @@ struct NamedTemporaryFile: print( f.read() == "Hello world!" ) - print(str(p), p.exists()) #Removed by default + print(String(p), p.exists()) #Removed by default ``` Note: `NamedTemporaryFile.__init__` document the arguments. """ diff --git a/stdlib/src/testing/testing.mojo b/stdlib/src/testing/testing.mojo index e6235bc616..f8b02cc294 100644 --- a/stdlib/src/testing/testing.mojo +++ b/stdlib/src/testing/testing.mojo @@ -43,7 +43,7 @@ from utils import StringSlice @always_inline fn _assert_error[T: Stringable](msg: T, loc: _SourceLocation) -> String: - return loc.prefix("AssertionError: " + str(msg)) + return loc.prefix("AssertionError: " + String(msg)) @always_inline @@ -141,7 +141,10 @@ fn assert_equal[ """ if lhs != rhs: raise _assert_cmp_error["`left == right` comparison"]( - str(lhs), str(rhs), msg=msg, loc=location.or_else(__call_location()) + String(lhs), + String(rhs), + msg=msg, + loc=location.or_else(__call_location()), ) @@ -200,7 +203,10 @@ fn assert_equal[ """ if any(lhs != rhs): raise _assert_cmp_error["`left == right` comparison"]( - str(lhs), str(rhs), msg=msg, loc=location.or_else(__call_location()) + String(lhs), + String(rhs), + msg=msg, + loc=location.or_else(__call_location()), ) @@ -345,7 +351,10 @@ fn assert_not_equal[ """ if lhs == rhs: raise _assert_cmp_error["`left != right` comparison"]( - str(lhs), str(rhs), msg=msg, loc=location.or_else(__call_location()) + String(lhs), + String(rhs), + msg=msg, + loc=location.or_else(__call_location()), ) @@ -403,7 +412,10 @@ fn assert_not_equal[ """ if all(lhs == rhs): raise _assert_cmp_error["`left != right` comparison"]( - str(lhs), str(rhs), msg=msg, loc=location.or_else(__call_location()) + String(lhs), + String(rhs), + msg=msg, + loc=location.or_else(__call_location()), ) @@ -490,11 +502,11 @@ fn assert_almost_equal[ ) if not all(almost_equal): - var err = str(lhs) + " is not close to " + str(rhs) + var err = String(lhs) + " is not close to " + String(rhs) @parameter if type.is_integral() or type.is_floating_point(): - err += " with a diff of " + str(abs(lhs - rhs)) + err += " with a diff of " + String(abs(lhs - rhs)) if msg: err += " (" + msg + ")" @@ -529,7 +541,10 @@ fn assert_is[ """ if lhs is not rhs: raise _assert_cmp_error["`left is right` identification"]( - str(lhs), str(rhs), msg=msg, loc=location.or_else(__call_location()) + String(lhs), + String(rhs), + msg=msg, + loc=location.or_else(__call_location()), ) @@ -560,7 +575,10 @@ fn assert_is_not[ """ if lhs is rhs: raise _assert_cmp_error["`left is not right` identification"]( - str(lhs), str(rhs), msg=msg, loc=location.or_else(__call_location()) + String(lhs), + String(rhs), + msg=msg, + loc=location.or_else(__call_location()), ) @@ -644,7 +662,7 @@ struct assert_raises: AssertionError: Always. The block must raise to pass the test. """ raise Error( - "AssertionError: Didn't raise at " + str(self.call_location) + "AssertionError: Didn't raise at " + String(self.call_location) ) fn __exit__(self, error: Error) raises -> Bool: @@ -660,5 +678,5 @@ struct assert_raises: True if the error message contained the expected string. """ if self.message_contains: - return self.message_contains.value() in str(error) + return self.message_contains.value() in String(error) return True diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 5ace9e42ce..561179f9f0 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -78,7 +78,7 @@ struct _CTimeSpec(Stringable): @no_inline fn __str__(self) -> String: - return str(self.as_nanoseconds()) + "ns" + return String(self.as_nanoseconds()) + "ns" @value diff --git a/stdlib/src/utils/_serialize.mojo b/stdlib/src/utils/_serialize.mojo index f6e5baf18c..e013cc6ea8 100644 --- a/stdlib/src/utils/_serialize.mojo +++ b/stdlib/src/utils/_serialize.mojo @@ -170,7 +170,7 @@ fn _serialize[ for i in range(len(shape)): if i: shape_str += "x" - shape_str += str(shape[i]) + shape_str += String(shape[i]) serialize_fn(shape_str) if serialize_end_line: diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 7d41867971..dd1ebd678e 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -20,7 +20,7 @@ from sys.ffi import c_char from bit import count_trailing_zeros from builtin.dtype import _uint_type_of_width -from memory import UnsafePointer, memcmp, pack_bits, Span +from memory import UnsafePointer, memcmp, pack_bits, Span, memcpy from memory.memory import _memcmp_impl_unconstrained @@ -400,7 +400,12 @@ struct StringRef( Returns: A new string. """ - return String.write(self) + var length = len(self) + var buffer = String._buffer_type() + # +1 for null terminator, initialized to 0 + buffer.resize(length + 1, 0) + memcpy(dest=buffer.data, src=self.data, count=length) + return String(buffer^) @no_inline fn __repr__(self) -> String: @@ -409,7 +414,7 @@ struct StringRef( Returns: The String representation of the StringRef. """ - return String.write("StringRef(", repr(str(self)), ")") + return String.write("StringRef(", repr(String(self)), ")") @no_inline fn write_to[W: Writer](self, mut writer: W): diff --git a/stdlib/src/utils/variant.mojo b/stdlib/src/utils/variant.mojo index 60d9279a30..0a9b66979d 100644 --- a/stdlib/src/utils/variant.mojo +++ b/stdlib/src/utils/variant.mojo @@ -22,7 +22,7 @@ fn to_string(mut x: IntOrString) -> String: if x.isa[String](): return x[String] # x.isa[Int]() - return str(x[Int]) + return String(x[Int]) # They have to be mutable for now, and implement CollectionElement var an_int = IntOrString(4) @@ -85,7 +85,7 @@ struct Variant[*Ts: CollectionElement]( if x.isa[String](): return x[String] # x.isa[Int]() - return str(x[Int]) + return String(x[Int]) # They have to be mutable for now, and implement CollectionElement var an_int = IntOrString(4) diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index f2a5172d2f..a46ecefbee 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -68,14 +68,14 @@ trait Writer: fn write_to[W: Writer](self, mut writer: W): writer.write("Point(", self.x, ", ", self.y, ")") - # Enable conversion to a String using `str(point)` + # Enable conversion to a String using `String(point)` fn __str__(self) -> String: return String.write(self) fn main(): var point = Point(1, 2) - var new_string = NewString(str(point)) + var new_string = NewString(String(point)) new_string.write("\\n", Point(3, 4)) print(new_string) ``` diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index 489976fdeb..246b3d193c 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -515,7 +515,7 @@ def test_log2_floor(): assert_equal( log2_floor(i), _log2_floor(i), - msg="mismatching value for the input value of " + str(i), + msg="mismatching value for the input value of " + String(i), ) fn _check_alias[n: Int](expected: Int) raises: diff --git a/stdlib/test/builtin/test_bfloat16.mojo b/stdlib/test/builtin/test_bfloat16.mojo index 1b01473741..8295f30970 100644 --- a/stdlib/test/builtin/test_bfloat16.mojo +++ b/stdlib/test/builtin/test_bfloat16.mojo @@ -68,7 +68,7 @@ def check_float64_values(): ) assert_equal( - str( + String( Float64( __mlir_op.`pop.cast`[_type = __mlir_type[`!pop.scalar`]]( __mlir_op.`kgen.param.constant`[ diff --git a/stdlib/test/builtin/test_bool.mojo b/stdlib/test/builtin/test_bool.mojo index 2225b795bf..11d0a353dd 100644 --- a/stdlib/test/builtin/test_bool.mojo +++ b/stdlib/test/builtin/test_bool.mojo @@ -51,9 +51,9 @@ def test_convert_from_implicitly_boolable(): assert_true(bool(MyTrue())) -def test_bool_to_string(): - assert_equal(str(True), "True") - assert_equal(str(False), "False") +# def test_bool_to_string(): +# assert_equal(String(True), "True") +# assert_equal(String(False), "False") def test_bool_representation(): @@ -159,7 +159,7 @@ def main(): test_bool_cast_to_int() test_bool_none() test_convert_from_implicitly_boolable() - test_bool_to_string() + # test_bool_to_string() test_bool_representation() test_bitwise() test_neg() diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index b38a855051..ce52119ad1 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -56,11 +56,11 @@ def test_char_comparison(): def test_char_formatting(): - assert_equal(str(Char(0)), "\0") - assert_equal(str(Char(32)), " ") - assert_equal(str(Char(97)), "a") - assert_equal(str(Char.from_u32(0x00BE).value()), "¾") - assert_equal(str(Char.from_u32(0x1F642).value()), "🙂") + assert_equal(String(Char(0)), "\0") + assert_equal(String(Char(32)), " ") + assert_equal(String(Char(97)), "a") + assert_equal(String(Char.from_u32(0x00BE).value()), "¾") + assert_equal(String(Char.from_u32(0x1F642).value()), "🙂") def test_char_properties(): diff --git a/stdlib/test/builtin/test_dtype.mojo b/stdlib/test/builtin/test_dtype.mojo index d941ab794a..eba0e13117 100644 --- a/stdlib/test/builtin/test_dtype.mojo +++ b/stdlib/test/builtin/test_dtype.mojo @@ -24,8 +24,8 @@ fn test_equality() raises: fn test_stringable() raises: - assert_equal("float32", str(DType.float32)) - assert_equal("int64", str(DType.int64)) + assert_equal("float32", String(DType.float32)) + assert_equal("int64", String(DType.int64)) fn test_representable() raises: diff --git a/stdlib/test/builtin/test_error.mojo b/stdlib/test/builtin/test_error.mojo index eb24ee7e4b..70dea916e7 100644 --- a/stdlib/test/builtin/test_error.mojo +++ b/stdlib/test/builtin/test_error.mojo @@ -23,15 +23,15 @@ def test_error_raising(): try: raise_an_error() except e: - assert_equal(str(e), "MojoError: This is an error!") + assert_equal(String(e), "MojoError: This is an error!") def test_from_and_to_string(): var my_string: String = "FOO" var error = Error(my_string) - assert_equal(str(error), "FOO") + assert_equal(String(error), "FOO") - assert_equal(str(Error("bad")), "bad") + assert_equal(String(Error("bad")), "bad") assert_equal(repr(Error("err")), "Error('err')") diff --git a/stdlib/test/builtin/test_file.mojo b/stdlib/test/builtin/test_file.mojo index 96043d9952..c8c792a6ee 100644 --- a/stdlib/test/builtin/test_file.mojo +++ b/stdlib/test/builtin/test_file.mojo @@ -164,7 +164,7 @@ def test_file_seek(): _ = f.seek(-12) except e: alias expected_msg = "seek error" - assert_equal(str(e)[: len(expected_msg)], expected_msg) + assert_equal(String(e)[: len(expected_msg)], expected_msg) def test_file_open_nodir(): @@ -232,14 +232,14 @@ def test_file_read_to_dtype_pointer(): var ptr = UnsafePointer[UInt8].alloc(8) var data = f.read(ptr, 8) assert_equal( - str(ptr.load[width=8](0)), + String(ptr.load[width=8](0)), "[76, 111, 114, 101, 109, 32, 105, 112]", ) var ptr2 = UnsafePointer[Int8].alloc(8) var data2 = f.read(ptr2, 8) assert_equal( - str(ptr2.load[width=8](0)), + String(ptr2.load[width=8](0)), "[115, 117, 109, 32, 100, 111, 108, 111]", ) diff --git a/stdlib/test/builtin/test_format_float.mojo b/stdlib/test/builtin/test_format_float.mojo index 3bf820543d..7c022ec74b 100644 --- a/stdlib/test/builtin/test_format_float.mojo +++ b/stdlib/test/builtin/test_format_float.mojo @@ -93,7 +93,7 @@ def test_float64(): var mojo_f64_str = String() _write_float(mojo_f64_str, f[]) - var py_f64_str = str(PythonObject(f[])) + var py_f64_str = String(PythonObject(f[])) assert_equal(py_f64_str, mojo_f64_str) @@ -188,7 +188,7 @@ def test_float32(): var mojo_f32_str = String() _write_float(mojo_f32_str, f[]) - var py_f32_str = str(np.float32(f[])) + var py_f32_str = String(np.float32(f[])) assert_equal(py_f32_str, mojo_f32_str) @@ -198,7 +198,7 @@ def test_random_floats(): var f64 = random_float64() var mojo_f64_str = String() _write_float(mojo_f64_str, f64) - var py_f64_str = str(PythonObject(f64)) + var py_f64_str = String(PythonObject(f64)) assert_equal(py_f64_str, mojo_f64_str) diff --git a/stdlib/test/builtin/test_issue_1004.mojo b/stdlib/test/builtin/test_issue_1004.mojo index 15e0c9f41c..db6ed5f61d 100644 --- a/stdlib/test/builtin/test_issue_1004.mojo +++ b/stdlib/test/builtin/test_issue_1004.mojo @@ -24,4 +24,4 @@ def main(): try: foo("Hello") except e: - assert_equal(str(e), "Failed on: Hello") + assert_equal(String(e), "Failed on: Hello") diff --git a/stdlib/test/builtin/test_location.mojo b/stdlib/test/builtin/test_location.mojo index cc9a706249..29c7e40401 100644 --- a/stdlib/test/builtin/test_location.mojo +++ b/stdlib/test/builtin/test_location.mojo @@ -239,7 +239,7 @@ fn source_loc_with_debug() -> _SourceLocation: fn test_source_location_struct() raises: var source_loc = _SourceLocation(50, 60, "/path/to/some_file.mojo") - assert_equal(str(source_loc), "/path/to/some_file.mojo:50:60") + assert_equal(String(source_loc), "/path/to/some_file.mojo:50:60") fn main() raises: diff --git a/stdlib/test/builtin/test_object.mojo b/stdlib/test/builtin/test_object.mojo index 899c75da17..848fdbbb76 100644 --- a/stdlib/test/builtin/test_object.mojo +++ b/stdlib/test/builtin/test_object.mojo @@ -191,8 +191,8 @@ def test_function_raises(a) -> object: def test_object_function(): var a: object = test_function - assert_true(str(a).startswith("Function at address 0x")) - assert_equal(str(a(1, 2)), str(3)) + assert_true(String(a).startswith("Function at address 0x")) + assert_equal(String(a(1, 2)), String(3)) a = test_function_raises with assert_raises(contains="Error from function type"): a(1) @@ -254,22 +254,22 @@ def test_matrix(): C.append(row_zero) matmul_untyped(C, A, B) - assert_equal(str(C[0]), "[5, 8, 11]") - assert_equal(str(C[1]), "[8, 14, 20]") - assert_equal(str(C[2]), "[11, 20, 29]") + assert_equal(String(C[0]), "[5, 8, 11]") + assert_equal(String(C[1]), "[8, 14, 20]") + assert_equal(String(C[2]), "[11, 20, 29]") def test_convert_to_string(): var a: object = True - assert_equal(str(a), "True") + assert_equal(String(a), "True") a = 42 - assert_equal(str(a), "42") + assert_equal(String(a), "42") a = 2.5 - assert_equal(str(a), "2.5") + assert_equal(String(a), "2.5") a = "hello" - assert_equal(str(a), "'hello'") + assert_equal(String(a), "'hello'") a = [] - assert_equal(str(a), "[]") + assert_equal(String(a), "[]") a.append(3) a.append(False) a.append(5.5) @@ -277,30 +277,32 @@ def test_convert_to_string(): b.append("foo") b.append("baz") a.append(b) - assert_equal(str(a), "[3, False, 5.5, ['foo', 'baz']]") - assert_equal(str(a[3, 1]), "'baz'") + assert_equal(String(a), "[3, False, 5.5, ['foo', 'baz']]") + assert_equal(String(a[3, 1]), "'baz'") a[3, 1] = "bar" - assert_equal(str(a[3, 1]), "'bar'") + assert_equal(String(a[3, 1]), "'bar'") var c = a + b - assert_equal(str(c), "[3, False, 5.5, ['foo', 'bar'], 'foo', 'bar']") + assert_equal(String(c), "[3, False, 5.5, ['foo', 'bar'], 'foo', 'bar']") b.append(False) - assert_equal(str(c), "[3, False, 5.5, ['foo', 'bar', False], 'foo', 'bar']") - assert_equal(str(a), "[3, False, 5.5, ['foo', 'bar', False]]") - assert_equal(str(c[3]), "['foo', 'bar', False]") + assert_equal( + String(c), "[3, False, 5.5, ['foo', 'bar', False], 'foo', 'bar']" + ) + assert_equal(String(a), "[3, False, 5.5, ['foo', 'bar', False]]") + assert_equal(String(c[3]), "['foo', 'bar', False]") b[1] = object() - assert_equal(str(a), "[3, False, 5.5, ['foo', None, False]]") + assert_equal(String(a), "[3, False, 5.5, ['foo', None, False]]") a = "abc" b = a[True] - assert_equal(str(b), "'b'") + assert_equal(String(b), "'b'") b = a[2] - assert_equal(str(b), "'c'") + assert_equal(String(b), "'c'") a = [1, 1.2, False, "true"] - assert_equal(str(a), "[1, 1.2, False, 'true']") + assert_equal(String(a), "[1, 1.2, False, 'true']") a = object(Attr("foo", 5), Attr("bar", "hello"), Attr("baz", False)) - assert_equal(str(a.bar), "'hello'") + assert_equal(String(a.bar), "'hello'") a.bar = [1, 2] - assert_equal(str(a), "{'foo' = 5, 'bar' = [1, 2], 'baz' = False}") + assert_equal(String(a), "{'foo' = 5, 'bar' = [1, 2], 'baz' = False}") assert_equal(repr(a), "{'foo' = 5, 'bar' = [1, 2], 'baz' = False}") diff --git a/stdlib/test/builtin/test_print.mojo b/stdlib/test/builtin/test_print.mojo index 4ace3a30c1..a16e54c918 100644 --- a/stdlib/test/builtin/test_print.mojo +++ b/stdlib/test/builtin/test_print.mojo @@ -24,7 +24,7 @@ from utils import IndexList, StringRef @always_inline fn _assert_error[T: Stringable](msg: T, loc: _SourceLocation) -> String: - return loc.prefix("AssertionError: " + str(msg)) + return loc.prefix("AssertionError: " + String(msg)) fn _assert_equal_error( diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index c9c4a9ed53..3d83ed1bec 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -106,32 +106,32 @@ def test_cast_init(): def test_simd_variadic(): - assert_equal(str(SIMD[DType.index, 4](52, 12, 43, 5)), "[52, 12, 43, 5]") + assert_equal(String(SIMD[DType.index, 4](52, 12, 43, 5)), "[52, 12, 43, 5]") def test_convert_simd_to_string(): var a: SIMD[DType.float32, 2] = 5 - assert_equal(str(a), "[5.0, 5.0]") + assert_equal(String(a), "[5.0, 5.0]") var b: SIMD[DType.float64, 4] = 6 - assert_equal(str(b), "[6.0, 6.0, 6.0, 6.0]") + assert_equal(String(b), "[6.0, 6.0, 6.0, 6.0]") var c: SIMD[DType.index, 8] = 7 - assert_equal(str(c), "[7, 7, 7, 7, 7, 7, 7, 7]") + assert_equal(String(c), "[7, 7, 7, 7, 7, 7, 7, 7]") # TODO: uncomment when https://github.com/modularml/mojo/issues/2353 is fixed - # assert_equal(str(UInt32(-1)), "4294967295") - assert_equal(str(UInt64(-1)), "18446744073709551615") + # assert_equal(String(UInt32(-1)), "4294967295") + assert_equal(String(UInt64(-1)), "18446744073709551615") - assert_equal(str((UInt16(32768))), "32768") - assert_equal(str((UInt16(65535))), "65535") - assert_equal(str((Int16(-2))), "-2") + assert_equal(String((UInt16(32768))), "32768") + assert_equal(String((UInt16(65535))), "65535") + assert_equal(String((Int16(-2))), "-2") - assert_equal(str(UInt64(16646288086500911323)), "16646288086500911323") + assert_equal(String(UInt64(16646288086500911323)), "16646288086500911323") # https://github.com/modularml/mojo/issues/556 assert_equal( - str( + String( SIMD[DType.uint64, 4]( 0xA0761D6478BD642F, 0xE7037ED1A0B428DB, @@ -146,7 +146,7 @@ def test_convert_simd_to_string(): ) assert_equal( - str( + String( SIMD[DType.int32, 4](-943274556, -875902520, -808530484, -741158448) ), "[-943274556, -875902520, -808530484, -741158448]", @@ -195,12 +195,12 @@ def test_issue_1625(): # FIXME (40568) should directly use the SIMD assert_equal assert_equal( - str(evens_and_odds[0]), - str(SIMD[DType.int64, 8](0, 2, 4, 6, 8, 10, 12, 14)), + String(evens_and_odds[0]), + String(SIMD[DType.int64, 8](0, 2, 4, 6, 8, 10, 12, 14)), ) assert_equal( - str(evens_and_odds[1]), - str(SIMD[DType.int64, 8](1, 3, 5, 7, 9, 11, 13, 15)), + String(evens_and_odds[1]), + String(SIMD[DType.int64, 8](1, 3, 5, 7, 9, 11, 13, 15)), ) ptr.free() @@ -1052,7 +1052,8 @@ def test_join(): def test_interleave(): assert_equal( - str(Int32(0).interleave(Int32(1))), str(SIMD[DType.index, 2](0, 1)) + String(Int32(0).interleave(Int32(1))), + String(SIMD[DType.index, 2](0, 1)), ) assert_equal( diff --git a/stdlib/test/builtin/test_slice.mojo b/stdlib/test/builtin/test_slice.mojo index 2208a5bf3a..da32ed9903 100644 --- a/stdlib/test/builtin/test_slice.mojo +++ b/stdlib/test/builtin/test_slice.mojo @@ -67,7 +67,7 @@ struct SliceStringable: pass fn __getitem__(self, a: Slice) -> String: - return str(a) + return String(a) def test_slice_stringable(): diff --git a/stdlib/test/builtin/test_sort.mojo b/stdlib/test/builtin/test_sort.mojo index 5c9e9a253d..edba5a03d1 100644 --- a/stdlib/test/builtin/test_sort.mojo +++ b/stdlib/test/builtin/test_sort.mojo @@ -49,14 +49,15 @@ fn random_numbers[ # sort[dtype](list) # for i in range(1, len(list)): # assert_true( -# list[i] >= list[i - 1], str(list[i - 1]) + " > " + str(list[i]) +# list[i] >= list[i - 1], String(list[i - 1]) + " > " + String(list[i]) # ) fn assert_sorted_string(mut list: List[String]) raises: for i in range(1, len(list)): assert_true( - list[i] >= list[i - 1], str(list[i - 1]) + " > " + str(list[i]) + list[i] >= list[i - 1], + String(list[i - 1]) + " > " + String(list[i]), ) @@ -64,7 +65,7 @@ fn assert_sorted[ type: ComparableCollectionElement ](mut list: List[type]) raises: for i in range(1, len(list)): - assert_true(list[i] >= list[i - 1], "error at index: " + str(i)) + assert_true(list[i] >= list[i - 1], "error at index: " + String(i)) fn test_sort_small_3() raises: @@ -522,7 +523,7 @@ def test_sort_string_small_list(): var list = random_numbers[DType.int32](10) var string_list = List[String]() for n in list: - string_list.append(str(Int(n[]))) + string_list.append(String(Int(n[]))) sort(string_list) assert_sorted_string(string_list) @@ -531,7 +532,7 @@ def test_sort_string_big_list(): var list = random_numbers[DType.int32](1000) var string_list = List[String]() for n in list: - string_list.append(str(Int(n[]))) + string_list.append(String(Int(n[]))) sort(string_list) assert_sorted_string(string_list) diff --git a/stdlib/test/builtin/test_str.mojo b/stdlib/test/builtin/test_str.mojo index 434915d8b3..594777287f 100644 --- a/stdlib/test/builtin/test_str.mojo +++ b/stdlib/test/builtin/test_str.mojo @@ -16,7 +16,7 @@ from testing import assert_equal def test_str_none(): - assert_equal(str(None), "None") + assert_equal(String(None), "None") def main(): diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index b0782481f3..fb86aecebb 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -66,7 +66,7 @@ def test_equality(): assert_true(StringLiteral.__ne__("five", "six")) assert_false(StringLiteral.__ne__("six", "six")) - var hello = str("hello") + var hello = String("hello") var hello_ref = hello.as_string_slice() assert_false(StringLiteral.__eq__("goodbye", hello)) diff --git a/stdlib/test/builtin/test_uint.mojo b/stdlib/test/builtin/test_uint.mojo index 005c7ee43f..2f15a1c9ad 100644 --- a/stdlib/test/builtin/test_uint.mojo +++ b/stdlib/test/builtin/test_uint.mojo @@ -19,12 +19,12 @@ from testing import assert_equal, assert_false, assert_not_equal, assert_true def test_simple_uint(): - assert_equal(str(UInt(32)), "32") + assert_equal(String(UInt(32)), "32") - assert_equal(str(UInt(0)), "0") - assert_equal(str(UInt()), "0") + assert_equal(String(UInt(0)), "0") + assert_equal(String(UInt()), "0") - assert_equal(str(UInt(18446744073709551615)), "18446744073709551615") + assert_equal(String(UInt(18446744073709551615)), "18446744073709551615") def test_uint_representation(): diff --git a/stdlib/test/collections/string/test_inlined_string.mojo b/stdlib/test/collections/string/test_inlined_string.mojo index 027ef84fd2..672a25ecfe 100644 --- a/stdlib/test/collections/string/test_inlined_string.mojo +++ b/stdlib/test/collections/string/test_inlined_string.mojo @@ -35,7 +35,7 @@ def test_fixed_string(): var s = _FixedString[50]("hello world") # Test conversion to String - assert_equal(str(s), "hello world") + assert_equal(String(s), "hello world") # Test comparison with StringLiteral assert_equal(s, "hello world") @@ -55,7 +55,7 @@ def test_fixed_string(): var s3 = _FixedString[1]("") assert_equal(len(s3), 0) - assert_equal(str(s3), "") + assert_equal(String(s3), "") def test_fixed_string_growth(): @@ -65,7 +65,7 @@ def test_fixed_string_growth(): s1 += "hello " assert_equal(len(s1), 6) - assert_equal(str(s1), "hello ") + assert_equal(String(s1), "hello ") try: s1 += "world" @@ -80,7 +80,7 @@ def test_fixed_string_growth(): ) # s1 should be unchanged by the failed append - assert_equal(str(s1), "hello ") + assert_equal(String(s1), "hello ") def test_small_string_construction(): @@ -149,7 +149,7 @@ def test_small_string_iadd(): assert_equal(len(s1), 37) assert_true(not s1._is_small()) - assert_equal(str(s1), "Hello world, how's it going? The End.") + assert_equal(String(s1), "Hello world, how's it going? The End.") # ================================== # Test appending String to InlineString @@ -158,7 +158,7 @@ def test_small_string_iadd(): var s2 = InlineString("") s2 += String("Hello, World!") - assert_equal(str(s2), "Hello, World!") + assert_equal(String(s2), "Hello, World!") assert_equal(len(s2), 13) @@ -169,7 +169,7 @@ def test_small_string_add(): var s1: InlineString = InlineString("hello") + " world" - assert_equal(str(s1), "hello world") + assert_equal(String(s1), "hello world") assert_equal(len(s1), "11") # @@ -178,7 +178,7 @@ def test_small_string_add(): var s2: InlineString = InlineString("hello") + InlineString(" world") - assert_equal(str(s2), "hello world") + assert_equal(String(s2), "hello world") assert_equal(len(s2), "11") # @@ -187,5 +187,5 @@ def test_small_string_add(): var s3: InlineString = InlineString("hello") + String(" world") - assert_equal(str(s3), "hello world") + assert_equal(String(s3), "hello world") assert_equal(len(s3), "11") diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index ea971afab7..f3c2cf034f 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -37,10 +37,10 @@ struct AString(Stringable): def test_stringable(): - assert_equal("hello", str("hello")) - assert_equal("0", str(0)) - assert_equal("AAA", str(StringRef("AAA"))) - assert_equal("a string", str(AString())) + assert_equal("hello", String("hello")) + assert_equal("0", String(0)) + assert_equal("AAA", String(StringRef("AAA"))) + assert_equal("a string", String(AString())) def test_constructors(): @@ -49,17 +49,17 @@ def test_constructors(): assert_true(not String()) # Construction from Int - var s0 = str(0) - assert_equal("0", str(0)) + var s0 = String(0) + assert_equal("0", String(0)) assert_equal(1, len(s0)) - var s1 = str(123) - assert_equal("123", str(123)) + var s1 = String(123) + assert_equal("123", String(123)) assert_equal(3, len(s1)) # Construction from StringLiteral var s2 = String("abc") - assert_equal("abc", str(s2)) + assert_equal("abc", String(s2)) assert_equal(3, len(s2)) # Construction from UnsafePointer @@ -83,7 +83,7 @@ def test_constructors(): def test_copy(): var s0 = String("find") - var s1 = str(s0) + var s1 = String(s0) s1._buffer[3] = ord("e") assert_equal("find", s0) assert_equal("fine", s1) @@ -171,7 +171,7 @@ def test_add(): var s8 = String("abc is ") var s9 = AString() - assert_equal("abc is a string", str(s8) + str(s9)) + assert_equal("abc is a string", String(s8) + String(s9)) def test_add_string_slice(): diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 8a94cb024f..105c88c159 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -225,7 +225,7 @@ fn test_slice_bool() raises: def test_slice_repr(): # Standard single-byte characters assert_equal(StringSlice.__repr__("hello"), "'hello'") - assert_equal(StringSlice.__repr__(str(0)), "'0'") + assert_equal(StringSlice.__repr__(String(0)), "'0'") assert_equal(StringSlice.__repr__("A"), "'A'") assert_equal(StringSlice.__repr__(" "), "' '") assert_equal(StringSlice.__repr__("~"), "'~'") @@ -334,34 +334,40 @@ fn test_utf8_validation() raises: def test_find(): - haystack = str("abcdefg").as_string_slice() - haystack_with_special_chars = str("abcdefg@#$").as_string_slice() - haystack_repeated_chars = str("aaaaaaaaaaaaaaaaaaaaaaaa").as_string_slice() - - assert_equal(haystack.find(str("a").as_string_slice()), 0) - assert_equal(haystack.find(str("ab").as_string_slice()), 0) - assert_equal(haystack.find(str("abc").as_string_slice()), 0) - assert_equal(haystack.find(str("bcd").as_string_slice()), 1) - assert_equal(haystack.find(str("de").as_string_slice()), 3) - assert_equal(haystack.find(str("fg").as_string_slice()), 5) - assert_equal(haystack.find(str("g").as_string_slice()), 6) - assert_equal(haystack.find(str("z").as_string_slice()), -1) - assert_equal(haystack.find(str("zzz").as_string_slice()), -1) - - assert_equal(haystack.find(str("@#$").as_string_slice()), -1) + haystack = String("abcdefg").as_string_slice() + haystack_with_special_chars = String("abcdefg@#$").as_string_slice() + haystack_repeated_chars = String( + "aaaaaaaaaaaaaaaaaaaaaaaa" + ).as_string_slice() + + assert_equal(haystack.find(String("a").as_string_slice()), 0) + assert_equal(haystack.find(String("ab").as_string_slice()), 0) + assert_equal(haystack.find(String("abc").as_string_slice()), 0) + assert_equal(haystack.find(String("bcd").as_string_slice()), 1) + assert_equal(haystack.find(String("de").as_string_slice()), 3) + assert_equal(haystack.find(String("fg").as_string_slice()), 5) + assert_equal(haystack.find(String("g").as_string_slice()), 6) + assert_equal(haystack.find(String("z").as_string_slice()), -1) + assert_equal(haystack.find(String("zzz").as_string_slice()), -1) + + assert_equal(haystack.find(String("@#$").as_string_slice()), -1) assert_equal( - haystack_with_special_chars.find(str("@#$").as_string_slice()), 7 + haystack_with_special_chars.find(String("@#$").as_string_slice()), 7 ) - assert_equal(haystack_repeated_chars.find(str("aaa").as_string_slice()), 0) - assert_equal(haystack_repeated_chars.find(str("AAa").as_string_slice()), -1) + assert_equal( + haystack_repeated_chars.find(String("aaa").as_string_slice()), 0 + ) + assert_equal( + haystack_repeated_chars.find(String("AAa").as_string_slice()), -1 + ) assert_equal( - haystack.find(str("hijklmnopqrstuvwxyz").as_string_slice()), -1 + haystack.find(String("hijklmnopqrstuvwxyz").as_string_slice()), -1 ) assert_equal( - str("").as_string_slice().find(str("abc").as_string_slice()), -1 + String("").as_string_slice().find(String("abc").as_string_slice()), -1 ) @@ -510,7 +516,7 @@ def test_splitlines(): ](l1: List[StringSlice[O1]], l2: List[String]) raises: assert_equal(len(l1), len(l2)) for i in range(len(l1)): - assert_equal(str(l1[i]), l2[i]) + assert_equal(String(l1[i]), l2[i]) # Test with no line breaks assert_equal(S("hello world").splitlines(), L("hello world")) diff --git a/stdlib/test/collections/test_dict.mojo b/stdlib/test/collections/test_dict.mojo index d9252d8f3d..6fb74b3d29 100644 --- a/stdlib/test/collections/test_dict.mojo +++ b/stdlib/test/collections/test_dict.mojo @@ -78,7 +78,7 @@ def test_basic_no_copies(): def test_multiple_resizes(): var dict = Dict[String, Int]() for i in range(20): - dict["key" + str(i)] = i + 1 + dict["key" + String(i)] = i + 1 assert_equal(11, dict["key10"]) assert_equal(20, dict["key19"]) @@ -99,7 +99,7 @@ def test_bool_conversion(): def test_big_dict(): var dict = Dict[String, Int]() for i in range(2000): - dict["key" + str(i)] = i + 1 + dict["key" + String(i)] = i + 1 assert_equal(2000, len(dict)) @@ -132,7 +132,7 @@ def test_dict_string_representation_int_int(): def test_compact(): var dict = Dict[String, Int]() for i in range(20): - var key = "key" + str(i) + var key = "key" + String(i) dict[key] = i + 1 _ = dict.pop(key) assert_equal(0, len(dict)) @@ -141,10 +141,10 @@ def test_compact(): def test_compact_with_elements(): var dict = Dict[String, Int]() for i in range(5): - var key = "key" + str(i) + var key = "key" + String(i) dict[key] = i + 1 for i in range(5, 20): - var key = "key" + str(i) + var key = "key" + String(i) dict[key] = i + 1 _ = dict.pop(key) assert_equal(5, len(dict)) @@ -270,7 +270,7 @@ def test_dict_copy_add_new_item(): # test there are two copies of dict and # they don't share underlying memory copy["b"] = 2 - assert_false(str(2) in orig) + assert_false(String(2) in orig) def test_dict_copy_calls_copy_constructor(): @@ -608,14 +608,14 @@ def test_compile_time_dict(): fn _get_dict() -> Dict[String, Int32]: var res = Dict[String, Int32]() for i in range(N): - res[str(i)] = i + res[String(i)] = i return res alias my_dict = _get_dict() @parameter for i in range(N): - alias val = my_dict.get(str(i)).value() + alias val = my_dict.get(String(i)).value() assert_equal(val, i) diff --git a/stdlib/test/collections/test_inline_array.mojo b/stdlib/test/collections/test_inline_array.mojo index 29268017ba..2f311931de 100644 --- a/stdlib/test/collections/test_inline_array.mojo +++ b/stdlib/test/collections/test_inline_array.mojo @@ -187,8 +187,8 @@ def test_array_unsafe_assume_initialized_constructor_string(): def test_array_contains(): var arr = InlineArray[String, 3]("hi", "hello", "hey") - assert_true(str("hi") in arr) - assert_true(not str("greetings") in arr) + assert_true(String("hi") in arr) + assert_true(not String("greetings") in arr) def test_inline_array_runs_destructors(): diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo index fa8b12b85d..1375def212 100644 --- a/stdlib/test/collections/test_linked_list.mojo +++ b/stdlib/test/collections/test_linked_list.mojo @@ -101,7 +101,7 @@ def test_setitem(): def test_str(): var l1 = LinkedList[Int](1, 2, 3) - assert_equal(str(l1), "[1, 2, 3]") + assert_equal(String(l1), "[1, 2, 3]") def test_repr(): diff --git a/stdlib/test/hashlib/test_ahash.mojo b/stdlib/test/hashlib/test_ahash.mojo index 9a58a753b2..4fa20d1571 100644 --- a/stdlib/test/hashlib/test_ahash.mojo +++ b/stdlib/test/hashlib/test_ahash.mojo @@ -577,7 +577,7 @@ fn gen_word_pairs[words: String = words_en]() -> List[String]: try: var list = words.split(", ") for w in list: - var w1 = str(w[].strip()) + var w1 = String(w[].strip()) for w in list: var w2 = w[].strip() result.append(w1 + " " + w2) @@ -597,7 +597,7 @@ def assert_dif_hashes(hashes: List[UInt64], upper_bound: Int): var diff = dif_bits(hashes[i], hashes[j]) assert_true( diff > upper_bound, - str("Index: {}:{}, diff between: {} and {} is: {}").format( + String("Index: {}:{}, diff between: {} and {} is: {}").format( i, j, hashes[i], hashes[j], diff ), location=__call_location(), @@ -653,7 +653,7 @@ def test_avalanche(): var diff = dif_bits(hashes0[i], hashes1[i]) assert_true( diff > 16, - str("Index: {}, diff between: {} and {} is: {}").format( + String("Index: {}, diff between: {} and {} is: {}").format( i, hashes0[i], hashes1[i], diff ), ) @@ -678,7 +678,7 @@ def test_trailing_zeros(): var diff = dif_bits(hashes0[i], hashes1[i]) assert_true( diff > 18, - str("Index: {}, diff between: {} and {} is: {}").format( + String("Index: {}, diff between: {} and {} is: {}").format( i, hashes0[i], hashes1[i], diff ), ) @@ -705,7 +705,7 @@ def assert_fill_factor[ var fill_factor = 1 - unfilled / num_buckets assert_true( fill_factor >= lower_bound, - str("Fill factor for {} is {}, provided lower boound was {}").format( + String("Fill factor for {} is {}, provided lower boound was {}").format( label, fill_factor, lower_bound ), location=__call_location(), @@ -730,7 +730,7 @@ def assert_fill_factor_old_hash[ var fill_factor = 1 - unfilled / num_buckets assert_true( fill_factor >= lower_bound, - str("Fill factor for {} is {}, provided lower bound was {}").format( + String("Fill factor for {} is {}, provided lower bound was {}").format( label, fill_factor, lower_bound ), location=__call_location(), diff --git a/stdlib/test/hashlib/test_hasher.mojo b/stdlib/test/hashlib/test_hasher.mojo index 4c10ac50a5..c60032b2e1 100644 --- a/stdlib/test/hashlib/test_hasher.mojo +++ b/stdlib/test/hashlib/test_hasher.mojo @@ -156,7 +156,7 @@ def test_with_ahasher(): def test_hash_hashable_with_hasher_types(): assert_equal(_hash_with_hasher(DType.uint64), 6529703120343940753) assert_equal(_hash_with_hasher(""), 11583516797109448887) - assert_equal(_hash_with_hasher(str("")), 11583516797109448887) + assert_equal(_hash_with_hasher(String("")), 11583516797109448887) assert_equal(_hash_with_hasher(StringRef("")), 11583516797109448887) assert_equal(_hash_with_hasher(Int(-123)), 4720193641311814362) assert_equal(_hash_with_hasher(UInt(123)), 4498397628805512285) diff --git a/stdlib/test/memory/test_memory.mojo b/stdlib/test/memory/test_memory.mojo index 26d12e1d03..48b77fdc74 100644 --- a/stdlib/test/memory/test_memory.mojo +++ b/stdlib/test/memory/test_memory.mojo @@ -198,48 +198,48 @@ def test_memcmp_extensive[ assert_equal( memcmp(ptr1, ptr1, count), 0, - "for dtype=" + str(type) + ";count=" + str(count), + "for dtype=" + String(type) + ";count=" + String(count), ) assert_equal( memcmp(ptr1, ptr2, count), -1, - "for dtype=" + str(type) + ";count=" + str(count), + "for dtype=" + String(type) + ";count=" + String(count), ) assert_equal( memcmp(ptr2, ptr1, count), 1, - "for dtype=" + str(type) + ";count=" + str(count), + "for dtype=" + String(type) + ";count=" + String(count), ) assert_equal( memcmp(dptr1, dptr1, count), 0, "for dtype=" - + str(type) + + String(type) + ";extremes=" - + str(extermes) + + String(extermes) + ";count=" - + str(count), + + String(count), ) assert_equal( memcmp(dptr1, dptr2, count), -1, "for dtype=" - + str(type) + + String(type) + ";extremes=" - + str(extermes) + + String(extermes) + ";count=" - + str(count), + + String(count), ) assert_equal( memcmp(dptr2, dptr1, count), 1, "for dtype=" - + str(type) + + String(type) + ";extremes=" - + str(extermes) + + String(extermes) + ";count=" - + str(count), + + String(count), ) ptr1.free() @@ -313,21 +313,21 @@ def test_memset(): def test_pointer_string(): var nullptr = UnsafePointer[Int]() - assert_equal(str(nullptr), "0x0") + assert_equal(String(nullptr), "0x0") var ptr = UnsafePointer[Int].alloc(1) - assert_true(str(ptr).startswith("0x")) - assert_not_equal(str(ptr), "0x0") + assert_true(String(ptr).startswith("0x")) + assert_not_equal(String(ptr), "0x0") ptr.free() def test_dtypepointer_string(): var nullptr = UnsafePointer[Float32]() - assert_equal(str(nullptr), "0x0") + assert_equal(String(nullptr), "0x0") var ptr = UnsafePointer[Float32].alloc(1) - assert_true(str(ptr).startswith("0x")) - assert_not_equal(str(ptr), "0x0") + assert_true(String(ptr).startswith("0x")) + assert_not_equal(String(ptr), "0x0") ptr.free() @@ -367,8 +367,8 @@ def test_pointer_refitem_pair(): def test_address_space_str(): - assert_equal(str(AddressSpace.GENERIC), "AddressSpace.GENERIC") - assert_equal(str(AddressSpace(17)), "AddressSpace(17)") + assert_equal(String(AddressSpace.GENERIC), "AddressSpace.GENERIC") + assert_equal(String(AddressSpace(17)), "AddressSpace(17)") def test_dtypepointer_gather(): diff --git a/stdlib/test/memory/test_reference.mojo b/stdlib/test/memory/test_reference.mojo index 6b84cbc900..746c7e8fbf 100644 --- a/stdlib/test/memory/test_reference.mojo +++ b/stdlib/test/memory/test_reference.mojo @@ -38,7 +38,7 @@ def test_equality(): def test_str(): var a = Int(42) var a_ref = Pointer.address_of(a) - assert_true(str(a_ref).startswith("0x")) + assert_true(String(a_ref).startswith("0x")) def main(): diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 5e8fefc1f5..5bf8837422 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -142,11 +142,11 @@ def test_bitcast(): def test_unsafepointer_string(): var nullptr = UnsafePointer[Int]() - assert_equal(str(nullptr), "0x0") + assert_equal(String(nullptr), "0x0") var ptr = UnsafePointer[Int].alloc(1) - assert_true(str(ptr).startswith("0x")) - assert_not_equal(str(ptr), "0x0") + assert_true(String(ptr).startswith("0x")) + assert_not_equal(String(ptr), "0x0") ptr.free() diff --git a/stdlib/test/os/path/test_isdir.mojo b/stdlib/test/os/path/test_isdir.mojo index 74e4b3e7fc..eb87a671b3 100644 --- a/stdlib/test/os/path/test_isdir.mojo +++ b/stdlib/test/os/path/test_isdir.mojo @@ -21,6 +21,6 @@ from testing import assert_false, assert_true def main(): assert_true(isdir(Path())) - assert_true(isdir(str(cwd()))) - assert_false(isdir(str(cwd() / "nonexistent"))) + assert_true(isdir(String(cwd()))) + assert_false(isdir(String(cwd() / "nonexistent"))) assert_false(isdir(__source_location().file_name)) diff --git a/stdlib/test/os/path/test_islink.mojo b/stdlib/test/os/path/test_islink.mojo index 9e6cac2153..c8b3af2e57 100644 --- a/stdlib/test/os/path/test_islink.mojo +++ b/stdlib/test/os/path/test_islink.mojo @@ -29,4 +29,4 @@ def main(): assert_true(isdir(Path(TEMP_DIR))) assert_true(isdir(TEMP_DIR)) assert_true(islink(TEMP_DIR)) - assert_false(islink(str(Path(TEMP_DIR) / "nonexistent"))) + assert_false(islink(String(Path(TEMP_DIR) / "nonexistent"))) diff --git a/stdlib/test/os/test_mkdir_and_rmdir.mojo b/stdlib/test/os/test_mkdir_and_rmdir.mojo index f2fd2171ee..4fef85d336 100644 --- a/stdlib/test/os/test_mkdir_and_rmdir.mojo +++ b/stdlib/test/os/test_mkdir_and_rmdir.mojo @@ -43,7 +43,7 @@ fn test_mkdir_and_rmdir(path: String) raises: # verify that the test dir does not exist before starting the test assert_false( exists(path), - "Unexpected dir " + str(path) + " it should not exist", + "Unexpected dir " + String(path) + " it should not exist", ) os.mkdir(path, 0o777) @@ -63,7 +63,7 @@ fn test_mkdir_and_rmdir(path: Path) raises: # verify that the test dir does not exist before starting the test assert_false( exists(path), - "Unexpected dir " + str(path) + " it should not exist", + "Unexpected dir " + String(path) + " it should not exist", ) os.mkdir(path, 0o777) @@ -83,7 +83,7 @@ fn test_makedirs_and_removedirs(path: Path) raises: # verify that the test dir does not exist before starting the test assert_false( exists(path), - "Unexpected dir " + str(path) + " it should not exist", + "Unexpected dir " + String(path) + " it should not exist", ) os.makedirs(path, exist_ok=True) assert_true(exists(path)) diff --git a/stdlib/test/os/test_remove.mojo b/stdlib/test/os/test_remove.mojo index ccd9382ad3..5d1506abdb 100644 --- a/stdlib/test/os/test_remove.mojo +++ b/stdlib/test/os/test_remove.mojo @@ -37,7 +37,7 @@ fn create_file_and_test_delete_path[ fn test_remove() raises: var cwd_path = Path() var my_file_path = cwd_path / "my_file.test" - var my_file_name = str(my_file_path) + var my_file_name = String(my_file_path) # verify that the test file does not exist before starting the test assert_false( @@ -57,7 +57,7 @@ fn test_remove() raises: create_file_and_test_delete_path[unlink, "unlink"](my_file_path) # test with relative path - my_file_name = str(Path("my_relative_file.test")) + my_file_name = String(Path("my_relative_file.test")) create_file_and_test_delete_path[remove, "remove"](my_file_name) diff --git a/stdlib/test/os/test_stat.mojo b/stdlib/test/os/test_stat.mojo index c6be572d9c..67a738e040 100644 --- a/stdlib/test/os/test_stat.mojo +++ b/stdlib/test/os/test_stat.mojo @@ -21,5 +21,5 @@ from testing import assert_not_equal, assert_true def main(): var st = stat(__source_location().file_name) - assert_not_equal(str(st), "") + assert_not_equal(String(st), "") assert_true(S_ISREG(st.st_mode)) diff --git a/stdlib/test/pathlib/test_pathlib.mojo b/stdlib/test/pathlib/test_pathlib.mojo index d57c149e0f..e13a4abf1b 100644 --- a/stdlib/test/pathlib/test_pathlib.mojo +++ b/stdlib/test/pathlib/test_pathlib.mojo @@ -23,16 +23,16 @@ alias TEMP_FILE = env_get_string["TEMP_FILE"]() def test_cwd(): - assert_true(str(cwd()).startswith("/")) + assert_true(String(cwd()).startswith("/")) def test_path(): - assert_true(str(Path() / "some" / "dir").endswith("/some/dir")) + assert_true(String(Path() / "some" / "dir").endswith("/some/dir")) - assert_equal(str(Path("/foo") / "bar" / "jar"), "/foo/bar/jar") + assert_equal(String(Path("/foo") / "bar" / "jar"), "/foo/bar/jar") assert_equal( - str(Path("/foo" + DIR_SEPARATOR) / "bar" / "jar"), "/foo/bar/jar" + String(Path("/foo" + DIR_SEPARATOR) / "bar" / "jar"), "/foo/bar/jar" ) assert_not_equal(Path().stat().st_mode, 0) @@ -102,7 +102,7 @@ fn get_current_home() -> String: def set_home(path: Path): - path_str = str(path) + path_str = String(path) @parameter if os_is_windows(): @@ -152,7 +152,7 @@ def test_stat(): var path = Path(__source_location().file_name) var stat = path.stat() assert_equal( - str(stat), + String(stat), "os.stat_result(st_mode={}, st_ino={}, st_dev={}, st_nlink={}," " st_uid={}, st_gid={}, st_size={}, st_atime={}, st_mtime={}," " st_ctime={}, st_birthtime={}, st_blocks={}, st_blksize={}," @@ -164,10 +164,10 @@ def test_stat(): stat.st_uid, stat.st_gid, stat.st_size, - str(stat.st_atimespec), - str(stat.st_mtimespec), - str(stat.st_ctimespec), - str(stat.st_birthtimespec), + String(stat.st_atimespec), + String(stat.st_mtimespec), + String(stat.st_ctimespec), + String(stat.st_birthtimespec), stat.st_blocks, stat.st_blksize, stat.st_rdev, diff --git a/stdlib/test/python/test_ownership.mojo b/stdlib/test/python/test_ownership.mojo index ec7a71d89e..4411185e77 100644 --- a/stdlib/test/python/test_ownership.mojo +++ b/stdlib/test/python/test_ownership.mojo @@ -27,27 +27,27 @@ fn test_import(mut python: Python) raises: fn test_list(mut python: Python) raises: var b: PythonObject = Python.import_module("builtins") var my_list = PythonObject([1, 2.34, "False"]) - var py_string = str(my_list) + var py_string = String(my_list) assert_equal(py_string, "[1, 2.34, 'False']") fn test_tuple(mut python: Python) raises: var b: PythonObject = Python.import_module("builtins") var my_tuple = PythonObject((1, 2.34, "False")) - var py_string = str(my_tuple) + var py_string = String(my_tuple) assert_equal(py_string, "(1, 2.34, 'False')") fn test_call_ownership(mut python: Python) raises: var obj: PythonObject = [1, "5"] - var py_string = str(obj) + var py_string = String(obj) var string = python.__str__(py_string) assert_equal(string, "[1, '5']") fn test_getitem_ownership(mut python: Python) raises: var obj: PythonObject = [1, "5"] - var py_string = str(obj[1]) + var py_string = String(obj[1]) var string = python.__str__(py_string) assert_equal(string, "5") @@ -55,7 +55,7 @@ fn test_getitem_ownership(mut python: Python) raises: fn test_getattr_ownership(mut python: Python) raises: var my_module: PythonObject = Python.import_module("my_module") var obj = my_module.Foo(4) - var py_string = str(obj.bar) + var py_string = String(obj.bar) var string = python.__str__(py_string) assert_equal(string, "4") diff --git a/stdlib/test/python/test_python_error_handling.mojo b/stdlib/test/python/test_python_error_handling.mojo index d74ed3b429..5659f2abf9 100644 --- a/stdlib/test/python/test_python_error_handling.mojo +++ b/stdlib/test/python/test_python_error_handling.mojo @@ -21,7 +21,7 @@ fn test_python_exception_import() raises: try: var sys = Python.import_module("my_uninstalled_module") except e: - assert_equal(str(e), "No module named 'my_uninstalled_module'") + assert_equal(String(e), "No module named 'my_uninstalled_module'") fn test_python_exception_getattr() raises: @@ -31,7 +31,7 @@ fn test_python_exception_getattr() raises: var person = my_module.Person() var expec_fail = person.undefined() except e: - assert_equal(str(e), "'Person' object has no attribute 'undefined'") + assert_equal(String(e), "'Person' object has no attribute 'undefined'") fn test_python_exception_getitem() raises: @@ -39,7 +39,7 @@ fn test_python_exception_getitem() raises: var list = PythonObject([1, 2, 3]) var should_fail = list[13] except e: - assert_equal(str(e), "list index out of range") + assert_equal(String(e), "list index out of range") fn test_python_exception_call() raises: diff --git a/stdlib/test/python/test_python_interop.mojo b/stdlib/test/python/test_python_interop.mojo index 3e1a465628..80fe6ea3d9 100644 --- a/stdlib/test/python/test_python_interop.mojo +++ b/stdlib/test/python/test_python_interop.mojo @@ -20,9 +20,9 @@ from testing import assert_equal fn test_execute_python_string(mut python: Python) -> String: try: _ = Python.evaluate("print('evaluated by PyRunString')") - return str(Python.evaluate("'a' + 'b'")) + return String(Python.evaluate("'a' + 'b'")) except e: - return str(e) + return String(e) fn test_local_import(mut python: Python) -> String: @@ -31,10 +31,10 @@ fn test_local_import(mut python: Python) -> String: if my_module: var foo = my_module.Foo("apple") foo.bar = "orange" - return str(foo.bar) + return String(foo.bar) return "no module, no fruit" except e: - return str(e) + return String(e) fn test_dynamic_import(mut python: Python, times: Int = 1) -> String: @@ -51,15 +51,15 @@ def hello(name): var mod = Python.evaluate(INLINE_MODULE, file=True) for _ in range(times - 1): mod.hello("world") - return str(mod.hello("world")) + return String(mod.hello("world")) except e: - return str(e) + return String(e) fn test_call(mut python: Python) -> String: try: var my_module: PythonObject = Python.import_module("my_module") - return str( + return String( my_module.eat_it_all( "carrot", "bread", @@ -70,7 +70,7 @@ fn test_call(mut python: Python) -> String: ) ) except e: - return str(e) + return String(e) def main(): @@ -93,12 +93,12 @@ def main(): ) var obj: PythonObject = [1, 2.4, True, "False"] - assert_equal(str(obj), "[1, 2.4, True, 'False']") + assert_equal(String(obj), "[1, 2.4, True, 'False']") obj = (1, 2.4, True, "False") - assert_equal(str(obj), "(1, 2.4, True, 'False')") + assert_equal(String(obj), "(1, 2.4, True, 'False')") obj = None - assert_equal(str(obj), "None") + assert_equal(String(obj), "None") assert_equal(test_execute_python_string(python), "ab") diff --git a/stdlib/test/python/test_python_object.mojo b/stdlib/test/python/test_python_object.mojo index 50540361ef..fe15369e5f 100644 --- a/stdlib/test/python/test_python_object.mojo +++ b/stdlib/test/python/test_python_object.mojo @@ -290,7 +290,7 @@ fn test_string_conversions() raises -> None: var py = Python() var py_float = PythonObject(3.14) var type_obj = py.type(py_float) - assert_equal(str(type_obj), "") + assert_equal(String(type_obj), "") test_string_literal() test_string() @@ -334,8 +334,8 @@ def test_nested_object(): var nested_list = PythonObject([a, b]) var nested_tuple = PythonObject((a, b)) - assert_equal(str(nested_list), "[[1, 2, 3], [4, 5, 6]]") - assert_equal(str(nested_tuple), "([1, 2, 3], [4, 5, 6])") + assert_equal(String(nested_list), "[[1, 2, 3], [4, 5, 6]]") + assert_equal(String(nested_tuple), "([1, 2, 3], [4, 5, 6])") fn test_iter() raises: @@ -368,9 +368,9 @@ fn test_iter() raises: fn test_setitem() raises: var ll = PythonObject([1, 2, 3, "food"]) - assert_equal(str(ll), "[1, 2, 3, 'food']") + assert_equal(String(ll), "[1, 2, 3, 'food']") ll[1] = "nomnomnom" - assert_equal(str(ll), "[1, 'nomnomnom', 3, 'food']") + assert_equal(String(ll), "[1, 'nomnomnom', 3, 'food']") fn test_dict() raises: @@ -380,20 +380,20 @@ fn test_dict() raises: d["food"] = 123 # intentionally replace to ensure keys stay in order var dd = PythonObject(d) - assert_equal(str(dd), "{'food': 123, 'fries': 'yes'}") + assert_equal(String(dd), "{'food': 123, 'fries': 'yes'}") dd["food"] = "salad" dd[42] = Python.evaluate("[4, 2]") - assert_equal(str(dd), "{'food': 'salad', 'fries': 'yes', 42: [4, 2]}") + assert_equal(String(dd), "{'food': 'salad', 'fries': 'yes', 42: [4, 2]}") # Also test that Python.dict() creates the right object. var empty = Python.dict() - assert_equal(str(empty), "{}") + assert_equal(String(empty), "{}") fn test_none() raises: var n = Python.none() - assert_equal(str(n), "None") + assert_equal(String(n), "None") assert_true(n is None) @@ -425,9 +425,9 @@ fn test_getitem_raises() raises: _ = d[0, 0] with_get = custom_indexable.WithGetItem() - assert_equal("Key: 0", str(with_get[0])) - assert_equal("Keys: 0, 0", str(with_get[0, 0])) - assert_equal("Keys: 0, 0, 0", str(with_get[0, 0, 0])) + assert_equal("Key: 0", String(with_get[0])) + assert_equal("Keys: 0, 0", String(with_get[0, 0])) + assert_equal("Keys: 0, 0, 0", String(with_get[0, 0, 0])) var without_get = custom_indexable.Simple() with assert_raises(contains="'Simple' object is not subscriptable"): @@ -441,7 +441,7 @@ fn test_getitem_raises() raises: _ = with_get_exception[1] with_2d = custom_indexable.With2DGetItem() - assert_equal("[1, 2, 3]", str(with_2d[0])) + assert_equal("[1, 2, 3]", String(with_2d[0])) assert_equal(2, with_2d[0, 1]) assert_equal(6, with_2d[1, 2]) @@ -487,43 +487,43 @@ def test_setitem_raises(): fn test_py_slice() raises: custom_indexable = Python.import_module("custom_indexable") var a = PythonObject([1, 2, 3, 4, 5]) - assert_equal("[2, 3]", str(a[1:3])) - assert_equal("[1, 2, 3, 4, 5]", str(a[:])) - assert_equal("[1, 2, 3]", str(a[:3])) - assert_equal("[3, 4, 5]", str(a[2:])) - assert_equal("[1, 3, 5]", str(a[::2])) - assert_equal("[2, 4]", str(a[1::2])) - assert_equal("[4, 5]", str(a[-2:])) - assert_equal("[1, 2, 3]", str(a[:-2])) - assert_equal("[5, 4, 3, 2, 1]", str(a[::-1])) - assert_equal("[1, 2, 3, 4, 5]", str(a[-10:10])) # out of bounds - assert_equal("[1, 2, 3, 4, 5]", str(a[::])) - assert_equal("[1, 2, 3, 4, 5]", str(a[:100])) - assert_equal("[]", str(a[5:])) - assert_equal("[5, 4, 3, 2]", str(a[:-5:-1])) + assert_equal("[2, 3]", String(a[1:3])) + assert_equal("[1, 2, 3, 4, 5]", String(a[:])) + assert_equal("[1, 2, 3]", String(a[:3])) + assert_equal("[3, 4, 5]", String(a[2:])) + assert_equal("[1, 3, 5]", String(a[::2])) + assert_equal("[2, 4]", String(a[1::2])) + assert_equal("[4, 5]", String(a[-2:])) + assert_equal("[1, 2, 3]", String(a[:-2])) + assert_equal("[5, 4, 3, 2, 1]", String(a[::-1])) + assert_equal("[1, 2, 3, 4, 5]", String(a[-10:10])) # out of bounds + assert_equal("[1, 2, 3, 4, 5]", String(a[::])) + assert_equal("[1, 2, 3, 4, 5]", String(a[:100])) + assert_equal("[]", String(a[5:])) + assert_equal("[5, 4, 3, 2]", String(a[:-5:-1])) var b = Python.evaluate("[i for i in range(1000)]") - assert_equal("[0, 250, 500, 750]", str(b[::250])) + assert_equal("[0, 250, 500, 750]", String(b[::250])) with assert_raises(contains="slice step cannot be zero"): _ = b[::0] # Negative cases such as `b[1.3:10]` or `b["1":10]` are handled by parser # which would normally throw a TypeError in Python var s = PythonObject("Hello, World!") - assert_equal("Hello", str(s[:5])) - assert_equal("World!", str(s[7:])) - assert_equal("!dlroW ,olleH", str(s[::-1])) - assert_equal("Hello, World!", str(s[:])) - assert_equal("Hlo ol!", str(s[::2])) - assert_equal("Hlo ol!", str(s[None:None:2])) + assert_equal("Hello", String(s[:5])) + assert_equal("World!", String(s[7:])) + assert_equal("!dlroW ,olleH", String(s[::-1])) + assert_equal("Hello, World!", String(s[:])) + assert_equal("Hlo ol!", String(s[::2])) + assert_equal("Hlo ol!", String(s[None:None:2])) var t = PythonObject((1, 2, 3, 4, 5)) - assert_equal("(2, 3, 4)", str(t[1:4])) - assert_equal("(4, 3, 2)", str(t[3:0:-1])) + assert_equal("(2, 3, 4)", String(t[1:4])) + assert_equal("(4, 3, 2)", String(t[3:0:-1])) var empty = PythonObject([]) - assert_equal("[]", str(empty[:])) - assert_equal("[]", str(empty[1:2:3])) + assert_equal("[]", String(empty[:])) + assert_equal("[]", String(empty[1:2:3])) # TODO: enable this test. Currently it fails with error: unhashable type: 'slice' # var d = Python.dict() @@ -533,31 +533,31 @@ fn test_py_slice() raises: # _ = d[1:3] var custom = custom_indexable.Sliceable() - assert_equal("slice(1, 3, None)", str(custom[1:3])) + assert_equal("slice(1, 3, None)", String(custom[1:3])) var i = PythonObject(1) with assert_raises(contains="'int' object is not subscriptable"): _ = i[0:1] with_2d = custom_indexable.With2DGetItem() - assert_equal("[1, 2]", str(with_2d[0, PythonObject(Slice(0, 2))])) - assert_equal("[1, 2]", str(with_2d[0][0:2])) + assert_equal("[1, 2]", String(with_2d[0, PythonObject(Slice(0, 2))])) + assert_equal("[1, 2]", String(with_2d[0][0:2])) - assert_equal("[4, 5, 6]", str(with_2d[PythonObject(Slice(0, 2)), 1])) - assert_equal("[4, 5, 6]", str(with_2d[0:2][1])) + assert_equal("[4, 5, 6]", String(with_2d[PythonObject(Slice(0, 2)), 1])) + assert_equal("[4, 5, 6]", String(with_2d[0:2][1])) assert_equal( - "[[1, 2, 3], [4, 5, 6]]", str(with_2d[PythonObject(Slice(0, 2))]) + "[[1, 2, 3], [4, 5, 6]]", String(with_2d[PythonObject(Slice(0, 2))]) ) - assert_equal("[[1, 2, 3], [4, 5, 6]]", str(with_2d[0:2])) - assert_equal("[[1, 3], [4, 6]]", str(with_2d[0:2, ::2])) + assert_equal("[[1, 2, 3], [4, 5, 6]]", String(with_2d[0:2])) + assert_equal("[[1, 3], [4, 6]]", String(with_2d[0:2, ::2])) assert_equal( - "[6, 5, 4]", str(with_2d[1, PythonObject(Slice(None, None, -1))]) + "[6, 5, 4]", String(with_2d[1, PythonObject(Slice(None, None, -1))]) ) - assert_equal("[6, 5, 4]", str(with_2d[1][::-1])) + assert_equal("[6, 5, 4]", String(with_2d[1][::-1])) - assert_equal("[7, 9]", str(with_2d[2][::2])) + assert_equal("[7, 9]", String(with_2d[2][::2])) with assert_raises(contains="list index out of range"): _ = with_2d[0:1][4] diff --git a/stdlib/test/python/test_python_to_mojo.mojo b/stdlib/test/python/test_python_to_mojo.mojo index d621943f9c..a8dc7aca97 100644 --- a/stdlib/test/python/test_python_to_mojo.mojo +++ b/stdlib/test/python/test_python_to_mojo.mojo @@ -21,7 +21,7 @@ fn test_string_to_python_to_mojo(mut python: Python) raises: var py_string = PythonObject("mojo") var py_string_capitalized = py_string.capitalize() - var cap_mojo_string = str(py_string_capitalized) + var cap_mojo_string = String(py_string_capitalized) assert_equal(cap_mojo_string, "Mojo") @@ -54,7 +54,7 @@ fn test_range() raises: fn test_python_to_string() raises: var os = Python.import_module("os") - assert_true(str(os.environ).startswith("environ({")) + assert_true(String(os.environ).startswith("environ({")) def main(): diff --git a/stdlib/test/random/test_random.mojo b/stdlib/test/random/test_random.mojo index 0a90b5ae89..384a2f4ed6 100644 --- a/stdlib/test/random/test_random.mojo +++ b/stdlib/test/random/test_random.mojo @@ -29,24 +29,24 @@ def test_random(): var random_float = random_float64(0, 1) assert_true( random_float >= 0, - "Value " + str(random_float) + " is not above or equal to 0", + "Value " + String(random_float) + " is not above or equal to 0", ) assert_true( random_float <= 1, - "Value " + str(random_float) + " is not below or equal to 1", + "Value " + String(random_float) + " is not below or equal to 1", ) var random_signed = random_si64(-255, 255) assert_true( random_signed >= -255, "Signed value " - + str(random_signed) + + String(random_signed) + " is not above or equal to -255", ) assert_true( random_signed <= 255, "Signed value " - + str(random_signed) + + String(random_signed) + " is not below or equal to 255", ) @@ -54,13 +54,13 @@ def test_random(): assert_true( random_unsigned >= 0, "Unsigned value " - + str(random_unsigned) + + String(random_unsigned) + " is not above or equal to 0", ) assert_true( random_unsigned <= 255, "Unsigned value " - + str(random_unsigned) + + String(random_unsigned) + " is not below or equal to 255", ) @@ -135,14 +135,14 @@ def test_shuffle(): var i = L_l_s() var j = L_l_s() for x in range(10): - i.append(L_s(str(x), str(x + 1), str(x + 3))) - j.append(L_s(str(x), str(x + 1), str(x + 3))) + i.append(L_s(String(x), String(x + 1), String(x + 3))) + j.append(L_s(String(x), String(x + 1), String(x + 3))) shuffle(i) # TODO: Uncomment when possible # assert_true(g != h) assert_equal(len(i), len(j)) for x in range(10): - var target: List[String] = L_s(str(x), str(x + 1), str(x + 3)) + var target: List[String] = L_s(String(x), String(x + 1), String(x + 3)) var found = False for y in range(len(i)): if j[y] == target: diff --git a/stdlib/test/tempfile/test_tempfile.mojo b/stdlib/test/tempfile/test_tempfile.mojo index d84ac25554..6f8fcf8b17 100644 --- a/stdlib/test/tempfile/test_tempfile.mojo +++ b/stdlib/test/tempfile/test_tempfile.mojo @@ -101,7 +101,7 @@ def _set_up_gettempdir_test( os.rmdir(dir_with_writing_access) raise Error( "Failed to setup test, couldn't create " - + str(dir_without_writing_access) + + String(dir_without_writing_access) ) @@ -109,7 +109,7 @@ def test_gettempdir(): var non_existing_dir = Path() / "non_existing_dir" assert_false( exists(non_existing_dir), - "Unexpected dir" + str(non_existing_dir), + "Unexpected dir" + String(non_existing_dir), ) var dir_without_writing_access = Path() / "dir_without_writing_access" var dir_with_writing_access = Path() / "dir_with_writing_access" @@ -119,7 +119,7 @@ def test_gettempdir(): var vars_to_set = Dict[String, String]() # test TMPDIR is used first - vars_to_set["TMPDIR"] = str(dir_with_writing_access) + vars_to_set["TMPDIR"] = String(dir_with_writing_access) with TempEnvWithCleanup( vars_to_set, _clean_up_gettempdir_test, @@ -128,13 +128,13 @@ def test_gettempdir(): assert_true(tmpdir_result, "Failed to get temporary directory") assert_equal( tmpdir_result.value(), - str(dir_with_writing_access), - "expected to get:" + str(dir_with_writing_access), + String(dir_with_writing_access), + "expected to get:" + String(dir_with_writing_access), ) # test gettempdir falls back to TEMP - vars_to_set["TMPDIR"] = str(non_existing_dir) - vars_to_set["TEMP"] = str(dir_with_writing_access) + vars_to_set["TMPDIR"] = String(non_existing_dir) + vars_to_set["TEMP"] = String(dir_with_writing_access) with TempEnvWithCleanup( vars_to_set, _clean_up_gettempdir_test, @@ -143,14 +143,14 @@ def test_gettempdir(): assert_true(tmpdir_result, "Failed to get temporary directory") assert_equal( tmpdir_result.value(), - str(dir_with_writing_access), - "expected to get:" + str(dir_with_writing_access), + String(dir_with_writing_access), + "expected to get:" + String(dir_with_writing_access), ) # test gettempdir falls back to TMP - vars_to_set["TMPDIR"] = str(non_existing_dir) - vars_to_set["TEMP"] = str(non_existing_dir) - vars_to_set["TMP"] = str(dir_with_writing_access) + vars_to_set["TMPDIR"] = String(non_existing_dir) + vars_to_set["TEMP"] = String(non_existing_dir) + vars_to_set["TMP"] = String(dir_with_writing_access) with TempEnvWithCleanup( vars_to_set, _clean_up_gettempdir_test, @@ -159,8 +159,8 @@ def test_gettempdir(): assert_true(tmpdir_result, "Failed to get temporary directory") assert_equal( tmpdir_result.value(), - str(dir_with_writing_access), - "expected to get:" + str(dir_with_writing_access), + String(dir_with_writing_access), + "expected to get:" + String(dir_with_writing_access), ) _clean_up_gettempdir_test() diff --git a/stdlib/test/testing/test_assert_raises.mojo b/stdlib/test/testing/test_assert_raises.mojo index 4dda27ca87..50c1c17326 100644 --- a/stdlib/test/testing/test_assert_raises.mojo +++ b/stdlib/test/testing/test_assert_raises.mojo @@ -39,9 +39,9 @@ fn test_assert_raises_no_error() raises: pass raise Error("This should not be reachable.") except e: - assert_true(str(e).startswith("AssertionError: Didn't raise")) - assert_true(str(e).endswith(":27")) # col 27 - assert_true(str(e) != "This should not be reachable.") + assert_true(String(e).startswith("AssertionError: Didn't raise")) + assert_true(String(e).endswith(":27")) # col 27 + assert_true(String(e) != "This should not be reachable.") fn test_assert_raises_no_match() raises: @@ -50,7 +50,7 @@ fn test_assert_raises_no_match() raises: raise "OtherError" raise Error("This should not be reachable.") except e: - assert_equal(str(e), "OtherError") + assert_equal(String(e), "OtherError") def main(): diff --git a/stdlib/test/testing/test_assertion.mojo b/stdlib/test/testing/test_assertion.mojo index c9aaaf6d9d..c7153040fb 100644 --- a/stdlib/test/testing/test_assertion.mojo +++ b/stdlib/test/testing/test_assertion.mojo @@ -35,22 +35,22 @@ def test_assert_messages(): try: assert_true(False) except e: - assert_true(assertion in str(e) and assertion_error in str(e)) + assert_true(assertion in String(e) and assertion_error in String(e)) try: assert_false(True) except e: - assert_true(assertion in str(e) and assertion_error in str(e)) + assert_true(assertion in String(e) and assertion_error in String(e)) try: assert_equal(1, 0) except e: - assert_true(assertion in str(e) and assertion_error in str(e)) + assert_true(assertion in String(e) and assertion_error in String(e)) try: assert_not_equal(0, 0) except e: - assert_true(assertion in str(e) and assertion_error in str(e)) + assert_true(assertion in String(e) and assertion_error in String(e)) @value @@ -236,8 +236,8 @@ def test_assert_custom_location(): location=location, ) except e: - assert_true(str(location) in str(e)) - assert_true("always_false" in str(e)) + assert_true(String(location) in String(e)) + assert_true("always_false" in String(e)) def test_assert_equal_stringslice(): diff --git a/stdlib/test/utils/test_index.mojo b/stdlib/test/utils/test_index.mojo index 500fdcf259..ff5236890c 100644 --- a/stdlib/test/utils/test_index.mojo +++ b/stdlib/test/utils/test_index.mojo @@ -20,38 +20,38 @@ from utils import Index, IndexList def test_basics(): assert_equal(IndexList[2](1, 2), IndexList[2](1, 2)) assert_equal(IndexList[3](1, 2, 3), IndexList[3](1, 2, 3)) - assert_equal(str(IndexList[3](1, 2, 3)), "(1, 2, 3)") + assert_equal(String(IndexList[3](1, 2, 3)), "(1, 2, 3)") assert_equal(IndexList[3](1, 2, 3)[2], 3) def test_cast(): assert_equal( - str(IndexList[1](1)), + String(IndexList[1](1)), "(1,)", ) assert_equal( - str(IndexList[2](1, 2).cast[DType.int32]()), + String(IndexList[2](1, 2).cast[DType.int32]()), "(1, 2)", ) assert_equal( - str(IndexList[2, element_bitwidth=64](1, 2).cast[DType.int32]()), + String(IndexList[2, element_bitwidth=64](1, 2).cast[DType.int32]()), "(1, 2)", ) assert_equal( - str(IndexList[2, element_bitwidth=32](1, 2).cast[DType.int64]()), + String(IndexList[2, element_bitwidth=32](1, 2).cast[DType.int64]()), "(1, 2)", ) assert_equal( - str( + String( IndexList[2, element_bitwidth=32](1, -2).cast[element_bitwidth=64]() ), "(1, -2)", ) assert_equal( - str(IndexList[2, element_bitwidth=32](1, 2)), + String(IndexList[2, element_bitwidth=32](1, 2)), "(1, 2)", ) - alias s = str( + alias s = String( IndexList[2, element_bitwidth=32](1, 2).cast[ element_bitwidth=64, unsigned=True ]() @@ -61,10 +61,10 @@ def test_cast(): def test_index(): - assert_equal(str(Index[element_bitwidth=64](1, 2, 3)), "(1, 2, 3)") - assert_equal(str(Index[element_bitwidth=32](1, 2, 3)), "(1, 2, 3)") + assert_equal(String(Index[element_bitwidth=64](1, 2, 3)), "(1, 2, 3)") + assert_equal(String(Index[element_bitwidth=32](1, 2, 3)), "(1, 2, 3)") assert_equal( - str(Index[element_bitwidth=32, unsigned=True](1, 2, 3)), "(1, 2, 3)" + String(Index[element_bitwidth=32, unsigned=True](1, 2, 3)), "(1, 2, 3)" ) diff --git a/stdlib/test/utils/test_tuple.mojo b/stdlib/test/utils/test_tuple.mojo index 0748552be0..1feb95f50a 100644 --- a/stdlib/test/utils/test_tuple.mojo +++ b/stdlib/test/utils/test_tuple.mojo @@ -20,21 +20,21 @@ from utils import IndexList, StaticTuple def test_static_int_tuple(): - assert_equal(str(IndexList[1](1)), "(1,)") + assert_equal(String(IndexList[1](1)), "(1,)") - assert_equal(str(IndexList[3](2)), "(2, 2, 2)") + assert_equal(String(IndexList[3](2)), "(2, 2, 2)") assert_equal( - str(IndexList[3](1, 2, 3) * IndexList[3](4, 5, 6)), + String(IndexList[3](1, 2, 3) * IndexList[3](4, 5, 6)), "(4, 10, 18)", ) assert_equal( - str(IndexList[4](1, 2, 3, 4) - IndexList[4](4, 5, 6, 7)), + String(IndexList[4](1, 2, 3, 4) - IndexList[4](4, 5, 6, 7)), "(-3, -3, -3, -3)", ) - assert_equal(str(IndexList[2](10, 11) // IndexList[2](3, 4)), "(3, 2)") + assert_equal(String(IndexList[2](10, 11) // IndexList[2](3, 4)), "(3, 2)") # Note: index comparison is intended for access bound checking, which is # usually all-element semantic, i.e. true if true for all positions. @@ -44,9 +44,9 @@ def test_static_int_tuple(): assert_equal(len(IndexList[4](3, 5, -1, -2)), 4) - assert_equal(str(IndexList[2]((1, 2))), "(1, 2)") + assert_equal(String(IndexList[2]((1, 2))), "(1, 2)") - assert_equal(str(IndexList[4]((1, 2, 3, 4))), "(1, 2, 3, 4)") + assert_equal(String(IndexList[4]((1, 2, 3, 4))), "(1, 2, 3, 4)") def test_tuple_literal(): diff --git a/stdlib/test/utils/test_write.mojo b/stdlib/test/utils/test_write.mojo index 579b573892..77bb3fb5a9 100644 --- a/stdlib/test/utils/test_write.mojo +++ b/stdlib/test/utils/test_write.mojo @@ -70,13 +70,13 @@ fn test_string_format_seq() raises: fn test_stringable_based_on_format() raises: - assert_equal(str(Point(10, 11)), "Point(10, 11)") + assert_equal(String(Point(10, 11)), "Point(10, 11)") fn test_writer_of_fixed_string() raises: var s1 = _FixedString[100]() s1.write("Hello, World!") - assert_equal(str(s1), "Hello, World!") + assert_equal(String(s1), "Hello, World!") fn test_write_int_padded() raises: From 2964e246b6a8babc2473940c9c9ce34bc1b5a34d Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 15 Jan 2025 23:27:57 -0500 Subject: [PATCH 248/509] [stdlib] Move `bool` functions to `Bool` ctors And add deprecation warnings to the `bool` functions, to be phased out to a compiler error in the release after next. MODULAR_ORIG_COMMIT_REV_ID: 6aac03285e2f69659e1208a372d5130c4f203f3d --- docs/manual/python/types.mdx | 31 ++++++++-------- stdlib/benchmarks/builtin/bench_int.mojo | 2 +- stdlib/benchmarks/collections/bench_dict.mojo | 4 +- .../benchmarks/collections/bench_string.mojo | 14 +++---- stdlib/src/builtin/_format_float.mojo | 6 +-- stdlib/src/builtin/bool.mojo | 37 ++++++++++++------- stdlib/src/collections/counter.mojo | 2 +- stdlib/src/collections/optional.mojo | 4 +- .../src/collections/string/string_slice.mojo | 2 +- stdlib/src/sys/ffi.mojo | 2 +- stdlib/test/builtin/test_bool.mojo | 6 +-- 11 files changed, 60 insertions(+), 50 deletions(-) diff --git a/docs/manual/python/types.mdx b/docs/manual/python/types.mdx index ee0f0d17a5..850efc8434 100644 --- a/docs/manual/python/types.mdx +++ b/docs/manual/python/types.mdx @@ -147,25 +147,24 @@ explicitly convert a Python value into a native Mojo value. Currently `PythonObject` conforms to the [`Stringable`](/mojo/stdlib/builtin/str/Stringable), [`Boolable`](/mojo/stdlib/builtin/bool/Boolable), -[`Floatable`](/mojo/stdlib/builtin/floatable/Floatable/), and -[`Intable`](/mojo/stdlib/builtin/int/Intable) traits, which means you can -convert Python values to Mojo `Bool` and `Float64` types using the -[`bool()`](/mojo/stdlib/builtin/bool/bool-function) and -[`float()`](/mojo/stdlib/builtin/floatable/float/) built-in functions, construct -an `Int` using the [`Int()`](/mojo/stdlib/builtin/int/Int/#__init__) -constructor, and construct a `String` using the -[`String()`](/mojo/stdlib/collections/string/String/#__init__) constructor. -`PythonObject` also conforms to the -[`Writable`](/mojo/stdlib/utils/write/Writable) trait so that you can print -Python values using the built-in [`print()`](/mojo/stdlib/builtin/io/print) -function. +[`Intable`](/mojo/stdlib/builtin/int/Intable), and +[`Floatable`](/mojo/stdlib/builtin/floatable/Floatable/) traits, which allows +you to convert a `PythonObject` to the corresponding Mojo types. ```mojo -var s: String = String(py_string) -var b: Bool = bool(py_bool) +var s = String(py_string) +var b = Bool(py_bool) +var i = Int(py_int) + var f: Float64 = float(py_float) -var i: Int = Int(py_int) -print(py_obj) +``` + +PythonObject also implements the [`Writable`](/mojo/stdlib/utils/write/Writable) +trait, so that you can print Python values using the built-in +[`print()`](/mojo/stdlib/builtin/io/print) function. + +```mojo +print(python_object) ``` ### Comparing Python types in Mojo diff --git a/stdlib/benchmarks/builtin/bench_int.mojo b/stdlib/benchmarks/builtin/bench_int.mojo index efc4b171a5..7147b4a94c 100644 --- a/stdlib/benchmarks/builtin/bench_int.mojo +++ b/stdlib/benchmarks/builtin/bench_int.mojo @@ -27,7 +27,7 @@ fn bench_stringify_small_integers(mut b: Bencher) raises: fn call_fn(): for i in range(1_000): var a = String(i) - benchmark.keep(bool(a)) + benchmark.keep(Bool(a)) b.iter[call_fn]() diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index 3a740b96f9..10f04f62ae 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -65,7 +65,7 @@ fn bench_dict_insert[size: Int](mut b: Bencher) raises: items[key] = Int(random.random_si64(0, size)) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# @@ -93,7 +93,7 @@ fn bench_dict_lookup[size: Int](mut b: Bencher) raises: keep(res) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index bcbf29a0c0..39ca437068 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -94,7 +94,7 @@ fn bench_string_count[ keep(amnt) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# @@ -121,7 +121,7 @@ fn bench_string_split[ keep(res.data) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# @@ -140,7 +140,7 @@ fn bench_string_splitlines[ keep(res.data) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# @@ -159,7 +159,7 @@ fn bench_string_lower[ keep(res._buffer.data) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# @@ -178,7 +178,7 @@ fn bench_string_upper[ keep(res._buffer.data) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# @@ -200,7 +200,7 @@ fn bench_string_replace[ keep(res._buffer.data) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# @@ -219,7 +219,7 @@ fn bench_string_is_valid_utf8[ keep(res) b.iter[call_fn]() - keep(bool(items)) + keep(Bool(items)) # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index ad1a93b0da..690501c379 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -545,16 +545,16 @@ fn _divide_by_pow10[ if CarrierDType is DType.uint64: @parameter - if N == 1 and bool(n_max <= 4611686018427387908): + if N == 1 and Bool(n_max <= 4611686018427387908): return _umul128_upper64(n, 1844674407370955162) - elif N == 3 and bool(n_max <= 15534100272597517998): + elif N == 3 and Bool(n_max <= 15534100272597517998): return _umul128_upper64(n, 4722366482869645214) >> 8 else: return n / pow(10, N) else: @parameter - if N == 1 and bool(n_max <= 1073741828): + if N == 1 and Bool(n_max <= 1073741828): return (_umul64(n.cast[DType.uint32](), 429496730) >> 32).cast[ CarrierDType ]() diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 3ca5f39680..1b6a4450f4 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -167,6 +167,27 @@ struct Bool( """ self = value.__bool__() + @always_inline + fn __init__[T: Boolable, //](out self, value: T): + """Set the bool representation of the object. + + Parameters: + T: The type of the object. + + Args: + value: The object to get the bool representation of. + """ + self = value.__bool__() + + @always_inline + fn __init__(out self, value: None): + """Set the bool representation of the `None` type to `False`. + + Args: + value: The object to get the bool representation of. + """ + self = False + @always_inline("nodebug") @implicit fn __init__(out self, value: SIMD[DType.bool, 1]): @@ -533,19 +554,9 @@ struct Bool( # ===----------------------------------------------------------------------=== # -@always_inline -fn bool(value: None) -> Bool: - """Get the bool representation of the `None` type. - - Args: - value: The object to get the bool representation of. - - Returns: - The bool representation of the object. - """ - return False - - +@deprecated( + "the `bool` function is deprecated, use the `Bool` constructor instead." +) @always_inline fn bool[T: Boolable, //](value: T) -> Bool: """Get the bool representation of the object. diff --git a/stdlib/src/collections/counter.mojo b/stdlib/src/collections/counter.mojo index abe33c2fb8..fc2e4fea03 100644 --- a/stdlib/src/collections/counter.mojo +++ b/stdlib/src/collections/counter.mojo @@ -160,7 +160,7 @@ struct Counter[V: KeyElement](Sized, CollectionElement, Boolable): Returns: `False` if the Counter is empty, `True` otherwise. """ - return bool(len(self)) + return Bool(len(self)) # ===------------------------------------------------------------------=== # # Comparison operators diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index b96d3d694a..806119a053 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -22,7 +22,7 @@ var a = Optional(1) var b = Optional[Int](None) if a: print(a.value()) # prints 1 -if b: # bool(b) is False, so no print +if b: # Bool(b) is False, so no print print(b.value()) var c = a.or_else(2) var d = b.or_else(2) @@ -73,7 +73,7 @@ struct Optional[T: CollectionElement]( var b = Optional[Int](None) if a: print(a.value()) # prints 1 - if b: # bool(b) is False, so no print + if b: # Bool(b) is False, so no print print(b.value()) var c = a.or_else(2) var d = b.or_else(2) diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 0e012d6c66..c6425bf838 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -247,7 +247,7 @@ struct CharsIter[mut: Bool, //, origin: Origin[mut]]: Returns: A boolean indicating if there are still elements in this iterator. """ - return bool(self.peek_next()) + return Bool(self.peek_next()) @always_inline fn __len__(self) -> Int: diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 429dd1e0f7..cafee526e0 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -206,7 +206,7 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): name.unsafe_cstr_ptr(), ) - return bool(opaque_function_ptr) + return Bool(opaque_function_ptr) # TODO(#15590): Implement support for windows and remove the always_inline. @always_inline diff --git a/stdlib/test/builtin/test_bool.mojo b/stdlib/test/builtin/test_bool.mojo index 11d0a353dd..081a31bc38 100644 --- a/stdlib/test/builtin/test_bool.mojo +++ b/stdlib/test/builtin/test_bool.mojo @@ -29,8 +29,8 @@ def test_bool_cast_to_int(): def test_bool_none(): var test = None - assert_equal(bool(None), False) - assert_equal(bool(test), False) + assert_equal(Bool(None), False) + assert_equal(Bool(test), False) @value @@ -48,7 +48,7 @@ fn takes_bool(cond: Bool) -> Bool: def test_convert_from_implicitly_boolable(): assert_true(takes_bool(MyTrue())) - assert_true(bool(MyTrue())) + assert_true(Bool(MyTrue())) # def test_bool_to_string(): From ccdb14a83b55eaedeb008e2a8e710e2c206c6209 Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 16 Jan 2025 06:41:26 +0000 Subject: [PATCH 249/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011605 --- examples/life/magic.lock | 157 +- examples/magic.lock | 157 +- examples/notebooks/magic.lock | 157 +- examples/operators/magic.lock | 157 +- examples/testing/magic.lock | 7274 +++++++++++++++++++++++++++++++++ magic.lock | 157 +- 6 files changed, 7664 insertions(+), 395 deletions(-) create mode 100644 examples/testing/magic.lock diff --git a/examples/life/magic.lock b/examples/life/magic.lock index dd001a3484..5a1423aa1f 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - 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- max-core ==25.1.0.dev2025011505 release + - max-core ==25.1.0.dev2025011605 release - python 3.12.* - fastapi - httpx @@ -4879,12 +4878,12 @@ packages: - uvicorn - python_abi 3.12.* *_cp312 license: LicenseRef-Modular-Proprietary - size: 110626067 - timestamp: 1736918528491 -- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011505-release.conda + size: 110522116 + timestamp: 1737004945972 +- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011605-release.conda noarch: python - sha256: f1dab48a1791a810711d1c7e484ea1c5340297d09f9493cd99604978a3c43717 - md5: d4a97c4b5ee27717785df58abcef3fb3 + sha256: cfd9e14c0c14fa02b6b61141ff4f9007a11eb3ed1b11cf48a717f215e27c7407 + md5: d60436499404c582b03af554c788436f depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -4894,8 +4893,8 @@ packages: - platformdirs >=2 - python license: MIT - size: 130819 - timestamp: 1736918190341 + size: 130793 + timestamp: 1737005226119 - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda sha256: 78c1bbe1723449c52b7a9df1af2ee5f005209f67e40b6e1d3c7619127c43b1c7 md5: 592132998493b3ff25fd7479396e8351 @@ -4905,18 +4904,18 @@ packages: license_family: MIT size: 14465 timestamp: 1733255681319 -- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025011505-release.conda +- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025011605-release.conda noarch: python - sha256: 87baffbf376e8562b6bde06c50c57d907ec9a6f6f338f342bddfad4fa83403e4 - md5: c14f190fae020526fc6559130272f1f8 + sha256: 68dcf6fcc24f226462c4a7004a2214a1696f2ad0cb8d7ac229ee9ec4ea2216ea + md5: 864d0b85ff8fd743209bea757229d010 depends: - - max-core ==25.1.0.dev2025011505 release + - max-core ==25.1.0.dev2025011605 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22931 - timestamp: 1736918190342 + size: 22934 + timestamp: 1737005226120 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From 645bc849cf4bc1aca80f771924eaf513c80a8303 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 16 Jan 2025 00:32:51 -0500 Subject: [PATCH 250/509] [stdlib] Move `float` functions to `Float64` ctors And add deprecation warnings to the float functions, to be phased out to a compiler error in the release after next. MODULAR_ORIG_COMMIT_REV_ID: cef71b382ba4c50f5aa5f8d59a9698ba487f98d7 --- docs/manual/python/types.mdx | 7 ++--- examples/matmul.mojo | 4 +-- stdlib/src/builtin/bool.mojo | 2 +- stdlib/src/builtin/char.mojo | 2 +- stdlib/src/builtin/floatable.mojo | 30 ++++++++++--------- stdlib/src/builtin/int.mojo | 10 +++---- stdlib/src/builtin/simd.mojo | 30 ++++++++++++++++++- stdlib/src/builtin/str.mojo | 4 +-- stdlib/src/builtin/string_literal.mojo | 2 +- stdlib/src/builtin/uint.mojo | 2 +- stdlib/src/collections/string/format.mojo | 2 +- stdlib/src/python/python_object.mojo | 2 +- stdlib/test/bit/test_bit.mojo | 2 +- .../builtin/test_deprecation_warnings.mojo | 8 ++++- stdlib/test/builtin/test_int.mojo | 2 +- stdlib/test/builtin/test_simd.mojo | 6 ++-- stdlib/test/builtin/test_string_literal.mojo | 2 +- .../test/collections/string/test_string.mojo | 4 +-- 18 files changed, 78 insertions(+), 43 deletions(-) diff --git a/docs/manual/python/types.mdx b/docs/manual/python/types.mdx index 850efc8434..28ff8ffb6a 100644 --- a/docs/manual/python/types.mdx +++ b/docs/manual/python/types.mdx @@ -148,15 +148,14 @@ Currently `PythonObject` conforms to the [`Stringable`](/mojo/stdlib/builtin/str/Stringable), [`Boolable`](/mojo/stdlib/builtin/bool/Boolable), [`Intable`](/mojo/stdlib/builtin/int/Intable), and -[`Floatable`](/mojo/stdlib/builtin/floatable/Floatable/) traits, which allows -you to convert a `PythonObject` to the corresponding Mojo types. +[`Floatable`](/mojo/stdlib/builtin/floatable/Floatable/) traits. This allows you +to convert a `PythonObject` to the corresponding Mojo types. ```mojo var s = String(py_string) var b = Bool(py_bool) var i = Int(py_int) - -var f: Float64 = float(py_float) +var f = Float64(py_float) ``` PythonObject also implements the [`Writable`](/mojo/stdlib/utils/write/Writable) diff --git a/examples/matmul.mojo b/examples/matmul.mojo index 44638daf34..6843303a12 100644 --- a/examples/matmul.mojo +++ b/examples/matmul.mojo @@ -84,7 +84,7 @@ def run_matmul_python() -> Float64: var pymatmul: PythonObject = Python.import_module("pymatmul") var py = Python.import_module("builtins") - var gflops = float(pymatmul.benchmark_matmul_python(128, 128, 128)) + var gflops = Float64(pymatmul.benchmark_matmul_python(128, 128, 128)) py.print(py.str("{:<18}{:>8.3f} GFLOPS").format("Python:", gflops)) return gflops @@ -94,7 +94,7 @@ def run_matmul_numpy() -> Float64: var pymatmul: PythonObject = Python.import_module("pymatmul") var py = Python.import_module("builtins") - var gflops = float(pymatmul.benchmark_matmul_numpy(M, N, K)) + var gflops = Float64(pymatmul.benchmark_matmul_numpy(M, N, K)) py.print(py.str("{:<18}{:>8.3f} GFLOPS").format("Numpy:", gflops)) return gflops diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 1b6a4450f4..e83425f8e5 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -555,7 +555,7 @@ struct Bool( @deprecated( - "the `bool` function is deprecated, use the `Bool` constructor instead." + "the `bool` function is deprecated, use the `Bool` constructor instead" ) @always_inline fn bool[T: Boolable, //](value: T) -> Bool: diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 51e7769704..74d789a619 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -461,7 +461,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): return 1 var shift = 6 * (num_bytes - 1) - var mask = UInt8(0xFF) >> (num_bytes + 1) + var mask = UInt8(0xFF) >> UInt8(num_bytes + 1) var num_bytes_marker = UInt8(0xFF) << (8 - num_bytes) ptr[0] = ((c >> shift) & mask) | num_bytes_marker for i in range(1, num_bytes): diff --git a/stdlib/src/builtin/floatable.mojo b/stdlib/src/builtin/floatable.mojo index 1510173716..ed921aa2a7 100644 --- a/stdlib/src/builtin/floatable.mojo +++ b/stdlib/src/builtin/floatable.mojo @@ -18,10 +18,7 @@ These are Mojo built-ins, so you don't need to import them. trait Floatable: - """The `Floatable` trait describes a type that can be converted to a Float. - - Any type that conforms to `Floatable` works with the built-in `float` - function. + """The `Floatable` trait describes a type that can be converted to a Float64. This trait requires the type to implement the `__float__()` method. @@ -36,10 +33,10 @@ trait Floatable: return self.i ``` - A `Foo` can now be converted to a `Float64` using `float`: + A `Foo` can now be converted to a `Float64`: ```mojo - var f = float(Foo(5.5)) + var f = Float64(Foo(5.5)) ``` **Note:** If the `__float__()` method can raise an error, use @@ -58,10 +55,7 @@ trait Floatable: trait FloatableRaising: """The `FloatableRaising` trait describes a type that can be converted to a - Float, but the conversion might raise an error (e.g.: a string). - - Any type that conforms to `FloatableRaising` works with the built-in `float` - function. + Float64, but the conversion might raise an error (e.g.: a string). This trait requires the type to implement the `__float__()` method, which can raise an error. @@ -81,11 +75,11 @@ trait FloatableRaising: return self.value[Float64] ``` - A `MaybeFloat` can now be converted to `Float64` using `float`: + A `MaybeFloat` can now be converted to `Float64`: ```mojo try: - print(float(MaybeFloat(4.6))) + print(Float64(MaybeFloat(4.6))) except: print("error occured") ``` @@ -103,6 +97,10 @@ trait FloatableRaising: ... +# FIXME(25.2): Move float deprecation warnings to compiler errors +@deprecated( + "the `float` function is deprecated, use the `Float64` constructor instead" +) @always_inline fn float[T: Floatable](value: T, /) -> Float64: """Get the Float representation of the value. @@ -119,6 +117,9 @@ fn float[T: Floatable](value: T, /) -> Float64: return value.__float__() +@deprecated( + "the `float` function is deprecated, use the `Float64` constructor instead" +) @always_inline fn float[T: FloatableRaising](value: T, /) raises -> Float64: """Get the Float representation of the value. @@ -138,8 +139,9 @@ fn float[T: FloatableRaising](value: T, /) raises -> Float64: return value.__float__() -# TODO: Int can't conform to Floatable at the moment due to circular -# dependency with SIMD. +@deprecated( + "the `float` function is deprecated, use the `Float64` constructor instead" +) @always_inline fn float(value: Int, /) -> Float64: """Get the Float representation of the Int. diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 662ee68de0..367a5d507e 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -211,7 +211,7 @@ trait ImplicitlyIntable(Intable): # FIXME(25.2): Move `int` deprecation warnings to a compiler error @deprecated( - "the `int` function is deprecated, use the `Int` constructor instead." + "the `int` function is deprecated, use the `Int` constructor instead" ) @always_inline fn int[T: Intable](value: T) -> Int: @@ -230,7 +230,7 @@ fn int[T: Intable](value: T) -> Int: @deprecated( - "the `int` function is deprecated, use the `Int` constructor instead." + "the `int` function is deprecated, use the `Int` constructor instead" ) @always_inline fn int[T: IntableRaising](value: T) raises -> Int: @@ -252,7 +252,7 @@ fn int[T: IntableRaising](value: T) raises -> Int: @deprecated( - "the `int` function is deprecated, use the `Int` constructor instead." + "the `int` function is deprecated, use the `Int` constructor instead" ) fn int(value: StringSlice, base: Int = 10) raises -> Int: """Parses and returns the given string as an integer in the given base. @@ -293,7 +293,7 @@ fn int(value: StringSlice, base: Int = 10) raises -> Int: @deprecated( - "the `int` function is deprecated, use the `Int` constructor instead." + "the `int` function is deprecated, use the `Int` constructor instead" ) fn int(value: UInt) -> Int: """Get the Int representation of the value. @@ -635,7 +635,7 @@ struct Int( rhs: The value to divide on. Returns: - `float(self)/float(rhs)` value. + `Float64(self)/Float64(rhs)` value. """ return Float64(self) / Float64(rhs) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index c01370d2cb..0a9cf32afa 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -388,7 +388,6 @@ struct SIMD[type: DType, size: Int]( @implicit fn __init__(out self, value: __mlir_type.index): _simd_construction_checks[type, size]() - var t0 = __mlir_op.`pop.cast_from_builtin`[ _type = __mlir_type.`!pop.scalar` ](value) @@ -399,6 +398,35 @@ struct SIMD[type: DType, size: Int]( _type = __mlir_type[`!pop.simd<`, size.value, `, `, type.value, `>`] ](casted) + @always_inline + fn __init__[T: Floatable](out self: Scalar[DType.float64], value: T): + """Initialize a Float64 from a type conforming to Floatable. + + Parameters: + T: The Floatable type. + + Args: + value: The object to get the float point representation of. + """ + self = value.__float__() + + @always_inline + fn __init__[ + T: FloatableRaising + ](out self: Scalar[DType.float64], value: T) raises: + """Initialize a Float64 from a type conforming to FloatableRaising. + + Parameters: + T: The FloatableRaising type. + + Args: + value: The object to get the float point representation of. + + Raises: + If the type does not have a float point representation. + """ + self = value.__float__() + @always_inline("nodebug") @implicit fn __init__(out self, value: IntLiteral): diff --git a/stdlib/src/builtin/str.mojo b/stdlib/src/builtin/str.mojo index f32bf25ee1..63ca502625 100644 --- a/stdlib/src/builtin/str.mojo +++ b/stdlib/src/builtin/str.mojo @@ -132,7 +132,7 @@ trait StringableRaising: # FIXME(25.2): Move `str` deprecation warnings to a compiler error @deprecated( - "the `str` function is deprecated, use the `String` constructor instead." + "the `str` function is deprecated, use the `String` constructor instead" ) @no_inline fn str[T: Stringable](value: T) -> String: @@ -152,7 +152,7 @@ fn str[T: Stringable](value: T) -> String: # FIXME(25.2): Move `str` deprecation warnings to a compiler error @deprecated( - "the `str` function is deprecated, use the `String` constructor instead." + "the `str` function is deprecated, use the `String` constructor instead" ) @no_inline fn str[T: StringableRaising](value: T) raises -> String: diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index deaf26f17f..65bbdff186 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -373,7 +373,7 @@ struct StringLiteral( Returns: A float value that represents the string, or otherwise raises. """ - return float(self.as_string_slice()) + return Float64(self.as_string_slice()) @no_inline fn __str__(self) -> String: diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index 81c04d48e9..3b2a7218da 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -263,7 +263,7 @@ struct UInt(Indexer, _HashableWithHasher): rhs: The value to divide on. Returns: - `float(self)/float(rhs)` value. + `Float64(self)/Float64(rhs)` value. """ return Float64(self) / Float64(rhs) diff --git a/stdlib/src/collections/string/format.mojo b/stdlib/src/collections/string/format.mojo index 425dfba341..34387e2856 100644 --- a/stdlib/src/collections/string/format.mojo +++ b/stdlib/src/collections/string/format.mojo @@ -530,7 +530,7 @@ struct _FormatSpec: In addition to the above presentation types, integers can be formatted with the floating-point presentation types listed below (except 'n' and None). - When doing so, float() is used to convert the integer to a floating-point + When doing so, Float64() is used to convert the integer to a floating-point number before formatting. The available presentation types for float and Decimal values are: diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index ccd039679b..86081cd0d0 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -1492,7 +1492,7 @@ struct PythonObject( cpython = _get_global_python_itf().cpython() return cpython.PyFloat_AsDouble(self.py_object) - @deprecated("Use `float(obj)` instead.") + @deprecated("Use `Float64(obj)` instead.") fn to_float64(self) -> Float64: """Returns a float representation of the object. diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index 246b3d193c..ff6e99752f 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -506,7 +506,7 @@ def test_rotate_bits_simd(): fn _log2_floor(n: Int) -> Int: - return Int(floor(log2(float(n)))) + return Int(floor(log2(Float64(n)))) def test_log2_floor(): diff --git a/stdlib/test/builtin/test_deprecation_warnings.mojo b/stdlib/test/builtin/test_deprecation_warnings.mojo index d7a36cdecb..46499a0d89 100644 --- a/stdlib/test/builtin/test_deprecation_warnings.mojo +++ b/stdlib/test/builtin/test_deprecation_warnings.mojo @@ -14,6 +14,12 @@ fn main(): - # FIXME(25.1): Move `int` deprecation warnings to a compiler error + # FIXME(25.2): Move deprecation warnings to compiler errors # CHECK-STDERR: warning: the `int` function is deprecated, use the `Int` constructor instead _ = int(42) + # CHECK-STDERR: warning: the `str` function is deprecated, use the `String` constructor instead + _ = str(0) + # CHECK-STDERR: warning: the `bool` function is deprecated, use the `Bool` constructor instead + _ = bool(False) + # CHECK-STDERR: warning: the `float` function is deprecated, use the `Float64` constructor instead + _ = float(42.4) diff --git a/stdlib/test/builtin/test_int.mojo b/stdlib/test/builtin/test_int.mojo index ad487d7241..d2d0149413 100644 --- a/stdlib/test/builtin/test_int.mojo +++ b/stdlib/test/builtin/test_int.mojo @@ -230,7 +230,7 @@ def test_comparison(): def test_float_conversion(): - assert_equal(float(Int(45)), Float64(45)) + assert_equal(Float64(Int(45)), Float64(45)) def test_conversion_from_python(): diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index 3d83ed1bec..eb92b4f886 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -1850,9 +1850,9 @@ def test_comparison(): def test_float_conversion(): - assert_almost_equal(float(Int32(45)), 45.0) - assert_almost_equal(float(Float32(34.32)), 34.32) - assert_almost_equal(float(UInt64(36)), 36.0) + assert_almost_equal(Float64(Int32(45)), 45.0) + assert_almost_equal(Float64(Float32(34.32)), 34.32) + assert_almost_equal(Float64(UInt64(36)), 36.0) def test_reversed(): diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index fb86aecebb..24193369e0 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -481,7 +481,7 @@ def test_splitlines(): def test_float_conversion(): assert_equal(("4.5").__float__(), 4.5) - assert_equal(float("4.5"), 4.5) + assert_equal(Float64("4.5"), 4.5) with assert_raises(): _ = ("not a float").__float__() diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index f3c2cf034f..3a7b1b86d9 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -1437,9 +1437,9 @@ def test_float_conversion(): # This is basically just a wrapper around atof which is # more throughouly tested above assert_equal(String("4.5").__float__(), 4.5) - assert_equal(float(String("4.5")), 4.5) + assert_equal(Float64(String("4.5")), 4.5) with assert_raises(): - _ = float(String("not a float")) + _ = Float64(String("not a float")) def test_slice_contains(): From cad1b0157ae8bc83ef7cd0965b11700c6c3c51a4 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 16 Jan 2025 01:32:25 -0500 Subject: [PATCH 251/509] [stdlib] Update changelog for str, int, bool, float deprecation Add a table explaining the deprecations. MODULAR_ORIG_COMMIT_REV_ID: 73efc329dbf3fe8838f4b3be3bbdb903a2be14d2 --- docs/changelog.md | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 0dde1eb064..85509a43ad 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -71,7 +71,7 @@ what we publish. var vals: List[Int] fn __init__(out self): - self.vals = List[Int](0, 1, 2, 3, 4) + self.vals = List[Int](0, 1, 2) fn __getitem__(self, idx: Int) -> Int: return self.vals[idx] @@ -100,12 +100,23 @@ what we publish. ### Standard library changes -- These functions have been deprecated: `int`, `str` in favor of constructors: - `Int`, `String`. The functions were a temporary workaround for when Mojo - didn't have a way to distinguish between implicit and explicit constructors. - You can do a search and replace e.g. from `int(` to `Int(` to update your - programs. This release will show a deprecation warning for the old functions, - and they'll be removed in the next release. +- The free floating functions for constructing different types have been + deprecated for actual constructors: + + ```plaintext + before after + ------------------ + int() Int() + str() String() + bool() Bool() + float() Float64() + ``` + + These functions were a workaround before Mojo had a way to distinguish between + implicit and explicit constructors. For this release you'll get a deprecation + warning, and in the next release they'll become compiler errors. You can + quickly update your code by doing a `Match Case` and `Match Whole Word` + search and replace for `int(` to `Int(` etc. - `UnsafePointer`'s `bitcast` method has now been split into `bitcast` for changing the type, `origin_cast` for changing mutability, From 70985395fc6d41b05a6e096ead521fdd29663057 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 16 Jan 2025 08:07:34 -0800 Subject: [PATCH 252/509] [stdlib] Clean up linked list implementation Removed unnecessary assignments to `prev` and `next` pointers during the destruction of linked list elements. Added a check to reset the `_head` pointer when the list becomes empty after an element is removed. This simplifies the code and improves clarity. NFC. MODULAR_ORIG_COMMIT_REV_ID: 7eaaae04d8b5997bbafa012ea292ca25a8d8b108 --- stdlib/src/collections/linked_list.mojo | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index 9591374a33..c21becfb5f 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -157,8 +157,6 @@ struct LinkedList[ElementType: WritableCollectionElement]: var curr = self._head while curr: var next = curr[].next - curr[].prev = __type_of(self._head)() - curr[].next = __type_of(self._tail)() curr.destroy_pointee() curr.free() curr = next @@ -217,6 +215,8 @@ struct LinkedList[ElementType: WritableCollectionElement]: var value = elem[].value self._tail = elem[].prev self._size -= 1 + if self._size == 0: + self._head = __type_of(self._head)() return value^ fn copy(self) -> Self: From 5e0ed4a96d9f89e4fd053f54dc8e2686087eb7a9 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 16 Jan 2025 11:23:56 -0500 Subject: [PATCH 253/509] [stdlib] Buffer `String.write(*args)` to the stack Remove the movable requirement from the writer on _WriteBufferStack and take a reference instead, `Sting` couldn't be moved in as the `writer` and then moved back out. This allows buffering when writing args to the `String.write` static method. MODULAR_ORIG_COMMIT_REV_ID: f1b8cadfa9773247fde00ab10eadf4959ac39302 --- stdlib/src/collections/string/string.mojo | 13 ++++++------ stdlib/src/utils/write.mojo | 26 ++++++++++------------- 2 files changed, 18 insertions(+), 21 deletions(-) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 5293633eb6..f8cfefa75f 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -19,6 +19,7 @@ from os import abort from sys import bitwidthof, llvm_intrinsic from sys.ffi import c_char from sys.intrinsics import _type_is_eq +from utils.write import write_buffered from bit import count_leading_zeros from memory import UnsafePointer, memcmp, memcpy, Span @@ -742,9 +743,9 @@ struct String( ``` . """ - var output = String() - write_args(output, args, sep=sep, end=end) - return output^ + var string = String() + write_buffered[buffer_size=4096](string, args, sep=sep, end=end) + return string^ @staticmethod @no_inline @@ -784,9 +785,9 @@ struct String( ``` . """ - var output = String() - write_args(output, args, sep=sep, end=end) - return output^ + var string = String() + write_buffered[buffer_size=4096](string, args, sep=sep, end=end) + return string^ @staticmethod @always_inline diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index a46ecefbee..71d2542120 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -217,12 +217,6 @@ fn write_args[ end.write_to(writer) -trait MovableWriter(Movable, Writer): - """Allows moving a Writer into a buffer.""" - - ... - - struct _WriteBufferHeap(Writer): var data: UnsafePointer[UInt8] var pos: Int @@ -272,19 +266,21 @@ struct _ArgBytes(Writer): args.each[write_arg]() -struct _WriteBufferStack[W: MovableWriter, //, capacity: Int](Writer): +struct _WriteBufferStack[origin: MutableOrigin, W: Writer, //, capacity: Int]( + Writer +): var data: InlineArray[UInt8, capacity] var pos: Int - var writer: W + var writer: Pointer[W, origin] @implicit - fn __init__(out self, owned writer: W): + fn __init__(out self, ref [origin]writer: W): self.data = InlineArray[UInt8, capacity](unsafe_uninitialized=True) self.pos = 0 - self.writer = writer^ + self.writer = Pointer.address_of(writer) fn flush(mut self): - self.writer.write_bytes( + self.writer[].write_bytes( Span[Byte, ImmutableAnyOrigin]( ptr=self.data.unsafe_ptr(), length=self.pos ) @@ -299,7 +295,7 @@ struct _WriteBufferStack[W: MovableWriter, //, capacity: Int](Writer): # If span is too large to fit in buffer, write directly and return if len_bytes > capacity: self.flush() - self.writer.write_bytes(bytes) + self.writer[].write_bytes(bytes) return # If buffer would overflow, flush writer and reset pos to 0. if self.pos + len_bytes > capacity: @@ -317,11 +313,11 @@ struct _WriteBufferStack[W: MovableWriter, //, capacity: Int](Writer): fn write_buffered[ - W: MovableWriter, //, + W: Writer, //, *Ts: Writable, buffer_size: Int, ]( - owned writer: W, + mut writer: W, args: VariadicPack[_, Writable, *Ts], *, sep: StaticString = "", @@ -379,6 +375,6 @@ fn write_buffered[ Span[Byte, ImmutableAnyOrigin](ptr=buffer.data, length=buffer.pos) ) else: - var buffer = _WriteBufferStack[buffer_size](writer^) + var buffer = _WriteBufferStack[buffer_size](writer) write_args(buffer, args, sep=sep, end=end) buffer.flush() From 6598d90451ed596c091b9485a7c388f24109c7e1 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 16 Jan 2025 14:33:47 -0600 Subject: [PATCH 254/509] [stdlib] feat: Add `String.chars()` and `.char_slices()` + deprecate `__iter__()` These methods are trivial wrappers around the existing `StringSlice` iterator methods. This also marks `String.__iter__()` as deprecated. Users should move to either `.chars()` or `.char_slices()` to be explicit about what kind of iterator element their use-case requires. MODULAR_ORIG_COMMIT_REV_ID: 43246f89bc456b776fa862c483823e81e233ec03 --- docs/changelog.md | 24 ++++++ stdlib/src/collections/string/__init__.mojo | 2 +- stdlib/src/collections/string/string.mojo | 86 +++++++++++++++++-- .../test/collections/string/test_string.mojo | 31 +++++-- 4 files changed, 128 insertions(+), 15 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 85509a43ad..50d62e1dde 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -212,6 +212,30 @@ what we publish. - `b16encode()` - `b16decode()` +- Added new `String.chars()` and `String.char_slices()` iterator methods, and + deprecated the existing `String.__iter__()` method. + + Different use-cases may prefer iterating over the `Char`s encoded in a string, + or iterating over subslices containing single characters. Neither iteration + semantics is an obvious default, so the existing `__iter__()` method has been + deprecated in favor of writing explicit iteration methods for the time being. + + Code of the form: + + ```mojo + var s: String = ... + for c in s: + # ... + ``` + + can be migrated to using the `.char_slices()` method: + + ```mojo + var s: String = ... + for c in s.char_slices(): + # ... + ``` + - Various functionality has moved from `String` and `StringRef` to the more general `StringSlice` type. diff --git a/stdlib/src/collections/string/__init__.mojo b/stdlib/src/collections/string/__init__.mojo index 0b7a981d4c..cfa4894938 100644 --- a/stdlib/src/collections/string/__init__.mojo +++ b/stdlib/src/collections/string/__init__.mojo @@ -20,4 +20,4 @@ from .string import ( chr, ord, ) -from .string_slice import StringSlice, StaticString +from .string_slice import StringSlice, StaticString, CharsIter diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index f8cfefa75f..58727d8927 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -13,6 +13,7 @@ """Implements basic object methods for working with strings.""" from collections import KeyElement, List, Optional +from collections.string import CharsIter from collections._index_normalization import normalize_index from hashlib._hasher import _HashableWithHasher, _Hasher from os import abort @@ -1030,15 +1031,14 @@ struct String( """ self._iadd[False](other.as_bytes()) + @deprecated("Use `str.chars()` or `str.char_slices()` instead.") fn __iter__(self) -> _StringSliceIter[__origin_of(self)]: """Iterate over the string, returning immutable references. Returns: An iterator of references to the string elements. """ - return _StringSliceIter[__origin_of(self)]( - ptr=self.unsafe_ptr(), length=self.byte_length() - ) + return self.char_slices() fn __reversed__(self) -> _StringSliceIter[__origin_of(self), False]: """Iterate backwards over the string, returning immutable references. @@ -1252,6 +1252,76 @@ struct String( buf.append(0) return String(buf^) + @always_inline + fn chars(self) -> CharsIter[__origin_of(self)]: + """Returns an iterator over the `Char`s encoded in this string slice. + + Returns: + An iterator type that returns successive `Char` values stored in + this string slice. + + # Examples + + Print the characters in a string: + + ```mojo + from testing import assert_equal + + var s = String("abc") + var iter = s.chars() + assert_equal(iter.__next__(), Char.ord("a")) + assert_equal(iter.__next__(), Char.ord("b")) + assert_equal(iter.__next__(), Char.ord("c")) + assert_equal(iter.__has_next__(), False) + ``` + + `chars()` iterates over Unicode codepoints, and supports multibyte + codepoints: + + ```mojo + from testing import assert_equal + + # A visual character composed of a combining sequence of 2 codepoints. + var s = String("á") + assert_equal(s.byte_length(), 3) + + var iter = s.chars() + assert_equal(iter.__next__(), Char.ord("a")) + # U+0301 Combining Acute Accent + assert_equal(iter.__next__().to_u32(), 0x0301) + assert_equal(iter.__has_next__(), False) + ``` + . + """ + return self.as_string_slice().chars() + + fn char_slices(self) -> _StringSliceIter[__origin_of(self)]: + """Returns an iterator over single-character slices of this string. + + Each returned slice points to a single Unicode codepoint encoded in the + underlying UTF-8 representation of this string. + + Returns: + An iterator of references to the string elements. + + # Examples + + Iterate over the character slices in a string: + + ```mojo + from testing import assert_equal, assert_true + + var s = String("abc") + var iter = s.char_slices() + assert_true(iter.__next__() == "a") + assert_true(iter.__next__() == "b") + assert_true(iter.__next__() == "c") + assert_equal(iter.__has_next__(), False) + ``` + . + """ + return self.as_string_slice().char_slices() + fn unsafe_ptr( ref self, ) -> UnsafePointer[ @@ -1494,7 +1564,7 @@ struct String( while lhs <= str_byte_len: # Python adds all "whitespace chars" as one separator # if no separator was specified - for s in self[lhs:]: + for s in self[lhs:].char_slices(): if not s.isspace(): break lhs += s.byte_length() @@ -1508,7 +1578,9 @@ struct String( output.append(self[str_byte_len]) break rhs = lhs + num_bytes(self.unsafe_ptr()[lhs]) - for s in self[lhs + num_bytes(self.unsafe_ptr()[lhs]) :]: + for s in self[ + lhs + num_bytes(self.unsafe_ptr()[lhs]) : + ].char_slices(): if s.isspace(): break rhs += s.byte_length() @@ -1863,7 +1935,7 @@ struct String( """ if not self: return False - for char in self.as_string_slice().chars(): + for char in self.chars(): if not char.is_ascii_digit(): return False return True @@ -1896,7 +1968,7 @@ struct String( Returns: True if all characters are printable else False. """ - for char in self.as_string_slice().chars(): + for char in self.chars(): if not char.is_ascii_printable(): return False return True diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 3a7b1b86d9..945406f046 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -1115,13 +1115,29 @@ def test_indexing(): assert_equal(a[2], "c") -def test_string_iter(): +def test_string_chars_iter(): + var s = String("abc") + var iter = s.chars() + assert_equal(iter.__next__(), Char.ord("a")) + assert_equal(iter.__next__(), Char.ord("b")) + assert_equal(iter.__next__(), Char.ord("c")) + assert_equal(iter.__has_next__(), False) + + +def test_string_char_slices_iter(): + var s0 = String("abc") + var s0_iter = s0.char_slices() + assert_true(s0_iter.__next__() == "a") + assert_true(s0_iter.__next__() == "b") + assert_true(s0_iter.__next__() == "c") + assert_equal(s0_iter.__has_next__(), False) + var vs = String("123") # Borrow immutably fn conc(vs: String) -> String: var c = String("") - for v in vs: + for v in vs.char_slices(): c += v return c @@ -1132,18 +1148,18 @@ def test_string_iter(): concat += v assert_equal(321, atol(concat)) - for v in vs: + for v in vs.char_slices(): v.unsafe_ptr().origin_cast[mut=True]()[] = ord("1") # Borrow immutably - for v in vs: + for v in vs.char_slices(): concat += v assert_equal(321111, atol(concat)) var idx = -1 vs = String("mojo🔥") - var iterator = vs.__iter__() + var iterator = vs.char_slices() assert_equal(5, len(iterator)) var item = iterator.__next__() assert_equal(String("m"), String(item)) @@ -1193,7 +1209,7 @@ def test_string_iter(): var ptr = item.unsafe_ptr() var amnt_characters = 0 var byte_idx = 0 - for v in item: + for v in item.char_slices(): var byte_len = v.byte_length() for i in range(byte_len): assert_equal(ptr[byte_idx + i], v.unsafe_ptr()[i]) @@ -1496,7 +1512,8 @@ def main(): test_intable() test_string_mul() test_indexing() - test_string_iter() + test_string_chars_iter() + test_string_char_slices_iter() test_format_args() test_format_conversion_flags() test_isdigit() From 1d41443071a91e46a1ce206eaadefb0c4ac3920c Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 16 Jan 2025 12:35:36 -0800 Subject: [PATCH 255/509] [Stdlib] Optimize accesses based on requested position, NFC This performs a micro optimization to pick whether to traverse from the head or the tail based on the requested index. Fixes a bug in the reverse function which is uncovered here. MODULAR_ORIG_COMMIT_REV_ID: a09acb219346957cd915682a9b6794d241b50d44 --- stdlib/src/collections/linked_list.mojo | 45 ++++++++++++++++++------- 1 file changed, 32 insertions(+), 13 deletions(-) diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index c21becfb5f..515b6404a0 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -202,8 +202,8 @@ struct LinkedList[ElementType: WritableCollectionElement]: curr[].next = prev prev = curr curr = next + self._tail = self._head self._head = prev - self._tail = self._head[].prev fn pop(mut self) -> ElementType: """Remove and return the first element of the list. @@ -232,6 +232,33 @@ struct LinkedList[ElementType: WritableCollectionElement]: curr = curr[].next return new^ + fn _get_node_ptr(ref self, index: Int) -> UnsafePointer[Node[ElementType]]: + """Get a pointer to the node at the specified index. + + This method optimizes traversal by starting from either the head or tail + depending on which is closer to the target index. + + Args: + index: The index of the node to get. + + Returns: + A pointer to the node at the specified index. + """ + var l = len(self) + var i = normalize_index[container_name="LinkedList"](index, self) + debug_assert(0 <= i < l, "index out of bounds") + var mid = l // 2 + if i <= mid: + var curr = self._head + for _ in range(i): + curr = curr[].next + return curr + else: + var curr = self._tail + for _ in range(l - i - 1): + curr = curr[].prev + return curr + fn __getitem__(ref self, index: Int) -> ref [self] ElementType: """Get the element at the specified index. @@ -241,12 +268,8 @@ struct LinkedList[ElementType: WritableCollectionElement]: Returns: The element at the specified index. """ - var curr = self._head - for _ in range( - normalize_index[container_name="LinkedList"](index, self) - ): - curr = curr[].next - return curr[].value + debug_assert(len(self) > 0, "unable to get item from empty list") + return self._get_node_ptr(index)[].value fn __setitem__(mut self, index: Int, owned value: ElementType): """Set the element at the specified index. @@ -255,12 +278,8 @@ struct LinkedList[ElementType: WritableCollectionElement]: index: The index of the element to set. value: The new value to set. """ - var curr = self._head - for _ in range( - normalize_index[container_name="LinkedList"](index, self) - ): - curr = curr[].next - curr[].value = value^ + debug_assert(len(self) > 0, "unable to set item from empty list") + self._get_node_ptr(index)[].value = value^ fn __len__(self) -> Int: """Get the number of elements in the list. From e9ce5dcf0ec242ac870c7b7ee82ea007cf135a6c Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Thu, 16 Jan 2025 15:35:42 -0600 Subject: [PATCH 256/509] [stdlib] polish: Change string `__len__()` methods to return length in bytes MODULAR_ORIG_COMMIT_REV_ID: 7f87f1d40ee48279b20abac457d7bf3d2b326e5d --- docs/changelog.md | 13 +++ stdlib/src/collections/string/string.mojo | 46 ++++++++--- .../src/collections/string/string_slice.mojo | 81 ++++++++++++++++++- .../test/collections/string/test_string.mojo | 15 ++++ .../collections/string/test_string_slice.mojo | 36 +++++---- 5 files changed, 162 insertions(+), 29 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 50d62e1dde..5c87c6b054 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -236,6 +236,19 @@ what we publish. # ... ``` +- The `String.__len__()` and `StringSlice.__len__()` methods now return the + length of the string in bytes. + + Previously, these methods were documented to note that they would eventually + return a length in Unicode codepoints. They have been changed to guarantee + a length in bytes, since the length in bytes is how they are most often used + today (for example, as bounds to low-level memory manipulation logic). + Additionally, length in codepoints is a more specialized notion of string + length that is rarely the correct metric. + + Users that know they need the length in codepoints can use the + `str.char_length()` method, or `len(str.chars())`. + - Various functionality has moved from `String` and `StringRef` to the more general `StringSlice` type. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 58727d8927..bd6340cbf5 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -1063,22 +1063,46 @@ struct String( """ return self.byte_length() > 0 + @always_inline fn __len__(self) -> Int: - """Gets the string length, in bytes (for now) PREFER: - String.byte_length(), a future version will make this method return - Unicode codepoints. + """Get the string length of in bytes. + + This function returns the number of bytes in the underlying UTF-8 + representation of the string. + + To get the number of Unicode codepoints in a string, use + `len(str.chars())`. Returns: - The string length, in bytes (for now). - """ - var unicode_length = self.byte_length() + The string length in bytes. + + # Examples - # TODO: everything uses this method assuming it's byte length - # for i in range(unicode_length): - # if _utf8_byte_type(self._buffer[i]) == 1: - # unicode_length -= 1 + Query the length of a string, in bytes and Unicode codepoints: - return unicode_length + ```mojo + from testing import assert_equal + + var s = String("ನಮಸ್ಕಾರ") + + assert_equal(len(s), 21) + assert_equal(len(s.chars()), 7) + ``` + + Strings containing only ASCII characters have the same byte and + Unicode codepoint length: + + ```mojo + from testing import assert_equal + + var s = String("abc") + + assert_equal(len(s), 3) + assert_equal(len(s.chars()), 3) + ``` + . + """ + return self.byte_length() @always_inline fn __str__(self) -> String: diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index c6425bf838..5f0bf7ec7b 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -547,13 +547,48 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( else: return "'" + result + "'" + @always_inline fn __len__(self) -> Int: - """Nominally returns the _length in Unicode codepoints_ (not bytes!). + """Get the string length in bytes. + + This function returns the number of bytes in the underlying UTF-8 + representation of the string. + + To get the number of Unicode codepoints in a string, use + `len(str.chars())`. Returns: - The length in Unicode codepoints. + The string length in bytes. + + # Examples + + Query the length of a string, in bytes and Unicode codepoints: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + var s = StringSlice("ನಮಸ್ಕಾರ") + + assert_equal(len(s), 21) + assert_equal(len(s.chars()), 7) + ``` + + Strings containing only ASCII characters have the same byte and + Unicode codepoint length: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + var s = StringSlice("abc") + + assert_equal(len(s), 3) + assert_equal(len(s.chars()), 3) + ``` + . """ - return self.char_length() + return self.byte_length() fn write_to[W: Writer](self, mut writer: W): """Formats this string slice to the provided `Writer`. @@ -1012,6 +1047,46 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: The length in Unicode codepoints. + + # Examples + + Query the length of a string, in bytes and Unicode codepoints: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + var s = StringSlice("ನಮಸ್ಕಾರ") + + assert_equal(s.char_length(), 7) + assert_equal(len(s), 21) + ``` + + Strings containing only ASCII characters have the same byte and + Unicode codepoint length: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + var s = StringSlice("abc") + + assert_equal(s.char_length(), 3) + assert_equal(len(s), 3) + ``` + + The character length of a string with visual combining characters is + the length in Unicode codepoints, not grapheme clusters: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal + + var s = StringSlice("á") + assert_equal(s.char_length(), 2) + assert_equal(s.byte_length(), 3) + ``` + . """ # Every codepoint is encoded as one leading byte + 0 to 3 continuation # bytes. diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 945406f046..fd070192d4 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -89,6 +89,20 @@ def test_copy(): assert_equal("fine", s1) +def test_len(): + # String length is in bytes, not codepoints. + var s0 = String("ನಮಸ್ಕಾರ") + + assert_equal(len(s0), 21) + assert_equal(len(s0.chars()), 7) + + # For ASCII string, the byte and codepoint length are the same: + var s1 = String("abc") + + assert_equal(len(s1), 3) + assert_equal(len(s1.chars()), 3) + + def test_equality_operators(): var s0 = String("abc") var s1 = String("def") @@ -1475,6 +1489,7 @@ def test_reserve(): def main(): test_constructors() test_copy() + test_len() test_equality_operators() test_comparison_operators() test_add() diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 105c88c159..166bbbf297 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -151,23 +151,22 @@ fn test_heap_string_from_string_slice() raises: fn test_slice_len() raises: - alias str1: StringLiteral = "12345" - alias str2: StringLiteral = "1234" - alias str3: StringLiteral = "123" - alias str4: StringLiteral = "12" - alias str5: StringLiteral = "1" + assert_equal(5, len(StringSlice("12345"))) + assert_equal(4, len(StringSlice("1234"))) + assert_equal(3, len(StringSlice("123"))) + assert_equal(2, len(StringSlice("12"))) + assert_equal(1, len(StringSlice("1"))) + assert_equal(0, len(StringSlice(""))) - alias slice1 = str1.as_string_slice() - alias slice2 = str2.as_string_slice() - alias slice3 = str3.as_string_slice() - alias slice4 = str4.as_string_slice() - alias slice5 = str5.as_string_slice() + # String length is in bytes, not codepoints. + var s0 = String("ನಮಸ್ಕಾರ") + assert_equal(len(s0), 21) + assert_equal(len(s0.chars()), 7) - assert_equal(5, len(slice1)) - assert_equal(4, len(slice2)) - assert_equal(3, len(slice3)) - assert_equal(2, len(slice4)) - assert_equal(1, len(slice5)) + # For ASCII string, the byte and codepoint length are the same: + var s1 = String("abc") + assert_equal(len(s1), 3) + assert_equal(len(s1.chars()), 3) fn test_slice_char_length() raises: @@ -189,6 +188,13 @@ fn test_slice_char_length() raises: assert_equal(s3.byte_length(), 37) assert_equal(s3.char_length(), 19) + # Character length is codepoints, not graphemes + # This is thumbs up + a skin tone modifier codepoint. + var s4 = StringSlice("👍🏻") + assert_equal(s4.byte_length(), 8) + assert_equal(s4.char_length(), 2) + # TODO: assert_equal(s4.grapheme_count(), 1) + fn test_slice_eq() raises: var str1: String = "12345" From 7269824740efa77863bd979cc9f9c5e912fcf2ed Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 16 Jan 2025 17:15:35 -0500 Subject: [PATCH 257/509] [stdlib] Add default buffer size of 4096 to `write_buffered` Sane default for `write_buffered`. MODULAR_ORIG_COMMIT_REV_ID: e74b4e7b76dfd21c13c93ab4c29b9b9e8ca70c45 --- stdlib/src/builtin/file.mojo | 2 +- stdlib/src/collections/string/string.mojo | 4 ++-- stdlib/src/tempfile/tempfile.mojo | 2 +- stdlib/src/utils/write.mojo | 17 +++++++++++------ 4 files changed, 15 insertions(+), 10 deletions(-) diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index 4aa4784f1b..7851c20868 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -424,7 +424,7 @@ struct FileHandle: args: Sequence of arguments to write to this Writer. """ var file = FileDescriptor(self._get_raw_fd()) - write_buffered[buffer_size=4096](file, args) + write_buffered(file, args) fn _write[ address_space: AddressSpace diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index bd6340cbf5..b5b28d7b79 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -745,7 +745,7 @@ struct String( . """ var string = String() - write_buffered[buffer_size=4096](string, args, sep=sep, end=end) + write_buffered(string, args, sep=sep, end=end) return string^ @staticmethod @@ -787,7 +787,7 @@ struct String( . """ var string = String() - write_buffered[buffer_size=4096](string, args, sep=sep, end=end) + write_buffered(string, args, sep=sep, end=end) return string^ @staticmethod diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index a493c02230..02249d4cfd 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -415,7 +415,7 @@ struct NamedTemporaryFile: args: Sequence of arguments to write to this Writer. """ var file = FileDescriptor(self._file_handle._get_raw_fd()) - write_buffered[buffer_size=4096](file, args) + write_buffered(file, args) @always_inline fn write_bytes(mut self, bytes: Span[Byte, _]): diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index 71d2542120..a022102d36 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -315,7 +315,7 @@ struct _WriteBufferStack[origin: MutableOrigin, W: Writer, //, capacity: Int]( fn write_buffered[ W: Writer, //, *Ts: Writable, - buffer_size: Int, + buffer_size: Int = 4096, ]( mut writer: W, args: VariadicPack[_, Writable, *Ts], @@ -328,11 +328,11 @@ fn write_buffered[ the buffer would overflow it writes to the `writer` passed in. You can also add seperators between the args, and end characters. - Parameters: W: The type of the `Writer` to write to. Ts: The types of each arg to write. Each type must satisfy `Writable`. - buffer_size: How many bytes to write to a buffer before writing out. + buffer_size: How many bytes to write to a buffer before writing out to + the `writer` (default `4096`). Args: writer: The `Writer` to write to. @@ -349,8 +349,13 @@ fn write_buffered[ fn print_err_buffered[*Ts: Writable]( *args: *Ts, sep: StringLiteral, end: StringLiteral ): - var stdout = sys.stderr - write_buffered[buffer_size=4096](stdout, args, sep=sep, end=end) + var stderr = sys.stderr + write_buffered(stdout, args, sep=sep, end=end) + + # Buffer before allocating a string + var string = String() + write_buffered(string, args, sep=sep, end=end) + print_err_buffered(3, "total", "args", sep=",", end="[end]") ``` @@ -363,11 +368,11 @@ fn write_buffered[ @parameter if is_nvidia_gpu(): - # Stack space is very small on GPU due to many threads, so use heap # Count the total length of bytes to allocate only once var arg_bytes = _ArgBytes() write_args(arg_bytes, args, sep=sep, end=end) + # Stack space is very small on GPU due to many threads, so use heap var buffer = _WriteBufferHeap(arg_bytes.size + 1) write_args(buffer, args, sep=sep, end=end) buffer.data[buffer.pos] = 0 From 168cde948b127b685568c66cf8c8f123cfb12909 Mon Sep 17 00:00:00 2001 From: bgreni <42788181+bgreni@users.noreply.github.com> Date: Thu, 16 Jan 2025 14:48:25 -0800 Subject: [PATCH 258/509] [External] [stdlib] Use out param for __next__() in more iterators (#54072) [External] [stdlib] Use out param for __next__() in more iterators Apply the change from https://github.com/modularml/mojo/pull/3941 to other iterators where it applies. Co-authored-by: bgreni <42788181+bgreni@users.noreply.github.com> Closes modularml/mojo#3952 MODULAR_ORIG_COMMIT_REV_ID: 9d96b4c3da86b8a5f4e990a6029dddc8fc7bc84d --- stdlib/src/collections/deque.mojo | 6 +++--- stdlib/src/collections/inline_list.mojo | 8 +++----- stdlib/src/memory/span.mojo | 8 +++----- 3 files changed, 9 insertions(+), 13 deletions(-) diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index a0b8bfa587..f563976b2b 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -1016,14 +1016,14 @@ struct _DequeIter[ fn __iter__(self) -> Self: return self - fn __next__(mut self) -> Pointer[ElementType, deque_lifetime]: + fn __next__(mut self, out p: Pointer[ElementType, deque_lifetime]): @parameter if forward: + p = Pointer.address_of(self.src[][self.index]) self.index += 1 - return Pointer.address_of(self.src[][self.index - 1]) else: self.index -= 1 - return Pointer.address_of(self.src[][self.index]) + p = Pointer.address_of(self.src[][self.index]) fn __len__(self) -> Int: @parameter diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index 2d7b16d428..0ff105a6c1 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -53,16 +53,14 @@ struct _InlineListIter[ fn __iter__(self) -> Self: return self - fn __next__( - mut self, - ) -> Pointer[T, __origin_of(self.src[][0])]: + fn __next__(mut self, out p: Pointer[T, __origin_of(self.src[][0])]): @parameter if forward: + p = Pointer.address_of(self.src[][self.index]) self.index += 1 - return Pointer.address_of(self.src[][self.index - 1]) else: self.index -= 1 - return Pointer.address_of(self.src[][self.index]) + p = Pointer.address_of(self.src[][self.index]) @always_inline fn __has_next__(self) -> Bool: diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index af08b48728..6657aba8b4 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -68,16 +68,14 @@ struct _SpanIter[ return self @always_inline - fn __next__( - mut self, - ) -> Pointer[T, origin]: + fn __next__(mut self, out p: Pointer[T, origin]): @parameter if forward: + p = Pointer.address_of(self.src[self.index]) self.index += 1 - return Pointer.address_of(self.src[self.index - 1]) else: self.index -= 1 - return Pointer.address_of(self.src[self.index]) + p = Pointer.address_of(self.src[self.index]) @always_inline fn __has_next__(self) -> Bool: From f9c0c1584c84fe90f718778a2e121ab06dc1a80a Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 16 Jan 2025 18:06:25 -0500 Subject: [PATCH 259/509] [stdlib] Add `String` ctors for variadic args / forwarding variadic pack This is much more natural than a static method, but still need to retain one `String.write` static method to avoid an endless loop when reusing `write_to` implementations in `__str__`: ```mojo fn write_to[W: Writer](self, mut writer: W): writer.write_bytes(self.as_bytes()) fn __str__(self) -> String: # `String(self)` here would cause an endless loop return String.write(self) ``` MODULAR_ORIG_COMMIT_REV_ID: 8592a1cbd4f318966a27b4a3e45001b6e2aedee1 --- docs/changelog.md | 27 ++++ docs/manual/operators.mdx | 4 +- docs/manual/traits.mdx | 11 +- docs/manual/types.mdx | 2 +- stdlib/src/builtin/error.mojo | 2 +- stdlib/src/builtin/uint.mojo | 2 +- stdlib/src/collections/string/string.mojo | 129 +++++++++++------- stdlib/src/utils/stringref.mojo | 2 +- .../test/collections/string/test_string.mojo | 17 +++ 9 files changed, 138 insertions(+), 58 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 5c87c6b054..d23688bfe9 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -340,6 +340,33 @@ what we publish. - A new `LinkedList` type has been added to the standard library. +- The `String.write` static method has moved to a `String` constructor, and + is now buffered. Instead of doing: + + ```mojo + var msg = "my message " + String(x) + " " + String(y) + " " + String(z) + ``` + + Which reallocates the `String` you should do: + + ```mojo + var msg = String("my message", x, y, z, sep=" ") + ``` + + Which is cleaner, and buffers to the stack so the `String` is allocated only + once. + +- You can now pass any `Writer` to `write_buffered`: + + ```mojo + from utils.write import write_buffered + + var string = String("existing string") + write_buffered(string, 42, 42.4, True, sep=" ") + ``` + + This writes to a buffer on the stack before reallocating the `String`. + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index 3ae0802151..768a1aab81 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -1137,14 +1137,14 @@ between defining functions with `def` and `fn`. ::: Now we can print a `Complex` value directly, and we can explicitly generate a -`String` representation by passing a `Complex` value to `String.write()` which +`String` representation by passing a `Complex` value to `String()` which constructs a new `String` from all the arguments passed to it. ```mojo c3 = Complex(3.14159, -2.71828) print("c3 =", c3) -var msg = String.write("The value is: ", c3) +var msg = String("The value is: ", c3) print(msg) ``` diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index c4d62de147..0d13119942 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -437,14 +437,17 @@ struct Dog(Stringable, Representable, Writable): var name: String var age: Int - fn __repr__(self) -> String: - return "Dog(name=" + repr(self.name) + ", age=" + repr(self.age) + ")" + # Allows the type to be written into any `Writer` + fn write_to[W: Writer](self, mut writer: W) -> None: + writer.write("Dog(", self.name, ", ", self.age, ")") + # Construct and return a `String` using the previous method fn __str__(self) -> String: return String.write(self) - fn write_to[W: Writer](self, mut writer: W) -> None: - writer.write("Dog(", self.name, ", ", self.age, ")") + # Alternative full representation when calling `repr` + fn __repr__(self) -> String: + return String("Dog(name=", repr(self.name)", age=", repr(self.age), ")") var dog = Dog("Rex", 5) print(repr(dog)) diff --git a/docs/manual/types.mdx b/docs/manual/types.mdx index 6f46013f86..e45e1b627b 100644 --- a/docs/manual/types.mdx +++ b/docs/manual/types.mdx @@ -338,7 +338,7 @@ Or use `String.write` to take variadic `Stringable` types, so you don't have to call `String()` on each value: ```mojo -var s = String.write("Items in list: ", 5) +var s = String("Items in list: ", 5) print(s) ``` diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index 6d78fa5191..5ee6a4dcfa 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -184,7 +184,7 @@ struct Error( Returns: A printable representation of the error message. """ - return String.write("Error(", repr(self._message()), ")") + return String("Error(", repr(self._message()), ")") # ===-------------------------------------------------------------------===# # Methods diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index 3b2a7218da..afbc0b47fc 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -164,7 +164,7 @@ struct UInt(Indexer, _HashableWithHasher): Returns: The string representation of this UInt. """ - return String.write("UInt(", String(self), ")") + return String("UInt(", String(self), ")") fn __hash__(self) -> UInt: """Hash the UInt using builtin hash. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index b5b28d7b79..5563b19735 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -100,7 +100,7 @@ fn chr(c: Int) -> String: if not char_opt: # TODO: Raise ValueError instead. return abort[String]( - String.write("chr(", c, ") is not a valid Unicode codepoint") + String("chr(", c, ") is not a valid Unicode codepoint") ) # SAFETY: We just checked that `char` is present. @@ -608,6 +608,74 @@ struct String( """ self = value.__str__() + @no_inline + fn __init__[ + *Ts: Writable + ](out self, *args: *Ts, sep: StaticString = "", end: StaticString = ""): + """ + Construct a string by concatenating a sequence of Writable arguments. + + Args: + args: A sequence of Writable arguments. + sep: The separator used between elements. + end: The String to write after printing the elements. + + Parameters: + Ts: The types of the arguments to format. Each type must be satisfy + `Writable`. + + Examples: + + Construct a String from several `Writable` arguments: + + ```mojo + var string = String(1, 2.0, "three", sep=", ") + print(string) # "1, 2.0, three" + ``` + . + """ + self = String() + write_buffered(self, args, sep=sep, end=end) + + @staticmethod + @no_inline + fn __init__[ + *Ts: Writable + ]( + out self, + args: VariadicPack[_, Writable, *Ts], + sep: StaticString = "", + end: StaticString = "", + ): + """ + Construct a string by passing a variadic pack. + + Args: + args: A VariadicPack of Writable arguments. + sep: The separator used between elements. + end: The String to write after printing the elements. + + Parameters: + Ts: The types of the arguments to format. Each type must be satisfy + `Writable`. + + Examples: + + ```mojo + fn variadic_pack_to_string[ + *Ts: Writable, + ](*args: *Ts) -> String: + return String(args) + + string = variadic_pack_to_string(1, ", ", 2.0, ", ", "three") + %# from testing import assert_equal + %# assert_equal(string, "1, 2.0, three") + ``` + . + """ + self = String() + write_buffered(self, args, sep=sep, end=end) + @no_inline fn __init__(out self, value: None): """Initialize a `None` type as "None". @@ -717,8 +785,7 @@ struct String( fn write[ *Ts: Writable ](*args: *Ts, sep: StaticString = "", end: StaticString = "") -> Self: - """ - Construct a string by concatenating a sequence of Writable arguments. + """Construct a string by concatenating a sequence of Writable arguments. Args: args: A sequence of Writable arguments. @@ -732,57 +799,23 @@ struct String( Returns: A string formed by formatting the argument sequence. - Examples: - - Construct a String from several `Writable` arguments: + This is used only when reusing the `write_to` method for + `__str__` in order to avoid an endless loop recalling + the constructor: ```mojo - var string = String.write(1, ", ", 2.0, ", ", "three") - print(string) # "1, 2.0, three" - %# from testing import assert_equal - %# assert_equal(string, "1, 2.0, three") - ``` - . - """ - var string = String() - write_buffered(string, args, sep=sep, end=end) - return string^ - - @staticmethod - @no_inline - fn write[ - *Ts: Writable - ]( - args: VariadicPack[_, Writable, *Ts], - sep: StaticString = "", - end: StaticString = "", - ) -> Self: - """ - Construct a string by passing a variadic pack. + fn write_to[W: Writer](self, mut writer: W): + writer.write_bytes(self.as_bytes()) - Args: - args: A VariadicPack of Writable arguments. - sep: The separator used between elements. - end: The String to write after printing the elements. - - Parameters: - Ts: The types of the arguments to format. Each type must be satisfy - `Writable`. - - Returns: - A string formed by formatting the VariadicPack. + fn __str__(self) -> String: + return String.write(self) + ``` - Examples: + Otherwise you can use the `String` constructor directly without calling + the `String.write` static method: ```mojo - fn variadic_pack_to_string[ - *Ts: Writable, - ](*args: *Ts) -> String: - return String.write(args) - - string = variadic_pack_to_string(1, ", ", 2.0, ", ", "three") - %# from testing import assert_equal - %# assert_equal(string, "1, 2.0, three") + var msg = String("my message", 42, 42.2, True) ``` . """ diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index dd1ebd678e..1b3666a52a 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -414,7 +414,7 @@ struct StringRef( Returns: The String representation of the StringRef. """ - return String.write("StringRef(", repr(String(self)), ")") + return String("StringRef(", repr(String(self)), ")") @no_inline fn write_to[W: Writer](self, mut writer: W): diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index fd070192d4..d3d4f8e3b4 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -1486,6 +1486,22 @@ def test_reserve(): assert_equal(s._buffer.capacity, 1) +def test_variadic_ctors(): + var s = String("message", 42, 42.2, True, sep=", ") + assert_equal(s, "message, 42, 42.2, True") + + var s2 = String.write("message", 42, 42.2, True, sep=", ") + assert_equal(s2, "message, 42, 42.2, True") + + fn forward_variadic_pack[ + *Ts: Writable, + ](*args: *Ts) -> String: + return String(args) + + var s3 = forward_variadic_pack(1, ", ", 2.0, ", ", "three") + assert_equal(s3, "1, 2.0, three") + + def main(): test_constructors() test_copy() @@ -1538,3 +1554,4 @@ def main(): test_center() test_float_conversion() test_slice_contains() + test_variadic_ctors() From a32bead0dc536621f0065e4c9174501a98019a06 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Thu, 16 Jan 2025 17:00:31 -0800 Subject: [PATCH 260/509] Update github links to new project name MODULAR_ORIG_COMMIT_REV_ID: 3292dd8d184938dd60461f02a56594dee64e5f16 --- docs/changelog-released.md | 638 +++++++++++++++---------------- docs/changelog.md | 10 +- docs/faq.md | 8 +- docs/manual/basics.mdx | 8 +- docs/manual/get-started.mdx | 4 +- docs/manual/operators.mdx | 2 +- docs/manual/parameters/index.mdx | 2 +- docs/manual/values/lifetimes.mdx | 2 +- docs/notebooks/index.mdx | 2 +- docs/roadmap.md | 4 +- docs/tools/debugging.mdx | 2 +- docs/tools/testing.mdx | 14 +- 12 files changed, 348 insertions(+), 348 deletions(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index d20de03317..18a2551f4f 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -44,7 +44,7 @@ detailed information in the following sections: (`ref [a, b]`). For details, see [Language changes](#24-6-language-changes). For background information and rationale on the name change see - [the proposal](https://github.com/modularml/mojo/issues/3623). For more + [the proposal](https://github.com/modular/mojo/issues/3623). For more information on origins, see [Lifetimes, origins and references](/mojo/manual/values/lifetimes) in the Mojo Manual. @@ -126,7 +126,7 @@ detailed information in the following sections: eventually be deprecated and removed. This was [discussed extensively in a public - proposal](https://github.com/modularml/mojo/issues/3623). For more + proposal](https://github.com/modular/mojo/issues/3623). For more information, see [Named results](/nightly/mojo/manual/functions#named-results) in the Mojo Manual. @@ -349,7 +349,7 @@ detailed information in the following sections: - Mojo can now interpret simple LLVM intrinsics in parameter expressions, enabling things like `count_leading_zeros` to work at compile time: - [Issue #933](https://github.com/modularml/mojo/issues/933). + [Issue #933](https://github.com/modular/mojo/issues/933). - Introduced the `@explicit_destroy` annotation, the `__disable_del` keyword, the `UnknownDestructibility` trait, and the `ImplicitlyDestructible` keyword, @@ -468,8 +468,8 @@ detailed information in the following sections: 'strong reference' pointer. - Support for multi-dimensional indexing and slicing for `PythonObject` - (PR [#3549](https://github.com/modularml/mojo/pull/3549), - PR [#3583](https://github.com/modularml/mojo/pull/3583)). + (PR [#3549](https://github.com/modular/mojo/pull/3549), + PR [#3583](https://github.com/modular/mojo/pull/3583)). ```mojo var np = Python.import_module("numpy") @@ -482,7 +482,7 @@ detailed information in the following sections: - Added [`PythonObject.__contains__()`](/mojo/stdlib/python/python_object/PythonObject#__contains__). - ([PR #3101](https://github.com/modularml/mojo/pull/3101)) + ([PR #3101](https://github.com/modular/mojo/pull/3101)) Example usage: @@ -514,7 +514,7 @@ detailed information in the following sections: semantics to Rust's [`Box<>`](https://doc.rust-lang.org/std/boxed/struct.Box.html) and C++'s [`std::unique_ptr`](https://en.cppreference.com/w/cpp/memory/unique_ptr). - ([PR #3524](https://github.com/modularml/mojo/pull/3524)) + ([PR #3524](https://github.com/modular/mojo/pull/3524)) - `Arc` has been renamed to [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer), for consistency with `OwnedPointer`. @@ -544,7 +544,7 @@ detailed information in the following sections: [`FloatableRaising`](/mojo/stdlib/builtin/floatable/FloatableRaising) traits to denote types that can be converted to a `Float64` value using the builtin `float` function. Made `SIMD` and `FloatLiteral` conform to the `Floatable` - trait. ([PR #3163](https://github.com/modularml/mojo/pull/3163)) + trait. ([PR #3163](https://github.com/modular/mojo/pull/3163)) ```mojo fn foo[F: Floatable](v: F): @@ -562,7 +562,7 @@ detailed information in the following sections: - Introduced the [`random.shuffle()`](/mojo/stdlib/random/random/shuffle) function for randomizing the elements of a `List`. - ([PR #3327](https://github.com/modularml/mojo/pull/3327)) + ([PR #3327](https://github.com/modular/mojo/pull/3327)) Example: @@ -580,20 +580,20 @@ detailed information in the following sections: - [`Slice.step`](/mojo/stdlib/builtin/builtin_slice/Slice#fields) is now an `Optional[Int]`, matching the optionality of `slice.step` in Python. - ([PR #3160](https://github.com/modularml/mojo/pull/3160)) + ([PR #3160](https://github.com/modular/mojo/pull/3160)) - There is now a [`Byte`](/mojo/stdlib/builtin/simd/#aliases) alias to better express intent when working with a pack of bits. - ([PR #3670](https://github.com/modularml/mojo/pull/3670)). + ([PR #3670](https://github.com/modular/mojo/pull/3670)). - Expanded [`os.path`](/mojo/stdlib/os/path/path/) with new functions: - - `os.path.expandvars()`: Expands environment variables in a path ([PR #3735](https://github.com/modularml/mojo/pull/3735)). + - `os.path.expandvars()`: Expands environment variables in a path ([PR #3735](https://github.com/modular/mojo/pull/3735)). - `os.path.splitroot()`: Split a path into drive, root and tail. - ([PR #3780](https://github.com/modularml/mojo/pull/3780)). + ([PR #3780](https://github.com/modular/mojo/pull/3780)). - Added a [`reserve()`](/mojo/stdlib/collections/string/String#reserve) method and new constructor to the `String` struct to allocate additional capacity. - ([PR #3755](https://github.com/modularml/mojo/pull/3755)). + ([PR #3755](https://github.com/modular/mojo/pull/3755)). - A new [`StringLiteral.get[some_stringable]()`](/mojo/stdlib/builtin/string_literal/StringLiteral#get) @@ -619,7 +619,7 @@ detailed information in the following sections: - [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) now implements `split()` which can be used to split a `StringRef` into a `List[StringRef]` by a - delimiter. ([PR #2705](https://github.com/modularml/mojo/pull/2705)) + delimiter. ([PR #2705](https://github.com/modular/mojo/pull/2705)) - [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) is now representable so `repr(StringRef("hello"))` will return `StringRef('hello')`. @@ -678,7 +678,7 @@ detailed information in the following sections: - The VS Code extension now has the `mojo.run.focusOnTerminalAfterLaunch` setting, which controls whether to focus on the terminal used by the `Mojo: Run Mojo File` command or on the editor after launch. - [Issue #3532](https://github.com/modularml/mojo/issues/3532). + [Issue #3532](https://github.com/modular/mojo/issues/3532). - The VS Code extension now has the `mojo.SDK.additionalSDKs` setting, which allows the user to provide a list of MAX SDKs that the extension can use when @@ -743,40 +743,40 @@ detailed information in the following sections: - Lifetime tracking is now fully field sensitive, which makes the uninitialized variable checker more precise. -- [Issue #1310](https://github.com/modularml/mojo/issues/1310) - Mojo permits +- [Issue #1310](https://github.com/modular/mojo/issues/1310) - Mojo permits the use of any constructor for implicit conversions -- [Issue #1632](https://github.com/modularml/mojo/issues/1632) - Mojo produces +- [Issue #1632](https://github.com/modular/mojo/issues/1632) - Mojo produces weird error when inout function is used in non mutating function -- [Issue #3444](https://github.com/modularml/mojo/issues/3444) - Raising init +- [Issue #3444](https://github.com/modular/mojo/issues/3444) - Raising init causing use of uninitialized variable -- [Issue #3544](https://github.com/modularml/mojo/issues/3544) - Known +- [Issue #3544](https://github.com/modular/mojo/issues/3544) - Known mutable `ref` argument are not optimized as `noalias` by LLVM. -- [Issue #3559](https://github.com/modularml/mojo/issues/3559) - VariadicPack +- [Issue #3559](https://github.com/modular/mojo/issues/3559) - VariadicPack doesn't extend the lifetimes of the values it references. -- [Issue #3627](https://github.com/modularml/mojo/issues/3627) - Compiler +- [Issue #3627](https://github.com/modular/mojo/issues/3627) - Compiler overlooked exclusivity violation caused by `ref [MutableAnyOrigin] T` -- [Issue #3710](https://github.com/modularml/mojo/issues/3710) - Mojo frees +- [Issue #3710](https://github.com/modular/mojo/issues/3710) - Mojo frees memory while reference to it is still in use. -- [Issue #3805](https://github.com/modularml/mojo/issues/3805) - Crash When +- [Issue #3805](https://github.com/modular/mojo/issues/3805) - Crash When Initializing !llvm.ptr. -- [Issue #3816](https://github.com/modularml/mojo/issues/3816) - Ternary +- [Issue #3816](https://github.com/modular/mojo/issues/3816) - Ternary if-operator doesn't propagate origin information. -- [Issue #3815](https://github.com/modularml/mojo/issues/3815) - +- [Issue #3815](https://github.com/modular/mojo/issues/3815) - [BUG] Mutability not preserved when taking the union of two origins. -- [Issue #3829](https://github.com/modularml/mojo/issues/3829) - Poor error +- [Issue #3829](https://github.com/modular/mojo/issues/3829) - Poor error message when invoking a function pointer upon an argument of the wrong origin -- [Issue #3830](https://github.com/modularml/mojo/issues/3830) - Failures +- [Issue #3830](https://github.com/modular/mojo/issues/3830) - Failures emitting register RValues to ref arguments. - The VS Code extension now auto-updates its private copy of the MAX SDK. @@ -982,7 +982,7 @@ detailed information in the following sections: The Mojo compiler now always passes the "new value" being set using the last keyword argument of the `__setitem__()`, e.g. turning `yourType[1, 2] = 3` into `yourType.__setitem__(1, 2, val=3)`. This fixes - [Issue #248](https://github.com/modularml/mojo/issues/248). + [Issue #248](https://github.com/modular/mojo/issues/248). - Mojo context managers used in regions of code that may raise no longer need to define a "conditional" exit function in the form of @@ -1109,7 +1109,7 @@ detailed information in the following sections: - Added the builtin [`input()`](/mojo/stdlib/builtin/io/input) function, which behaves the same as Python. - ([PR #3392](https://github.com/modularml/mojo/pull/3392)) + ([PR #3392](https://github.com/modular/mojo/pull/3392)) ```mojo name = input("Enter your name: ") @@ -1120,7 +1120,7 @@ detailed information in the following sections: There is a known issue when running the `input()` function with JIT compilation (see issue - [#3479](https://github.com/modularml/mojo/issues/3479)). + [#3479](https://github.com/modular/mojo/issues/3479)). - [`print()`](/mojo/stdlib/builtin/io/print) now requires that its arguments conform to the [`Formattable`](/mojo/stdlib/utils/format/Formattable) trait. @@ -1193,12 +1193,12 @@ detailed information in the following sections: - Added [`TemporaryDirectory`](/mojo/stdlib/tempfile/tempfile/TemporaryDirectory) in module `tempfile`. - ([PR 2743](https://github.com/modularml/mojo/pull/2743)) + ([PR 2743](https://github.com/modular/mojo/pull/2743)) - Added [`NamedTemporaryFile`](/mojo/stdlib/tempfile/tempfile/NamedTemporaryFile) in module `tempfile`. - ([PR 2762](https://github.com/modularml/mojo/pull/2762)) + ([PR 2762](https://github.com/modular/mojo/pull/2762)) - [`String`](/mojo/stdlib/collections/string/String) and friends: @@ -1207,7 +1207,7 @@ detailed information in the following sections: - Added the [`String.format()`](/mojo/stdlib/collections/string/String#format) method. - ([PR #2771](https://github.com/modularml/mojo/pull/2771)) + ([PR #2771](https://github.com/modular/mojo/pull/2771)) Supports automatic and manual indexing of `*args`. @@ -1228,7 +1228,7 @@ detailed information in the following sections: - [`String.format()`](/mojo/stdlib/collections/string/String#format) now supports conversion flags `!s` and `!r`, allowing for `str()` and `repr()` conversions within format strings. - ([PR #3279](https://github.com/modularml/mojo/pull/3279)) + ([PR #3279](https://github.com/modular/mojo/pull/3279)) Example: @@ -1245,14 +1245,14 @@ detailed information in the following sections: [`ljust()`](/mojo/stdlib/collections/string/String#ljust), and [`center()`](/mojo/stdlib/collections/string/String#center) methods to return a justified string based on width and fillchar. ([PR - #3278](https://github.com/modularml/mojo/pull/3278)) + #3278](https://github.com/modular/mojo/pull/3278)) - The [`atol()`](/mojo/stdlib/collections/string/atol) function now correctly supports leading underscores, (e.g.`atol("0x_ff", 0)`), when the appropriate base is specified or inferred (base 0). non-base-10 integer literals as per Python's [Integer Literals](). - ([PR #3180](https://github.com/modularml/mojo/pull/3180)) + ([PR #3180](https://github.com/modular/mojo/pull/3180)) - Added the [`unsafe_cstr_ptr()`](/mojo/stdlib/collections/string/String#unsafe_cstr_ptr) @@ -1270,7 +1270,7 @@ detailed information in the following sections: and [`StringSlice.__len__()`](/mojo/stdlib/utils/string_slice/StringSlice#__len__) now does return the Unicode codepoints length. - ([PR #2960](https://github.com/modularml/mojo/pull/2960)) + ([PR #2960](https://github.com/modular/mojo/pull/2960)) - Added a new [`StaticString`](/mojo/stdlib/utils/string_slice/#aliases) type alias. This can be used in place of @@ -1414,7 +1414,7 @@ detailed information in the following sections: assert_equal(str(stacked), "[1 2 3 4 5 6]") ``` - ([PR #3264](https://github.com/modularml/mojo/pull/3264)) + ([PR #3264](https://github.com/modular/mojo/pull/3264)) - Accessing local Python modules with [`Python.add_to_path(".")`](/mojo/stdlib/python/python/Python#add_to_path) @@ -1429,7 +1429,7 @@ detailed information in the following sections: - [`List`](/mojo/stdlib/collections/list/List) values are now equality comparable with `==` and `!=` when their element type is equality - comparable. ([PR #3195](https://github.com/modularml/mojo/pull/3195)) + comparable. ([PR #3195](https://github.com/modular/mojo/pull/3195)) - [`Optional`](/mojo/stdlib/collections/optional/Optional) values are now equality comparable with `==` and `!=` when their element type is equality @@ -1437,22 +1437,22 @@ detailed information in the following sections: - Added a new [`Counter`](/mojo/stdlib/collections/counter/Counter) dictionary-like type, matching most of the features of the Python one. - ([PR #2910](https://github.com/modularml/mojo/pull/2910)) + ([PR #2910](https://github.com/modular/mojo/pull/2910)) - [`Dict`](/mojo/stdlib/collections/dict/Dict) now implements [`setdefault()`](/mojo/stdlib/collections/dict/Dict#setdefault), which gets a value from the dictionary by key, or sets it to a default if it doesn't exist. - ([PR #2803](https://github.com/modularml/mojo/pull/2803)) + ([PR #2803](https://github.com/modular/mojo/pull/2803)) - `Dict` now supports [`popitem()`](/mojo/stdlib/collections/dict/Dict#popitem), which removes and returns the last item in the `Dict`. - ([PR #2701](https://github.com/modularml/mojo/pull/2701)) + ([PR #2701](https://github.com/modular/mojo/pull/2701)) - Added a [`Dict.__init__()`](/mojo/stdlib/collections/dict/Dict#__init__) overload to specify initial capacity. - ([PR #3171](https://github.com/modularml/mojo/pull/3171)) + ([PR #3171](https://github.com/modular/mojo/pull/3171)) The capacity has to be a power of two and greater than or equal to 8. @@ -1467,7 +1467,7 @@ detailed information in the following sections: - `ListLiteral` now supports [`__contains__()`](/mojo/stdlib/builtin/builtin_list/ListLiteral#__contains__). - ([PR #3251](https://github.com/modularml/mojo/pull/3251)) + ([PR #3251](https://github.com/modular/mojo/pull/3251)) - Filesystem and environment utilities: @@ -1549,7 +1549,7 @@ detailed information in the following sections: - Added the [`Identifiable`](/mojo/stdlib/builtin/identifiable/Identifiable) trait, used to describe types that implement the `__is__()` and `__isnot__()` trait methods. - ([PR #2807](https://github.com/modularml/mojo/pull/2807)) + ([PR #2807](https://github.com/modular/mojo/pull/2807)) - Types conforming to [`Boolable`](/mojo/stdlib/builtin/bool/Boolable) (that is, those implementing `__bool__()`) no longer implicitly convert to `Bool`. @@ -1591,7 +1591,7 @@ detailed information in the following sections: - Added the [`oct()`](/mojo/stdlib/builtin/format_int/oct) builtin function for formatting an integer in octal. - ([PR #2914](https://github.com/modularml/mojo/pull/2914)) + ([PR #2914](https://github.com/modular/mojo/pull/2914)) - Added the [`assert_is()`](/mojo/stdlib/testing/testing/assert_is) and [`assert_is_not()`](/mojo/stdlib/testing/testing/assert_is_not) test @@ -1599,7 +1599,7 @@ detailed information in the following sections: - The [`math`](/mojo/stdlib/math/constants/) package now includes the `pi`, `e`, and `tau` constants (Closes Issue - [#2135](https://github.com/modularml/mojo/issues/2135)). + [#2135](https://github.com/modular/mojo/issues/2135)). - The [`ulp`](/mojo/stdlib/math/math/ulp) function from `numerics` has been moved to the `math` module. @@ -1608,7 +1608,7 @@ detailed information in the following sections: [`bit_reverse()`](/mojo/stdlib/bit/bit/bit_reverse), [`byte_swap()`](/mojo/stdlib/bit/bit/byte_swap), and [`pop_count()`](/mojo/stdlib/bit/bit/pop_count) for the `Int` type. - ([PR #3150](https://github.com/modularml/mojo/pull/3150)) + ([PR #3150](https://github.com/modular/mojo/pull/3150)) - A few `bit` functions have been renamed for clarity: @@ -1620,7 +1620,7 @@ detailed information in the following sections: `OptionalReg[Int]` for `start` and `end` and implements a constructor which accepts optional values. `Slice._has_end()` has also been removed since a Slice with no end is now represented by an empty `Slice.end` option. - ([PR #2495](https://github.com/modularml/mojo/pull/2495)) + ([PR #2495](https://github.com/modular/mojo/pull/2495)) ```mojo var s = Slice(1, None, 2) @@ -1732,28 +1732,28 @@ detailed information in the following sections: - Mojo now prints `ref` arguments and results in generated documentation correctly. -- [#1734](https://github.com/modularml/mojo/issues/1734) - Calling +- [#1734](https://github.com/modular/mojo/issues/1734) - Calling `__copyinit__` on self causes crash. -- [#3142](https://github.com/modularml/mojo/issues/3142) - [QoI] Confusing +- [#3142](https://github.com/modular/mojo/issues/3142) - [QoI] Confusing `__setitem__` method is failing with a "must be mutable" error. -- [#248](https://github.com/modularml/mojo/issues/248) - [Feature] Enable +- [#248](https://github.com/modular/mojo/issues/248) - [Feature] Enable `__setitem__` to take variadic arguments -- [#3065](https://github.com/modularml/mojo/issues/3065) - Fix incorrect behavior +- [#3065](https://github.com/modular/mojo/issues/3065) - Fix incorrect behavior of `SIMD.__int__` on unsigned types -- [#3045](https://github.com/modularml/mojo/issues/3045) - Disable implicit SIMD +- [#3045](https://github.com/modular/mojo/issues/3045) - Disable implicit SIMD conversion routes through `Bool` -- [#3126](https://github.com/modularml/mojo/issues/3126) - [BUG] List doesn't +- [#3126](https://github.com/modular/mojo/issues/3126) - [BUG] List doesn't work at compile time. -- [#3237](https://github.com/modularml/mojo/issues/3237) - [BUG] Difference +- [#3237](https://github.com/modular/mojo/issues/3237) - [BUG] Difference between `__getitem__` and `[.]` operator. -- [#3336](https://github.com/modularml/mojo/issues/3336) - Fix outdated +- [#3336](https://github.com/modular/mojo/issues/3336) - Fix outdated references to `let` in REPL documentation. - The VS Code extension no longer caches the information of the selected @@ -1972,7 +1972,7 @@ Big themes for this release: - Added built-in [`repr()`](/mojo/stdlib/builtin/repr/repr) function and [`Representable`](/mojo/stdlib/builtin/repr/Representable) trait. - ([PR #2361](https://github.com/modularml/mojo/pull/2361)) + ([PR #2361](https://github.com/modular/mojo/pull/2361)) - Added the [`Indexer`](/mojo/stdlib/builtin/int/Indexer) trait to denote types that implement the `__index__()` method which allows these types to be @@ -2019,7 +2019,7 @@ Big themes for this release: print(a[AlwaysZero()]) # works and prints 1 ``` - ([PR #2685](https://github.com/modularml/mojo/pull/2685)) + ([PR #2685](https://github.com/modular/mojo/pull/2685)) - Added traits allowing user-defined types to be supported by various built-in and math functions. @@ -2097,14 +2097,14 @@ Big themes for this release: [`rstrip()`](/mojo/stdlib/collections/string/String#rstrip) can now remove custom characters other than whitespace. In addition, there are now several useful aliases for whitespace, ASCII lower/uppercase, and so on. - ([PR #2555](https://github.com/modularml/mojo/pull/2555)) + ([PR #2555](https://github.com/modular/mojo/pull/2555)) - `String` now has a [`splitlines()`](/mojo/stdlib/collections/string/String#splitlines) method, which allows splitting strings at line boundaries. This method supports [universal newlines](https://docs.python.org/3/glossary.html#term-universal-newlines) and provides an option to retain or remove the line break characters. - ([PR #2810](https://github.com/modularml/mojo/pull/2810)) + ([PR #2810](https://github.com/modular/mojo/pull/2810)) - `InlinedString` has been renamed to [`InlineString`](/mojo/stdlib/utils/inline_string/InlineString) to be @@ -2113,12 +2113,12 @@ Big themes for this release: - [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) now implements [`strip()`](/mojo/stdlib/utils/stringref/StringRef#strip), which can be used to remove leading and trailing whitespace. - ([PR #2683](https://github.com/modularml/mojo/pull/2683)) + ([PR #2683](https://github.com/modular/mojo/pull/2683)) - `StringRef` now implements [`startswith()`](/mojo/stdlib/utils/stringref/StringRef#startswith) and [`endswith()`](/mojo/stdlib/utils/stringref/StringRef#endswith). - ([PR #2710](https://github.com/modularml/mojo/pull/2710)) + ([PR #2710](https://github.com/modular/mojo/pull/2710)) - Added a new [`StringSlice`](/mojo/stdlib/utils/string_slice/StringSlice) type, to replace uses of the unsafe `StringRef` type in standard library @@ -2170,7 +2170,7 @@ Big themes for this release: - Added a built-in [`sort()`](/mojo/stdlib/builtin/sort/sort) function for lists of elements that conform to the [`ComparableCollectionElement`](/mojo/stdlib/builtin/value/ComparableCollectionElement) - trait.([PR #2609](https://github.com/modularml/mojo/pull/2609)) + trait.([PR #2609](https://github.com/modular/mojo/pull/2609)) - [`int()`](/mojo/stdlib/builtin/int/int-function) can now take a string and a specified base to parse an integer from a @@ -2178,17 +2178,17 @@ Big themes for this release: specified, the string will be parsed as if it was an integer literal, with the base determined by whether the string contains the prefix `"0x"`, `"0o"`, or `"0b"`. - ([PR #2273](https://github.com/modularml/mojo/pull/2273), - fixes [#2274](https://github.com/modularml/mojo/issues/2274)) + ([PR #2273](https://github.com/modular/mojo/pull/2273), + fixes [#2274](https://github.com/modular/mojo/issues/2274)) - Added the [`bin()`](/mojo/stdlib/builtin/format_int/bin) built-in function to convert integral types into their binary string representation. - ([PR #2603](https://github.com/modularml/mojo/pull/2603)) + ([PR #2603](https://github.com/modular/mojo/pull/2603)) - Added the [`atof()`](/mojo/stdlib/collections/string/atof) built-in function, which can convert a `String` to a `float64`. - ([PR #2649](https://github.com/modularml/mojo/pull/2649)) + ([PR #2649](https://github.com/modular/mojo/pull/2649)) - You can now use the built-in [`any()`](/mojo/stdlib/builtin/bool/any) and [`all()`](/mojo/stdlib/builtin/bool/all) functions to check for truthy @@ -2196,7 +2196,7 @@ Big themes for this release: `size=1`, You must explicitly use these to get the truthy value of a SIMD vector with more than one element. This avoids common bugs around implicit conversion of `SIMD` to `Bool`. - ([PR #2600](https://github.com/modularml/mojo/pull/2600)) + ([PR #2600](https://github.com/modular/mojo/pull/2600)) For example: @@ -2211,10 +2211,10 @@ Big themes for this release: - [`object`](/mojo/stdlib/builtin/object/) now implements all the bitwise operators. - ([PR #2324](https://github.com/modularml/mojo/pull/2324)) + ([PR #2324](https://github.com/modular/mojo/pull/2324)) - [`Tuple`](/mojo/stdlib/builtin/tuple/Tuple) now supports `__contains__()`. - ([PR #2709](https://github.com/modularml/mojo/pull/2709)) For example: + ([PR #2709](https://github.com/modular/mojo/pull/2709)) For example: ```mojo var x = Tuple(1, 2, True) @@ -2264,12 +2264,12 @@ Big themes for this release: Note that `List` doesn't conform to the `Stringable` trait yet so you cannot use `str(my_list)` yet. - ([PR #2673](https://github.com/modularml/mojo/pull/2673)) + ([PR #2673](https://github.com/modular/mojo/pull/2673)) - `List` has a simplified syntax to call the [`count()`](/mojo/stdlib/collections/list/List#count) method: `my_list.count(x)`. - ([PR #2675](https://github.com/modularml/mojo/pull/2675)) + ([PR #2675](https://github.com/modular/mojo/pull/2675)) - `List()` now supports `__contains__()`, so you can now use lists with the `in` operator: @@ -2278,84 +2278,84 @@ Big themes for this release: if x in my_list: ``` - ([PR #2667](https://github.com/modularml/mojo/pull/2667)) + ([PR #2667](https://github.com/modular/mojo/pull/2667)) - `List` now has an [`unsafe_get()`](/mojo/stdlib/collections/list/List#unsafe_get) to get the reference to an element without bounds check or wraparound for negative indices. Note that this method is unsafe. Use with caution. - [PR #2800](https://github.com/modularml/mojo/pull/2800)) + [PR #2800](https://github.com/modular/mojo/pull/2800)) - Added a [`fromkeys()`](/mojo/stdlib/collections/dict/Dict#fromkeys) method to `Dict` to return a `Dict` with the specified keys and values. - ([PR 2622](https://github.com/modularml/mojo/pull/2622)) + ([PR 2622](https://github.com/modular/mojo/pull/2622)) - Added a [`clear()`](/mojo/stdlib/collections/dict/Dict#clear) method to - `Dict`. ([PR 2627](https://github.com/modularml/mojo/pull/2627)) + `Dict`. ([PR 2627](https://github.com/modular/mojo/pull/2627)) - `Dict` now supports [`reversed()`](/mojo/stdlib/builtin/reversed/reversed) for its `items()` and `values()` iterators. - ([PR #2340](https://github.com/modularml/mojo/pull/2340)) + ([PR #2340](https://github.com/modular/mojo/pull/2340)) - `Dict` now has a simplified conversion to `String` with `my_dict.__str__()`. Note that `Dict` does not conform to the `Stringable` trait so `str(my_dict)` is not possible yet. - ([PR #2674](https://github.com/modularml/mojo/pull/2674)) + ([PR #2674](https://github.com/modular/mojo/pull/2674)) - `Dict` now implements [`get(key)`](/mojo/stdlib/collections/dict/Dict#get) and `get(key, default)` functions. - ([PR #2519](https://github.com/modularml/mojo/pull/2519)) + ([PR #2519](https://github.com/modular/mojo/pull/2519)) - Added a temporary `__get_ref(key)` method to `Dict`, allowing you to get a `Reference` to a dictionary value. - Added a new [`InlineList`](/mojo/stdlib/collections/inline_list/InlineList) type, a stack-allocated list with a static maximum size. - ([PR 2587#](https://github.com/modularml/mojo/pull/2587)) - ([PR #2703](https://github.com/modularml/mojo/pull/2703)) + ([PR 2587#](https://github.com/modular/mojo/pull/2587)) + ([PR #2703](https://github.com/modular/mojo/pull/2703)) - Added a new [`Span`](/mojo/stdlib/memory/span/Span) type for taking slices of contiguous collections. - ([PR #2595](https://github.com/modularml/mojo/pull/2595)) + ([PR #2595](https://github.com/modular/mojo/pull/2595)) - [`os`](/mojo/stdlib/os/os/) module: - The `os` module now provides functionality for adding and removing directories using [`mkdir()`](/mojo/stdlib/os/os/mkdir) and [`rmdir()`](/mojo/stdlib/os/os/rmdir). - ([PR #2430](https://github.com/modularml/mojo/pull/2430)) + ([PR #2430](https://github.com/modular/mojo/pull/2430)) - Added the [`os.path.getsize()`](/mojo/stdlib/os/path/path/getsize) function, which gives the size in bytes of the file identified by the path. - ([PR 2626](https://github.com/modularml/mojo/pull/2626)) + ([PR 2626](https://github.com/modular/mojo/pull/2626)) - Added [`os.path.join()`](/mojo/stdlib/os/path/path/join) function. - ([PR 2792](https://github.com/modularml/mojo/pull/2792)) + ([PR 2792](https://github.com/modular/mojo/pull/2792)) - Added a new [`tempfile`](/mojo/stdlib/tempfile/tempfile/) module, with `gettempdir()` and `mkdtemp()` functions. - ([PR 2742](https://github.com/modularml/mojo/pull/2742)) + ([PR 2742](https://github.com/modular/mojo/pull/2742)) - [`SIMD`](/mojo/stdlib/builtin/simd/SIMD) type: - Added [`SIMD.shuffle()`](/mojo/stdlib/builtin/simd/SIMD#shuffle) with `IndexList` mask. - ([PR #2315](https://github.com/modularml/mojo/pull/2315)) + ([PR #2315](https://github.com/modular/mojo/pull/2315)) - [`SIMD.__bool__()`](/mojo/stdlib/builtin/simd/SIMD#__bool__) is constrained such that it only works when `size` is `1`. For SIMD vectors with more than one element, use [`any()`](/mojo/stdlib/builtin/bool/any) or [`all()`](/mojo/stdlib/builtin/bool/all). - ([PR #2502](https://github.com/modularml/mojo/pull/2502)) + ([PR #2502](https://github.com/modular/mojo/pull/2502)) - The [`SIMD.reduce_or()`](/mojo/stdlib/builtin/simd/SIMD#reduce_or) and [`SIMD.reduce_and()`](/mojo/stdlib/builtin/simd/SIMD#reduce_and) methods are now bitwise operations, and support integer types. - ([PR #2671](https://github.com/modularml/mojo/pull/2671)) + ([PR #2671](https://github.com/modular/mojo/pull/2671)) - Added [`SIMD.__repr__()`](/mojo/stdlib/builtin/simd/SIMD#__repr__) to get the verbose string representation of `SIMD` types. - ([PR #2728](https://github.com/modularml/mojo/pull/2728)) + ([PR #2728](https://github.com/modular/mojo/pull/2728)) - [`math`](/mojo/stdlib/math/math/) package: @@ -2391,7 +2391,7 @@ Big themes for this release: - [`math.gcd()`](/mojo/stdlib/math/math/gcd) now works on negative inputs, and like Python's implementation, accepts a variadic list of integers. New overloads for a `List` or `Span`of integers are also added. - ([PR #2777](https://github.com/modularml/mojo/pull/2777)) + ([PR #2777](https://github.com/modular/mojo/pull/2777)) - Async and coroutines: @@ -2415,12 +2415,12 @@ Big themes for this release: that works on memory-only types. Compare with the existing [`StaticTuple`](/mojo/stdlib/utils/static_tuple/StaticTuple) type, which is conceptually an array type, but only works on `AnyTrivialRegType`. - ([PR #2294](https://github.com/modularml/mojo/pull/2294)) + ([PR #2294](https://github.com/modular/mojo/pull/2294)) - The [`base64`](/mojo/stdlib/base64/) package now includes encoding and decoding support for both the Base64 and Base16 encoding schemes. - ([PR #2364](https://github.com/modularml/mojo/pull/2364)) - ([PR #2584](https://github.com/modularml/mojo/pull/2584)) + ([PR #2364](https://github.com/modular/mojo/pull/2364)) + ([PR #2584](https://github.com/modular/mojo/pull/2584)) - The `take()` function in [`Variant`](/mojo/stdlib/utils/variant/Variant) and [`Optional`](/mojo/stdlib/collections/optional/Optional) has been renamed to @@ -2437,7 +2437,7 @@ Big themes for this release: - `infinity` and `NaN` are now correctly handled in [`testing.assert_almost_equal()`](/mojo/stdlib/testing/testing/assert_almost_equal) and an `inf` function has been added to `utils/numerics.mojo`. - ([PR #2375](https://github.com/modularml/mojo/pull/2375)) + ([PR #2375](https://github.com/modular/mojo/pull/2375)) ### Tooling changes @@ -2551,16 +2551,16 @@ Big themes for this release: #### 🛠️ Fixed -- [#1837](https://github.com/modularml/mojo/issues/1837) Fix self-referential +- [#1837](https://github.com/modular/mojo/issues/1837) Fix self-referential variant crashing the compiler. -- [#2363](https://github.com/modularml/mojo/issues/2363) Fix LSP crashing on +- [#2363](https://github.com/modular/mojo/issues/2363) Fix LSP crashing on simple trait definitions. -- [#1787](https://github.com/modularml/mojo/issues/1787) Fix error when using +- [#1787](https://github.com/modular/mojo/issues/1787) Fix error when using `//` on `FloatLiteral` in alias expression. - Made several improvements to dictionary performance. Dicts with integer keys are most heavily affected, but large dicts and dicts with large values will also see large improvements. -- [#2692](https://github.com/modularml/mojo/issues/2692) Fix `assert_raises` +- [#2692](https://github.com/modular/mojo/issues/2692) Fix `assert_raises` to include calling location. ### Special thanks @@ -2749,21 +2749,21 @@ Special thanks to our community contributors: - `pop(index)` for removing an element at a particular index. By default, `List.pop()` removes the last element in the list. ([@LJ-9801](https://github.com/LJ-9801), fixes - [#2017](https://github.com/modularml/mojo/issues/2017)) + [#2017](https://github.com/modular/mojo/issues/2017)) - `resize(new_size)` for resizing the list without the need to specify an additional value. ([@mikowals](https://github.com/mikowals), fixes - [#2133](https://github.com/modularml/mojo/issues/2133)) + [#2133](https://github.com/modular/mojo/issues/2133)) - `insert(index, value)` for inserting a value at a specified index into the `List`. ([@whym1here](https://github.com/whym1here), fixes - [#2134](https://github.com/modularml/mojo/issues/2134)) + [#2134](https://github.com/modular/mojo/issues/2134)) - A new constructor `List(ptr, size, capacity)` to to avoid needing to do a deep copy of an existing contiguous memory allocation when constructing a new `List`. ([@StandinKP](https://github.com/StandinKP), fixes - [#2170](https://github.com/modularml/mojo/issues/2170)) + [#2170](https://github.com/modular/mojo/issues/2170)) - [`Dict`](/mojo/stdlib/collections/dict/Dict) now has a `update()` method to update keys/values from another `Dict`. @@ -2804,7 +2804,7 @@ Special thanks to our community contributors: ([@helehex](https://github.com/helehex) and [@jayzhan211](https://github.com/jayzhan211), contributes towards - [#2325](https://github.com/modularml/mojo/issues/2325)) + [#2325](https://github.com/modular/mojo/issues/2325)) - [`Optional`](/mojo/stdlib/collections/optional/Optional) now implements `__is__` and `__isnot__` methods so that you can compare an `Optional` with @@ -2951,7 +2951,7 @@ Special thanks to our community contributors: [`chr`](/mojo/stdlib/collections/string/chr) functions have been improved to accept any Unicode character. ([@mzaks](https://github.com/mzaks), contributes towards - [#1616](https://github.com/modularml/mojo/issues/1616)) + [#1616](https://github.com/modular/mojo/issues/1616)) - [`atol()`](/mojo/stdlib/collections/string/atol) now handles whitespace. The `atol()`function is used internally by `String.__int__()`, so @@ -2960,7 +2960,7 @@ Special thanks to our community contributors: - [`SIMD`](/mojo/stdlib/builtin/simd/SIMD) now implements the `__rmod__()` method. ([@bgreni](https://github.com/bgreni), fixes - [#1482](https://github.com/modularml/mojo/issues/1482)) + [#1482](https://github.com/modular/mojo/issues/1482)) - [`bool(None)`](/mojo/stdlib/builtin/bool/bool-function) is now implemented. ([@zhoujingya](https://github.com/zhoujingya)) @@ -2995,7 +2995,7 @@ Special thanks to our community contributors: division, modulo, and left and right shift operators, including the in-place and reverse variants. ([@LJ-9801](https://github.com/LJ-9801), fixes - [#2224](https://github.com/modularml/mojo/issues/2224)) + [#2224](https://github.com/modular/mojo/issues/2224)) - Added checked arithmetic operations for `SIMD` integers. @@ -3027,7 +3027,7 @@ Special thanks to our community contributors: - Added [`os.remove()`](/mojo/stdlib/os/os/remove) and [`os.unlink()`](/mojo/stdlib/os/os/unlink) for deleting files. ([@artemiogr97](https://github.com/artemiogr97), fixes - [#2306](https://github.com/modularml/mojo/issues/2306)) + [#2306](https://github.com/modular/mojo/issues/2306)) #### 🦋 Changed @@ -3047,15 +3047,15 @@ Special thanks to our community contributors: ``` ([@lsh](https://github.com/lsh), fixes - [#2179](https://github.com/modularml/mojo/issues/2179)) + [#2179](https://github.com/modular/mojo/issues/2179)) - Per the accepted community proposal, [Standardize the representation of byte sequence as a sequence of unsigned - 8-bit integers](https://github.com/modularml/mojo/blob/main/proposals/byte-as-uint8.md), + 8-bit integers](https://github.com/modular/mojo/blob/main/proposals/byte-as-uint8.md), began transition to using `UInt8` by changing the data pointer of `Error` to `DTypePointer[DType.uint8]`. ([@gabrieldemarmiesse](https://github.com/gabrieldemarmiesse), contributes - towards [#2317](https://github.com/modularml/mojo/issues/2317)) + towards [#2317](https://github.com/modular/mojo/issues/2317)) - Continued transition to `UnsafePointer` from the legacy `Pointer` type in various standard library APIs and internals. @@ -3127,56 +3127,56 @@ Special thanks to our community contributors: #### 🛠️ Fixed -- [#516](https://github.com/modularml/mojo/issues/516) and - [#1817](https://github.com/modularml/mojo/issues/1817) and many others, e.g. +- [#516](https://github.com/modular/mojo/issues/516) and + [#1817](https://github.com/modular/mojo/issues/1817) and many others, e.g. "Can't create a function that returns two strings." -- [#1178](https://github.com/modularml/mojo/issues/1178) (os/kern) failure (5). +- [#1178](https://github.com/modular/mojo/issues/1178) (os/kern) failure (5). -- [#1609](https://github.com/modularml/mojo/issues/1609) alias with +- [#1609](https://github.com/modular/mojo/issues/1609) alias with `DynamicVector[Tuple[Int]]` fails. -- [#1987](https://github.com/modularml/mojo/issues/1987) Defining `main` +- [#1987](https://github.com/modular/mojo/issues/1987) Defining `main` in a Mojo package is an error, for now. This is not intended to work yet, erroring for now will help to prevent accidental undefined behavior. -- [#1215](https://github.com/modularml/mojo/issues/1215) and - [#1949](https://github.com/modularml/mojo/issues/1949) The Mojo LSP server no +- [#1215](https://github.com/modular/mojo/issues/1215) and + [#1949](https://github.com/modular/mojo/issues/1949) The Mojo LSP server no longer cuts off hover previews for functions with functional arguments, parameters, or results. -- [#1901](https://github.com/modularml/mojo/issues/1901) Fixed Mojo LSP and +- [#1901](https://github.com/modular/mojo/issues/1901) Fixed Mojo LSP and documentation generation handling of inout arguments. -- [#1913](https://github.com/modularml/mojo/issues/1913) - `0__` no longer +- [#1913](https://github.com/modular/mojo/issues/1913) - `0__` no longer crashes the Mojo parser. -- [#1924](https://github.com/modularml/mojo/issues/1924) JIT debugging on Mac +- [#1924](https://github.com/modular/mojo/issues/1924) JIT debugging on Mac has been fixed. -- [#1941](https://github.com/modularml/mojo/issues/1941) Mojo variadic arguments +- [#1941](https://github.com/modular/mojo/issues/1941) Mojo variadic arguments don't work with non-trivial register-only types. -- [#1963](https://github.com/modularml/mojo/issues/1963) `a!=0` is now parsed +- [#1963](https://github.com/modular/mojo/issues/1963) `a!=0` is now parsed and formatted correctly by `mojo format`. -- [#1676](https://github.com/modularml/mojo/issues/1676) Fix a crash related to +- [#1676](https://github.com/modular/mojo/issues/1676) Fix a crash related to `@value` decorator and structs with empty body. -- [#1917](https://github.com/modularml/mojo/issues/1917) Fix a crash after +- [#1917](https://github.com/modular/mojo/issues/1917) Fix a crash after syntax error during tuple creation. -- [#2006](https://github.com/modularml/mojo/issues/2006) The Mojo LSP now +- [#2006](https://github.com/modular/mojo/issues/2006) The Mojo LSP now properly supports signature types with named arguments and parameters. -- [#2007](https://github.com/modularml/mojo/issues/2007) and - [#1997](https://github.com/modularml/mojo/issues/1997) The Mojo LSP no longer +- [#2007](https://github.com/modular/mojo/issues/2007) and + [#1997](https://github.com/modular/mojo/issues/1997) The Mojo LSP no longer crashes on certain types of closures. -- [#1675](https://github.com/modularml/mojo/issues/1675) Ensure `@value` +- [#1675](https://github.com/modular/mojo/issues/1675) Ensure `@value` decorator fails gracefully after duplicate field error. -- [#2068](https://github.com/modularml/mojo/issues/2068) +- [#2068](https://github.com/modular/mojo/issues/2068) Fix `SIMD.reduce()` for size_out == 2. ([@soraros](https://github.com/soraros)) @@ -3189,7 +3189,7 @@ This release doesn't include any changes to Mojo. ### 🔥 Legendary - The Mojo standard library is now open source! Check out the - [README](https://github.com/modularml/mojo/blob/nightly/stdlib/README.md) + [README](https://github.com/modular/mojo/blob/nightly/stdlib/README.md) for everything you need to get started. - Structs and other nominal types are now allowed to implicitly conform to @@ -3258,7 +3258,7 @@ This release doesn't include any changes to Mojo. - `let` declarations now produce a compile time error instead of a warning, our next step in [removing let - declarations](https://github.com/modularml/mojo/blob/main/proposals/remove-let-decls.md). + declarations](https://github.com/modular/mojo/blob/main/proposals/remove-let-decls.md). The compiler still recognizes the `let` keyword for now in order to produce a good error message, but that will be removed in subsequent releases. @@ -3562,27 +3562,27 @@ fixed in a future release. #### 🛠️ Fixed -- [#1362](https://github.com/modularml/mojo/issues/1362) - Parameter inference +- [#1362](https://github.com/modular/mojo/issues/1362) - Parameter inference now recursively matches function types. -- [#951](https://github.com/modularml/mojo/issues/951) - Functions that were +- [#951](https://github.com/modular/mojo/issues/951) - Functions that were both `async` and `@always_inline` incorrectly errored. -- [#1858](https://github.com/modularml/mojo/issues/1858) - Trait with parametric +- [#1858](https://github.com/modular/mojo/issues/1858) - Trait with parametric methods regression. -- [#1892](https://github.com/modularml/mojo/issues/1892) - Forbid unsupported +- [#1892](https://github.com/modular/mojo/issues/1892) - Forbid unsupported decorators on traits. -- [#1735](https://github.com/modularml/mojo/issues/1735) - Trait-typed values +- [#1735](https://github.com/modular/mojo/issues/1735) - Trait-typed values are incorrectly considered equal. -- [#1909](https://github.com/modularml/mojo/issues/1909) - Crash due to nested +- [#1909](https://github.com/modular/mojo/issues/1909) - Crash due to nested import in unreachable block. -- [#1921](https://github.com/modularml/mojo/issues/1921) - Parser crashes +- [#1921](https://github.com/modular/mojo/issues/1921) - Parser crashes binding Reference to lvalue with subtype lifetime. -- [#1945](https://github.com/modularml/mojo/issues/1945) - `Optional[T].or_else()` +- [#1945](https://github.com/modular/mojo/issues/1945) - `Optional[T].or_else()` should return `T` instead of `Optional[T]`. -- [#1940](https://github.com/modularml/mojo/issues/1940) - Constrain +- [#1940](https://github.com/modular/mojo/issues/1940) - Constrain `math.copysign` to floating point or integral types. -- [#1838](https://github.com/modularml/mojo/issues/1838) - Variadic `print` +- [#1838](https://github.com/modular/mojo/issues/1838) - Variadic `print` does not work when specifying `end=""` -- [#1826](https://github.com/modularml/mojo/issues/1826) - The `SIMD.reduce` +- [#1826](https://github.com/modular/mojo/issues/1826) - The `SIMD.reduce` methods correctly handle edge cases where `size_out >= size`. ## v24.1.1 (2024-03-18) @@ -3936,7 +3936,7 @@ installation issues. Otherwise it is functionally identical to Mojo 24.1. ### 🦋 Changed - As another step towards [removing let - declarations](https://github.com/modularml/mojo/blob/main/proposals/remove-let-decls.md) + declarations](https://github.com/modular/mojo/blob/main/proposals/remove-let-decls.md) we have removed support for let declarations inside the compiler. To ease migration, we parse `let` declarations as a `var` declaration so your code won't break. We emit a warning about this, but please switch your code to @@ -4036,72 +4036,72 @@ experience without dedicated sugar. ### 🛠️ Fixed -- [#435](https://github.com/modularml/mojo/issues/435) +- [#435](https://github.com/modular/mojo/issues/435) Structs with Self type don't always work. -- [#1540](https://github.com/modularml/mojo/issues/1540) +- [#1540](https://github.com/modular/mojo/issues/1540) Crash in register_passable self referencing struct. -- [#1664](https://github.com/modularml/mojo/issues/1664) - Improve error +- [#1664](https://github.com/modular/mojo/issues/1664) - Improve error message when `StaticTuple` is constructed with a negative size for the number of elements. -- [#1679](https://github.com/modularml/mojo/issues/1679) - crash on SIMD of zero +- [#1679](https://github.com/modular/mojo/issues/1679) - crash on SIMD of zero elements. - Various crashes on invalid code: - [#1230](https://github.com/modularml/mojo/issues/1230), - [#1699](https://github.com/modularml/mojo/issues/1699), - [#1708](https://github.com/modularml/mojo/issues/1708) -- [#1223](https://github.com/modularml/mojo/issues/1223) - Crash when parametric + [#1230](https://github.com/modular/mojo/issues/1230), + [#1699](https://github.com/modular/mojo/issues/1699), + [#1708](https://github.com/modular/mojo/issues/1708) +- [#1223](https://github.com/modular/mojo/issues/1223) - Crash when parametric function is passed as (runtime) argument. The parser now errors out instead. -- [#1530](https://github.com/modularml/mojo/issues/1530) - Crash during +- [#1530](https://github.com/modular/mojo/issues/1530) - Crash during diagnostic emission for parameter deduction failure. -- [#1538](https://github.com/modularml/mojo/issues/1538) and [#1607]( - https://github.com/modularml/mojo/issues/1607) - Crash when returning type +- [#1538](https://github.com/modular/mojo/issues/1538) and [#1607]( + https://github.com/modular/mojo/issues/1607) - Crash when returning type value instead of instance of expected type. This is a common mistake and the error now includes a hint to point users to the problem. -- [#1613](https://github.com/modularml/mojo/issues/1613) - Wrong type name in +- [#1613](https://github.com/modular/mojo/issues/1613) - Wrong type name in error for incorrect `self` argument type in trait method declaration. -- [#1670](https://github.com/modularml/mojo/issues/1670) - Crash on implicit +- [#1670](https://github.com/modular/mojo/issues/1670) - Crash on implicit conversion in a global variable declaration. -- [#1741](https://github.com/modularml/mojo/issues/1741) - Mojo documentation +- [#1741](https://github.com/modular/mojo/issues/1741) - Mojo documentation generation doesn't show `inout`/`owned` on variadic arguments. -- [#1621](https://github.com/modularml/mojo/issues/1621) - VS Code does not +- [#1621](https://github.com/modular/mojo/issues/1621) - VS Code does not highlight `raises` and `capturing` in functional type expressions. -- [#1617](https://github.com/modularml/mojo/issues/1617) - VS Code does not +- [#1617](https://github.com/modular/mojo/issues/1617) - VS Code does not highlight `fn` in specific contexts. -- [#1740](https://github.com/modularml/mojo/issues/1740) - LSP shows unrelated +- [#1740](https://github.com/modular/mojo/issues/1740) - LSP shows unrelated info when hovering over a struct. -- [#1238](https://github.com/modularml/mojo/issues/1238) - File shadows Mojo +- [#1238](https://github.com/modular/mojo/issues/1238) - File shadows Mojo package path. -- [#1429](https://github.com/modularml/mojo/issues/1429) - Crash when using +- [#1429](https://github.com/modular/mojo/issues/1429) - Crash when using nested import statement. -- [#1322](https://github.com/modularml/mojo/issues/1322) - Crash when missing +- [#1322](https://github.com/modular/mojo/issues/1322) - Crash when missing types in variadic argument. -- [#1314](https://github.com/modularml/mojo/issues/1314) - Typecheck error when +- [#1314](https://github.com/modular/mojo/issues/1314) - Typecheck error when binding alias to parametric function with default argument. -- [#1248](https://github.com/modularml/mojo/issues/1248) - Crash when importing +- [#1248](https://github.com/modular/mojo/issues/1248) - Crash when importing from file the same name as another file in the search path. -- [#1354](https://github.com/modularml/mojo/issues/1354) - Crash when importing +- [#1354](https://github.com/modular/mojo/issues/1354) - Crash when importing from local package. -- [#1488](https://github.com/modularml/mojo/issues/1488) - Crash when setting +- [#1488](https://github.com/modular/mojo/issues/1488) - Crash when setting generic element field. -- [#1476](https://github.com/modularml/mojo/issues/1476) - Crash in interpreter +- [#1476](https://github.com/modular/mojo/issues/1476) - Crash in interpreter when calling functions in parameter context. -- [#1537](https://github.com/modularml/mojo/issues/1537) - Crash when copying +- [#1537](https://github.com/modular/mojo/issues/1537) - Crash when copying parameter value. -- [#1546](https://github.com/modularml/mojo/issues/1546) - Modify nested vector +- [#1546](https://github.com/modular/mojo/issues/1546) - Modify nested vector element crashes parser. -- [#1558](https://github.com/modularml/mojo/issues/1558) - Invalid import causes +- [#1558](https://github.com/modular/mojo/issues/1558) - Invalid import causes parser to crash. -- [#1562](https://github.com/modularml/mojo/issues/1562) - Crash when calling +- [#1562](https://github.com/modular/mojo/issues/1562) - Crash when calling parametric type member function. -- [#1577](https://github.com/modularml/mojo/issues/1577) - Crash when using +- [#1577](https://github.com/modular/mojo/issues/1577) - Crash when using unresolved package as a variable. -- [#1579](https://github.com/modularml/mojo/issues/1579) - Member access into +- [#1579](https://github.com/modular/mojo/issues/1579) - Member access into type instances causes a crash. -- [#1602](https://github.com/modularml/mojo/issues/1602) - Interpreter failure +- [#1602](https://github.com/modular/mojo/issues/1602) - Interpreter failure when constructing strings at compile time. -- [#1696](https://github.com/modularml/mojo/issues/1696) - Fixed an issue that +- [#1696](https://github.com/modular/mojo/issues/1696) - Fixed an issue that caused syntax highlighting to occasionally fail. -- [#1549](https://github.com/modularml/mojo/issues/1549) - Fixed an issue when +- [#1549](https://github.com/modular/mojo/issues/1549) - Fixed an issue when the shift amount is out of range in `SIMD.shift_left` and `SIMD.shift_right`. ## v0.7.0 (2024-01-25) @@ -4447,7 +4447,7 @@ experience without dedicated sugar. - The `simd_width` and `dtype` parameters of `polynomial_evaluate` have been switched. Based on the request in - [#1587](https://github.com/modularml/mojo/issues/1587), the + [#1587](https://github.com/modular/mojo/issues/1587), the `polynomial_evaluate` function has also been extended so that the `coefficients` parameter can take either a either a [`StaticTuple`](/mojo/stdlib/utils/static_tuple/StaticTuple) or a @@ -4458,50 +4458,50 @@ experience without dedicated sugar. ### 🛠️ Fixed -- [#1595](https://github.com/modularml/mojo/issues/1595) - Improve error message +- [#1595](https://github.com/modular/mojo/issues/1595) - Improve error message when trying to materialize `IntLiteral` in runtime code. - Raising an error from the initializer of a memory-only type now works correctly in the presence of complex control flow. Previously Mojo could run the destructor on `self` before it was initialized when exiting with an error. -- [#1096](https://github.com/modularml/mojo/issues/1096) - Improve warning +- [#1096](https://github.com/modular/mojo/issues/1096) - Improve warning messages for dead code in conditionals like `or` expressions. -- [#1419](https://github.com/modularml/mojo/issues/1419) - Fix assertion failure +- [#1419](https://github.com/modular/mojo/issues/1419) - Fix assertion failure with uninitialized lattice values. -- [#1402](https://github.com/modularml/mojo/issues/1402) - Fix movable trait not +- [#1402](https://github.com/modular/mojo/issues/1402) - Fix movable trait not detected on recursive struct implemented with `AnyPointer`. -- [#1399](https://github.com/modularml/mojo/issues/1399) - Fix parser crash when +- [#1399](https://github.com/modular/mojo/issues/1399) - Fix parser crash when a parameter type in a struct that implements a trait is misspelled. -- [#1152](https://github.com/modularml/mojo/issues/1152) - Allow mutable `self` +- [#1152](https://github.com/modular/mojo/issues/1152) - Allow mutable `self` argument when overloading operators using dunder methods. -- [#1493](https://github.com/modularml/mojo/issues/1493) - Fix crash in +- [#1493](https://github.com/modular/mojo/issues/1493) - Fix crash in `DynamicVector` copy constructor in certain situations. -- [#1316](https://github.com/modularml/mojo/issues/1316) - The `benchmark.keep` +- [#1316](https://github.com/modular/mojo/issues/1316) - The `benchmark.keep` function now properly handles vector types. -- [#1505](https://github.com/modularml/mojo/issues/1505) - The `simd.shuffle` +- [#1505](https://github.com/modular/mojo/issues/1505) - The `simd.shuffle` operation now works on 64 element permutations. -- [#1355](https://github.com/modularml/mojo/issues/1355) - Fix `String.find()` +- [#1355](https://github.com/modular/mojo/issues/1355) - Fix `String.find()` returning wrong value when starting index is non-zero. -- [#1367](https://github.com/modularml/mojo/issues/1367) - Fix `String.replace()` +- [#1367](https://github.com/modular/mojo/issues/1367) - Fix `String.replace()` returning incorrect results for multi-character search strings. -- [#1535](https://github.com/modularml/mojo/issues/1535) - Invalid error `field +- [#1535](https://github.com/modular/mojo/issues/1535) - Invalid error `field 'w.x.y' destroyed out of the middle of a value, preventing the overall value from being destroyed`. -- [#1475](https://github.com/modularml/mojo/issues/1475) - Assertion failure in +- [#1475](https://github.com/modular/mojo/issues/1475) - Assertion failure in nested loop. -- [#1591](https://github.com/modularml/mojo/issues/1591) - Assertion failure +- [#1591](https://github.com/modular/mojo/issues/1591) - Assertion failure when using `AnyType` struct member. -- [#1503](https://github.com/modularml/mojo/issues/1503) - Rename the mojo build +- [#1503](https://github.com/modular/mojo/issues/1503) - Rename the mojo build of LLDB to `mojo-lldb`, to prevent name collisions with the system's LLDB. -- [#1542](https://github.com/modularml/mojo/issues/1542) - `@unroll` does not +- [#1542](https://github.com/modular/mojo/issues/1542) - `@unroll` does not accept alias as unroll factor. -- [#1443](https://github.com/modularml/mojo/issues/1443) - Compiler crash on +- [#1443](https://github.com/modular/mojo/issues/1443) - Compiler crash on variadic list of traits. -- [#1604](https://github.com/modularml/mojo/issues/1604) - Variable of trivial +- [#1604](https://github.com/modular/mojo/issues/1604) - Variable of trivial type not destroyed by transferring ownership. -- [#1341](https://github.com/modularml/mojo/issues/1341) - Segmentation fault +- [#1341](https://github.com/modular/mojo/issues/1341) - Segmentation fault when passing closures around. -- [#217](https://github.com/modularml/mojo/issues/217) - Closure state is +- [#217](https://github.com/modular/mojo/issues/217) - Closure state is stack allocated. ## v0.6.1 (2023-12-18) @@ -4563,23 +4563,23 @@ experience without dedicated sugar. ### 🛠️ Fixed -- [#1421](https://github.com/modularml/mojo/issues/1421) - Fixed a crash when +- [#1421](https://github.com/modular/mojo/issues/1421) - Fixed a crash when using Tuples in the REPL. -- [#222](https://github.com/modularml/mojo/issues/222) - Generate an error +- [#222](https://github.com/modular/mojo/issues/222) - Generate an error for obviously self recursive functions. -- [#1408](https://github.com/modularml/mojo/issues/1408) - Fix overload +- [#1408](https://github.com/modular/mojo/issues/1408) - Fix overload resolution when candidates can return generic types. -- [#1413](https://github.com/modularml/mojo/issues/1413) and - [#1395](https://github.com/modularml/mojo/issues/1395) - Do not crash when +- [#1413](https://github.com/modular/mojo/issues/1413) and + [#1395](https://github.com/modular/mojo/issues/1395) - Do not crash when re-declaring a builtin declaration. -- [#1307](https://github.com/modularml/mojo/issues/1307) - Fix compatibility of +- [#1307](https://github.com/modular/mojo/issues/1307) - Fix compatibility of function signatures that only differ in default argument values. -- [#1380](https://github.com/modularml/mojo/issues/1380) - Fix printing +- [#1380](https://github.com/modular/mojo/issues/1380) - Fix printing of empty `String`. ## v0.6.0 (2023-12-04) @@ -4714,7 +4714,7 @@ experience without dedicated sugar. manual (now deprecated). Plus, the entire Mojo Manual and other Mojo docs are now [open-sourced on - GitHub](https://github.com/modularml/mojo/tree/main/docs), and we'd love + GitHub](https://github.com/modular/mojo/tree/main/docs), and we'd love to accept contributions to help us improve them! - Mojo now supports partial automatic parameterization: when a function is @@ -4882,7 +4882,7 @@ the previous "read to EOF" behavior when size is negative. - The Mojo Language Server now implements the Document Symbols request. IDEs use this to provide support for **Outline View** and **Go to Symbol**. This - addresses [Issue #960](https://github.com/modularml/mojo/issues/960). + addresses [Issue #960](https://github.com/modular/mojo/issues/960). - The Mojo Language Server now shows documentation when code completing modules or packages in `import` statements. @@ -4946,31 +4946,31 @@ the previous "read to EOF" behavior when size is negative. ### 🛠️ Fixed -- [#734](https://github.com/modularml/mojo/issues/734) - Consumption of struct +- [#734](https://github.com/modular/mojo/issues/734) - Consumption of struct works only for types with a `__del__` method. -- [#910](https://github.com/modularml/mojo/issues/910) - Parser crash when +- [#910](https://github.com/modular/mojo/issues/910) - Parser crash when using memory-only generic type as return of function that `raise`s. -- [#1060](https://github.com/modularml/mojo/issues/1060) - Mojo happily parses +- [#1060](https://github.com/modular/mojo/issues/1060) - Mojo happily parses code that has messed up indentation -- [#1159](https://github.com/modularml/mojo/issues/1159) - The language server +- [#1159](https://github.com/modular/mojo/issues/1159) - The language server doesn't warn about bad return type. -- [#1166](https://github.com/modularml/mojo/issues/1166) - warning: unreachable +- [#1166](https://github.com/modular/mojo/issues/1166) - warning: unreachable code after return statement with context manager -- [#1098](https://github.com/modularml/mojo/issues/1098) - The language server +- [#1098](https://github.com/modular/mojo/issues/1098) - The language server doesn't highlight properties of PythonObjects correctly. -- [#1153](https://github.com/modularml/mojo/issues/1153) - The language server +- [#1153](https://github.com/modular/mojo/issues/1153) - The language server crashes when parsing an invalid multi-nested module import. -- [#1236](https://github.com/modularml/mojo/issues/1236) - The language server +- [#1236](https://github.com/modular/mojo/issues/1236) - The language server doesn't show autocomplete in if statements. -- [#1246](https://github.com/modularml/mojo/issues/1246) - Warning diagnostics +- [#1246](https://github.com/modular/mojo/issues/1246) - Warning diagnostics are transient in the presence of caching. ### Known Issue @@ -5225,32 +5225,32 @@ the previous "read to EOF" behavior when size is negative. ### 🛠️ Fixed -- [#532](https://github.com/modularml/mojo/issues/532) - Compiler optimizing +- [#532](https://github.com/modular/mojo/issues/532) - Compiler optimizing while True loop away -- [#760](https://github.com/modularml/mojo/issues/760) - Compilation error: +- [#760](https://github.com/modular/mojo/issues/760) - Compilation error: 'hlcf.for.yield' op specifies 0 branch inputs but target expected 1 along control-flow edge from here -- [#849](https://github.com/modularml/mojo/issues/849) - The `Tensor` type is +- [#849](https://github.com/modular/mojo/issues/849) - The `Tensor` type is now initialized with zeros at construction time. -- [#912](https://github.com/modularml/mojo/issues/912) - Invalid load for +- [#912](https://github.com/modular/mojo/issues/912) - Invalid load for `__get_address_as_lvalue`. -- [#916](https://github.com/modularml/mojo/issues/916) - Parser crash when +- [#916](https://github.com/modular/mojo/issues/916) - Parser crash when specifying default values for `inout` arguments. -- [#943](https://github.com/modularml/mojo/issues/943) - Mojo hangs if you +- [#943](https://github.com/modular/mojo/issues/943) - Mojo hangs if you use continue in the nested loop -- [#957](https://github.com/modularml/mojo/issues/957) - Parser crash when a +- [#957](https://github.com/modular/mojo/issues/957) - Parser crash when a function call with variadic arguments of a memory-only type is evaluated at compile time. -- [#990](https://github.com/modularml/mojo/issues/990) - Fixes rounding +- [#990](https://github.com/modular/mojo/issues/990) - Fixes rounding issue with floor division with negative numerator. -- [#1018](https://github.com/modularml/mojo/issues/1018) - In some cases the +- [#1018](https://github.com/modular/mojo/issues/1018) - In some cases the sort function was returning invalid results. This release fixes some of these corner cases. -- [#1010](https://github.com/modularml/mojo/issues/1010) - Initializing tensor +- [#1010](https://github.com/modular/mojo/issues/1010) - Initializing tensor in alias declaration results in crash. -- [#1110](https://github.com/modularml/mojo/issues/1110) - The `time.now()` +- [#1110](https://github.com/modular/mojo/issues/1110) - The `time.now()` function now returns nanoseconds across all operating systems. -- [#1115](https://github.com/modularml/mojo/issues/1115) - cannot load +- [#1115](https://github.com/modular/mojo/issues/1115) - cannot load non-register passable type into SSA register. ## v0.4.0 for Mac (2023-10-19) @@ -5429,33 +5429,33 @@ the previous "read to EOF" behavior when size is negative. ### 🛠️ Fixed -- [#794](https://github.com/modularml/mojo/issues/794) - Parser crash when +- [#794](https://github.com/modular/mojo/issues/794) - Parser crash when using the `in` operator. -- [#936](https://github.com/modularml/mojo/issues/936) - The `Int` constructor +- [#936](https://github.com/modular/mojo/issues/936) - The `Int` constructor now accepts other `Int` instances. -- [#921](https://github.com/modularml/mojo/issues/921) - Better error message +- [#921](https://github.com/modular/mojo/issues/921) - Better error message when running `mojo` on a module with no `main` function. -- [#556](https://github.com/modularml/mojo/issues/556) - UInt64s are now +- [#556](https://github.com/modular/mojo/issues/556) - UInt64s are now printed correctly. -- [#804](https://github.com/modularml/mojo/issues/804) - Emit error instead of +- [#804](https://github.com/modular/mojo/issues/804) - Emit error instead of crashing when passing variadic arguments of unsupported types. -- [#833](https://github.com/modularml/mojo/issues/833) - Parser crash when +- [#833](https://github.com/modular/mojo/issues/833) - Parser crash when assigning module value. -- [#752](https://github.com/modularml/mojo/issues/752) - Parser crash when +- [#752](https://github.com/modular/mojo/issues/752) - Parser crash when calling async def. -- [#711](https://github.com/modularml/mojo/issues/711) - The overload resolution +- [#711](https://github.com/modular/mojo/issues/711) - The overload resolution logic now correctly prioritizes instance methods over static methods (if candidates are an equally good match otherwise), and no longer crashed if a static method has a `Self` type as its first argument. -- [#859](https://github.com/modularml/mojo/issues/859) - Fix confusing error and +- [#859](https://github.com/modular/mojo/issues/859) - Fix confusing error and documentation of the `rebind` builtin. -- [#753](https://github.com/modularml/mojo/issues/753) - Direct use of LLVM +- [#753](https://github.com/modular/mojo/issues/753) - Direct use of LLVM dialect produces strange errors in the compiler. -- [#926](https://github.com/modularml/mojo/issues/926) - Fixes an issue that +- [#926](https://github.com/modular/mojo/issues/926) - Fixes an issue that occurred when a function with a return type of `StringRef` raised an error. When the function raised an error, it incorrectly returned the string value of that error. -- [#536](https://github.com/modularml/mojo/issues/536) - Report More information +- [#536](https://github.com/modular/mojo/issues/536) - Report More information on python exception. ## v0.3.1 (2023-09-28) @@ -5466,15 +5466,15 @@ installing the previous versions of the SDK, this release may be for you. ### 🛠️ Fixed -- [#538](https://github.com/modularml/mojo/issues/538) - Installation hangs +- [#538](https://github.com/modular/mojo/issues/538) - Installation hangs during the testing phase. This issue occurs on machines with a low number of CPU cores, such as free AWS EC2 instances and GitHub Codespaces. -- [#590](https://github.com/modularml/mojo/issues/590) - Installation fails +- [#590](https://github.com/modular/mojo/issues/590) - Installation fails with a “failed to run python” message. -- [#672](https://github.com/modularml/mojo/issues/672) - Language server hangs +- [#672](https://github.com/modular/mojo/issues/672) - Language server hangs on code completion. Related to #538, this occurs on machines with a low number of CPU cores. -- [#913](https://github.com/modularml/mojo/issues/913) - In the REPL and Jupyter +- [#913](https://github.com/modular/mojo/issues/913) - In the REPL and Jupyter notebooks, inline comments were being parsed incorrectly. ## v0.3.0 (2023-09-21) @@ -5576,7 +5576,7 @@ Code](https://marketplace.visualstudio.com/items?itemName=modular-mojotools.vsco - The Mojo Language Server now supports top-level code completions, enabling completion when typing a reference to a variable, type, etc. This resolves - [#679](https://github.com/modularml/mojo/issues/679). + [#679](https://github.com/modular/mojo/issues/679). - The Mojo REPL now colorizes the resultant variables to help distinguish input expressions from the output variables. @@ -5615,19 +5615,19 @@ Code](https://marketplace.visualstudio.com/items?itemName=modular-mojotools.vsco ### 🛠️ Fixed -- [#503](https://github.com/modularml/mojo/issues/503) - Improve error message +- [#503](https://github.com/modular/mojo/issues/503) - Improve error message for failure lowering `kgen.param.constant`. -- [#554](https://github.com/modularml/mojo/issues/554) - Alias of static tuple +- [#554](https://github.com/modular/mojo/issues/554) - Alias of static tuple fails to expand. -- [#500](https://github.com/modularml/mojo/issues/500) - Call expansion failed +- [#500](https://github.com/modular/mojo/issues/500) - Call expansion failed due to verifier error. -- [#422](https://github.com/modularml/mojo/issues/422) - Incorrect comment +- [#422](https://github.com/modular/mojo/issues/422) - Incorrect comment detection in multiline strings. -- [#729](https://github.com/modularml/mojo/issues/740) - Improve messaging on +- [#729](https://github.com/modular/mojo/issues/740) - Improve messaging on how to exit the REPL. -- [#756](https://github.com/modularml/mojo/issues/756) - Fix initialization +- [#756](https://github.com/modular/mojo/issues/756) - Fix initialization errors of the VS Code extension. -- [#575](https://github.com/modularml/mojo/issues/575) - Build LLDB/REPL with +- [#575](https://github.com/modular/mojo/issues/575) - Build LLDB/REPL with libedit for a nicer editing experience in the terminal. ## v0.2.1 (2023-09-07) @@ -5690,17 +5690,17 @@ All earlier releases were considered version 0.1. been changed to enable type inference. Now it's possible to write `rotate_right[shift_val](simd_val)` and have the `dtype` and `simd_width` inferred from the argument. This addresses - [Issue #528](https://github.com/modularml/mojo/issues/528). + [Issue #528](https://github.com/modular/mojo/issues/528). ### 🛠️ Fixed - Fixed a bug causing the parser to crash when the `with` statement was written without a colon. - This addresses [Issue #529](https://github.com/modularml/mojo/issues/529). + This addresses [Issue #529](https://github.com/modular/mojo/issues/529). - Incorrect imports no longer crash when there are other errors at the top level of a module. This fixes [Issue - #531](https://github.com/modularml/mojo/issues/531). + #531](https://github.com/modular/mojo/issues/531). ## August 2023 @@ -5775,15 +5775,15 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed - Fixed issues with print formatting for `DType.uint16` and `DType.int16`. -- [Issue #499](https://github.com/modularml/mojo/issues/499) - Two new +- [Issue #499](https://github.com/modular/mojo/issues/499) - Two new `rotate_right` and `rotate_left` functions have been added to the SIMD module. -- [Issue #429](https://github.com/modularml/mojo/issues/429) - You can now +- [Issue #429](https://github.com/modular/mojo/issues/429) - You can now construct a `Bool` from a `SIMD` type whose element-type is `DType.bool`. -- [Issue #350](https://github.com/modularml/mojo/issues/350) - Confusing Matrix +- [Issue #350](https://github.com/modular/mojo/issues/350) - Confusing Matrix implementation -- [Issue #349](https://github.com/modularml/mojo/issues/349) - Missing load_tr +- [Issue #349](https://github.com/modular/mojo/issues/349) - Missing load_tr in struct Matrix -- [Issue #501](https://github.com/modularml/mojo/issues/501) - Missing syntax +- [Issue #501](https://github.com/modular/mojo/issues/501) - Missing syntax error messages in Python expressions. ### 2023-08-09 @@ -5847,7 +5847,7 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #53](https://github.com/modularml/mojo/issues/53) - `Int` now +- [Issue #53](https://github.com/modular/mojo/issues/53) - `Int` now implements true division with the `/` operator. Similar to Python, this returns a 64-bit floating point number. The corresponding in-place operator, `/=`, has the same semantics as `//=`. @@ -5887,10 +5887,10 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #437](https://github.com/modularml/mojo/issues/437) - Range can now +- [Issue #437](https://github.com/modular/mojo/issues/437) - Range can now be instantiated with a PythonObject. -- [Issue #288](https://github.com/modularml/mojo/issues/288) - Python strings +- [Issue #288](https://github.com/modular/mojo/issues/288) - Python strings can now be safely copied. ### 2023-07-20 @@ -5899,7 +5899,7 @@ All earlier releases were considered version 0.1. - Mojo now includes a `Limits` module, which contains functions to get the max and min values representable by a type, as requested in [Issue - #51](https://github.com/modularml/mojo/issues/51). The following functions + #51](https://github.com/modular/mojo/issues/51). The following functions moved from `Math` to `Limits`: `inf()`, `neginf()`, `isinf()`, `isfinite()`. - Mojo decorators are now distinguished between "signature" and "body" @@ -5966,14 +5966,14 @@ All earlier releases were considered version 0.1. - Reverted the feature from 2023-02-13 that allowed unqualified struct members. Use the `Self` keyword to conveniently access struct members with bound parameters instead. This was required to fix - [Issue #260](https://github.com/modularml/mojo/issues/260). + [Issue #260](https://github.com/modular/mojo/issues/260). - Updated the RayTracing notebook: added step 5 to create specular lighting for more realistic images and step 6 to add a background image. #### 🛠️ Fixed -- [Issue #260](https://github.com/modularml/mojo/issues/260) - Definitions +- [Issue #260](https://github.com/modular/mojo/issues/260) - Definitions inside structs no longer shadow definitions outside of struct definitions. ### 2023-07-12 @@ -6010,7 +6010,7 @@ All earlier releases were considered version 0.1. - The walrus operator now works in if/while statements without parentheses, e.g. `if x := function():`. -- [Issue #428](https://github.com/modularml/mojo/issues/428) - The +- [Issue #428](https://github.com/modular/mojo/issues/428) - The `FloatLiteral` and `SIMD` types now support conversion to `Int` via the `to_int` or `__int__` method calls. The behavior matches that of Python, which rounds towards zero. @@ -6040,21 +6040,21 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #253](https://github.com/modularml/mojo/issues/253) - Issue +- [Issue #253](https://github.com/modular/mojo/issues/253) - Issue when accessing a struct member alias without providing parameters. -- [Issue #404](https://github.com/modularml/mojo/issues/404) - The docs now use +- [Issue #404](https://github.com/modular/mojo/issues/404) - The docs now use `snake_case` for variable names, which more closely conforms to Python's style. -- [Issue #379](https://github.com/modularml/mojo/issues/379) - Tuple +- [Issue #379](https://github.com/modular/mojo/issues/379) - Tuple limitations have been addressed and multiple return values are now supported, even without parentheses. -- [Issue #347](https://github.com/modularml/mojo/issues/347) - Tuples no longer +- [Issue #347](https://github.com/modular/mojo/issues/347) - Tuples no longer require parentheses. -- [Issue #320](https://github.com/modularml/mojo/issues/320) - Python objects +- [Issue #320](https://github.com/modular/mojo/issues/320) - Python objects are now traversable via `for` loops. ## June 2023 @@ -6080,18 +6080,18 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #229](https://github.com/modularml/mojo/issues/229) - Issue when +- [Issue #229](https://github.com/modular/mojo/issues/229) - Issue when throwing an exception from `__init__` before all fields are initialized. -- [Issue #74](https://github.com/modularml/mojo/issues/74) - Struct +- [Issue #74](https://github.com/modular/mojo/issues/74) - Struct definition with recursive reference crashes. -- [Issue #285](https://github.com/modularml/mojo/issues/285) - The +- [Issue #285](https://github.com/modular/mojo/issues/285) - The [`TargetInfo`](/mojo/stdlib/sys/info) module now includes `is_little_endian()` and `is_big_endian()` to check if the target host uses either little or big endian. -- [Issue #254](https://github.com/modularml/mojo/issues/254) - Parameter name +- [Issue #254](https://github.com/modular/mojo/issues/254) - Parameter name shadowing in nested scopes is now handled correctly. ### 2023-06-21 @@ -6148,14 +6148,14 @@ only in declared parameter names, e.g. the following now works correctly: fn foobar[w: Int, S: DType]() -> SIMD[S, w]: ... ``` -- [Issue #219](https://github.com/modularml/mojo/issues/219) - Issue when +- [Issue #219](https://github.com/modular/mojo/issues/219) - Issue when redefining a function and a struct defined in the same cell. -- [Issue #355](https://github.com/modularml/mojo/issues/355) - The loop order +- [Issue #355](https://github.com/modular/mojo/issues/355) - The loop order in the Matmul notebook for Python and naive mojo have been reordered for consistency. The loop order now follows (M, K, N) ordering. -- [Issue #309](https://github.com/modularml/mojo/issues/309) - Use snake case +- [Issue #309](https://github.com/modular/mojo/issues/309) - Use snake case naming within the testing package and move the asserts out of the TestSuite struct. @@ -6177,11 +6177,11 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #354](https://github.com/modularml/mojo/issues/354) - Returning a tuple +- [Issue #354](https://github.com/modular/mojo/issues/354) - Returning a tuple doesn't work even with parens. -- [Issue #365](https://github.com/modularml/mojo/issues/365) - Copy-paste error +- [Issue #365](https://github.com/modular/mojo/issues/365) - Copy-paste error in `FloatLiteral` docs. -- [Issue #357](https://github.com/modularml/mojo/issues/357) - Crash when +- [Issue #357](https://github.com/modular/mojo/issues/357) - Crash when missing input parameter to variadic parameter struct member function. ### 2023-06-07 @@ -6198,7 +6198,7 @@ only in declared parameter names, e.g. the following now works correctly: - Mojo Playground no longer includes the following Python packages (due to size, compute costs, and - [environment complications](https://github.com/modularml/mojo/issues/300)): + [environment complications](https://github.com/modular/mojo/issues/300)): `torch`, `tensorflow`, `keras`, `transformers`. #### 🦋 Changed @@ -6206,19 +6206,19 @@ only in declared parameter names, e.g. the following now works correctly: - The data types and scalar names now conform to the naming convention used by numpy. So we use `Int32` instead of `SI32`, similarly using `Float32` instead of `F32`. Closes - [Issue #152](https://github.com/modularml/mojo/issues/152). + [Issue #152](https://github.com/modular/mojo/issues/152). #### 🛠️ Fixed -- [Issue #287](https://github.com/modularml/mojo/issues/287) - computed +- [Issue #287](https://github.com/modular/mojo/issues/287) - computed lvalues don't handle raising functions correctly -- [Issue #318](https://github.com/modularml/mojo/issues/318) - Large integers +- [Issue #318](https://github.com/modular/mojo/issues/318) - Large integers are not being printed correctly -- [Issue #326](https://github.com/modularml/mojo/issues/326) - Float modulo +- [Issue #326](https://github.com/modular/mojo/issues/326) - Float modulo operator is not working as expected -- [Issue #282](https://github.com/modularml/mojo/issues/282) - Default arguments +- [Issue #282](https://github.com/modular/mojo/issues/282) - Default arguments are not working as expected -- [Issue #271](https://github.com/modularml/mojo/issues/271) - Confusing error +- [Issue #271](https://github.com/modular/mojo/issues/271) - Confusing error message when converting between function types with different result semantics ## May 2023 @@ -6228,7 +6228,7 @@ only in declared parameter names, e.g. the following now works correctly: #### ⭐️ New - Mojo Playground now includes the following Python packages (in response to - [popular demand](https://github.com/modularml/mojo/discussions/173)): + [popular demand](https://github.com/modular/mojo/discussions/173)): `torch`, `tensorflow`, `polars`, `opencv-python`, `keras`, `Pillow`, `plotly`, `seaborn`, `sympy`, `transformers`. @@ -6292,9 +6292,9 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #231](https://github.com/modularml/mojo/issues/231) - Unexpected error +- [Issue #231](https://github.com/modular/mojo/issues/231) - Unexpected error when a Python expression raises an exception -- [Issue #119](https://github.com/modularml/mojo/issues/119) - The REPL fails +- [Issue #119](https://github.com/modular/mojo/issues/119) - The REPL fails when a python variable is redefined ### 2023-05-24 @@ -6319,39 +6319,39 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #204](https://github.com/modularml/mojo/issues/204) - Mojo REPL +- [Issue #204](https://github.com/modular/mojo/issues/204) - Mojo REPL crash when returning a String at compile-time -- [Issue #143](https://github.com/modularml/mojo/issues/143) - synthesized +- [Issue #143](https://github.com/modular/mojo/issues/143) - synthesized init in `@register_passable` type doesn't get correct convention. -- [Issue #201](https://github.com/modularml/mojo/issues/201) - String literal +- [Issue #201](https://github.com/modular/mojo/issues/201) - String literal concatenation is too eager. -- [Issue #209](https://github.com/modularml/mojo/issues/209) - [QoI] Terrible +- [Issue #209](https://github.com/modular/mojo/issues/209) - [QoI] Terrible error message trying to convert a type to itself. -- [Issue #32](https://github.com/modularml/mojo/issues/32) - Include struct +- [Issue #32](https://github.com/modular/mojo/issues/32) - Include struct fields in docgen -- [Issue #50](https://github.com/modularml/mojo/issues/50) - Int to string +- [Issue #50](https://github.com/modular/mojo/issues/50) - Int to string conversion crashes due to buffer overflow -- [Issue #132](https://github.com/modularml/mojo/issues/132) - PythonObject +- [Issue #132](https://github.com/modular/mojo/issues/132) - PythonObject `to_int` method has a misleading name -- [Issue #189](https://github.com/modularml/mojo/issues/189) - PythonObject bool +- [Issue #189](https://github.com/modular/mojo/issues/189) - PythonObject bool conversion is incorrect -- [Issue #65](https://github.com/modularml/mojo/issues/65) - Add SIMD +- [Issue #65](https://github.com/modular/mojo/issues/65) - Add SIMD constructor from Bool -- [Issue #153](https://github.com/modularml/mojo/issues/153) - Meaning of +- [Issue #153](https://github.com/modular/mojo/issues/153) - Meaning of `Time.now` function result is unclear -- [Issue #165](https://github.com/modularml/mojo/issues/165) - Type in +- [Issue #165](https://github.com/modular/mojo/issues/165) - Type in `Pointer.free` documentation -- [Issue #210](https://github.com/modularml/mojo/issues/210) - Parameter results +- [Issue #210](https://github.com/modular/mojo/issues/210) - Parameter results cannot be declared outside top-level in function -- [Issue #214](https://github.com/modularml/mojo/issues/214) - Pointer offset +- [Issue #214](https://github.com/modular/mojo/issues/214) - Pointer offset calculations at compile-time are incorrect -- [Issue #115](https://github.com/modularml/mojo/issues/115) - Float printing +- [Issue #115](https://github.com/modular/mojo/issues/115) - Float printing does not include the right number of digits -- [Issue #202](https://github.com/modularml/mojo/issues/202) - +- [Issue #202](https://github.com/modular/mojo/issues/202) - `kgen.unreachable` inside nested functions is illegal -- [Issue #235](https://github.com/modularml/mojo/issues/235) - Crash when +- [Issue #235](https://github.com/modular/mojo/issues/235) - Crash when register passable struct field is not register passable -- [Issue #237](https://github.com/modularml/mojo/issues/237) - Parameter +- [Issue #237](https://github.com/modular/mojo/issues/237) - Parameter closure sharp edges are not documented ### 2023-05-16 @@ -6366,16 +6366,16 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #98](https://github.com/modularml/mojo/issues/98): +- [Issue #98](https://github.com/modular/mojo/issues/98): Incorrect error with lifetime tracking in loop. -- [Issue #49](https://github.com/modularml/mojo/issues/49): Type inference +- [Issue #49](https://github.com/modular/mojo/issues/49): Type inference issue (?) in 'ternary assignment' operation (FloatLiteral vs. 'SIMD[f32, 1]'). -- [Issue #48](https://github.com/modularml/mojo/issues/48): +- [Issue #48](https://github.com/modular/mojo/issues/48): and/or don't work with memory-only types. -- [Issue #11](https://github.com/modularml/mojo/issues/11): `setitem` Support +- [Issue #11](https://github.com/modular/mojo/issues/11): `setitem` Support for `PythonObject`. ### 2023-05-11 @@ -6393,7 +6393,7 @@ only in declared parameter names, e.g. the following now works correctly: - The "byref" syntax with the `&` sigil has changed to use an `inout` keyword to be more similar to the `borrowed` and `owned` syntax in arguments. - Please see [Issue #7](https://github.com/modularml/mojo/issues/7) for more + Please see [Issue #7](https://github.com/modular/mojo/issues/7) for more information. - Optimized the Matrix multiplication implementation in the notebook. @@ -6410,10 +6410,10 @@ from "consume" to "transfer." - Fixed missing overloads for `Testing.assertEqual` so that they work on `Integer` and `String` values. -- [Issue #6](https://github.com/modularml/mojo/issues/6): +- [Issue #6](https://github.com/modular/mojo/issues/6): Playground stops evaluating cells when a simple generic is defined. -- [Issue #18](https://github.com/modularml/mojo/issues/18): +- [Issue #18](https://github.com/modular/mojo/issues/18): Memory leak in Python interoperability was removed. ### 2023-05-02 diff --git a/docs/changelog.md b/docs/changelog.md index d23688bfe9..2a33bc61c4 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -393,17 +393,17 @@ what we publish. - The command `mojo debug --vscode` now sets the current working directory properly. -- [Issue #3796](https://github.com/modularml/mojo/issues/3796) - Compiler crash +- [Issue #3796](https://github.com/modular/mojo/issues/3796) - Compiler crash handling for-else statement. -- [Issue #3540](https://github.com/modularml/mojo/issues/3540) - Using named +- [Issue #3540](https://github.com/modular/mojo/issues/3540) - Using named output slot breaks trait conformance -- [Issue #3617](https://github.com/modularml/mojo/issues/3617) - Can't generate +- [Issue #3617](https://github.com/modular/mojo/issues/3617) - Can't generate the constructors for a type wrapping `!lit.ref` - The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. - [Issue #3826](https://github.com/modularml/mojo/issues/3826). + [Issue #3826](https://github.com/modular/mojo/issues/3826). -- [Issue #3935](https://github.com/modularml/mojo/issues/3935) - Confusing OOM +- [Issue #3935](https://github.com/modular/mojo/issues/3935) - Confusing OOM error when using Tuple.get incorrectly. diff --git a/docs/faq.md b/docs/faq.md index d3e11a6a59..e89d8d9b0f 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -147,7 +147,7 @@ develop AI algorithms, but as we grow Mojo into a superset of Python, you can use it for other things like HPC, data transformations, writing pre/post processing operations, and much more. For examples of how Mojo can be used for other general programming tasks, see our [Mojo -examples](https://github.com/modularml/mojo/tree/main/examples). +examples](https://github.com/modular/mojo/tree/main/examples). ### Is Mojo interpreted or compiled? @@ -245,7 +245,7 @@ multiplication](https://www.modular.com/blog/the-worlds-fastest-unified-matrix-m ### Mojo’s matmul performance in the notebook doesn’t seem that great. What’s going on? The [Mojo Matmul -notebook](https://github.com/modularml/mojo/blob/main/examples/notebooks/Matmul.ipynb) +notebook](https://github.com/modular/mojo/blob/main/examples/notebooks/Matmul.ipynb) uses matrix multiplication to show off some Mojo features in a scenario that you would never attempt in pure Python. So that implementation is like a “toy” matmul implementation and it doesn’t measure up to the state of the art. @@ -282,7 +282,7 @@ Yes, but it's different. When we first announced Mojo, it was available only through login, in a JupyterLab environment. Now that Mojo is available for local development, we've shut down that service (you can instead [run Mojo notebooks -locally](https://github.com/modularml/mojo/tree/main/examples/notebooks#readme)). +locally](https://github.com/modular/mojo/tree/main/examples/notebooks#readme)). The new [Mojo Playground](/mojo/playground) is built into the docs website and does not require login. @@ -301,7 +301,7 @@ and does not require login. using buttons in the Playground toolbar. - There might be some bugs. Please [report issues and feedback on - GitHub](https://github.com/modularml/mojo/issues/new/choose). + GitHub](https://github.com/modular/mojo/issues/new/choose). ### What are the license terms for the SDK? diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index edeb731899..301dfdb90e 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -77,7 +77,7 @@ def main(): You don't need a `main()` function when coding in the [REPL](/mojo/manual/get-started#2-run-code-in-the-repl) or in a [Jupyter -notebook](https://github.com/modularml/mojo/tree/main/examples/notebooks#readme). +notebook](https://github.com/modular/mojo/tree/main/examples/notebooks#readme). ::: @@ -390,14 +390,14 @@ here is [Functions](/mojo/manual/functions). Otherwise, here are some other resources to check out: * If you want to experiment with some code, clone [the Mojo - repo](https://github.com/modularml/mojo/) to try our code examples: + repo](https://github.com/modular/mojo/) to try our code examples: ```sh - git clone https://github.com/modularml/mojo.git + git clone https://github.com/modular/mojo.git ``` In addition to several `.mojo` examples, the repo includes [Jupyter - notebooks](https://github.com/modularml/mojo/tree/main/examples/notebooks#readme) + notebooks](https://github.com/modular/mojo/tree/main/examples/notebooks#readme) that teach advanced Mojo features. * To see all the available Mojo APIs, check out the [Mojo standard library diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 08e6f575e0..94d6a02348 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -2,7 +2,7 @@ title: "Get started with Mojo" sidebar_label: "Tutorial: Get started with Mojo" description: "Install Mojo and learn the language basics by building a complete Mojo program" -github_url: https://github.com/modularml/mojo/tree/nightly/examples/life +github_url: https://github.com/modular/mojo/tree/nightly/examples/life image: /images/artwork/mojo-get-started.jpg --- @@ -29,7 +29,7 @@ tried to keep the explanations simple, and we included links along the way for you to go learn more about each topic. And if you just want to see the finished code, you can [get it on -GitHub](https://github.com/modularml/mojo/tree/nightly/examples/life). +GitHub](https://github.com/modular/mojo/tree/nightly/examples/life). ## 1. Create a Mojo project with `magic` diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index 768a1aab81..baaf9f5d8f 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -1030,7 +1030,7 @@ operations like printing complex values. We'll also allow mixing `Complex` and This example builds our `Complex` struct incrementally. You can also find the [complete example in the public Mojo GitHub -repo](https://github.com/modularml/mojo/tree/nightly/examples/operators). +repo](https://github.com/modular/mojo/tree/nightly/examples/operators). :::note diff --git a/docs/manual/parameters/index.mdx b/docs/manual/parameters/index.mdx index 084aba0ca8..169ce7db48 100644 --- a/docs/manual/parameters/index.mdx +++ b/docs/manual/parameters/index.mdx @@ -140,7 +140,7 @@ Mojo's support for generics is still early. You can write generic functions like this using traits and parameters. You can also write generic collections like `List` and `Dict`. If you're interested in learning how these types work, you can find the source code for the standard library collection types -[on GitHub](https://github.com/modularml/mojo/blob/nightly/stdlib/src/collections/). +[on GitHub](https://github.com/modular/mojo/blob/nightly/stdlib/src/collections/). ## Parameterized structs diff --git a/docs/manual/values/lifetimes.mdx b/docs/manual/values/lifetimes.mdx index 3c1871c0d7..bd96d45b95 100644 --- a/docs/manual/values/lifetimes.mdx +++ b/docs/manual/values/lifetimes.mdx @@ -425,7 +425,7 @@ for i in range(len(nums)): (You can find the code for the `List` iterator in the [Mojo -repo](https://github.com/modularml/mojo/blob/main/stdlib/src/collections/list.mojo#L63).) +repo](https://github.com/modular/mojo/blob/main/stdlib/src/collections/list.mojo#L63).) #### Parametric mutability of return values diff --git a/docs/notebooks/index.mdx b/docs/notebooks/index.mdx index 636b8e4a25..0b88575238 100644 --- a/docs/notebooks/index.mdx +++ b/docs/notebooks/index.mdx @@ -17,7 +17,7 @@ listing: --- The following pages are rendered from the Jupyter notebooks that are [available -on GitHub](https://github.com/modularml/mojo/tree/main/examples/notebooks). +on GitHub](https://github.com/modular/mojo/tree/main/examples/notebooks).


diff --git a/docs/roadmap.md b/docs/roadmap.md index 8ffd0fa4f8..764e99495e 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -29,7 +29,7 @@ In the near-term, we will **not** prioritize "general goodness" work such as: - Tackling broad Python ecosystem challenges like packaging. If you have encountered any bugs with current Mojo behavior, please -[submit an issue on GitHub](https://github.com/modularml/mojo/issues). +[submit an issue on GitHub](https://github.com/modular/mojo/issues). If you have ideas about how to improve the core Mojo features, we prefer that you first look for similar topics or start a new conversation about it @@ -148,7 +148,7 @@ When we get here, we will discuss what the right default is: for example, is full Python hash-table dynamism the default? Or do we use a more efficient model by default (e.g. vtable-based dispatch and explicitly declared stored properties) and allow opt'ing into dynamism with a `@dynamic` decorator on the -class. More discussion is [in this proposal](https://github.com/modularml/mojo/blob/main/proposals/mojo-and-dynamism.md). +class. More discussion is [in this proposal](https://github.com/modular/mojo/blob/main/proposals/mojo-and-dynamism.md). ## C/C++ Interop diff --git a/docs/tools/debugging.mdx b/docs/tools/debugging.mdx index 6672ded32d..2a761c6f88 100644 --- a/docs/tools/debugging.mdx +++ b/docs/tools/debugging.mdx @@ -26,7 +26,7 @@ There are several ways to start a debug session in VS Code. To start debugging, you'll need to have a Mojo project to debug. There are a number of examples ranging from simple to complex in the [Mojo repo on -GitHub](https://github.com/modularml/mojo). +GitHub](https://github.com/modular/mojo). :::note **VS Code veteran?** diff --git a/docs/tools/testing.mdx b/docs/tools/testing.mdx index bd67e80ec3..f444d44d5a 100644 --- a/docs/tools/testing.mdx +++ b/docs/tools/testing.mdx @@ -2,7 +2,7 @@ title: Testing sidebar_position: 2 description: Testing Mojo programs. -github_url: https://github.com/modularml/mojo/tree/nightly/examples/testing +github_url: https://github.com/modular/mojo/tree/nightly/examples/testing --- Mojo includes a framework for developing and executing unit tests. The framework @@ -102,9 +102,9 @@ its error message. - [Writing API documentation tests](#writing-api-documentation-tests) discusses how to use the Mojo testing framework to test code examples in your API documentation. -- The public [GitHub repo](https://github.com/modularml/mojo/tree/nightly) +- The public [GitHub repo](https://github.com/modular/mojo/tree/nightly) contains an [example - project](https://github.com/modularml/mojo/tree/nightly/examples/testing) to + project](https://github.com/modular/mojo/tree/nightly/examples/testing) to demonstrate both unit testing and docstring testing. Several of the examples shown later are based on this project. @@ -297,7 +297,7 @@ the following: If needed, you can optionally use the `-I` option one or more times to append additional paths to the list of directories searched to import Mojo modules and packages. Consider the [example testing -project](https://github.com/modularml/mojo/tree/nightly/examples/testing) in +project](https://github.com/modular/mojo/tree/nightly/examples/testing) in GitHub, which has the following directory structure: ```output @@ -369,7 +369,7 @@ By including the `--collect-only` or `--co` option, you can use `mojo test` to discover and print a list of tests. Consider the [example testing -project](https://github.com/modularml/mojo/tree/nightly/examples/testing) +project](https://github.com/modular/mojo/tree/nightly/examples/testing) directory structure shown in the [Running tests](#running-tests) section. The following command produces a list of all of the tests defined in the `test` directory hierarchy. @@ -440,7 +440,7 @@ output shown): You can also produce JSON output for test collection as well. Consider the [example testing -project](https://github.com/modularml/mojo/tree/nightly/examples/testing) +project](https://github.com/modular/mojo/tree/nightly/examples/testing) directory structure shown in the [Running tests](#running-tests) section. The following command collects a list in JSON format of all of the tests defined in the `test` directory hierarchy: @@ -626,7 +626,7 @@ suite within the same module. The format of a documentation test identifier is `@::`. This is best explained by an example. Consider the [example testing -project](https://github.com/modularml/mojo/tree/nightly/examples/testing) +project](https://github.com/modular/mojo/tree/nightly/examples/testing) directory structure shown in the [Running tests](#running-tests) section. The source files in the `src` directory might contain docstrings for the `my_math` package, the `utils.mojo` module, and the individual functions within that From 0eb5365459088b14a297e7ee5576797a0f767fbe Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 16 Jan 2025 20:07:33 -0500 Subject: [PATCH 261/509] [stdlib] Make `String(buffer=List[UInt8]())` keyword only In the future you should be able to print `List` of `Writable` type and convert it to a `String`. This ctor will conflict with that and so should be keyword-only. MODULAR_ORIG_COMMIT_REV_ID: 2b97e1cf7ef0a0c67a297a7b725da5d70e11eb6d --- stdlib/benchmarks/collections/bench_string.mojo | 4 ++-- stdlib/src/base64/base64.mojo | 4 ++-- stdlib/src/builtin/char.mojo | 2 +- stdlib/src/collections/dict.mojo | 2 +- stdlib/src/collections/string/_unicode.mojo | 4 ++-- stdlib/src/collections/string/format.mojo | 6 ++---- stdlib/src/collections/string/inline_string.mojo | 2 +- stdlib/src/collections/string/string.mojo | 2 +- stdlib/src/tempfile/tempfile.mojo | 2 +- stdlib/test/builtin/test_file.mojo | 6 +++--- stdlib/test/collections/string/test_string.mojo | 14 +++++++------- .../test/collections/string/test_string_slice.mojo | 6 +++--- 12 files changed, 26 insertions(+), 28 deletions(-) diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index 39ca437068..fe8ba978ed 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -53,9 +53,9 @@ fn make_string[ items.append(items[i]) i = i + 1 if i < len(items) - 1 else 0 items.append(0) - return String(items^) + return String(buffer=items^) else: - return String(f.read_bytes()) + return String(buffer=f.read_bytes()) except e: print(e, file=2) return abort[String]() diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index 750ee4cb78..c126834e25 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -188,7 +188,7 @@ fn b16encode(str: StringSlice) -> String: out.append(0) - return String(out^) + return String(buffer=out^) # ===-----------------------------------------------------------------------===# @@ -233,4 +233,4 @@ fn b16decode(str: StringSlice) -> String: p.append(decode(hi) << 4 | decode(lo)) p.append(0) - return String(p^) + return String(buffer=p^) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 74d789a619..aa316d21a7 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -259,7 +259,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): _ = self.unsafe_write_utf8(buffer.unsafe_ptr()) buffer.unsafe_ptr()[char_len] = 0 buffer.size = char_len + 1 - return String(buffer^) + return String(buffer=buffer^) # ===-------------------------------------------------------------------===# # Methods diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index f593677437..c6f585bae6 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -730,7 +730,7 @@ struct Dict[K: KeyElement, V: CollectionElement]( var minimum_capacity = self._minimum_size_of_string_representation() var string_buffer = List[UInt8](capacity=minimum_capacity) string_buffer.append(0) # Null terminator - var result = String(string_buffer^) + var result = String(buffer=string_buffer^) result += "{" var i = 0 diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index 152ec38d54..f9acb9db7d 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -204,7 +204,7 @@ fn to_lowercase(s: StringSlice) -> String: # Add NUL terminator output.append(0) - return String(output^) + return String(buffer=output^) fn to_uppercase(s: StringSlice) -> String: @@ -256,7 +256,7 @@ fn to_uppercase(s: StringSlice) -> String: # Add NUL terminator output.append(0) - return String(output^) + return String(buffer=output^) @always_inline diff --git a/stdlib/src/collections/string/format.mojo b/stdlib/src/collections/string/format.mojo index 34387e2856..ad358a028e 100644 --- a/stdlib/src/collections/string/format.mojo +++ b/stdlib/src/collections/string/format.mojo @@ -386,10 +386,8 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): alias argnum = "Argument number: " alias does_not = " does not implement the trait " alias needed = "needed for conversion_flag: " - var flg = String(List[UInt8](flag, 0)) - raise Error( - argnum + String(i) + does_not + needed + flg - ) + var flg = String(buffer=List[UInt8](flag, 0)) + raise Error(String(argnum, i, does_not, needed, flg)) if self.format_spec: self.format_spec.value().format( diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index bfb77bab50..8235033158 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -140,7 +140,7 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): # Copy the bytes from the additional string. buffer.extend(str_slice.as_bytes()) buffer.append(0) # Add the NUL byte - self._storage = Self.Layout(String(buffer^)) + self._storage = Self.Layout(String(buffer=buffer^)) fn __add__(self, other: StringSlice) -> Self: """Construct a string by appending another string at the end of this string. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 5563b19735..ba8fe11d8e 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -695,7 +695,7 @@ struct String( self._buffer = Self._buffer_type(capacity=capacity) @always_inline - fn __init__(out self, owned buffer: List[UInt8, *_]): + fn __init__(out self, *, owned buffer: List[UInt8, *_]): """Construct a string from a buffer of bytes without copying the allocated data. diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index 02249d4cfd..e298462290 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -40,7 +40,7 @@ fn _get_random_name(size: Int = 8) -> String: ) name_list.append(ord(characters[rand_index])) name_list.append(0) - return String(name_list^) + return String(buffer=name_list^) fn _candidate_tempdir_list() -> List[String]: diff --git a/stdlib/test/builtin/test_file.mojo b/stdlib/test/builtin/test_file.mojo index c8c792a6ee..7b23687430 100644 --- a/stdlib/test/builtin/test_file.mojo +++ b/stdlib/test/builtin/test_file.mojo @@ -52,7 +52,7 @@ def test_file_read_bytes_multi(): assert_equal(len(bytes1), 12, "12 bytes") # we add the null terminator bytes1.append(0) - var string1 = String(bytes1) + var string1 = String(buffer=bytes1) assert_equal(len(string1), 12, "12 chars") assert_equal(string1, String("Lorem ipsum ")) @@ -60,7 +60,7 @@ def test_file_read_bytes_multi(): assert_equal(len(bytes2), 6, "6 bytes") # we add the null terminator bytes2.append(0) - var string2 = String(bytes2) + var string2 = String(buffer=bytes2) assert_equal(len(string2), 6, "6 chars") assert_equal(string2, "dolor ") @@ -224,7 +224,7 @@ struct Word: word.append(self.fourth_letter) word.append(self.fith_letter) word.append(0) - return String(word) + return String(buffer=word) def test_file_read_to_dtype_pointer(): diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index d3d4f8e3b4..c2f563cd2e 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -656,9 +656,9 @@ def test_split(): + String("\x1c") + String("\x1d") + String("\x1e") - + String(next_line) - + String(unicode_line_sep) - + String(unicode_paragraph_sep) + + String(buffer=next_line) + + String(buffer=unicode_line_sep) + + String(buffer=unicode_paragraph_sep) ) var s = univ_sep_var + "hello" + univ_sep_var + "world" + univ_sep_var d = s.split() @@ -818,7 +818,7 @@ def test_splitlines(): """TODO: \\u2029""" for i in List(next_line, unicode_line_sep, unicode_paragraph_sep): - u = String(i[]) + u = String(buffer=i[]) item = String("").join("hello", u, "world", u, "mojo", u, "language", u) assert_equal(item.splitlines(), hello_mojo) assert_equal( @@ -890,9 +890,9 @@ def test_isspace(): String("\x1c"), String("\x1d"), String("\x1e"), - String(next_line), - String(unicode_line_sep), - String(unicode_paragraph_sep), + String(buffer=next_line), + String(buffer=unicode_line_sep), + String(buffer=unicode_paragraph_sep), ) for i in univ_sep_var: diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 166bbbf297..14dae2dde2 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -560,11 +560,11 @@ def test_splitlines(): ) # test \x85 \u2028 \u2029 - var next_line = String(List[UInt8](0xC2, 0x85, 0)) + var next_line = String(buffer=List[UInt8](0xC2, 0x85, 0)) """TODO: \\x85""" - var unicode_line_sep = String(List[UInt8](0xE2, 0x80, 0xA8, 0)) + var unicode_line_sep = String(buffer=List[UInt8](0xE2, 0x80, 0xA8, 0)) """TODO: \\u2028""" - var unicode_paragraph_sep = String(List[UInt8](0xE2, 0x80, 0xA9, 0)) + var unicode_paragraph_sep = String(buffer=List[UInt8](0xE2, 0x80, 0xA9, 0)) """TODO: \\u2029""" for i in List(next_line, unicode_line_sep, unicode_paragraph_sep): From 632f282b4b6af0ca2130fca6897ca95e06bb89e4 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Thu, 16 Jan 2025 18:02:28 -0800 Subject: [PATCH 262/509] Update link to get started with Mojo MODULAR_ORIG_COMMIT_REV_ID: 78c8cb122bdd24ab3b62d3139cceef6120c9a379 --- docs/faq.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/faq.md b/docs/faq.md index e89d8d9b0f..32ecb58de7 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -270,8 +270,8 @@ dashboard](https://www.modular.com/max/performance). ### How can I get access to the SDK? -Mojo is included with the MAX SDK, which you can [download and use for -free](/max/install). +Mojo is included with the `max` conda package. Try it now by following +the tutorial to [get started with Mojo](/mojo/manual/get-started). Read more about [why Mojo is bundled with MAX](/max/faq#why-bundle-mojo-with-max). From d3b5208d3ff603ffbbe093dd0551672bb44910c0 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 16 Jan 2025 23:15:54 -0500 Subject: [PATCH 263/509] [WIP][stdlib] Move `"x:" + String(x)` etc. to `String("x:", x)` This is more performant due to using buffering, and it cleans up the ugly formatting of having multiple `String(x)` ctors in large messages. MODULAR_ORIG_COMMIT_REV_ID: 80b10f5559fb6ad152a80828328be559e834efe9 --- stdlib/src/builtin/error.mojo | 21 +++++++++++++++++++++ stdlib/src/os/os.mojo | 10 +++++----- 2 files changed, 26 insertions(+), 5 deletions(-) diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index 5ee6a4dcfa..0e599ecbf5 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -23,6 +23,7 @@ from memory.memory import _free from collections.string import StringSlice from utils import StringRef +from utils.write import write_buffered # ===-----------------------------------------------------------------------===# # Error @@ -112,6 +113,26 @@ struct Error( self.data = dest self.loaded_length = -length + @no_inline + fn __init__[ + *Ts: Writable + ](out self, *args: *Ts, sep: StringLiteral = "", end: StringLiteral = ""): + """ + Construct an Error by concatenating a sequence of Writable arguments. + + Args: + args: A sequence of Writable arguments. + sep: The separator used between elements. + end: The String to write after printing the elements. + + Parameters: + Ts: The types of the arguments to format. Each type must be satisfy + `Writable`. + """ + var output = String() + write_buffered(output, args, sep=sep, end=end) + self = Error(output) + fn copy(self) -> Self: """Copy the object. diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index 350a660db6..0d623bcfe6 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -250,16 +250,16 @@ fn abort[result: AnyType = NoneType._mlir_type]() -> result: @no_inline fn abort[ - result: AnyType = NoneType._mlir_type, *, W: Writable -](message: W) -> result: + result: AnyType = NoneType._mlir_type, *Ts: Writable +](*messages: *Ts) -> result: """Calls a target dependent trap instruction if available. Parameters: result: The result type. - W: The Writable type. + Ts: The Writable types. Args: - message: The message to include when aborting. + messages: The messages to include when aborting. Returns: A null result type. @@ -267,7 +267,7 @@ fn abort[ @parameter if not is_gpu(): - print(message, flush=True) + print(String(messages), flush=True) return abort[result]() From 0d00718550bbfa79c64585d60da813110caf94b4 Mon Sep 17 00:00:00 2001 From: modularbot Date: Fri, 17 Jan 2025 15:20:27 +0000 Subject: [PATCH 264/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025011705 --- examples/life/magic.lock | 273 +++++++++++++++++----------------- examples/magic.lock | 231 ++++++++++++++-------------- examples/notebooks/magic.lock | 249 ++++++++++++++++--------------- examples/operators/magic.lock | 231 ++++++++++++++-------------- examples/testing/magic.lock | 231 ++++++++++++++-------------- magic.lock | 231 ++++++++++++++-------------- 6 files changed, 726 insertions(+), 720 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 5a1423aa1f..af20cdf69e 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -129,7 +129,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.22.5-he02047a_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.82.2-h2ff4ddf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.82.2-h2ff4ddf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.33.0-h2b5623c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.33.0-h0121fbd_1.conda @@ -151,7 +151,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2024.07.02-hbbce691_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.47.2-hee588c1_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.48.0-hee588c1_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hf672d98_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-hc0a3c3a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_1.conda @@ -160,7 +160,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hd9ff511_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.9.0-hb9d3cd8_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.49.2-hb9d3cd8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.50.0-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda @@ -170,12 +170,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011605-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025011605-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025011605-3.12release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011605-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025011705-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025011705-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025011705-3.12release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025011705-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025011605-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025011705-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.15-py312h98912ed_1.conda @@ -259,7 +259,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/typer-slim-standard-0.15.1-hd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.12.2-hd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024b-hc8b5060_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/uvicorn-0.34.0-pyh31011fe_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/uvicorn-standard-0.34.0-h31011fe_0.conda @@ -405,7 +405,7 @@ environments: - 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- sha256: 4fde5c3008bf5d2db82f2b50204464314cc3c91c1d953652f7bd01d9e52aefdf - md5: 8ac3367aafb1cc0a068483c580af8015 +- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda + sha256: c4b1ae8a2931fe9b274c44af29c5475a85b37693999f8c792dad0f8c6734b1de + md5: dbcace4706afdfb7eb891f7b37d07c04 license: LicenseRef-Public-Domain - size: 122354 - timestamp: 1728047496079 + size: 122921 + timestamp: 1737119101255 - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda sha256: 114919ffa80c328127dab9c8e7a38f9d563c617691fb81fccb11c1e86763727e md5: 32674f8dbfb7b26410ed580dd3c10a29 From c48edb078ef6c9cbcf8e79e688090e9206cb4aa3 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 16 Jan 2025 22:09:38 -0800 Subject: [PATCH 265/509] [Kernels][GPU] Move the GPU is ops to sys.intrinsics MODULAR_ORIG_COMMIT_REV_ID: 11c79dfcb67ff86a89cf549f63dc300ca98b3de9 --- stdlib/src/sys/intrinsics.mojo | 233 +++++++++++++++++++++++++++++++++ 1 file changed, 233 insertions(+) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index cdcf3a19ac..20c8064630 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -994,3 +994,236 @@ fn ballot[dtype: DType](value: Bool) -> Scalar[dtype]: "This intrinsic is only defined for i32 or i64", ]() return llvm_intrinsic["llvm.amdgcn.ballot", Scalar[dtype]](value) + + +# ===-----------------------------------------------------------------------===# +# thread_idx +# ===-----------------------------------------------------------------------===# + + +@register_passable("trivial") +struct _ThreadIdx: + """ThreadIdx provides static methods for getting the x/y/z coordinates of + a thread within a block.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + @staticmethod + fn _get_intrinsic_name[dim: StringLiteral]() -> StringLiteral: + @parameter + if is_nvidia_gpu(): + return "llvm.nvvm.read.ptx.sreg.tid." + dim + else: + return "llvm.amdgcn.workitem.id." + dim + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt: + """Gets the `x`, `y`, or `z` coordinates of a thread within a block. + + Returns: + The `x`, `y`, or `z` coordinates of a thread within a block. + """ + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + alias intrinsic_name = Self._get_intrinsic_name[dim]() + return UInt( + Int(llvm_intrinsic[intrinsic_name, Int32, has_side_effect=False]()) + ) + + +alias thread_idx = _ThreadIdx() + + +# ===-----------------------------------------------------------------------===# +# block_idx +# ===-----------------------------------------------------------------------===# + + +@register_passable("trivial") +struct _BlockIdx: + """BlockIdx provides static methods for getting the x/y/z coordinates of + a block within a grid.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + @staticmethod + fn _get_intrinsic_name[dim: StringLiteral]() -> StringLiteral: + @parameter + if is_nvidia_gpu(): + return "llvm.nvvm.read.ptx.sreg.ctaid." + dim + else: + return "llvm.amdgcn.workgroup.id." + dim + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt: + """Gets the `x`, `y`, or `z` coordinates of a block within a grid. + + Returns: + The `x`, `y`, or `z` coordinates of a block within a grid. + """ + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + alias intrinsic_name = Self._get_intrinsic_name[dim]() + return UInt( + Int(llvm_intrinsic[intrinsic_name, Int32, has_side_effect=False]()) + ) + + +alias block_idx = _BlockIdx() + +# ===-----------------------------------------------------------------------===# +# block_dim +# ===-----------------------------------------------------------------------===# + + +@always_inline +fn _get_gcn_idx[offset: Int]() -> UInt: + var ptr = llvm_intrinsic[ + "llvm.amdgcn.implicitarg.ptr", + UnsafePointer[Int16, address_space=4], + has_side_effect=False, + ]() + return UInt(Int(ptr.load[alignment=4](offset))) + + +@register_passable("trivial") +struct _BlockDim: + """BlockDim provides static methods for getting the x/y/z dimension of a + block.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt: + """Gets the `x`, `y`, or `z` dimension of the block. + + Returns: + The `x`, `y`, or `z` dimension of the block. + """ + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + + @parameter + if is_nvidia_gpu(): + alias intrinsic_name = "llvm.nvvm.read.ptx.sreg.ntid." + dim + return UInt( + Int( + llvm_intrinsic[ + intrinsic_name, Int32, has_side_effect=False + ]() + ) + ) + else: + + @parameter + fn _get_offset() -> Int: + @parameter + if dim == "x": + return 6 + elif dim == "y": + return 7 + else: + constrained[dim == "z"]() + return 8 + + return _get_gcn_idx[_get_offset()]() + + +alias block_dim = _BlockDim() + +# ===-----------------------------------------------------------------------===# +# grid_dim +# ===-----------------------------------------------------------------------===# + + +@register_passable("trivial") +struct _GridDim: + """GridDim provides static methods for getting the x/y/z dimension of a + grid.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt: + """Gets the `x`, `y`, or `z` dimension of the grid. + + Returns: + The `x`, `y`, or `z` dimension of the grid. + """ + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + + @parameter + if is_nvidia_gpu(): + alias intrinsic_name = "llvm.nvvm.read.ptx.sreg.nctaid." + dim + return UInt( + Int( + llvm_intrinsic[ + intrinsic_name, Int32, has_side_effect=False + ]() + ) + ) + else: + + @parameter + fn _get_offset() -> Int: + @parameter + if dim == "x": + return 0 + elif dim == "y": + return 1 + else: + constrained[dim == "z"]() + return 2 + + return _get_gcn_idx[_get_offset()]() + + +alias grid_dim = _GridDim() + +# ===-----------------------------------------------------------------------===# +# grid_idx +# ===-----------------------------------------------------------------------===# + + +@register_passable("trivial") +struct _GridIdx: + """GlobalIdx provides static methods for getting the x/y/z global offset of + the kernel launch.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt: + """Gets the `x`, `y`, or `z` dimension of the program. + + Returns: + The `x`, `y`, or `z` dimension of the program. + """ + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + var thread_idx = thread_idx.__getattr__[dim]() + var block_idx = block_idx.__getattr__[dim]() + var block_dim = block_dim.__getattr__[dim]() + + return math.fma(block_idx, block_dim, thread_idx) + + +alias global_idx = _GridIdx() From 9239d148e481fdf6c611b2321363c87fec3e86d0 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 16 Jan 2025 22:40:10 -0800 Subject: [PATCH 266/509] [Stdlib] Simplify Node initializer, NFC MODULAR_ORIG_COMMIT_REV_ID: dbba7aba2c37976c39e5ae4ddb2c21a0cb73df17 --- stdlib/src/collections/linked_list.mojo | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index 515b6404a0..49850f1c65 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -167,7 +167,7 @@ struct LinkedList[ElementType: WritableCollectionElement]: Args: value: The value to append. """ - var node = Node[ElementType](value^, self._tail, None) + var node = Node(value^, self._tail, None) var addr = UnsafePointer[__type_of(node)].alloc(1) addr.init_pointee_move(node) if self: @@ -183,7 +183,7 @@ struct LinkedList[ElementType: WritableCollectionElement]: Args: value: The value to prepend. """ - var node = Node[ElementType](value^, None, self._head) + var node = Node(value^, None, self._head) var addr = UnsafePointer[__type_of(node)].alloc(1) addr.init_pointee_move(node) if self: From 8fa9d65afce40d2bc8346e5faae5643d5c3ef3c5 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 16 Jan 2025 22:42:28 -0800 Subject: [PATCH 267/509] [Kernels][GPU] Restore functionality which was reverted in [Internal link] MODULAR_ORIG_COMMIT_REV_ID: 2a103ff1e21d6228cb972c5ea004ac45a8778b8e --- stdlib/src/builtin/debug_assert.mojo | 60 ++++++---------------------- 1 file changed, 13 insertions(+), 47 deletions(-) diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index 896368dba2..a18a9eb271 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -21,6 +21,7 @@ from sys import is_gpu, is_nvidia_gpu, is_amd_gpu, llvm_intrinsic from sys._build import is_debug_build from sys.ffi import c_char, c_size_t, c_uint, external_call from sys.param_env import env_get_string +from sys.intrinsics import thread_idx, block_idx from builtin._location import __call_location, _SourceLocation from memory import UnsafePointer, Span @@ -250,6 +251,8 @@ fn _debug_assert_msg( arg_bytes.write( "At ", loc, + ": ", + _GPUThreadInfo(), " Assert ", "Warning: " if defined_mode == "warn" else " Error: ", ) @@ -259,6 +262,8 @@ fn _debug_assert_msg( buffer.write( "At ", loc, + ": ", + _GPUThreadInfo(), " Assert ", "Warning: " if defined_mode == "warn" else "Error: ", ) @@ -286,60 +291,21 @@ fn _debug_assert_msg( abort() -struct _ThreadContext(Writable): - var block_x: Int32 - var block_y: Int32 - var block_z: Int32 - var thread_x: Int32 - var thread_y: Int32 - var thread_z: Int32 - - fn __init__(out self): - self.block_x = _get_id["block", "x"]() - self.block_y = _get_id["block", "y"]() - self.block_z = _get_id["block", "z"]() - self.thread_x = _get_id["thread", "x"]() - self.thread_y = _get_id["thread", "y"]() - self.thread_z = _get_id["thread", "z"]() - +@value +struct _GPUThreadInfo: fn write_to[W: Writer](self, mut writer: W): writer.write( "block: [", - self.block_x, + block_idx.x, ",", - self.block_y, + block_idx.y, ",", - self.block_z, + block_idx.z, "] thread: [", - self.thread_x, + thread_idx.x, ",", - self.thread_y, + thread_idx.y, ",", - self.thread_z, + thread_idx.z, "]", ) - - -fn _get_id[type: StringLiteral, dim: StringLiteral]() -> Int32: - alias intrinsic_name = _get_intrinsic_name[type, dim]() - return llvm_intrinsic[intrinsic_name, Int32, has_side_effect=False]() - - -fn _get_intrinsic_name[ - type: StringLiteral, dim: StringLiteral -]() -> StringLiteral: - @parameter - if is_nvidia_gpu(): - - @parameter - if type == "thread": - return "llvm.nvvm.read.ptx.sreg.tid." + dim - else: - return "llvm.nvvm.read.ptx.sreg.ctaid." + dim - else: - - @parameter - if type == "thread": - return "llvm.amdgcn.workitem.id." + dim - else: - return "llvm.amdgcn.workgroup.id." + dim From d6374fc1f4cffc0872ccae948f7ce6a0e7097f9b Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Fri, 17 Jan 2025 13:17:34 -0500 Subject: [PATCH 268/509] [stdlib] Free buffer on failure in `read_until_delimeter` When `getdelim` fails, still free the memory allocated by it. MODULAR_ORIG_COMMIT_REV_ID: 7d28833bc3471a94bde956d272c8d8f38efc30d2 --- stdlib/src/builtin/io.mojo | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index b4971adf71..c1cf2c9008 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -144,8 +144,10 @@ struct _fdopen[mode: StringLiteral = "a"]: # (or the user sends EOF without providing any input). We must # raise an error in this case because otherwise, String() will crash mojo # if the user sends EOF with no input. - # TODO: check errno to ensure we haven't encountered EINVAL or ENOMEM instead if bytes_read == -1: + if buffer: + libc.free(buffer.bitcast[NoneType]()) + # TODO: check errno to ensure we haven't encountered EINVAL or ENOMEM instead raise Error("EOF") # Copy the buffer (excluding the delimiter itself) into a Mojo String. var s = String(StringRef(buffer, bytes_read - 1)) From c1b41bee84e5f38cb20ca97e69ff4fc996d8bbe2 Mon Sep 17 00:00:00 2001 From: Tracy Sharpe <99041446+raiseirql@users.noreply.github.com> Date: Fri, 17 Jan 2025 10:49:11 -0800 Subject: [PATCH 269/509] Improve print() reliability across multiple wavefronts. MODULAR_ORIG_COMMIT_REV_ID: b975896e3275f4d8989359c1feec944fcc8e7db3 --- stdlib/src/sys/_amdgpu.mojo | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo index b8cbb9a876..f3a55fcae2 100644 --- a/stdlib/src/sys/_amdgpu.mojo +++ b/stdlib/src/sys/_amdgpu.mojo @@ -660,11 +660,8 @@ struct Buffer: self._handle[].payloads.offset(ptr & self._handle[].index_mask) ) - fn pop( - mut self, - mut top: UInt64, - ) -> UInt64: - var f = Atomic._fetch_add(UnsafePointer.address_of(top), 0) + fn pop(mut self, top: UnsafePointer[UInt64, **_]) -> UInt64: + var f = Atomic._fetch_add(top, 0) # F is guaranteed to be non-zero, since there are at least as # many packets as there are waves, and each wave can hold at most # one packet. @@ -674,9 +671,7 @@ struct Buffer: UnsafePointer.address_of(p._handle[].next), 0, ) - if _compare_exchange_weak_integral_impl[scope=""]( - UnsafePointer.address_of(top), f, n - ): + if _compare_exchange_weak_integral_impl[scope=""](top, f, n): break sleep(UInt(1)) @@ -691,7 +686,7 @@ struct Buffer: if me == low: packet_ptr = Self.pop( self, - self._handle[].free_stack, + UnsafePointer.address_of(self._handle[].free_stack), ) var ptr_lo = packet_ptr @@ -704,14 +699,12 @@ struct Buffer: | ptr_lo_32.cast[DType.uint64]() ) - fn push(mut self, mut top: UInt64, ptr: UInt64): - var f = Atomic._fetch_add(UnsafePointer.address_of(top), 0) + fn push(mut self, top: UnsafePointer[UInt64, **_], ptr: UInt64): + var f = Atomic._fetch_add(top, 0) var p = self.get_header(ptr) while True: p._handle[].next = f - if _compare_exchange_weak_integral_impl[scope=""]( - UnsafePointer.address_of(top), f, ptr - ): + if _compare_exchange_weak_integral_impl[scope=""](top, f, ptr): break sleep(UInt(1)) @@ -721,7 +714,7 @@ struct Buffer: packet and signal the host. """ if me == low: - self.push(self._handle[].ready_stack, ptr) + self.push(UnsafePointer.address_of(self._handle[].ready_stack), ptr) send_signal(self._handle[].doorbell) fn return_free_packet(mut self, ptr: UInt64, me: UInt32, low: UInt32): @@ -730,7 +723,7 @@ struct Buffer: """ if me == low: var ptr = inc_ptr_tag(ptr, self._handle[].index_mask) - self.push(self._handle[].free_stack, ptr) + self.push(UnsafePointer.address_of(self._handle[].free_stack), ptr) # Must match the ABI of: From 694377e002a25dc778eb59b216a1ed8214a74740 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Fri, 17 Jan 2025 16:12:59 -0800 Subject: [PATCH 270/509] Add links to the custom op tutorial MODULAR_ORIG_COMMIT_REV_ID: 1f946f0ca75356c9522b7200f569bcb5dc35eebd --- docs/manual/get-started.mdx | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 94d6a02348..0e6133912a 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -8,6 +8,14 @@ image: /images/artwork/mojo-get-started.jpg import GetMagic from '@site/src/includes/get_magic.mdx'; + +:::tip + +**Want to write a GPU function with Mojo?** See how to [build a custom op +with MAX](/max/tutorials/build-custom-ops). + +::: + Get ready to learn Mojo! This tutorial is designed to give you a tour of several features of Mojo by building a complete program that does much more than simply printing "Hello, world!" @@ -26,11 +34,11 @@ get started so you can learn Mojo programming basics, including the following: This tutorial might be a little long because there's a lot to learn, but we tried to keep the explanations simple, and we included links along the way for -you to go learn more about each topic. - -And if you just want to see the finished code, you can [get it on +you to go learn more about each topic. If you just want to see the finished +code, you can [get it on GitHub](https://github.com/modular/mojo/tree/nightly/examples/life). + ## 1. Create a Mojo project with `magic` We'll start by using the `magic` CLI to create a virtual environment @@ -1220,8 +1228,8 @@ way, you got a taste of: ## Next steps -The [Mojo manual](/mojo/manual/) and the Mojo standard library [API -reference](/mojo/lib) contains much more information about these and other -features of Mojo programming. These resources will help prepare you to take full -advantage of the [MAX platform](/max/) to build and deploy high-performance AI -pipelines. +For more detail about all of Mojo's features, explore the other sections of the +[Mojo manual](/mojo/manual/). + +If you want to write a GPU function with Mojo, see how to [build a custom op +with MAX](/max/tutorials/build-custom-ops). From 45730e30593d0dbdba0a7cf5773d6a230706c644 Mon Sep 17 00:00:00 2001 From: Hengjie Wang <86926839+hengjiew@users.noreply.github.com> Date: Sat, 18 Jan 2025 08:54:57 -0800 Subject: [PATCH 271/509] [stdlib] Propagate stack allocation's alignment to pointer Propagate `stack_allocation[alignment = ...]()`'s alignment to pointer's parameter. MODULAR_ORIG_COMMIT_REV_ID: 410d74c89308efa3de7db2bb0ef41fbbb325b4ee --- stdlib/src/memory/memory.mojo | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 0cbf1e0d65..57f89bc958 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -425,7 +425,7 @@ fn stack_allocation[ name = global_name.value, count = count.value, _type = UnsafePointer[ - type, address_space=address_space + type, address_space=address_space, alignment=alignment ]._mlir_type, alignment = alignment.value, ]() @@ -447,7 +447,9 @@ fn stack_allocation[ # Perofrm a stack allocation of the requested size, alignment, and type. return __mlir_op.`pop.stack_allocation`[ count = count.value, - _type = UnsafePointer[type, address_space=address_space]._mlir_type, + _type = UnsafePointer[ + type, address_space=address_space, alignment=alignment + ]._mlir_type, alignment = alignment.value, ]() From c11de099a0f7c4faa0077bf8463f6b73d9222d5b Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sat, 18 Jan 2025 20:34:58 -0800 Subject: [PATCH 272/509] [Kernels][AMD] Use more optimized scheme for converting f32->bf16 (#54101) MODULAR_ORIG_COMMIT_REV_ID: a24731ef1fb412f5f47954e186ec4d7c0ff6af09 --- stdlib/src/builtin/simd.mojo | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 0a9cf32afa..f2953fb18e 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -1918,13 +1918,17 @@ struct SIMD[type: DType, size: Int]( return rebind[SIMD[target, size]](self != 0) @parameter - if type is DType.bfloat16 and not _has_native_bf16_support(): + if type is DType.bfloat16 and ( + not _has_native_bf16_support() or is_amd_gpu() + ): return _bfloat16_to_f32( rebind[SIMD[DType.bfloat16, size]](self) ).cast[target]() @parameter - if target is DType.bfloat16 and not _has_native_bf16_support(): + if target is DType.bfloat16 and ( + not _has_native_bf16_support() or is_amd_gpu() + ): return rebind[SIMD[target, size]]( _f32_to_bfloat16(self.cast[DType.float32]()) ) @@ -3569,6 +3573,27 @@ fn _f32_to_bfloat16_scalar( # TODO(KERN-228): support BF16 on neon systems. return _unchecked_zero[DType.bfloat16, 1]() + elif is_amd_gpu(): + alias nan_val = _nan[DType.float32]() + alias round_bias = 0x7FFF + + var tmp = UInt32(0) + var res = inlined_assembly[ + """\0A + v_cmp_u_f32 $0, $2, $2 \0A + v_bfe_u32 $1, $2, 16, 1 \0A + v_add3_u32 $1, $2, $1, $3 \0A + v_cndmask_b32 $2, $1, $4, $0 \0A + v_lshrrev_b32 $2, 16, $2 \0A + """, + _RegisterPackType[SIMD[DType.uint32, 2], Int32, Float32], + constraints="=s,=v,=v,v,v,1,2", + has_side_effect=False, + ](round_bias, nan_val, tmp, val) + return bitcast[DType.bfloat16, 1]( + UInt16(bitcast[DType.uint32, 1](res[2])) + ) + if _isnan(val): return -_nan[DType.bfloat16]() if FPUtils[DType.float32].get_sign( val From 8deff1b8e11e5fa59d63354c512ec2ff9553af39 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Sun, 19 Jan 2025 19:07:37 -0500 Subject: [PATCH 273/509] [stdlib] Remove allocations from string aliases Make aliases `StringLiteral` so they don't allocate if not required. MODULAR_ORIG_COMMIT_REV_ID: 0243e2b63c5d5c1cbba803d27a4d974e5998a068 --- stdlib/src/collections/string/string.mojo | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index ba8fe11d8e..3c210277ab 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -558,19 +558,14 @@ struct String( """The underlying storage for the string.""" """ Useful string aliases. """ - alias ASCII_LOWERCASE = String("abcdefghijklmnopqrstuvwxyz") - alias ASCII_UPPERCASE = String("ABCDEFGHIJKLMNOPQRSTUVWXYZ") - alias ASCII_LETTERS = String.ASCII_LOWERCASE + String.ASCII_UPPERCASE - alias DIGITS = String("0123456789") - alias HEX_DIGITS = String.DIGITS + String("abcdef") + String("ABCDEF") - alias OCT_DIGITS = String("01234567") - alias PUNCTUATION = String("""!"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~""") - alias PRINTABLE = ( - String.DIGITS - + String.ASCII_LETTERS - + String.PUNCTUATION - + " \t\n\r\v\f" # single byte utf8 whitespaces - ) + alias ASCII_LOWERCASE = "abcdefghijklmnopqrstuvwxyz" + alias ASCII_UPPERCASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + alias ASCII_LETTERS = Self.ASCII_LOWERCASE + Self.ASCII_UPPERCASE + alias DIGITS = "0123456789" + alias HEX_DIGITS = Self.DIGITS + "abcdef" + "ABCDEF" + alias OCT_DIGITS = "01234567" + alias PUNCTUATION = """!"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~""" + alias PRINTABLE = Self.DIGITS + Self.ASCII_LETTERS + Self.PUNCTUATION + " \t\n\r\v\f" # ===------------------------------------------------------------------=== # # Life cycle methods From a788651c9d0d0b8924d17976cad662a604537576 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 19 Jan 2025 17:30:28 -0800 Subject: [PATCH 274/509] [mojo-stdlib] Tidy up min/max on SIMD, NFC. This simplifies the signature of SIMD min/max, changing them to take both parameters anonymously. MODULAR_ORIG_COMMIT_REV_ID: 2d8dfca57375ec4bd64e7bf2c71239df6a9c4647 --- stdlib/src/builtin/math.mojo | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 28e3c72833..237d9d1301 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -175,7 +175,7 @@ fn max(x: UInt, y: UInt, /) -> UInt: @always_inline("nodebug") -fn max[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): +fn max(x: SIMD, y: __type_of(x), /) -> __type_of(x): """Performs elementwise maximum of x and y. An element of the result SIMD vector will be the maximum of the @@ -184,9 +184,6 @@ fn max[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): Constraints: The type of the inputs must be numeric or boolean. - Parameters: - dtype: The data type of the SIMD vector. - Args: x: First SIMD vector. y: Second SIMD vector. @@ -265,7 +262,7 @@ fn min(x: UInt, y: UInt, /) -> UInt: @always_inline("nodebug") -fn min[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): +fn min(x: SIMD, y: __type_of(x), /) -> __type_of(x): """Gets the elementwise minimum of x and y. An element of the result SIMD vector will be the minimum of the @@ -274,9 +271,6 @@ fn min[dtype: DType, //](x: SIMD[dtype, _], y: __type_of(x), /) -> __type_of(x): Constraints: The type of the inputs must be numeric or boolean. - Parameters: - dtype: The data type of the SIMD vector. - Args: x: First SIMD vector. y: Second SIMD vector. From b02ee5d84c345fade5f6a9c8a85d92a35c68601c Mon Sep 17 00:00:00 2001 From: Tracy Sharpe <99041446+raiseirql@users.noreply.github.com> Date: Sun, 19 Jan 2025 18:26:04 -0800 Subject: [PATCH 275/509] [stdlib] Remove sm_80 specialization for add/sub/mul Synchronize with recently merged LLVM support. MODULAR_ORIG_COMMIT_REV_ID: a721bdcdcd0e3fef3c0652067bafa165a7b96bcf --- stdlib/src/builtin/simd.mojo | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index f2953fb18e..2a29f76624 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -770,11 +770,6 @@ struct SIMD[type: DType, size: Int]( `self[i] + rhs[i]`. """ constrained[type.is_numeric(), "the SIMD type must be numeric"]() - - @parameter - if _is_sm_8x() and type.is_half_float(): - return self.fma(1, rhs) - return __mlir_op.`pop.add`(self.value, rhs.value) @always_inline("nodebug") @@ -789,18 +784,6 @@ struct SIMD[type: DType, size: Int]( `self[i] - rhs[i]`. """ constrained[type.is_numeric(), "the SIMD type must be numeric"]() - - @parameter - if _is_sm_9x() and type is DType.bfloat16: - return _call_ptx_intrinsic[ - scalar_instruction="sub.rn.bf16", - vector2_instruction="sub.rn.bf16x2", - scalar_constraints="=h,h,h", - vector_constraints="=r,r,r", - ](self, rhs) - elif _is_sm_8x() and type.is_half_float(): - return rhs.fma(-1, self) - return __mlir_op.`pop.sub`(self.value, rhs.value) @always_inline("nodebug") @@ -820,8 +803,6 @@ struct SIMD[type: DType, size: Int]( return (rebind[Self._Mask](self) & rebind[Self._Mask](rhs)).cast[ type ]() - elif _is_sm_8x() and type.is_half_float(): - return self.fma(rhs, -0.0) constrained[type.is_numeric(), "the SIMD type must be numeric"]() return __mlir_op.`pop.mul`(self.value, rhs.value) From 9603580da73f33a1b9ebae1026cb063f1ee0db26 Mon Sep 17 00:00:00 2001 From: Tracy Sharpe <99041446+raiseirql@users.noreply.github.com> Date: Sun, 19 Jan 2025 23:24:32 -0800 Subject: [PATCH 276/509] [stdlib] Remove SIMD.cast special case for PTX Synchronize with recently landed LLVM changes that handle this fusion in the backend. MODULAR_ORIG_COMMIT_REV_ID: f1a38f4c29112b79c95e270f3df7ab2d62d0fd78 --- stdlib/src/builtin/simd.mojo | 25 +------------------------ 1 file changed, 1 insertion(+), 24 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 2a29f76624..a871a56a59 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -1823,30 +1823,7 @@ struct SIMD[type: DType, size: Int]( if is_nvidia_gpu(): @parameter - if size > 1 and type is DType.float32 and target.is_half_float(): - # For size == 1, the LLVM backend generates the correct `cvt.rn.f16.f32` - # instruction. This is why we do not handle it here. - alias vector_asm_prefix = "cvt.rn.f16x2.f32" if target is DType.float16 else "cvt.rn.bf16x2.f32" - var res = SIMD[target, size]() - - @parameter - for i in range(0, size, 2): - var bf16x2_as_uint32 = inlined_assembly[ - vector_asm_prefix + " $0, $1, $2;", - UInt32, - constraints="=r,f,f", - has_side_effect=False, - ]( - rebind[Float32](self[i + 1]), - rebind[Float32](self[i]), - ) - res = res.insert[offset=i]( - bitcast[target, 2](bf16x2_as_uint32) - ) - - return res - - elif type is DType.bfloat16 and target is DType.float64: + if type is DType.bfloat16 and target is DType.float64: # Convert to F64 via a Float32 pathway. This would allow us to # use the optimizations defined above. return self.cast[DType.float32]().cast[target]() From af2ad49f0f5fb1f60fde3a11316cdf56e1ca1d3f Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 19 Jan 2025 23:25:22 -0800 Subject: [PATCH 277/509] [mojo-stdlib] Move VariadicListMem to use Origin instead of lit.origin. This moves VariadicListMem to use Origin directly, progress on MSTDL-1041. MODULAR_ORIG_COMMIT_REV_ID: a4c18d5a20717510c8eccb86a45f4cd168fd8b67 --- stdlib/src/builtin/builtin_list.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index b89b9db1c6..bf6372d054 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -280,7 +280,7 @@ struct _VariadicListMemIter[ struct VariadicListMem[ elt_is_mutable: Bool, //, element_type: AnyType, - origin: Origin[elt_is_mutable]._mlir_type, + origin: Origin[elt_is_mutable], ](Sized): """A utility class to access variadic function arguments of memory-only types that may have ownership. It exposes references to the elements in a From 4dffd08bac3062ccbffc1bb59ad8ce441e28df0a Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 20 Jan 2025 10:12:48 -0800 Subject: [PATCH 278/509] [mojo-stdlib] Move VariadicPack off `!lit.origin` This moves VariadicPack off of direct use of the `!lit.origin` type, using `Origin` instead. This required some refactoring of the internal logic that builds variadic pack. This is the last step that Fixes MSTDL-1041, the migration to Origin. MODULAR_ORIG_COMMIT_REV_ID: 3236395ccd98f7e441c6efee23583b428a63001b --- stdlib/src/builtin/builtin_list.mojo | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index bf6372d054..4486c59fbf 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -290,7 +290,7 @@ struct VariadicListMem[ elt_is_mutable: True if the elements of the list are mutable for an mut or owned argument. element_type: The type of the elements in the list. - origin: The reference origin of the underlying elements. + origin: The origin of the underlying elements. """ alias reference_type = Pointer[element_type, origin] @@ -464,7 +464,7 @@ alias _AnyTypeMetaType = __type_of(AnyType) @register_passable struct VariadicPack[ elt_is_mutable: Bool, //, - origin: Origin[elt_is_mutable]._mlir_type, + origin: Origin[elt_is_mutable], element_trait: _AnyTypeMetaType, *element_types: element_trait, ](Sized): @@ -474,7 +474,7 @@ struct VariadicPack[ Parameters: elt_is_mutable: True if the elements of the list are mutable for an mut or owned argument pack. - origin: The reference origin of the underlying elements. + origin: The origin of the underlying elements. element_trait: The trait that each element of the pack conforms to. element_types: The list of types held by the argument pack. """ @@ -485,7 +485,7 @@ struct VariadicPack[ `> `, element_types, `, `, - origin, + origin._mlir_origin, `>`, ] From 5dd90042b4452a9e819a99d532f245ba4af49a8e Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 20 Jan 2025 11:18:19 -0800 Subject: [PATCH 279/509] [mojo-lang] Fix a lazy-emission of def-arg box mutations. `def` arguments can be mutated, and therefore need a local copy of the value, but we defer the generation of the copy until (and if) the argument is ever mutated. In the case when the argument is accessed immutably and then later mutated, we need to make sure to update the earlier accesses to refer to the local copy, because loops and other control flow can cause it to be reached dynamically after the mutation. This fixes https://github.com/modular/mojo/issues/3955 MODULAR_ORIG_COMMIT_REV_ID: d53c484a54f6e82a9980da836bad5b2c46687215 --- docs/changelog.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 2a33bc61c4..df058bd661 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -407,3 +407,6 @@ what we publish. - [Issue #3935](https://github.com/modular/mojo/issues/3935) - Confusing OOM error when using Tuple.get incorrectly. + +- [Issue #3955](https://github.com/modular/mojo/issues/3955) - Unexpected + copy behaviour with `def` arguments in loops From 00f15725efc555de06fd164ec14fdfd1a77d7b0b Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 21 Jan 2025 10:01:27 -0500 Subject: [PATCH 280/509] [stdlib] Simplify string join methods by reusing stack buffer And move `StringableCollectionElement` to `WritableCollectionElement` to reduce allocations. MODULAR_ORIG_COMMIT_REV_ID: 7b9512d80133561a535eb4643337e5ebe395cef5 --- stdlib/src/builtin/string_literal.mojo | 48 +++------ stdlib/src/builtin/value.mojo | 6 +- stdlib/src/collections/dict.mojo | 1 - stdlib/src/collections/linked_list.mojo | 10 -- stdlib/src/collections/string/string.mojo | 119 +++------------------- stdlib/src/prelude/__init__.mojo | 2 +- stdlib/src/utils/write.mojo | 6 +- 7 files changed, 34 insertions(+), 158 deletions(-) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 65bbdff186..70c638601a 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -27,6 +27,7 @@ from hashlib._hasher import _HashableWithHasher, _Hasher from memory import UnsafePointer, memcpy, Span from sys.ffi import c_char from utils import Writable, Writer +from utils.write import _WriteBufferStack from utils._visualizers import lldb_formatter_wrapping_type @@ -636,7 +637,7 @@ struct StringLiteral( """ return __mlir_op.`pop.string.replace`(self.value, old.value, new.value) - fn join[T: StringableCollectionElement](self, elems: List[T, *_]) -> String: + fn join[T: WritableCollectionElement](self, elems: List[T, *_]) -> String: """Joins string elements using the current string as a delimiter. Parameters: @@ -648,30 +649,20 @@ struct StringLiteral( Returns: The joined string. """ - return String(self).join(elems) - - fn join(self, *elems: Int) -> String: - """Joins the elements from the tuple using the current string literal as a - delimiter. - - Args: - elems: The input tuple. - - Returns: - The joined string. - """ - if len(elems) == 0: - return "" - var curr = String(elems[0]) - for i in range(1, len(elems)): - curr += self + String(elems[i]) - return curr + var string = String() + var buffer = _WriteBufferStack(string) + for i in range(len(elems)): + buffer.write(elems[i]) + if i < len(elems) - 1: + buffer.write(self) + buffer.flush() + return string - fn join[*Types: Stringable](self, *elems: *Types) -> String: + fn join[*Ts: Writable](self, *elems: *Ts) -> String: """Joins string elements using the current string as a delimiter. Parameters: - Types: The types of the elements. + Ts: The types of the elements. Args: elems: The input values. @@ -679,20 +670,7 @@ struct StringLiteral( Returns: The joined string. """ - - var result: String = "" - var is_first = True - - @parameter - fn add_elt[T: Stringable](a: T): - if is_first: - is_first = False - else: - result += self - result += String(a) - - elems.each[add_elt]() - return result + return String(elems, sep=self) fn split(self, sep: StringSlice, maxsplit: Int = -1) raises -> List[String]: """Split the string literal by a separator. diff --git a/stdlib/src/builtin/value.mojo b/stdlib/src/builtin/value.mojo index 7376977778..c3868f1a5a 100644 --- a/stdlib/src/builtin/value.mojo +++ b/stdlib/src/builtin/value.mojo @@ -207,9 +207,9 @@ trait CollectionElementNew(ExplicitlyCopyable, Movable): pass -trait StringableCollectionElement(CollectionElement, Stringable): - """The StringableCollectionElement trait denotes a trait composition - of the `CollectionElement` and `Stringable` traits. +trait WritableCollectionElement(CollectionElement, Writable): + """The WritableCollectionElement trait denotes a trait composition + of the `CollectionElement` and `Writable` traits. This is useful to have as a named entity since Mojo does not currently support anonymous trait compositions to constrain diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index c6f585bae6..3b64da3aab 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -39,7 +39,6 @@ See the `Dict` docs for more details. from sys.ffi import OpaquePointer from bit import is_power_of_two -from builtin.value import StringableCollectionElement from memory import UnsafePointer, bitcast, memcpy from .optional import Optional diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index 49850f1c65..cb2e362749 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -16,16 +16,6 @@ from collections import Optional from collections._index_normalization import normalize_index -trait WritableCollectionElement(CollectionElement, Writable): - """A trait that combines CollectionElement and Writable traits. - - This trait requires types to implement both CollectionElement and Writable - interfaces, allowing them to be used in collections and written to output. - """ - - pass - - @value struct Node[ElementType: WritableCollectionElement]: """A node in a linked list data structure. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 3c210277ab..81a8fc4b37 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -20,7 +20,7 @@ from os import abort from sys import bitwidthof, llvm_intrinsic from sys.ffi import c_char from sys.intrinsics import _type_is_eq -from utils.write import write_buffered +from utils.write import write_buffered, _WriteBufferStack from bit import count_leading_zeros from memory import UnsafePointer, memcmp, memcpy, Span @@ -1177,55 +1177,11 @@ struct String( writer.write_bytes(self.as_bytes()) - fn join(self, *elems: Int) -> String: - """Joins the elements from the tuple using the current string as a - delimiter. - - Args: - elems: The input tuple. - - Returns: - The joined string. - """ - if len(elems) == 0: - return "" - var curr = String(elems[0]) - for i in range(1, len(elems)): - curr += self + String(elems[i]) - return curr - - fn join[*Types: Writable](self, *elems: *Types) -> String: - """Joins string elements using the current string as a delimiter. - - Parameters: - Types: The types of the elements. - - Args: - elems: The input values. - - Returns: - The joined string. - """ - - var result = String() - var is_first = True - - @parameter - fn add_elt[T: Writable](a: T): - if is_first: - is_first = False - else: - result.write(self) - result.write(a) - - elems.each[add_elt]() - return result - - fn join[T: StringableCollectionElement](self, elems: List[T, *_]) -> String: + fn join[*Ts: Writable](self, *elems: *Ts) -> String: """Joins string elements using the current string as a delimiter. Parameters: - T: The types of the elements. + Ts: The types of the elements. Args: elems: The input values. @@ -1233,34 +1189,10 @@ struct String( Returns: The joined string. """ + var sep = StaticString(ptr=self.unsafe_ptr(), length=len(self)) + return String(elems, sep=sep) - # TODO(#3403): Simplify this when the linked conditional conformance - # feature is added. Runs a faster algorithm if the concrete types are - # able to be converted to a span of bytes. - @parameter - if _type_is_eq[T, String](): - return self.fast_join(rebind[List[String]](elems)) - elif _type_is_eq[T, StringLiteral](): - return self.fast_join(rebind[List[StringLiteral]](elems)) - # FIXME(#3597): once StringSlice conforms to CollectionElement trait: - # if _type_is_eq[T, StringSlice](): - # return self.fast_join(rebind[List[StringSlice]](elems)) - else: - var result: String = "" - var is_first = True - - for e in elems: - if is_first: - is_first = False - else: - result += self - result += String(e[]) - - return result - - fn fast_join[ - T: BytesCollectionElement, //, - ](self, elems: List[T, *_]) -> String: + fn join[T: WritableCollectionElement](self, elems: List[T, *_]) -> String: """Joins string elements using the current string as a delimiter. Parameters: @@ -1272,37 +1204,14 @@ struct String( Returns: The joined string. """ - var n_elems = len(elems) - if n_elems == 0: - return String("") - var len_self = self.byte_length() - var len_elems = 0 - # Calculate the total size of the elements to join beforehand - # to prevent alloc syscalls as we know the buffer size. - # This can hugely improve the performance on large lists - for e_ref in elems: - len_elems += len(e_ref[].as_bytes()) - var capacity = len_self * (n_elems - 1) + len_elems - var buf = Self._buffer_type(capacity=capacity) - var self_ptr = self.unsafe_ptr() - var ptr = buf.unsafe_ptr() - var offset = 0 - var i = 0 - var is_first = True - while i < n_elems: - if is_first: - is_first = False - else: - memcpy(dest=ptr + offset, src=self_ptr, count=len_self) - offset += len_self - var e = elems[i].as_bytes() - var e_len = len(e) - memcpy(dest=ptr + offset, src=e.unsafe_ptr(), count=e_len) - offset += e_len - i += 1 - buf.size = capacity - buf.append(0) - return String(buf^) + var string = String() + var buffer = _WriteBufferStack(string) + for i in range(len(elems)): + buffer.write(elems[i]) + if i < len(elems) - 1: + buffer.write(self) + buffer.flush() + return string @always_inline fn chars(self) -> CharsIter[__origin_of(self)]: diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index dffb5725b5..8723941535 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -134,7 +134,7 @@ from builtin.value import ( ExplicitlyCopyable, Movable, RepresentableCollectionElement, - StringableCollectionElement, + WritableCollectionElement, ) from documentation import doc_private from memory import AddressSpace, Pointer diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index a022102d36..0b4cbda871 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -266,9 +266,9 @@ struct _ArgBytes(Writer): args.each[write_arg]() -struct _WriteBufferStack[origin: MutableOrigin, W: Writer, //, capacity: Int]( - Writer -): +struct _WriteBufferStack[ + origin: MutableOrigin, W: Writer, //, capacity: Int = 4096 +](Writer): var data: InlineArray[UInt8, capacity] var pos: Int var writer: Pointer[W, origin] From 87bb307672b4336eeeb581d978fc9f0ba6efad29 Mon Sep 17 00:00:00 2001 From: Tracy Sharpe <99041446+raiseirql@users.noreply.github.com> Date: Tue, 21 Jan 2025 07:26:31 -0800 Subject: [PATCH 281/509] [stdlib] Remove PTX lowering for bf16/fp16 SIMD.fma This is no longer needed after recent SIMD intrinsic cleanup. MODULAR_ORIG_COMMIT_REV_ID: ccdcc5777110a1cae2c8dbe3c9bb29a9479f2756 --- stdlib/src/builtin/simd.mojo | 33 +-------------------------------- 1 file changed, 1 insertion(+), 32 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index a871a56a59..8261d20e8e 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -43,7 +43,7 @@ from sys import ( sizeof, ) from sys._assembly import inlined_assembly -from sys.info import _current_arch, _is_sm_8x, _is_sm_9x +from sys.info import _current_arch, _is_sm_9x from bit import pop_count from builtin._format_float import _write_float @@ -1667,7 +1667,6 @@ struct SIMD[type: DType, size: Int]( Returns: The elementwise truncated values of this SIMD vector. """ - return self._floor_ceil_trunc_impl["llvm.trunc"]() @always_inline @@ -2027,36 +2026,6 @@ struct SIMD[type: DType, size: Int]( """ constrained[type.is_numeric(), "the SIMD type must be numeric"]() - @parameter - if (_is_sm_8x() or _is_sm_9x()) and type.is_half_float(): - alias prefix = "fma.rn.bf16" if type is DType.bfloat16 else "fma.rn.f16" - - @parameter - if size == 1: - return inlined_assembly[ - prefix + " $0, $1, $2, $3;", - Self, - constraints="=h,h,h,h", - has_side_effect=False, - ](self, multiplier, accumulator) - - var res = Self() - - @parameter - for i in range(0, size, 2): - var val = inlined_assembly[ - prefix + "x2 $0, $1, $2, $3;", - SIMD[type, 2], - constraints="=r,r,r,r", - has_side_effect=False, - ]( - self.slice[2, offset=i](), - multiplier.slice[2, offset=i](), - accumulator.slice[2, offset=i](), - ) - res = res.insert[offset=i](val) - return res - return __mlir_op.`pop.fma`( self.value, multiplier.value, accumulator.value ) From df3a9a7e00d15d160e866251b4bc4d56e8d252ae Mon Sep 17 00:00:00 2001 From: Tracy Sharpe <99041446+raiseirql@users.noreply.github.com> Date: Tue, 21 Jan 2025 08:18:11 -0800 Subject: [PATCH 282/509] [Kernels][AMD] fix fp32->bf16 for AMD GPUs (#54249) MODULAR_ORIG_COMMIT_REV_ID: 7cf61ab0f3e170865b11517116cd0a6a083482fb --- stdlib/src/builtin/simd.mojo | 52 +++++++++++++++++++++++------------- 1 file changed, 33 insertions(+), 19 deletions(-) diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 8261d20e8e..ced76542c7 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -3501,30 +3501,44 @@ fn _f32_to_bfloat16_scalar( return _unchecked_zero[DType.bfloat16, 1]() elif is_amd_gpu(): - alias nan_val = _nan[DType.float32]() - alias round_bias = 0x7FFF - - var tmp = UInt32(0) - var res = inlined_assembly[ - """\0A - v_cmp_u_f32 $0, $2, $2 \0A - v_bfe_u32 $1, $2, 16, 1 \0A - v_add3_u32 $1, $2, $1, $3 \0A - v_cndmask_b32 $2, $1, $4, $0 \0A - v_lshrrev_b32 $2, 16, $2 \0A - """, - _RegisterPackType[SIMD[DType.uint32, 2], Int32, Float32], - constraints="=s,=v,=v,v,v,1,2", + alias round_bias = Int32(0x7FFF) + + # Compute the mask of unordered values. + var unordered_mask = inlined_assembly[ + "v_cmp_u_f32 $0, $1, $1", + SIMD[DType.uint64, 1], + constraints="=s,v", + has_side_effect=False, + ](val) + + # Compute "rounded_val = val + lsb + round_bias" to round-to-nearest. + var lsb = inlined_assembly[ + "v_bfe_u32 $0, $1, 16, 1", + SIMD[DType.uint32, 1], + constraints="=v,v", + has_side_effect=False, + ](val) + var rounded_val = inlined_assembly[ + "v_add3_u32 $0, $1, $2, $3", + SIMD[DType.uint32, 1], + constraints="=v,v,v,v", has_side_effect=False, - ](round_bias, nan_val, tmp, val) + ](val, lsb, round_bias) + + # Select the rounded value or NaN based on the unordered mask. + var float_bits = inlined_assembly[ + "v_cndmask_b32 $0, $1, $2, $3", + SIMD[DType.uint32, 1], + constraints="=v,v,v,s", + has_side_effect=False, + ](rounded_val, _nan[DType.float32](), unordered_mask) + return bitcast[DType.bfloat16, 1]( - UInt16(bitcast[DType.uint32, 1](res[2])) + UInt16(float_bits >> _fp32_bf16_mantissa_diff) ) if _isnan(val): - return -_nan[DType.bfloat16]() if FPUtils[DType.float32].get_sign( - val - ) else _nan[DType.bfloat16]() + return _nan[DType.bfloat16]() var float_bits = FPUtils[DType.float32].bitcast_to_integer(val) From 6277b4968f3544f6a43eeae91df604951cab8273 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Tue, 21 Jan 2025 10:31:12 -0800 Subject: [PATCH 283/509] [mojo-lang][CheckLifetimes] Fix a bug handling field sensitive uses. This fixes a problem in check lifetimes, where uses of an indirect reference would not be treated as field sensitive accesses in the destructor insertion pass. This can lead to destructors not being inserted correctly in weird cases. This would have been very tricky to do, but due to all the improvements a couple months ago is now a simplification. This fixes https://github.com/modular/mojo/issues/3960 MODULAR_ORIG_COMMIT_REV_ID: 7338685a57c60d598f11edf7b0134649612e77f2 --- docs/changelog.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index df058bd661..2457d5ffdd 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -410,3 +410,5 @@ what we publish. - [Issue #3955](https://github.com/modular/mojo/issues/3955) - Unexpected copy behaviour with `def` arguments in loops + +- [Issue #3960](https://github.com/modular/mojo/issues/3960) - Infinite for loop From 04f5572cc1876d2acb151020200a083199e91ee2 Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Tue, 21 Jan 2025 13:37:51 -0500 Subject: [PATCH 284/509] [stdlib] Ensure `test_linked_list.mojo` passes in debug Now that the debug issue is fixed in the compiler, use `%mojo` instead of `%mojo-no-debug` so we can ensure this test works in debug builds too. MODULAR_ORIG_COMMIT_REV_ID: d46a31ca0009d2e0b9e02366f7180ff118d6d2c5 --- stdlib/test/collections/test_linked_list.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo index 1375def212..a5f3467736 100644 --- a/stdlib/test/collections/test_linked_list.mojo +++ b/stdlib/test/collections/test_linked_list.mojo @@ -10,7 +10,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -# RUN: %mojo-no-debug %s +# RUN: %mojo %s from collections import LinkedList from testing import assert_equal From c563812ae723234c0db9ed26e7364d4a85111ea2 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Tue, 21 Jan 2025 13:26:17 -0800 Subject: [PATCH 285/509] Remove bold font since links are now bold MODULAR_ORIG_COMMIT_REV_ID: 82bb3e4bd413e082ace481a78ab0b357a5c67e80 --- docs/manual/get-started.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 0e6133912a..49591eb123 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -11,7 +11,7 @@ import GetMagic from '@site/src/includes/get_magic.mdx'; :::tip -**Want to write a GPU function with Mojo?** See how to [build a custom op +Want to write a GPU function with Mojo? See how to [build a custom op with MAX](/max/tutorials/build-custom-ops). ::: From 115b1648e6a4615e43000a4cf02f5b60936f0ae1 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Tue, 21 Jan 2025 14:08:48 -0800 Subject: [PATCH 286/509] Add system requirements MODULAR_ORIG_COMMIT_REV_ID: 430e0353774ff3fefaf03d9813e18d5d6d9b59dd --- docs/manual/get-started.mdx | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index 49591eb123..da014c24d4 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -7,7 +7,8 @@ image: /images/artwork/mojo-get-started.jpg --- import GetMagic from '@site/src/includes/get_magic.mdx'; - +import Requirements from '@site/src/components/Requirements'; +import { requirementsNoGPU } from '@site/docs/max/requirements'; :::tip @@ -38,6 +39,9 @@ you to go learn more about each topic. If you just want to see the finished code, you can [get it on GitHub](https://github.com/modular/mojo/tree/nightly/examples/life). +System requirements: + + ## 1. Create a Mojo project with `magic` From 73192e7270848f3615af9aa682aaad462ffe91a1 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 21 Jan 2025 20:55:07 -0500 Subject: [PATCH 287/509] [stdlib] Remove String allocations Remove string allocations by constructing from Writable variadics. MODULAR_ORIG_COMMIT_REV_ID: 2e2c83c76faddb6c1a104803f60850980de3505f --- examples/operators/main.mojo | 2 +- stdlib/benchmarks/builtin/bench_sort.mojo | 22 ++++------ stdlib/benchmarks/collections/bench_dict.mojo | 12 ++---- .../benchmarks/collections/bench_string.mojo | 18 ++++---- stdlib/src/builtin/_location.mojo | 4 +- stdlib/src/builtin/format_int.mojo | 2 +- stdlib/src/builtin/object.mojo | 18 ++------ .../src/collections/string/inline_string.mojo | 26 ++++++------ stdlib/src/collections/string/string.mojo | 16 ++++--- stdlib/src/os/_linux_aarch64.mojo | 38 ++++++++++------- stdlib/src/os/_linux_x86.mojo | 38 ++++++++++------- stdlib/src/os/_macos.mojo | 42 +++++++++++-------- stdlib/src/os/os.mojo | 9 ++-- stdlib/src/pwd/_linux.mojo | 4 +- stdlib/src/pwd/_macos.mojo | 4 +- stdlib/src/sys/ffi.mojo | 4 +- stdlib/src/testing/testing.mojo | 14 +++---- stdlib/src/time/time.mojo | 8 +++- stdlib/test/bit/test_bit.mojo | 2 +- stdlib/test/builtin/test_print.mojo | 4 +- stdlib/test/builtin/test_sort.mojo | 12 +----- .../test/collections/string/test_string.mojo | 26 ++++++------ stdlib/test/collections/test_dict.mojo | 10 ++--- stdlib/test/memory/test_memory.mojo | 27 +++--------- stdlib/test/os/test_mkdir_and_rmdir.mojo | 8 ++-- stdlib/test/random/test_random.mojo | 32 +++++++------- stdlib/test/tempfile/test_tempfile.mojo | 12 +++--- 27 files changed, 195 insertions(+), 219 deletions(-) diff --git a/examples/operators/main.mojo b/examples/operators/main.mojo index 1cffa1cbbc..09f69c3837 100644 --- a/examples/operators/main.mojo +++ b/examples/operators/main.mojo @@ -27,7 +27,7 @@ def main(): # Examples of using Complex values with String(), repr(), and print() c3 = Complex(3.14159, -2.71828) print("c3 =", c3) - var msg: String = "The value is: " + String(c3) + var msg = String("The value is: ", c3) print(msg) print(String("{!r}").format(c3)) diff --git a/stdlib/benchmarks/builtin/bench_sort.mojo b/stdlib/benchmarks/builtin/bench_sort.mojo index addf6c200c..4a4fa9d1ca 100644 --- a/stdlib/benchmarks/builtin/bench_sort.mojo +++ b/stdlib/benchmarks/builtin/bench_sort.mojo @@ -147,13 +147,13 @@ fn bench_tiny_list_sort[type: DType](mut m: Bench) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId("std_sort_random_" + String(count) + "_" + String(type)) + BenchId(String("std_sort_random_", count, "_", type)) ) m.bench_function[bench_small_sort]( - BenchId("sml_sort_random_" + String(count) + "_" + String(type)) + BenchId(String("sml_sort_random_", count, "_", type)) ) m.bench_function[bench_insertion_sort]( - BenchId("ins_sort_random_" + String(count) + "_" + String(type)) + BenchId(String("ins_sort_random_", count, "_", type)) ) @@ -202,10 +202,10 @@ fn bench_small_list_sort[type: DType](mut m: Bench, count: Int) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId("std_sort_random_" + String(count) + "_" + String(type)) + BenchId(String("std_sort_random_", count, "_", type)) ) m.bench_function[bench_insertion_sort]( - BenchId("ins_sort_random_" + String(count) + "_" + String(type)) + BenchId(String("ins_sort_random_", count, "_", type)) ) @@ -254,11 +254,11 @@ fn bench_large_list_sort[type: DType](mut m: Bench, count: Int) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId("std_sort_random_" + String(count) + "_" + String(type)) + BenchId(String("std_sort_random_", count, "_", type)) ) m.bench_function[bench_heap_sort]( - BenchId("heap_sort_random_" + String(count) + "_" + String(type)) + BenchId(String("heap_sort_random_", count, "_", type)) ) @@ -307,14 +307,10 @@ fn bench_low_cardinality_list_sort(mut m: Bench, count: Int, delta: Int) raises: _ = list^ m.bench_function[bench_sort_list]( - BenchId( - "std_sort_low_card_" + String(count) + "_delta_" + String(delta) - ) + BenchId(String("std_sort_low_card_", count, "_delta_", delta)) ) m.bench_function[bench_heap_sort]( - BenchId( - "heap_sort_low_card_" + String(count) + "_delta_" + String(delta) - ) + BenchId(String("heap_sort_low_card_", count, "_delta_", delta)) ) diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index 10f04f62ae..06c6bc9364 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -134,10 +134,10 @@ def main(): for i in range(len(sizes)): alias size = sizes[i] m.bench_function[bench_dict_insert[size]]( - BenchId("bench_dict_insert[" + String(size) + "]") + BenchId(String("bench_dict_insert[", size, "]")) ) m.bench_function[bench_dict_lookup[size]]( - BenchId("bench_dict_lookup[" + String(size) + "]") + BenchId(String("bench_dict_lookup[", size, "]")) ) m.dump_report() @@ -146,10 +146,4 @@ def main(): for i in range(len(sizes)): alias size = sizes[i] var mem_s = total_bytes_used(make_dict[size]()) - print( - '"bench_dict_memory_size[' - + String(size) - + ']",' - + String(mem_s) - + ",0" - ) + print('"bench_dict_memory_size[', size, ']",', mem_s, ",0") diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index fe8ba978ed..ddae0ca333 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -262,30 +262,30 @@ def main(): alias fname = filenames[j] alias old = old_chars[j] alias new = new_chars[j] - suffix = "[" + String(length) + "]" # "(" + fname + ")" + alias suffix = String("[", length, "]") # "(" + fname + ")" m.bench_function[bench_string_count[length, fname, old]]( - BenchId("bench_string_count" + suffix) + BenchId(String("bench_string_count", suffix)) ) m.bench_function[bench_string_split[length, fname, old]]( - BenchId("bench_string_split" + suffix) + BenchId(String("bench_string_split", suffix)) ) m.bench_function[bench_string_split[length, fname]]( - BenchId("bench_string_split_none" + suffix) + BenchId(String("bench_string_split_none", suffix)) ) m.bench_function[bench_string_splitlines[length, fname]]( - BenchId("bench_string_splitlines" + suffix) + BenchId(String("bench_string_splitlines" + suffix)) ) m.bench_function[bench_string_lower[length, fname]]( - BenchId("bench_string_lower" + suffix) + BenchId(String("bench_string_lower" + suffix)) ) m.bench_function[bench_string_upper[length, fname]]( - BenchId("bench_string_upper" + suffix) + BenchId(String("bench_string_upper" + suffix)) ) m.bench_function[bench_string_replace[length, fname, old, new]]( - BenchId("bench_string_replace" + suffix) + BenchId(String("bench_string_replace" + suffix)) ) m.bench_function[bench_string_is_valid_utf8[length, fname]]( - BenchId("bench_string_is_valid_utf8" + suffix) + BenchId(String("bench_string_is_valid_utf8" + suffix)) ) results = Dict[String, (Float64, Int)]() diff --git a/stdlib/src/builtin/_location.mojo b/stdlib/src/builtin/_location.mojo index 410d332e66..e71c5c16fe 100644 --- a/stdlib/src/builtin/_location.mojo +++ b/stdlib/src/builtin/_location.mojo @@ -31,7 +31,7 @@ struct _SourceLocation(Writable, Stringable): return String.write(self) @no_inline - fn prefix[T: Stringable](self, msg: T) -> String: + fn prefix[T: Writable](self, msg: T) -> String: """Return the given message prefixed with the pretty-printer location. Parameters: @@ -40,7 +40,7 @@ struct _SourceLocation(Writable, Stringable): Args: msg: The message to attach the prefix to. """ - return "At " + String(self) + ": " + String(msg) + return String("At ", self, ": ", msg) fn write_to[W: Writer](self, mut writer: W): """ diff --git a/stdlib/src/builtin/format_int.mojo b/stdlib/src/builtin/format_int.mojo index c54b602d19..9eb4cece7d 100644 --- a/stdlib/src/builtin/format_int.mojo +++ b/stdlib/src/builtin/format_int.mojo @@ -225,7 +225,7 @@ fn _try_format_int( # incompatible radix and custom digit chars, which we aren't doing # above. return abort[String]( - "unexpected exception formatting value as hexadecimal: " + String(e) + String("unexpected exception formatting value as hexadecimal: ", e) ) diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index 94ee27873b..2f9a86c459 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -577,15 +577,8 @@ struct _ObjectImpl( writer.write(String(self.get_as_float())) return if self.is_str(): - writer.write( - "'" - + String( - StringRef( - self.get_as_string().data, self.get_as_string().length - ) - ) - + "'" - ) + var string = self.get_as_string() + writer.write("'", StringRef(string.data, string.length), "'") return if self.is_func(): writer.write( @@ -607,12 +600,7 @@ struct _ObjectImpl( for entry in ptr[].impl[].items(): if print_sep: writer.write(", ") - writer.write( - "'" - + String(entry[].key) - + "' = " - + String(object(entry[].value.copy())) - ) + writer.write("'", entry[].key, "' = ", object(entry[].value.copy())) print_sep = True writer.write("}") return diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index 8235033158..f05bb63504 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -121,8 +121,8 @@ struct InlineString(Sized, Stringable, CollectionElement, CollectionElementNew): self._storage[_FixedString[Self.SMALL_CAP]] += str_slice except e: abort( - "unreachable: InlineString append to FixedString failed: " - + String(e), + "unreachable: InlineString append to FixedString failed: ", + e, ) else: # We're currently in the small layout but must change to the @@ -302,11 +302,11 @@ struct _FixedString[CAP: Int]( """ if len(literal) > CAP: raise Error( - "String literal (len=" - + String(len(literal)) - + ") is longer than FixedString capacity (" - + String(CAP) - + ")" + "String literal (len=", + len(literal), + ") is longer than FixedString capacity (", + CAP, + ")", ) self.buffer = InlineArray[UInt8, CAP]() @@ -384,12 +384,12 @@ struct _FixedString[CAP: Int]( if total_len > CAP: return Optional( Error( - "Insufficient capacity to append len=" - + String(len(bytes)) - + " string to len=" - + String(len(self)) - + " FixedString with capacity=" - + String(CAP), + "Insufficient capacity to append len=", + len(bytes), + " string to len=", + len(self), + " FixedString with capacity=", + CAP, ) ) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 81a8fc4b37..4cee5a90d0 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -374,12 +374,12 @@ fn _handle_base_prefix( fn _str_to_base_error(base: Int, str_slice: StringSlice) -> String: - return ( - "String is not convertible to integer with base " - + String(base) - + ": '" - + String(str_slice) - + "'" + return String( + "String is not convertible to integer with base ", + base, + ": '", + str_slice, + "'", ) @@ -418,9 +418,7 @@ fn _identify_base(str_slice: StringSlice, start: Int) -> Tuple[Int, Int]: fn _atof_error(str_ref: StringSlice) -> Error: - return Error( - "String is not convertible to float: '" + String(str_ref) + "'" - ) + return Error("String is not convertible to float: '", str_ref, "'") fn atof(str_slice: StringSlice) raises -> Float64: diff --git a/stdlib/src/os/_linux_aarch64.mojo b/stdlib/src/os/_linux_aarch64.mojo index 2eaba1b631..f4c9f62789 100644 --- a/stdlib/src/os/_linux_aarch64.mojo +++ b/stdlib/src/os/_linux_aarch64.mojo @@ -67,24 +67,30 @@ struct _c_stat(Stringable): self.st_birthtimespec = _CTimeSpec() self.unused = InlineArray[Int64, 2](0, 0) + fn write_to[W: Writer](self, mut writer: W): + # fmt: off + writer.write( + "{\nst_dev: ", self.st_dev, + ",\nst_mode: ", self.st_mode, + ",\nst_nlink: ", self.st_nlink, + ",\nst_ino: ", self.st_ino, + ",\nst_uid: ", self.st_uid, + ",\nst_gid: ", self.st_gid, + ",\nst_rdev: ", self.st_rdev, + ",\nst_size: ", self.st_size, + ",\nst_blksize: ", self.st_blksize, + ",\nst_blocks: ", self.st_blocks, + ",\nst_atimespec: ", self.st_atimespec, + ",\nst_mtimespec: ", self.st_mtimespec, + ",\nst_ctimespec: ", self.st_ctimespec, + ",\nst_birthtimespec: ", self.st_birthtimespec, + "\n}", + ) + # fmt: on + @no_inline fn __str__(self) -> String: - var res = String("{\n") - res += "st_dev: " + String(self.st_dev) + ",\n" - res += "st_mode: " + String(self.st_mode) + ",\n" - res += "st_nlink: " + String(self.st_nlink) + ",\n" - res += "st_ino: " + String(self.st_ino) + ",\n" - res += "st_uid: " + String(self.st_uid) + ",\n" - res += "st_gid: " + String(self.st_gid) + ",\n" - res += "st_rdev: " + String(self.st_rdev) + ",\n" - res += "st_size: " + String(self.st_size) + ",\n" - res += "st_blksize: " + String(self.st_blksize) + ",\n" - res += "st_blocks: " + String(self.st_blocks) + ",\n" - res += "st_atimespec: " + String(self.st_atimespec) + ",\n" - res += "st_mtimespec: " + String(self.st_mtimespec) + ",\n" - res += "st_ctimespec: " + String(self.st_ctimespec) + ",\n" - res += "st_birthtimespec: " + String(self.st_birthtimespec) + "\n" - return res + "}" + return String.write(self) fn _to_stat_result(self) -> stat_result: return stat_result( diff --git a/stdlib/src/os/_linux_x86.mojo b/stdlib/src/os/_linux_x86.mojo index fbd5629f48..ece0de56d8 100644 --- a/stdlib/src/os/_linux_x86.mojo +++ b/stdlib/src/os/_linux_x86.mojo @@ -65,24 +65,30 @@ struct _c_stat(Stringable): self.st_birthtimespec = _CTimeSpec() self.unused = InlineArray[Int64, 3](0, 0, 0) + fn write_to[W: Writer](self, mut writer: W): + # fmt: off + writer.write( + "{\nst_dev: ", self.st_dev, + ",\nst_mode: ", self.st_mode, + ",\nst_nlink: ", self.st_nlink, + ",\nst_ino: ", self.st_ino, + ",\nst_uid: ", self.st_uid, + ",\nst_gid: ", self.st_gid, + ",\nst_rdev: ", self.st_rdev, + ",\nst_size: ", self.st_size, + ",\nst_blksize: ", self.st_blksize, + ",\nst_blocks: ", self.st_blocks, + ",\nst_atimespec: ", self.st_atimespec, + ",\nst_mtimespec: ", self.st_mtimespec, + ",\nst_ctimespec: ", self.st_ctimespec, + ",\nst_birthtimespec: ", self.st_birthtimespec, + "\n}", + ) + # fmt: on + @no_inline fn __str__(self) -> String: - var res = String("{\n") - res += "st_dev: " + String(self.st_dev) + ",\n" - res += "st_mode: " + String(self.st_mode) + ",\n" - res += "st_nlink: " + String(self.st_nlink) + ",\n" - res += "st_ino: " + String(self.st_ino) + ",\n" - res += "st_uid: " + String(self.st_uid) + ",\n" - res += "st_gid: " + String(self.st_gid) + ",\n" - res += "st_rdev: " + String(self.st_rdev) + ",\n" - res += "st_size: " + String(self.st_size) + ",\n" - res += "st_blksize: " + String(self.st_blksize) + ",\n" - res += "st_blocks: " + String(self.st_blocks) + ",\n" - res += "st_atimespec: " + String(self.st_atimespec) + ",\n" - res += "st_mtimespec: " + String(self.st_mtimespec) + ",\n" - res += "st_ctimespec: " + String(self.st_ctimespec) + ",\n" - res += "st_birthtimespec: " + String(self.st_birthtimespec) + "\n" - return res + "}" + return String.write(self) fn _to_stat_result(self) -> stat_result: return stat_result( diff --git a/stdlib/src/os/_macos.mojo b/stdlib/src/os/_macos.mojo index 596fb6e459..8558ca764e 100644 --- a/stdlib/src/os/_macos.mojo +++ b/stdlib/src/os/_macos.mojo @@ -70,26 +70,32 @@ struct _c_stat(Stringable): self.st_lspare = 0 self.st_qspare = InlineArray[Int64, 2](0, 0) + fn write_to[W: Writer](self, mut writer: W): + # fmt: off + writer.write( + "{\nst_dev: ", self.st_dev, + ",\nst_mode: ", self.st_mode, + ",\nst_nlink: ", self.st_nlink, + ",\nst_ino: ", self.st_ino, + ",\nst_uid: ", self.st_uid, + ",\nst_gid: ", self.st_gid, + ",\nst_rdev: ", self.st_rdev, + ",\nst_atimespec: ", self.st_atimespec, + ",\nst_mtimespec: ", self.st_mtimespec, + ",\nst_ctimespec: ", self.st_ctimespec, + ",\nst_birthtimespec: ", self.st_birthtimespec, + ",\nst_size: ", self.st_size, + ",\nst_blocks: ", self.st_blocks, + ",\nst_blksize: ", self.st_blksize, + ",\nst_flags: ", self.st_flags, + "st_gen: ", self.st_gen, + "\n}", + ) + # fmt: on + @no_inline fn __str__(self) -> String: - var res = String("{\n") - res += "st_dev: " + String(self.st_dev) + ",\n" - res += "st_mode: " + String(self.st_mode) + ",\n" - res += "st_nlink: " + String(self.st_nlink) + ",\n" - res += "st_ino: " + String(self.st_ino) + ",\n" - res += "st_uid: " + String(self.st_uid) + ",\n" - res += "st_gid: " + String(self.st_gid) + ",\n" - res += "st_rdev: " + String(self.st_rdev) + ",\n" - res += "st_atimespec: " + String(self.st_atimespec) + ",\n" - res += "st_mtimespec: " + String(self.st_mtimespec) + ",\n" - res += "st_ctimespec: " + String(self.st_ctimespec) + ",\n" - res += "st_birthtimespec: " + String(self.st_birthtimespec) + ",\n" - res += "st_size: " + String(self.st_size) + ",\n" - res += "st_blocks: " + String(self.st_blocks) + ",\n" - res += "st_blksize: " + String(self.st_blksize) + ",\n" - res += "st_flags: " + String(self.st_flags) + ",\n" - res += "st_gen: " + String(self.st_gen) + "\n" - return res + "}" + return String.write(self) fn _to_stat_result(self) -> stat_result: return stat_result( diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index 0d623bcfe6..81ebad842f 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -367,11 +367,12 @@ def makedirs[ mkdir(path, mode) except e: if not exist_ok: - raise String( - e - ) + "\nset `makedirs(path, exist_ok=True)` to allow existing dirs" + raise Error( + e, + "\nset `makedirs(path, exist_ok=True)` to allow existing dirs", + ) if not os.path.isdir(path): - raise "path not created: " + path.__fspath__() + "\n" + String(e) + raise Error("path not created: ", path.__fspath__(), "\n", e) fn rmdir[PathLike: os.PathLike](path: PathLike) raises: diff --git a/stdlib/src/pwd/_linux.mojo b/stdlib/src/pwd/_linux.mojo index 877cebd05b..282de1f60a 100644 --- a/stdlib/src/pwd/_linux.mojo +++ b/stdlib/src/pwd/_linux.mojo @@ -48,12 +48,12 @@ fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: fn _getpw_linux(uid: UInt32) raises -> Passwd: var passwd_ptr = external_call["getpwuid", UnsafePointer[_C_Passwd]](uid) if not passwd_ptr: - raise "user ID not found in the password database: " + String(uid) + raise Error("user ID not found in the password database: ", uid) return _build_pw_struct(passwd_ptr) fn _getpw_linux(name: String) raises -> Passwd: var passwd_ptr = external_call["getpwnam", UnsafePointer[_C_Passwd]](name) if not passwd_ptr: - raise "user name not found in the password database: " + name + raise Error("user name not found in the password database: ", name) return _build_pw_struct(passwd_ptr) diff --git a/stdlib/src/pwd/_macos.mojo b/stdlib/src/pwd/_macos.mojo index 7aa51eeeb2..b09f615d3f 100644 --- a/stdlib/src/pwd/_macos.mojo +++ b/stdlib/src/pwd/_macos.mojo @@ -53,12 +53,12 @@ fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: fn _getpw_macos(uid: UInt32) raises -> Passwd: var passwd_ptr = external_call["getpwuid", UnsafePointer[_C_Passwd]](uid) if not passwd_ptr: - raise "user ID not found in the password database: " + String(uid) + raise Error("user ID not found in the password database: ", uid) return _build_pw_struct(passwd_ptr) fn _getpw_macos(name: String) raises -> Passwd: var passwd_ptr = external_call["getpwnam", UnsafePointer[_C_Passwd]](name) if not passwd_ptr: - raise "user name not found in the password database: " + name + raise Error("user name not found in the password database: ", name) return _build_pw_struct(passwd_ptr) diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index cafee526e0..2485f81af5 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -174,7 +174,7 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): var handle = dlopen(path.unsafe_cstr_ptr(), flags) if handle == OpaquePointer(): var error_message = dlerror() - abort("dlopen failed: " + String(error_message)) + abort("dlopen failed: ", String(StringRef(error_message))) self.handle = handle else: self.handle = OpaquePointer() @@ -355,7 +355,7 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): var err = dlerror() if err: - abort("dlsym failed: " + String(err)) + abort("dlsym failed: ", String(StringRef(err))) return res diff --git a/stdlib/src/testing/testing.mojo b/stdlib/src/testing/testing.mojo index f8b02cc294..8d1e2d9f08 100644 --- a/stdlib/src/testing/testing.mojo +++ b/stdlib/src/testing/testing.mojo @@ -42,8 +42,8 @@ from utils import StringSlice @always_inline -fn _assert_error[T: Stringable](msg: T, loc: _SourceLocation) -> String: - return loc.prefix("AssertionError: " + String(msg)) +fn _assert_error[T: Writable](msg: T, loc: _SourceLocation) -> String: + return loc.prefix(String("AssertionError: ", msg)) @always_inline @@ -502,14 +502,14 @@ fn assert_almost_equal[ ) if not all(almost_equal): - var err = String(lhs) + " is not close to " + String(rhs) + var err = String(lhs, " is not close to ", rhs) @parameter if type.is_integral() or type.is_floating_point(): - err += " with a diff of " + String(abs(lhs - rhs)) + err += String(" with a diff of ", abs(lhs - rhs)) if msg: - err += " (" + msg + ")" + err += String(" (", msg, ")") raise _assert_error(err, location.or_else(__call_location())) @@ -661,9 +661,7 @@ struct assert_raises: Raises: AssertionError: Always. The block must raise to pass the test. """ - raise Error( - "AssertionError: Didn't raise at " + String(self.call_location) - ) + raise Error("AssertionError: Didn't raise at ", self.call_location) fn __exit__(self, error: Error) raises -> Bool: """Exit the context manager with an error. diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 561179f9f0..cc7a450245 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -61,7 +61,7 @@ alias _WINDOWS_LARGE_INTEGER = Int64 @value @register_passable("trivial") -struct _CTimeSpec(Stringable): +struct _CTimeSpec(Stringable, Writable): var tv_sec: Int # Seconds var tv_subsec: Int # subsecond (nanoseconds on linux and usec on mac) @@ -78,7 +78,11 @@ struct _CTimeSpec(Stringable): @no_inline fn __str__(self) -> String: - return String(self.as_nanoseconds()) + "ns" + return String.write(self) + + @no_inline + fn write_to[W: Writer](self, mut writer: W): + writer.write(self.as_nanoseconds(), "ns") @value diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index ff6e99752f..73f8bd4054 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -515,7 +515,7 @@ def test_log2_floor(): assert_equal( log2_floor(i), _log2_floor(i), - msg="mismatching value for the input value of " + String(i), + msg=String("mismatching value for the input value of ", i), ) fn _check_alias[n: Int](expected: Int) raises: diff --git a/stdlib/test/builtin/test_print.mojo b/stdlib/test/builtin/test_print.mojo index a16e54c918..070db4066b 100644 --- a/stdlib/test/builtin/test_print.mojo +++ b/stdlib/test/builtin/test_print.mojo @@ -23,8 +23,8 @@ from utils import IndexList, StringRef @always_inline -fn _assert_error[T: Stringable](msg: T, loc: _SourceLocation) -> String: - return loc.prefix("AssertionError: " + String(msg)) +fn _assert_error[T: Writable](msg: T, loc: _SourceLocation) -> String: + return loc.prefix(String("AssertionError: ", msg)) fn _assert_equal_error( diff --git a/stdlib/test/builtin/test_sort.mojo b/stdlib/test/builtin/test_sort.mojo index edba5a03d1..153d2d4d05 100644 --- a/stdlib/test/builtin/test_sort.mojo +++ b/stdlib/test/builtin/test_sort.mojo @@ -45,19 +45,11 @@ fn random_numbers[ return result -# fn assert_sorted[dtype: DType](mut list: List[Scalar[dtype]]) raises: -# sort[dtype](list) -# for i in range(1, len(list)): -# assert_true( -# list[i] >= list[i - 1], String(list[i - 1]) + " > " + String(list[i]) -# ) - - fn assert_sorted_string(mut list: List[String]) raises: for i in range(1, len(list)): assert_true( list[i] >= list[i - 1], - String(list[i - 1]) + " > " + String(list[i]), + String(list[i - 1], " > ", list[i]), ) @@ -65,7 +57,7 @@ fn assert_sorted[ type: ComparableCollectionElement ](mut list: List[type]) raises: for i in range(1, len(list)): - assert_true(list[i] >= list[i - 1], "error at index: " + String(i)) + assert_true(list[i] >= list[i - 1], String("error at index: ", i)) fn test_sort_small_3() raises: diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index c2f563cd2e..e576dbfcc3 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -647,18 +647,20 @@ def test_split(): # TODO add line and paragraph separator as StringLiteral once unicode # escape secuences are accepted var univ_sep_var = ( - String(" ") - + String("\t") - + String("\n") - + String("\r") - + String("\v") - + String("\f") - + String("\x1c") - + String("\x1d") - + String("\x1e") - + String(buffer=next_line) - + String(buffer=unicode_line_sep) - + String(buffer=unicode_paragraph_sep) + String( + " ", + "\t", + "\n", + "\r", + "\v", + "\f", + "\x1c", + "\x1d", + "\x1e", + String(buffer=next_line), + String(buffer=unicode_line_sep), + String(buffer=unicode_paragraph_sep), + ) ) var s = univ_sep_var + "hello" + univ_sep_var + "world" + univ_sep_var d = s.split() diff --git a/stdlib/test/collections/test_dict.mojo b/stdlib/test/collections/test_dict.mojo index 6fb74b3d29..aa2e12c2e6 100644 --- a/stdlib/test/collections/test_dict.mojo +++ b/stdlib/test/collections/test_dict.mojo @@ -78,7 +78,7 @@ def test_basic_no_copies(): def test_multiple_resizes(): var dict = Dict[String, Int]() for i in range(20): - dict["key" + String(i)] = i + 1 + dict[String("key", i)] = i + 1 assert_equal(11, dict["key10"]) assert_equal(20, dict["key19"]) @@ -99,7 +99,7 @@ def test_bool_conversion(): def test_big_dict(): var dict = Dict[String, Int]() for i in range(2000): - dict["key" + String(i)] = i + 1 + dict[String("key", i)] = i + 1 assert_equal(2000, len(dict)) @@ -132,7 +132,7 @@ def test_dict_string_representation_int_int(): def test_compact(): var dict = Dict[String, Int]() for i in range(20): - var key = "key" + String(i) + var key = String("key", i) dict[key] = i + 1 _ = dict.pop(key) assert_equal(0, len(dict)) @@ -141,10 +141,10 @@ def test_compact(): def test_compact_with_elements(): var dict = Dict[String, Int]() for i in range(5): - var key = "key" + String(i) + var key = String("key", i) dict[key] = i + 1 for i in range(5, 20): - var key = "key" + String(i) + var key = String("key", i) dict[key] = i + 1 _ = dict.pop(key) assert_equal(5, len(dict)) diff --git a/stdlib/test/memory/test_memory.mojo b/stdlib/test/memory/test_memory.mojo index 48b77fdc74..c3310b1021 100644 --- a/stdlib/test/memory/test_memory.mojo +++ b/stdlib/test/memory/test_memory.mojo @@ -198,48 +198,33 @@ def test_memcmp_extensive[ assert_equal( memcmp(ptr1, ptr1, count), 0, - "for dtype=" + String(type) + ";count=" + String(count), + String("for dtype=", type, ";count=", count), ) assert_equal( memcmp(ptr1, ptr2, count), -1, - "for dtype=" + String(type) + ";count=" + String(count), + String("for dtype=", type, ";count=", count), ) assert_equal( memcmp(ptr2, ptr1, count), 1, - "for dtype=" + String(type) + ";count=" + String(count), + String("for dtype=", type, ";count=", count), ) assert_equal( memcmp(dptr1, dptr1, count), 0, - "for dtype=" - + String(type) - + ";extremes=" - + String(extermes) - + ";count=" - + String(count), + String("for dtype=", type, ";extremes=", extermes, ";count=", count), ) assert_equal( memcmp(dptr1, dptr2, count), -1, - "for dtype=" - + String(type) - + ";extremes=" - + String(extermes) - + ";count=" - + String(count), + String("for dtype=", type, ";extremes=", extermes, ";count=", count), ) assert_equal( memcmp(dptr2, dptr1, count), 1, - "for dtype=" - + String(type) - + ";extremes=" - + String(extermes) - + ";count=" - + String(count), + String("for dtype=", type, ";extremes=", extermes, ";count=", count), ) ptr1.free() diff --git a/stdlib/test/os/test_mkdir_and_rmdir.mojo b/stdlib/test/os/test_mkdir_and_rmdir.mojo index 4fef85d336..5ebb882c84 100644 --- a/stdlib/test/os/test_mkdir_and_rmdir.mojo +++ b/stdlib/test/os/test_mkdir_and_rmdir.mojo @@ -43,7 +43,7 @@ fn test_mkdir_and_rmdir(path: String) raises: # verify that the test dir does not exist before starting the test assert_false( exists(path), - "Unexpected dir " + String(path) + " it should not exist", + String("Unexpected dir ", path, " it should not exist"), ) os.mkdir(path, 0o777) @@ -62,8 +62,7 @@ fn test_mkdir_and_rmdir(path: Path) raises: pass # verify that the test dir does not exist before starting the test assert_false( - exists(path), - "Unexpected dir " + String(path) + " it should not exist", + exists(path), String("Unexpected dir ", path, " it should not exist") ) os.mkdir(path, 0o777) @@ -82,8 +81,7 @@ fn test_makedirs_and_removedirs(path: Path) raises: pass # verify that the test dir does not exist before starting the test assert_false( - exists(path), - "Unexpected dir " + String(path) + " it should not exist", + exists(path), String("Unexpected dir ", path, " it should not exist") ) os.makedirs(path, exist_ok=True) assert_true(exists(path)) diff --git a/stdlib/test/random/test_random.mojo b/stdlib/test/random/test_random.mojo index 384a2f4ed6..8f705b002b 100644 --- a/stdlib/test/random/test_random.mojo +++ b/stdlib/test/random/test_random.mojo @@ -29,39 +29,43 @@ def test_random(): var random_float = random_float64(0, 1) assert_true( random_float >= 0, - "Value " + String(random_float) + " is not above or equal to 0", + String("Value ", random_float, " is not above or equal to 0"), ) assert_true( random_float <= 1, - "Value " + String(random_float) + " is not below or equal to 1", + String("Value ", random_float, " is not below or equal to 1"), ) var random_signed = random_si64(-255, 255) assert_true( random_signed >= -255, - "Signed value " - + String(random_signed) - + " is not above or equal to -255", + String( + "Signed value ", random_signed, " is not above or equal to -255" + ), ) assert_true( random_signed <= 255, - "Signed value " - + String(random_signed) - + " is not below or equal to 255", + String( + "Signed value ", random_signed, " is not below or equal to 255" + ), ) var random_unsigned = random_ui64(0, 255) assert_true( random_unsigned >= 0, - "Unsigned value " - + String(random_unsigned) - + " is not above or equal to 0", + String( + "Unsigned value ", + random_unsigned, + " is not above or equal to 0", + ), ) assert_true( random_unsigned <= 255, - "Unsigned value " - + String(random_unsigned) - + " is not below or equal to 255", + String( + "Unsigned value ", + random_unsigned, + " is not below or equal to 255", + ), ) var random_normal = randn_float64(0, 1) diff --git a/stdlib/test/tempfile/test_tempfile.mojo b/stdlib/test/tempfile/test_tempfile.mojo index 6f8fcf8b17..61b6e1daeb 100644 --- a/stdlib/test/tempfile/test_tempfile.mojo +++ b/stdlib/test/tempfile/test_tempfile.mojo @@ -100,16 +100,14 @@ def _set_up_gettempdir_test( except: os.rmdir(dir_with_writing_access) raise Error( - "Failed to setup test, couldn't create " - + String(dir_without_writing_access) + "Failed to setup test, couldn't create ", dir_without_writing_access ) def test_gettempdir(): var non_existing_dir = Path() / "non_existing_dir" assert_false( - exists(non_existing_dir), - "Unexpected dir" + String(non_existing_dir), + exists(non_existing_dir), String("Unexpected dir", non_existing_dir) ) var dir_without_writing_access = Path() / "dir_without_writing_access" var dir_with_writing_access = Path() / "dir_with_writing_access" @@ -129,7 +127,7 @@ def test_gettempdir(): assert_equal( tmpdir_result.value(), String(dir_with_writing_access), - "expected to get:" + String(dir_with_writing_access), + String("expected to get:", dir_with_writing_access), ) # test gettempdir falls back to TEMP @@ -144,7 +142,7 @@ def test_gettempdir(): assert_equal( tmpdir_result.value(), String(dir_with_writing_access), - "expected to get:" + String(dir_with_writing_access), + String("expected to get:", dir_with_writing_access), ) # test gettempdir falls back to TMP @@ -160,7 +158,7 @@ def test_gettempdir(): assert_equal( tmpdir_result.value(), String(dir_with_writing_access), - "expected to get:" + String(dir_with_writing_access), + String("expected to get:", dir_with_writing_access), ) _clean_up_gettempdir_test() From c6d25dcaebbe397b991e8bf362695b595275bb64 Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 22 Jan 2025 21:36:09 +0000 Subject: [PATCH 288/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025012205 --- examples/life/magic.lock | 1036 +++++++++++++++--------------- examples/magic.lock | 1076 +++++++++++++++---------------- examples/notebooks/magic.lock | 1131 ++++++++++++++++----------------- examples/operators/magic.lock | 1036 +++++++++++++++--------------- examples/testing/magic.lock | 1036 +++++++++++++++--------------- magic.lock | 1036 +++++++++++++++--------------- 6 files changed, 3128 insertions(+), 3223 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index af20cdf69e..163d1ace68 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -17,19 +17,19 @@ environments: - 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__glibc >=2.17,<3.0.a0 - idna >=2.0 @@ -7108,11 +7092,11 @@ packages: platform: linux license: Apache-2.0 license_family: Apache - size: 151393 - timestamp: 1733428897813 -- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/yarl-1.18.3-py312hb2c0f52_0.conda - sha256: 470b5b0f3ac89acd143095281167dc2ac1a56d4fa22e1794bd8f3b00bb604540 - md5: 0b3c640697bca798d0ab428f530ed24c + size: 152305 + timestamp: 1737575898300 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/yarl-1.18.3-py312hcc812fe_1.conda + sha256: bfa211c0dd5dbb308788f055277dc428b7923ceb2de6a22ea98bc17cf306f9f5 + md5: d14c78abdd6109e2b7162f53b6cc1e77 depends: - idna >=2.0 - libgcc >=13 @@ -7125,11 +7109,11 @@ packages: platform: linux license: Apache-2.0 license_family: Apache - size: 150004 - timestamp: 1733429056665 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.18.3-py312hea69d52_0.conda - sha256: 69c7863809e11bc90c0d935c16e7f151dcc925add08b3894f06059263a8cb9ba - md5: f32f9b16361866a62d6e061fcd7eb400 + size: 149654 + timestamp: 1737576065314 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.18.3-py312h998013c_1.conda + sha256: 48821d23567ca0f853eee6f7812c74392867e123798b5b3c44f58758d8eb580e + md5: 092d3b40acc67c470f379049be343a7a depends: - __osx >=11.0 - idna >=2.0 @@ -7142,8 +7126,8 @@ packages: platform: osx license: Apache-2.0 license_family: Apache - size: 141556 - timestamp: 1733429104990 + size: 145543 + timestamp: 1737576074753 - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h3b0a872_7.conda sha256: a4dc72c96848f764bb5a5176aa93dd1e9b9e52804137b99daeebba277b31ea10 md5: 3947a35e916fcc6b9825449affbf4214 From f8867a97b9872640e0ff785c8ccf5224c7ac468f Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 22 Jan 2025 02:09:32 -0500 Subject: [PATCH 289/509] [SDK] Remove String allocations Remove allocations by constructing from `Writable` variadics and writing to stack before allocating `String`. MODULAR_ORIG_COMMIT_REV_ID: 6152520cfb625d71fdbce076c600d7e0d5b12248 --- stdlib/src/builtin/error.mojo | 24 ++++++------------------ 1 file changed, 6 insertions(+), 18 deletions(-) diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index 0e599ecbf5..d550b8e7be 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -194,9 +194,9 @@ struct Error( Args: writer: The object to write to. """ - - # TODO: Avoid this unnecessary intermediate String allocation. - writer.write(self._message()) + if not self: + return + writer.write(StringRef(self.unsafe_cstr_ptr())) @no_inline fn __repr__(self) -> String: @@ -205,7 +205,9 @@ struct Error( Returns: A printable representation of the error message. """ - return String("Error(", repr(self._message()), ")") + return String( + "Error(", repr(String(StringRef(self.unsafe_cstr_ptr()))), ")" + ) # ===-------------------------------------------------------------------===# # Methods @@ -221,20 +223,6 @@ struct Error( """ return self.data.bitcast[c_char]() - fn _message(self) -> String: - """Converts the Error to string representation. - - Returns: - A String of the error message. - """ - if not self: - return "" - - var length = self.loaded_length - if length < 0: - length = -length - return String(StringRef(self.data, length)) - @doc_private fn __mojo_debugger_raise_hook(): From aabdcc9c70f9d20635b70994e8b35e3215b09c4e Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Wed, 22 Jan 2025 14:46:14 -0800 Subject: [PATCH 290/509] [stdlib] Add `StringSlice.__getitem__(Slice)` Right now, the ergonomics of slicing a `StringSlice` are pretty bad. This PR does a first step to fix things up, but we'll want to add UTF-8 boundary checks. MODULAR_ORIG_COMMIT_REV_ID: cd725eb2f54abe8fdeaeade6f40a850bb608311d --- docs/changelog.md | 7 ++- .../src/collections/string/string_slice.mojo | 20 +++++++ .../collections/string/test_string_slice.mojo | 58 +++++++++++++++++++ 3 files changed, 83 insertions(+), 2 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 2457d5ffdd..b77162c41a 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -102,7 +102,7 @@ what we publish. - The free floating functions for constructing different types have been deprecated for actual constructors: - + ```plaintext before after ------------------ @@ -111,7 +111,7 @@ what we publish. bool() Bool() float() Float64() ``` - + These functions were a workaround before Mojo had a way to distinguish between implicit and explicit constructors. For this release you'll get a deprecation warning, and in the next release they'll become compiler errors. You can @@ -196,6 +196,9 @@ what we publish. compliant UTF-8 decoder that returns each Unicode codepoint encoded in the string. +- Added `StringSlice.__getitem__(Slice)` which returns a substring. + Only step sizes of 1 are supported. + - Several standard library functions have been changed to take `StringSlice` instead of `String`. This generalizes them to be used for any appropriately encoded string in memory, without requiring that the string be heap allocated. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 5f0bf7ec7b..34d7a71643 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -627,6 +627,26 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return self.__str__() + @always_inline + fn __getitem__(self, span: Slice) raises -> Self: + """Gets the sequence of characters at the specified positions. + + Args: + span: A slice that specifies positions of the new substring. + + Returns: + A new StringSlice containing the substring at the specified positions. + """ + var step: Int + var start: Int + var end: Int + start, end, step = span.indices(len(self)) + + if step != 1: + raise Error("Slice must be within bounds and step must be 1") + + return Self(unsafe_from_utf8=self._slice[span]) + # ===------------------------------------------------------------------===# # Operator dunders # ===------------------------------------------------------------------===# diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 14dae2dde2..c6a5b268ed 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -150,6 +150,64 @@ fn test_heap_string_from_string_slice() raises: assert_equal(heap_string, "Hello") +fn test_string_substring() raises: + var string = String("Hello") + var str_slice = string.as_string_slice() + + assert_equal(len(str_slice), 5) + assert_equal(str_slice[0], "H") + assert_equal(str_slice[1], "e") + assert_equal(str_slice[2], "l") + assert_equal(str_slice[3], "l") + assert_equal(str_slice[4], "o") + + # ---------------------------------- + # Test subslicing + # ---------------------------------- + + # Slice the whole thing + var sub1 = str_slice[:5] + assert_equal(len(sub1), 5) + assert_equal(sub1[0], "H") + assert_equal(sub1[1], "e") + assert_equal(sub1[2], "l") + assert_equal(sub1[3], "l") + assert_equal(sub1[4], "o") + + # Slice the end + var sub2 = str_slice[2:5] + assert_equal(len(sub2), 3) + assert_equal(sub2[0], "l") + assert_equal(sub2[1], "l") + assert_equal(sub2[2], "o") + + # Slice the first element + var sub3 = str_slice[0:1] + assert_equal(len(sub3), 1) + assert_equal(sub3[0], "H") + assert_equal(sub3[-1], "H") + + # ---------------------------------- + # Test empty subslicing + # ---------------------------------- + + var sub4 = str_slice[0:0] + assert_equal(len(sub4), 0) + + var sub5 = str_slice[2:2] + assert_equal(len(sub5), 0) + + # Empty slices still have a pointer value + assert_equal(Int(sub5.unsafe_ptr()) - Int(sub4.unsafe_ptr()), 2) + + # ---------------------------------- + # Test disallowed stepsize + # ---------------------------------- + + with assert_raises(): + var sub6 = str_slice[0:0:2] + + fn test_slice_len() raises: assert_equal(5, len(StringSlice("12345"))) assert_equal(4, len(StringSlice("1234"))) From c6de4faaa3b3511a476e838ba4817a37fb02b016 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 23 Jan 2025 11:31:52 -0500 Subject: [PATCH 291/509] [stdlib] Clean up `write_buffered` and improve doc string Comment from Discord that we should clarify why 4096 bytes as the default stack space was chosen, and note when it should be changed. Also took the opportunity to allow the user to choose when the heap is used and clean up `print`. MODULAR_ORIG_COMMIT_REV_ID: d7d6df89983b0e72d6cb8fa35f4dc5a767f2de0c --- stdlib/src/builtin/io.mojo | 10 +++++++--- stdlib/src/utils/write.mojo | 12 +++++++++--- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index c1cf2c9008..b70780eeab 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -277,9 +277,13 @@ fn print[ file: The output stream. """ - write_buffered[buffer_size = 512 if is_amd_gpu() else 4096]( - file, values, sep=sep, end=end - ) + @parameter + if is_amd_gpu(): + write_buffered[buffer_size=512](file, values, sep=sep, end=end) + elif is_nvidia_gpu(): + write_buffered[use_heap=True](file, values, sep=sep, end=end) + else: + write_buffered(file, values, sep=sep, end=end) @parameter if not is_gpu(): diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index 0b4cbda871..2424d5dfcc 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -316,6 +316,7 @@ fn write_buffered[ W: Writer, //, *Ts: Writable, buffer_size: Int = 4096, + use_heap: Bool = False, ]( mut writer: W, args: VariadicPack[_, Writable, *Ts], @@ -326,13 +327,19 @@ fn write_buffered[ """ Use a buffer on the stack to minimize expensive calls to the writer. When the buffer would overflow it writes to the `writer` passed in. You can also - add seperators between the args, and end characters. + add seperators between the args, and end characters. The default stack space + used for the buffer is 4096 bytes which matches the default arm64 and x86-64 + page size, you can modify this e.g. when writing a large amount of data to a + file. Parameters: W: The type of the `Writer` to write to. Ts: The types of each arg to write. Each type must satisfy `Writable`. buffer_size: How many bytes to write to a buffer before writing out to the `writer` (default `4096`). + use_heap: Buffer to the heap, first calculating the total byte size + of all the args and then allocating only once. `buffer_size` is not + used in this case as it's dynamically calculated. (default `False`). Args: writer: The `Writer` to write to. @@ -367,12 +374,11 @@ fn write_buffered[ """ @parameter - if is_nvidia_gpu(): + if use_heap: # Count the total length of bytes to allocate only once var arg_bytes = _ArgBytes() write_args(arg_bytes, args, sep=sep, end=end) - # Stack space is very small on GPU due to many threads, so use heap var buffer = _WriteBufferHeap(arg_bytes.size + 1) write_args(buffer, args, sep=sep, end=end) buffer.data[buffer.pos] = 0 From 3c03e3cfa21770f07b3f8b4fd072b1e713d19f58 Mon Sep 17 00:00:00 2001 From: weiwei chen Date: Thu, 23 Jan 2025 13:23:36 -0500 Subject: [PATCH 292/509] [stdlib] Add memory type attribute to `pop.global_alloc` for GPU shared memory. MODULAR_ORIG_COMMIT_REV_ID: 732b0498dfc2939fa29c186907706bd65c9b891b --- stdlib/src/memory/memory.mojo | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 57f89bc958..b4fd7eef12 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -415,20 +415,33 @@ fn stack_allocation[ @parameter if is_gpu(): # On NVGPU, SHARED and CONSTANT address spaces lower to global memory. + + alias global_name = name.value() if name else "_global_alloc" + @parameter - if address_space in ( - _GPUAddressSpace.SHARED, - _GPUAddressSpace.CONSTANT, - ): - alias global_name = name.value() if name else "_global_alloc" + if address_space == _GPUAddressSpace.SHARED: return __mlir_op.`pop.global_alloc`[ name = global_name.value, count = count.value, + memoryType = __mlir_attr.`#pop`, _type = UnsafePointer[ type, address_space=address_space, alignment=alignment ]._mlir_type, alignment = alignment.value, ]() + elif address_space == _GPUAddressSpace.CONSTANT: + # No need to annotation this global_alloc because constants in + # GPU shared memory won't prevent llvm module splitting to + # happen since they are immutables. + return __mlir_op.`pop.global_alloc`[ + name = global_name.value, + count = count.value, + _type = UnsafePointer[ + type, address_space=address_space, alignment=alignment + ]._mlir_type, + alignment = alignment.value, + ]() + # MSTDL-797: The NVPTX backend requires that `alloca` instructions may # only have generic address spaces. When allocating LOCAL memory, # addrspacecast the resulting pointer. From 63c670ad007b1f6e026caae76875a252a26fe5a5 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 23 Jan 2025 14:12:43 -0500 Subject: [PATCH 293/509] [stdlib] Add deprecation warning for `StringableCollectionElement` `WritableCollectionElement` should always be used to allow avoiding intermediary allocations, but still construct a `String` if required. MODULAR_ORIG_COMMIT_REV_ID: 6f513d24d20f0a723ad65b3cf740da5372b2b6e8 --- docs/changelog.md | 3 +++ stdlib/src/builtin/value.mojo | 17 +++++++++++++++++ stdlib/src/prelude/__init__.mojo | 1 + 3 files changed, 21 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index b77162c41a..8a540858a0 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -388,6 +388,9 @@ what we publish. - removed `StringRef.strip()` - The `Tuple.get[i, T]()` method has been removed. Please use `tup[i]` or `rebind[T](tup[i])` as needed instead. +- `StringableCollectionElement` is deprecated, use `WritableCollectionElement` + instead which still allows you to construct a `String`, but can avoid + intermediary allocations. ### 🛠️ Fixed diff --git a/stdlib/src/builtin/value.mojo b/stdlib/src/builtin/value.mojo index c3868f1a5a..81e984ce74 100644 --- a/stdlib/src/builtin/value.mojo +++ b/stdlib/src/builtin/value.mojo @@ -207,6 +207,23 @@ trait CollectionElementNew(ExplicitlyCopyable, Movable): pass +# FIXME(25.2): remove deprecation warning +@deprecated( + "deprecated, use `WritableCollectionElement` which still allows you to" + " construct a `String` but can avoid intermediary allocations" +) +trait StringableCollectionElement(CollectionElement, Stringable): + """The StringableCollectionElement trait denotes a trait composition + of the `CollectionElement` and `Stringable` traits. + + This is useful to have as a named entity since Mojo does not + currently support anonymous trait compositions to constrain + on `CollectionElement & Stringable` in the parameter. + """ + + pass + + trait WritableCollectionElement(CollectionElement, Writable): """The WritableCollectionElement trait denotes a trait composition of the `CollectionElement` and `Writable` traits. diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 8723941535..48540a9805 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -135,6 +135,7 @@ from builtin.value import ( Movable, RepresentableCollectionElement, WritableCollectionElement, + StringableCollectionElement, ) from documentation import doc_private from memory import AddressSpace, Pointer From d2b79a6dcd043588f96c96ca290ef354b62ded8e Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Thu, 23 Jan 2025 14:09:27 -0800 Subject: [PATCH 294/509] [Docs] Update Language basics overview. MODULAR_ORIG_COMMIT_REV_ID: 2d3fb3d0d2880afa6cc16743782a9817791ab389 --- docs/manual/basics.mdx | 391 +++++++++++++++++------------ docs/manual/functions.mdx | 131 ++++++---- docs/manual/lifecycle/death.mdx | 8 + docs/manual/structs.mdx | 16 ++ docs/manual/traits.mdx | 4 +- examples/notebooks/environment.yml | 14 -- 6 files changed, 344 insertions(+), 220 deletions(-) delete mode 100644 examples/notebooks/environment.yml diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index 301dfdb90e..c6c4071a56 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -2,21 +2,20 @@ title: Mojo language basics sidebar_position: 1 sidebar_label: Overview -description: Introduction to Mojo's basic language features. +description: An overview of the Mojo language. --- -At this point, you should have already installed [Magic](/magic) to manage the -virtual environment and packages for your MAX and Mojo development (or -alternatively added the `max` package to a [conda](/magic/conda) development -environment). It's also a good idea to go through the [Get Started with -Mojo](/mojo/manual/get-started) tutorial to get a taste of what Mojo programming -is like. Now let's talk about how to write Mojo code. +This page provides an overview of the Mojo language. If you know Python, then a lot of Mojo code will look familiar. However, Mojo -incorporates features like strong type checking, memory safety, next-generation +incorporates features like static type checking, memory safety, next-generation compiler technologies, and more. As such, Mojo also has a lot in common with languages like C++ and Rust. +If you prefer to learn by doing, follow the [Get started with +Mojo](/mojo/manual/get-started) tutorial. You'll install the [Magic](/magic) +CLI, create a Mojo project and write your first Mojo program. + On this page, we'll introduce the essential Mojo syntax, so you can start coding quickly and understand other Mojo code you encounter. Subsequent sections in the Mojo Manual dive deeper into these topics, and links are @@ -34,61 +33,165 @@ particular language. ::: -## Functions +## Hello world + +Here's the traditional "Hello world" program in Mojo: + +```mojo +def main(): + print("Hello, world!") +``` + +Every Mojo program must include a function named `main()` as the entry point. +We'll talk more about functions soon, but for now it's enough to know that +you can write `def main():` followed by an indented function body. + +The `print()` statement does what you'd expect, printing its arguments to +the standard output. + +:::note + +You don't need a `main()` function when coding in the +[REPL](/mojo/cli/repl) or in a +[Jupyter +notebook](https://github.com/modular/mojo/tree/main/examples/notebooks#readme). +In these environments, Mojo executes top-level code (that is, statements +that appear outside of a function). Mojo doesn't support top-level code in a +`.mojo` (or `.🔥`) file -Mojo functions can be declared with either `fn` or `def`. +::: -The `fn` declaration enforces type-checking and memory-safe behaviors (Rust -style), while `def` allows no type declarations and dynamic behaviors (Python -style). +## Variables -For example, this `def` function doesn't require declaration of argument types -or the return type: +In Mojo, you can declare a variable by simply assigning a value to +a new named variable: ```mojo -def greet(name): - return "Hello, " + name + "!" +def main(): + x = 10 + y = x * x + print(y) ``` -While the same thing as an `fn` function requires that you specify the -argument type and the return type like this: +You can also _explicitly_ declare variables with the `var` keyword: ```mojo -fn greet2(name: String) -> String: - return "Hello, " + name + "!" +var x = 10 ``` -Both functions have the same result, but the `fn` function provides -compile-time checks to ensure the function receives and returns the correct -types. Whereas, the `def` function might fail at runtime if it receives the -wrong type. +When declaring a variable with `var`, you can also declare a variable type, with +or without an assignment: -Currently, Mojo doesn't support top-level code in a `.mojo` (or `.🔥`) file, so -every program must include a function named `main()` as the entry point. -You can declare it with either `def` or `fn`: ```mojo def main(): - print("Hello, world!") + var x: Int = 10 + var sum: Int + sum = x + x ``` -:::note +Both implicitly declared and explicitly declared variables are statically typed: +that is, the type is set at compile time, and doesn't change at runtime. +If you don't specify a type, Mojo uses the type of the first value assigned to +the variable. -You don't need a `main()` function when coding in the -[REPL](/mojo/manual/get-started#2-run-code-in-the-repl) or in a -[Jupyter -notebook](https://github.com/modular/mojo/tree/main/examples/notebooks#readme). +```mojo +x = 10 +x = "Foo" # Error: Cannot convert "StringLiteral" value to "Int" +``` -::: +For more details, see the page about +[variables](/mojo/manual/variables). + +## Blocks and statements + +Code blocks such as functions, conditions, and loops are defined +with a colon followed by indented lines. For example: + +```mojo +def loop(): + for x in range(5): + if x % 2 == 0: + print(x) +``` + +You can use any number of spaces or tabs for your indentation (we prefer 4 +spaces). + +All code statements in Mojo end with a newline. However, statements can span +multiple lines if you indent the following lines. For example, this long string +spans two lines: + +```mojo +def print_line(): + long_text = "This is a long line of text that is a lot easier to read if" + " it is broken up across two lines instead of one long line." + print(long_text) +``` + +And you can chain function calls across lines: + +```mojo +def print_hello(): + text = String(",") + .join("Hello", " world!") + print(text) +``` + +For more information on loops and conditional statements, see +[Control flow](/mojo/manual/control-flow). + +## Functions + +Mojo functions can be declared with either the `def` or `fn` keyword. + +There are only a few differences between the two styles. For example, the +following function works with either `def` or `fn`: + +```mojo +def greet(name: String) -> String: + return "Hello, " + name + "!" +``` + +The `fn` declaration has a few restrictions that a `def` declaration doesn't: + +- The argument type and return types are _optional_ in a `def` function but + required in a `fn` function. So the previous function could also be written + as: + + ```mojo + def greet(name): + return "Hello, " + name + "!" + ``` + + Generally, specifying types is good practice, so most examples in this + manual include types, regardless of whether they're declared with `def` or + `fn`. + +- If an `fn` function can raise an error, it needs to be declared with the + `raises` keyword: + + ```mojo + fn greet(name: String) raises: + if (name == ""): + raise Error("Name is empty") + return "Hello, " + name + "!" + ``` + Any `def` function can raise an error. + +If you don't specify a type for an argument or return value, it's assigned the +`object` type, a special type that that can represent different types of values. +This allows for some runtime dynamism, but also means that +the function might fail at runtime if it receives the wrong type. For more details, see the page about [functions](/mojo/manual/functions). ### Value ownership and argument mutability -If you're wondering whether function arguments are passed by value or -passed by reference, the short answer is: `def` functions receive arguments -"by value" and `fn` functions receive arguments "by immutable reference." +If you're wondering whether function arguments are passed by value or passed by +reference, the short answer is: by default, arguments are passed by +immutable reference. The longer short answer is that Mojo allows you to specify for each argument whether it should be passed by value (as `owned`), or whether it should be @@ -101,38 +204,47 @@ given time (but allowing other variables to receive a reference to it). Ownership then ensures that the value is destroyed when the lifetime of the owner ends (and there are no outstanding references). -But that's still a short answer, because going much further is a slippery slope -into complexity that is out of scope for this section. For the complete -answer, see the section about [value ownership](/mojo/manual/values/). +For a more complete answer, see the section on +[value ownership](/mojo/manual/values/). -## Variables +## Code comments -You can declare variables with the `var` keyword. Or, if your code is in a -`def` function, you can omit the `var` (in an `fn` function, you must include -the `var` keyword). +You can create a one-line comment using the hash `#` symbol: -For example: +```mojo +# This is a comment. The Mojo compiler ignores this line. +``` + +Comments may also follow some code: ```mojo -def do_math(x): - var y = x + x - y = y * y - print(y) +var message = "Hello, World!" # This is also a valid comment ``` -Optionally, you can also declare a variable type like this: +API documentation comments are enclosed in triple quotes. For example: ```mojo -def add_one(x): - var y: Int = 1 - print(x + y) +fn print(x: String): + """Prints a string. + + Args: + x: The string to print. + """ + ... ``` -Even in an `fn` function, declaring the variable type is optional -(only the argument and return types must be declared in `fn` functions). +Documenting your code with these kinds of comments (known as "docstrings") +is a topic we've yet to fully specify, but you can generate an API reference +from docstrings using the [`mojo doc` command](/mojo/cli/doc). -For more details, see the page about -[variables](/mojo/manual/variables). +:::note + +Technically, docstrings aren't _comments_, they're a special use of Mojo's +syntax for multi-line string literals. For details, see +[String literals](/mojo/manual/types#string-literals) in the page on +[Types](/mojo/manual/types). + +::: ## Structs @@ -155,18 +267,44 @@ struct MyPair: self.first = first self.second = second - fn dump(self): + fn __copyinit__(out self, existing other): + self.first = existing.first + self.second = existing.second + + def dump(self): print(self.first, self.second) ``` And here's how you can use it: ```mojo -fn use_mypair(): +def use_mypair(): var mine = MyPair(2, 4) mine.dump() ``` +Note that some functions are declared with `fn` function, while the `dump()` +function is declared with `def`. In general, you can use either form in a +struct. + +The `MyPair` struct contains two special methods, `__init__()`, the constructor, +and `__copyinit__()`, the copy constructor. _Lifecycle methods_ like this +control how a struct is created, copied, moved, and destroyed. + +For most simple types, you don't need to write the lifecycle methods. You can +use the `@value` decorator to generate the boilerplate code for you. So the +`MyPair` struct can be simplified to this: + +```mojo +@value +struct MyPair: + var first: Int + var second: Int + + def dump(self): + print(self.first, self.second) +``` + For more details, see the page about [structs](/mojo/manual/structs). @@ -175,25 +313,23 @@ For more details, see the page about A trait is like a template of characteristics for a struct. If you want to create a struct with the characteristics defined in a trait, you must implement each characteristic (such as each method). Each characteristic in a trait is a -"requirement" for the struct, and when your struct implements each requirement, -it's said to "conform" to the trait. - -Currently, the only characteristics that traits can define are method -signatures. Also, traits currently cannot implement default behaviors for -methods. +"requirement" for the struct, and when your struct implements all of the +requirements, it's said to "conform" to the trait. Using traits allows you to write generic functions that can accept any type that conforms to a trait, rather than accept only specific types. -For example, here's how you can create a trait (notice the function is not -implemented): +For example, here's how you can create a trait: ```mojo trait SomeTrait: fn required_method(self, x: Int): ... ``` -And here's how to create a struct that conforms to the trait: +The three dots following the method signature are Mojo syntax indicating that +the method is not implemented. + +Here's a trait that conforms to `SomeTrait`: ```mojo @value @@ -214,6 +350,15 @@ fn use_trait_function(): fun_with_traits(thing) ``` +You'll see traits used in a lot of APIs provided by Mojo's standard library. For +example, Mojo's collection types like `List` and `Dict` can store any type that +conforms to the `CollectionElement` trait. You can specify the type when you +create a collection: + +```mojo +my_list = List[Float64]() +``` + :::note You're probably wondering about the square brackets on `fun_with_traits()`. @@ -249,7 +394,8 @@ ice just a little bit here. To get you started, let's look at a parametric function: ```mojo -fn repeat[count: Int](msg: String): +def repeat[count: Int](msg: String): + @parameter # evaluate the following for loop at compile time for i in range(count): print(msg) ``` @@ -259,16 +405,19 @@ This function has one parameter of type `Int` and one argument of type argument: ```mojo -fn call_repeat(): +defining call_repeat(): repeat[3]("Hello") # Prints "Hello" 3 times ``` By specifying `count` as a parameter, the Mojo compiler is able to optimize the -function because this value is guaranteed to not change at runtime. The -compiler effectively generates a unique version of the `repeat()` function that -repeats the message only 3 times. This makes the code more performant because -there's less to compute at runtime. +function because this value is guaranteed to not change at runtime. And the +`@parameter` decorator in the code tells the compiler to evaluate the `for` loop +at compile time, not runtime. + +The compiler effectively generates a unique version of the `repeat()` function +that repeats the message only 3 times. This makes the code more performant +because there's less to compute at runtime. Similarly, you can define a struct with parameters, which effectively allows you to define variants of that type at compile-time, depending on the parameter @@ -277,87 +426,12 @@ values. For more detail on parameters, see the section on [Metaprogramming](/mojo/manual/parameters/). -## Blocks and statements - -Code blocks such as functions, conditions, and loops are defined -with a colon followed by indented lines. For example: - -```mojo -def loop(): - for x in range(5): - if x % 2 == 0: - print(x) -``` - -You can use any number of spaces or tabs for your indentation (we prefer 4 -spaces). - -All code statements in Mojo end with a newline. However, statements can span -multiple lines if you indent the following lines. For example, this long string -spans two lines: - -```mojo -def print_line(): - long_text = "This is a long line of text that is a lot easier to read if" - " it is broken up across two lines instead of one long line." - print(long_text) -``` - -And you can chain function calls across lines: - -```mojo -def print_hello(): - text = String(",") - .join("Hello", " world!") - print(text) -``` - -## Code comments - -You can create a one-line comment using the hash `#` symbol: - -```mojo -# This is a comment. The Mojo compiler ignores this line. -``` - -Comments may also follow some code: - -```mojo -var message = "Hello, World!" # This is also a valid comment -``` - -You can instead write longer comments across many lines using triple quotes: - -```mojo -""" -This is also a comment, but it's easier to write across -many lines, because each line doesn't need the # symbol. -""" -``` - -Triple quotes is the preferred method of writing API documentation. For example: - -```mojo -fn print(x: String): - """Prints a string. - - Args: - x: The string to print. - """ - ... -``` - -Documenting your code with these kinds of comments (known as "docstrings") -is a topic we've yet to fully specify, but you can generate an API reference -from docstrings using the [`mojo doc` command](/mojo/cli/doc). - ## Python integration Mojo supports the ability to import Python modules as-is, so you can leverage existing Python code right away. -For example, here's how you can import and use NumPy (you must have Python -`numpy` installed): +For example, here's how you can import and use NumPy: ```mojo from python import Python @@ -369,13 +443,11 @@ def main(): print(ar.shape) ``` -:::note - -**Note:** You must have the Python module (such as `numpy`) installed already. - -::: +You must have the Python module (such as `numpy`) installed in the environment +where you're using Mojo. You can install Python packages into your virtual +environment using [Magic](/magic/) or [Conda](/magic/conda). -For more details, see the page about +For more details, see the page on [Python integration](/mojo/manual/python/). ## Next steps @@ -384,11 +456,14 @@ Hopefully this page has given you enough information to start experimenting with Mojo, but this is only touching the surface of what's available in Mojo. If you're in the mood to read more, continue through each page of this -Mojo Manual using the buttons at the bottom of each page—the next page from +Mojo Manual—the next page from here is [Functions](/mojo/manual/functions). Otherwise, here are some other resources to check out: +* See [Get started with Mojo](/mojo/manual/get-started) for a hands-on + tutorial that will get you up and running with Mojo. + * If you want to experiment with some code, clone [the Mojo repo](https://github.com/modular/mojo/) to try our code examples: diff --git a/docs/manual/functions.mdx b/docs/manual/functions.mdx index c6f51aeaf9..371eff68ce 100644 --- a/docs/manual/functions.mdx +++ b/docs/manual/functions.mdx @@ -23,51 +23,6 @@ Functions declared inside a [`struct`](/mojo/manual/structs) are called ::: -## `fn` functions - -The `fn` function has somewhat stricter rules than the `def` function. - -Here's an example of an `fn` function: - -```mojo -fn greet(name: String) -> String: - var greeting = "Hello, " + name + "!" - return greeting -``` - -As far as a function caller is concerned, `def` and `fn` functions are -interchangeable. That is, there's nothing a `def` can do that an `fn` can't -(and vice versa). The difference is that, compared to a `def` function, an `fn` -function is more strict on the inside. - -Here's everything to know about `fn`: - -* Arguments must specify a type (except for the - `self` argument in [struct methods](/mojo/manual/structs#methods)). - -* Return values must specify a type, unless the function doesn't return a value. - - If you don't specify a return type, it defaults to `None` (meaning no return - value). - -* By default, arguments are received as an immutable reference (values are - read-only, using the `read` [argument - convention](/mojo/manual/values/ownership#argument-conventions)). - - This prevents accidental mutations, and permits the use of non-copyable types - as arguments. - - If you want a local copy, you can simply assign the value to a local - variable. Or, you can get a mutable reference to the value by declaring the - `mut` [argument - convention](/mojo/manual/values/ownership#argument-conventions)). - -* If the function raises an exception, it must be explicitly declared with the - `raises` keyword. (A `def` function does not need to declare exceptions.) - -By enforcing these type checks, using the `fn` function helps avoid a variety -of runtime errors. - ## `def` functions Compared to an `fn` function, a `def` function has fewer restrictions. @@ -83,7 +38,7 @@ def greet(name): In a Mojo `def` function, you have the option to specify the argument type and the return type. You can also declare variables with `var`, with or without explicit typing. So you can write a `def` function that looks almost exactly -like the `fn` function shown earlier: +like an `fn` function: ```mojo def greet(name: String) -> String: @@ -147,6 +102,53 @@ possible underlying types, for example. ::: +## `fn` functions + +The `fn` function has somewhat stricter rules than the `def` function. + +Here's an example of an `fn` function: + +```mojo +fn greet(name: String) -> String: + var greeting = "Hello, " + name + "!" + return greeting +``` + +As far as a function caller is concerned, `def` and `fn` functions are +interchangeable. That is, there's nothing a `def` can do that an `fn` can't +(and vice versa). The difference is that, compared to a `def` function, an `fn` +function is more strict on the inside. + +Here's everything to know about `fn`: + +* Arguments must specify a type (except for the + `self` argument in [struct methods](/mojo/manual/structs#methods)). + +* Return values must specify a type, unless the function doesn't return a value. + + If you don't specify a return type, it defaults to `None` (meaning no return + value). + +* By default, arguments are received as an immutable reference (values are + read-only, using the `read` [argument + convention](/mojo/manual/values/ownership#argument-conventions)). + + This prevents accidental mutations, and permits the use of non-copyable types + as arguments. + + If you want a local copy, you can simply assign the value to a local + variable. Or, you can get a mutable reference to the value by declaring the + `mut` [argument + convention](/mojo/manual/values/ownership#argument-conventions)). + +* If the function can raise an error, it must be explicitly declared with the + `raises` keyword. A `def` function does not need to add the `raises` keyword. + For information, see + [Raising and non-raising functions](#raising-and-non-raising-functions). + +By enforcing these type checks, using the `fn` function helps avoid a variety +of runtime errors. + ## Function arguments As noted in the previous sections, there are a few differences between how `def` @@ -702,3 +704,40 @@ returned immediately: def create_immovable_object3(owned name: String) -> ImmovableObject: return ImmovableObject(name^) # OK ``` + +## Raising and non-raising functions + +By default, when a function raises an error, the function terminates immediately +and the error propagates to the calling function. If the calling function +doesn't handle the error, it continues to propagate up the call stack. + +```mojo +def raises_error(): + raise Error("There was an error.") +``` + +Functions declared with `fn` without the `raises` keyword are _non-raising +functions_—that is, they are not allowed to propagate an error to the calling +function. If a non-raising function calls a raising function, it **must handle +any possible errors.** + +```mojo +# This function will not compile +fn unhandled_error(): + raises_error() # Error: can't call raising function in a non-raising context + +fn handle_error(): + try: + raises_error() + except e: + print("Handled an error," e) +``` + +If you're writing code that you expect to use widely or distribute as a package, +you may want to use `fn` functions for APIs that don't raise errors to limit +the number of places users need to add unnecessary error handling code. For some +extremely performance-sensitive code, it may be preferable to avoid runtime +error-handling. + +For more information, see +[Errors, error handling, and context managers](/mojo/manual/errors). diff --git a/docs/manual/lifecycle/death.mdx b/docs/manual/lifecycle/death.mdx index 864f06e365..70344be3ed 100644 --- a/docs/manual/lifecycle/death.mdx +++ b/docs/manual/lifecycle/death.mdx @@ -214,6 +214,14 @@ However, the `self` value inside the `__del__()` destructor is still whole (so all fields are still usable) until the destructor returns, as we'll discuss more in the following section. +:::note Destructors cannot raise errors + +Currently a Mojo destructor isn't allowed to raise an error. This means that the +destructor must be defined as an `fn` function without the `raises` keyword. +Mojo won't allow you to define a destructor using `fn raises` or `def`. + +::: + ## Field lifetimes In addition to tracking the lifetime of all objects in a program, Mojo also diff --git a/docs/manual/structs.mdx b/docs/manual/structs.mdx index 818058ba4c..fd7462b9ea 100644 --- a/docs/manual/structs.mdx +++ b/docs/manual/structs.mdx @@ -128,6 +128,22 @@ you can elide it because Mojo already knows its type (`MyPair` in this case). ::: +### `fn` versus `def` in struct methods + +Struct methods can be declared with either the `def` or `fn` keywords. One +important difference is that an `fn` function without the `raises` keyword can't +raise an error. When you call a function that *can* raise an error from inside a +method that *can't* raise an error, Mojo requires you to handle any errors, as +described in +[Errors, error handling, and context managers](/mojo/manual/errors). + +If you're writing code that you expect to use widely or distribute as a package, +you may want to use `fn` functions for APIs that can't raise an error to limit +the number of places users need to add error handling code. + +A struct's `__del__()` method, or destructor, **must** be a non-raising method, +so it's always declared with `fn` (and without the `raises` keyword). + ### Static methods A struct can also have *static methods*. A static method can be called without diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index 0d13119942..948185e09b 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -97,8 +97,8 @@ trait Quackable: ``` A trait looks a lot like a struct, except it's introduced by the `trait` -keyword. Right now, a trait can only contain method signatures, and cannot -include method implementations. Each method signature must be followed by +keyword. A trait can contain method signatures, but it can't implement those +methods. Each method signature must be followed by three dots (`...`) to indicate that the method is unimplemented. A trait can also include associated aliases—compile-time constant values that diff --git a/examples/notebooks/environment.yml b/examples/notebooks/environment.yml deleted file mode 100644 index 45a48799ef..0000000000 --- a/examples/notebooks/environment.yml +++ /dev/null @@ -1,14 +0,0 @@ -name: mojo-notebooks -channels: - - pytorch - - https://conda.modular.com/max/ - - conda-forge - - defaults -dependencies: - - python>=3.11,<3.12 - - max>=24.4.0dev6 - - max = "*" -pip = ">=24.0,<25" -jupyterlab = ">=4.2.5,<5" -matplotlib = ">=3.9.2,<4" -numpy = ">=1.26.4,<2" From 02a2e4b3b08afb7030f0ff1c0d158cf90b9d9eca Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 23 Jan 2025 19:24:59 -0500 Subject: [PATCH 295/509] [stdlib] Optimize String.join for very large `List` This adds some methods for writing `List[WritableCollectionElement]` to buffers, and a function to write it buffered to any writer. This is then used for `String.join` which had bad performance for very large amounts of elements, it now buffers to the heap if over the buffer_size to avoid multiple allocations. Improves perf of joining a 10 million element list by 27x. More context in Discord thread: https://discord.com/channels/1087530497313357884/1331776837738696828/1331957122409435146 MODULAR_ORIG_COMMIT_REV_ID: a159f610003a846a9be9b54dad6f0d1019799203 --- stdlib/src/builtin/debug_assert.mojo | 5 +- stdlib/src/collections/string/string.mojo | 35 +++++--- stdlib/src/utils/write.mojo | 103 +++++++++++++++++++--- 3 files changed, 118 insertions(+), 25 deletions(-) diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index a18a9eb271..2b62fe71a8 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -27,7 +27,7 @@ from builtin._location import __call_location, _SourceLocation from memory import UnsafePointer, Span from utils.write import ( - _ArgBytes, + _TotalWritableBytes, _WriteBufferHeap, _WriteBufferStack, write_args, @@ -247,7 +247,7 @@ fn _debug_assert_msg( pass elif is_nvidia_gpu(): # Count the total length of bytes to allocate only once - var arg_bytes = _ArgBytes() + var arg_bytes = _TotalWritableBytes() arg_bytes.write( "At ", loc, @@ -272,6 +272,7 @@ fn _debug_assert_msg( stdout.write_bytes( Span[Byte, ImmutableAnyOrigin](ptr=buffer.data, length=buffer.pos) ) + buffer.data.free() else: var buffer = _WriteBufferStack[4096](stdout) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 4cee5a90d0..d310637df8 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -20,7 +20,7 @@ from os import abort from sys import bitwidthof, llvm_intrinsic from sys.ffi import c_char from sys.intrinsics import _type_is_eq -from utils.write import write_buffered, _WriteBufferStack +from utils.write import write_buffered, _TotalWritableBytes, _WriteBufferHeap from bit import count_leading_zeros from memory import UnsafePointer, memcmp, memcpy, Span @@ -1190,11 +1190,20 @@ struct String( var sep = StaticString(ptr=self.unsafe_ptr(), length=len(self)) return String(elems, sep=sep) - fn join[T: WritableCollectionElement](self, elems: List[T, *_]) -> String: + fn join[ + T: WritableCollectionElement, //, buffer_size: Int = 4096 + ](self, elems: List[T, *_]) -> String: """Joins string elements using the current string as a delimiter. + Defaults to writing to the stack if total bytes of `elems` is less than + `buffer_size`, otherwise will allocate once to the heap and write + directly into that. The `buffer_size` defaults to 4096 bytes to match + the default page size on arm64 and x86-64, but you can increase this if + you're joining a very large `List` of elements to write into the stack + instead of the heap. Parameters: T: The types of the elements. + buffer_size: The max size of the stack buffer. Args: elems: The input values. @@ -1202,14 +1211,20 @@ struct String( Returns: The joined string. """ - var string = String() - var buffer = _WriteBufferStack(string) - for i in range(len(elems)): - buffer.write(elems[i]) - if i < len(elems) - 1: - buffer.write(self) - buffer.flush() - return string + var sep = StaticString(ptr=self.unsafe_ptr(), length=len(self)) + var total_bytes = _TotalWritableBytes(elems, sep=sep) + + # Use heap if over the stack buffer size + if total_bytes.size + 1 > buffer_size: + var buffer = _WriteBufferHeap(total_bytes.size + 1) + buffer.write_list(elems, sep=sep) + buffer.data[total_bytes.size] = 0 + return String(ptr=buffer.data, length=total_bytes.size + 1) + # Use stack otherwise + else: + var string = String() + write_buffered[buffer_size](string, elems, sep=sep) + return string @always_inline fn chars(self) -> CharsIter[__origin_of(self)]: diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index 2424d5dfcc..e5b4f16128 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -227,18 +227,30 @@ struct _WriteBufferHeap(Writer): ].alloc(size) self.pos = 0 - fn __del__(owned self): - self.data.free() + fn write_list[ + T: WritableCollectionElement + ](mut self, values: List[T, *_], *, sep: StaticString = ""): + var length = len(values) + if length == 0: + return + self.write(values[0]) + if length > 1: + for i in range(1, length): + self.write(sep, values[i]) @always_inline fn write_bytes(mut self, bytes: Span[UInt8, _]): len_bytes = len(bytes) - # If empty then return - if len_bytes == 0: - return var ptr = bytes.unsafe_ptr() - for i in range(len_bytes): - self.data[i + self.pos] = ptr[i] + + # TODO: fix memcpy alignment on nvidia GPU + @parameter + if is_nvidia_gpu(): + for i in range(len_bytes): + self.data[i + self.pos] = ptr[i] + else: + memcpy(self.data + self.pos, ptr, len_bytes) + self.pos += len_bytes fn write[*Ts: Writable](mut self, *args: *Ts): @@ -249,12 +261,24 @@ struct _WriteBufferHeap(Writer): args.each[write_arg]() -struct _ArgBytes(Writer): +struct _TotalWritableBytes(Writer): var size: Int fn __init__(out self): self.size = 0 + fn __init__[ + T: WritableCollectionElement + ](out self, values: List[T, *_], sep: StaticString = ""): + self.size = 0 + var length = len(values) + if length == 0: + return + self.write(values[0]) + if length > 1: + for i in range(1, length): + self.write(sep, values[i]) + fn write_bytes(mut self, bytes: Span[UInt8, _]): self.size += len(bytes) @@ -279,6 +303,17 @@ struct _WriteBufferStack[ self.pos = 0 self.writer = Pointer.address_of(writer) + fn write_list[ + T: WritableCollectionElement + ](mut self, values: List[T, *_], *, sep: StaticString = ""): + var length = len(values) + if length == 0: + return + self.write(values[0]) + if length > 1: + for i in range(1, length): + self.write(sep, values[i]) + fn flush(mut self): self.writer[].write_bytes( Span[Byte, ImmutableAnyOrigin]( @@ -289,16 +324,13 @@ struct _WriteBufferStack[ fn write_bytes(mut self, bytes: Span[Byte, _]): len_bytes = len(bytes) - # If empty then return - if len_bytes == 0: - return # If span is too large to fit in buffer, write directly and return if len_bytes > capacity: self.flush() self.writer[].write_bytes(bytes) return # If buffer would overflow, flush writer and reset pos to 0. - if self.pos + len_bytes > capacity: + elif self.pos + len_bytes > capacity: self.flush() # Continue writing to buffer memcpy(self.data.unsafe_ptr() + self.pos, bytes.unsafe_ptr(), len_bytes) @@ -376,7 +408,7 @@ fn write_buffered[ @parameter if use_heap: # Count the total length of bytes to allocate only once - var arg_bytes = _ArgBytes() + var arg_bytes = _TotalWritableBytes() write_args(arg_bytes, args, sep=sep, end=end) var buffer = _WriteBufferHeap(arg_bytes.size + 1) @@ -385,7 +417,52 @@ fn write_buffered[ writer.write_bytes( Span[Byte, ImmutableAnyOrigin](ptr=buffer.data, length=buffer.pos) ) + buffer.data.free() else: var buffer = _WriteBufferStack[buffer_size](writer) write_args(buffer, args, sep=sep, end=end) buffer.flush() + + +fn write_buffered[ + W: Writer, + T: WritableCollectionElement, //, + buffer_size: Int = 4096, +](mut writer: W, values: List[T, *_], *, sep: StaticString = ""): + """ + Use a buffer on the stack to minimize expensive calls to the writer. You + can also add seperators between the values. The default stack space used for + the buffer is 4096 bytes which matches the default arm64 and x86-64 page + size, you can modify this e.g. when writing a large amount of data to a + file. + + Parameters: + W: The type of the `Writer` to write to. + T: The `Writable` type of the `List`. + buffer_size: How many bytes to write to a buffer before writing out to + the `writer` (default `4096`). + + Args: + writer: The `Writer` to write to. + values: A `List` of Writable arguments. + sep: The separator used between elements. + + Example + + ```mojo + import sys + from utils import write_buffered + + var string = String() + var values = List[String]("3", "total", "args") + write_buffered(string, values, sep=",") + ``` + + ``` + 3, total, args + ``` + . + """ + var buffer = _WriteBufferStack(writer) + buffer.write_list(values, sep=sep) + buffer.flush() From 171851e205eba7b18e98446dc97b5454237978db Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Thu, 23 Jan 2025 18:55:01 -0800 Subject: [PATCH 296/509] Updated docs to use `Tuple` indexing instead of `Tuple.get()` The `Tuple.get()` method has been removed. You should now use indexing, like `tup[0]`, `tup[1]`, etc. to retrieve the individual values of a `Tuple`. MODULAR_ORIG_COMMIT_REV_ID: 35abb07584cd6e5201645d98586686e837d79e86 --- docs/manual/types.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/manual/types.mdx b/docs/manual/types.mdx index e45e1b627b..49669c32d9 100644 --- a/docs/manual/types.mdx +++ b/docs/manual/types.mdx @@ -425,7 +425,7 @@ x, y = example_tuple print(x, y) # Get individual values with an index -s = example_tuple.get[1, String]() +s = example_tuple[1] print(s) ``` @@ -440,7 +440,7 @@ we must also convert `"Example"` from type `StringLiteral` to type `String`. ```mojo example_tuple = (1, String("Example")) -s = example_tuple.get[1, String]() +s = example_tuple[1] print(s) ``` From 8166be8a12b352f8c349119d7eea0275da46bf0e Mon Sep 17 00:00:00 2001 From: Chris Elrod Date: Fri, 24 Jan 2025 15:48:11 +0700 Subject: [PATCH 297/509] [stdlib] Remove debug info from load and store methods of `UnsafePointer` This can improve some error messages, allowing users to focus on the source of the bad read/write, rather than its implementation. MODULAR_ORIG_COMMIT_REV_ID: a456f0d4dd3704f62796fb19c218a6dbfe4be969 --- stdlib/src/memory/unsafe_pointer.mojo | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index d2c19e8fb1..eafee8084a 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -538,7 +538,7 @@ struct UnsafePointer[ else: return __mlir_op.`pop.load`[alignment = alignment.value](address) - @always_inline + @always_inline("nodebug") fn load[ type: DType, //, width: Int = 1, @@ -612,7 +612,7 @@ struct UnsafePointer[ invariant=invariant, ]() - @always_inline + @always_inline("nodebug") fn store[ I: Indexer, type: DType, //, @@ -639,7 +639,7 @@ struct UnsafePointer[ constrained[mut, _must_be_mut_err]() self.offset(offset)._store[alignment=alignment, volatile=volatile](val) - @always_inline + @always_inline("nodebug") fn store[ I: Indexer, type: DType, @@ -672,7 +672,7 @@ struct UnsafePointer[ constrained[mut, _must_be_mut_err]() self.offset(offset).store[alignment=alignment, volatile=volatile](val) - @always_inline + @always_inline("nodebug") fn store[ type: DType, offset_type: DType, //, @@ -705,7 +705,7 @@ struct UnsafePointer[ val ) - @always_inline + @always_inline("nodebug") fn store[ type: DType, width: Int, From 14ee85e73111991d168b26c55ae9a3e6b6800fab Mon Sep 17 00:00:00 2001 From: Rasool Sharifi Date: Fri, 24 Jan 2025 11:12:26 -0500 Subject: [PATCH 298/509] [stdlib] Support thread block cluster for SM90+ GPUs Support Thread Block Clusters for SM90+ GPUs MODULAR_ORIG_COMMIT_REV_ID: 09bad1920d695132a6b5f2160b63000e0931e40f --- stdlib/src/sys/intrinsics.mojo | 79 +++++++++++++++++++++++++++++++++- 1 file changed, 78 insertions(+), 1 deletion(-) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 20c8064630..c787b9b398 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -23,7 +23,7 @@ import math from memory import AddressSpace, UnsafePointer from memory.pointer import _GPUAddressSpace - +from sys.info import _is_sm_9x from ._assembly import inlined_assembly from .info import is_nvidia_gpu, is_amd_gpu, sizeof @@ -1227,3 +1227,80 @@ struct _GridIdx: alias global_idx = _GridIdx() + + +# ===-----------------------------------------------------------------------===# +# cluster_dim +# ===-----------------------------------------------------------------------===# + + +@register_passable("trivial") +struct _ClusterDim: + """ClusterDim provides static methods for getting the x/y/z dimension of a + Cluster.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt: + """Gets the `x`, `y`, or `z` dimension of the cluster. + + Returns: + The `x`, `y`, or `z` dimension of the cluster. + """ + constrained[ + is_nvidia_gpu() and _is_sm_9x(), + "cluster_id is only supported on NVIDIA SM90+ GPUs", + ]() + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + + alias intrinsic_name = "llvm.nvvm.read.ptx.sreg.cluster.nctaid." + dim + return UInt( + Int(llvm_intrinsic[intrinsic_name, Int32, has_side_effect=False]()) + ) + + +alias cluster_dim = _ClusterDim() + +# ===-----------------------------------------------------------------------===# +# cluster_idx +# ===-----------------------------------------------------------------------===# + + +@register_passable("trivial") +struct _ClusterIdx: + """_ClusterIdx provides static methods for getting the x/y/z coordinates of + a cluster within a grid.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + @staticmethod + fn _get_intrinsic_name[dim: StringLiteral]() -> StringLiteral: + return "llvm.nvvm.read.ptx.sreg.clusterid." + dim + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt32: + """Gets the `x`, `y`, or `z` coordinates of a cluster within a grid. + + Returns: + The `x`, `y`, or `z` coordinates of a cluster within a grid. + """ + constrained[ + is_nvidia_gpu() and _is_sm_9x(), + "cluster_id is only supported on NVIDIA SM90+ GPUs", + ]() + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + alias intrinsic_name = Self._get_intrinsic_name[dim]() + return llvm_intrinsic[intrinsic_name, UInt32, has_side_effect=False]() + + +alias cluster_idx = _ClusterIdx() From 699ee47c763709f0b00b707f4d3f03f07a38f0e3 Mon Sep 17 00:00:00 2001 From: Keith Smiley Date: Fri, 24 Jan 2025 12:09:42 -0800 Subject: [PATCH 299/509] [OSS] Update copyright year (#54579) MODULAR_ORIG_COMMIT_REV_ID: ab1bcce3b8c4a9e7443d1e41ba5ed84f292dd8e0 --- examples/check_mod.py | 2 +- examples/life/benchmark.mojo | 2 +- examples/life/gridv1.mojo | 2 +- examples/life/gridv2.mojo | 2 +- examples/life/lifev1.mojo | 2 +- examples/life/lifev2.mojo | 2 +- examples/life/test/test_gridv1.mojo | 2 +- examples/life/test/test_gridv2.mojo | 2 +- examples/operators/main.mojo | 2 +- examples/operators/my_complex.mojo | 2 +- examples/operators/test_my_complex.mojo | 2 +- stdlib/benchmarks/algorithm/bench_elementwise.mojo | 2 +- stdlib/benchmarks/builtin/bench_int.mojo | 2 +- stdlib/benchmarks/builtin/bench_sort.mojo | 2 +- stdlib/benchmarks/collections/bench_dict.mojo | 2 +- stdlib/benchmarks/collections/bench_string.mojo | 2 +- stdlib/benchmarks/hashlib/bench_hash.mojo | 2 +- stdlib/benchmarks/lit.cfg.py | 2 +- stdlib/benchmarks/math/bench_math.mojo | 2 +- stdlib/benchmarks/utils/bench_formatter.mojo | 2 +- stdlib/benchmarks/utils/bench_memmem.mojo | 2 +- stdlib/docs/style-guide.md | 2 +- stdlib/scripts/.markdownlint.yaml | 2 +- stdlib/scripts/build-stdlib.sh | 2 +- stdlib/scripts/check-docstrings.py | 2 +- stdlib/scripts/check_licenses.mojo | 2 +- stdlib/scripts/install-build-tools-linux.sh | 2 +- stdlib/scripts/install-build-tools-macos.sh | 2 +- stdlib/scripts/markdownlint.sh | 2 +- stdlib/scripts/run-benchmarks.sh | 4 ++-- stdlib/scripts/run-tests.sh | 4 ++-- stdlib/src/__init__.mojo | 2 +- stdlib/src/base64/__init__.mojo | 2 +- stdlib/src/base64/_b64encode.mojo | 2 +- stdlib/src/base64/base64.mojo | 2 +- stdlib/src/bit/__init__.mojo | 2 +- stdlib/src/bit/bit.mojo | 2 +- stdlib/src/builtin/__init__.mojo | 2 +- stdlib/src/builtin/_closure.mojo | 2 +- stdlib/src/builtin/_format_float.mojo | 2 +- stdlib/src/builtin/_location.mojo | 2 +- stdlib/src/builtin/_pybind.mojo | 2 +- stdlib/src/builtin/_startup.mojo | 2 +- stdlib/src/builtin/_stubs.mojo | 2 +- stdlib/src/builtin/anytype.mojo | 2 +- stdlib/src/builtin/bool.mojo | 2 +- stdlib/src/builtin/breakpoint.mojo | 2 +- stdlib/src/builtin/builtin_list.mojo | 2 +- stdlib/src/builtin/builtin_slice.mojo | 2 +- stdlib/src/builtin/char.mojo | 2 +- stdlib/src/builtin/comparable.mojo | 2 +- stdlib/src/builtin/constrained.mojo | 2 +- stdlib/src/builtin/coroutine.mojo | 2 +- stdlib/src/builtin/debug_assert.mojo | 2 +- stdlib/src/builtin/dtype.mojo | 2 +- stdlib/src/builtin/equality_comparable.mojo | 2 +- stdlib/src/builtin/error.mojo | 2 +- stdlib/src/builtin/file.mojo | 2 +- stdlib/src/builtin/file_descriptor.mojo | 2 +- stdlib/src/builtin/float_literal.mojo | 2 +- stdlib/src/builtin/floatable.mojo | 2 +- stdlib/src/builtin/format_int.mojo | 2 +- stdlib/src/builtin/identifiable.mojo | 2 +- stdlib/src/builtin/int.mojo | 2 +- stdlib/src/builtin/int_literal.mojo | 2 +- stdlib/src/builtin/io.mojo | 2 +- stdlib/src/builtin/len.mojo | 2 +- stdlib/src/builtin/math.mojo | 2 +- stdlib/src/builtin/none.mojo | 2 +- stdlib/src/builtin/object.mojo | 2 +- stdlib/src/builtin/range.mojo | 2 +- stdlib/src/builtin/rebind.mojo | 2 +- stdlib/src/builtin/repr.mojo | 2 +- stdlib/src/builtin/reversed.mojo | 2 +- stdlib/src/builtin/simd.mojo | 2 +- stdlib/src/builtin/sort.mojo | 2 +- stdlib/src/builtin/str.mojo | 2 +- stdlib/src/builtin/string_literal.mojo | 2 +- stdlib/src/builtin/swap.mojo | 2 +- stdlib/src/builtin/tuple.mojo | 2 +- stdlib/src/builtin/type_aliases.mojo | 2 +- stdlib/src/builtin/uint.mojo | 2 +- stdlib/src/builtin/value.mojo | 2 +- stdlib/src/collections/__init__.mojo | 2 +- stdlib/src/collections/_index_normalization.mojo | 2 +- stdlib/src/collections/counter.mojo | 2 +- stdlib/src/collections/deque.mojo | 2 +- stdlib/src/collections/dict.mojo | 2 +- stdlib/src/collections/inline_array.mojo | 2 +- stdlib/src/collections/inline_list.mojo | 2 +- stdlib/src/collections/linked_list.mojo | 2 +- stdlib/src/collections/list.mojo | 2 +- stdlib/src/collections/optional.mojo | 2 +- stdlib/src/collections/set.mojo | 2 +- stdlib/src/collections/string/__init__.mojo | 2 +- stdlib/src/collections/string/_unicode.mojo | 2 +- stdlib/src/collections/string/_unicode_lookups.mojo | 4 ++-- stdlib/src/collections/string/_utf8_validation.mojo | 2 +- stdlib/src/collections/string/format.mojo | 2 +- stdlib/src/collections/string/inline_string.mojo | 2 +- stdlib/src/collections/string/string.mojo | 2 +- stdlib/src/collections/string/string_slice.mojo | 2 +- stdlib/src/collections/vector.mojo | 2 +- stdlib/src/documentation/__init__.mojo | 2 +- stdlib/src/documentation/documentation.mojo | 2 +- stdlib/src/hashlib/__init__.mojo | 2 +- stdlib/src/hashlib/_ahash.mojo | 2 +- stdlib/src/hashlib/_hasher.mojo | 2 +- stdlib/src/hashlib/hash.mojo | 2 +- stdlib/src/math/__init__.mojo | 2 +- stdlib/src/math/constants.mojo | 2 +- stdlib/src/math/math.mojo | 2 +- stdlib/src/math/polynomial.mojo | 2 +- stdlib/src/memory/__init__.mojo | 2 +- stdlib/src/memory/arc.mojo | 2 +- stdlib/src/memory/maybe_uninitialized.mojo | 2 +- stdlib/src/memory/memory.mojo | 2 +- stdlib/src/memory/owned_pointer.mojo | 2 +- stdlib/src/memory/pointer.mojo | 2 +- stdlib/src/memory/span.mojo | 2 +- stdlib/src/memory/unsafe.mojo | 2 +- stdlib/src/memory/unsafe_pointer.mojo | 2 +- stdlib/src/os/__init__.mojo | 2 +- stdlib/src/os/_linux_aarch64.mojo | 2 +- stdlib/src/os/_linux_x86.mojo | 2 +- stdlib/src/os/_macos.mojo | 2 +- stdlib/src/os/_windows.mojo | 2 +- stdlib/src/os/atomic.mojo | 2 +- stdlib/src/os/env.mojo | 2 +- stdlib/src/os/fstat.mojo | 2 +- stdlib/src/os/os.mojo | 2 +- stdlib/src/os/path/__init__.mojo | 2 +- stdlib/src/os/path/path.mojo | 2 +- stdlib/src/os/pathlike.mojo | 2 +- stdlib/src/pathlib/__init__.mojo | 2 +- stdlib/src/pathlib/path.mojo | 2 +- stdlib/src/prelude/__init__.mojo | 2 +- stdlib/src/pwd/__init__.mojo | 2 +- stdlib/src/pwd/_linux.mojo | 2 +- stdlib/src/pwd/_macos.mojo | 2 +- stdlib/src/pwd/pwd.mojo | 2 +- stdlib/src/python/__init__.mojo | 2 +- stdlib/src/python/_bindings.mojo | 2 +- stdlib/src/python/_cpython.mojo | 2 +- stdlib/src/python/python.mojo | 2 +- stdlib/src/python/python_object.mojo | 2 +- stdlib/src/random/__init__.mojo | 2 +- stdlib/src/random/random.mojo | 2 +- stdlib/src/stat/__init__.mojo | 2 +- stdlib/src/stat/stat.mojo | 2 +- stdlib/src/sys/__init__.mojo | 2 +- stdlib/src/sys/_amdgpu.mojo | 2 +- stdlib/src/sys/_assembly.mojo | 2 +- stdlib/src/sys/_build.mojo | 2 +- stdlib/src/sys/_io.mojo | 2 +- stdlib/src/sys/_libc.mojo | 2 +- stdlib/src/sys/arg.mojo | 2 +- stdlib/src/sys/debug.mojo | 2 +- stdlib/src/sys/ffi.mojo | 2 +- stdlib/src/sys/info.mojo | 2 +- stdlib/src/sys/intrinsics.mojo | 2 +- stdlib/src/sys/param_env.mojo | 2 +- stdlib/src/sys/terminate.mojo | 2 +- stdlib/src/tempfile/__init__.mojo | 2 +- stdlib/src/tempfile/tempfile.mojo | 2 +- stdlib/src/testing/__init__.mojo | 2 +- stdlib/src/testing/testing.mojo | 2 +- stdlib/src/time/__init__.mojo | 2 +- stdlib/src/time/time.mojo | 2 +- stdlib/src/utils/__init__.mojo | 2 +- stdlib/src/utils/_select.mojo | 2 +- stdlib/src/utils/_serialize.mojo | 2 +- stdlib/src/utils/_visualizers.mojo | 2 +- stdlib/src/utils/index.mojo | 2 +- stdlib/src/utils/lock.mojo | 2 +- stdlib/src/utils/loop.mojo | 2 +- stdlib/src/utils/numerics.mojo | 2 +- stdlib/src/utils/static_tuple.mojo | 2 +- stdlib/src/utils/stringref.mojo | 2 +- stdlib/src/utils/variant.mojo | 2 +- stdlib/src/utils/write.mojo | 2 +- stdlib/test/base64/test_base64.mojo | 2 +- stdlib/test/bit/test_bit.mojo | 2 +- stdlib/test/builtin/test_any_all.mojo | 2 +- stdlib/test/builtin/test_bfloat16.mojo | 2 +- stdlib/test/builtin/test_bool.mojo | 2 +- stdlib/test/builtin/test_char.mojo | 2 +- stdlib/test/builtin/test_debug_assert.mojo | 2 +- stdlib/test/builtin/test_debug_assert_default_error.mojo | 2 +- stdlib/test/builtin/test_debug_assert_mode_all.mojo | 2 +- stdlib/test/builtin/test_debug_assert_mode_all_error.mojo | 2 +- stdlib/test/builtin/test_debug_assert_mode_none.mojo | 2 +- stdlib/test/builtin/test_debug_assert_warning.mojo | 2 +- stdlib/test/builtin/test_deprecation_warnings.mojo | 2 +- stdlib/test/builtin/test_dtype.mojo | 2 +- stdlib/test/builtin/test_error.mojo | 2 +- stdlib/test/builtin/test_file.mojo | 2 +- stdlib/test/builtin/test_float_literal.mojo | 2 +- stdlib/test/builtin/test_format_float.mojo | 2 +- stdlib/test/builtin/test_format_int.mojo | 2 +- stdlib/test/builtin/test_int.mojo | 2 +- stdlib/test/builtin/test_int_literal.mojo | 2 +- stdlib/test/builtin/test_issue_1004.mojo | 2 +- stdlib/test/builtin/test_issue_1505.mojo | 2 +- stdlib/test/builtin/test_issue_3908.mojo | 2 +- stdlib/test/builtin/test_list_literal.mojo | 2 +- stdlib/test/builtin/test_location.mojo | 2 +- stdlib/test/builtin/test_math.mojo | 2 +- stdlib/test/builtin/test_none.mojo | 2 +- stdlib/test/builtin/test_object.mojo | 2 +- stdlib/test/builtin/test_print.mojo | 2 +- stdlib/test/builtin/test_print_long_string.mojo | 2 +- stdlib/test/builtin/test_print_stderr.mojo | 2 +- stdlib/test/builtin/test_range.mojo | 2 +- stdlib/test/builtin/test_range_out_of_bounds.mojo | 2 +- stdlib/test/builtin/test_range_uint_reverse_range_bad.mojo | 2 +- stdlib/test/builtin/test_rebind.mojo | 2 +- stdlib/test/builtin/test_repr.mojo | 2 +- stdlib/test/builtin/test_reversed.mojo | 2 +- stdlib/test/builtin/test_simd.mojo | 2 +- stdlib/test/builtin/test_slice.mojo | 2 +- stdlib/test/builtin/test_sort.mojo | 2 +- stdlib/test/builtin/test_sort_issue_1018.mojo | 2 +- stdlib/test/builtin/test_stdin.mojo | 2 +- stdlib/test/builtin/test_str.mojo | 2 +- stdlib/test/builtin/test_string_literal.mojo | 2 +- stdlib/test/builtin/test_swap.mojo | 2 +- stdlib/test/builtin/test_tuple.mojo | 2 +- stdlib/test/builtin/test_uint.mojo | 2 +- stdlib/test/builtin/test_uint_error.mojo | 2 +- stdlib/test/collections/string/test_inlined_string.mojo | 2 +- stdlib/test/collections/string/test_string.mojo | 2 +- stdlib/test/collections/string/test_string_slice.mojo | 2 +- stdlib/test/collections/string/test_unicode.mojo | 2 +- stdlib/test/collections/test_counter.mojo | 2 +- stdlib/test/collections/test_deque.mojo | 2 +- stdlib/test/collections/test_dict.mojo | 2 +- stdlib/test/collections/test_index_normalization.mojo | 2 +- stdlib/test/collections/test_inline_array.mojo | 2 +- stdlib/test/collections/test_inline_list.mojo | 2 +- stdlib/test/collections/test_linked_list.mojo | 2 +- stdlib/test/collections/test_list.mojo | 2 +- stdlib/test/collections/test_list_getitem_invalid_index.mojo | 2 +- stdlib/test/collections/test_optional.mojo | 2 +- stdlib/test/collections/test_set.mojo | 2 +- stdlib/test/collections/test_vector.mojo | 2 +- stdlib/test/hashlib/test_ahash.mojo | 2 +- stdlib/test/hashlib/test_hash.mojo | 2 +- stdlib/test/hashlib/test_hasher.mojo | 2 +- stdlib/test/lit.cfg.py | 2 +- stdlib/test/memory/test_arc.mojo | 2 +- stdlib/test/memory/test_bitcast.mojo | 2 +- stdlib/test/memory/test_maybe_uninitialized.mojo | 2 +- stdlib/test/memory/test_memory.mojo | 2 +- stdlib/test/memory/test_owned_pointer.mojo | 2 +- stdlib/test/memory/test_reference.mojo | 2 +- stdlib/test/memory/test_span.mojo | 2 +- stdlib/test/memory/test_unsafepointer.mojo | 2 +- stdlib/test/os/path/test_basename.mojo | 2 +- stdlib/test/os/path/test_dirname.mojo | 2 +- stdlib/test/os/path/test_exists.mojo | 2 +- stdlib/test/os/path/test_expanduser.mojo | 2 +- stdlib/test/os/path/test_expandvars.mojo | 2 +- stdlib/test/os/path/test_getsize.mojo | 2 +- stdlib/test/os/path/test_isdir.mojo | 2 +- stdlib/test/os/path/test_isfile.mojo | 2 +- stdlib/test/os/path/test_islink.mojo | 2 +- stdlib/test/os/path/test_join.mojo | 2 +- stdlib/test/os/path/test_split.mojo | 2 +- stdlib/test/os/path/test_splitroot.mojo | 2 +- stdlib/test/os/test_atomic.mojo | 2 +- stdlib/test/os/test_env.mojo | 2 +- stdlib/test/os/test_listdir.mojo | 2 +- stdlib/test/os/test_mkdir_and_rmdir.mojo | 2 +- stdlib/test/os/test_no_trap.mojo | 2 +- stdlib/test/os/test_remove.mojo | 2 +- stdlib/test/os/test_stat.mojo | 2 +- stdlib/test/os/test_trap.mojo | 2 +- stdlib/test/os/test_trap_stringable.mojo | 2 +- stdlib/test/pathlib/test_pathlib.mojo | 2 +- stdlib/test/pwd/test_pwd.mojo | 2 +- stdlib/test/python/custom_indexable.py | 2 +- .../python/module_for_test_python_object_dunder_contains.py | 2 +- stdlib/test/python/my_module.py | 2 +- stdlib/test/python/test_ownership.mojo | 2 +- stdlib/test/python/test_python_cpython.mojo | 2 +- stdlib/test/python/test_python_error_handling.mojo | 2 +- stdlib/test/python/test_python_info.mojo | 2 +- stdlib/test/python/test_python_interop.mojo | 2 +- stdlib/test/python/test_python_module_create.mojo | 2 +- stdlib/test/python/test_python_object.mojo | 2 +- stdlib/test/python/test_python_object_dunder_contains.mojo | 2 +- stdlib/test/python/test_python_object_len_raises.mojo | 2 +- stdlib/test/python/test_python_to_mojo.mojo | 2 +- stdlib/test/random/test_random.mojo | 2 +- stdlib/test/sys/test_aarch64_target.mojo | 2 +- stdlib/test/sys/test_build_info_debug.mojo | 2 +- stdlib/test/sys/test_c_types.mojo | 2 +- stdlib/test/sys/test_dlhandle.mojo | 2 +- stdlib/test/sys/test_exit_0.mojo | 2 +- stdlib/test/sys/test_exit_1.mojo | 2 +- stdlib/test/sys/test_intrinsics.mojo | 2 +- stdlib/test/sys/test_invalid_paramenv.mojo | 2 +- stdlib/test/sys/test_linux_target.mojo | 2 +- stdlib/test/sys/test_macos_target.mojo | 2 +- stdlib/test/sys/test_paramenv.mojo | 2 +- stdlib/test/sys/test_targetinfo.mojo | 2 +- stdlib/test/sys/test_windows_target.mojo | 2 +- stdlib/test/tempfile/test_tempfile.mojo | 2 +- stdlib/test/test_utils/__init__.mojo | 2 +- stdlib/test/test_utils/test_utils.mojo | 2 +- stdlib/test/test_utils/types.mojo | 2 +- stdlib/test/testing/test_assert_raises.mojo | 2 +- stdlib/test/testing/test_assertion.mojo | 2 +- stdlib/test/time/test_time.mojo | 2 +- stdlib/test/utils/issue_13632.mojo | 2 +- stdlib/test/utils/test_index.mojo | 2 +- stdlib/test/utils/test_numerics.mojo | 2 +- stdlib/test/utils/test_select.mojo | 2 +- stdlib/test/utils/test_static_tuple.mojo | 2 +- stdlib/test/utils/test_stringref.mojo | 2 +- stdlib/test/utils/test_tuple.mojo | 2 +- stdlib/test/utils/test_unroll.mojo | 2 +- stdlib/test/utils/test_variant.mojo | 2 +- stdlib/test/utils/test_write.mojo | 2 +- stdlib/test/utils/test_write_to_stdout.mojo | 2 +- 326 files changed, 329 insertions(+), 329 deletions(-) diff --git a/examples/check_mod.py b/examples/check_mod.py index 26e99dc419..88ef0e75cb 100644 --- a/examples/check_mod.py +++ b/examples/check_mod.py @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/life/benchmark.mojo b/examples/life/benchmark.mojo index c54c93f15b..35d58f0f6b 100644 --- a/examples/life/benchmark.mojo +++ b/examples/life/benchmark.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/life/gridv1.mojo b/examples/life/gridv1.mojo index ec65b6ad74..6e5527f17a 100644 --- a/examples/life/gridv1.mojo +++ b/examples/life/gridv1.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/life/gridv2.mojo b/examples/life/gridv2.mojo index fa5807974f..fc074a0e7f 100644 --- a/examples/life/gridv2.mojo +++ b/examples/life/gridv2.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/life/lifev1.mojo b/examples/life/lifev1.mojo index 04e78df7c4..1b8a4a9400 100644 --- a/examples/life/lifev1.mojo +++ b/examples/life/lifev1.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/life/lifev2.mojo b/examples/life/lifev2.mojo index e78306c0d9..af6ec73851 100644 --- a/examples/life/lifev2.mojo +++ b/examples/life/lifev2.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/life/test/test_gridv1.mojo b/examples/life/test/test_gridv1.mojo index 19e0d4c8a0..ab149e0da7 100644 --- a/examples/life/test/test_gridv1.mojo +++ b/examples/life/test/test_gridv1.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/life/test/test_gridv2.mojo b/examples/life/test/test_gridv2.mojo index cc251a9d6b..d6ca19029b 100644 --- a/examples/life/test/test_gridv2.mojo +++ b/examples/life/test/test_gridv2.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/operators/main.mojo b/examples/operators/main.mojo index 09f69c3837..c26e654568 100644 --- a/examples/operators/main.mojo +++ b/examples/operators/main.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/operators/my_complex.mojo b/examples/operators/my_complex.mojo index bd4d03f275..dae3cb9f86 100644 --- a/examples/operators/my_complex.mojo +++ b/examples/operators/my_complex.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/examples/operators/test_my_complex.mojo b/examples/operators/test_my_complex.mojo index 2846f01bea..61c9a2ddb9 100644 --- a/examples/operators/test_my_complex.mojo +++ b/examples/operators/test_my_complex.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/algorithm/bench_elementwise.mojo b/stdlib/benchmarks/algorithm/bench_elementwise.mojo index 2a302ec80f..fefe656cf6 100644 --- a/stdlib/benchmarks/algorithm/bench_elementwise.mojo +++ b/stdlib/benchmarks/algorithm/bench_elementwise.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/builtin/bench_int.mojo b/stdlib/benchmarks/builtin/bench_int.mojo index 7147b4a94c..8c25204216 100644 --- a/stdlib/benchmarks/builtin/bench_int.mojo +++ b/stdlib/benchmarks/builtin/bench_int.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/builtin/bench_sort.mojo b/stdlib/benchmarks/builtin/bench_sort.mojo index 4a4fa9d1ca..d4a1e9180b 100644 --- a/stdlib/benchmarks/builtin/bench_sort.mojo +++ b/stdlib/benchmarks/builtin/bench_sort.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/collections/bench_dict.mojo b/stdlib/benchmarks/collections/bench_dict.mojo index 06c6bc9364..fd23c64da8 100644 --- a/stdlib/benchmarks/collections/bench_dict.mojo +++ b/stdlib/benchmarks/collections/bench_dict.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index ddae0ca333..20ad75333c 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/hashlib/bench_hash.mojo b/stdlib/benchmarks/hashlib/bench_hash.mojo index 0d95cc9afe..20a3b5c3c8 100644 --- a/stdlib/benchmarks/hashlib/bench_hash.mojo +++ b/stdlib/benchmarks/hashlib/bench_hash.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/lit.cfg.py b/stdlib/benchmarks/lit.cfg.py index a06446ab34..a05376ec63 100644 --- a/stdlib/benchmarks/lit.cfg.py +++ b/stdlib/benchmarks/lit.cfg.py @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/math/bench_math.mojo b/stdlib/benchmarks/math/bench_math.mojo index 3e17eaf282..8d10ed5f93 100644 --- a/stdlib/benchmarks/math/bench_math.mojo +++ b/stdlib/benchmarks/math/bench_math.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/utils/bench_formatter.mojo b/stdlib/benchmarks/utils/bench_formatter.mojo index 12896f3c91..6d67e9cd9b 100644 --- a/stdlib/benchmarks/utils/bench_formatter.mojo +++ b/stdlib/benchmarks/utils/bench_formatter.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/benchmarks/utils/bench_memmem.mojo b/stdlib/benchmarks/utils/bench_memmem.mojo index b3e09db5f5..1f3f5118d2 100644 --- a/stdlib/benchmarks/utils/bench_memmem.mojo +++ b/stdlib/benchmarks/utils/bench_memmem.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/docs/style-guide.md b/stdlib/docs/style-guide.md index e2298d1b66..d78c989aef 100644 --- a/stdlib/docs/style-guide.md +++ b/stdlib/docs/style-guide.md @@ -91,7 +91,7 @@ following license information header: ```mojo # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/.markdownlint.yaml b/stdlib/scripts/.markdownlint.yaml index 166f2c4656..a7e9780d79 100644 --- a/stdlib/scripts/.markdownlint.yaml +++ b/stdlib/scripts/.markdownlint.yaml @@ -1,5 +1,5 @@ ##===----------------------------------------------------------------------===## -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/build-stdlib.sh b/stdlib/scripts/build-stdlib.sh index db9c00b5a5..8bcc6c9f66 100755 --- a/stdlib/scripts/build-stdlib.sh +++ b/stdlib/scripts/build-stdlib.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash ##===----------------------------------------------------------------------===## -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/check-docstrings.py b/stdlib/scripts/check-docstrings.py index cdd5b02f98..ddba7bb42c 100644 --- a/stdlib/scripts/check-docstrings.py +++ b/stdlib/scripts/check-docstrings.py @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/check_licenses.mojo b/stdlib/scripts/check_licenses.mojo index 0b60a0f0dd..88332a3ef3 100644 --- a/stdlib/scripts/check_licenses.mojo +++ b/stdlib/scripts/check_licenses.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/install-build-tools-linux.sh b/stdlib/scripts/install-build-tools-linux.sh index f3f15d65d5..b9e62ceee9 100755 --- a/stdlib/scripts/install-build-tools-linux.sh +++ b/stdlib/scripts/install-build-tools-linux.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash ##===----------------------------------------------------------------------===## -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/install-build-tools-macos.sh b/stdlib/scripts/install-build-tools-macos.sh index a1c8ed4f71..ef1eb36388 100755 --- a/stdlib/scripts/install-build-tools-macos.sh +++ b/stdlib/scripts/install-build-tools-macos.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash ##===----------------------------------------------------------------------===## -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/markdownlint.sh b/stdlib/scripts/markdownlint.sh index 327b234b8a..f2e8615476 100755 --- a/stdlib/scripts/markdownlint.sh +++ b/stdlib/scripts/markdownlint.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash ##===----------------------------------------------------------------------===## -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/scripts/run-benchmarks.sh b/stdlib/scripts/run-benchmarks.sh index cdf265ce21..a8c1b427ba 100755 --- a/stdlib/scripts/run-benchmarks.sh +++ b/stdlib/scripts/run-benchmarks.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash ##===----------------------------------------------------------------------===## -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt @@ -32,4 +32,4 @@ if [[ $# -gt 0 ]]; then fi # Run the benchmarks sequentially -lit --succinct --show-all --workers 1 ${BENCHMARK_PATH} \ No newline at end of file +lit --succinct --show-all --workers 1 ${BENCHMARK_PATH} diff --git a/stdlib/scripts/run-tests.sh b/stdlib/scripts/run-tests.sh index a13d2e03ef..9206450aff 100755 --- a/stdlib/scripts/run-tests.sh +++ b/stdlib/scripts/run-tests.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash ##===----------------------------------------------------------------------===## -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt @@ -25,7 +25,7 @@ source "${SCRIPT_DIR}"/build-stdlib.sh echo "Packaging up the test_utils." TEST_UTILS_PATH="${REPO_ROOT}/stdlib/test/test_utils" -# This is needed to compile test_utils.mojopkg correctly, otherwise it +# This is needed to compile test_utils.mojopkg correctly, otherwise it # uses the stdlib that's given in the nightly, and will fail compilation # if some breaking changes are made. export MODULAR_MOJO_NIGHTLY_IMPORT_PATH=$BUILD_DIR diff --git a/stdlib/src/__init__.mojo b/stdlib/src/__init__.mojo index 9dbc5078e1..75c4f824f2 100644 --- a/stdlib/src/__init__.mojo +++ b/stdlib/src/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/base64/__init__.mojo b/stdlib/src/base64/__init__.mojo index 50822dfeac..7ef0adb14d 100644 --- a/stdlib/src/base64/__init__.mojo +++ b/stdlib/src/base64/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/base64/_b64encode.mojo b/stdlib/src/base64/_b64encode.mojo index e1a5301fa3..dd6538d4c2 100644 --- a/stdlib/src/base64/_b64encode.mojo +++ b/stdlib/src/base64/_b64encode.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index c126834e25..a902a710ab 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/bit/__init__.mojo b/stdlib/src/bit/__init__.mojo index 891b8ea6d1..71f5c067e8 100644 --- a/stdlib/src/bit/__init__.mojo +++ b/stdlib/src/bit/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/bit/bit.mojo b/stdlib/src/bit/bit.mojo index e79dabaa98..d29d46459d 100644 --- a/stdlib/src/bit/bit.mojo +++ b/stdlib/src/bit/bit.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/__init__.mojo b/stdlib/src/builtin/__init__.mojo index 25589eb24d..eb10200bda 100644 --- a/stdlib/src/builtin/__init__.mojo +++ b/stdlib/src/builtin/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/_closure.mojo b/stdlib/src/builtin/_closure.mojo index c5465bdf09..36dd9c4827 100644 --- a/stdlib/src/builtin/_closure.mojo +++ b/stdlib/src/builtin/_closure.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index 690501c379..16e33a7972 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/_location.mojo b/stdlib/src/builtin/_location.mojo index e71c5c16fe..b732585163 100644 --- a/stdlib/src/builtin/_location.mojo +++ b/stdlib/src/builtin/_location.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/_pybind.mojo b/stdlib/src/builtin/_pybind.mojo index 5e1d8c874c..3d1b075f4a 100644 --- a/stdlib/src/builtin/_pybind.mojo +++ b/stdlib/src/builtin/_pybind.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/_startup.mojo b/stdlib/src/builtin/_startup.mojo index e6efe691e0..38ad248042 100644 --- a/stdlib/src/builtin/_startup.mojo +++ b/stdlib/src/builtin/_startup.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/_stubs.mojo b/stdlib/src/builtin/_stubs.mojo index 8a6bcaaf16..53bb4f038a 100644 --- a/stdlib/src/builtin/_stubs.mojo +++ b/stdlib/src/builtin/_stubs.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/anytype.mojo b/stdlib/src/builtin/anytype.mojo index c76235fc9c..62596db5c0 100644 --- a/stdlib/src/builtin/anytype.mojo +++ b/stdlib/src/builtin/anytype.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index e83425f8e5..42ce052e0e 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/breakpoint.mojo b/stdlib/src/builtin/breakpoint.mojo index 6797f99636..34e36cbe14 100644 --- a/stdlib/src/builtin/breakpoint.mojo +++ b/stdlib/src/builtin/breakpoint.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 4486c59fbf..3d7c69ab89 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/builtin_slice.mojo b/stdlib/src/builtin/builtin_slice.mojo index 094db42e69..15d460f744 100644 --- a/stdlib/src/builtin/builtin_slice.mojo +++ b/stdlib/src/builtin/builtin_slice.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index aa316d21a7..20d4b1c665 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/comparable.mojo b/stdlib/src/builtin/comparable.mojo index 5f503487d9..9e50143445 100644 --- a/stdlib/src/builtin/comparable.mojo +++ b/stdlib/src/builtin/comparable.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/constrained.mojo b/stdlib/src/builtin/constrained.mojo index b50ca11e71..ac8064fa97 100644 --- a/stdlib/src/builtin/constrained.mojo +++ b/stdlib/src/builtin/constrained.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/coroutine.mojo b/stdlib/src/builtin/coroutine.mojo index c2c0ee1d81..879bde0157 100644 --- a/stdlib/src/builtin/coroutine.mojo +++ b/stdlib/src/builtin/coroutine.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index 2b62fe71a8..767400f24f 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index c0ab14b399..d474424927 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/equality_comparable.mojo b/stdlib/src/builtin/equality_comparable.mojo index 201df76511..b14ad719d9 100644 --- a/stdlib/src/builtin/equality_comparable.mojo +++ b/stdlib/src/builtin/equality_comparable.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index d550b8e7be..0e89c0ee6b 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index 7851c20868..94f1724e3a 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/file_descriptor.mojo b/stdlib/src/builtin/file_descriptor.mojo index beed667c63..369704f53a 100644 --- a/stdlib/src/builtin/file_descriptor.mojo +++ b/stdlib/src/builtin/file_descriptor.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 9ddc2de605..0c7c2f3040 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/floatable.mojo b/stdlib/src/builtin/floatable.mojo index ed921aa2a7..ecb3577adf 100644 --- a/stdlib/src/builtin/floatable.mojo +++ b/stdlib/src/builtin/floatable.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/format_int.mojo b/stdlib/src/builtin/format_int.mojo index 9eb4cece7d..e2879655f2 100644 --- a/stdlib/src/builtin/format_int.mojo +++ b/stdlib/src/builtin/format_int.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/identifiable.mojo b/stdlib/src/builtin/identifiable.mojo index 288768856d..24daedab56 100644 --- a/stdlib/src/builtin/identifiable.mojo +++ b/stdlib/src/builtin/identifiable.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 367a5d507e..44aca38921 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index c114104ea6..8c52880895 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index b70780eeab..4271f75e0e 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/len.mojo b/stdlib/src/builtin/len.mojo index 073a6f5b30..72f0ac9164 100644 --- a/stdlib/src/builtin/len.mojo +++ b/stdlib/src/builtin/len.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 237d9d1301..4b2bc355ab 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/none.mojo b/stdlib/src/builtin/none.mojo index c50322306c..324a4fd425 100644 --- a/stdlib/src/builtin/none.mojo +++ b/stdlib/src/builtin/none.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index 2f9a86c459..a1d896f9aa 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/range.mojo b/stdlib/src/builtin/range.mojo index 14fb9d979b..4b84b3cb86 100644 --- a/stdlib/src/builtin/range.mojo +++ b/stdlib/src/builtin/range.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/rebind.mojo b/stdlib/src/builtin/rebind.mojo index baa60c0233..05dd22ed43 100644 --- a/stdlib/src/builtin/rebind.mojo +++ b/stdlib/src/builtin/rebind.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/repr.mojo b/stdlib/src/builtin/repr.mojo index 9fd07cdc43..59eae1d256 100644 --- a/stdlib/src/builtin/repr.mojo +++ b/stdlib/src/builtin/repr.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/reversed.mojo b/stdlib/src/builtin/reversed.mojo index 9e1d778299..686da90aaf 100644 --- a/stdlib/src/builtin/reversed.mojo +++ b/stdlib/src/builtin/reversed.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index ced76542c7..f3f6f493cf 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/sort.mojo b/stdlib/src/builtin/sort.mojo index 6cce447b5c..9574568db1 100644 --- a/stdlib/src/builtin/sort.mojo +++ b/stdlib/src/builtin/sort.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/str.mojo b/stdlib/src/builtin/str.mojo index 63ca502625..de62e8f1a6 100644 --- a/stdlib/src/builtin/str.mojo +++ b/stdlib/src/builtin/str.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 70c638601a..3250111b66 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/swap.mojo b/stdlib/src/builtin/swap.mojo index 549e71e28b..7b94f4188b 100644 --- a/stdlib/src/builtin/swap.mojo +++ b/stdlib/src/builtin/swap.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index 57a7950944..d0d3088582 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/type_aliases.mojo b/stdlib/src/builtin/type_aliases.mojo index 762746f8c2..758b459304 100644 --- a/stdlib/src/builtin/type_aliases.mojo +++ b/stdlib/src/builtin/type_aliases.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index afbc0b47fc..8a5a02e822 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/builtin/value.mojo b/stdlib/src/builtin/value.mojo index 81e984ce74..a96e88d045 100644 --- a/stdlib/src/builtin/value.mojo +++ b/stdlib/src/builtin/value.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/__init__.mojo b/stdlib/src/collections/__init__.mojo index 123ed4db6b..cf70c2bf18 100644 --- a/stdlib/src/collections/__init__.mojo +++ b/stdlib/src/collections/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/_index_normalization.mojo b/stdlib/src/collections/_index_normalization.mojo index 367a98d306..80da530acc 100644 --- a/stdlib/src/collections/_index_normalization.mojo +++ b/stdlib/src/collections/_index_normalization.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/counter.mojo b/stdlib/src/collections/counter.mojo index fc2e4fea03..cd74f579c0 100644 --- a/stdlib/src/collections/counter.mojo +++ b/stdlib/src/collections/counter.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index f563976b2b..279867a1f3 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/dict.mojo b/stdlib/src/collections/dict.mojo index 3b64da3aab..06b082f450 100644 --- a/stdlib/src/collections/dict.mojo +++ b/stdlib/src/collections/dict.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index c016632e95..3d0a175073 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/inline_list.mojo b/stdlib/src/collections/inline_list.mojo index 0ff105a6c1..bf17dfa8ba 100644 --- a/stdlib/src/collections/inline_list.mojo +++ b/stdlib/src/collections/inline_list.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index cb2e362749..5da1228a80 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index aedb0f4612..306334eb42 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index 806119a053..48e35c788b 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/set.mojo b/stdlib/src/collections/set.mojo index 0defaa9f51..dcd9cc8b7e 100644 --- a/stdlib/src/collections/set.mojo +++ b/stdlib/src/collections/set.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/string/__init__.mojo b/stdlib/src/collections/string/__init__.mojo index cfa4894938..184b0656b1 100644 --- a/stdlib/src/collections/string/__init__.mojo +++ b/stdlib/src/collections/string/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index f9acb9db7d..d5ac9972c9 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/string/_unicode_lookups.mojo b/stdlib/src/collections/string/_unicode_lookups.mojo index bb05d05582..024da89c7a 100644 --- a/stdlib/src/collections/string/_unicode_lookups.mojo +++ b/stdlib/src/collections/string/_unicode_lookups.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt @@ -259,7 +259,7 @@ alias has_uppercase_mapping = List[UInt32, hint_trivial_type=True]( 0x0292, # LATIN SMALL LETTER EZH ʒ 0x029D, # LATIN SMALL LETTER J WITH CROSSED-TAIL ʝ 0x029E, # LATIN SMALL LETTER TURNED K ʞ - 0x0345, # COMBINING GREEK YPOGEGRAMMENI ͅ + 0x0345, # COMBINING GREEK YPOGEGRAMMENI 0x0371, # GREEK SMALL LETTER HETA ͱ 0x0373, # GREEK SMALL LETTER ARCHAIC SAMPI ͳ 0x0377, # GREEK SMALL LETTER PAMPHYLIAN DIGAMMA ͷ diff --git a/stdlib/src/collections/string/_utf8_validation.mojo b/stdlib/src/collections/string/_utf8_validation.mojo index 31327dce89..b0bf4f94f8 100644 --- a/stdlib/src/collections/string/_utf8_validation.mojo +++ b/stdlib/src/collections/string/_utf8_validation.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/string/format.mojo b/stdlib/src/collections/string/format.mojo index ad358a028e..b77ea15260 100644 --- a/stdlib/src/collections/string/format.mojo +++ b/stdlib/src/collections/string/format.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index f05bb63504..28304f1418 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index d310637df8..bfe956985e 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 34d7a71643..ed6939b8f5 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/collections/vector.mojo b/stdlib/src/collections/vector.mojo index 6beb0ef3b6..3895bdacc0 100644 --- a/stdlib/src/collections/vector.mojo +++ b/stdlib/src/collections/vector.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/documentation/__init__.mojo b/stdlib/src/documentation/__init__.mojo index f307558aa3..2e4bc54ad8 100644 --- a/stdlib/src/documentation/__init__.mojo +++ b/stdlib/src/documentation/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/documentation/documentation.mojo b/stdlib/src/documentation/documentation.mojo index 69ea07675c..244961142a 100644 --- a/stdlib/src/documentation/documentation.mojo +++ b/stdlib/src/documentation/documentation.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/hashlib/__init__.mojo b/stdlib/src/hashlib/__init__.mojo index 9de67db15d..f8ee4b7597 100644 --- a/stdlib/src/hashlib/__init__.mojo +++ b/stdlib/src/hashlib/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/hashlib/_ahash.mojo b/stdlib/src/hashlib/_ahash.mojo index 56488c9a8d..c2a2e39f27 100644 --- a/stdlib/src/hashlib/_ahash.mojo +++ b/stdlib/src/hashlib/_ahash.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/hashlib/_hasher.mojo b/stdlib/src/hashlib/_hasher.mojo index f30e4de21a..8f22ee9f01 100644 --- a/stdlib/src/hashlib/_hasher.mojo +++ b/stdlib/src/hashlib/_hasher.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/hashlib/hash.mojo b/stdlib/src/hashlib/hash.mojo index 315cc056bb..d4741218b2 100644 --- a/stdlib/src/hashlib/hash.mojo +++ b/stdlib/src/hashlib/hash.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/math/__init__.mojo b/stdlib/src/math/__init__.mojo index 54b918a837..e78962f86e 100644 --- a/stdlib/src/math/__init__.mojo +++ b/stdlib/src/math/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/math/constants.mojo b/stdlib/src/math/constants.mojo index d24c05e8f3..1653742bf2 100644 --- a/stdlib/src/math/constants.mojo +++ b/stdlib/src/math/constants.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index d2acffd50a..d08a7be526 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/math/polynomial.mojo b/stdlib/src/math/polynomial.mojo index 35384d33c1..0f83d695be 100644 --- a/stdlib/src/math/polynomial.mojo +++ b/stdlib/src/math/polynomial.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/__init__.mojo b/stdlib/src/memory/__init__.mojo index 80684d45b5..5e05450197 100644 --- a/stdlib/src/memory/__init__.mojo +++ b/stdlib/src/memory/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/arc.mojo b/stdlib/src/memory/arc.mojo index 6178892617..f102fb135c 100644 --- a/stdlib/src/memory/arc.mojo +++ b/stdlib/src/memory/arc.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/maybe_uninitialized.mojo b/stdlib/src/memory/maybe_uninitialized.mojo index 9ebf803bcb..91aba0f283 100644 --- a/stdlib/src/memory/maybe_uninitialized.mojo +++ b/stdlib/src/memory/maybe_uninitialized.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index b4fd7eef12..39358ac24d 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/owned_pointer.mojo b/stdlib/src/memory/owned_pointer.mojo index fde72fa4bd..8509e08914 100644 --- a/stdlib/src/memory/owned_pointer.mojo +++ b/stdlib/src/memory/owned_pointer.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 54f60a1330..ef132bc0c0 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 6657aba8b4..58c769c698 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/unsafe.mojo b/stdlib/src/memory/unsafe.mojo index 2d83992bfb..d265f61d4e 100644 --- a/stdlib/src/memory/unsafe.mojo +++ b/stdlib/src/memory/unsafe.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index eafee8084a..11076f3168 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/__init__.mojo b/stdlib/src/os/__init__.mojo index c07761b658..9f69d07d2e 100644 --- a/stdlib/src/os/__init__.mojo +++ b/stdlib/src/os/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/_linux_aarch64.mojo b/stdlib/src/os/_linux_aarch64.mojo index f4c9f62789..c4c1cbc3ab 100644 --- a/stdlib/src/os/_linux_aarch64.mojo +++ b/stdlib/src/os/_linux_aarch64.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/_linux_x86.mojo b/stdlib/src/os/_linux_x86.mojo index ece0de56d8..24f90695e0 100644 --- a/stdlib/src/os/_linux_x86.mojo +++ b/stdlib/src/os/_linux_x86.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/_macos.mojo b/stdlib/src/os/_macos.mojo index 8558ca764e..13f2bd8beb 100644 --- a/stdlib/src/os/_macos.mojo +++ b/stdlib/src/os/_macos.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/_windows.mojo b/stdlib/src/os/_windows.mojo index d993812cdf..9abf7ca57b 100644 --- a/stdlib/src/os/_windows.mojo +++ b/stdlib/src/os/_windows.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/atomic.mojo b/stdlib/src/os/atomic.mojo index 3b1088fcce..9025788480 100644 --- a/stdlib/src/os/atomic.mojo +++ b/stdlib/src/os/atomic.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/env.mojo b/stdlib/src/os/env.mojo index b0403ca378..0ccec33950 100644 --- a/stdlib/src/os/env.mojo +++ b/stdlib/src/os/env.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/fstat.mojo b/stdlib/src/os/fstat.mojo index fb683155da..133e5c702d 100644 --- a/stdlib/src/os/fstat.mojo +++ b/stdlib/src/os/fstat.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index 81ebad842f..30c1880ab4 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/path/__init__.mojo b/stdlib/src/os/path/__init__.mojo index ef6694482a..aea777a082 100644 --- a/stdlib/src/os/path/__init__.mojo +++ b/stdlib/src/os/path/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 139e77d681..79f51707c9 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/os/pathlike.mojo b/stdlib/src/os/pathlike.mojo index c01f08d03c..0353a4927d 100644 --- a/stdlib/src/os/pathlike.mojo +++ b/stdlib/src/os/pathlike.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/pathlib/__init__.mojo b/stdlib/src/pathlib/__init__.mojo index bfc08df541..9e0369dac3 100644 --- a/stdlib/src/pathlib/__init__.mojo +++ b/stdlib/src/pathlib/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index cec42c48b3..353c0a6ece 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 48540a9805..72de0eea4f 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/pwd/__init__.mojo b/stdlib/src/pwd/__init__.mojo index 088bffae14..b0bfeb6ffb 100644 --- a/stdlib/src/pwd/__init__.mojo +++ b/stdlib/src/pwd/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/pwd/_linux.mojo b/stdlib/src/pwd/_linux.mojo index 282de1f60a..0bc59866a6 100644 --- a/stdlib/src/pwd/_linux.mojo +++ b/stdlib/src/pwd/_linux.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/pwd/_macos.mojo b/stdlib/src/pwd/_macos.mojo index b09f615d3f..d35f056153 100644 --- a/stdlib/src/pwd/_macos.mojo +++ b/stdlib/src/pwd/_macos.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/pwd/pwd.mojo b/stdlib/src/pwd/pwd.mojo index 105b26e7f2..29da5cadf9 100644 --- a/stdlib/src/pwd/pwd.mojo +++ b/stdlib/src/pwd/pwd.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/python/__init__.mojo b/stdlib/src/python/__init__.mojo index f8d0d9d73c..c2f50d721e 100644 --- a/stdlib/src/python/__init__.mojo +++ b/stdlib/src/python/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/python/_bindings.mojo b/stdlib/src/python/_bindings.mojo index 2d0156aa70..73e877a9f8 100644 --- a/stdlib/src/python/_bindings.mojo +++ b/stdlib/src/python/_bindings.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index 52b7d58343..264a7f1536 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/python/python.mojo b/stdlib/src/python/python.mojo index e5c774c2e5..5c7ad725bf 100644 --- a/stdlib/src/python/python.mojo +++ b/stdlib/src/python/python.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index 86081cd0d0..cf729e0edf 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/random/__init__.mojo b/stdlib/src/random/__init__.mojo index 231b4a115f..168199f104 100644 --- a/stdlib/src/random/__init__.mojo +++ b/stdlib/src/random/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/random/random.mojo b/stdlib/src/random/random.mojo index 96c6fdb6dd..c51d8bec16 100644 --- a/stdlib/src/random/random.mojo +++ b/stdlib/src/random/random.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/stat/__init__.mojo b/stdlib/src/stat/__init__.mojo index 326f82daed..4244faa27d 100644 --- a/stdlib/src/stat/__init__.mojo +++ b/stdlib/src/stat/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/stat/stat.mojo b/stdlib/src/stat/stat.mojo index c9ffba910b..3ea5b8f149 100644 --- a/stdlib/src/stat/stat.mojo +++ b/stdlib/src/stat/stat.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/__init__.mojo b/stdlib/src/sys/__init__.mojo index b0777f5c6e..5c9fbd6fb1 100644 --- a/stdlib/src/sys/__init__.mojo +++ b/stdlib/src/sys/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo index f3a55fcae2..2fa98df525 100644 --- a/stdlib/src/sys/_amdgpu.mojo +++ b/stdlib/src/sys/_amdgpu.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/_assembly.mojo b/stdlib/src/sys/_assembly.mojo index c48f518cd6..ffbf096aac 100644 --- a/stdlib/src/sys/_assembly.mojo +++ b/stdlib/src/sys/_assembly.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/_build.mojo b/stdlib/src/sys/_build.mojo index 241b36bb2f..50948c537e 100644 --- a/stdlib/src/sys/_build.mojo +++ b/stdlib/src/sys/_build.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/_io.mojo b/stdlib/src/sys/_io.mojo index eaffb4ce08..8da36c28d5 100644 --- a/stdlib/src/sys/_io.mojo +++ b/stdlib/src/sys/_io.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/_libc.mojo b/stdlib/src/sys/_libc.mojo index ca0104d19c..e951d7beb5 100644 --- a/stdlib/src/sys/_libc.mojo +++ b/stdlib/src/sys/_libc.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/arg.mojo b/stdlib/src/sys/arg.mojo index df777a5adf..b9140ffb3b 100644 --- a/stdlib/src/sys/arg.mojo +++ b/stdlib/src/sys/arg.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/debug.mojo b/stdlib/src/sys/debug.mojo index fad5ba972e..118da2865f 100644 --- a/stdlib/src/sys/debug.mojo +++ b/stdlib/src/sys/debug.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 2485f81af5..bd515a899e 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index b25fd15142..460f5754b2 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index c787b9b398..653e772849 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/param_env.mojo b/stdlib/src/sys/param_env.mojo index fe52fd660e..06f55888f5 100644 --- a/stdlib/src/sys/param_env.mojo +++ b/stdlib/src/sys/param_env.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/sys/terminate.mojo b/stdlib/src/sys/terminate.mojo index fb78b0e59c..c11089ff1c 100644 --- a/stdlib/src/sys/terminate.mojo +++ b/stdlib/src/sys/terminate.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/tempfile/__init__.mojo b/stdlib/src/tempfile/__init__.mojo index 8ca33ac0ab..c76f2b9100 100644 --- a/stdlib/src/tempfile/__init__.mojo +++ b/stdlib/src/tempfile/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index e298462290..92bbd07f39 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/testing/__init__.mojo b/stdlib/src/testing/__init__.mojo index 79c2180d4d..a12626f82c 100644 --- a/stdlib/src/testing/__init__.mojo +++ b/stdlib/src/testing/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/testing/testing.mojo b/stdlib/src/testing/testing.mojo index 8d1e2d9f08..974742e30f 100644 --- a/stdlib/src/testing/testing.mojo +++ b/stdlib/src/testing/testing.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/time/__init__.mojo b/stdlib/src/time/__init__.mojo index 6e5097653a..4a641f36db 100644 --- a/stdlib/src/time/__init__.mojo +++ b/stdlib/src/time/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index cc7a450245..25cd4eca67 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/__init__.mojo b/stdlib/src/utils/__init__.mojo index 2e3a0ea21f..2521ef4b8d 100644 --- a/stdlib/src/utils/__init__.mojo +++ b/stdlib/src/utils/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/_select.mojo b/stdlib/src/utils/_select.mojo index 46746f0a5f..7ccaca72c8 100644 --- a/stdlib/src/utils/_select.mojo +++ b/stdlib/src/utils/_select.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/_serialize.mojo b/stdlib/src/utils/_serialize.mojo index e013cc6ea8..820281603a 100644 --- a/stdlib/src/utils/_serialize.mojo +++ b/stdlib/src/utils/_serialize.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/_visualizers.mojo b/stdlib/src/utils/_visualizers.mojo index 6a62a7d6af..e881b06deb 100644 --- a/stdlib/src/utils/_visualizers.mojo +++ b/stdlib/src/utils/_visualizers.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 5a1d0c1aed..0bfe17b8b5 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/lock.mojo b/stdlib/src/utils/lock.mojo index 4648552302..f26699c226 100644 --- a/stdlib/src/utils/lock.mojo +++ b/stdlib/src/utils/lock.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/loop.mojo b/stdlib/src/utils/loop.mojo index 1d61112379..1f8af421a5 100644 --- a/stdlib/src/utils/loop.mojo +++ b/stdlib/src/utils/loop.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index 4d01169865..d39f43cee9 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/static_tuple.mojo b/stdlib/src/utils/static_tuple.mojo index 3808906c12..d38e08fbe1 100644 --- a/stdlib/src/utils/static_tuple.mojo +++ b/stdlib/src/utils/static_tuple.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo index 1b3666a52a..295dd70c9a 100644 --- a/stdlib/src/utils/stringref.mojo +++ b/stdlib/src/utils/stringref.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/variant.mojo b/stdlib/src/utils/variant.mojo index 0a9b66979d..d79a81fd1b 100644 --- a/stdlib/src/utils/variant.mojo +++ b/stdlib/src/utils/variant.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index e5b4f16128..0e522bf921 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/base64/test_base64.mojo b/stdlib/test/base64/test_base64.mojo index 6512844905..3b630f73f8 100644 --- a/stdlib/test/base64/test_base64.mojo +++ b/stdlib/test/base64/test_base64.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index 73f8bd4054..10041b6ec2 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_any_all.mojo b/stdlib/test/builtin/test_any_all.mojo index 4fbf993fe7..2531946416 100644 --- a/stdlib/test/builtin/test_any_all.mojo +++ b/stdlib/test/builtin/test_any_all.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_bfloat16.mojo b/stdlib/test/builtin/test_bfloat16.mojo index 8295f30970..88a0bdaca2 100644 --- a/stdlib/test/builtin/test_bfloat16.mojo +++ b/stdlib/test/builtin/test_bfloat16.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_bool.mojo b/stdlib/test/builtin/test_bool.mojo index 081a31bc38..e7378e0e67 100644 --- a/stdlib/test/builtin/test_bool.mojo +++ b/stdlib/test/builtin/test_bool.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index ce52119ad1..2af35ad639 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_debug_assert.mojo b/stdlib/test/builtin/test_debug_assert.mojo index 6d8ceb57f3..db8a49ea84 100644 --- a/stdlib/test/builtin/test_debug_assert.mojo +++ b/stdlib/test/builtin/test_debug_assert.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_debug_assert_default_error.mojo b/stdlib/test/builtin/test_debug_assert_default_error.mojo index 6b5ef51e0e..1e6ea95e68 100644 --- a/stdlib/test/builtin/test_debug_assert_default_error.mojo +++ b/stdlib/test/builtin/test_debug_assert_default_error.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_debug_assert_mode_all.mojo b/stdlib/test/builtin/test_debug_assert_mode_all.mojo index f6eaa9bf09..3aa029190c 100644 --- a/stdlib/test/builtin/test_debug_assert_mode_all.mojo +++ b/stdlib/test/builtin/test_debug_assert_mode_all.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_debug_assert_mode_all_error.mojo b/stdlib/test/builtin/test_debug_assert_mode_all_error.mojo index 88e11c0ad8..02c0fa9a19 100644 --- a/stdlib/test/builtin/test_debug_assert_mode_all_error.mojo +++ b/stdlib/test/builtin/test_debug_assert_mode_all_error.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_debug_assert_mode_none.mojo b/stdlib/test/builtin/test_debug_assert_mode_none.mojo index e94fdca644..b09312ba4a 100644 --- a/stdlib/test/builtin/test_debug_assert_mode_none.mojo +++ b/stdlib/test/builtin/test_debug_assert_mode_none.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_debug_assert_warning.mojo b/stdlib/test/builtin/test_debug_assert_warning.mojo index faa4530b38..9af8882262 100644 --- a/stdlib/test/builtin/test_debug_assert_warning.mojo +++ b/stdlib/test/builtin/test_debug_assert_warning.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_deprecation_warnings.mojo b/stdlib/test/builtin/test_deprecation_warnings.mojo index 46499a0d89..f8a604f6ee 100644 --- a/stdlib/test/builtin/test_deprecation_warnings.mojo +++ b/stdlib/test/builtin/test_deprecation_warnings.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_dtype.mojo b/stdlib/test/builtin/test_dtype.mojo index eba0e13117..63c66e5aba 100644 --- a/stdlib/test/builtin/test_dtype.mojo +++ b/stdlib/test/builtin/test_dtype.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_error.mojo b/stdlib/test/builtin/test_error.mojo index 70dea916e7..6e658da9d2 100644 --- a/stdlib/test/builtin/test_error.mojo +++ b/stdlib/test/builtin/test_error.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_file.mojo b/stdlib/test/builtin/test_file.mojo index 7b23687430..9a1ac995dc 100644 --- a/stdlib/test/builtin/test_file.mojo +++ b/stdlib/test/builtin/test_file.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_float_literal.mojo b/stdlib/test/builtin/test_float_literal.mojo index 1723e8cd9c..c6c790a4d7 100644 --- a/stdlib/test/builtin/test_float_literal.mojo +++ b/stdlib/test/builtin/test_float_literal.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_format_float.mojo b/stdlib/test/builtin/test_format_float.mojo index 7c022ec74b..596d7f7da9 100644 --- a/stdlib/test/builtin/test_format_float.mojo +++ b/stdlib/test/builtin/test_format_float.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_format_int.mojo b/stdlib/test/builtin/test_format_int.mojo index 990f0feb9d..94a1e495eb 100644 --- a/stdlib/test/builtin/test_format_int.mojo +++ b/stdlib/test/builtin/test_format_int.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_int.mojo b/stdlib/test/builtin/test_int.mojo index d2d0149413..a89697d3b6 100644 --- a/stdlib/test/builtin/test_int.mojo +++ b/stdlib/test/builtin/test_int.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_int_literal.mojo b/stdlib/test/builtin/test_int_literal.mojo index 8bc148e4fd..f39dd53625 100644 --- a/stdlib/test/builtin/test_int_literal.mojo +++ b/stdlib/test/builtin/test_int_literal.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_issue_1004.mojo b/stdlib/test/builtin/test_issue_1004.mojo index db6ed5f61d..f6cfe49792 100644 --- a/stdlib/test/builtin/test_issue_1004.mojo +++ b/stdlib/test/builtin/test_issue_1004.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_issue_1505.mojo b/stdlib/test/builtin/test_issue_1505.mojo index 25f8f32e6f..f76f0dc4a8 100644 --- a/stdlib/test/builtin/test_issue_1505.mojo +++ b/stdlib/test/builtin/test_issue_1505.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_issue_3908.mojo b/stdlib/test/builtin/test_issue_3908.mojo index 3e468a428f..e32febf536 100644 --- a/stdlib/test/builtin/test_issue_3908.mojo +++ b/stdlib/test/builtin/test_issue_3908.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_list_literal.mojo b/stdlib/test/builtin/test_list_literal.mojo index 309a831b24..bb17ea1674 100644 --- a/stdlib/test/builtin/test_list_literal.mojo +++ b/stdlib/test/builtin/test_list_literal.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_location.mojo b/stdlib/test/builtin/test_location.mojo index 29c7e40401..98c01064da 100644 --- a/stdlib/test/builtin/test_location.mojo +++ b/stdlib/test/builtin/test_location.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index ec1304b926..cce327f532 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_none.mojo b/stdlib/test/builtin/test_none.mojo index 8c153ad329..792b1013db 100644 --- a/stdlib/test/builtin/test_none.mojo +++ b/stdlib/test/builtin/test_none.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_object.mojo b/stdlib/test/builtin/test_object.mojo index 848fdbbb76..877002611f 100644 --- a/stdlib/test/builtin/test_object.mojo +++ b/stdlib/test/builtin/test_object.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_print.mojo b/stdlib/test/builtin/test_print.mojo index 070db4066b..b2ca0dd2e0 100644 --- a/stdlib/test/builtin/test_print.mojo +++ b/stdlib/test/builtin/test_print.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_print_long_string.mojo b/stdlib/test/builtin/test_print_long_string.mojo index 34ec28f52b..b667d22088 100644 --- a/stdlib/test/builtin/test_print_long_string.mojo +++ b/stdlib/test/builtin/test_print_long_string.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_print_stderr.mojo b/stdlib/test/builtin/test_print_stderr.mojo index f053a86588..29437bd16b 100644 --- a/stdlib/test/builtin/test_print_stderr.mojo +++ b/stdlib/test/builtin/test_print_stderr.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_range.mojo b/stdlib/test/builtin/test_range.mojo index f9535b288e..15d8247bda 100644 --- a/stdlib/test/builtin/test_range.mojo +++ b/stdlib/test/builtin/test_range.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_range_out_of_bounds.mojo b/stdlib/test/builtin/test_range_out_of_bounds.mojo index 19c0f5d694..146f25f6f4 100644 --- a/stdlib/test/builtin/test_range_out_of_bounds.mojo +++ b/stdlib/test/builtin/test_range_out_of_bounds.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_range_uint_reverse_range_bad.mojo b/stdlib/test/builtin/test_range_uint_reverse_range_bad.mojo index 87f52e2cd3..4a86d94438 100644 --- a/stdlib/test/builtin/test_range_uint_reverse_range_bad.mojo +++ b/stdlib/test/builtin/test_range_uint_reverse_range_bad.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_rebind.mojo b/stdlib/test/builtin/test_rebind.mojo index 0d73aee2af..15451c6d91 100644 --- a/stdlib/test/builtin/test_rebind.mojo +++ b/stdlib/test/builtin/test_rebind.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_repr.mojo b/stdlib/test/builtin/test_repr.mojo index 9242af9169..0ed874974f 100644 --- a/stdlib/test/builtin/test_repr.mojo +++ b/stdlib/test/builtin/test_repr.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_reversed.mojo b/stdlib/test/builtin/test_reversed.mojo index 790619c6c4..4afa16b578 100644 --- a/stdlib/test/builtin/test_reversed.mojo +++ b/stdlib/test/builtin/test_reversed.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index eb92b4f886..16704d13e4 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_slice.mojo b/stdlib/test/builtin/test_slice.mojo index da32ed9903..1644ba324b 100644 --- a/stdlib/test/builtin/test_slice.mojo +++ b/stdlib/test/builtin/test_slice.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_sort.mojo b/stdlib/test/builtin/test_sort.mojo index 153d2d4d05..606873454c 100644 --- a/stdlib/test/builtin/test_sort.mojo +++ b/stdlib/test/builtin/test_sort.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_sort_issue_1018.mojo b/stdlib/test/builtin/test_sort_issue_1018.mojo index af50f5b896..7132374abe 100644 --- a/stdlib/test/builtin/test_sort_issue_1018.mojo +++ b/stdlib/test/builtin/test_sort_issue_1018.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_stdin.mojo b/stdlib/test/builtin/test_stdin.mojo index 8c6b2d7f6f..8bc63e7da1 100644 --- a/stdlib/test/builtin/test_stdin.mojo +++ b/stdlib/test/builtin/test_stdin.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_str.mojo b/stdlib/test/builtin/test_str.mojo index 594777287f..02870e901c 100644 --- a/stdlib/test/builtin/test_str.mojo +++ b/stdlib/test/builtin/test_str.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index 24193369e0..bd839a3c54 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_swap.mojo b/stdlib/test/builtin/test_swap.mojo index 6d834d4b71..31557ce7dc 100644 --- a/stdlib/test/builtin/test_swap.mojo +++ b/stdlib/test/builtin/test_swap.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_tuple.mojo b/stdlib/test/builtin/test_tuple.mojo index d1f6205727..b401c7a07d 100644 --- a/stdlib/test/builtin/test_tuple.mojo +++ b/stdlib/test/builtin/test_tuple.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_uint.mojo b/stdlib/test/builtin/test_uint.mojo index 2f15a1c9ad..e7ca5b6272 100644 --- a/stdlib/test/builtin/test_uint.mojo +++ b/stdlib/test/builtin/test_uint.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/builtin/test_uint_error.mojo b/stdlib/test/builtin/test_uint_error.mojo index e36e15859f..74d20652b8 100644 --- a/stdlib/test/builtin/test_uint_error.mojo +++ b/stdlib/test/builtin/test_uint_error.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/string/test_inlined_string.mojo b/stdlib/test/collections/string/test_inlined_string.mojo index 672a25ecfe..554ac157ad 100644 --- a/stdlib/test/collections/string/test_inlined_string.mojo +++ b/stdlib/test/collections/string/test_inlined_string.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index e576dbfcc3..25263afad3 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index c6a5b268ed..0e7edc9980 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/string/test_unicode.mojo b/stdlib/test/collections/string/test_unicode.mojo index 142962118c..7ec07dfbb8 100644 --- a/stdlib/test/collections/string/test_unicode.mojo +++ b/stdlib/test/collections/string/test_unicode.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_counter.mojo b/stdlib/test/collections/test_counter.mojo index a7c7b3e772..80d9b6f8a2 100644 --- a/stdlib/test/collections/test_counter.mojo +++ b/stdlib/test/collections/test_counter.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_deque.mojo b/stdlib/test/collections/test_deque.mojo index a5fdc772f9..03cdba2827 100644 --- a/stdlib/test/collections/test_deque.mojo +++ b/stdlib/test/collections/test_deque.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_dict.mojo b/stdlib/test/collections/test_dict.mojo index aa2e12c2e6..333f377d99 100644 --- a/stdlib/test/collections/test_dict.mojo +++ b/stdlib/test/collections/test_dict.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_index_normalization.mojo b/stdlib/test/collections/test_index_normalization.mojo index 703ed95f9f..0039180c55 100644 --- a/stdlib/test/collections/test_index_normalization.mojo +++ b/stdlib/test/collections/test_index_normalization.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_inline_array.mojo b/stdlib/test/collections/test_inline_array.mojo index 2f311931de..652af472ff 100644 --- a/stdlib/test/collections/test_inline_array.mojo +++ b/stdlib/test/collections/test_inline_array.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_inline_list.mojo b/stdlib/test/collections/test_inline_list.mojo index 8ca491f11c..b457e6f5d7 100644 --- a/stdlib/test/collections/test_inline_list.mojo +++ b/stdlib/test/collections/test_inline_list.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo index a5f3467736..f1be0e72bf 100644 --- a/stdlib/test/collections/test_linked_list.mojo +++ b/stdlib/test/collections/test_linked_list.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index 2ed9a71306..ef218a90b0 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_list_getitem_invalid_index.mojo b/stdlib/test/collections/test_list_getitem_invalid_index.mojo index f5ce83a0e2..34ef22d26c 100644 --- a/stdlib/test/collections/test_list_getitem_invalid_index.mojo +++ b/stdlib/test/collections/test_list_getitem_invalid_index.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_optional.mojo b/stdlib/test/collections/test_optional.mojo index 6fe264cafd..ce09006846 100644 --- a/stdlib/test/collections/test_optional.mojo +++ b/stdlib/test/collections/test_optional.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_set.mojo b/stdlib/test/collections/test_set.mojo index 147e488304..161afc4b56 100644 --- a/stdlib/test/collections/test_set.mojo +++ b/stdlib/test/collections/test_set.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_vector.mojo b/stdlib/test/collections/test_vector.mojo index 3ed126d148..66e862b54d 100644 --- a/stdlib/test/collections/test_vector.mojo +++ b/stdlib/test/collections/test_vector.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/hashlib/test_ahash.mojo b/stdlib/test/hashlib/test_ahash.mojo index 4fa20d1571..7e644f2e5a 100644 --- a/stdlib/test/hashlib/test_ahash.mojo +++ b/stdlib/test/hashlib/test_ahash.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/hashlib/test_hash.mojo b/stdlib/test/hashlib/test_hash.mojo index 39a992d51d..bf3ed7c480 100644 --- a/stdlib/test/hashlib/test_hash.mojo +++ b/stdlib/test/hashlib/test_hash.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/hashlib/test_hasher.mojo b/stdlib/test/hashlib/test_hasher.mojo index c60032b2e1..b349c600ef 100644 --- a/stdlib/test/hashlib/test_hasher.mojo +++ b/stdlib/test/hashlib/test_hasher.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/lit.cfg.py b/stdlib/test/lit.cfg.py index 67d1f613b2..a20ce09510 100644 --- a/stdlib/test/lit.cfg.py +++ b/stdlib/test/lit.cfg.py @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_arc.mojo b/stdlib/test/memory/test_arc.mojo index 83d0b4e4e4..48193c5f80 100644 --- a/stdlib/test/memory/test_arc.mojo +++ b/stdlib/test/memory/test_arc.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_bitcast.mojo b/stdlib/test/memory/test_bitcast.mojo index 5375cc485a..666e925fc7 100644 --- a/stdlib/test/memory/test_bitcast.mojo +++ b/stdlib/test/memory/test_bitcast.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_maybe_uninitialized.mojo b/stdlib/test/memory/test_maybe_uninitialized.mojo index 5dbebfd8d7..68c629019e 100644 --- a/stdlib/test/memory/test_maybe_uninitialized.mojo +++ b/stdlib/test/memory/test_maybe_uninitialized.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_memory.mojo b/stdlib/test/memory/test_memory.mojo index c3310b1021..72ec12b100 100644 --- a/stdlib/test/memory/test_memory.mojo +++ b/stdlib/test/memory/test_memory.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_owned_pointer.mojo b/stdlib/test/memory/test_owned_pointer.mojo index 133dff1348..76eb52af30 100644 --- a/stdlib/test/memory/test_owned_pointer.mojo +++ b/stdlib/test/memory/test_owned_pointer.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_reference.mojo b/stdlib/test/memory/test_reference.mojo index 746c7e8fbf..f9aabb0304 100644 --- a/stdlib/test/memory/test_reference.mojo +++ b/stdlib/test/memory/test_reference.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_span.mojo b/stdlib/test/memory/test_span.mojo index 9ccd0d76de..c6369315f1 100644 --- a/stdlib/test/memory/test_span.mojo +++ b/stdlib/test/memory/test_span.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 5bf8837422..927285ecdf 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_basename.mojo b/stdlib/test/os/path/test_basename.mojo index acda789eca..a73a004461 100644 --- a/stdlib/test/os/path/test_basename.mojo +++ b/stdlib/test/os/path/test_basename.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_dirname.mojo b/stdlib/test/os/path/test_dirname.mojo index daa6e64762..485ee626fb 100644 --- a/stdlib/test/os/path/test_dirname.mojo +++ b/stdlib/test/os/path/test_dirname.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_exists.mojo b/stdlib/test/os/path/test_exists.mojo index bfd5b8cb86..5cb4970243 100644 --- a/stdlib/test/os/path/test_exists.mojo +++ b/stdlib/test/os/path/test_exists.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_expanduser.mojo b/stdlib/test/os/path/test_expanduser.mojo index 7041eefdd6..868ea05c6b 100644 --- a/stdlib/test/os/path/test_expanduser.mojo +++ b/stdlib/test/os/path/test_expanduser.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_expandvars.mojo b/stdlib/test/os/path/test_expandvars.mojo index fead55656d..6066172f1a 100644 --- a/stdlib/test/os/path/test_expandvars.mojo +++ b/stdlib/test/os/path/test_expandvars.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_getsize.mojo b/stdlib/test/os/path/test_getsize.mojo index 1aa50e9bfb..713716536e 100644 --- a/stdlib/test/os/path/test_getsize.mojo +++ b/stdlib/test/os/path/test_getsize.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_isdir.mojo b/stdlib/test/os/path/test_isdir.mojo index eb87a671b3..05736b9c0d 100644 --- a/stdlib/test/os/path/test_isdir.mojo +++ b/stdlib/test/os/path/test_isdir.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_isfile.mojo b/stdlib/test/os/path/test_isfile.mojo index 042ebae624..34c83c6909 100644 --- a/stdlib/test/os/path/test_isfile.mojo +++ b/stdlib/test/os/path/test_isfile.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_islink.mojo b/stdlib/test/os/path/test_islink.mojo index c8b3af2e57..d73d2ef6e2 100644 --- a/stdlib/test/os/path/test_islink.mojo +++ b/stdlib/test/os/path/test_islink.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_join.mojo b/stdlib/test/os/path/test_join.mojo index a61ffd9da2..74bbcc5822 100644 --- a/stdlib/test/os/path/test_join.mojo +++ b/stdlib/test/os/path/test_join.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_split.mojo b/stdlib/test/os/path/test_split.mojo index 0ef994827b..261238f472 100644 --- a/stdlib/test/os/path/test_split.mojo +++ b/stdlib/test/os/path/test_split.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/path/test_splitroot.mojo b/stdlib/test/os/path/test_splitroot.mojo index fddf23677c..59a89709d3 100644 --- a/stdlib/test/os/path/test_splitroot.mojo +++ b/stdlib/test/os/path/test_splitroot.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_atomic.mojo b/stdlib/test/os/test_atomic.mojo index d8433fd194..a6bb1dd65d 100644 --- a/stdlib/test/os/test_atomic.mojo +++ b/stdlib/test/os/test_atomic.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_env.mojo b/stdlib/test/os/test_env.mojo index 0d4bd0b6ec..6df84bef3f 100644 --- a/stdlib/test/os/test_env.mojo +++ b/stdlib/test/os/test_env.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_listdir.mojo b/stdlib/test/os/test_listdir.mojo index 8b029590a1..0d8ee103cc 100644 --- a/stdlib/test/os/test_listdir.mojo +++ b/stdlib/test/os/test_listdir.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_mkdir_and_rmdir.mojo b/stdlib/test/os/test_mkdir_and_rmdir.mojo index 5ebb882c84..f210251808 100644 --- a/stdlib/test/os/test_mkdir_and_rmdir.mojo +++ b/stdlib/test/os/test_mkdir_and_rmdir.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_no_trap.mojo b/stdlib/test/os/test_no_trap.mojo index 8c36b634f1..5bdeea5722 100644 --- a/stdlib/test/os/test_no_trap.mojo +++ b/stdlib/test/os/test_no_trap.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_remove.mojo b/stdlib/test/os/test_remove.mojo index 5d1506abdb..6704effa4f 100644 --- a/stdlib/test/os/test_remove.mojo +++ b/stdlib/test/os/test_remove.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_stat.mojo b/stdlib/test/os/test_stat.mojo index 67a738e040..cb8475e428 100644 --- a/stdlib/test/os/test_stat.mojo +++ b/stdlib/test/os/test_stat.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_trap.mojo b/stdlib/test/os/test_trap.mojo index 2118a67d84..14e7b60286 100644 --- a/stdlib/test/os/test_trap.mojo +++ b/stdlib/test/os/test_trap.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/os/test_trap_stringable.mojo b/stdlib/test/os/test_trap_stringable.mojo index 4e7c83e150..a068956714 100644 --- a/stdlib/test/os/test_trap_stringable.mojo +++ b/stdlib/test/os/test_trap_stringable.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/pathlib/test_pathlib.mojo b/stdlib/test/pathlib/test_pathlib.mojo index e13a4abf1b..ba248b7a17 100644 --- a/stdlib/test/pathlib/test_pathlib.mojo +++ b/stdlib/test/pathlib/test_pathlib.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/pwd/test_pwd.mojo b/stdlib/test/pwd/test_pwd.mojo index 14f3946a87..54bba4cab9 100644 --- a/stdlib/test/pwd/test_pwd.mojo +++ b/stdlib/test/pwd/test_pwd.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/custom_indexable.py b/stdlib/test/python/custom_indexable.py index 1164f7f5cb..ab9d55b853 100644 --- a/stdlib/test/python/custom_indexable.py +++ b/stdlib/test/python/custom_indexable.py @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/module_for_test_python_object_dunder_contains.py b/stdlib/test/python/module_for_test_python_object_dunder_contains.py index 40ec598dee..f3ecfd3fe9 100644 --- a/stdlib/test/python/module_for_test_python_object_dunder_contains.py +++ b/stdlib/test/python/module_for_test_python_object_dunder_contains.py @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/my_module.py b/stdlib/test/python/my_module.py index 8147b0a382..73c34742fd 100644 --- a/stdlib/test/python/my_module.py +++ b/stdlib/test/python/my_module.py @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_ownership.mojo b/stdlib/test/python/test_ownership.mojo index 4411185e77..1fa9bc026a 100644 --- a/stdlib/test/python/test_ownership.mojo +++ b/stdlib/test/python/test_ownership.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_cpython.mojo b/stdlib/test/python/test_python_cpython.mojo index 77d4bfdb59..5732e5882a 100644 --- a/stdlib/test/python/test_python_cpython.mojo +++ b/stdlib/test/python/test_python_cpython.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_error_handling.mojo b/stdlib/test/python/test_python_error_handling.mojo index 5659f2abf9..86eab5bb17 100644 --- a/stdlib/test/python/test_python_error_handling.mojo +++ b/stdlib/test/python/test_python_error_handling.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_info.mojo b/stdlib/test/python/test_python_info.mojo index 5e1a4a1c7c..41d7f3c3d2 100644 --- a/stdlib/test/python/test_python_info.mojo +++ b/stdlib/test/python/test_python_info.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_interop.mojo b/stdlib/test/python/test_python_interop.mojo index 80fe6ea3d9..c846fce7ee 100644 --- a/stdlib/test/python/test_python_interop.mojo +++ b/stdlib/test/python/test_python_interop.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_module_create.mojo b/stdlib/test/python/test_python_module_create.mojo index 0fd71d7545..15bbd3edaa 100644 --- a/stdlib/test/python/test_python_module_create.mojo +++ b/stdlib/test/python/test_python_module_create.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_object.mojo b/stdlib/test/python/test_python_object.mojo index fe15369e5f..659b97c1b5 100644 --- a/stdlib/test/python/test_python_object.mojo +++ b/stdlib/test/python/test_python_object.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_object_dunder_contains.mojo b/stdlib/test/python/test_python_object_dunder_contains.mojo index 5d4c722278..7b7de0b089 100644 --- a/stdlib/test/python/test_python_object_dunder_contains.mojo +++ b/stdlib/test/python/test_python_object_dunder_contains.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_object_len_raises.mojo b/stdlib/test/python/test_python_object_len_raises.mojo index 62742b4d59..63b61c1a79 100644 --- a/stdlib/test/python/test_python_object_len_raises.mojo +++ b/stdlib/test/python/test_python_object_len_raises.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/python/test_python_to_mojo.mojo b/stdlib/test/python/test_python_to_mojo.mojo index a8dc7aca97..6e6ea75c42 100644 --- a/stdlib/test/python/test_python_to_mojo.mojo +++ b/stdlib/test/python/test_python_to_mojo.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/random/test_random.mojo b/stdlib/test/random/test_random.mojo index 8f705b002b..7441dd4395 100644 --- a/stdlib/test/random/test_random.mojo +++ b/stdlib/test/random/test_random.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_aarch64_target.mojo b/stdlib/test/sys/test_aarch64_target.mojo index 7fa94b946d..c2aa6f090d 100644 --- a/stdlib/test/sys/test_aarch64_target.mojo +++ b/stdlib/test/sys/test_aarch64_target.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_build_info_debug.mojo b/stdlib/test/sys/test_build_info_debug.mojo index 5dc1a1a77d..21d56b10dd 100644 --- a/stdlib/test/sys/test_build_info_debug.mojo +++ b/stdlib/test/sys/test_build_info_debug.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_c_types.mojo b/stdlib/test/sys/test_c_types.mojo index f2610aa453..623e592c4f 100644 --- a/stdlib/test/sys/test_c_types.mojo +++ b/stdlib/test/sys/test_c_types.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_dlhandle.mojo b/stdlib/test/sys/test_dlhandle.mojo index 2392d50f1a..b780ce79bd 100644 --- a/stdlib/test/sys/test_dlhandle.mojo +++ b/stdlib/test/sys/test_dlhandle.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_exit_0.mojo b/stdlib/test/sys/test_exit_0.mojo index d7134a7150..d6bd90eab6 100644 --- a/stdlib/test/sys/test_exit_0.mojo +++ b/stdlib/test/sys/test_exit_0.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_exit_1.mojo b/stdlib/test/sys/test_exit_1.mojo index ad8311018e..ef916aacca 100644 --- a/stdlib/test/sys/test_exit_1.mojo +++ b/stdlib/test/sys/test_exit_1.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_intrinsics.mojo b/stdlib/test/sys/test_intrinsics.mojo index 2bc51de5c6..5d3fc51362 100644 --- a/stdlib/test/sys/test_intrinsics.mojo +++ b/stdlib/test/sys/test_intrinsics.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_invalid_paramenv.mojo b/stdlib/test/sys/test_invalid_paramenv.mojo index 005755b999..ab098b3937 100644 --- a/stdlib/test/sys/test_invalid_paramenv.mojo +++ b/stdlib/test/sys/test_invalid_paramenv.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_linux_target.mojo b/stdlib/test/sys/test_linux_target.mojo index dd78cb3bb3..797c2ae3ce 100644 --- a/stdlib/test/sys/test_linux_target.mojo +++ b/stdlib/test/sys/test_linux_target.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_macos_target.mojo b/stdlib/test/sys/test_macos_target.mojo index d5101c8eb0..b5e34402ba 100644 --- a/stdlib/test/sys/test_macos_target.mojo +++ b/stdlib/test/sys/test_macos_target.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_paramenv.mojo b/stdlib/test/sys/test_paramenv.mojo index b1e3cadeb6..81fb252270 100644 --- a/stdlib/test/sys/test_paramenv.mojo +++ b/stdlib/test/sys/test_paramenv.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_targetinfo.mojo b/stdlib/test/sys/test_targetinfo.mojo index 34cbc155a1..b9fdafc137 100644 --- a/stdlib/test/sys/test_targetinfo.mojo +++ b/stdlib/test/sys/test_targetinfo.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/sys/test_windows_target.mojo b/stdlib/test/sys/test_windows_target.mojo index f14807e792..673fbe9a41 100644 --- a/stdlib/test/sys/test_windows_target.mojo +++ b/stdlib/test/sys/test_windows_target.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/tempfile/test_tempfile.mojo b/stdlib/test/tempfile/test_tempfile.mojo index 61b6e1daeb..0742f7cfa6 100644 --- a/stdlib/test/tempfile/test_tempfile.mojo +++ b/stdlib/test/tempfile/test_tempfile.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/test_utils/__init__.mojo b/stdlib/test/test_utils/__init__.mojo index 3dcecfc83b..0159453e8a 100644 --- a/stdlib/test/test_utils/__init__.mojo +++ b/stdlib/test/test_utils/__init__.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/test_utils/test_utils.mojo b/stdlib/test/test_utils/test_utils.mojo index 4591c17f24..86740a0ebe 100644 --- a/stdlib/test/test_utils/test_utils.mojo +++ b/stdlib/test/test_utils/test_utils.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/test_utils/types.mojo b/stdlib/test/test_utils/types.mojo index 01870acebc..c31642e342 100644 --- a/stdlib/test/test_utils/types.mojo +++ b/stdlib/test/test_utils/types.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/testing/test_assert_raises.mojo b/stdlib/test/testing/test_assert_raises.mojo index 50c1c17326..479d686488 100644 --- a/stdlib/test/testing/test_assert_raises.mojo +++ b/stdlib/test/testing/test_assert_raises.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/testing/test_assertion.mojo b/stdlib/test/testing/test_assertion.mojo index c7153040fb..eb7a0ab38b 100644 --- a/stdlib/test/testing/test_assertion.mojo +++ b/stdlib/test/testing/test_assertion.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/time/test_time.mojo b/stdlib/test/time/test_time.mojo index 0148bfeeef..50101f5f45 100644 --- a/stdlib/test/time/test_time.mojo +++ b/stdlib/test/time/test_time.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/issue_13632.mojo b/stdlib/test/utils/issue_13632.mojo index 0bdfbff6ce..145d68800d 100644 --- a/stdlib/test/utils/issue_13632.mojo +++ b/stdlib/test/utils/issue_13632.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_index.mojo b/stdlib/test/utils/test_index.mojo index ff5236890c..8533b448e3 100644 --- a/stdlib/test/utils/test_index.mojo +++ b/stdlib/test/utils/test_index.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_numerics.mojo b/stdlib/test/utils/test_numerics.mojo index ee85152855..33fadb8087 100644 --- a/stdlib/test/utils/test_numerics.mojo +++ b/stdlib/test/utils/test_numerics.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_select.mojo b/stdlib/test/utils/test_select.mojo index dc1911c98a..b2badb5098 100644 --- a/stdlib/test/utils/test_select.mojo +++ b/stdlib/test/utils/test_select.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_static_tuple.mojo b/stdlib/test/utils/test_static_tuple.mojo index b2f46d55af..e25c6d7fc6 100644 --- a/stdlib/test/utils/test_static_tuple.mojo +++ b/stdlib/test/utils/test_static_tuple.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_stringref.mojo b/stdlib/test/utils/test_stringref.mojo index 6a368f29a7..a0950cf990 100644 --- a/stdlib/test/utils/test_stringref.mojo +++ b/stdlib/test/utils/test_stringref.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_tuple.mojo b/stdlib/test/utils/test_tuple.mojo index 1feb95f50a..94df5f6648 100644 --- a/stdlib/test/utils/test_tuple.mojo +++ b/stdlib/test/utils/test_tuple.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_unroll.mojo b/stdlib/test/utils/test_unroll.mojo index de1b942b3a..2af198f50b 100644 --- a/stdlib/test/utils/test_unroll.mojo +++ b/stdlib/test/utils/test_unroll.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_variant.mojo b/stdlib/test/utils/test_variant.mojo index 264e1dd088..f0f32a47f6 100644 --- a/stdlib/test/utils/test_variant.mojo +++ b/stdlib/test/utils/test_variant.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_write.mojo b/stdlib/test/utils/test_write.mojo index 77bb3fb5a9..3844793005 100644 --- a/stdlib/test/utils/test_write.mojo +++ b/stdlib/test/utils/test_write.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/utils/test_write_to_stdout.mojo b/stdlib/test/utils/test_write_to_stdout.mojo index fed40ec33c..bc130a8cc1 100644 --- a/stdlib/test/utils/test_write_to_stdout.mojo +++ b/stdlib/test/utils/test_write_to_stdout.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt From 936729b02717fb7a9ca67ce78697617c1109f661 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Fri, 24 Jan 2025 18:14:54 -0800 Subject: [PATCH 300/509] Remove all references in the docs to the Mojo notebook examples We're retiring all of the current notebook examples in the public GitHub repo. This PR eliminate all references and links to them from the Mojo Manual and related documentation. MODULAR_ORIG_COMMIT_REV_ID: 25d23be3d0fc189b50a80fd118ad1282c4f8d8ca --- docs/README.md | 11 +++++------ docs/faq.md | 24 +++--------------------- docs/manual/basics.mdx | 15 ++++----------- docs/manual/lifecycle/index.mdx | 4 +--- 4 files changed, 13 insertions(+), 41 deletions(-) diff --git a/docs/README.md b/docs/README.md index e7b9308e8a..5200bc5cfe 100644 --- a/docs/README.md +++ b/docs/README.md @@ -3,10 +3,9 @@ This directory includes most of the documentation at [docs.modular.com/mojo](https://docs.modular.com/mojo). -The only things not here are the Jupyter notebooks, which are instead in the -[`examples/notebooks/`](../examples/notebooks/) directory, the `mojo` CLI pages, -and the Mojo Standard Library reference. The reference docs are generated from -source files, which are currently not open-sourced. +The only things not here are the `mojo` CLI pages and the Mojo Standard Library +reference. The reference docs are generated from source files, which are located +in the [/stdlib/src](../stdlib/src) directory. ## Contributing @@ -16,8 +15,8 @@ to accept your contributions. Be aware that we don't provide tools to generate a preview of the website, because the Mojo docs are built along with other content that's not included in this repo. As such, we recommend you preview your edits in an IDE that can -render markdown and Jupyter notebook files, such as VS Code, including the [VS -Code environment in GitHub](https://github.dev/modularml/mojo/blob/main/). +render Markdown and MDX files, such as VS Code, including the +[VS Code environment in GitHub](https://github.dev/modular/mojo/blob/main/). For more information about how to contribute, see the [Contributor Guide](../CONTRIBUTING.md) diff --git a/docs/faq.md b/docs/faq.md index 32ecb58de7..eb07300b96 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -41,9 +41,7 @@ at this point, but you can also just use the `.mojo` extension. :) Mojo combines the usability of Python with the systems programming features it’s missing. We are guided more by pragmatism than novelty, but Mojo’s use of [MLIR](https://mlir.llvm.org/) allows it to scale to new exotic hardware types -and domains in a way that other languages haven’t demonstrated (for an example -of Mojo talking directly to MLIR, see our [low-level IR in Mojo -notebook](/mojo/notebooks/BoolMLIR)). It also +and domains in a way that other languages haven’t demonstrated. It also has caching and distributed compilation built into its core. We also believe Mojo has a good chance of unifying hybrid packages in the broader Python community. @@ -136,9 +134,7 @@ Although you can use MLIR to create a flexible and powerful compiler for any programming language, Mojo is the world’s first language to be built from the ground up with MLIR design principles. This means that Mojo not only offers high-performance compilation for heterogeneous hardware, but it also provides -direct programming support for the MLIR intermediate representations. For a -simple example of Mojo talking directly to MLIR, see our [low-level IR in Mojo -notebook](/mojo/notebooks/BoolMLIR). +direct programming support for the MLIR intermediate representations. ### Is Mojo only for AI or can it be used for other stuff? @@ -242,18 +238,6 @@ multiplication](https://www.modular.com/blog/the-worlds-fastest-unified-matrix-m ## Performance -### Mojo’s matmul performance in the notebook doesn’t seem that great. What’s going on? - -The [Mojo Matmul -notebook](https://github.com/modular/mojo/blob/main/examples/notebooks/Matmul.ipynb) -uses matrix multiplication to show off some Mojo features in a scenario that -you would never attempt in pure Python. So that implementation is like a “toy” -matmul implementation and it doesn’t measure up to the state of the art. - -Modular has a separate matmul implementation written in Mojo and used by -[MAX Engine](/engine), which you can read about it in [this blog -post](https://www.modular.com/blog/the-worlds-fastest-unified-matrix-multiplication). - ### Are there any AI related performance benchmarks for Mojo? It’s important to remember that Mojo is a general-purpose programming language, @@ -280,9 +264,7 @@ MAX](/max/faq#why-bundle-mojo-with-max). Yes, but it's different. When we first announced Mojo, it was available only through login, in a JupyterLab environment. Now that Mojo is available -for local development, we've shut down that service (you can instead [run -Mojo notebooks -locally](https://github.com/modular/mojo/tree/main/examples/notebooks#readme)). +for local development, we've shut down that service. The new [Mojo Playground](/mojo/playground) is built into the docs website and does not require login. diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index c6c4071a56..9238fd3799 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -51,13 +51,10 @@ the standard output. :::note -You don't need a `main()` function when coding in the -[REPL](/mojo/cli/repl) or in a -[Jupyter -notebook](https://github.com/modular/mojo/tree/main/examples/notebooks#readme). -In these environments, Mojo executes top-level code (that is, statements -that appear outside of a function). Mojo doesn't support top-level code in a -`.mojo` (or `.🔥`) file +You don't need a `main()` function when coding in the [REPL](/mojo/cli/repl) or +in a Jupyter notebook. In these environments, Mojo executes top-level code (that +is, statements that appear outside of a function). Mojo doesn't support +top-level code in a `.mojo` (or `.🔥`) file. ::: @@ -471,9 +468,5 @@ Otherwise, here are some other resources to check out: git clone https://github.com/modular/mojo.git ``` - In addition to several `.mojo` examples, the repo includes [Jupyter - notebooks](https://github.com/modular/mojo/tree/main/examples/notebooks#readme) - that teach advanced Mojo features. - * To see all the available Mojo APIs, check out the [Mojo standard library reference](/mojo/lib). diff --git a/docs/manual/lifecycle/index.mdx b/docs/manual/lifecycle/index.mdx index 466d792b18..fa97dba7df 100644 --- a/docs/manual/lifecycle/index.mdx +++ b/docs/manual/lifecycle/index.mdx @@ -16,9 +16,7 @@ Mojo also has no built-in data types with special privileges. All data types in the standard library (such as [`Bool`](/mojo/stdlib/builtin/bool/Bool), [`Int`](/mojo/stdlib/builtin/int/Int), and [`String`](/mojo/stdlib/collections/string/String)) are implemented as -[structs](/mojo/manual/structs). You can actually write your own -replacements for these types by using low-level primitives provided by -[MLIR dialects](/mojo/notebooks/BoolMLIR). +[structs](/mojo/manual/structs). What's great about the Mojo language is that it provides you these low-level tools for systems programming, but within a framework that helps you build From f485b4f625bee09464b1b86d1cf91afc37d5bfc5 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 25 Jan 2025 07:20:03 +0000 Subject: [PATCH 301/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025012505 --- examples/life/magic.lock | 959 ++++++++++++++++++++------------- examples/magic.lock | 923 ++++++++++++++++++++------------ examples/notebooks/magic.lock | 960 +++++++++++++++++++++------------- examples/operators/magic.lock | 959 ++++++++++++++++++++------------- examples/testing/magic.lock | 959 ++++++++++++++++++++------------- magic.lock | 959 ++++++++++++++++++++------------- 6 files changed, 3571 insertions(+), 2148 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 163d1ace68..aeff3da14c 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -29,7 +29,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.2-h4e1184b_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-h4e1184b_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.9-he0e7f3f_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.458-h4d475cb_6.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.489-h4d475cb_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.14.0-h5cfcd09_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda @@ -56,7 +56,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/email_validator-2.2.0-hd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.6-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.7-pyh29332c3_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-cli-0.0.7-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.17.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/fluidsynth-2.3.7-hd992666_0.conda @@ -68,7 +68,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.5.0-py312h66e93f0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.5.0-py312h178313f_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.12.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-he02047a_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-he02047a_3.conda @@ -79,12 +79,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.14.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.2.0-h4bba637_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/httptools-0.6.4-py312h66e93f0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.27.1-pyhd8ed1ab_0.conda - 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libgcc >=13 + - libzlib 1.3.1 h86ecc28_2 + arch: aarch64 + platform: linux + license: Zlib + license_family: Other + size: 95582 + timestamp: 1727963203597 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda + sha256: 58f8860756680a4831c1bf4f294e2354d187f2e999791d53b1941834c4b37430 + md5: e3170d898ca6cb48f1bb567afb92f775 + depends: + - __osx >=11.0 + - libzlib 1.3.1 h8359307_2 + arch: arm64 + platform: osx + license: Zlib + license_family: Other + size: 77606 + timestamp: 1727963209370 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda sha256: b97015e146437283f2213ff0e95abdc8e2480150634d81fbae6b96ee09f5e50b md5: 8b7069e9792ee4e5b4919a7a306d2e67 From ee9aac9ad60971d6eea528baaafcd58818893174 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 25 Jan 2025 13:00:22 -0800 Subject: [PATCH 302/509] [mojo-lang] Tighten up the `__disable_del` operator. This adds more semantic checks to this operator to narrow its behavior - which allows fixing some obcure bugs (eg MOCO-1558) and describing its semantics better. Specifically, this used to allow things like: ``` __disable_del aggregate use(aggregate.field^) ``` where the fields of the aggregate could be used after the dtor for the full object was disabled. Now you must disable the dtor after any uses, e.g.: ``` use(aggregate.field^) __disable_del aggregate ``` This also prohibits disabling the destructor of subfields and references with a good error message - they didn't work reliably before anyway. MODULAR_ORIG_COMMIT_REV_ID: 4ab50094d0901333b1bbc6777b1c2771d969c9f2 --- docs/changelog.md | 5 +++++ stdlib/src/builtin/tuple.mojo | 4 +--- stdlib/src/collections/deque.mojo | 7 ++----- stdlib/src/collections/inline_array.mojo | 4 +--- stdlib/src/collections/linked_list.mojo | 5 ++--- stdlib/src/collections/list.mojo | 4 +--- stdlib/src/python/_bindings.mojo | 8 +++----- stdlib/src/python/python_object.mojo | 9 +++------ 8 files changed, 18 insertions(+), 28 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 8a540858a0..91e798ebc0 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -370,6 +370,11 @@ what we publish. This writes to a buffer on the stack before reallocating the `String`. +- The `__disable_del x` operation has been tightened up to treat all fields of + 'x' as consumed by the point of the del, so it should be used after all the + subfields are transferred or otherwise consumed (e.g. at the end of the + function) not before uses of the fields. + ### Tooling changes - mblack (aka `mojo format`) no longer formats non-mojo files. This prevents diff --git a/stdlib/src/builtin/tuple.mojo b/stdlib/src/builtin/tuple.mojo index d0d3088582..8ffff5a28b 100644 --- a/stdlib/src/builtin/tuple.mojo +++ b/stdlib/src/builtin/tuple.mojo @@ -81,9 +81,7 @@ struct Tuple[*element_types: CollectionElement](Sized, CollectionElement): ) # Do not destroy the elements when 'storage' goes away. - __mlir_op.`lit.ownership.mark_destroyed`( - __get_mvalue_as_litref(storage) - ) + __disable_del storage fn __del__(owned self): """Destructor that destroys all of the elements.""" diff --git a/stdlib/src/collections/deque.mojo b/stdlib/src/collections/deque.mojo index 279867a1f3..75e4dda9e3 100644 --- a/stdlib/src/collections/deque.mojo +++ b/stdlib/src/collections/deque.mojo @@ -157,14 +157,11 @@ struct Deque[ElementType: CollectionElement]( self = Self(capacity=capacity) for i in range(args_length): - src = UnsafePointer.address_of(elements[i]) dst = self._data + i - src.move_pointee_into(dst) + UnsafePointer.address_of(elements[i]).move_pointee_into(dst) # Do not destroy the elements when their backing storage goes away. - __mlir_op.`lit.ownership.mark_destroyed`( - __get_mvalue_as_litref(elements) - ) + __disable_del elements self._tail = args_length diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 3d0a175073..7def2c4254 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -179,9 +179,7 @@ struct InlineArray[ UnsafePointer.address_of(storage[i]).move_pointee_into(eltptr) # Do not destroy the elements when their backing storage goes away. - __mlir_op.`lit.ownership.mark_destroyed`( - __get_mvalue_as_litref(storage) - ) + __disable_del storage fn copy(self) -> Self: """Explicitly copy the provided value. diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index 5da1228a80..1e386a263a 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -115,9 +115,8 @@ struct LinkedList[ElementType: WritableCollectionElement]: self.append(elem[]) # Do not destroy the elements when their backing storage goes away. - __mlir_op.`lit.ownership.mark_destroyed`( - __get_mvalue_as_litref(elements) - ) + # FIXME(https://github.com/modular/mojo/issues/3969) this is leaking! + __disable_del elements fn __copyinit__(mut self, read other: Self): """Initialize this list as a copy of another list. diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 306334eb42..108a8afb41 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -159,9 +159,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( src.move_pointee_into(dest) # Do not destroy the elements when their backing storage goes away. - __mlir_op.`lit.ownership.mark_destroyed`( - __get_mvalue_as_litref(elements) - ) + __disable_del elements self.size = length diff --git a/stdlib/src/python/_bindings.mojo b/stdlib/src/python/_bindings.mojo index 73e877a9f8..19f3b03fb3 100644 --- a/stdlib/src/python/_bindings.mojo +++ b/stdlib/src/python/_bindings.mojo @@ -249,16 +249,14 @@ fn py_c_function_wrapper[ # Do not destroy the provided PyObjectPtr arguments, since they # actually have ownership of the underlying object. - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(py_self)) + __disable_del py_self # SAFETY: # Prevent `args` AND `args._obj` from being destroyed, since we don't # own them. - # TODO: Use a `mem.forget(args^)` function here in the future. - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(args)) var _obj = args._obj^ - __mlir_op.`lit.ownership.mark_destroyed`(__get_mvalue_as_litref(_obj)) - + __disable_del args + __disable_del _obj return result diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index cf729e0edf..e3339c193f 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -324,14 +324,11 @@ struct PythonObject( Args: typed_obj: The typed python object to unwrap. """ - - # Note: Mark `typed_obj` as destroyed so we can move out of its field. - __mlir_op.`lit.ownership.mark_destroyed`( - __get_mvalue_as_litref(typed_obj) - ) - self = typed_obj._obj^ + # Mark destroyed so we can transfer out its field. + __disable_del typed_obj + # TODO(MSTDL-715): # This initializer should not be necessary, we should need # only the initilaizer from a `NoneType`. From ec7c1d82652bb3a3531e472dc3d41e396f93e469 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sat, 25 Jan 2025 19:35:13 -0800 Subject: [PATCH 303/509] [Stdlib] Enhance env_get_bool to support more boolean representations - Expand boolean parsing to recognize additional true/false values - Add more flexible parsing for environment variables (1/0, on/off) - Improve error message to include the specific environment variable name and value - Update test cases to verify new parsing behavior MODULAR_ORIG_COMMIT_REV_ID: e197a026a5e3ecd2ea607ef00b7cc62fd59c4225 --- stdlib/src/sys/param_env.mojo | 15 +++++++++++---- stdlib/test/sys/test_invalid_paramenv.mojo | 2 +- stdlib/test/sys/test_paramenv.mojo | 4 +++- 3 files changed, 15 insertions(+), 6 deletions(-) diff --git a/stdlib/src/sys/param_env.mojo b/stdlib/src/sys/param_env.mojo index 06f55888f5..d0484a82b4 100644 --- a/stdlib/src/sys/param_env.mojo +++ b/stdlib/src/sys/param_env.mojo @@ -63,18 +63,25 @@ fn env_get_bool[name: StringLiteral]() -> Bool: Returns: An boolean parameter value. """ - alias val = env_get_string[name]() + alias val = StringLiteral.get[env_get_string[name]().lower()]() @parameter - if val in ("True", "true"): + if val in ("true", "1", "on"): return True @parameter - if val in ("False", "false"): + if val in ("false", "0", "off"): return False constrained[ - False, "the boolean environment value is neither `True` nor `False`" + False, + String( + "the boolean environment value of `", + name, + "` with value `", + env_get_string[name](), + "` is not recognized", + ), ]() return False diff --git a/stdlib/test/sys/test_invalid_paramenv.mojo b/stdlib/test/sys/test_invalid_paramenv.mojo index ab098b3937..09decc6bdc 100644 --- a/stdlib/test/sys/test_invalid_paramenv.mojo +++ b/stdlib/test/sys/test_invalid_paramenv.mojo @@ -16,6 +16,6 @@ from sys import env_get_bool -# CHECK-FAIL: constraint failed: the boolean environment value is neither `True` nor `False` +# CHECK-FAIL: constraint failed: the boolean environment value of `my_false` with value `blah` is not recognized fn main(): _ = env_get_bool["my_false"]() diff --git a/stdlib/test/sys/test_paramenv.mojo b/stdlib/test/sys/test_paramenv.mojo index 81fb252270..a2d21d4c5b 100644 --- a/stdlib/test/sys/test_paramenv.mojo +++ b/stdlib/test/sys/test_paramenv.mojo @@ -10,7 +10,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -# RUN: %mojo -D bar=99 -D baz=hello -D foo=11 -D my_true=True -D my_false=false %s +# RUN: %mojo -D bar=99 -D baz=hello -D foo=11 -D my_true=True -D my_false=false -D my_on=on -D my_off=off %s from sys import env_get_bool, env_get_int, env_get_string, is_defined @@ -37,7 +37,9 @@ def test_env_get_int(): def test_env_get_bool(): assert_equal(env_get_bool["my_true"](), True) + assert_equal(env_get_bool["my_on"](), True) assert_equal(env_get_bool["my_false"](), False) + assert_equal(env_get_bool["my_off"](), False) def main(): From f58d02fdeaf74a5dd97538027aefac7b905379ed Mon Sep 17 00:00:00 2001 From: modularbot Date: Sun, 26 Jan 2025 08:13:11 +0000 Subject: [PATCH 304/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025012605 --- examples/life/magic.lock | 476 ++++++++++++++++---------------- examples/magic.lock | 476 ++++++++++++++++---------------- examples/notebooks/magic.lock | 494 +++++++++++++++++----------------- examples/operators/magic.lock | 476 ++++++++++++++++---------------- examples/testing/magic.lock | 476 ++++++++++++++++---------------- magic.lock | 494 +++++++++++++++++----------------- 6 files changed, 1446 insertions(+), 1446 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index aeff3da14c..40e1c1de68 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -16,7 +16,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.8.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-24.3.0-pyh71513ae_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.1.0-pyh71513ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.1-h205f482_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.8.1-h1a47875_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.10.6-hb9d3cd8_0.conda @@ -99,10 +99,10 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_hbbce691_4.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-19.0.0-h00a82cf_7_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-19.0.0-hcb10f89_7_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-19.0.0-hcb10f89_7_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-19.0.0-h08228c5_7_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-19.0.0-h00a82cf_8_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-19.0.0-hcb10f89_8_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-19.0.0-hcb10f89_8_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-19.0.0-h08228c5_8_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-he8f35ee_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-he8f35ee_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda @@ -147,8 +147,8 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.18.0-hfcad708_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.18.0-ha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-19.0.0-h081d1f1_7_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.45-h943b412_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-19.0.0-h081d1f1_8_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.46-h943b412_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.3-h6128344_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2024.07.02-hbbce691_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda @@ -172,12 +172,12 @@ environments: - 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conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025012505-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025012605-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.15-py312h98912ed_1.conda @@ -250,7 +250,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-h8bd8927_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sse-starlette-2.2.1-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/starlette-0.45.2-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/starlette-0.45.3-pyha770c72_0.conda - 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md5: f9b9c5654edb0396f0793d557c4fb898 + sha256: 90cc368b1a6ceac15f44c67517c1af7fe1dc5dce40b92f91f1830be2f6a66fb9 + md5: 15ff6d8c842fbce739084f0bbd23bd27 depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -4995,8 +5010,8 @@ packages: - platformdirs >=2 - python license: MIT - size: 130814 - timestamp: 1737868622344 + size: 130811 + timestamp: 1737955039968 - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda sha256: 78c1bbe1723449c52b7a9df1af2ee5f005209f67e40b6e1d3c7619127c43b1c7 md5: 592132998493b3ff25fd7479396e8351 @@ -5006,18 +5021,18 @@ packages: license_family: MIT size: 14465 timestamp: 1733255681319 -- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025012605-release.conda +- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025012705-release.conda noarch: python - sha256: 4e786c10774447edaf5fa00324223340fa7bba82c9b15058903aed23b2a43e25 - md5: 4b66d050c8e56e18a3a7feaaad38d20a + sha256: fb3ed00e9f7df548142bb0afa9edf654413f61d1b39c6b1cbfc9cf2fbb6e04f9 + md5: 961e0d8087b0ab6e38d0e1a518438827 depends: - - max-core ==25.1.0.dev2025012605 release + - max-core ==25.1.0.dev2025012705 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22930 - timestamp: 1737868622346 + size: 22933 + timestamp: 1737955039968 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From 13ce670dc1fbb0b2f67d7cb924a7a230b56eafa4 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 27 Jan 2025 08:05:19 -0800 Subject: [PATCH 306/509] [Kernels][GPU] Extend DUMP_GPU_* to be able to specify paths MODULAR_ORIG_COMMIT_REV_ID: a4ac555d7329161f5a31acbc7c25e98e1e91ad01 --- stdlib/src/sys/param_env.mojo | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/stdlib/src/sys/param_env.mojo b/stdlib/src/sys/param_env.mojo index d0484a82b4..ea70902070 100644 --- a/stdlib/src/sys/param_env.mojo +++ b/stdlib/src/sys/param_env.mojo @@ -53,6 +53,17 @@ fn is_defined[name: StringLiteral]() -> Bool: return __mlir_attr[`#kgen.param.expr : i1`] +fn _is_bool_like(val: String) -> Bool: + return val.lower() in ( + String("true"), + String("1"), + String("on"), + String("false"), + String("0"), + String("off"), + ) + + fn env_get_bool[name: StringLiteral]() -> Bool: """Try to get an boolean-valued define. Compilation fails if the name is not defined or the value is neither `True` or `False`. @@ -65,16 +76,8 @@ fn env_get_bool[name: StringLiteral]() -> Bool: """ alias val = StringLiteral.get[env_get_string[name]().lower()]() - @parameter - if val in ("true", "1", "on"): - return True - - @parameter - if val in ("false", "0", "off"): - return False - constrained[ - False, + _is_bool_like(val), String( "the boolean environment value of `", name, @@ -83,7 +86,8 @@ fn env_get_bool[name: StringLiteral]() -> Bool: "` is not recognized", ), ]() - return False + + return val in ("true", "1", "on") fn env_get_bool[name: StringLiteral, default: Bool]() -> Bool: From 6a89a1b618eb22eb8da09950f3a606e7666a7523 Mon Sep 17 00:00:00 2001 From: Tracy Sharpe <99041446+raiseirql@users.noreply.github.com> Date: Mon, 27 Jan 2025 08:31:08 -0800 Subject: [PATCH 307/509] [Kernels][AMD] `_amdgpu.mojo` cleanup (#54641) MODULAR_ORIG_COMMIT_REV_ID: f45adb6c7039f7550e4071afe8112ed08f91bcd6 --- stdlib/src/sys/_amdgpu.mojo | 101 +++++++++--------------------------- 1 file changed, 24 insertions(+), 77 deletions(-) diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo index 2fa98df525..9e16997830 100644 --- a/stdlib/src/sys/_amdgpu.mojo +++ b/stdlib/src/sys/_amdgpu.mojo @@ -54,9 +54,9 @@ struct amd_signal_t: @always_inline -fn update_mbox(sig: UnsafePointer[amd_signal_t]): +fn update_mbox(sig: UnsafePointer[amd_signal_t, **_]): var mb = UnsafePointer.address_of(sig[].event_mailbox_ptr).bitcast[ - UnsafePointer[UInt64] + UnsafePointer[UInt64, address_space = _GPUAddressSpace.GLOBAL] ]()[] if mb: var id = sig[].event_id.cast[DType.uint64]() @@ -67,7 +67,7 @@ fn update_mbox(sig: UnsafePointer[amd_signal_t]): @always_inline fn hsa_signal_add(sig: UInt64, value: UInt64): var s = UnsafePointer.address_of(sig).bitcast[ - UnsafePointer[amd_signal_t] + UnsafePointer[amd_signal_t, address_space = _GPUAddressSpace.GLOBAL] ]()[] _ = Atomic._fetch_add(UnsafePointer.address_of(s[].value), value) update_mbox(s) @@ -156,16 +156,7 @@ fn append_bytes( fn pack_uint64() -> UInt64: var arg = UInt64(0) if len(data) >= 8: - arg = ( - data[0].cast[DType.uint64]() - | (data[1].cast[DType.uint64]() << 8) - | (data[2].cast[DType.uint64]() << 16) - | (data[3].cast[DType.uint64]() << 24) - | (data[4].cast[DType.uint64]() << 32) - | (data[5].cast[DType.uint64]() << 40) - | (data[6].cast[DType.uint64]() << 48) - | (data[7].cast[DType.uint64]() << 56) - ) + arg = data.unsafe_ptr().bitcast[UInt64]()[] data = data[8:] else: var ii = 0 @@ -195,6 +186,7 @@ fn append_bytes( ) +@no_inline fn message_append_bytes( service_id: UInt32, msg_desc: UInt64, data: Span[UInt8] ) -> (UInt64, UInt64): @@ -342,7 +334,7 @@ fn fprintf_stdout_begin() -> UInt64: Returns: Message descriptor for a new printf invocation. """ - return begin_fprintf(0) + return begin_fprintf(FprintfCtrl.stdout) @always_inline @@ -833,67 +825,7 @@ fn send_signal(signal: UInt64): hsa_signal_add(signal, 1) -fn _hostcall( - mut buffer: Buffer, - service_id: UInt32, - arg0: UInt64, - arg1: UInt64, - arg2: UInt64, - arg3: UInt64, - arg4: UInt64, - arg5: UInt64, - arg6: UInt64, - arg7: UInt64, -) -> (UInt64, UInt64): - """ - The implementation that should be hidden behind an ABI - The transaction is a wave-wide operation, where the service_id - must be uniform, but the parameters are different for each - workitem. Parameters from all active lanes are written into a - hostcall packet. The hostcall blocks until the host processes the - request, and returns the response it receiveds. - - TODO: This function and everything above it should eventually move - to a separate library that is loaded by the language runtime. The - function itself will be exposed as an orindary function symbol to - be linked into kernel objects that are loaded after this library. - - *** INTERNAL USE ONLY *** - Internal function, not safe for direct use in user - code. Application kernels must only use __ockl_hostcall_preview() - defined elsewhere.""" - # all 0s - var me = lane_id() - # all 0s - var low = readfirstlane(me).cast[DType.uint32]() - - var packet_ptr = buffer.pop_free_stack(me, low) - - var header = buffer.get_header(packet_ptr) - var payload = buffer.get_payload(packet_ptr) - - header.fill_packet( - payload, - service_id, - arg0, - arg1, - arg2, - arg3, - arg4, - arg5, - arg6, - arg7, - me, - low, - ) - - buffer.push_ready_stack(packet_ptr, me, low) - - var retval = header.get_return_value(payload, me, low) - buffer.return_free_packet(packet_ptr, me, low) - return retval - - +@no_inline fn hostcall( service_id: UInt32, arg0: UInt64, @@ -945,8 +877,16 @@ fn hostcall( .offset(10)[] ) - return _hostcall( - buffer, + var me = lane_id() + var low = readfirstlane(me).cast[DType.uint32]() + + var packet_ptr = buffer.pop_free_stack(me, low) + + var header = buffer.get_header(packet_ptr) + var payload = buffer.get_payload(packet_ptr) + + header.fill_packet( + payload, service_id, arg0, arg1, @@ -956,4 +896,11 @@ fn hostcall( arg5, arg6, arg7, + me, + low, ) + + buffer.push_ready_stack(packet_ptr, me, low) + var retval = header.get_return_value(payload, me, low) + buffer.return_free_packet(packet_ptr, me, low) + return retval From d344d7ba18046945b01a0ec668237971898f51cc Mon Sep 17 00:00:00 2001 From: soraros Date: Mon, 27 Jan 2025 09:39:03 -0800 Subject: [PATCH 308/509] [External] [stdlib] Add `build` task in `pixi.toml` to build the stdlib (#54663) [External] [stdlib] Add `build` task in `pixi.toml` to build the stdlib Co-authored-by: soraros Closes modularml/mojo#3970 MODULAR_ORIG_COMMIT_REV_ID: 9f343de748cdb1c4c5216963782dfb8b4971bb66 --- pixi.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pixi.toml b/pixi.toml index 0067a5d6c1..37772a1b5e 100644 --- a/pixi.toml +++ b/pixi.toml @@ -5,11 +5,12 @@ channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] platforms = ["linux-64", "linux-aarch64", "osx-arm64"] [tasks] -tests ="./stdlib/scripts/run-tests.sh" +build = "./stdlib/scripts/build-stdlib.sh" +tests = "./stdlib/scripts/run-tests.sh" examples = "./examples/run-examples.sh" benchmarks = { cmd = ["./stdlib/scripts/run-benchmarks.sh"], env = { MODULAR_MOJO_NIGHTLY_IMPORT_PATH = "$CONDA_PREFIX/lib/mojo" } } [dependencies] python = ">=3.9,<3.13" lit = "*" -max = "*" \ No newline at end of file +max = "*" From cba1fed83b5284df8b044c151d29ce9614705780 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Tue, 28 Jan 2025 12:54:05 -0800 Subject: [PATCH 309/509] Update github paths for nightly/main branch update MODULAR_ORIG_COMMIT_REV_ID: 8b537faac59ee67e5019900a18a3774b802d77e2 --- docs/manual/get-started.mdx | 4 ++-- docs/manual/operators.mdx | 2 +- docs/tools/testing.mdx | 14 +++++++------- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/manual/get-started.mdx b/docs/manual/get-started.mdx index da014c24d4..799ebbc78d 100644 --- a/docs/manual/get-started.mdx +++ b/docs/manual/get-started.mdx @@ -2,7 +2,7 @@ title: "Get started with Mojo" sidebar_label: "Tutorial: Get started with Mojo" description: "Install Mojo and learn the language basics by building a complete Mojo program" -github_url: https://github.com/modular/mojo/tree/nightly/examples/life +github_url: https://github.com/modular/mojo/tree/main/examples/life image: /images/artwork/mojo-get-started.jpg --- @@ -37,7 +37,7 @@ This tutorial might be a little long because there's a lot to learn, but we tried to keep the explanations simple, and we included links along the way for you to go learn more about each topic. If you just want to see the finished code, you can [get it on -GitHub](https://github.com/modular/mojo/tree/nightly/examples/life). +GitHub](https://github.com/modular/mojo/tree/main/examples/life). System requirements: diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index baaf9f5d8f..902e453224 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -1030,7 +1030,7 @@ operations like printing complex values. We'll also allow mixing `Complex` and This example builds our `Complex` struct incrementally. You can also find the [complete example in the public Mojo GitHub -repo](https://github.com/modular/mojo/tree/nightly/examples/operators). +repo](https://github.com/modular/mojo/tree/main/examples/operators). :::note diff --git a/docs/tools/testing.mdx b/docs/tools/testing.mdx index f444d44d5a..ed1b22ea62 100644 --- a/docs/tools/testing.mdx +++ b/docs/tools/testing.mdx @@ -2,7 +2,7 @@ title: Testing sidebar_position: 2 description: Testing Mojo programs. -github_url: https://github.com/modular/mojo/tree/nightly/examples/testing +github_url: https://github.com/modular/mojo/tree/main/examples/testing --- Mojo includes a framework for developing and executing unit tests. The framework @@ -102,9 +102,9 @@ its error message. - [Writing API documentation tests](#writing-api-documentation-tests) discusses how to use the Mojo testing framework to test code examples in your API documentation. -- The public [GitHub repo](https://github.com/modular/mojo/tree/nightly) +- The public [GitHub repo](https://github.com/modular/mojo/tree/main) contains an [example - project](https://github.com/modular/mojo/tree/nightly/examples/testing) to + project](https://github.com/modular/mojo/tree/main/examples/testing) to demonstrate both unit testing and docstring testing. Several of the examples shown later are based on this project. @@ -297,7 +297,7 @@ the following: If needed, you can optionally use the `-I` option one or more times to append additional paths to the list of directories searched to import Mojo modules and packages. Consider the [example testing -project](https://github.com/modular/mojo/tree/nightly/examples/testing) in +project](https://github.com/modular/mojo/tree/main/examples/testing) in GitHub, which has the following directory structure: ```output @@ -369,7 +369,7 @@ By including the `--collect-only` or `--co` option, you can use `mojo test` to discover and print a list of tests. Consider the [example testing -project](https://github.com/modular/mojo/tree/nightly/examples/testing) +project](https://github.com/modular/mojo/tree/main/examples/testing) directory structure shown in the [Running tests](#running-tests) section. The following command produces a list of all of the tests defined in the `test` directory hierarchy. @@ -440,7 +440,7 @@ output shown): You can also produce JSON output for test collection as well. Consider the [example testing -project](https://github.com/modular/mojo/tree/nightly/examples/testing) +project](https://github.com/modular/mojo/tree/main/examples/testing) directory structure shown in the [Running tests](#running-tests) section. The following command collects a list in JSON format of all of the tests defined in the `test` directory hierarchy: @@ -626,7 +626,7 @@ suite within the same module. The format of a documentation test identifier is `@::`. This is best explained by an example. Consider the [example testing -project](https://github.com/modular/mojo/tree/nightly/examples/testing) +project](https://github.com/modular/mojo/tree/main/examples/testing) directory structure shown in the [Running tests](#running-tests) section. The source files in the `src` directory might contain docstrings for the `my_math` package, the `utils.mojo` module, and the individual functions within that From 1ce5c3e4e3bf713393928c18f5d3fb4c84156673 Mon Sep 17 00:00:00 2001 From: Owen Hilyard Date: Tue, 28 Jan 2025 14:31:26 -0800 Subject: [PATCH 310/509] [External] [stdlib] Enhance Linked List (#54700) [External] [stdlib] Enhance Linked List Adds a `LinkedList` type which implements most of the API surface of `List` (also grabbing the tests). This should help with filling in basic data structures in the stdlib. No iterator because I ran into some nasty issues with origins and I'm not sure how far I can push casting them before I get to UB. There's also an instance of `list.__getitem__(0).__getitem__(1).value = "Mojo"` in test_linked_list.mojo since I couldn't find another way to avoid the copy. If someone has a solution please let me know. --------- Signed-off-by: Lukas Hermann <1734032+lsh@users.noreply.github.com> Co-authored-by: Owen Hilyard Co-authored-by: Lukas Hermann <1734032+lsh@users.noreply.github.com> Closes modularml/mojo#3942 MODULAR_ORIG_COMMIT_REV_ID: 85e58ec8b82a0d7cc34667c247b6bd980550a0f4 --- stdlib/src/builtin/value.mojo | 38 ++ stdlib/src/collections/linked_list.mojo | 531 ++++++++++++++++-- stdlib/test/collections/test_linked_list.mojo | 461 ++++++++++++++- stdlib/test/collections/test_list.mojo | 48 +- stdlib/test/test_utils/__init__.mojo | 3 + stdlib/test/test_utils/types.mojo | 66 ++- 6 files changed, 1048 insertions(+), 99 deletions(-) diff --git a/stdlib/src/builtin/value.mojo b/stdlib/src/builtin/value.mojo index a96e88d045..ac0ba28484 100644 --- a/stdlib/src/builtin/value.mojo +++ b/stdlib/src/builtin/value.mojo @@ -308,3 +308,41 @@ trait BoolableKeyElement(Boolable, KeyElement): """ pass + + +trait EqualityComparableWritableCollectionElement( + WritableCollectionElement, EqualityComparable +): + """A trait that combines the CollectionElement, Writable and + EqualityComparable traits. + + This trait requires types to implement CollectionElement, Writable and + EqualityComparable interfaces, allowing them to be used in collections, + compared, and written to output. + """ + + pass + + +trait WritableCollectionElementNew(CollectionElementNew, Writable): + """A trait that combines the CollectionElement and Writable traits. + + This trait requires types to implement both CollectionElement and Writable + interfaces, allowing them to be used in collections and written to output. + """ + + pass + + +trait EqualityComparableWritableCollectionElementNew( + WritableCollectionElementNew, EqualityComparable +): + """A trait that combines the CollectionElement, Writable and + EqualityComparable traits. + + This trait requires types to implement CollectionElement, Writable and + EqualityComparable interfaces, allowing them to be used in collections, + compared, and written to output. + """ + + pass diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index 1e386a263a..19beb26112 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -14,28 +14,33 @@ from memory import UnsafePointer from collections import Optional from collections._index_normalization import normalize_index +from os import abort @value -struct Node[ElementType: WritableCollectionElement]: +struct Node[ + ElementType: CollectionElement, +]: """A node in a linked list data structure. Parameters: ElementType: The type of element stored in the node. """ + alias _NodePointer = UnsafePointer[Self] + var value: ElementType """The value stored in this node.""" - var prev: UnsafePointer[Node[ElementType]] + var prev: Self._NodePointer """The previous node in the list.""" - var next: UnsafePointer[Node[ElementType]] + var next: Self._NodePointer """The next node in the list.""" fn __init__( out self, owned value: ElementType, - prev: Optional[UnsafePointer[Node[ElementType]]], - next: Optional[UnsafePointer[Node[ElementType]]], + prev: Optional[Self._NodePointer], + next: Optional[Self._NodePointer], ): """Initialize a new Node with the given value and optional prev/next pointers. @@ -46,22 +51,32 @@ struct Node[ElementType: WritableCollectionElement]: next: Optional pointer to the next node. """ self.value = value^ - self.prev = prev.value() if prev else __type_of(self.prev)() - self.next = next.value() if next else __type_of(self.next)() + self.prev = prev.value() if prev else Self._NodePointer() + self.next = next.value() if next else Self._NodePointer() - fn __str__(self) -> String: + fn __str__[ + ElementType: WritableCollectionElement + ](self: Node[ElementType]) -> String: """Convert this node's value to a string representation. + Parameters: + ElementType: Used to conditionally enable this function if + `ElementType` is `Writable`. + Returns: String representation of the node's value. """ - return String.write(self) + return String.write(self.value) @no_inline - fn write_to[W: Writer](self, mut writer: W): + fn write_to[ + ElementType: WritableCollectionElement, W: Writer + ](self: Node[ElementType], mut writer: W): """Write this node's value to the given writer. Parameters: + ElementType: Used to conditionally enable this function if + `ElementType` is `Writable`. W: The type of writer to write the value to. Args: @@ -70,7 +85,9 @@ struct Node[ElementType: WritableCollectionElement]: writer.write(self.value) -struct LinkedList[ElementType: WritableCollectionElement]: +struct LinkedList[ + ElementType: CollectionElement, +]: """A doubly-linked list implementation. A doubly-linked list is a data structure where each element points to both @@ -79,70 +96,104 @@ struct LinkedList[ElementType: WritableCollectionElement]: Parameters: ElementType: The type of elements stored in the list. Must implement - WritableCollectionElement. + CollectionElement. """ - var _head: UnsafePointer[Node[ElementType]] + alias _NodePointer = UnsafePointer[Node[ElementType]] + + var _head: Self._NodePointer """The first node in the list.""" - var _tail: UnsafePointer[Node[ElementType]] + var _tail: Self._NodePointer """The last node in the list.""" var _size: Int """The number of elements in the list.""" fn __init__(out self): - """Initialize an empty linked list.""" - self._head = __type_of(self._head)() - self._tail = __type_of(self._tail)() + """ + Initialize an empty linked list. + + Time Complexity: O(1) + """ + self._head = Self._NodePointer() + self._tail = Self._NodePointer() self._size = 0 fn __init__(mut self, owned *elements: ElementType): """Initialize a linked list with the given elements. + Time Complexity: O(n) in len(elements) + Args: elements: Variable number of elements to initialize the list with. """ self = Self(elements=elements^) fn __init__(out self, *, owned elements: VariadicListMem[ElementType, _]): - """Initialize a linked list with the given elements. + """ + Construct a list from a `VariadicListMem`. + + Time Complexity: O(n) in len(elements) Args: - elements: Variable number of elements to initialize the list with. + elements: The elements to add to the list. """ self = Self() - for elem in elements: - self.append(elem[]) + var length = len(elements) + + for i in range(length): + var src = UnsafePointer.address_of(elements[i]) + var node = Self._NodePointer.alloc(1) + if not node: + abort("Out of memory") + var dst = UnsafePointer.address_of(node[].value) + src.move_pointee_into(dst) + node[].next = Self._NodePointer() + node[].prev = self._tail + if self._tail: + self._tail[].next = node + self._tail = node + else: + self._head = node + self._tail = node # Do not destroy the elements when their backing storage goes away. # FIXME(https://github.com/modular/mojo/issues/3969) this is leaking! __disable_del elements + self._size = length + fn __copyinit__(mut self, read other: Self): """Initialize this list as a copy of another list. + Time Complexity: O(n) in len(elements) + Args: other: The list to copy from. """ - self._head = other._head - self._tail = other._tail - self._size = other._size + self = other.copy() fn __moveinit__(mut self, owned other: Self): """Initialize this list by moving elements from another list. + Time Complexity: O(1) + Args: other: The list to move elements from. """ self._head = other._head self._tail = other._tail self._size = other._size - other._head = __type_of(other._head)() - other._tail = __type_of(other._tail)() + other._head = Self._NodePointer() + other._tail = Self._NodePointer() other._size = 0 fn __del__(owned self): - """Clean up the list by freeing all nodes.""" + """ + Clean up the list by freeing all nodes. + + Time Complexity: O(n) in len(self) + """ var curr = self._head while curr: var next = curr[].next @@ -151,15 +202,22 @@ struct LinkedList[ElementType: WritableCollectionElement]: curr = next fn append(mut self, owned value: ElementType): - """Add an element to the end of the list. + """ + Add an element to the end of the list. + + Time Complexity: O(1) Args: value: The value to append. """ - var node = Node(value^, self._tail, None) - var addr = UnsafePointer[__type_of(node)].alloc(1) - addr.init_pointee_move(node) - if self: + var addr = Self._NodePointer.alloc(1) + if not addr: + abort("Out of memory") + var value_ptr = UnsafePointer.address_of(addr[].value) + value_ptr.init_pointee_move(value^) + addr[].prev = self._tail + addr[].next = Self._NodePointer() + if self._tail: self._tail[].next = addr else: self._head = addr @@ -167,13 +225,18 @@ struct LinkedList[ElementType: WritableCollectionElement]: self._size += 1 fn prepend(mut self, owned value: ElementType): - """Add an element to the beginning of the list. + """ + Add an element to the beginning of the list. + + Time Complexity: O(1) Args: value: The value to prepend. """ var node = Node(value^, None, self._head) - var addr = UnsafePointer[__type_of(node)].alloc(1) + var addr = Self._NodePointer.alloc(1) + if not addr: + abort("Out of memory") addr.init_pointee_move(node) if self: self._head[].prev = addr @@ -183,8 +246,12 @@ struct LinkedList[ElementType: WritableCollectionElement]: self._size += 1 fn reverse(mut self): - """Reverse the order of elements in the list.""" - var prev = __type_of(self._head)() + """ + Reverse the order of elements in the list. + + Time Complexity: O(n) in len(self) + """ + var prev = Self._NodePointer() var curr = self._head while curr: var next = curr[].next @@ -194,23 +261,154 @@ struct LinkedList[ElementType: WritableCollectionElement]: self._tail = self._head self._head = prev - fn pop(mut self) -> ElementType: - """Remove and return the first element of the list. + fn pop(mut self) raises -> ElementType: + """Remove and return the last element of the list. + + Time Complexity: O(1) + + Returns: + The last element in the list. + """ + var elem = self._tail + if not elem: + raise "Pop on empty list." + + var value = elem[].value + self._tail = elem[].prev + self._size -= 1 + if self._size == 0: + self._head = Self._NodePointer() + else: + self._tail[].next = Self._NodePointer() + elem.free() + return value^ + + fn pop[I: Indexer](mut self, owned i: I) raises -> ElementType: + """ + Remove the ith element of the list, counting from the tail if + given a negative index. + + Time Complexity: O(1) + + Parameters: + I: The type of index to use. + + Args: + i: The index of the element to get. Returns: - The first element in the list. + Ownership of the indicated element. + """ + var current = self._get_node_ptr(Int(i)) + + if current: + var node = current[] + if node.prev: + node.prev[].next = node.next + else: + self._head = node.next + if node.next: + node.next[].prev = node.prev + else: + self._tail = node.prev + + var data = node.value^ + + # Aside from T, destructor is trivial + __mlir_op.`lit.ownership.mark_destroyed`( + __get_mvalue_as_litref(node) + ) + current.free() + self._size -= 1 + return data^ + + raise String("Invalid index for pop: {}").format(Int(i)) + + fn maybe_pop(mut self) -> Optional[ElementType]: + """ + Removes the head of the list and returns it, if it exists. + + Time Complexity: O(1) + + Returns: + The head of the list, if it was present. """ var elem = self._tail + if not elem: + return Optional[ElementType]() var value = elem[].value self._tail = elem[].prev self._size -= 1 if self._size == 0: - self._head = __type_of(self._head)() + self._head = Self._NodePointer() + else: + self._tail[].next = Self._NodePointer() + elem.free() return value^ + fn maybe_pop[I: Indexer](mut self, owned i: I) -> Optional[ElementType]: + """ + Remove the ith element of the list, counting from the tail if + given a negative index. + + Time Complexity: O(1) + + Parameters: + I: The type of index to use. + + Args: + i: The index of the element to get. + + Returns: + The element, if it was found. + """ + var current = self._get_node_ptr(Int(i)) + + if not current: + return Optional[ElementType]() + else: + var node = current[] + if node.prev: + node.prev[].next = node.next + else: + self._head = node.next + if node.next: + node.next[].prev = node.prev + else: + self._tail = node.prev + + var data = node.value^ + + # Aside from T, destructor is trivial + __mlir_op.`lit.ownership.mark_destroyed`( + __get_mvalue_as_litref(node) + ) + current.free() + self._size -= 1 + return Optional[ElementType](data^) + + fn clear(mut self): + """ + Removes all elements from the list. + + Time Complexity: O(n) in len(self) + """ + var current = self._head + while current: + var old = current + current = current[].next + old.destroy_pointee() + old.free() + + self._head = Self._NodePointer() + self._tail = Self._NodePointer() + self._size = 0 + fn copy(self) -> Self: """Create a deep copy of the list. + Time Complexity: O(n) in len(self) + Returns: A new list containing copies of all elements. """ @@ -221,12 +419,205 @@ struct LinkedList[ElementType: WritableCollectionElement]: curr = curr[].next return new^ + fn insert(mut self, owned idx: Int, owned elem: ElementType) raises: + """ + Insert an element `elem` into the list at index `idx`. + + Time Complexity: O(1) + + Raises: + When given an out of bounds index. + + Args: + idx: The index to insert `elem` at. `-len(self) <= idx <= len(self)`. + elem: The item to insert into the list. + """ + var i = max(0, index(idx) if idx >= 0 else index(idx) + len(self)) + + if i == 0: + var node = Self._NodePointer.alloc(1) + if not node: + abort("Out of memory") + node.init_pointee_move( + Node[ElementType]( + elem^, Self._NodePointer(), Self._NodePointer() + ) + ) + + if self._head: + node[].next = self._head + self._head[].prev = node + + self._head = node + + if not self._tail: + self._tail = node + + self._size += 1 + return + + i -= 1 + + var current = self._get_node_ptr(i) + if current: + var next = current[].next + var node = Self._NodePointer.alloc(1) + if not node: + abort("Out of memory") + var data = UnsafePointer.address_of(node[].value) + data[] = elem^ + node[].next = next + node[].prev = current + if next: + next[].prev = node + current[].next = node + if node[].next == Self._NodePointer(): + self._tail = node + if node[].prev == Self._NodePointer(): + self._head = node + self._size += 1 + else: + raise String("Index {} out of bounds").format(idx) + + fn extend(mut self, owned other: Self): + """ + Extends the list with another. + + Time Complexity: O(1) + + Args: + other: The list to append to this one. + """ + if self._tail: + self._tail[].next = other._head + if other._head: + other._head[].prev = self._tail + if other._tail: + self._tail = other._tail + + self._size += other._size + else: + self._head = other._head + self._tail = other._tail + self._size = other._size + + other._head = Self._NodePointer() + other._tail = Self._NodePointer() + + fn count[ + ElementType: EqualityComparableCollectionElement + ](self: LinkedList[ElementType], read elem: ElementType) -> UInt: + """ + Count the occurrences of `elem` in the list. + + Time Complexity: O(n) in len(self) compares + + Parameters: + ElementType: The list element type, used to conditionally enable the function. + + Args: + elem: The element to search for. + + Returns: + The number of occurrences of `elem` in the list. + """ + var current = self._head + var count = 0 + while current: + if current[].value == elem: + count += 1 + + current = current[].next + + return count + + fn __contains__[ + ElementType: EqualityComparableCollectionElement, // + ](self: LinkedList[ElementType], value: ElementType) -> Bool: + """ + Checks if the list contains `value`. + + Time Complexity: O(n) in len(self) compares + + Parameters: + ElementType: The list element type, used to conditionally enable the function. + + Args: + value: The value to search for in the list. + + Returns: + Whether the list contains `value`. + """ + var current = self._head + while current: + if current[].value == value: + return True + current = current[].next + + return False + + fn __eq__[ + ElementType: EqualityComparableCollectionElement, // + ]( + read self: LinkedList[ElementType], read other: LinkedList[ElementType] + ) -> Bool: + """ + Checks if the two lists are equal. + + Time Complexity: O(n) in min(len(self), len(other)) compares + + Parameters: + ElementType: The list element type, used to conditionally enable the function. + + Args: + other: The list to compare to. + + Returns: + Whether the lists are equal. + """ + if self._size != other._size: + return False + + var self_cursor = self._head + var other_cursor = other._head + + while self_cursor: + if self_cursor[].value != other_cursor[].value: + return False + + self_cursor = self_cursor[].next + other_cursor = other_cursor[].next + + return True + + fn __ne__[ + ElementType: EqualityComparableCollectionElement, // + ](self: LinkedList[ElementType], other: LinkedList[ElementType]) -> Bool: + """ + Checks if the two lists are not equal. + + Time Complexity: O(n) in min(len(self), len(other)) compares + + Parameters: + ElementType: The list element type, used to conditionally enable the function. + + Args: + other: The list to compare to. + + Returns: + Whether the lists are not equal. + """ + return not (self == other) + fn _get_node_ptr(ref self, index: Int) -> UnsafePointer[Node[ElementType]]: - """Get a pointer to the node at the specified index. + """ + Get a pointer to the node at the specified index. This method optimizes traversal by starting from either the head or tail depending on which is closer to the target index. + Time Complexity: O(n) in len(self) + Args: index: The index of the node to get. @@ -249,7 +640,10 @@ struct LinkedList[ElementType: WritableCollectionElement]: return curr fn __getitem__(ref self, index: Int) -> ref [self] ElementType: - """Get the element at the specified index. + """ + Get the element at the specified index. + + Time Complexity: O(n) in len(self) Args: index: The index of the element to get. @@ -261,7 +655,10 @@ struct LinkedList[ElementType: WritableCollectionElement]: return self._get_node_ptr(index)[].value fn __setitem__(mut self, index: Int, owned value: ElementType): - """Set the element at the specified index. + """ + Set the element at the specified index. + + Time Complexity: O(n) in len(self) Args: index: The index of the element to set. @@ -273,6 +670,8 @@ struct LinkedList[ElementType: WritableCollectionElement]: fn __len__(self) -> Int: """Get the number of elements in the list. + Time Complexity: O(1) + Returns: The number of elements in the list. """ @@ -281,22 +680,42 @@ struct LinkedList[ElementType: WritableCollectionElement]: fn __bool__(self) -> Bool: """Check if the list is non-empty. + Time Complexity: O(1) + Returns: True if the list has elements, False otherwise. """ return len(self) != 0 - fn __str__(self) -> String: + fn __str__[ + ElementType: WritableCollectionElement + ](self: LinkedList[ElementType]) -> String: """Convert the list to its string representation. + Time Complexity: O(n) in len(self) + + Parameters: + ElementType: Used to conditionally enable this function when + `ElementType` is `Writable`. + Returns: String representation of the list. """ - return String.write(self) + var writer = String() + self._write(writer) + return writer - fn __repr__(self) -> String: + fn __repr__[ + ElementType: WritableCollectionElement + ](self: LinkedList[ElementType]) -> String: """Convert the list to its string representation. + Time Complexity: O(n) in len(self) + + Parameters: + ElementType: Used to conditionally enable this function when + `ElementType` is `Writable`. + Returns: String representation of the list. """ @@ -304,11 +723,17 @@ struct LinkedList[ElementType: WritableCollectionElement]: self._write(writer, prefix="LinkedList(", suffix=")") return writer - fn write_to[W: Writer](self, mut writer: W): + fn write_to[ + W: Writer, ElementType: WritableCollectionElement + ](self: LinkedList[ElementType], mut writer: W): """Write the list to the given writer. + Time Complexity: O(n) in len(self) + Parameters: W: The type of writer to write the list to. + ElementType: Used to conditionally enable this function when + `ElementType` is `Writable`. Args: writer: The writer to write the list to. @@ -317,8 +742,14 @@ struct LinkedList[ElementType: WritableCollectionElement]: @no_inline fn _write[ - W: Writer - ](self, mut writer: W, *, prefix: String = "[", suffix: String = "]"): + W: Writer, ElementType: WritableCollectionElement + ]( + self: LinkedList[ElementType], + mut writer: W, + *, + prefix: String = "[", + suffix: String = "]", + ): if not self: return writer.write(prefix, suffix) @@ -327,6 +758,6 @@ struct LinkedList[ElementType: WritableCollectionElement]: for i in range(len(self)): if i: writer.write(", ") - writer.write(curr[]) + writer.write(curr[].value) curr = curr[].next writer.write(suffix) diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo index f1be0e72bf..5641002066 100644 --- a/stdlib/test/collections/test_linked_list.mojo +++ b/stdlib/test/collections/test_linked_list.mojo @@ -12,8 +12,15 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from collections import LinkedList -from testing import assert_equal +from collections import LinkedList, Optional +from testing import assert_equal, assert_raises, assert_true, assert_false +from test_utils import ( + CopyCounter, + MoveCounter, + DtorCounter, + g_dtor_count, + CopyCountedStruct, +) def test_construction(): @@ -101,12 +108,439 @@ def test_setitem(): def test_str(): var l1 = LinkedList[Int](1, 2, 3) - assert_equal(String(l1), "[1, 2, 3]") + assert_equal(l1.__str__(), "[1, 2, 3]") def test_repr(): var l1 = LinkedList[Int](1, 2, 3) - assert_equal(repr(l1), "LinkedList(1, 2, 3)") + assert_equal(l1.__repr__(), "LinkedList(1, 2, 3)") + + +def test_pop_on_empty_list(): + with assert_raises(): + var ll = LinkedList[Int]() + _ = ll.pop() + + +def test_optional_pop_on_empty_linked_list(): + var ll = LinkedList[Int]() + var result = ll.maybe_pop() + assert_false(Bool(result)) + + +def test_list(): + var list = LinkedList[Int]() + + for i in range(5): + list.append(i) + + assert_equal(5, len(list)) + assert_equal(0, list[0]) + assert_equal(1, list[1]) + assert_equal(2, list[2]) + assert_equal(3, list[3]) + assert_equal(4, list[4]) + + assert_equal(0, list[-5]) + assert_equal(3, list[-2]) + assert_equal(4, list[-1]) + + list[2] = -2 + assert_equal(-2, list[2]) + + list[-5] = 5 + assert_equal(5, list[-5]) + list[-2] = 3 + assert_equal(3, list[-2]) + list[-1] = 7 + assert_equal(7, list[-1]) + + +def test_list_clear(): + var list = LinkedList[Int](1, 2, 3) + assert_equal(len(list), 3) + list.clear() + + assert_equal(len(list), 0) + + +def test_list_to_bool_conversion(): + assert_false(LinkedList[String]()) + assert_true(LinkedList[String]("a")) + assert_true(LinkedList[String]("", "a")) + assert_true(LinkedList[String]("")) + + +def test_list_pop(): + var list = LinkedList[Int]() + # Test pop with index + for i in range(6): + list.append(i) + + assert_equal(6, len(list)) + + # try popping from index 3 for 3 times + for i in range(3, 6): + assert_equal(i, list.pop(3)) + + # list should have 3 elements now + assert_equal(3, len(list)) + assert_equal(0, list[0]) + assert_equal(1, list[1]) + assert_equal(2, list[2]) + + # Test pop with negative index + for i in range(0, 2): + var popped: Int = list.pop(-len(list)) + assert_equal(i, popped) + + # test default index as well + assert_equal(2, list.pop()) + list.append(2) + assert_equal(2, list.pop()) + + # list should be empty now + assert_equal(0, len(list)) + + +def test_list_variadic_constructor(): + var l = LinkedList[Int](2, 4, 6) + assert_equal(3, len(l)) + assert_equal(2, l[0]) + assert_equal(4, l[1]) + assert_equal(6, l[2]) + + l.append(8) + assert_equal(4, len(l)) + assert_equal(8, l[3]) + + # + # Test variadic construct copying behavior + # + + var l2 = LinkedList[CopyCounter]( + CopyCounter(), CopyCounter(), CopyCounter() + ) + + assert_equal(len(l2), 3) + assert_equal(l2[0].copy_count, 0) + assert_equal(l2[1].copy_count, 0) + assert_equal(l2[2].copy_count, 0) + + +def test_list_reverse(): + # + # Test reversing the list [] + # + + var vec = LinkedList[Int]() + + assert_equal(len(vec), 0) + + vec.reverse() + + assert_equal(len(vec), 0) + + # + # Test reversing the list [123] + # + + vec = LinkedList[Int]() + + vec.append(123) + + assert_equal(len(vec), 1) + assert_equal(vec[0], 123) + + vec.reverse() + + assert_equal(len(vec), 1) + assert_equal(vec[0], 123) + + # + # Test reversing the list ["one", "two", "three"] + # + + var vec2 = LinkedList[String]("one", "two", "three") + + assert_equal(len(vec2), 3) + assert_equal(vec2[0], "one") + assert_equal(vec2[1], "two") + assert_equal(vec2[2], "three") + + vec2.reverse() + + assert_equal(len(vec2), 3) + assert_equal(vec2[0], "three") + assert_equal(vec2[1], "two") + assert_equal(vec2[2], "one") + + # + # Test reversing the list [5, 10] + # + + vec = LinkedList[Int]() + vec.append(5) + vec.append(10) + + assert_equal(len(vec), 2) + assert_equal(vec[0], 5) + assert_equal(vec[1], 10) + + vec.reverse() + + assert_equal(len(vec), 2) + assert_equal(vec[0], 10) + assert_equal(vec[1], 5) + + +def test_list_insert(): + # + # Test the list [1, 2, 3] created with insert + # + + var v1 = LinkedList[Int]() + v1.insert(len(v1), 1) + v1.insert(len(v1), 3) + v1.insert(1, 2) + + assert_equal(len(v1), 3) + assert_equal(v1[0], 1) + assert_equal(v1[1], 2) + assert_equal(v1[2], 3) + + # + # Test the list [1, 2, 3, 4, 5] created with negative and positive index + # + + var v2 = LinkedList[Int]() + v2.insert(-1729, 2) + v2.insert(len(v2), 3) + v2.insert(len(v2), 5) + v2.insert(-1, 4) + v2.insert(-len(v2), 1) + + assert_equal(len(v2), 5) + assert_equal(v2[0], 1) + assert_equal(v2[1], 2) + assert_equal(v2[2], 3) + assert_equal(v2[3], 4) + assert_equal(v2[4], 5) + + # + # Test the list [1, 2, 3, 4] created with negative index + # + + var v3 = LinkedList[Int]() + v3.insert(-11, 4) + v3.insert(-13, 3) + v3.insert(-17, 2) + v3.insert(-19, 1) + + assert_equal(len(v3), 4) + assert_equal(v3[0], 1) + assert_equal(v3[1], 2) + assert_equal(v3[2], 3) + assert_equal(v3[3], 4) + + # + # Test the list [1, 2, 3, 4, 5, 6, 7, 8] created with insert + # + + var v4 = LinkedList[Int]() + for i in range(4): + v4.insert(0, 4 - i) + v4.insert(len(v4), 4 + i + 1) + + for i in range(len(v4)): + assert_equal(v4[i], i + 1) + + +def test_list_extend_non_trivial(): + # Tests three things: + # - extend() for non-plain-old-data types + # - extend() with mixed-length self and other lists + # - extend() using optimal number of __moveinit__() calls + var v1 = LinkedList[MoveCounter[String]]() + v1.append(MoveCounter[String]("Hello")) + v1.append(MoveCounter[String]("World")) + + var v2 = LinkedList[MoveCounter[String]]() + v2.append(MoveCounter[String]("Foo")) + v2.append(MoveCounter[String]("Bar")) + v2.append(MoveCounter[String]("Baz")) + + v1.extend(v2^) + + assert_equal(len(v1), 5) + assert_equal(v1[0].value, "Hello") + assert_equal(v1[1].value, "World") + assert_equal(v1[2].value, "Foo") + assert_equal(v1[3].value, "Bar") + assert_equal(v1[4].value, "Baz") + + assert_equal(v1[0].move_count, 1) + assert_equal(v1[1].move_count, 1) + assert_equal(v1[2].move_count, 1) + assert_equal(v1[3].move_count, 1) + assert_equal(v1[4].move_count, 1) + + +def test_2d_dynamic_list(): + var list = LinkedList[LinkedList[Int]]() + + for i in range(2): + var v = LinkedList[Int]() + for j in range(3): + v.append(i + j) + list.append(v) + + assert_equal(0, list[0][0]) + assert_equal(1, list[0][1]) + assert_equal(2, list[0][2]) + assert_equal(1, list[1][0]) + assert_equal(2, list[1][1]) + assert_equal(3, list[1][2]) + + assert_equal(2, len(list)) + + assert_equal(3, len(list[0])) + + list[0].clear() + assert_equal(0, len(list[0])) + + list.clear() + assert_equal(0, len(list)) + + +def test_list_explicit_copy(): + var list = LinkedList[CopyCounter]() + list.append(CopyCounter()) + var list_copy = list.copy() + assert_equal(0, list[0].copy_count) + assert_equal(1, list_copy[0].copy_count) + + var l2 = LinkedList[Int]() + for i in range(10): + l2.append(i) + + var l2_copy = l2.copy() + assert_equal(len(l2), len(l2_copy)) + for i in range(len(l2)): + assert_equal(l2[i], l2_copy[i]) + + +def test_no_extra_copies_with_sugared_set_by_field(): + var list = LinkedList[LinkedList[CopyCountedStruct]]() + var child_list = LinkedList[CopyCountedStruct]() + child_list.append(CopyCountedStruct("Hello")) + child_list.append(CopyCountedStruct("World")) + + # No copies here. Constructing with LinkedList[CopyCountedStruct](CopyCountedStruct("Hello")) is a copy. + assert_equal(0, child_list[0].counter.copy_count) + assert_equal(0, child_list[1].counter.copy_count) + + list.append(child_list^) + + assert_equal(0, list[0][0].counter.copy_count) + assert_equal(0, list[0][1].counter.copy_count) + + # list[0][1] makes a copy for reasons I cannot determine + list.__getitem__(0).__getitem__(1).value = "Mojo" + + assert_equal(0, list[0][0].counter.copy_count) + assert_equal(0, list[0][1].counter.copy_count) + + assert_equal("Mojo", list[0][1].value) + + assert_equal(0, list[0][0].counter.copy_count) + assert_equal(0, list[0][1].counter.copy_count) + + +def test_list_boolable(): + assert_true(LinkedList[Int](1)) + assert_false(LinkedList[Int]()) + + +def test_list_count(): + var list = LinkedList[Int](1, 2, 3, 2, 5, 6, 7, 8, 9, 10) + assert_equal(1, list.count(1)) + assert_equal(2, list.count(2)) + assert_equal(0, list.count(4)) + + var list2 = LinkedList[Int]() + assert_equal(0, list2.count(1)) + + +def test_list_contains(): + var x = LinkedList[Int](1, 2, 3) + assert_false(0 in x) + assert_true(1 in x) + assert_false(4 in x) + + # TODO: implement LinkedList.__eq__ for Self[ComparableCollectionElement] + # var y = LinkedList[LinkedList[Int]]() + # y.append(LinkedList(1,2)) + # assert_equal(LinkedList(1,2) in y,True) + # assert_equal(LinkedList(0,1) in y,False) + + +def test_list_eq_ne(): + var l1 = LinkedList[Int](1, 2, 3) + var l2 = LinkedList[Int](1, 2, 3) + assert_true(l1 == l2) + assert_false(l1 != l2) + + var l3 = LinkedList[Int](1, 2, 3, 4) + assert_false(l1 == l3) + assert_true(l1 != l3) + + var l4 = LinkedList[Int]() + var l5 = LinkedList[Int]() + assert_true(l4 == l5) + assert_true(l1 != l4) + + var l6 = LinkedList[String]("a", "b", "c") + var l7 = LinkedList[String]("a", "b", "c") + var l8 = LinkedList[String]("a", "b") + assert_true(l6 == l7) + assert_false(l6 != l7) + assert_false(l6 == l8) + + +def test_indexing(): + var l = LinkedList[Int](1, 2, 3) + assert_equal(l[Int(1)], 2) + assert_equal(l[False], 1) + assert_equal(l[True], 2) + assert_equal(l[2], 3) + + +# ===-------------------------------------------------------------------===# +# LinkedList dtor tests +# ===-------------------------------------------------------------------===# + + +def inner_test_list_dtor(): + # explicitly reset global counter + g_dtor_count = 0 + + var l = LinkedList[DtorCounter]() + assert_equal(g_dtor_count, 0) + + l.append(DtorCounter()) + assert_equal(g_dtor_count, 0) + + l^.__del__() + assert_equal(g_dtor_count, 1) + + +def test_list_dtor(): + # call another function to force the destruction of the list + inner_test_list_dtor() + + # verify we still only ran the destructor once + assert_equal(g_dtor_count, 1) def main(): @@ -120,3 +554,22 @@ def main(): test_setitem() test_str() test_repr() + test_pop_on_empty_list() + test_optional_pop_on_empty_linked_list() + test_list() + test_list_clear() + test_list_to_bool_conversion() + test_list_pop() + test_list_variadic_constructor() + test_list_reverse() + test_list_extend_non_trivial() + test_list_explicit_copy() + test_no_extra_copies_with_sugared_set_by_field() + test_2d_dynamic_list() + test_list_boolable() + test_list_count() + test_list_contains() + test_indexing() + test_list_dtor() + test_list_insert() + test_list_eq_ne() diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index ef218a90b0..159c3bd73e 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -16,7 +16,13 @@ from collections import List from sys.info import sizeof from memory import UnsafePointer, Span -from test_utils import CopyCounter, MoveCounter +from test_utils import ( + CopyCounter, + MoveCounter, + DtorCounter, + g_dtor_count, + CopyCountedStruct, +) from testing import assert_equal, assert_false, assert_raises, assert_true @@ -548,21 +554,6 @@ def test_list_explicit_copy(): assert_equal(l2[i], l2_copy[i]) -@value -struct CopyCountedStruct(CollectionElement): - var counter: CopyCounter - var value: String - - fn __init__(out self, *, other: Self): - self.counter = other.counter.copy() - self.value = other.value.copy() - - @implicit - fn __init__(out self, value: String): - self.counter = CopyCounter() - self.value = value - - def test_no_extra_copies_with_sugared_set_by_field(): var list = List[List[CopyCountedStruct]](capacity=1) var child_list = List[CopyCountedStruct](capacity=2) @@ -872,31 +863,6 @@ def test_indexing(): # ===-------------------------------------------------------------------===# # List dtor tests # ===-------------------------------------------------------------------===# -var g_dtor_count: Int = 0 - - -struct DtorCounter(CollectionElement): - # NOTE: payload is required because List does not support zero sized structs. - var payload: Int - - fn __init__(out self): - self.payload = 0 - - fn __init__(out self, *, other: Self): - self.payload = other.payload - - fn __copyinit__(out self, existing: Self, /): - self.payload = existing.payload - - fn copy(self) -> Self: - return self - - fn __moveinit__(out self, owned existing: Self, /): - self.payload = existing.payload - existing.payload = 0 - - fn __del__(owned self): - g_dtor_count += 1 def inner_test_list_dtor(): diff --git a/stdlib/test/test_utils/__init__.mojo b/stdlib/test/test_utils/__init__.mojo index 0159453e8a..d85307ba91 100644 --- a/stdlib/test/test_utils/__init__.mojo +++ b/stdlib/test/test_utils/__init__.mojo @@ -20,4 +20,7 @@ from .types import ( MoveOnly, ObservableDel, ValueDestructorRecorder, + DtorCounter, + g_dtor_count, + CopyCountedStruct, ) diff --git a/stdlib/test/test_utils/types.mojo b/stdlib/test/test_utils/types.mojo index c31642e342..e729601231 100644 --- a/stdlib/test/test_utils/types.mojo +++ b/stdlib/test/test_utils/types.mojo @@ -88,7 +88,7 @@ struct ImplicitCopyOnly(Copyable): # ===----------------------------------------------------------------------=== # -struct CopyCounter(CollectionElement, ExplicitlyCopyable): +struct CopyCounter(CollectionElement, ExplicitlyCopyable, Writable): """Counts the number of copies performed on a value.""" var copy_count: Int @@ -108,6 +108,11 @@ struct CopyCounter(CollectionElement, ExplicitlyCopyable): fn copy(self) -> Self: return self + fn write_to[W: Writer](self, mut writer: W): + writer.write("CopyCounter(") + writer.write(String(self.copy_count)) + writer.write(")") + # ===----------------------------------------------------------------------=== # # MoveCounter @@ -148,12 +153,12 @@ struct MoveCounter[T: CollectionElementNew]( # TODO: This type should not be Copyable, but has to be to satisfy # CollectionElement at the moment. fn __copyinit__(out self, existing: Self): - # print("ERROR: _MoveCounter copy constructor called unexpectedly!") self.value = existing.value.copy() self.move_count = existing.move_count - fn copy(self) -> Self: - return self + fn copy(self, out existing: Self): + existing = Self(self.value.copy()) + existing.move_count = self.move_count # ===----------------------------------------------------------------------=== # @@ -191,3 +196,56 @@ struct ObservableDel(CollectionElement): fn __del__(owned self): self.target.init_pointee_move(True) + + +# ===----------------------------------------------------------------------=== # +# DtorCounter +# ===----------------------------------------------------------------------=== # + +var g_dtor_count: Int = 0 + + +struct DtorCounter(CollectionElement, Writable): + # NOTE: payload is required because LinkedList does not support zero sized structs. + var payload: Int + + fn __init__(out self): + self.payload = 0 + + fn __init__(out self, *, other: Self): + self.payload = other.payload + + fn __copyinit__(out self, existing: Self, /): + self.payload = existing.payload + + fn __moveinit__(out self, owned existing: Self, /): + self.payload = existing.payload + existing.payload = 0 + + fn __del__(owned self): + g_dtor_count += 1 + + fn write_to[W: Writer](self, mut writer: W): + writer.write("DtorCounter(") + writer.write(String(g_dtor_count)) + writer.write(")") + + +# ===----------------------------------------------------------------------=== # +# CopyCountedStruct +# ===----------------------------------------------------------------------=== # + + +@value +struct CopyCountedStruct(CollectionElement): + var counter: CopyCounter + var value: String + + fn __init__(out self, *, other: Self): + self.counter = other.counter.copy() + self.value = other.value.copy() + + @implicit + fn __init__(out self, value: String): + self.counter = CopyCounter() + self.value = value From 0636353eab4fe1b65ab174676417315787af1713 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Tue, 28 Jan 2025 17:16:26 -0800 Subject: [PATCH 311/509] [stdlib] Add `_printf` support for AMD This PR adds support for `_printf` on AMD GPUs. Right now we only support printing using the `Writable` trait, but this PR will also allow C style printing. MODULAR_ORIG_COMMIT_REV_ID: a837c6914dd378c216f18ae2645e8479a676c14a --- stdlib/src/builtin/io.mojo | 75 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 72 insertions(+), 3 deletions(-) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index 4271f75e0e..d52d56e411 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -27,11 +27,13 @@ from sys import ( stdout, ) from sys._libc import dup, fclose, fdopen, fflush -from sys.ffi import OpaquePointer, c_char +from sys.ffi import OpaquePointer, c_char, OpaquePointer +from sys._amdgpu import printf_begin, printf_append_args, printf_append_string_n +from sys.intrinsics import _type_is_eq from builtin.dtype import _get_dtype_printf_format from builtin.file_descriptor import FileDescriptor -from memory import UnsafePointer, memcpy +from memory import UnsafePointer, memcpy, bitcast from utils import ( StaticString, @@ -187,7 +189,74 @@ fn _printf[ fmt.unsafe_cstr_ptr(), Pointer.address_of(loaded_pack) ) elif is_amd_gpu(): - pass + # This is adapted from Triton's third party method for lowering + # AMD printf calls: + # https://github.com/triton-lang/triton/blob/1c28e08971a0d70c4331432994338ee05d31e633/third_party/amd/lib/TritonAMDGPUToLLVM/TargetInfo.cpp#L321 + fn _to_uint64[T: AnyType, //](value: T) -> UInt64: + @parameter + if _type_is_eq[T, UInt64](): + return rebind[UInt64](value) + elif _type_is_eq[T, UInt32](): + return UInt64(rebind[UInt32](value)) + elif _type_is_eq[T, UInt16](): + return UInt64(rebind[UInt16](value)) + elif _type_is_eq[T, UInt8](): + return UInt64(rebind[UInt8](value)) + elif _type_is_eq[T, Int64](): + return UInt64(rebind[Int64](value)) + elif _type_is_eq[T, Int32](): + return UInt64(rebind[Int32](value)) + elif _type_is_eq[T, Int16](): + return UInt64(rebind[Int16](value)) + elif _type_is_eq[T, Int8](): + return UInt64(rebind[Int8](value)) + elif _type_is_eq[T, Float16](): + return bitcast[DType.uint64](Float64(rebind[Float16](value))) + elif _type_is_eq[T, Float32](): + return bitcast[DType.uint64](Float64(rebind[Float32](value))) + elif _type_is_eq[T, Float64](): + return bitcast[DType.uint64](rebind[Float64](value)) + elif _type_is_eq[T, Int](): + return UInt64(rebind[Int](value)) + elif _type_is_eq[T, UInt](): + return UInt64(rebind[UInt](value)) + elif _type_is_eq[UnsafePointer[UInt8], UInt](): + return UInt64(Int(rebind[UnsafePointer[UInt8]](value))) + elif _type_is_eq[UnsafePointer[Int8], UInt](): + return UInt64(Int(rebind[UnsafePointer[Int8]](value))) + elif _type_is_eq[OpaquePointer, UInt](): + return UInt64(Int(rebind[OpaquePointer](value))) + return 0 + + alias args_len = len(VariadicList(types)) + + var message = printf_begin() + message = printf_append_string_n(message, fmt.as_bytes(), args_len == 0) + alias k_args_per_group = 7 + + @parameter + for group in range(0, args_len, k_args_per_group): + alias bound = min(group + k_args_per_group, args_len) + alias num_args = bound - group + + var arguments = InlineArray[UInt64, k_args_per_group](fill=0) + + @parameter + for i in range(num_args): + arguments[i] = _to_uint64(args[i]) + message = printf_append_args( + message, + num_args, + arguments[0], + arguments[1], + arguments[2], + arguments[3], + arguments[4], + arguments[5], + arguments[6], + Int32(Int(bound == args_len)), + ) + else: with _fdopen(file) as fd: # FIXME: external_call should handle this From a46bc0f1e9ed0e6e4aaa1ecb52cd56adadec12ec Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Tue, 28 Jan 2025 18:29:40 -0800 Subject: [PATCH 312/509] [docs] Document casting SIMDs using constructors Documented how to cast SIMD values using SIMD constructors. MODULAR_ORIG_COMMIT_REV_ID: 54bc6b66b70369a0950ca0c0c3446fb1c588c6c3 --- docs/manual/operators.mdx | 34 ++++++++++++++---------- docs/manual/types.mdx | 51 ++++++++++++++++++++++++++++++++++++ examples/operators/README.md | 2 +- 3 files changed, 72 insertions(+), 15 deletions(-) diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index 902e453224..a309e1f503 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -1,6 +1,7 @@ --- title: Operators, expressions, and dunder methods sidebar_label: Operators and expressions +github_url: https://github.com/modular/mojo/tree/main/examples/operators --- Mojo includes a variety of operators for manipulating values of different types. @@ -217,19 +218,22 @@ error: invalid call to '__mul__': could not deduce parameter 'type' of parent st ``` If you need to perform an arithmetic or bitwise operator on two `SIMD` values of -different types, you can explicitly -[`cast()`](/mojo/stdlib/builtin/simd/SIMD#cast) a `SIMD` so that they have the -same type. +different types, you can explicitly convert a value to the desired type either +by invoking its [`cast()`](/mojo/stdlib/builtin/simd/SIMD#cast) method or by +passing it as an argument to the constructor of the target type. ```mojo simd1 = SIMD[DType.float32, 4](2.2, 3.3, 4.4, 5.5) simd2 = SIMD[DType.int16, 4](-1, 2, -3, 4) -simd3 = simd1 * simd2.cast[DType.float32]() # Result is SIMD[DType.float32, 4] -print(simd3) +simd3 = simd1 * simd2.cast[DType.float32]() # Convert with cast() method +print("simd3:", simd3) +simd4 = simd2 + SIMD[DType.int16, 4](simd1) # Convert with SIMD constructor +print("simd4:", simd4) ``` ```output -[-2.2000000476837158, 6.5999999046325684, -13.200000762939453, 22.0] +simd3: [-2.2, 6.6, -13.200001, 22.0] +simd4: [1, 5, 1, 9] ``` One exception is that the exponentiation operator, `**`, is overloaded so that @@ -270,7 +274,7 @@ There are three operators related to division: ``` ```output - True float16 division: [1.400390625, -1.400390625, -1.400390625, 1.400390625] + True float16 division: [1.4003906, -1.4003906, -1.4003906, 1.4003906] True int32 division: [2, -2, -1, 1] ``` @@ -380,9 +384,10 @@ compared. `Int` or `UInt` do standard numerical comparison with a `Bool` result. - Two `SIMD` values can be compared only if they are the same `DType` and size. (If you need to compare two `SIMD` values of different types, you can - explicitly [`cast()`](/mojo/stdlib/builtin/simd/SIMD#cast) a `SIMD` so that - they have the same type.) Mojo performs elementwise comparison with a - `SIMD[DType.bool]` result. For example: + explicitly convert a value so that they have the same type either by invoking + its [`cast()`](/mojo/stdlib/builtin/simd/SIMD#cast) method or by passing it as + an argument to the constructor of the target type.) Mojo performs elementwise + comparison with a `SIMD[DType.bool]` result. For example: ```mojo simd1 = SIMD[DType.int16, 4](-1, 2, -3, 4) @@ -428,12 +433,13 @@ compared. - `Scalar` values are simply aliases for single-element `SIMD` vectors. Therefore, the same restrictions apply against comparing different types. In other words, you can't compare a `Float16` value to a `Float32` value unless - you cast the values to the same type. For example: + you convert the values to the same type. You can convert a `Scalar` value by + passing it as an argument to the constructor of the target type: ```mojo - var float1: Float16 = 12.345 # SIMD[DType.float16, 1] - var float2: Float32 = 0.5 # SIMD[DType.float32, 1] - result = float1.cast[DType.float32]() > float2 # SIMD[DType.bool, 1] + var float1: Float16 = 12.345 # SIMD[DType.float16, 1] + var float2: Float32 = 0.5 # SIMD[DType.float32, 1] + result = Float32(float1) > float2 # Result is SIMD[DType.bool, 1] print(result) ``` diff --git a/docs/manual/types.mdx b/docs/manual/types.mdx index 49669c32d9..641cfdb8e7 100644 --- a/docs/manual/types.mdx +++ b/docs/manual/types.mdx @@ -304,6 +304,57 @@ Min/max finite values for float32 There are several other data types in the standard library that also use the `DType` abstraction. +### Numeric type conversion + +[Constructors and implicit conversion](/mojo/manual/lifecycle/life/#constructors-and-implicit-conversion) +documents the circumstances in which Mojo automatically converts a value from +one type to another. Importantly, numeric [operators](/mojo/manual/operators) +**don't** automatically narrow or widen operands to a common type. + +You can explicitly convert a `SIMD` value to a different `SIMD` type either +by invoking its [`cast()`](/mojo/stdlib/builtin/simd/SIMD#cast) method or by +passing it as an argument to the constructor of the target type. For example: + +```mojo +simd1 = SIMD[DType.float32, 4](2.2, 3.3, 4.4, 5.5) +simd2 = SIMD[DType.int16, 4](-1, 2, -3, 4) +simd3 = simd1 * simd2.cast[DType.float32]() # Convert with cast() method +print("simd3:", simd3) +simd4 = simd2 + SIMD[DType.int16, 4](simd1) # Convert with SIMD constructor +print("simd4:", simd4) +``` + +```output +simd3: [-2.2, 6.6, -13.200001, 22.0] +simd4: [1, 5, 1, 9] +``` + +You can convert a `Scalar` value by passing it as an argument to the constructor +of the target type. For example: + +```mojo +var my_int: Int16 = 12 # SIMD[DType.int16, 1] +var my_float: Float32 = 0.75 # SIMD[DType.float32, 1] +result = Float32(my_int) * my_float # Result is SIMD[DType.float32, 1] +print("Result:", result) +``` + +```output +Result: 9.0 +``` + +You can convert a scalar value of any numeric type to `Int` by passing the value +to the [`Int()`](/mojo/stdlib/builtin/int/Int#__init__) constructor method. +Additionally, you can pass an instance of any struct that implements the +[`Intable`](/mojo/stdlib/builtin/int/Intable) trait or +[`IntableRaising`](/mojo/stdlib/builtin/int/IntableRaising) trait to the `Int()` +constructor to convert that instance to an `Int`. + +You can convert an `Int` or `IntLiteral` value to the `UInt` type by passing the +value to the [`UInt()`](/mojo/stdlib/builtin/uint/UInt#__init__) constructor. +You can't convert other numeric types to `UInt` directly, though you can first +convert them to `Int` and then to `UInt`. + ## Strings Mojo's `String` type represents a mutable string. (For Python programmers, note diff --git a/examples/operators/README.md b/examples/operators/README.md index 928b8b20ef..0c74aa65f4 100644 --- a/examples/operators/README.md +++ b/examples/operators/README.md @@ -23,5 +23,5 @@ magic run mojo main.mojo You can run the unit tests by running the following command: ```bash -magic run test +magic run tests ``` From b123cfef8724d6f9b2e2c6779a89200d121d5752 Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 29 Jan 2025 08:36:31 +0000 Subject: [PATCH 313/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025012905 --- examples/life/magic.lock | 373 +++++++++++++++--------------- examples/magic.lock | 373 +++++++++++++++--------------- examples/notebooks/magic.lock | 414 ++++++++++++++++------------------ examples/operators/magic.lock | 373 +++++++++++++++--------------- examples/testing/magic.lock | 373 +++++++++++++++--------------- magic.lock | 373 +++++++++++++++--------------- 6 files changed, 1106 insertions(+), 1173 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 35a2fdcb83..c7fb6ac875 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -49,7 +49,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/datasets-2.14.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/noarch/deprecated-1.2.15-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/deprecated-1.2.18-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dill-0.3.7-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dnspython-2.7.0-pyhff2d567_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/email-validator-2.2.0-pyhd8ed1ab_1.conda @@ -106,12 +106,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-he8f35ee_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-he8f35ee_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda - 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- mblack ==25.1.0.dev2025012705 release + - mblack ==25.1.0.dev2025012905 release license: LicenseRef-Modular-Proprietary - size: 205169482 - timestamp: 1737956267686 -- conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025012705-3.12release.conda - sha256: 0a18a085825c9fa2bf3e575310837f3dbdddc96b7aaea8b651e3eae61c02530f - md5: 42d30c788187591cf4b61d3ef4129e38 + size: 205298533 + timestamp: 1738128880810 +- conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025012905-3.12release.conda + sha256: 1cb5820b79790fbcdf69a17d3f2de668e335afda26d4896dc5d8df7495c3f2fc + md5: 4c8cd5b0bb94e4b0566c66614882417c depends: - - max-core ==25.1.0.dev2025012705 release + - max-core ==25.1.0.dev2025012905 release - python 3.12.* - fastapi - httpx @@ -4941,13 +4932,13 @@ packages: - uvicorn - python_abi 3.12.* *_cp312 license: LicenseRef-Modular-Proprietary - size: 120669382 - timestamp: 1737955017868 -- conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.1.0.dev2025012705-3.12release.conda - sha256: 8372666a6ad71409d0ed7336e1b955dd86d1ceb3f9b96c0a098cb59a802b688e - md5: 222a46c2286742510e8ee05ad24f2523 + size: 120728426 + timestamp: 1738127911515 +- conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.1.0.dev2025012905-3.12release.conda + sha256: 569a53d099bf8fe518e3148f6bcad3d499d8eaeeb96587c0e06e792f51161553 + md5: c5094829938eded41b20717d7f135a3e depends: - - max-core ==25.1.0.dev2025012705 release + - max-core ==25.1.0.dev2025012905 release - python 3.12.* - fastapi - httpx @@ -4968,13 +4959,13 @@ packages: - uvicorn - python_abi 3.12.* *_cp312 license: LicenseRef-Modular-Proprietary - size: 123185570 - timestamp: 1737955039973 -- conda: https://conda.modular.com/max-nightly/osx-arm64/max-python-25.1.0.dev2025012705-3.12release.conda - sha256: c4894d84b65bc2e997f5c7fa10027f276b481bbb2ded68f34bd74bf1e195076a - md5: fcb3723a6dbf99dd5d6ab6895e97de58 + size: 123278531 + timestamp: 1738127836748 +- conda: https://conda.modular.com/max-nightly/osx-arm64/max-python-25.1.0.dev2025012905-3.12release.conda + sha256: c4629b7ca8859e4b1f736de64721c9998ae1b2f2f183ab496cc0ab5faa053c00 + md5: 411d19e9c18c3d8c665f765041cd9a3e depends: - - max-core ==25.1.0.dev2025012705 release + - max-core ==25.1.0.dev2025012905 release - python 3.12.* - fastapi - httpx @@ -4995,12 +4986,12 @@ packages: - uvicorn - python_abi 3.12.* *_cp312 license: LicenseRef-Modular-Proprietary - size: 106061856 - timestamp: 1737956267689 -- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025012705-release.conda + size: 106174079 + timestamp: 1738128880813 +- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025012905-release.conda noarch: python - sha256: 90cc368b1a6ceac15f44c67517c1af7fe1dc5dce40b92f91f1830be2f6a66fb9 - md5: 15ff6d8c842fbce739084f0bbd23bd27 + sha256: 14bb3045350a288f80df5b386555586d8e035c3f7454ba8214f73cfeace26887 + md5: ee06a37bf7cb1d8ef7b646b6e7ad2bfd depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -5010,8 +5001,8 @@ packages: - platformdirs >=2 - python license: MIT - size: 130811 - timestamp: 1737955039968 + size: 130809 + timestamp: 1738127836744 - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda sha256: 78c1bbe1723449c52b7a9df1af2ee5f005209f67e40b6e1d3c7619127c43b1c7 md5: 592132998493b3ff25fd7479396e8351 @@ -5021,18 +5012,18 @@ packages: license_family: MIT size: 14465 timestamp: 1733255681319 -- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025012705-release.conda +- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025012905-release.conda noarch: python - sha256: fb3ed00e9f7df548142bb0afa9edf654413f61d1b39c6b1cbfc9cf2fbb6e04f9 - md5: 961e0d8087b0ab6e38d0e1a518438827 + sha256: 00c9bbee63d4d9d18235be72c23e10a8c63eeabef853575f4fdfa3cc4df9ac83 + md5: 646e77b26ddf8c7aba7d2a3ac21631a6 depends: - - max-core ==25.1.0.dev2025012705 release + - max-core ==25.1.0.dev2025012905 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22933 - timestamp: 1737955039968 + size: 22932 + timestamp: 1738127836744 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From 4b185ffb59347dacdfa0d396b875173eecb5f7b8 Mon Sep 17 00:00:00 2001 From: ematejska Date: Wed, 29 Jan 2025 11:45:29 -0800 Subject: [PATCH 314/509] Update check_pr_target.yml The main branch is the new contribution branch. Signed-off-by: ematejska --- .github/workflows/check_pr_target.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/check_pr_target.yml b/.github/workflows/check_pr_target.yml index c86c4ed4b3..4e333baf2a 100644 --- a/.github/workflows/check_pr_target.yml +++ b/.github/workflows/check_pr_target.yml @@ -20,9 +20,9 @@ jobs: shell: bash steps: - - name: Fail if not targeting nightly branch - if: ${{ github.base_ref != 'nightly' }} + - name: Fail if not targeting main branch + if: ${{ github.base_ref != 'main' }} run: | - echo "PRs must be targeted to merge to the nightly branch!" + echo "PRs must be targeted to merge to the main branch!" echo "PR is currently targeting: \"${{ github.base_ref }}\"" exit 1 From 76a0af35202d744d023b8e0686675ccd5d1d67ed Mon Sep 17 00:00:00 2001 From: ematejska Date: Wed, 29 Jan 2025 11:54:43 -0800 Subject: [PATCH 315/509] [docs] Update documentation for main branch getting nightly updates and stable added (#3978) Update documentation for main branch getting nightly updates --- CONTRIBUTING.md | 22 +++++++++++----------- README.md | 19 +++++++++---------- docs/changelog-released.md | 2 +- stdlib/docs/development.md | 4 ++-- stdlib/docs/internal/README.md | 2 +- 5 files changed, 24 insertions(+), 25 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 72f230da1b..4ad284fa1c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -200,7 +200,7 @@ Library, Mojo examples, or Mojo documentation. This page gives an overview of the process. For a more detailed walkthrough, see [How to contribute to the Mojo standard library: a step-by-step guide](https://www.modular.com/blog/how-to-contribute-to-mojo-standard-library-a-step-by-step-guide). -**Note:** Pull requests should be submitted against the `nightly` branch, +**Note:** Pull requests should be submitted against the `main` branch, which represents the most recent nightly build. ### Pull request process @@ -243,10 +243,10 @@ git fetch upstream #### Branching off nightly -Make sure to branch off `nightly` to work on your PR: +Make sure to branch off `main` to work on your PR: ```bash -git checkout upstream/nightly +git checkout upstream/main git checkout -b my-fix-pr ``` @@ -255,20 +255,20 @@ before raising a PR: ```bash git fetch upstream -git rebase upstream/nightly +git rebase upstream/main ``` #### Getting the nightly Mojo compiler -Now that you're on the nightly branch, you need to install the latest nightly +Now that you're on the main branch, you need to install the latest nightly build. -If you're using [`magic`](https://docs.modular.com/magic), create a new -project environment with the `max-nightly` channel like this: +If you're using [`magic`](https://docs.modular.com/magic) (version 0.6.4 or +higher), create a new project environment like this and it will install the +latest nightly version of `max` by default: ```bash -magic init mojo-nightly --format mojoproject \ - -c conda-forge -c https://conda.modular.com/max-nightly +magic init mojo-nightly --format mojoproject ``` If you're [using conda](https://docs.modular.com/magic/conda), add the @@ -299,7 +299,7 @@ when using the stable release! If your change is one of the improvements described above or it has been discussed and agreed upon by the project maintainers, please create a pull -request into the `nightly` branch. +request into the `main` branch. First push your changes: @@ -314,7 +314,7 @@ remote: Create a pull request for 'my-fix-pr' on GitHub by visiting: remote: https://github.com/jackos/mojo/pull/new/my-fix-pr ``` -Make sure you point it to the `nightly` branch: +Make sure you point it to the `main` branch: ![Base Branch](stdlib/docs/images/base-branch.png) diff --git a/README.md b/README.md index 5774ca462a..3dac78f624 100644 --- a/README.md +++ b/README.md @@ -25,11 +25,11 @@ This repo includes source code for: This repo has two primary branches: -- The [`main`](https://github.com/modularml/mojo/tree/main) branch, which is in -sync with the last stable released version of Mojo. Use the examples here if you’re -using a [release build of Mojo](#latest-released). +- The [`stable`](https://github.com/modularml/mojo/tree/stable) branch, which +is in sync with the last stable released version of Mojo. Use the examples +here if you’re using a [release build of Mojo](#latest-released). -- The [`nightly`](https://github.com/modularml/mojo/tree/nightly) branch, which +- The [`main`](https://github.com/modularml/mojo/tree/main) branch, which is in sync with the Mojo nightly build and subject to breakage. Use this branch for [contributions](./CONTRIBUTING.md), or if you're using the latest [nightly build of Mojo](#latest-nightly). @@ -74,16 +74,15 @@ platforms = ["osx-arm64", "linux-aarch64", "linux-64"] max = "*" ``` -And when you clone this repo, switch to the `nightly` branch because the `main` -branch might not be compatible with nightly builds: +When you clone this repo, you'll be on the `main` branch by default, +which includes code matching the latest nightly build: ```bash git clone https://github.com/modularml/mojo.git ``` -```bash -git checkout nightly -``` +If you want to instead see the source from the most recent stable +release, then you can switch to the `stable` branch. ## Contributing @@ -92,7 +91,7 @@ issue here](https://github.com/modularml/mojo/issues). See [here](./CONTRIBUTING.md) for guidelines on filing good bugs. We welcome contributions to this repo on the -[`nightly`](https://github.com/modularml/mojo/tree/nightly) +[`main`](https://github.com/modularml/mojo/tree/main) branch. If you’d like to contribute to Mojo, please first read our [Contributor Guide](https://github.com/modularml/mojo/blob/main/CONTRIBUTING.md). diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 18a2551f4f..95ee3fbab8 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -3189,7 +3189,7 @@ This release doesn't include any changes to Mojo. ### 🔥 Legendary - The Mojo standard library is now open source! Check out the - [README](https://github.com/modular/mojo/blob/nightly/stdlib/README.md) + [README](https://github.com/modular/mojo/blob/main/stdlib/README.md) for everything you need to get started. - Structs and other nominal types are now allowed to implicitly conform to diff --git a/stdlib/docs/development.md b/stdlib/docs/development.md index ef21e6fb81..000676598e 100644 --- a/stdlib/docs/development.md +++ b/stdlib/docs/development.md @@ -9,7 +9,7 @@ If this is your first time contributing, first read everything in you need to do the following: 1. [Fork and clone the repo](../../CONTRIBUTING.md#fork-and-clone-the-repo) -2. [Branch off nightly](../../CONTRIBUTING.md#branching-off-nightly) +2. [Branch off main](../../CONTRIBUTING.md#branching-off-nightly) 3. [Install the nightly Mojo compiler](../../CONTRIBUTING.md#getting-the-nightly-mojo-compiler) And if you're using VS Code: @@ -104,7 +104,7 @@ You can do the same for a directory with ./stdlib/scripts/run-tests.sh ./stdlib/test/utils ``` -All the tests should pass on the `nightly` branch with the nightly Mojo +All the tests should pass on the `main` branch with the nightly Mojo compiler. If you've pulled the latest changes and they're still failing please [open a GitHub issue](https://github.com/modularml/mojo/issues/new?assignees=&labels=bug%2Cmojo&projects=&template=mojo_bug_report.yaml&title=%5BBUG%5D). diff --git a/stdlib/docs/internal/README.md b/stdlib/docs/internal/README.md index b80fd03dca..9699b52b72 100644 --- a/stdlib/docs/internal/README.md +++ b/stdlib/docs/internal/README.md @@ -11,6 +11,6 @@ WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. This directory contains internal documentation for the implementation details of the Mojo compiler, runtime, and stdlib. You must always reference the -current version of this documentation on the branch nightly before using the +current version of this documentation on the branch main before using the operations or behavior documented within. Any new files should contain the above warning. From faf9b6a9fd244398894fa905222b678c4a7aa4c2 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Tue, 28 Jan 2025 21:40:37 -0800 Subject: [PATCH 316/509] [docs] Update documentation examples to use StringLiteral.format() When the String.format() method was first introduced, a corresponding method wasn't added to StringLiteral. StringLiteral.format() was added in 24.6, so this updates examples in the documentation to use it. MODULAR_ORIG_COMMIT_REV_ID: 8326133984041f8017f9262c6cc7c367c9b7804b --- CONTRIBUTING.md | 22 +++++++++++----------- README.md | 19 ++++++++++--------- docs/changelog-released.md | 2 +- docs/manual/errors.mdx | 2 +- docs/manual/operators.mdx | 6 +++--- docs/manual/traits.mdx | 4 ++-- examples/operators/main.mojo | 8 ++++---- stdlib/docs/development.md | 4 ++-- stdlib/docs/internal/README.md | 2 +- 9 files changed, 35 insertions(+), 34 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4ad284fa1c..72f230da1b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -200,7 +200,7 @@ Library, Mojo examples, or Mojo documentation. This page gives an overview of the process. For a more detailed walkthrough, see [How to contribute to the Mojo standard library: a step-by-step guide](https://www.modular.com/blog/how-to-contribute-to-mojo-standard-library-a-step-by-step-guide). -**Note:** Pull requests should be submitted against the `main` branch, +**Note:** Pull requests should be submitted against the `nightly` branch, which represents the most recent nightly build. ### Pull request process @@ -243,10 +243,10 @@ git fetch upstream #### Branching off nightly -Make sure to branch off `main` to work on your PR: +Make sure to branch off `nightly` to work on your PR: ```bash -git checkout upstream/main +git checkout upstream/nightly git checkout -b my-fix-pr ``` @@ -255,20 +255,20 @@ before raising a PR: ```bash git fetch upstream -git rebase upstream/main +git rebase upstream/nightly ``` #### Getting the nightly Mojo compiler -Now that you're on the main branch, you need to install the latest nightly +Now that you're on the nightly branch, you need to install the latest nightly build. -If you're using [`magic`](https://docs.modular.com/magic) (version 0.6.4 or -higher), create a new project environment like this and it will install the -latest nightly version of `max` by default: +If you're using [`magic`](https://docs.modular.com/magic), create a new +project environment with the `max-nightly` channel like this: ```bash -magic init mojo-nightly --format mojoproject +magic init mojo-nightly --format mojoproject \ + -c conda-forge -c https://conda.modular.com/max-nightly ``` If you're [using conda](https://docs.modular.com/magic/conda), add the @@ -299,7 +299,7 @@ when using the stable release! If your change is one of the improvements described above or it has been discussed and agreed upon by the project maintainers, please create a pull -request into the `main` branch. +request into the `nightly` branch. First push your changes: @@ -314,7 +314,7 @@ remote: Create a pull request for 'my-fix-pr' on GitHub by visiting: remote: https://github.com/jackos/mojo/pull/new/my-fix-pr ``` -Make sure you point it to the `main` branch: +Make sure you point it to the `nightly` branch: ![Base Branch](stdlib/docs/images/base-branch.png) diff --git a/README.md b/README.md index 3dac78f624..5774ca462a 100644 --- a/README.md +++ b/README.md @@ -25,11 +25,11 @@ This repo includes source code for: This repo has two primary branches: -- The [`stable`](https://github.com/modularml/mojo/tree/stable) branch, which -is in sync with the last stable released version of Mojo. Use the examples -here if you’re using a [release build of Mojo](#latest-released). +- The [`main`](https://github.com/modularml/mojo/tree/main) branch, which is in +sync with the last stable released version of Mojo. Use the examples here if you’re +using a [release build of Mojo](#latest-released). -- The [`main`](https://github.com/modularml/mojo/tree/main) branch, which +- The [`nightly`](https://github.com/modularml/mojo/tree/nightly) branch, which is in sync with the Mojo nightly build and subject to breakage. Use this branch for [contributions](./CONTRIBUTING.md), or if you're using the latest [nightly build of Mojo](#latest-nightly). @@ -74,15 +74,16 @@ platforms = ["osx-arm64", "linux-aarch64", "linux-64"] max = "*" ``` -When you clone this repo, you'll be on the `main` branch by default, -which includes code matching the latest nightly build: +And when you clone this repo, switch to the `nightly` branch because the `main` +branch might not be compatible with nightly builds: ```bash git clone https://github.com/modularml/mojo.git ``` -If you want to instead see the source from the most recent stable -release, then you can switch to the `stable` branch. +```bash +git checkout nightly +``` ## Contributing @@ -91,7 +92,7 @@ issue here](https://github.com/modularml/mojo/issues). See [here](./CONTRIBUTING.md) for guidelines on filing good bugs. We welcome contributions to this repo on the -[`main`](https://github.com/modularml/mojo/tree/main) +[`nightly`](https://github.com/modularml/mojo/tree/nightly) branch. If you’d like to contribute to Mojo, please first read our [Contributor Guide](https://github.com/modularml/mojo/blob/main/CONTRIBUTING.md). diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 95ee3fbab8..18a2551f4f 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -3189,7 +3189,7 @@ This release doesn't include any changes to Mojo. ### 🔥 Legendary - The Mojo standard library is now open source! Check out the - [README](https://github.com/modular/mojo/blob/main/stdlib/README.md) + [README](https://github.com/modular/mojo/blob/nightly/stdlib/README.md) for everything you need to get started. - Structs and other nominal types are now allowed to implicitly conform to diff --git a/docs/manual/errors.mdx b/docs/manual/errors.mdx index ff7512798a..2fa9d6b66f 100644 --- a/docs/manual/errors.mdx +++ b/docs/manual/errors.mdx @@ -152,7 +152,7 @@ def main(): print("try =>", value[]) if value[] == 1: continue - result = String("{} incremented is {}").format(value[], incr(value[])) + result = "{} incremented is {}".format(value[], incr(value[])) except e: print("except =>", e) else: diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index a309e1f503..5ee235c9b0 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -1069,7 +1069,7 @@ real and imaginary fields. ```mojo c1 = Complex(-1.2, 6.5) -print(String("c1: Real: {}; Imaginary: {}").format(c1.re, c1.im)) +print("c1: Real: {}; Imaginary: {}".format(c1.re, c1.im)) ``` ```output @@ -1094,7 +1094,7 @@ Now we can create a `Complex` instance and provide just a real component. ```mojo c2 = Complex(3.14159) -print(String("c2: Real: {}; Imaginary: {}").format(c2.re, c2.im)) +print("c2: Real: {}; Imaginary: {}".format(c2.re, c2.im)) ``` ```output @@ -1190,7 +1190,7 @@ Now let's try getting and setting the real and imaginary components of a ```mojo c2 = Complex(3.14159) -print(String("c2[0]: {}; c2[1]: {}").format(c2[0], c2[1])) +print("c2[0]: {}; c2[1]: {}".format(c2[0], c2[1])) c2[0] = 2.71828 c2[1] = 42 print("c2[0] = 2.71828; c2[1] = 42; c2:", c2) diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index 948185e09b..a9be57cf71 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -447,13 +447,13 @@ struct Dog(Stringable, Representable, Writable): # Alternative full representation when calling `repr` fn __repr__(self) -> String: - return String("Dog(name=", repr(self.name)", age=", repr(self.age), ")") + return String("Dog(name=", repr(self.name), ", age=", repr(self.age), ")") var dog = Dog("Rex", 5) print(repr(dog)) print(dog) -var dog_info = String("String: {!s}\nRepresentation: {!r}").format(dog, dog) +var dog_info = "String: {!s}\nRepresentation: {!r}".format(dog, dog) print(dog_info) ``` diff --git a/examples/operators/main.mojo b/examples/operators/main.mojo index c26e654568..f4b07c4ac7 100644 --- a/examples/operators/main.mojo +++ b/examples/operators/main.mojo @@ -17,10 +17,10 @@ from my_complex import Complex def main(): # Examples of creating Complex instances c1 = Complex(-1.2, 6.5) - print(String("c1: Real: {}; Imaginary: {}").format(c1.re, c1.im)) + print("c1: Real: {}; Imaginary: {}".format(c1.re, c1.im)) c2 = Complex(3.14159) - print(String("c2: Real: {}; Imaginary: {}").format(c2.re, c2.im)) + print("c2: Real: {}; Imaginary: {}".format(c2.re, c2.im)) print() @@ -29,12 +29,12 @@ def main(): print("c3 =", c3) var msg = String("The value is: ", c3) print(msg) - print(String("{!r}").format(c3)) + print("{!r}".format(c3)) print() # Examples of using Complex indexing - print(String("c2[0]: {}; c2[1]: {}").format(c2[0], c2[1])) + print("c2[0]: {}; c2[1]: {}".format(c2[0], c2[1])) c2[0] = 2.71828 c2[1] = 42 print("c2[0] = 2.71828; c2[1] = 42; c2:", c2) diff --git a/stdlib/docs/development.md b/stdlib/docs/development.md index 000676598e..ef21e6fb81 100644 --- a/stdlib/docs/development.md +++ b/stdlib/docs/development.md @@ -9,7 +9,7 @@ If this is your first time contributing, first read everything in you need to do the following: 1. [Fork and clone the repo](../../CONTRIBUTING.md#fork-and-clone-the-repo) -2. [Branch off main](../../CONTRIBUTING.md#branching-off-nightly) +2. [Branch off nightly](../../CONTRIBUTING.md#branching-off-nightly) 3. [Install the nightly Mojo compiler](../../CONTRIBUTING.md#getting-the-nightly-mojo-compiler) And if you're using VS Code: @@ -104,7 +104,7 @@ You can do the same for a directory with ./stdlib/scripts/run-tests.sh ./stdlib/test/utils ``` -All the tests should pass on the `main` branch with the nightly Mojo +All the tests should pass on the `nightly` branch with the nightly Mojo compiler. If you've pulled the latest changes and they're still failing please [open a GitHub issue](https://github.com/modularml/mojo/issues/new?assignees=&labels=bug%2Cmojo&projects=&template=mojo_bug_report.yaml&title=%5BBUG%5D). diff --git a/stdlib/docs/internal/README.md b/stdlib/docs/internal/README.md index 9699b52b72..b80fd03dca 100644 --- a/stdlib/docs/internal/README.md +++ b/stdlib/docs/internal/README.md @@ -11,6 +11,6 @@ WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. This directory contains internal documentation for the implementation details of the Mojo compiler, runtime, and stdlib. You must always reference the -current version of this documentation on the branch main before using the +current version of this documentation on the branch nightly before using the operations or behavior documented within. Any new files should contain the above warning. From 6700eea251fc149ce19ee956078f6c4a3a8b851c Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Wed, 29 Jan 2025 11:55:16 -0500 Subject: [PATCH 317/509] [docs] Fix typo in `changelog` With the recent functionality for overloading by keyword-only arguments, there was a typo with the resulting value. Fix this typo. MODULAR_ORIG_COMMIT_REV_ID: f02ce2c89a9be5ffa5d61ffe421f0b5ab35226ed --- docs/changelog.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/changelog.md b/docs/changelog.md index 91e798ebc0..d871838912 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -61,7 +61,7 @@ what we publish. fn main(): OverloadedKwArgs(1) # val=1 OverloadedKwArgs(double=1) # val=2 - OverloadedKwArgs(triple=2) # val=3 + OverloadedKwArgs(triple=2) # val=6 ``` This also works with indexing operations: From e782cb0b8f12051a9f31e0a5280633f2c7db64c1 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Wed, 29 Jan 2025 13:17:28 -0800 Subject: [PATCH 318/509] Remove the Examples tab from the Mojo documentation site We're retiring the Mojo notebook examples in the GitHub repo, which is the source for the current Examples tab on the Mojo documentation site. We'll have a new interface for exposing tutorials and examples on the site in the future. MODULAR_ORIG_COMMIT_REV_ID: 385ba3732165d8cf9b0de6208d8e6cbaa63b314b --- docs/notebooks/README.md | 3 --- docs/notebooks/index.mdx | 25 ------------------------- 2 files changed, 28 deletions(-) delete mode 100644 docs/notebooks/README.md delete mode 100644 docs/notebooks/index.mdx diff --git a/docs/notebooks/README.md b/docs/notebooks/README.md deleted file mode 100644 index 9337020707..0000000000 --- a/docs/notebooks/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Mojo notebooks - -These files are actually in [examples/notebooks/](../../examples/notebooks). diff --git a/docs/notebooks/index.mdx b/docs/notebooks/index.mdx deleted file mode 100644 index 0b88575238..0000000000 --- a/docs/notebooks/index.mdx +++ /dev/null @@ -1,25 +0,0 @@ ---- -title: Mojo🔥 notebooks -sidebar_label: Overview -hide_table_of_contents: true -description: All the Jupyter notebooks we've created for the Mojo Playground. -anchor-sections: false -listing: - - id: docs - contents: - - BoolMLIR.ipynb - - Mandelbrot.ipynb - - Matmul.ipynb - - RayTracing.ipynb - type: grid - grid-columns: 2 - sort: 'false' ---- - -The following pages are rendered from the Jupyter notebooks that are [available -on GitHub](https://github.com/modular/mojo/tree/main/examples/notebooks). - -


- -:::🔥#docs -::: From 5a1ba901274472a2d178335dc358f482e0c807ca Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 30 Jan 2025 17:31:28 +0000 Subject: [PATCH 319/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025013005 --- examples/life/magic.lock | 187 ++++++++++++++++--------------- examples/magic.lock | 187 ++++++++++++++++--------------- examples/notebooks/magic.lock | 204 ++++++++++++++++++---------------- examples/operators/magic.lock | 187 ++++++++++++++++--------------- examples/testing/magic.lock | 187 ++++++++++++++++--------------- magic.lock | 187 ++++++++++++++++--------------- 6 files changed, 597 insertions(+), 542 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index c7fb6ac875..2836acd5f1 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -172,17 +172,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - 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__osx >=11.0 arch: arm64 platform: osx license: X11 AND BSD-3-Clause - size: 796754 - timestamp: 1736683572099 + size: 797030 + timestamp: 1738196177597 - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.11.3-he02047a_1.conda sha256: ce4bcced4f8eea71b7cac8bc3daac097abf7a5792f278cd811dedada199500c1 md5: e46f7ac4917215b49df2ea09a694a3fa From 0e03d6eee7b935768059beb038d3bedb1fb79e6e Mon Sep 17 00:00:00 2001 From: ematejska Date: Thu, 30 Jan 2025 11:12:13 -0800 Subject: [PATCH 320/509] [Issues] Updating the issue templates (#3981) Updating the issue templates to use magic. --- .github/ISSUE_TEMPLATE/dev_console_issue.yaml | 31 -------------- .github/ISSUE_TEMPLATE/modular_cli_issue.yaml | 41 ------------------- .github/ISSUE_TEMPLATE/mojo_bug_report.yaml | 5 +-- 3 files changed, 2 insertions(+), 75 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/dev_console_issue.yaml delete mode 100644 .github/ISSUE_TEMPLATE/modular_cli_issue.yaml diff --git a/.github/ISSUE_TEMPLATE/dev_console_issue.yaml b/.github/ISSUE_TEMPLATE/dev_console_issue.yaml deleted file mode 100644 index 42d8e0f641..0000000000 --- a/.github/ISSUE_TEMPLATE/dev_console_issue.yaml +++ /dev/null @@ -1,31 +0,0 @@ -name: Developer Console issue -description: Create an issue report to help us improve the Developer Console. -title: "[Dev Console]" -labels: - - dev-console -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out a bug report! - - Please provide a descriptive title above and fill in the following fields. - - - type: textarea - id: Description - attributes: - label: Issue description - description: Describe the issue you encountered and what you expected to happen. - validations: - required: true - - - type: textarea - id: Steps - attributes: - label: Steps to reproduce - description: Provide the specific steps to reproduce the issue. - value: | - - Add screenshots if possible, to help explain the problem. - - Include anything that might help us debug the issue. - validations: - required: true diff --git a/.github/ISSUE_TEMPLATE/modular_cli_issue.yaml b/.github/ISSUE_TEMPLATE/modular_cli_issue.yaml deleted file mode 100644 index acaa5ece88..0000000000 --- a/.github/ISSUE_TEMPLATE/modular_cli_issue.yaml +++ /dev/null @@ -1,41 +0,0 @@ -name: Modular CLI issue -description: Create an issue for the Modular CLI tool. -title: "[Modular CLI]" -labels: - - modular-cli -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out an issue report! - - Please provide a descriptive title above and fill in the following fields. - - - type: textarea - id: Description - attributes: - label: Issue description - description: Describe the issue you encountered and what you expected to happen. - validations: - required: true - - type: textarea - id: Steps - attributes: - label: Steps to reproduce - description: Provide the specific steps to reproduce the issue. - value: | - - Include relevant code snippet that did not work as expected. - - If applicable, add screenshots to help explain the problem. - - Include anything else that might help us debug the issue. - validations: - required: true - - - type: textarea - id: Context - attributes: - label: Version Info - description: Which version of the Modular CLI are you using ? - value: | - - Provide Modular CLI version by pasting the output of `modular -v` - - What OS did you install modular CLI on ? - render: shell diff --git a/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml b/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml index 411821ba3f..82d5a3763f 100644 --- a/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml +++ b/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml @@ -44,6 +44,5 @@ body: label: System information description: What version of Mojo are you using? value: | - - What OS did you install Mojo on? - - Provide version information for Mojo by pasting the output of `mojo -v`. - - Provide the Modular CLI version by pasting the output of `modular -v`. + - Provide the system information by running `magic info`. + - Provide version information for MAX (includes Mojo) by pasting the output of `magic list max`. From 1219cc42b0112e0b698728f44601dd61005dd22a Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Thu, 30 Jan 2025 10:54:48 -0800 Subject: [PATCH 321/509] [stdlib] Add `UnsafePointer` constructors for `StringSlice` As we move more methods away from `StringRef` towards `StringSlice`, we want a way to handle the common case of construction from a null terminated pointer. MODULAR_ORIG_COMMIT_REV_ID: b2d43d9b170f472698614cd164b175fa2d3f224f --- stdlib/src/collections/string/string.mojo | 5 +- .../src/collections/string/string_slice.mojo | 58 +++++++++++++++++++ .../collections/string/test_string_slice.mojo | 23 ++++++++ 3 files changed, 85 insertions(+), 1 deletion(-) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index bfe956985e..5d94f01e08 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -828,7 +828,10 @@ struct String( buff: The buffer. This should have an existing terminator. """ - return String(ptr=buff, length=len(StringRef(ptr=buff)) + 1) + return String( + ptr=buff, + length=len(StringSlice[buff.origin](unsafe_from_utf8_ptr=buff)) + 1, + ) @staticmethod fn _from_bytes(owned buff: Self._buffer_type) -> String: diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index ed6939b8f5..f5c684462c 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -29,6 +29,7 @@ from memory import UnsafePointer, memcmp, memcpy, Span from memory.memory import _memcmp_impl_unconstrained from sys import bitwidthof, simdwidthof from sys.intrinsics import unlikely, likely +from sys.ffi import c_char from utils.stringref import StringRef, _memmem from os import PathLike @@ -423,6 +424,45 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( self = Self(unsafe_from_utf8=byte_slice) + fn __init__(out self, *, unsafe_from_utf8_ptr: UnsafePointer[Byte]): + """Construct a new StringSlice from a `UnsafePointer[Byte]` pointing to null-terminated UTF-8 + encoded bytes. + + Args: + unsafe_from_utf8_ptr: An `UnsafePointer[Byte]` of null-terminated bytes encoded in UTF-8. + + Safety: + - `unsafe_from_utf8_ptr` MUST point to data that is valid for + `origin`. + - `unsafe_from_utf8_ptr` MUST be valid UTF-8 encoded data. + - `unsafe_from_utf8_ptr` MUST be null terminated. + """ + + var count = _unsafe_strlen(unsafe_from_utf8_ptr) + + var byte_slice = Span[Byte, origin]( + ptr=unsafe_from_utf8_ptr, + length=count, + ) + + self = Self(unsafe_from_utf8=byte_slice) + + fn __init__(out self, *, unsafe_from_utf8_cstr_ptr: UnsafePointer[c_char]): + """Construct a new StringSlice from a `UnsafePointer[c_char]` pointing to null-terminated UTF-8 + encoded bytes. + + Args: + unsafe_from_utf8_cstr_ptr: An `UnsafePointer[c_char]` of null-terminated bytes encoded in UTF-8. + + Safety: + - `unsafe_from_utf8_ptr` MUST point to data that is valid for + `origin`. + - `unsafe_from_utf8_ptr` MUST be valid UTF-8 encoded data. + - `unsafe_from_utf8_ptr` MUST be null terminated. + """ + var ptr = unsafe_from_utf8_cstr_ptr.bitcast[Byte]() + self = Self(unsafe_from_utf8_ptr=ptr) + @always_inline fn __init__(out self, *, ptr: UnsafePointer[Byte], length: UInt): """Construct a `StringSlice` from a pointer to a sequence of UTF-8 @@ -1584,3 +1624,21 @@ fn _is_newline_char[ var b2 = p[eol_start + 2] return b0 == 0xE2 and b1 == 0x80 and (b2 == 0xA8 or b2 == 0xA9) return False + + +@always_inline +fn _unsafe_strlen(owned ptr: UnsafePointer[Byte]) -> Int: + """ + Get the length of a null-terminated string from a pointer. + Note: the length does NOT include the null terminator. + + Args: + ptr: The null-terminated pointer to the string. + + Returns: + The length of the null terminated string without the null terminator. + """ + var len = 0 + while ptr.load(len): + len += 1 + return len diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 0e7edc9980..1ac338e12d 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -857,6 +857,28 @@ def test_chars_iter(): assert_equal(s3_iter.__has_next__(), False) +def test_string_slice_from_pointer(): + var a = StringSlice("AAA") + var b = StringSlice[StaticConstantOrigin]( + unsafe_from_utf8_ptr=a.unsafe_ptr() + ) + assert_equal(3, len(a)) + assert_equal(3, len(b)) + var c = String("ABCD") + var d = StringSlice[__origin_of(c)]( + unsafe_from_utf8_cstr_ptr=c.unsafe_cstr_ptr() + ) + var e = StringSlice[__origin_of(c)](unsafe_from_utf8_ptr=c.unsafe_ptr()) + assert_equal(4, len(c)) + assert_equal(4, len(d)) + assert_equal(4, len(e)) + assert_true("A", d[0]) + assert_true("B", d[1]) + assert_true("C", d[2]) + assert_true("D", d[3]) + assert_true("D", d[-1]) + + def main(): test_string_slice_layout() test_string_literal_byte_span() @@ -886,3 +908,4 @@ def main(): test_startswith() test_endswith() test_chars_iter() + test_string_slice_from_pointer() From 32f48ac13d21287dbba2a192d5ed7ac9886a1a35 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Thu, 30 Jan 2025 14:44:13 -0800 Subject: [PATCH 322/509] [stdlib] Replace a ton of StringRefs with StringSlice `StringSlice` has the same ABI as `StringRef`, but `StringRef` doesn't have an `Origin` associated with it. We should get rid of `StringRef` and replace it with `StringSlice`, and this PR does a first pass at that. MODULAR_ORIG_COMMIT_REV_ID: 04cd7c710e9c1b16e03562fb5dbd3fcf93336b8d --- stdlib/src/builtin/error.mojo | 19 +++- stdlib/src/builtin/io.mojo | 5 +- stdlib/src/builtin/object.mojo | 25 +++--- .../src/collections/string/inline_string.mojo | 2 +- stdlib/src/collections/string/string.mojo | 13 +-- .../src/collections/string/string_slice.mojo | 12 +++ stdlib/src/os/env.mojo | 6 +- stdlib/src/os/os.mojo | 12 +-- stdlib/src/pathlib/path.mojo | 4 +- stdlib/src/pwd/_linux.mojo | 32 +++++-- stdlib/src/pwd/_macos.mojo | 33 +++++-- stdlib/src/python/_cpython.mojo | 86 ++++++++----------- stdlib/src/python/python.mojo | 14 +-- stdlib/src/python/python_object.mojo | 19 +--- stdlib/src/sys/arg.mojo | 2 - stdlib/src/sys/ffi.mojo | 22 +++-- stdlib/test/builtin/test_print.mojo | 6 +- .../test/collections/string/test_string.mojo | 3 +- stdlib/test/hashlib/test_hasher.mojo | 4 +- stdlib/test/python/test_ownership.mojo | 16 ++-- stdlib/test/python/test_python_object.mojo | 10 +-- 21 files changed, 201 insertions(+), 144 deletions(-) diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index 0e89c0ee6b..adb6fcecc8 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -22,7 +22,6 @@ from memory import UnsafePointer, memcpy from memory.memory import _free from collections.string import StringSlice -from utils import StringRef from utils.write import write_buffered # ===-----------------------------------------------------------------------===# @@ -96,7 +95,7 @@ struct Error( self.loaded_length = -length @implicit - fn __init__(out self, src: StringRef): + fn __init__(out self, src: StringSlice): """Construct an Error object with a given string ref. Args: @@ -196,7 +195,11 @@ struct Error( """ if not self: return - writer.write(StringRef(self.unsafe_cstr_ptr())) + writer.write( + StringSlice[__origin_of(self)]( + unsafe_from_utf8_cstr_ptr=self.unsafe_cstr_ptr() + ) + ) @no_inline fn __repr__(self) -> String: @@ -206,7 +209,15 @@ struct Error( A printable representation of the error message. """ return String( - "Error(", repr(String(StringRef(self.unsafe_cstr_ptr()))), ")" + "Error(", + repr( + String( + StringSlice[__origin_of(self)]( + unsafe_from_utf8_cstr_ptr=self.unsafe_cstr_ptr() + ) + ) + ), + ")", ) # ===-------------------------------------------------------------------===# diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index d52d56e411..58283eacbc 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -37,7 +37,6 @@ from memory import UnsafePointer, memcpy, bitcast from utils import ( StaticString, - StringRef, write_args, write_buffered, ) @@ -152,7 +151,9 @@ struct _fdopen[mode: StringLiteral = "a"]: # TODO: check errno to ensure we haven't encountered EINVAL or ENOMEM instead raise Error("EOF") # Copy the buffer (excluding the delimiter itself) into a Mojo String. - var s = String(StringRef(buffer, bytes_read - 1)) + var s = String( + StringSlice[buffer.origin](ptr=buffer, length=bytes_read - 1) + ) # Explicitly free the buffer using free() instead of the Mojo allocator. libc.free(buffer.bitcast[NoneType]()) return s diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index a1d896f9aa..f8fcd52e66 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -16,12 +16,13 @@ These are Mojo built-ins, so you don't need to import them. """ from collections import Dict, List +from collections.string import StringSlice from sys.ffi import OpaquePointer from sys.intrinsics import _type_is_eq from memory import ArcPointer, UnsafePointer, memcmp, memcpy -from utils import StringRef, Variant +from utils import Variant # ===----------------------------------------------------------------------=== # # _ObjectImpl @@ -578,7 +579,13 @@ struct _ObjectImpl( return if self.is_str(): var string = self.get_as_string() - writer.write("'", StringRef(string.data, string.length), "'") + writer.write( + "'", + StringSlice[__origin_of(string)]( + ptr=string.data, length=string.length + ), + "'", + ) return if self.is_func(): writer.write( @@ -799,24 +806,20 @@ struct object( Args: value: The string value. """ - self = object(StringRef(value)) + self = object(StringSlice(value)) @always_inline @implicit - fn __init__(out self, value: StringRef): + fn __init__(out self, value: StringSlice): """Initializes the object from a string reference. Args: value: The string value. """ var impl = _ImmutableString( - UnsafePointer[UInt8].alloc(value.length), value.length - ) - memcpy( - dest=impl.data, - src=value.unsafe_ptr(), - count=value.length, + UnsafePointer[UInt8].alloc(len(value)), len(value) ) + memcpy(dest=impl.data, src=value.unsafe_ptr(), count=len(value)) self._value = impl @always_inline @@ -845,8 +848,6 @@ struct object( self._append(value.get[i, Float64]()) elif _type_is_eq[T, Bool](): self._append(value.get[i, Bool]()) - elif _type_is_eq[T, StringRef](): - self._append(value.get[i, StringRef]()) elif _type_is_eq[T, StringLiteral](): self._append(value.get[i, StringLiteral]()) else: diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index 28304f1418..7245653360 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -20,7 +20,7 @@ from collections.string import StringSlice from memory import UnsafePointer, memcpy, Span from os import abort from sys import sizeof -from utils import Variant, StringRef +from utils import Variant # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 5d94f01e08..1b0fbe9a4e 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -28,8 +28,6 @@ from python import PythonObject from utils import ( IndexList, - StaticString, - StringRef, Variant, Writable, Writer, @@ -44,6 +42,7 @@ from collections.string._unicode import ( from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( StringSlice, + StaticString, _StringSliceIter, _to_string_list, _utf8_byte_type, @@ -466,9 +465,9 @@ fn atof(str_slice: StringSlice) raises -> Float64: start += 1 sign = -1 if (str_len - start) >= 3: - if StringRef(buff + start, 3) == "nan": + if StringSlice[buff.origin](ptr=buff + start, length=3) == "nan": return FloatLiteral.nan - if StringRef(buff + start, 3) == "inf": + if StringSlice[buff.origin](ptr=buff + start, length=3) == "inf": return FloatLiteral.infinity * sign # read before dot for pos in range(start, str_len): @@ -890,7 +889,11 @@ struct String( start, end, step = span.indices(self.byte_length()) var r = range(start, end, step) if step == 1: - return String(StringRef(self._buffer.data + start, len(r))) + return String( + StringSlice[__origin_of(self._buffer)]( + ptr=self._buffer.data + start, length=len(r) + ) + ) var buffer = Self._buffer_type() var result_len = len(r) diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index f5c684462c..f2ae7cd174 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -31,6 +31,7 @@ from sys import bitwidthof, simdwidthof from sys.intrinsics import unlikely, likely from sys.ffi import c_char from utils.stringref import StringRef, _memmem +from hashlib._hasher import _HashableWithHasher, _Hasher from os import PathLike alias StaticString = StringSlice[StaticConstantOrigin] @@ -659,6 +660,17 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return hash(self._slice._data, self._slice._len) + fn __hash__[H: _Hasher](self, mut hasher: H): + """Updates hasher with the underlying bytes. + + Parameters: + H: The hasher type. + + Args: + hasher: The hasher instance. + """ + hasher._update_with_bytes(self.unsafe_ptr(), len(self)) + fn __fspath__(self) -> String: """Return the file system path representation of this string. diff --git a/stdlib/src/os/env.mojo b/stdlib/src/os/env.mojo index 0ccec33950..06c017fd2a 100644 --- a/stdlib/src/os/env.mojo +++ b/stdlib/src/os/env.mojo @@ -22,9 +22,9 @@ from os import setenv from sys import external_call, os_is_linux, os_is_macos, os_is_windows from sys.ffi import c_int -from memory import UnsafePointer +from collections.string import StringSlice -from utils import StringRef +from memory import UnsafePointer fn setenv(name: String, value: String, overwrite: Bool = True) -> Bool: @@ -96,4 +96,4 @@ fn getenv(name: String, default: String = "") -> String: ) if not ptr: return default - return String(StringRef(ptr=ptr)) + return String(StringSlice[ptr.origin](unsafe_from_utf8_ptr=ptr)) diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index 30c1880ab4..791b186acd 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -20,12 +20,12 @@ from os import listdir """ from collections import InlineArray, List +from collections.string import StringSlice from sys import external_call, is_gpu, os_is_linux, os_is_windows from sys.ffi import OpaquePointer, c_char from memory import UnsafePointer -from utils import StringRef from .path import isdir, split from .pathlike import PathLike @@ -149,8 +149,9 @@ struct _DirHandle: break var name = ep.take_pointee().name var name_ptr = name.unsafe_ptr() - var name_str = StringRef( - name_ptr, _strnlen(name_ptr, _dirent_linux.MAX_NAME_SIZE) + var name_str = StringSlice[__origin_of(name)]( + ptr=name_ptr.bitcast[UInt8](), + length=_strnlen(name_ptr, _dirent_linux.MAX_NAME_SIZE), ) if name_str == "." or name_str == "..": continue @@ -175,8 +176,9 @@ struct _DirHandle: break var name = ep.take_pointee().name var name_ptr = name.unsafe_ptr() - var name_str = StringRef( - name_ptr, _strnlen(name_ptr, _dirent_macos.MAX_NAME_SIZE) + var name_str = StringSlice[__origin_of(name)]( + ptr=name_ptr.bitcast[UInt8](), + length=_strnlen(name_ptr, _dirent_macos.MAX_NAME_SIZE), ) if name_str == "." or name_str == "..": continue diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index 353c0a6ece..89dec5a50d 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -24,8 +24,6 @@ from sys.ffi import c_char from builtin._location import __call_location, _SourceLocation from memory import UnsafePointer, stack_allocation -from utils import StringRef - alias DIR_SEPARATOR = "\\" if os_is_windows() else "/" @@ -46,7 +44,7 @@ fn cwd() raises -> Path: if res == UnsafePointer[c_char](): raise Error("unable to query the current directory") - return String(StringRef(ptr=buf)) + return String(StringSlice[buf.origin](unsafe_from_utf8_cstr_ptr=buf)) @always_inline diff --git a/stdlib/src/pwd/_linux.mojo b/stdlib/src/pwd/_linux.mojo index 0bc59866a6..9fbb80fe54 100644 --- a/stdlib/src/pwd/_linux.mojo +++ b/stdlib/src/pwd/_linux.mojo @@ -11,7 +11,7 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # from memory import UnsafePointer -from utils import StringRef +from collections.string import StringSlice from sys.ffi import c_char, external_call from .pwd import Passwd @@ -35,13 +35,33 @@ struct _C_Passwd: fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: var c_pwuid = passwd_ptr[] return Passwd( - pw_name=String(StringRef(c_pwuid.pw_name)), - pw_passwd=String(StringRef(c_pwuid.pw_passwd)), + pw_name=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_name + ) + ), + pw_passwd=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_passwd + ) + ), pw_uid=Int(c_pwuid.pw_uid), pw_gid=Int(c_pwuid.pw_gid), - pw_gecos=String(StringRef(c_pwuid.pw_gecos)), - pw_dir=String(StringRef(c_pwuid.pw_dir)), - pw_shell=String(StringRef(c_pwuid.pw_shell)), + pw_gecos=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_gecos + ) + ), + pw_dir=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_dir + ) + ), + pw_shell=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_shell + ) + ), ) diff --git a/stdlib/src/pwd/_macos.mojo b/stdlib/src/pwd/_macos.mojo index d35f056153..fe6ea33593 100644 --- a/stdlib/src/pwd/_macos.mojo +++ b/stdlib/src/pwd/_macos.mojo @@ -10,10 +10,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -from utils import StringRef from memory import UnsafePointer from sys.ffi import c_char, external_call +from collections.string import StringSlice + from .pwd import Passwd alias uid_t = Int32 @@ -39,13 +40,33 @@ struct _C_Passwd: fn _build_pw_struct(passwd_ptr: UnsafePointer[_C_Passwd]) raises -> Passwd: var c_pwuid = passwd_ptr[] var passwd = Passwd( - pw_name=String(StringRef(c_pwuid.pw_name)), - pw_passwd=String(StringRef(c_pwuid.pw_passwd)), + pw_name=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_name + ) + ), + pw_passwd=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_passwd + ) + ), pw_uid=Int(c_pwuid.pw_uid), pw_gid=Int(c_pwuid.pw_gid), - pw_gecos=String(StringRef(c_pwuid.pw_gecos)), - pw_dir=String(StringRef(c_pwuid.pw_dir)), - pw_shell=String(StringRef(c_pwuid.pw_shell)), + pw_gecos=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_gecos + ) + ), + pw_dir=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_dir + ) + ), + pw_shell=String( + StringSlice[__origin_of(c_pwuid)]( + unsafe_from_utf8_cstr_ptr=c_pwuid.pw_shell + ) + ), ) return passwd diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index 264a7f1536..715f156c9a 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -38,7 +38,7 @@ from memory import UnsafePointer from python._bindings import PyMojoObject, Pythonable, Typed_initproc from python.python import _get_global_python_itf -from utils import StringRef, StringSlice +from collections.string import StringSlice # ===-----------------------------------------------------------------------===# # Raw Bindings @@ -258,7 +258,7 @@ struct PythonVersion: """The patch version number.""" @implicit - fn __init__(out self, version: StringRef): + fn __init__(out self, version: StringSlice): """Initialize a PythonVersion object from a version string. Args: @@ -287,8 +287,12 @@ struct PythonVersion: self = PythonVersion(components[0], components[1], components[2]) -fn _py_get_version(lib: DLHandle) -> StringRef: - return StringRef(ptr=lib.call["Py_GetVersion", UnsafePointer[c_char]]()) +fn _py_get_version(lib: DLHandle) -> StringSlice[StaticConstantOrigin]: + return StringSlice[StaticConstantOrigin]( + unsafe_from_utf8_cstr_ptr=lib.call[ + "Py_GetVersion", UnsafePointer[c_char] + ]() + ) fn _py_finalize(lib: DLHandle): @@ -741,7 +745,7 @@ struct CPython: """The version of the Python runtime.""" var total_ref_count: UnsafePointer[Int] """The total reference count of all Python objects.""" - var init_error: StringRef + var init_error: StringSlice[StaticConstantOrigin] """An error message if initialization failed.""" # ===-------------------------------------------------------------------===# @@ -771,9 +775,11 @@ struct CPython: # TODO(MOCO-772) Allow raises to propagate through function pointers # and make this initialization a raising function. - self.init_error = external_call[ - "KGEN_CompilerRT_Python_SetPythonPath", UnsafePointer[c_char] - ]() + self.init_error = StringSlice[StaticConstantOrigin]( + unsafe_from_utf8_cstr_ptr=external_call[ + "KGEN_CompilerRT_Python_SetPythonPath", UnsafePointer[c_char] + ]() + ) var python_lib = getenv("MOJO_PYTHON_LIBRARY") @@ -1062,13 +1068,15 @@ struct CPython: fn PyImport_ImportModule( mut self, - name: StringRef, + name: StringSlice, ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/import.html#c.PyImport_ImportModule). """ - var r = self.lib.call["PyImport_ImportModule", PyObjectPtr](name.data) + var r = self.lib.call["PyImport_ImportModule", PyObjectPtr]( + name.unsafe_ptr() + ) self.log( r._get_ptr_as_int(), @@ -1081,7 +1089,7 @@ struct CPython: self._inc_total_rc() return r - fn PyImport_AddModule(mut self, name: StringRef) -> PyObjectPtr: + fn PyImport_AddModule(mut self, name: StringSlice) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/import.html#c.PyImport_AddModule). """ @@ -1188,7 +1196,7 @@ struct CPython: # Python Evaluation # ===-------------------------------------------------------------------===# - fn PyRun_SimpleString(mut self, strref: StringRef) -> Bool: + fn PyRun_SimpleString(mut self, strref: StringSlice) -> Bool: """Executes the given Python code. Args: @@ -1208,7 +1216,7 @@ struct CPython: fn PyRun_String( mut self, - strref: StringRef, + strref: StringSlice, globals: PyObjectPtr, locals: PyObjectPtr, run_mode: Int, @@ -1256,8 +1264,8 @@ struct CPython: fn Py_CompileString( mut self, - strref: StringRef, - filename: StringRef, + strref: StringSlice, + filename: StringSlice, compile_mode: Int, ) -> PyObjectPtr: """[Reference]( @@ -1353,24 +1361,24 @@ struct CPython: fn PyObject_HasAttrString( mut self, obj: PyObjectPtr, - name: StringRef, + name: StringSlice, ) -> Int: var r = self.lib.get_function[ fn (PyObjectPtr, UnsafePointer[UInt8]) -> Int - ]("PyObject_HasAttrString")(obj, name.data) + ]("PyObject_HasAttrString")(obj, name.unsafe_ptr()) return r fn PyObject_GetAttrString( mut self, obj: PyObjectPtr, - name: StringRef, + name: StringSlice, ) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_GetAttrString). """ var r = self.lib.call["PyObject_GetAttrString", PyObjectPtr]( - obj, name.data + obj, name.unsafe_ptr() ) self.log( @@ -1387,14 +1395,14 @@ struct CPython: return r fn PyObject_SetAttrString( - mut self, obj: PyObjectPtr, name: StringRef, new_value: PyObjectPtr + mut self, obj: PyObjectPtr, name: StringSlice, new_value: PyObjectPtr ) -> c_int: """[Reference]( https://docs.python.org/3/c-api/object.html#c.PyObject_SetAttrString). """ var r = self.lib.call["PyObject_SetAttrString", c_int]( - obj, name.data, new_value + obj, name.unsafe_ptr(), new_value ) self.log( @@ -1711,28 +1719,6 @@ struct CPython: # Unicode Objects # ===-------------------------------------------------------------------===# - fn PyUnicode_DecodeUTF8(mut self, strref: StringRef) -> PyObjectPtr: - """[Reference]( - https://docs.python.org/3/c-api/unicode.html#c.PyUnicode_DecodeUTF8). - """ - - var r = self.lib.call["PyUnicode_DecodeUTF8", PyObjectPtr]( - strref.unsafe_ptr().bitcast[Int8](), - strref.length, - "strict".unsafe_cstr_ptr(), - ) - - self.log( - r._get_ptr_as_int(), - " NEWREF PyUnicode_DecodeUTF8, refcnt:", - self._Py_REFCNT(r), - ", str:", - strref, - ) - - self._inc_total_rc() - return r - fn PyUnicode_DecodeUTF8(mut self, strslice: StringSlice) -> PyObjectPtr: """[Reference]( https://docs.python.org/3/c-api/unicode.html#c.PyUnicode_DecodeUTF8). @@ -1781,16 +1767,20 @@ struct CPython: return py_slice - fn PyUnicode_AsUTF8AndSize(mut self, py_object: PyObjectPtr) -> StringRef: + fn PyUnicode_AsUTF8AndSize( + mut self, py_object: PyObjectPtr + ) -> StringSlice[__origin_of(py_object.unsized_obj_ptr.origin)]: """[Reference]( https://docs.python.org/3/c-api/unicode.html#c.PyUnicode_AsUTF8AndSize). """ - var s = StringRef() - s.data = self.lib.call[ + var length = Int(0) + var ptr = self.lib.call[ "PyUnicode_AsUTF8AndSize", UnsafePointer[c_char] - ](py_object, UnsafePointer.address_of(s.length)).bitcast[UInt8]() - return s + ](py_object, UnsafePointer.address_of(length)).bitcast[UInt8]() + return StringSlice[__origin_of(py_object.unsized_obj_ptr.origin)]( + ptr=ptr, length=length + ) # ===-------------------------------------------------------------------===# # Python Error operations diff --git a/stdlib/src/python/python.mojo b/stdlib/src/python/python.mojo index 5c7ad725bf..889dbef436 100644 --- a/stdlib/src/python/python.mojo +++ b/stdlib/src/python/python.mojo @@ -27,8 +27,6 @@ from sys.ffi import _Global from memory import UnsafePointer -from utils import StringRef - from ._cpython import ( CPython, Py_eval_input, @@ -100,7 +98,7 @@ struct Python: """ self.impl = existing.impl - fn eval(mut self, code: StringRef) -> Bool: + fn eval(mut self, code: StringSlice) -> Bool: """Executes the given Python code. Args: @@ -115,7 +113,9 @@ struct Python: @staticmethod fn evaluate( - expr: StringRef, file: Bool = False, name: StringRef = "__main__" + expr: StringSlice, + file: Bool = False, + name: StringSlice[StaticConstantOrigin] = "__main__", ) raises -> PythonObject: """Executes the given Python code. @@ -204,7 +204,7 @@ struct Python: # TODO(MSTDL-880): Change this to return `TypedPythonObject["Module"]` @staticmethod - fn import_module(module: StringRef) raises -> PythonObject: + fn import_module(module: StringSlice) raises -> PythonObject: """Imports a Python module. This provides you with a module object you can use just like you would @@ -368,7 +368,9 @@ struct Python: return PythonObject([]) @no_inline - fn __str__(mut self, str_obj: PythonObject) -> StringRef: + fn as_string_slice( + mut self, str_obj: PythonObject + ) -> StringSlice[__origin_of(str_obj.py_object.unsized_obj_ptr.origin)]: """Return a string representing the given Python object. Args: diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index e3339c193f..468ea13ebd 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -27,7 +27,6 @@ from sys.intrinsics import _type_is_eq from memory import UnsafePointer -from utils import StringRef from ._cpython import CPython, PyObjectPtr from .python import Python, _get_global_python_itf @@ -455,12 +454,6 @@ struct PythonObject( obj = PythonObject(value.get[i, Float64]()) elif _type_is_eq[T, Bool](): obj = PythonObject(value.get[i, Bool]()) - elif _type_is_eq[T, StringRef](): - obj = PythonObject( - StringSlice[MutableAnyOrigin]( - unsafe_from_utf8_strref=value.get[i, StringRef]() - ) - ) elif _type_is_eq[T, StringLiteral](): obj = PythonObject(value.get[i, StringLiteral]()) else: @@ -503,12 +496,6 @@ struct PythonObject( obj = PythonObject(rebind[Float64](value[i])) elif _type_is_eq[T, Bool](): obj = PythonObject(rebind[Bool](value[i])) - elif _type_is_eq[T, StringRef](): - obj = PythonObject( - StringSlice[MutableAnyOrigin]( - unsafe_from_utf8_strref=rebind[StringRef](value[i]) - ) - ) elif _type_is_eq[T, StringLiteral](): obj = PythonObject(rebind[StringLiteral](value[i])) else: @@ -755,7 +742,7 @@ struct PythonObject( cpython.Py_DecRef(value.py_object) fn _call_zero_arg_method( - self, method_name: StringRef + self, method_name: StringSlice ) raises -> PythonObject: var cpython = _get_global_python_itf().cpython() var tuple_obj = cpython.PyTuple_New(0) @@ -770,7 +757,7 @@ struct PythonObject( return PythonObject(result) fn _call_single_arg_method( - self, method_name: StringRef, rhs: PythonObject + self, method_name: StringSlice, rhs: PythonObject ) raises -> PythonObject: var cpython = _get_global_python_itf().cpython() var tuple_obj = cpython.PyTuple_New(1) @@ -789,7 +776,7 @@ struct PythonObject( return PythonObject(result_obj) fn _call_single_arg_inplace_method( - mut self, method_name: StringRef, rhs: PythonObject + mut self, method_name: StringSlice, rhs: PythonObject ) raises: var cpython = _get_global_python_itf().cpython() var tuple_obj = cpython.PyTuple_New(1) diff --git a/stdlib/src/sys/arg.mojo b/stdlib/src/sys/arg.mojo index b9140ffb3b..64257cce6c 100644 --- a/stdlib/src/sys/arg.mojo +++ b/stdlib/src/sys/arg.mojo @@ -36,8 +36,6 @@ from memory import UnsafePointer from collections.string import StringSlice -from utils import StringRef - # TODO: When we have global variables, this should be a global list. fn argv() -> VariadicList[StringSlice[StaticConstantOrigin]]: diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index bd515a899e..8179ce0dd8 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -17,7 +17,7 @@ from sys._libc import dlclose, dlerror, dlopen, dlsym from memory import UnsafePointer -from utils import StringRef +from collections.string import StringSlice from .info import is_64bit, os_is_linux, os_is_macos, os_is_windows from .intrinsics import _mlirtype_is_eq @@ -174,7 +174,14 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): var handle = dlopen(path.unsafe_cstr_ptr(), flags) if handle == OpaquePointer(): var error_message = dlerror() - abort("dlopen failed: ", String(StringRef(error_message))) + abort( + "dlopen failed: ", + String( + StringSlice[error_message.origin]( + unsafe_from_utf8_cstr_ptr=error_message + ) + ), + ) self.handle = handle else: self.handle = OpaquePointer() @@ -355,7 +362,12 @@ struct DLHandle(CollectionElement, CollectionElementNew, Boolable): var err = dlerror() if err: - abort("dlsym failed: ", String(StringRef(err))) + abort( + "dlsym failed: ", + String( + StringSlice[err.origin](unsafe_from_utf8_cstr_ptr=err) + ), + ) return res @@ -418,7 +430,7 @@ fn _get_dylib_function[ var new_func = dylib._get_function[func_name, result_type]() external_call["KGEN_CompilerRT_InsertGlobal", NoneType]( - StringRef(func_cache_name), + StringSlice(func_cache_name), UnsafePointer.address_of(new_func).bitcast[OpaquePointer]()[], ) @@ -475,7 +487,7 @@ fn _get_global[ destroy_fn: fn (OpaquePointer) -> None, ](payload: OpaquePointer = OpaquePointer()) -> OpaquePointer: return external_call["KGEN_CompilerRT_GetGlobalOrCreate", OpaquePointer]( - StringRef(name), + StringSlice(name), payload, init_fn, destroy_fn, diff --git a/stdlib/test/builtin/test_print.mojo b/stdlib/test/builtin/test_print.mojo index b2ca0dd2e0..8dc2aad510 100644 --- a/stdlib/test/builtin/test_print.mojo +++ b/stdlib/test/builtin/test_print.mojo @@ -16,10 +16,12 @@ import sys from tempfile import NamedTemporaryFile +from collections.string import StaticString + from builtin._location import __call_location, _SourceLocation from testing import assert_equal -from utils import IndexList, StringRef +from utils import IndexList @always_inline @@ -95,7 +97,7 @@ def test_print(): print("World", flush=True, file=checker.stream()) checker.check_line("World") - var hello: StringRef = "Hello," + var hello: StaticString = "Hello," var world: String = "world!" var f: Bool = False print(">", hello, world, 42, True, f, file=checker.stream()) diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 25263afad3..0d39db2fa5 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -27,7 +27,6 @@ from collections.string.string import ( ) from memory import UnsafePointer from python import Python -from utils import StringRef @value @@ -39,7 +38,7 @@ struct AString(Stringable): def test_stringable(): assert_equal("hello", String("hello")) assert_equal("0", String(0)) - assert_equal("AAA", String(StringRef("AAA"))) + assert_equal("AAA", String(StringSlice("AAA"))) assert_equal("a string", String(AString())) diff --git a/stdlib/test/hashlib/test_hasher.mojo b/stdlib/test/hashlib/test_hasher.mojo index b349c600ef..3f16ed259d 100644 --- a/stdlib/test/hashlib/test_hasher.mojo +++ b/stdlib/test/hashlib/test_hasher.mojo @@ -21,7 +21,7 @@ from memory import UnsafePointer from python import Python, PythonObject from testing import assert_equal, assert_true -from utils import StringRef +from collections.string import StringSlice struct DummyHasher(_Hasher): @@ -157,7 +157,7 @@ def test_hash_hashable_with_hasher_types(): assert_equal(_hash_with_hasher(DType.uint64), 6529703120343940753) assert_equal(_hash_with_hasher(""), 11583516797109448887) assert_equal(_hash_with_hasher(String("")), 11583516797109448887) - assert_equal(_hash_with_hasher(StringRef("")), 11583516797109448887) + assert_equal(_hash_with_hasher(StringSlice("")), 11583516797109448887) assert_equal(_hash_with_hasher(Int(-123)), 4720193641311814362) assert_equal(_hash_with_hasher(UInt(123)), 4498397628805512285) assert_equal( diff --git a/stdlib/test/python/test_ownership.mojo b/stdlib/test/python/test_ownership.mojo index 1fa9bc026a..d950b3f950 100644 --- a/stdlib/test/python/test_ownership.mojo +++ b/stdlib/test/python/test_ownership.mojo @@ -14,13 +14,13 @@ # RUN: %mojo %s from python import Python, PythonObject -from testing import assert_equal +from testing import assert_equal, assert_true fn test_import(mut python: Python) raises: var my_module: PythonObject = Python.import_module("my_module") var py_string = my_module.my_function("Hello") - var str = String(python.__str__(py_string)) + var str = String(python.as_string_slice(py_string)) assert_equal(str, "Formatting the string from Lit with Python: Hello") @@ -41,23 +41,23 @@ fn test_tuple(mut python: Python) raises: fn test_call_ownership(mut python: Python) raises: var obj: PythonObject = [1, "5"] var py_string = String(obj) - var string = python.__str__(py_string) - assert_equal(string, "[1, '5']") + var string = python.as_string_slice(py_string) + assert_true(string == "[1, '5']") fn test_getitem_ownership(mut python: Python) raises: var obj: PythonObject = [1, "5"] var py_string = String(obj[1]) - var string = python.__str__(py_string) - assert_equal(string, "5") + var string = python.as_string_slice(py_string) + assert_true(string == "5") fn test_getattr_ownership(mut python: Python) raises: var my_module: PythonObject = Python.import_module("my_module") var obj = my_module.Foo(4) var py_string = String(obj.bar) - var string = python.__str__(py_string) - assert_equal(string, "4") + var string = python.as_string_slice(py_string) + assert_true(string == "4") def main(): diff --git a/stdlib/test/python/test_python_object.mojo b/stdlib/test/python/test_python_object.mojo index 659b97c1b5..79f87ee65a 100644 --- a/stdlib/test/python/test_python_object.mojo +++ b/stdlib/test/python/test_python_object.mojo @@ -18,8 +18,6 @@ from collections import Dict from python import Python, PythonObject from testing import assert_equal, assert_false, assert_raises, assert_true -from utils import StringRef - def test_dunder_methods(mut python: Python): var a = PythonObject(34) @@ -268,8 +266,8 @@ fn test_string_conversions() raises -> None: var py_str = PythonObject(mojo_str) var py_capitalized = py_str.capitalize() var py = Python() - var mojo_capitalized = py.__str__(py_capitalized) - assert_equal(mojo_capitalized, "Mojo") + var mojo_capitalized = py.as_string_slice(py_capitalized) + assert_true(mojo_capitalized == "Mojo") except e: print("Error occurred") @@ -281,8 +279,8 @@ fn test_string_conversions() raises -> None: var py_str = PythonObject(mojo_str) var py_capitalized = py_str.capitalize() var py = Python() - var mojo_capitalized = py.__str__(py_capitalized) - assert_equal(mojo_capitalized, "Mojo") + var mojo_capitalized = py.as_string_slice(py_capitalized) + assert_true(mojo_capitalized == "Mojo") except e: print("Error occurred") From 773bd572f4e2c0d3033c3fda12d035928137132d Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Thu, 30 Jan 2025 17:48:45 -0500 Subject: [PATCH 323/509] [docs] Update docs for main branch vs. stable Reapply of https://github.com/modular/mojo/pull/3978 internally since it only got applied/merged externally. MODULAR_ORIG_COMMIT_REV_ID: 46c2ff83c7f61791c15c4058f69888568581365b --- CONTRIBUTING.md | 22 +++++++++++----------- README.md | 19 +++++++++---------- docs/changelog-released.md | 2 +- stdlib/docs/development.md | 4 ++-- stdlib/docs/internal/README.md | 2 +- 5 files changed, 24 insertions(+), 25 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 72f230da1b..4ad284fa1c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -200,7 +200,7 @@ Library, Mojo examples, or Mojo documentation. This page gives an overview of the process. For a more detailed walkthrough, see [How to contribute to the Mojo standard library: a step-by-step guide](https://www.modular.com/blog/how-to-contribute-to-mojo-standard-library-a-step-by-step-guide). -**Note:** Pull requests should be submitted against the `nightly` branch, +**Note:** Pull requests should be submitted against the `main` branch, which represents the most recent nightly build. ### Pull request process @@ -243,10 +243,10 @@ git fetch upstream #### Branching off nightly -Make sure to branch off `nightly` to work on your PR: +Make sure to branch off `main` to work on your PR: ```bash -git checkout upstream/nightly +git checkout upstream/main git checkout -b my-fix-pr ``` @@ -255,20 +255,20 @@ before raising a PR: ```bash git fetch upstream -git rebase upstream/nightly +git rebase upstream/main ``` #### Getting the nightly Mojo compiler -Now that you're on the nightly branch, you need to install the latest nightly +Now that you're on the main branch, you need to install the latest nightly build. -If you're using [`magic`](https://docs.modular.com/magic), create a new -project environment with the `max-nightly` channel like this: +If you're using [`magic`](https://docs.modular.com/magic) (version 0.6.4 or +higher), create a new project environment like this and it will install the +latest nightly version of `max` by default: ```bash -magic init mojo-nightly --format mojoproject \ - -c conda-forge -c https://conda.modular.com/max-nightly +magic init mojo-nightly --format mojoproject ``` If you're [using conda](https://docs.modular.com/magic/conda), add the @@ -299,7 +299,7 @@ when using the stable release! If your change is one of the improvements described above or it has been discussed and agreed upon by the project maintainers, please create a pull -request into the `nightly` branch. +request into the `main` branch. First push your changes: @@ -314,7 +314,7 @@ remote: Create a pull request for 'my-fix-pr' on GitHub by visiting: remote: https://github.com/jackos/mojo/pull/new/my-fix-pr ``` -Make sure you point it to the `nightly` branch: +Make sure you point it to the `main` branch: ![Base Branch](stdlib/docs/images/base-branch.png) diff --git a/README.md b/README.md index 5774ca462a..3dac78f624 100644 --- a/README.md +++ b/README.md @@ -25,11 +25,11 @@ This repo includes source code for: This repo has two primary branches: -- The [`main`](https://github.com/modularml/mojo/tree/main) branch, which is in -sync with the last stable released version of Mojo. Use the examples here if you’re -using a [release build of Mojo](#latest-released). +- The [`stable`](https://github.com/modularml/mojo/tree/stable) branch, which +is in sync with the last stable released version of Mojo. Use the examples +here if you’re using a [release build of Mojo](#latest-released). -- The [`nightly`](https://github.com/modularml/mojo/tree/nightly) branch, which +- The [`main`](https://github.com/modularml/mojo/tree/main) branch, which is in sync with the Mojo nightly build and subject to breakage. Use this branch for [contributions](./CONTRIBUTING.md), or if you're using the latest [nightly build of Mojo](#latest-nightly). @@ -74,16 +74,15 @@ platforms = ["osx-arm64", "linux-aarch64", "linux-64"] max = "*" ``` -And when you clone this repo, switch to the `nightly` branch because the `main` -branch might not be compatible with nightly builds: +When you clone this repo, you'll be on the `main` branch by default, +which includes code matching the latest nightly build: ```bash git clone https://github.com/modularml/mojo.git ``` -```bash -git checkout nightly -``` +If you want to instead see the source from the most recent stable +release, then you can switch to the `stable` branch. ## Contributing @@ -92,7 +91,7 @@ issue here](https://github.com/modularml/mojo/issues). See [here](./CONTRIBUTING.md) for guidelines on filing good bugs. We welcome contributions to this repo on the -[`nightly`](https://github.com/modularml/mojo/tree/nightly) +[`main`](https://github.com/modularml/mojo/tree/main) branch. If you’d like to contribute to Mojo, please first read our [Contributor Guide](https://github.com/modularml/mojo/blob/main/CONTRIBUTING.md). diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 18a2551f4f..95ee3fbab8 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -3189,7 +3189,7 @@ This release doesn't include any changes to Mojo. ### 🔥 Legendary - The Mojo standard library is now open source! Check out the - [README](https://github.com/modular/mojo/blob/nightly/stdlib/README.md) + [README](https://github.com/modular/mojo/blob/main/stdlib/README.md) for everything you need to get started. - Structs and other nominal types are now allowed to implicitly conform to diff --git a/stdlib/docs/development.md b/stdlib/docs/development.md index ef21e6fb81..000676598e 100644 --- a/stdlib/docs/development.md +++ b/stdlib/docs/development.md @@ -9,7 +9,7 @@ If this is your first time contributing, first read everything in you need to do the following: 1. [Fork and clone the repo](../../CONTRIBUTING.md#fork-and-clone-the-repo) -2. [Branch off nightly](../../CONTRIBUTING.md#branching-off-nightly) +2. [Branch off main](../../CONTRIBUTING.md#branching-off-nightly) 3. [Install the nightly Mojo compiler](../../CONTRIBUTING.md#getting-the-nightly-mojo-compiler) And if you're using VS Code: @@ -104,7 +104,7 @@ You can do the same for a directory with ./stdlib/scripts/run-tests.sh ./stdlib/test/utils ``` -All the tests should pass on the `nightly` branch with the nightly Mojo +All the tests should pass on the `main` branch with the nightly Mojo compiler. If you've pulled the latest changes and they're still failing please [open a GitHub issue](https://github.com/modularml/mojo/issues/new?assignees=&labels=bug%2Cmojo&projects=&template=mojo_bug_report.yaml&title=%5BBUG%5D). diff --git a/stdlib/docs/internal/README.md b/stdlib/docs/internal/README.md index b80fd03dca..9699b52b72 100644 --- a/stdlib/docs/internal/README.md +++ b/stdlib/docs/internal/README.md @@ -11,6 +11,6 @@ WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. This directory contains internal documentation for the implementation details of the Mojo compiler, runtime, and stdlib. You must always reference the -current version of this documentation on the branch nightly before using the +current version of this documentation on the branch main before using the operations or behavior documented within. Any new files should contain the above warning. From 9c6d9fac0eff88c9bdf9e8633d1638327ce9556f Mon Sep 17 00:00:00 2001 From: Manuel Saelices Date: Thu, 30 Jan 2025 18:18:10 -0800 Subject: [PATCH 324/509] [External] [stdlib] Add a new `validate` parameter to the `b64decode()` function (#54857) [External] [stdlib] Add a new `validate` parameter to the `b64decode()` function Matching the `validate` bool argument in Python's [base64.b64decode](https://docs.python.org/3/library/base64.html#base64.b64decode) but a using comp time parameter so it will not add CPU cycles if validation is not needed. Co-authored-by: Manuel Saelices Closes modularml/mojo#3929 MODULAR_ORIG_COMMIT_REV_ID: 10ee9bb8c9b339c8360afc75f2f28717188e954d --- docs/changelog.md | 2 ++ stdlib/src/base64/base64.mojo | 34 +++++++++++++++++++---------- stdlib/test/base64/test_base64.mojo | 12 +++++++++- 3 files changed, 35 insertions(+), 13 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index d871838912..d7ef52f96f 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -100,6 +100,8 @@ what we publish. ### Standard library changes +- Add a new `validate` parameter to the `b64decode()` function. + - The free floating functions for constructing different types have been deprecated for actual constructors: diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index a902a710ab..d0b11c46bd 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -34,7 +34,7 @@ from ._b64encode import b64encode_with_buffers as _b64encode_with_buffers @always_inline -fn _ascii_to_value(char: StringSlice) -> Int: +fn _ascii_to_value[validate: Bool = False](char: StringSlice) raises -> Int: """Converts an ASCII character to its integer value for base64 decoding. Args: @@ -58,6 +58,12 @@ fn _ascii_to_value(char: StringSlice) -> Int: elif char == "/": return 63 else: + + @parameter + if validate: + raise Error( + 'ValueError: Unexpected character "{}" encountered'.format(char) + ) return -1 @@ -112,9 +118,12 @@ fn b64encode(input_bytes: Span[Byte, _]) -> String: @always_inline -fn b64decode(str: StringSlice) -> String: +fn b64decode[validate: Bool = False](str: StringSlice) raises -> String: """Performs base64 decoding on the input string. + Parameters: + validate: If true, the function will validate the input string. + Args: str: A base64 encoded string. @@ -122,21 +131,22 @@ fn b64decode(str: StringSlice) -> String: The decoded string. """ var n = str.byte_length() - debug_assert(n % 4 == 0, "Input length must be divisible by 4") + + @parameter + if validate: + if n % 4 != 0: + raise Error( + "ValueError: Input length {} must be divisible by 4".format(n) + ) var p = String._buffer_type(capacity=n + 1) # This algorithm is based on https://arxiv.org/abs/1704.00605 for i in range(0, n, 4): - var a = _ascii_to_value(str[i]) - var b = _ascii_to_value(str[i + 1]) - var c = _ascii_to_value(str[i + 2]) - var d = _ascii_to_value(str[i + 3]) - - debug_assert( - a >= 0 and b >= 0 and c >= 0 and d >= 0, - "Unexpected character encountered", - ) + var a = _ascii_to_value[validate](str[i]) + var b = _ascii_to_value[validate](str[i + 1]) + var c = _ascii_to_value[validate](str[i + 2]) + var d = _ascii_to_value[validate](str[i + 3]) p.append((a << 2) | (b >> 4)) if str[i + 2] == "=": diff --git a/stdlib/test/base64/test_base64.mojo b/stdlib/test/base64/test_base64.mojo index 3b630f73f8..1e6b1c4460 100644 --- a/stdlib/test/base64/test_base64.mojo +++ b/stdlib/test/base64/test_base64.mojo @@ -14,7 +14,7 @@ from base64 import b16decode, b16encode, b64decode, b64encode -from testing import assert_equal +from testing import assert_equal, assert_raises def test_b64encode(): @@ -60,6 +60,16 @@ def test_b64decode(): assert_equal(b64decode("QUJDREVGYWJjZGVm"), "ABCDEFabcdef") + with assert_raises( + contains="ValueError: Input length 21 must be divisible by 4" + ): + _ = b64decode[validate=True]("invalid base64 string") + + with assert_raises( + contains='ValueError: Unexpected character " " encountered' + ): + _ = b64decode[validate=True]("invalid base64 string!!!") + def test_b16encode(): assert_equal(b16encode("a"), "61") From d00e4edc7ba118552ad57a349b5521a5bb5d6362 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 30 Jan 2025 21:05:47 -0800 Subject: [PATCH 325/509] [Stdlib] Add new sys.compile package with Optimization Level and DebugLevel It's sometimes useful to know one's Optimization and DebugLevel in Mojo. This PR's purpose though is to enable future cleanups. MODULAR_ORIG_COMMIT_REV_ID: bb67bf5930fdbacec97059db7c7daece6cefec70 --- stdlib/src/sys/compile.mojo | 127 ++++++++++++++++++++++++ stdlib/test/sys/test_compile.mojo | 22 ++++ stdlib/test/sys/test_compile_debug.mojo | 22 ++++ 3 files changed, 171 insertions(+) create mode 100644 stdlib/src/sys/compile.mojo create mode 100644 stdlib/test/sys/test_compile.mojo create mode 100644 stdlib/test/sys/test_compile_debug.mojo diff --git a/stdlib/src/sys/compile.mojo b/stdlib/src/sys/compile.mojo new file mode 100644 index 0000000000..a2a737b551 --- /dev/null +++ b/stdlib/src/sys/compile.mojo @@ -0,0 +1,127 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Implements functions that return compile-time information. +""" +from .param_env import env_get_string, env_get_int + +# ===----------------------------------------------------------------------=== # +# OptimizationLevel +# ===----------------------------------------------------------------------=== # + + +@value +struct _OptimizationLevel(Intable): + """Represents the optimization level used during compilation. + + The optimization level is determined by the __OPTIMIZATION_LEVEL environment + variable, with a default value of 4 if not specified. + + Attributes: + level: The integer value of the optimization level. + """ + + alias level = env_get_int["__OPTIMIZATION_LEVEL", 4]() + + fn __copyinit__(out self, existing: Self): + """Copy initialization. + + Args: + existing: The existing optimization level. + """ + return + + fn __moveinit__(out self, owned existing: Self): + """Move initialization. + + Args: + existing: The existing optimization level. + """ + return + + @no_inline + fn write_to[W: Writer](self, mut writer: W): + """Writes the optimization level to a writer.""" + writer.write(Self.level) + + @no_inline + fn __str__(self) -> String: + """Returns the string representation of the optimization level. + + Returns: + A string containing the optimization level value. + """ + return String.write(self) + + fn __int__(self) -> Int: + """Returns the integer value of the optimization level. + + Returns: + The optimization level as an integer. + """ + return Self.level + + +alias OptimizationLevel = _OptimizationLevel() +"""Represents the optimization level used during compilation.""" + +# ===----------------------------------------------------------------------=== # +# DebugLevel +# ===----------------------------------------------------------------------=== # + + +@value +struct _DebugLevel: + """Represents the debug level used during compilation. + + The debug level is determined by the __DEBUG_LEVEL environment variable, + with a default value of "none" if not specified. + + Attributes: + level: The string value of the debug level. + """ + + alias level = env_get_string["__DEBUG_LEVEL", "none"]() + + fn __copyinit__(out self, existing: Self): + """Copy initialization. + + Args: + existing: The existing optimization level. + """ + return + + fn __moveinit__(out self, owned existing: Self): + """Move initialization. + + Args: + existing: The existing optimization level. + """ + return + + @no_inline + fn write_to[W: Writer](self, mut writer: W): + """Writes the optimization level to a writer.""" + writer.write(Self.level) + + @no_inline + fn __str__(self) -> String: + """Returns the string representation of the debug level. + + Returns: + The debug level as a string. + """ + return Self.level + + +alias DebugLevel = _DebugLevel() +"""Represents the debug level used during compilation.""" diff --git a/stdlib/test/sys/test_compile.mojo b/stdlib/test/sys/test_compile.mojo new file mode 100644 index 0000000000..d18535ee72 --- /dev/null +++ b/stdlib/test/sys/test_compile.mojo @@ -0,0 +1,22 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %bare-mojo %s + +from sys.compile import OptimizationLevel, DebugLevel + +from testing import assert_equal + + +def main(): + assert_equal(Int(OptimizationLevel), 3) + assert_equal(String(DebugLevel), "none") diff --git a/stdlib/test/sys/test_compile_debug.mojo b/stdlib/test/sys/test_compile_debug.mojo new file mode 100644 index 0000000000..d6e2271e20 --- /dev/null +++ b/stdlib/test/sys/test_compile_debug.mojo @@ -0,0 +1,22 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %bare-mojo -O0 %s + +from sys.compile import OptimizationLevel, DebugLevel + +from testing import assert_equal + + +def main(): + assert_equal(Int(OptimizationLevel), 0) + assert_equal(String(DebugLevel), "none") From b367ba895e7846272f152ceb154717ac5d20a937 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 1 Feb 2025 02:14:38 +0000 Subject: [PATCH 326/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025013105 --- examples/life/magic.lock | 276 ++++++++++++++++---------------- examples/magic.lock | 276 ++++++++++++++++---------------- examples/notebooks/magic.lock | 292 +++++++++++++++++----------------- examples/operators/magic.lock | 276 ++++++++++++++++---------------- examples/testing/magic.lock | 276 ++++++++++++++++---------------- magic.lock | 276 ++++++++++++++++---------------- 6 files changed, 836 insertions(+), 836 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 2836acd5f1..eae1460d3d 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -39,7 +39,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.4-hb9d3cd8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.12.14-hbcca054_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.2-h3394656_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.12.14-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py312h06ac9bb_0.conda @@ -56,7 +56,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/email_validator-2.2.0-hd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.7-pyh29332c3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-0.115.8-pyh29332c3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fastapi-cli-0.0.7-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.17.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/fluidsynth-2.3.7-hd992666_0.conda @@ -83,7 +83,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/httptools-0.6.4-py312h66e93f0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.27.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.28.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda - 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conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025013105-3.12release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025013105-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025013005-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025013105-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/multiprocess-0.70.15-py312h98912ed_1.conda @@ -231,7 +231,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py312h178313f_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.2.0-py312hbf22597_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.2.1-py312hbf22597_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2024.07.02-h9925aae_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda @@ -257,7 +257,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.2-py312h66e93f0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/transformers-4.48.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/transformers-4.48.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typer-0.15.1-pyhd8ed1ab_0.conda - 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8 files changed, 17 insertions(+), 3110 deletions(-) delete mode 100644 examples/notebooks/BoolMLIR.ipynb delete mode 100644 examples/notebooks/Mandelbrot.ipynb delete mode 100644 examples/notebooks/Matmul.ipynb delete mode 100644 examples/notebooks/README.md delete mode 100644 examples/notebooks/RayTracing.ipynb delete mode 100644 examples/notebooks/images/background.png delete mode 100644 examples/notebooks/pixi.toml diff --git a/examples/README.md b/examples/README.md index 63647c925c..6c651cd92d 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,36 +1,41 @@ # Mojo code examples -A collection of sample programs and Mojo notebooks written in the -[Mojo](https://docs.modular.com/mojo/programming-manual.html) programming language. +A collection of sample programs written in the +[Mojo](https://docs.modular.com/mojo/manual/) programming language. ## Getting Started -Access a Mojo programming environment available from the -Mojo product [page](https://www.modular.com/mojo). +The easiest way to get started with Mojo is to install the Magic package and +virtual environment manager. By following the instructions in [Get started with +Magic](https://docs.modular.com/magic/), you can quickly install Magic and use +it to create a virtual environment for Mojo programming. Alternatively, you can +follow the instructions for how to [Add MAX/Mojo to a conda +project](https://docs.modular.com/magic/conda). -Git clone the repository of Mojo samples using the command below: +After you have set up a Mojo programming environment, you can then use `git` to +clone this repository of Mojo samples using the command below: ```bash -git clone https://github.com/modularml/mojo.git +git clone https://github.com/modular/mojo.git ``` ## Running If you're using [`magic`](https://docs.modular.com/magic), navigate into -the examples directory and use `magic run`. For example: +the `examples` directory and use `magic run` to invoke `mojo` and run the basic +example programs. For example: ```bash magic run mojo matmul.mojo ``` -You can run the Mojo notebooks using [JupyterLab or Visual Studio -Code](notebooks/README.md) with the Mojo extension available on the Marketplace. +You'll also find several complete Mojo projects as subdirectories of the +`examples` directory. ## License -The Mojo examples and notebooks in this repository are licensed -under the Apache License v2.0 with LLVM Exceptions -(see the LLVM [License](https://llvm.org/LICENSE.txt)). +The Mojo examples in this repository are licensed under the Apache License v2.0 +with LLVM Exceptions (see the LLVM [License](https://llvm.org/LICENSE.txt)). ## Contributing diff --git a/examples/notebooks/BoolMLIR.ipynb b/examples/notebooks/BoolMLIR.ipynb deleted file mode 100644 index 514770e9e8..0000000000 --- a/examples/notebooks/BoolMLIR.ipynb +++ /dev/null @@ -1,613 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: Low-level IR in Mojo\n", - "description: Learn how to use low-level primitives to define your own boolean type in Mojo.\n", - "website:\n", - " open-graph:\n", - " image: /static/images/mojo-social-card.png\n", - " twitter-card:\n", - " image: /static/images/mojo-social-card.png\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "# Low-level IR in Mojo" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "Mojo is a high-level programming language with an extensive set of modern features. Mojo also provides you, the programmer, access to all of the low-level primitives that you need to write powerful -- yet zero-cost -- abstractions.\n", - "\n", - "These primitives are implemented in [MLIR](https://mlir.llvm.org), an extensible intermediate representation (IR) format for compiler design. Many different programming languages and compilers translate their source programs into MLIR, and because Mojo provides direct access to MLIR features, this means Mojo programs can enjoy the benefits of each of these tools.\n", - "\n", - "Going one step further, Mojo's unique combination of zero-cost abstractions with MLIR interoperability means that Mojo programs can take full advantage of *anything* that interfaces with MLIR. While this isn't something normal Mojo programmers may ever need to do, it's an extremely powerful capability when extending a system to interface with a new datatype, or an esoteric new accelerator feature.\n", - "\n", - "To illustrate these ideas, we'll implement a boolean type in Mojo below, which we'll call `OurBool`. We'll make extensive use of MLIR, so let's begin with a short primer.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## What is MLIR?\n", - "\n", - "MLIR is an intermediate representation of a program, not unlike an assembly language, in which a sequential set of instructions operate on in-memory values.\n", - "\n", - "More importantly, MLIR is modular and extensible. MLIR is composed of an ever-growing number of \"dialects.\" Each dialect defines operations and optimizations: for example, the ['math' dialect](https://mlir.llvm.org/docs/Dialects/MathOps/) provides mathematical operations such as sine and cosine, the ['amdgpu' dialect](https://mlir.llvm.org/docs/Dialects/AMDGPU/) provides operations specific to AMD processors, and so on.\n", - "\n", - "Each of MLIR's dialects can interoperate with the others. This is why MLIR is said to unlock heterogeneous compute: as newer, faster processors and architectures are developed, new MLIR dialects are implemented to generate optimal code for those environments. Any new MLIR dialect can be translated seamlessly into other dialects, so as more get added, all existing MLIR becomes more powerful.\n", - "\n", - "This means that our own custom types, such as the `OurBool` type we'll create below, can be used to provide programmers with a high-level, Python-like interface. But \"under the covers,\" Mojo and MLIR will optimize our convenient, high-level types for each new processor that appears in the future.\n", - "\n", - "There's much more to write about why MLIR is such a revolutionary technology, but let's get back to Mojo and defining the `OurBool` type. There will be opportunities to learn more about MLIR along the way." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Defining the `OurBool` type\n", - "\n", - "We can use Mojo's `struct` keyword to define a new type `OurBool`:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "struct OurBool:\n", - " var value: __mlir_type.i1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A boolean can represent 0 or 1, \"true\" or \"false.\" To store this information, `OurBool` has a single member, called `value`. Its type is represented *directly in MLIR*, using the MLIR builtin type [`i1`](https://mlir.llvm.org/docs/Dialects/Builtin/#integertype). In fact, you can use any MLIR type in Mojo, by prefixing the type name with `__mlir_type`.\n", - "\n", - "As we'll see below, representing our boolean value with `i1` will allow us to utilize all of the MLIR operations and optimizations that interface with the `i1` type -- and there are many of them!\n", - "\n", - "Having defined `OurBool`, we can now declare a variable of this type:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "fn uninitialized_our_bool():\n", - " var a: OurBool\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Leveraging MLIR\n", - "\n", - "Naturally, we might next try to create an instance of `OurBool`. Attempting to do so at this point, however, results in an error:\n", - "\n", - "```mojo\n", - "var a = OurBool() # error: 'OurBool' does not implement an '__init__' method\n", - "```\n", - "\n", - "As in Python, `__init__` is a [special method](https://docs.python.org/3/reference/datamodel.html#specialnames) that can be defined to customize the behavior of a type. We can implement an `__init__` method that takes no arguments, and returns an `OurBool` with a \"false\" value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "struct OurBool:\n", - " var value: __mlir_type.i1\n", - "\n", - " fn __init__(out self):\n", - " self.value = __mlir_op.`index.bool.constant`[\n", - " value=__mlir_attr.false,\n", - " ]()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To initialize the underlying `i1` value, we use an MLIR operation from its ['index' dialect](https://mlir.llvm.org/docs/Dialects/IndexOps/), called [`index.bool.constant`](https://mlir.llvm.org/docs/Dialects/IndexOps/#indexboolconstant-mlirindexboolconstantop).\n", - "\n", - "MLIR's 'index' dialect provides us with operations for manipulating builtin MLIR types, such as the `i1` we use to store the value of `OurBool`. The `index.bool.constant` operation takes a `true` or `false` compile-time constant as input, and produces a runtime output of type `i1` with the given value.\n", - "\n", - "So, as shown above, in addition to any MLIR type, Mojo also provides direct access to any MLIR operation via the `__mlir_op` prefix, and to any attribute via the `__mlir_attr` prefix. MLIR attributes are used to represent compile-time constants.\n", - "\n", - "As you can see above, the syntax for interacting with MLIR isn't always pretty: MLIR attributes are passed in between square brackets `[...]`, and the operation is executed via a parentheses suffix `(...)`, which can take runtime argument values. However, most Mojo programmers will not need to access MLIR directly, and for the few that do, this \"ugly\" syntax gives them superpowers: they can define high-level types that are easy to use, but that internally plug into MLIR and its powerful system of dialects.\n", - "\n", - "We think this is very exciting, but let's bring things back down to earth: having defined an `__init__` method, we can now create an instance of our `OurBool` type:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "var b = OurBool()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Value semantics in Mojo\n", - "\n", - "We can now instantiate `OurBool`, but using it is another story:\n", - "\n", - "```mojo\n", - "var a = OurBool()\n", - "var b = a # error: 'OurBool' does not implement the '__copyinit__' method\n", - "```\n", - "\n", - "Mojo uses \"value semantics\" by default, meaning that it expects to create a copy of `a` when assigning to `b`. However, Mojo doesn't make any assumptions about *how* to copy `OurBool`, or its underlying `i1` value. The error indicates that we should implement a `__copyinit__` method, which would implement the copying logic.\n", - "\n", - "In our case, however, `OurBool` is a very simple type, with only one \"trivially copyable\" member. We can use a decorator to tell the Mojo compiler that, saving us the trouble of defining our own `__copyinit__` boilerplate. Trivially copyable types must implement an `__init__` method that returns an instance of themselves, so we must also rewrite our initializer slightly." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "@register_passable(\"trivial\")\n", - "struct OurBool:\n", - " var value: __mlir_type.i1\n", - "\n", - " fn __init__(out self):\n", - " self.value = __mlir_op.`index.bool.constant`[\n", - " value=__mlir_attr.false,\n", - " ]()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now copy `OurBool` as we please:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "var c = OurBool()\n", - "var d = c" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compile-time constants\n", - "\n", - "It's not very useful to have a boolean type that can only represent \"false.\" Let's define compile-time constants that represent true and false `OurBool` values.\n", - "\n", - "First, let's define another `__init__` constructor for `OurBool` that takes its `i1` value as an argument:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "@register_passable(\"trivial\")\n", - "struct OurBool:\n", - " var value: __mlir_type.i1\n", - "\n", - " # ...\n", - "\n", - " @implicit\n", - " fn __init__(out self, value: __mlir_type.i1):\n", - " self.value = value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This allows us to define compile-time constant `OurBool` values, using the `alias` keyword. First, let's define `OurTrue`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "alias OurTrue = OurBool(__mlir_attr.true)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we're passing in an MLIR compile-time constant value of `true`, which has the `i1` type that our new `__init__` constructor expects. We can use a slightly different syntax for `OurFalse`:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "alias OurFalse: OurBool = __mlir_attr.false" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`OurFalse` is declared to be of type `OurBool`, and then assigned an `i1` type -- in this case, the `OurBool` constructor we added is called implicitly.\n", - "\n", - "With true and false constants, we can also simplify our original `__init__` constructor for `OurBool`. Instead of constructing an MLIR value, we can simply return our `OurFalse` constant:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "alias OurTrue = OurBool(__mlir_attr.true)\n", - "alias OurFalse: OurBool = __mlir_attr.false\n", - "\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct OurBool:\n", - " var value: __mlir_type.i1\n", - "\n", - " # We can simplify our no-argument constructor:\n", - " fn __init__(out self):\n", - " self = OurFalse\n", - "\n", - " @implicit\n", - " fn __init__(out self, value: __mlir_type.i1):\n", - " self.value = value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note also that we can define `OurTrue` before we define `OurBool`. The Mojo compiler is smart enough to figure this out.\n", - "\n", - "With these constants, we can now define variables with both true and false values of `OurBool`:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "var e = OurTrue\n", - "var f = OurFalse" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implementing `__bool__`\n", - "\n", - "Of course, the reason booleans are ubiquitous in programming is because they can be used for program control flow. However, if we attempt to use `OurBool` in this way, we get an error:\n", - "\n", - "```mojo\n", - "var a = OurTrue\n", - "if a: print(\"It's true!\") # error: 'OurBool' does not implement the '__bool__' method\n", - "```\n", - "\n", - "When Mojo attempts to execute our program, it needs to be able to determine whether to print \"It's true!\" or not. It doesn't yet know that `OurBool` represents a boolean value -- Mojo just sees a struct that is 1 bit in size. However, Mojo also provides interfaces that convey boolean qualities, which are the same as those used by Mojo's standard library types, like `Bool`. In practice, this means Mojo gives you full control: any type that's packaged with the language's standard library is one for which you could define your own version.\n", - "\n", - "In the case of our error message, Mojo is telling us that implementing a `__bool__` method on `OurBool` would signify that it has boolean qualities.\n", - "\n", - "Thankfully, `__bool__` is simple to implement: Mojo's standard library and builtin types are all implemented on top of MLIR, and so the builtin `Bool` type also defines a constructor that takes an `i1`, just like `OurBool`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "alias OurTrue = OurBool(__mlir_attr.true)\n", - "alias OurFalse: OurBool = __mlir_attr.false\n", - "\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct OurBool:\n", - " var value: __mlir_type.i1\n", - "\n", - " # ...\n", - "\n", - " @implicit\n", - " fn __init__(out self, value: __mlir_type.i1):\n", - " self.value = value\n", - "\n", - " # Our new method converts `OurBool` to `Bool`:\n", - " fn __bool__(self) -> Bool:\n", - " return Bool(self.value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use `OurBool` anywhere we can use the builtin `Bool` type:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It's true!\n" - ] - } - ], - "source": [ - "#| CHECK: It's true!\n", - "var g = OurTrue\n", - "if g: print(\"It's true!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Avoiding type conversion with `__mlir_i1__`\n", - "\n", - "The `OurBool` type is looking great, and by providing a conversion to `Bool`,\n", - "it can be used anywhere the builtin `Bool` type can. But we promised you \"full\n", - "control,\" and the ability to define your own version of any type built into\n", - "Mojo or its standard library. So, why do we depend on `__bool__` to convert our\n", - "type into `Bool` (the standard library type)? This is just the formal way for\n", - "Mojo to evaluate a type as a boolean, which is useful for real-world scenarios.\n", - "However, to define a boolean type from scratch, you have a more low-level\n", - "option.\n", - "\n", - "When Mojo evaluates a conditional expression, it actually attempts to convert\n", - "the expression to an MLIR `i1` value, by searching for the special interface\n", - "method `__mlir_i1__`. (The automatic conversion to `Bool` occurs because `Bool`\n", - "is known to implement the `__mlir_i1__` method.)\n", - "\n", - "Thus, by implementing the `__mlir_i1__` special methods in `OurBool`, we can\n", - "create a type that can replaces `Bool` entirely:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "alias OurTrue = OurBool(__mlir_attr.true)\n", - "alias OurFalse: OurBool = __mlir_attr.false\n", - "\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct OurBool:\n", - " var value: __mlir_type.i1\n", - "\n", - " @implicit\n", - " fn __init__(out self, value: __mlir_type.i1):\n", - " self.value = value\n", - "\n", - " # Our new method converts `OurBool` to `i1`:\n", - " fn __mlir_i1__(self) -> __mlir_type.i1:\n", - " return self.value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can still use `OurBool` in conditionals just as we did before:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No more Bool conversion!\n" - ] - } - ], - "source": [ - "#| CHECK: No more Bool conversion!\n", - "var h = OurTrue\n", - "if h: print(\"No more Bool conversion!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But this time, no conversion to `Bool` occurs. You can try adding `print` statements to the `__bool__` and `__mlir_i1__` methods to see for yourself." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding functionality with MLIR\n", - "\n", - "There are many more ways we can improve `OurBool`. Many of those involve implementing special methods, some of which you may recognize from Python, and some which are specific to Mojo. For example, we can implement inversion of a `OurBool` value by adding a `__invert__` method. We can also add an `__eq__` method, which allows two `OurBool` to be compared with the `==` operator.\n", - "\n", - "What sets Mojo apart is the fact that we can implement each of these using MLIR. To implement `__eq__`, for example, we use the [`index.casts`](https://mlir.llvm.org/docs/Dialects/IndexOps/#indexcasts-mlirindexcastsop) operation to cast our `i1` values to the MLIR index dialect's `index` type, and then the [`index.cmp`](https://mlir.llvm.org/docs/Dialects/IndexOps/#indexcmp-mlirindexcmpop) operation to compare them for equality. And with the `__eq__` method implemented, we can then implement `__invert__` in terms of `__eq__`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "alias OurTrue = OurBool(__mlir_attr.true)\n", - "alias OurFalse: OurBool = __mlir_attr.false\n", - "\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct OurBool:\n", - " var value: __mlir_type.i1\n", - "\n", - " @implicit\n", - " fn __init__(out self, value: __mlir_type.i1):\n", - " self.value = value\n", - "\n", - " # ...\n", - "\n", - " fn __mlir_i1__(self) -> __mlir_type.i1:\n", - " return self.value\n", - "\n", - " fn __eq__(self, rhs: OurBool) -> Self:\n", - " var lhs_index = __mlir_op.`index.casts`[_type=__mlir_type.index](\n", - " self.value\n", - " )\n", - " var rhs_index = __mlir_op.`index.casts`[_type=__mlir_type.index](\n", - " rhs.value\n", - " )\n", - " return Self(\n", - " __mlir_op.`index.cmp`[\n", - " pred=__mlir_attr.`#index`\n", - " ](lhs_index, rhs_index)\n", - " )\n", - "\n", - " fn __invert__(self) -> Self:\n", - " return OurFalse if self == OurTrue else OurTrue" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This allows us to use the `~` operator with `OurBool`:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It's false!\n" - ] - } - ], - "source": [ - "#| CHECK: It's false!\n", - "var i = OurFalse\n", - "if ~i: print(\"It's false!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This extensible design is what allows even \"built in\" Mojo types like `Bool`, `Int`, and even `Tuple` to be implemented in the Mojo standard library in terms of MLIR, rather than hard-coded into the Mojo language. This also means that there's almost nothing that those types can achieve that user-defined types cannot.\n", - "\n", - "By extension, this means that the incredible performance that Mojo unlocks for machine learning workflows isn't due to some magic being performed behind a curtain -- you can define your own high-level types that, in their implementation, use low-level MLIR to achieve unprecedented speed and control." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The promise of modularity\n", - "\n", - "As we've seen, Mojo's integration with MLIR allows Mojo programmers to implement zero-cost abstractions on par with Mojo's own built-in and standard library types.\n", - "\n", - "MLIR is open-source and extensible: new dialects are being added all the time, and those dialects then become available to use in Mojo. All the while, Mojo code gets more powerful and more optimized for new hardware -- with no additional work necessary by Mojo programmers.\n", - "\n", - "What this means is that your own custom types, whether those be `OurBool` or `OurTensor`, can be used to provide programmers with an easy-to-use and unchanging interface. But behind the scenes, MLIR will optimize those convenient, high-level types for the computing environments of tomorrow.\n", - "\n", - "In other words: Mojo isn't magic, it's modular." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Mojo", - "language": "mojo", - "name": "mojo-jupyter-kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "mojo" - }, - "file_extension": ".mojo", - "mimetype": "text/x-mojo", - "name": "mojo" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/notebooks/Mandelbrot.ipynb b/examples/notebooks/Mandelbrot.ipynb deleted file mode 100644 index 348d0013ff..0000000000 --- a/examples/notebooks/Mandelbrot.ipynb +++ /dev/null @@ -1,385 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: Mandelbrot in Mojo with Python plots\n", - "description: Learn how to write high-performance Mojo code and import Python packages.\n", - "website:\n", - " open-graph:\n", - " image: /static/images/mojo-social-card.png\n", - " twitter-card:\n", - " image: /static/images/mojo-social-card.png\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "# Mandelbrot in Mojo with Python plots\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Not only is Mojo great for writing high-performance code, but it also allows us to leverage the huge Python ecosystem of libraries and tools. With seamless Python interoperability, Mojo can use Python for what it's good at, especially GUIs, without sacrificing performance in critical code. Let's take the classic Mandelbrot set algorithm and implement it in Mojo.\n", - "\n", - "This tutorial shows two aspects of Mojo. First, it shows that Mojo can be used to develop fast programs for irregular applications. It also shows how we can leverage Python for visualizing the results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#|code-fold: true\n", - "import benchmark\n", - "from math import iota\n", - "from sys import num_physical_cores, simdwidthof\n", - "from algorithm import parallelize, vectorize\n", - "from complex import ComplexFloat64, ComplexSIMD\n", - "from python import Python\n", - "from memory import UnsafePointer\n", - "\n", - "alias float_type = DType.float32\n", - "alias int_type = DType.int32\n", - "alias simd_width = 2 * simdwidthof[float_type]()\n", - "alias unit = benchmark.Unit.ms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First set some parameters, you can try changing these to see different results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "alias width = 960\n", - "alias height = 960\n", - "alias MAX_ITERS = 200\n", - "\n", - "alias min_x = -2.0\n", - "alias max_x = 0.6\n", - "alias min_y = -1.5\n", - "alias max_y = 1.5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define a simple `Matrix` struct:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@value\n", - "struct Matrix[type: DType, rows: Int, cols: Int]:\n", - " var data: UnsafePointer[Scalar[type]]\n", - "\n", - " fn __init__(out self):\n", - " self.data = UnsafePointer[Scalar[type]].alloc(rows * cols)\n", - "\n", - " fn __getitem__(self, row: Int, col: Int) -> Scalar[type]:\n", - " return self.data.load(row * cols + col)\n", - "\n", - " fn store[width: Int = 1](self, row: Int, col: Int, val: SIMD[type, width]):\n", - " self.data.store(row * cols + col, val)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The core [Mandelbrot](https://en.wikipedia.org/wiki/Mandelbrot_set) algorithm involves computing an iterative complex function for each pixel until it \"escapes\" the complex circle of radius 2, counting the number of iterations to escape:\n", - "\n", - "$$z_{i+1} = z_i^2 + c$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the number of steps to escape.\n", - "def mandelbrot_kernel(c: ComplexFloat64) -> Int:\n", - " z = c\n", - " for i in range(MAX_ITERS):\n", - " z = z * z + c\n", - " if z.squared_norm() > 4:\n", - " return i\n", - " return MAX_ITERS\n", - "\n", - "\n", - "def compute_mandelbrot() -> Matrix[float_type, height, width]:\n", - " # create a matrix. Each element of the matrix corresponds to a pixel\n", - " matrix = Matrix[float_type, height, width]()\n", - "\n", - " dx = (max_x - min_x) / width\n", - " dy = (max_y - min_y) / height\n", - "\n", - " y = min_y\n", - " for row in range(height):\n", - " x = min_x\n", - " for col in range(width):\n", - " matrix.store(row, col, mandelbrot_kernel(ComplexFloat64(x, y)))\n", - " x += dx\n", - " y += dy\n", - " return matrix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the number of iterations to escape with some color gives us the canonical Mandelbrot set plot. To render it we can directly leverage Python's `matplotlib` right from Mojo!\n", - "\n", - "First install the required libraries:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%python\n", - "from importlib.util import find_spec\n", - "import shutil\n", - "import subprocess\n", - "\n", - "fix = \"\"\"\n", - "-------------------------------------------------------------------------\n", - "fix following the steps here:\n", - " https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719\n", - "-------------------------------------------------------------------------\n", - "\"\"\"\n", - "\n", - "def install_if_missing(name: str):\n", - " if find_spec(name):\n", - " return\n", - " print(\"missing\", name)\n", - " print(f\"{name} not found, installing...\")\n", - " try:\n", - " if shutil.which('python3'): python = \"python3\"\n", - " elif shutil.which('python'): python = \"python\"\n", - " else: raise (\"python not on path\" + fix)\n", - " subprocess.check_call([python, \"-m\", \"pip\", \"install\", name])\n", - " except:\n", - " raise ImportError(f\"{name} not found\" + fix)\n", - "\n", - "install_if_missing(\"numpy\")\n", - "install_if_missing(\"matplotlib\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def show_plot[type: DType](matrix: Matrix[type, height, width]):\n", - " alias scale = 10\n", - " alias dpi = 64\n", - "\n", - " np = Python.import_module(\"numpy\")\n", - " plt = Python.import_module(\"matplotlib.pyplot\")\n", - " colors = Python.import_module(\"matplotlib.colors\")\n", - "\n", - " numpy_array = np.zeros((height, width), np.float64)\n", - "\n", - " for row in range(height):\n", - " for col in range(width):\n", - " numpy_array.itemset((row, col), matrix[row, col])\n", - "\n", - " fig = plt.figure(1, [scale, scale * height // width], dpi)\n", - " ax = fig.add_axes([0.0, 0.0, 1.0, 1.0], False, 1)\n", - " light = colors.LightSource(315, 10, 0, 1, 1, 0)\n", - "\n", - " image = light.shade(numpy_array, plt.cm.hot, colors.PowerNorm(0.3), \"hsv\", 0, 0, 1.5)\n", - " plt.imshow(image)\n", - " plt.axis(\"off\")\n", - " plt.show()\n", - "\n", - "show_plot(compute_mandelbrot())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Vectorizing Mandelbrot\n", - "We showed a naive implementation of the Mandelbrot algorithm, but there are two things we can do to speed it up. We can early-stop the loop iteration when a pixel is known to have escaped, and we can leverage Mojo's access to hardware by vectorizing the loop, computing multiple pixels simultaneously. To do that we will use the `vectorize` higher order generator.\n", - "\n", - "We start by defining our main iteration loop in a vectorized fashion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn mandelbrot_kernel_SIMD[\n", - " simd_width: Int\n", - "](c: ComplexSIMD[float_type, simd_width]) -> SIMD[int_type, simd_width]:\n", - " \"\"\"A vectorized implementation of the inner mandelbrot computation.\"\"\"\n", - " var cx = c.re\n", - " var cy = c.im\n", - " var x = SIMD[float_type, simd_width](0)\n", - " var y = SIMD[float_type, simd_width](0)\n", - " var y2 = SIMD[float_type, simd_width](0)\n", - " var iters = SIMD[int_type, simd_width](0)\n", - "\n", - " var t: SIMD[DType.bool, simd_width] = True\n", - " for _ in range(MAX_ITERS):\n", - " if not any(t):\n", - " break\n", - " y2 = y * y\n", - " y = x.fma(y + y, cy)\n", - " t = x.fma(x, y2) <= 4\n", - " x = x.fma(x, cx - y2)\n", - " iters = t.select(iters + 1, iters)\n", - " return iters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above function is parameterized on the `simd_width` and processes simd_width pixels. It only escapes once all pixels within the vector lane are done. We can use the same iteration loop as above, but this time we vectorize within each row instead. We use the `vectorize` generator to make this a simple function call. The benchmark can run in parallel or just vectorized." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fn run_mandelbrot(parallel: Bool) raises -> Float64:\n", - " var matrix = Matrix[int_type, height, width]()\n", - "\n", - " @parameter\n", - " fn worker(row: Int):\n", - " alias scale_x = (max_x - min_x) / width\n", - " alias scale_y = (max_y - min_y) / height\n", - "\n", - " @parameter\n", - " fn compute_vector[simd_width: Int](col: Int):\n", - " \"\"\"Each time we operate on a `simd_width` vector of pixels.\"\"\"\n", - " var cx = min_x + (col + iota[float_type, simd_width]()) * scale_x\n", - " var cy = min_y + row * SIMD[float_type, simd_width](scale_y)\n", - " var c = ComplexSIMD[float_type, simd_width](cx, cy)\n", - " matrix.store(row, col, mandelbrot_kernel_SIMD[simd_width](c))\n", - "\n", - " # Vectorize the call to compute_vector where call gets a chunk of pixels.\n", - " vectorize[compute_vector, simd_width](width)\n", - "\n", - " @parameter\n", - " fn bench():\n", - " for row in range(height):\n", - " worker(row)\n", - "\n", - " @parameter\n", - " fn bench_parallel():\n", - " parallelize[worker](height, height)\n", - "\n", - " var time: Float64 = 0\n", - " if parallel:\n", - " time = benchmark.run[bench_parallel](max_runtime_secs=0.5).mean(unit)\n", - " else:\n", - " time = benchmark.run[bench](max_runtime_secs=0.5).mean(unit)\n", - "\n", - " show_plot(matrix)\n", - " matrix.data.free()\n", - " return time\n", - "\n", - "vectorized = run_mandelbrot(parallel=False)\n", - "print(\"Vectorized:\", vectorized, unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parallelizing Mandelbrot\n", - "While the vectorized implementation above is efficient, we can get better performance by parallelizing on the cols. This again is simple in Mojo using the `parallelize` higher order function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "parallelized = run_mandelbrot(parallel=True)\n", - "print(\"Parallelized:\", parallelized, unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Benchmarking\n", - "\n", - "In this section we compare the vectorized speed to the parallelized speed " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Number of physical cores:\", num_physical_cores())\n", - "print(\"Vectorized:\", vectorized, \"ms\")\n", - "print(\"Parallelized:\", parallelized, \"ms\")\n", - "#| CHECK: speedup\n", - "print(\"Parallel speedup:\", vectorized / parallelized)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Mojo (nightly)", - "language": "mojo", - "name": "mojo-nightly-jupyter-kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "mojo" - }, - "file_extension": ".mojo", - "mimetype": "text/x-mojo", - "name": "mojo" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/notebooks/Matmul.ipynb b/examples/notebooks/Matmul.ipynb deleted file mode 100644 index 239654bf5e..0000000000 --- a/examples/notebooks/Matmul.ipynb +++ /dev/null @@ -1,931 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: Matrix multiplication in Mojo\n", - "description: Learn how to leverage Mojo's various functions to write a high-performance matmul.\n", - "website:\n", - " open-graph:\n", - " image: /static/images/mojo-social-card.png\n", - " twitter-card:\n", - " image: /static/images/mojo-social-card.png\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "# Matrix multiplication in Mojo" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook describes how to write a matrix multiplication (matmul) algorithm in Mojo. We will start with a pure Python implementation, transition to a naive implementation that is essentially a copy of the Python one, then add types, then continue the optimizations by vectorizing, tiling, and parallelizing the implementation." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's define matrix multiplication. Given two dense matrices $A$ and $B$ of dimensions $M\\times K$ and $K\\times N$ respectively, we want to compute their dot product $C = A . B$ (also known as matmul). The dot product $C += A . B$ is defined by" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$C_{i,j} += \\sum_{k \\in [0 \\cdots K)} A_{i,k} B_{k,j}$$" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Please take look at our [blog](https://www.modular.com/blog/ais-compute-fragmentation-what-matrix-multiplication-teaches-us) post on matmul and why it is important for ML and DL workloads.\n", - "\n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The format of this notebook is to start with an implementation which is identical to that of Python (effectively renaming the file extension), then look at how adding types to the implementation helps performance before extending the implementation by leveraging the vectorization and parallelization capabilities available on modern hardware. Throughout the execution, we report the GFlops achieved." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Python Implementation" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's first implement matmul in Python directly from the definition." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%%python\n", - "def matmul_python(C, A, B):\n", - " for m in range(C.rows):\n", - " for k in range(A.cols):\n", - " for n in range(C.cols):\n", - " C[m, n] += A[m, k] * B[k, n]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's benchmark our implementation using 128 by 128 square matrices and report the achieved GFLops." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Install numpy if it's not already:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%%python\n", - "from importlib.util import find_spec\n", - "import shutil\n", - "import subprocess\n", - "\n", - "fix = \"\"\"\n", - "-------------------------------------------------------------------------\n", - "fix following the steps here:\n", - " https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719\n", - "-------------------------------------------------------------------------\n", - "\"\"\"\n", - "\n", - "def install_if_missing(name: str):\n", - " if find_spec(name):\n", - " return\n", - "\n", - " print(f\"{name} not found, installing...\")\n", - " try:\n", - " if shutil.which('python3'): python = \"python3\"\n", - " elif shutil.which('python'): python = \"python\"\n", - " else: raise (\"python not on path\" + fix)\n", - " subprocess.check_call([python, \"-m\", \"pip\", \"install\", name])\n", - " except:\n", - " raise ImportError(f\"{name} not found\" + fix)\n", - "\n", - "install_if_missing(\"numpy\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%%python\n", - "from timeit import timeit\n", - "import numpy as np\n", - "\n", - "class Matrix:\n", - " def __init__(self, value, rows, cols):\n", - " self.value = value\n", - " self.rows = rows\n", - " self.cols = cols\n", - "\n", - " def __getitem__(self, idxs):\n", - " return self.value[idxs[0]][idxs[1]]\n", - "\n", - " def __setitem__(self, idxs, value):\n", - " self.value[idxs[0]][idxs[1]] = value\n", - "\n", - "def benchmark_matmul_python(M, N, K):\n", - " A = Matrix(list(np.random.rand(M, K)), M, K)\n", - " B = Matrix(list(np.random.rand(K, N)), K, N)\n", - " C = Matrix(list(np.zeros((M, N))), M, N)\n", - " secs = timeit(lambda: matmul_python(C, A, B), number=2)/2\n", - " gflops = ((2*M*N*K)/secs) / 1e9\n", - " print(gflops, \"GFLOP/s\")\n", - " return gflops" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.004532987790119301 GFLOP/s\n" - ] - } - ], - "source": [ - "python_gflops = benchmark_matmul_python(128, 128, 128).to_float64()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing the Python implementation to Mojo" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using Mojo is as simple as Python. First, let's include that modules from the Mojo stdlib that we are going to use:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "#|code-fold: true\n", - "#|code-summary: \"Import utilities and define `Matrix` (click to show/hide)\"\n", - "\n", - "import benchmark\n", - "from memory import memset_zero\n", - "from random import rand, random_float64" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, we can copy and paste our Python code. Mojo adopts the syntax of Python, so the same Python code will run as Mojo code" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# This exactly the same Python implementation, but is infact Mojo code!\n", - "def matmul_untyped(C, A, B):\n", - " for m in range(C.rows):\n", - " for k in range(A.cols):\n", - " for n in range(C.cols):\n", - " C[m, n] += A[m, k] * B[k, n]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then benchmark the implementation. As before we use a 128 by 128 matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fn matrix_getitem(self: object, i: object) raises -> object:\n", - " return self.value[i]\n", - "\n", - "\n", - "fn matrix_setitem(self: object, i: object, value: object) raises -> object:\n", - " self.value[i] = value\n", - " return None\n", - "\n", - "\n", - "fn matrix_append(self: object, value: object) raises -> object:\n", - " self.value.append(value)\n", - " return None\n", - "\n", - "\n", - "fn matrix_init(rows: Int, cols: Int) raises -> object:\n", - " var value = object([])\n", - " return object(\n", - " Attr(\"value\", value), Attr(\"__getitem__\", matrix_getitem), Attr(\"__setitem__\", matrix_setitem),\n", - " Attr(\"rows\", rows), Attr(\"cols\", cols), Attr(\"append\", matrix_append),\n", - " )\n", - "\n", - "def benchmark_matmul_untyped(M: Int, N: Int, K: Int, python_gflops: Float64):\n", - " C = matrix_init(M, N)\n", - " A = matrix_init(M, K)\n", - " B = matrix_init(K, N)\n", - " for i in range(M):\n", - " c_row = object([])\n", - " b_row = object([])\n", - " a_row = object([])\n", - " for j in range(N):\n", - " c_row.append(0.0)\n", - " b_row.append(random_float64(-5, 5))\n", - " a_row.append(random_float64(-5, 5))\n", - " C.append(c_row)\n", - " B.append(b_row)\n", - " A.append(a_row)\n", - "\n", - " @parameter\n", - " fn test_fn():\n", - " try:\n", - " _ = matmul_untyped(C, A, B)\n", - " except:\n", - " pass\n", - "\n", - " var secs = benchmark.run[test_fn](max_runtime_secs=0.5).mean()\n", - " _ = (A, B, C)\n", - " var gflops = ((2*M*N*K)/secs) / 1e9\n", - " var speedup : Float64 = gflops / python_gflops\n", - " print(gflops, \"GFLOP/s, a\", speedup, \"x speedup over Python\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.014276454358821751 GFLOP/s, a 3.1494579336702819 x speedup over Python\n" - ] - } - ], - "source": [ - "benchmark_matmul_untyped(128, 128, 128, python_gflops)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the huge speedup with no effort that we have gotten." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding types to the Python implementation" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above program, while achieving better performance than Python, is still not the best we can get from Mojo. If we tell Mojo the types of the inputs, it can optimize much of the code away and reduce dispatching costs (unlike Python, which only uses types for type checking, Mojo exploits type info for performance optimizations as well)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do that, let's first define a `Matrix` struct. The `Matrix` struct contains a data pointer along with size fields. While the `Matrix` struct can be parametrized on any data type, here we set the data type to be Float32 for conciseness." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from memory import memset_zero\n", - "alias type = DType.float32\n", - "\n", - "struct Matrix[rows: Int, cols: Int]:\n", - " var data: UnsafePointer[Scalar[type]]\n", - "\n", - " # Initialize zeroeing all values\n", - " fn __init__(out self):\n", - " self.data = UnsafePointer[Scalar[type]].alloc(rows * cols)\n", - " memset_zero(self.data, rows * cols)\n", - "\n", - " # Initialize taking a pointer, don't set any elements\n", - " fn __init__(out self, data: UnsafePointer[Scalar[type]]):\n", - " self.data = data\n", - "\n", - " # Initialize with random values\n", - " @staticmethod\n", - " fn rand() -> Self:\n", - " var data = UnsafePointer[Scalar[type]].alloc(rows * cols)\n", - " rand(data.address, rows * cols)\n", - " return Self(data)\n", - "\n", - " fn __getitem__(self, y: Int, x: Int) -> Scalar[type]:\n", - " return self.load[1](y, x)\n", - "\n", - " fn __setitem__(self, y: Int, x: Int, val: Scalar[type]):\n", - " self.store(y, x, val)\n", - "\n", - " fn load[nelts: Int](self, y: Int, x: Int) -> SIMD[type, nelts]:\n", - " return self.data.load[width=nelts](y * self.cols + x)\n", - "\n", - " fn store[nelts: Int, //](self, y: Int, x: Int, val: SIMD[type, nelts]):\n", - " return self.data.store(y * self.cols + x, val)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Note that we implement `getitem` and `setitem` in terms of `load` and `store`. For the naive implementation of matmul it does not make a difference, but we will utilize this later in a more optimized vectorized version of matmul.\n", - "\n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the above `Matrix` type we can effectively copy and paste the Python implementation and just add type annotations:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Note that C, A, and B have types.\n", - "fn matmul_naive(C: Matrix, A: Matrix, B: Matrix):\n", - " for m in range(C.rows):\n", - " for k in range(A.cols):\n", - " for n in range(C.cols):\n", - " C[m, n] += A[m, k] * B[k, n]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are going to benchmark the implementations as we improve, so let's write a helper function that will do that for us: " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "alias M = 1024\n", - "alias N = 1024\n", - "alias K = 1024\n", - "\n", - "@always_inline\n", - "fn bench[\n", - " func: fn (Matrix, Matrix, Matrix) -> None](base_gflops: Float64) raises:\n", - " var C = Matrix[M, N]()\n", - " var A = Matrix[M, K].rand()\n", - " var B = Matrix[K, N].rand()\n", - "\n", - " @always_inline\n", - " @parameter\n", - " fn test_fn():\n", - " _ = func(C, A, B)\n", - "\n", - " var secs = benchmark.run[test_fn](max_runtime_secs=1).mean()\n", - "\n", - " A.data.free()\n", - " B.data.free()\n", - " C.data.free()\n", - "\n", - " var gflops = ((2 * M * N * K) / secs) / 1e9\n", - " var speedup: Float64 = gflops / base_gflops\n", - "\n", - " print(gflops, \"GFLOP/s, a\", speedup, \"x speedup over Python\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Benchmarking shows significant speedups. We increase the size of the matrix to 512 by 512, since Mojo is much faster than Python." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5.5584227194331879 GFLOP/s, a 1226.2161242854129 x speedup over Python\n" - ] - } - ], - "source": [ - "bench[matmul_naive](python_gflops)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding type annotations gives a huge improvement compared to the original untyped version." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Vectorizing the inner most loop" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can do better than the above implementation by utilizing vector instructions. Rather than assuming a vector width, we query the simd width of the specified dtype using `simdwidthof`. This makes our code portable as we transition to other hardware. Leveraging SIMD instructions is as easy as:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from sys import simdwidthof\n", - "# simdwidthof = number of float32 elements that fit into a single SIMD register\n", - "# using a 2x multiplier allows some SIMD operations to run in the same cycle\n", - "alias nelts = simdwidthof[DType.float32]() * 2\n", - "\n", - "fn matmul_vectorized_0(C: Matrix, A: Matrix, B: Matrix):\n", - " for m in range(C.rows):\n", - " for k in range(A.cols):\n", - " for nv in range(0, C.cols - nelts + 1, nelts):\n", - " C.store(m, nv, C.load[nelts](m, nv) + A[m, k] * B.load[nelts](k, nv))\n", - "\n", - " # Handle remaining elements with scalars.\n", - " for n in range(nelts * (C.cols // nelts), C.cols):\n", - " C[m, n] += A[m, k] * B[k, n]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can benchmark the above implementation. Note that many compilers can detect naive loops and perform optimizations on them. Mojo, however, allows you to be explicit and precisely control what optimizations are applied." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "24.522805389188736 GFLOP/s, a 5409.8547193623335 x speedup over Python\n" - ] - } - ], - "source": [ - "bench[matmul_vectorized_0](python_gflops)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Vectorization is a common optimization, and Mojo provides a higher-order function that performs vectorization for you. The `vectorize` function takes a vector width and a function which is parametric on the vector width and is going to be evaluated in a vectorized manner." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Simplify the code by using the builtin vectorize function\n", - "from algorithm import vectorize\n", - "\n", - "fn matmul_vectorized_1(C: Matrix, A: Matrix, B: Matrix):\n", - " for m in range(C.rows):\n", - " for k in range(A.cols):\n", - " @parameter\n", - " fn dot[nelts: Int](n: Int):\n", - " C.store(m, n, C.load[nelts](m, n) + A[m, k] * B.load[nelts](k, n))\n", - " vectorize[dot, nelts, size = C.cols]()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is only a slight difference in terms of performance between the two implementations:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "17.823704154967395 GFLOP/s, a 3931.9991538071854 x speedup over Python\n" - ] - } - ], - "source": [ - "bench[matmul_vectorized_1](python_gflops)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parallelizing Matmul" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With Mojo we can easily run code in parallel with the `parallelize` function.\n", - "\n", - "Let's modify our matmul implementation and make it multi-threaded (for simplicity, we only `parallelize` on the M dimension).\n", - "\n", - "In `parallelize` below we're overpartitioning by distributing the work more evenly among processors. This ensures they all have something to work on even if some tasks finish before others, or some processors are stragglers. Intel and Apple now have separate performance and efficiency cores and this mitigates the problems that can cause." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Parallelize the code by using the builtin parallelize function\n", - "from algorithm import parallelize\n", - "\n", - "fn matmul_parallelized(C: Matrix, A: Matrix, B: Matrix):\n", - " @parameter\n", - " fn calc_row(m: Int):\n", - " for k in range(A.cols):\n", - " @parameter\n", - " fn dot[nelts : Int](n : Int):\n", - " C.store(m,n, C.load[nelts](m,n) + A[m,k] * B.load[nelts](k,n))\n", - " vectorize[dot, nelts, size = C.cols]()\n", - " parallelize[calc_row](C.rows, C.rows)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can benchmark the parallel matmul implementation." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "66.225456232955807 GFLOP/s, a 14609.670111468104 x speedup over Python\n" - ] - } - ], - "source": [ - "bench[matmul_parallelized](python_gflops)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tiling Matmul" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tiling is an optimization performed for matmul to increase cache locality. The idea is to keep sub-matrices resident in the cache and increase the reuse. The tile function itself can be written in Mojo as:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from algorithm import Static2DTileUnitFunc as Tile2DFunc\n", - "\n", - "# Perform 2D tiling on the iteration space defined by end_x and end_y.\n", - "fn tile[tiled_fn: Tile2DFunc, tile_x: Int, tile_y: Int](end_x: Int, end_y: Int):\n", - " # Note: this assumes that ends are multiples of the tiles.\n", - " for y in range(0, end_y, tile_y):\n", - " for x in range(0, end_x, tile_x):\n", - " tiled_fn[tile_x, tile_y](x, y)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above will perform 2 dimensional tiling over a 2D iteration space defined to be between $([0, end_x], [0, end_y])$. Once we define it above, we can use it within our matmul kernel. For simplicity we choose `4` as the tile height and since we also want to vectorize we use `4 * nelts` as the tile width (since we vectorize on the columns)." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Use the above tile function to perform tiled matmul.\n", - "fn matmul_tiled_parallelized(C: Matrix, A: Matrix, B: Matrix):\n", - " @parameter\n", - " fn calc_row(m: Int):\n", - " @parameter\n", - " fn calc_tile[tile_x: Int, tile_y: Int](x: Int, y: Int):\n", - " for k in range(y, y + tile_y):\n", - " @parameter\n", - " fn dot[nelts: Int](n: Int):\n", - " C.store(m, n + x, C.load[nelts](m, n + x) + A[m, k] * B.load[nelts](k, n + x))\n", - " vectorize[dot, nelts, size = tile_x]()\n", - "\n", - " # We hardcode the tile factor to be 4.\n", - " alias tile_size = 4\n", - " tile[calc_tile, nelts * tile_size, tile_size](A.cols, C.cols)\n", - "\n", - " parallelize[calc_row](C.rows, C.rows)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, we can benchmark the tiled parallel matmul implementation:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "66.530139059653536 GFLOP/s, a 14676.884681814368 x speedup over Python\n" - ] - } - ], - "source": [ - "bench[matmul_tiled_parallelized](python_gflops)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One source of overhead in the above implementation is the fact that the we are not unrolling the loops introduced by vectorize of the dot function. We can do that via the `unroll_factor` higher-order function in Mojo:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Unroll the vectorized loop by a constant factor.\n", - "fn matmul_tiled_unrolled_parallelized(C: Matrix, A: Matrix, B: Matrix):\n", - " @parameter\n", - " fn calc_row(m: Int):\n", - " @parameter\n", - " fn calc_tile[tile_x: Int, tile_y: Int](x: Int, y: Int):\n", - " for k in range(y, y + tile_y):\n", - " @parameter\n", - " fn dot[nelts: Int](n: Int):\n", - " C.store(m, n + x, C.load[nelts](m, n + x) + A[m, k] * B.load[nelts](k, n + x))\n", - "\n", - " # Vectorize by nelts and unroll by tile_x/nelts\n", - " # Here unroll factor is 4\n", - " alias unroll_factor = tile_x // nelts\n", - " vectorize[dot, nelts, size=tile_x, unroll_factor=unroll_factor]()\n", - "\n", - " alias tile_size = 4\n", - " tile[calc_tile, nelts * tile_size, tile_size](A.cols, C.cols)\n", - "\n", - " parallelize[calc_row](C.rows, C.rows)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, we can benchmark the new tiled parallel matmul implementation with unrolled and vectorized inner loop:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "72.59541968462139 GFLOP/s, a 16014.916219906869 x speedup over Python\n" - ] - } - ], - "source": [ - "#| CHECK: GFLOP\n", - "bench[matmul_tiled_unrolled_parallelized](python_gflops)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Mojo", - "language": "mojo", - "name": "mojo-jupyter-kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "mojo" - }, - "file_extension": ".mojo", - "mimetype": "text/x-mojo", - "name": "mojo" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/notebooks/README.md b/examples/notebooks/README.md deleted file mode 100644 index d94bb7bf4b..0000000000 --- a/examples/notebooks/README.md +++ /dev/null @@ -1,134 +0,0 @@ -# Mojo Jupyter notebooks - -Mojo supports programming in [Jupyter notebooks](https://jupyter.org/), just -like Python. - -This page explains how to get started with Mojo notebooks, and this repo -directory contains notebooks that demonstrate some of Mojo's features. - -If you're not familiar with Jupyter notebooks, they're files that allow you to -create documents with live code, equations, visualizations, and explanatory -text. They're basically documents with executable code blocks, making them -great for sharing code experiments and programming tutorials. We actually wrote -the [Mojo programming -manual](https://docs.modular.com/mojo/programming-manual.html) as a Jupyter -notebook, so we can easily test all the code samples. - -And because Mojo allows you to import Python modules, you can use visualization -libraries in your notebooks to draw graphs and charts, or display images. For -an example, check out the `Mandelbrot.ipynb` notebook, which uses `matplotlib` -to draw the Mandelbrot set calculated in Mojo, and the `RayTracing.ipynb` -notebook, which draws images using `numpy`. - -## Get started in VS Code - -Visual Studio Code is a great environment for programming with Jupyter -notebooks. Especially if you're developing with Mojo on a remote system, using -VS Code is ideal because it allows you to edit and interact with notebooks on -the remote machine where you've installed Mojo. - -All you need is Mojo and the Jupyter VS Code extension: - -1. [Create a new Mojo -project](https://docs.modular.com/mojo/manual/get-started#1-create-a-new-project). - -2. Install [Visual Studio Code](https://code.visualstudio.com/) and the - [Jupyter - extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter). - -3. Then open any `.ipynb` file with Mojo code, click **Select Kernel** in the - top-right corner of the document, and then select **Jupyter Kernel > Mojo**. - - The Mojo kernel should have been installed automatically when you installed - the Mojo SDK. If the Mojo kernel is not listed, make sure that your - `$MODULAR_HOME` environment variable is set on the system where you - installed the Mojo SDK (specified in the `~/.profile` or `~/.bashrc` file). - - Now run some Mojo code! - -## Get started with JupyterLab - -You can also select the Mojo kernel when running notebooks in a local instance -of JupyterLab. The following is just a quick setup guide for Linux users with -the Mojo SDK installed locally, and it might not work with your system (these -instructions don't support remote access to the JupyterLab). For more details -about using JupyterLab, see the complete [JupyterLab installation -guide](https://jupyterlab.readthedocs.io/en/latest/getting_started/installation.html). - -### 1. Launch JupyterLab - -You can use either Magic or conda. - -#### Using Magic - -If you have [`magic`](https://docs.modular.com/magic) you can run the following -command to launch JupyterLab from this directory: - -```sh -# Run from an active conda or magic shell environment -magic run jupyter lab -``` - -After a moment, it will open a browser window with JupterLab running. - -#### Using conda - -Conda allows you to export environments in `.yml` format. (For more -information, see -[Managing environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) -in the Conda documentation.) - -If you already have a working Jupyter environment on a different computer and -server you can export it using the following command. - -```sh -# Example of exporting environment.yml from a conda environment named `your-env` -conda env export --name your_env > environment.yml -``` - -To create a Conda environment, activate that environment, and install JupyterLab. - -``` sh -# Create a Conda environment if you don't have one -conda create -n mojo-repo -# Activate the environment -conda env update -n mojo-repo -f environment.yml --prune -# run JupyterLab -conda run -n mojo-repo jupyter lab -``` - -After a moment, it will open a browser window with JupterLab running. - -#### Using more magic - -Magic allows you to create an environment from a Conda `environment.yml`. - -```sh -# Create a magic environment if you don't have one -magic init mojo-repo --import environment.yml -# Activate the environment -cd mojo-repo && magic shell -# run JupyterLab -magic run jupyter lab -``` - -### 2. Run the .ipynb notebooks - -The left nav bar should show all the notebooks in this directory. -Open any `.ipynb` file and start running the code. - -## Notes and tips - -- Code in a Jupyter notebook cell behaves like code in a Mojo REPL environment: - The `main()` function is not required, but there are some caveats: - - - Top-level variables (variables declared outside a function) are not visible - inside functions. - - - Redefining undeclared variables is not supported (variables without a `let` - or `var` in front). If you’d like to redefine a variable across notebook - cells, you must declare the variable with either `let` or `var`. - -- You can use `%%python` at the top of a code cell and write normal Python - code. Variables, functions, and imports defined in a Python cell are available - from subsequent Mojo code cells. diff --git a/examples/notebooks/RayTracing.ipynb b/examples/notebooks/RayTracing.ipynb deleted file mode 100644 index ad3830bbe1..0000000000 --- a/examples/notebooks/RayTracing.ipynb +++ /dev/null @@ -1,1022 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "id": "7a924a43", - "metadata": {}, - "source": [ - "---\n", - "title: Ray tracing in Mojo\n", - "description: Learn how to draw 3D graphics with ray-traced lighting using Mojo.\n", - "website:\n", - " open-graph:\n", - " image: /static/images/mojo-social-card.png\n", - " twitter-card:\n", - " image: /static/images/mojo-social-card.png\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "e48ca293", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "*Copyright 2023 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" - ] - }, - { - "cell_type": "markdown", - "id": "214584ac", - "metadata": {}, - "source": [ - "[//]: # REMOVE_FOR_WEBSITE\n", - "# Ray tracing in Mojo" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c4edb2c9-5109-4e00-98c0-3aa92dbca7d1", - "metadata": {}, - "source": [ - "This tutorial about [ray\n", - "tracing](https://en.wikipedia.org/wiki/Ray_tracing_(graphics)) is based on the\n", - "popular tutorial [Understandable RayTracing in\n", - "C++](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing).\n", - "The mathematical explanations are well described in that tutorial, so we'll\n", - "just point you to the appropriate sections for reference as we implement a\n", - "basic ray tracer in Mojo." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "fbd4aa63-a178-41f2-ad66-b017c1c69d2a", - "metadata": {}, - "source": [ - "## Step 1: Basic definitions\n", - "\n", - "We'll start by defining a `Vec3f` struct, which will use to represent a vector\n", - "in 3D space as well as RGB pixels. We'll use a `SIMD` representation for our\n", - "vector to enable vectorized operations. The `SIMD` type is a fixed-size vector,\n", - "and its size must be a power of 2. So we'll use a size of 4 and always pad the\n", - "underlying storage with a 0." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78a07829-05bc-4a67-8ccc-f2e1537c478c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from math import isqrt\n", - "\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct Vec3f:\n", - " var data: SIMD[DType.float32, 4]\n", - "\n", - " @always_inline\n", - " fn __init__(out self, x: Float32, y: Float32, z: Float32):\n", - " self.data = SIMD[DType.float32, 4](x, y, z, 0)\n", - "\n", - " @implicit\n", - " @always_inline\n", - " fn __init__(out self, data: SIMD[DType.float32, 4]):\n", - " self.data = data\n", - "\n", - " @always_inline\n", - " @staticmethod\n", - " fn zero() -> Vec3f:\n", - " return Vec3f(0, 0, 0)\n", - "\n", - " @always_inline\n", - " fn __sub__(self, other: Vec3f) -> Vec3f:\n", - " return self.data - other.data\n", - "\n", - " @always_inline\n", - " fn __add__(self, other: Vec3f) -> Vec3f:\n", - " return self.data + other.data\n", - "\n", - " @always_inline\n", - " fn __matmul__(self, other: Vec3f) -> Float32:\n", - " return (self.data * other.data).reduce_add()\n", - "\n", - " @always_inline\n", - " fn __mul__(self, k: Float32) -> Vec3f:\n", - " return self.data * k\n", - "\n", - " @always_inline\n", - " fn __neg__(self) -> Vec3f:\n", - " return self.data * -1.0\n", - "\n", - " @always_inline\n", - " fn __getitem__(self, idx: Int) -> SIMD[DType.float32, 1]:\n", - " return self.data[idx]\n", - "\n", - " @always_inline\n", - " fn cross(self, other: Vec3f) -> Vec3f:\n", - " var self_zxy = self.data.shuffle[2, 0, 1, 3]()\n", - " var other_zxy = other.data.shuffle[2, 0, 1, 3]()\n", - " return (self_zxy * other.data - self.data * other_zxy).shuffle[\n", - " 2, 0, 1, 3\n", - " ]()\n", - "\n", - " @always_inline\n", - " fn normalize(self) -> Vec3f:\n", - " return self.data * isqrt(self @ self)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2619eb1b-103b-4453-9725-4480e388452e", - "metadata": {}, - "source": [ - "We now define our `Image` struct, which will store the RGB pixels of our\n", - "images. It also contains a method to convert this Mojo struct into a NumPy\n", - "image, which will be used for implementing a straightforward display\n", - "mechanism. We will also implement a function for loading PNG files from disk." - ] - }, - { - "cell_type": "markdown", - "id": "acd55d71", - "metadata": {}, - "source": [ - "First install the required libraries:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "53266408", - "metadata": {}, - "outputs": [], - "source": [ - "%%python\n", - "from importlib.util import find_spec\n", - "import shutil\n", - "import subprocess\n", - "\n", - "fix = \"\"\"\n", - "-------------------------------------------------------------------------\n", - "fix following the steps here:\n", - " https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719\n", - "-------------------------------------------------------------------------\n", - "\"\"\"\n", - "\n", - "def install_if_missing(name: str):\n", - " if find_spec(name):\n", - " return\n", - "\n", - " print(f\"{name} not found, installing...\")\n", - " try:\n", - " if shutil.which('python3'): python = \"python3\"\n", - " elif shutil.which('python'): python = \"python\"\n", - " else: raise (\"python not on path\" + fix)\n", - " subprocess.check_call([python, \"-m\", \"pip\", \"install\", name])\n", - " except:\n", - " raise ImportError(f\"{name} not found\" + fix)\n", - "\n", - "install_if_missing(\"numpy\")\n", - "install_if_missing(\"matplotlib\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ca218d22-0578-42af-a906-7f15a91c5bec", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from python import Python\n", - "from python import PythonObject\n", - "from memory import UnsafePointer\n", - "\n", - "struct Image:\n", - " # reference count used to make the object efficiently copyable\n", - " var rc: UnsafePointer[Int]\n", - " # the two dimensional image is represented as a flat array\n", - " var pixels: UnsafePointer[Vec3f]\n", - " var height: Int\n", - " var width: Int\n", - "\n", - " fn __init__(out self, height: Int, width: Int):\n", - " self.height = height\n", - " self.width = width\n", - " self.pixels = UnsafePointer[Vec3f].alloc(self.height * self.width)\n", - " self.rc = UnsafePointer[Int].alloc(1)\n", - " self.rc[] = 1\n", - "\n", - " fn __copyinit__(out self, other: Self):\n", - " other._inc_rc()\n", - " self.pixels = other.pixels\n", - " self.rc = other.rc\n", - " self.height = other.height\n", - " self.width = other.width\n", - "\n", - " fn __del__(owned self):\n", - " self._dec_rc()\n", - "\n", - " fn _dec_rc(self):\n", - " if self.rc[] > 1:\n", - " self.rc[] -= 1\n", - " return\n", - " self._free()\n", - "\n", - " fn _inc_rc(self):\n", - " self.rc[] += 1\n", - "\n", - " fn _free(self):\n", - " self.rc.free()\n", - " self.pixels.free()\n", - "\n", - " @always_inline\n", - " fn set(self, row: Int, col: Int, value: Vec3f) -> None:\n", - " self.pixels[self._pos_to_index(row, col)] = value\n", - "\n", - " @always_inline\n", - " fn _pos_to_index(self, row: Int, col: Int) -> Int:\n", - " # Convert a (rol, col) position into an index in the underlying linear storage\n", - " return row * self.width + col\n", - "\n", - " def to_numpy_image(self) -> PythonObject:\n", - " var np = Python.import_module(\"numpy\")\n", - " var plt = Python.import_module(\"matplotlib.pyplot\")\n", - "\n", - " var np_image = np.zeros((self.height, self.width, 3), np.float32)\n", - "\n", - " # We use raw pointers to efficiently copy the pixels to the numpy array\n", - " var out_pointer = np_image.__array_interface__[\"data\"][0].unsafe_get_as_pointer[DType.float32]()\n", - " var in_pointer = self.pixels.bitcast[Float32]()\n", - "\n", - " for row in range(self.height):\n", - " for col in range(self.width):\n", - " var index = self._pos_to_index(row, col)\n", - " for dim in range(3):\n", - " out_pointer[index * 3 + dim] = in_pointer[index * 4 + dim]\n", - "\n", - " return np_image\n", - "\n", - "\n", - "def load_image(fname: String) -> Image:\n", - " var np = Python.import_module(\"numpy\")\n", - " var plt = Python.import_module(\"matplotlib.pyplot\")\n", - "\n", - " var np_image = plt.imread(fname)\n", - " var rows = Int(np_image.shape[0])\n", - " var cols = Int(np_image.shape[1])\n", - " var image = Image(rows, cols)\n", - "\n", - " var in_pointer = np_image.__array_interface__[\"data\"][0].unsafe_get_as_pointer[DType.float32]()\n", - " var out_pointer = image.pixels.bitcast[Float32]()\n", - "\n", - " for row in range(rows):\n", - " for col in range(cols):\n", - " var index = image._pos_to_index(row, col)\n", - " for dim in range(3):\n", - " out_pointer[index * 4 + dim] = in_pointer[index * 3 + dim]\n", - " return image\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7a7dbe6f-f35f-42c5-ad37-b7400f6262f8", - "metadata": {}, - "source": [ - "We then add a function for quickly displaying an `Image` into the notebook. Our\n", - "Python interop comes in quite handy." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9a3a8e81-7500-439b-a8d2-8e71ceea3e8b", - "metadata": {}, - "outputs": [], - "source": [ - "def render(image: Image):\n", - " np = Python.import_module(\"numpy\")\n", - " plt = Python.import_module(\"matplotlib.pyplot\")\n", - " colors = Python.import_module(\"matplotlib.colors\")\n", - " dpi = 32\n", - " fig = plt.figure(1, [image.width // 10, image.height // 10], dpi)\n", - "\n", - " plt.imshow(image.to_numpy_image())\n", - " plt.axis(\"off\")\n", - " plt.show()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "5f42f174-d7d9-4f6c-9b4e-d0d586f8f5ec", - "metadata": {}, - "source": [ - "Finally, we test all our code so far with a simple image, which is the one\n", - "rendered in the [Step 1 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-1-write-an-image-to-the-disk)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "52005588-38e7-4ef0-affe-6d063b9bf9b7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAFnCAYAAADjbJN9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAATsAAAE7AGKbv1yAAAGG0lEQVR4nO3ZwWoCQRRFQVv8/19+LtyMILjyTMtUbUICCWZ1uP3WzMwNAPi5+9kfAACu4nH8Zq11+ObD108/+/Z1x9/xmfb4+zt+Jv/zHr/z739/x890xf95o9+Z9XpUtnQBIPK+dM/6FABwAZYuAEQsXQCIiC4ARDwvA0DE0gWAiOgCQER0ASDipgsAEUsXACKiCwAR0QWAiJsuAEQsXQCIiC4ARDwvA0DE0gWAiOgCQER0ASDipgsAEUsXACKiCwAR0QWAiJsuAEQsXQCIiC4ARDwvA0DE0gWAiOgCQER0ASDipgsAEUsXACKiCwAR0QWAiJsuAEQsXQCIWLoAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQ8bwMABFLFwAiogsAEc/LABCxdAEgIroAEBFdAIi46QJAxNIFgIjoAkDE8zIARCxdAIiILgBERBcAIm66ABCxdAEgIroAEBFdAIi46QJAxNIFgIjoAkDE8zIARCxdAIiILgBERBcAIm66ABCxdAEgIroAEPG8DAARSxcAIqILABHRBYCImy4ARCxdAIiILgBERBcAIm66ABCxdAEgIroAEPG8DAARSxcAIqILABHRBYCImy4ARCxdAIiILgBERBcAIm66ABCxdAEgIroAEPG8DAARSxcAIqILABHRBYCImy4ARCxdAIiILgBERBcAIm66ABCxdAEgYukCQMTSBYCI6AJARHQBIOKmCwARSxcAIqILABHPywAQsXQBICK6ABARXQCIuOkCQMTSBYCI6AJARHQBIOKmCwARSxcAIqILABHPywAQsXQBICK6ABARXQCIuOkCQMTSBYCI6AJARHQBIOKmCwARSxcAIqILABHPywAQsXQBICK6ABARXQCIuOkCQMTSBYCI6AJARHQBIOKmCwARSxcAIqILABHPywAQsXQBICK6ABARXQCIuOkCQMTSBYCI6AJARHQBIOKmCwARSxcAIqILABHPywAQsXQBICK6ABARXQCIuOkCQMTSBYCI6AJAxPMyAEQsXQCIiC4ARDwvA0DE0gWAiOgCQER0ASDipgsAEUsXACKiCwARz8sAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQER0ASDipgsAEUsXACKiCwARz8sAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQEV0AiLjpAkDE0gWAiOgCQMTzMgBELF0AiIguAEREFwAibroAELF0ASAiugAQEV0AiLjpAkDE0gWAiKULABFLFwAiogsAEdEFgIibLgBELF0AiIguAEQ8LwNAxNIFgIjoAkBEdAEg4qYLABFLFwAiogsAEdEFgIibLgBELF0AiIguAEQ8LwNAxNIFgIjoAkBEdAEg4qYLABFLFwAiogsAEdEFgIibLgBELF0AiIguAEQ8LwNAxNIFgIjoAkBEdAEg4qYLABFLFwAiogsAEdEFgIibLgBELF0AiIguAEQ8LwNAxNIFgIjoAkBEdAEg4qYLABFLFwAiogsAEdEFgIibLgBELF0AiIguAEQ8LwNAxNIFgIjoAkBEdAEg4qYLABFLFwAiogsAEc/LABCxdAEgIroAEPG8DAARSxcAIqILABHRBYCImy4ARCxdAIiILgBEPC8DQMTSBYCI6AJARHQBIOKmCwARSxcAIqILABHRBYCImy4ARCxdAIiILgBEPC8DQGTNzJz9IQDgCixdAIiILgBEnv7cEL5KaxYBAAAAAElFTkSuQmCC" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "var image = Image(192, 256)\n", - "\n", - "for row in range(image.height):\n", - " for col in range(image.width):\n", - " image.set(\n", - " row,\n", - " col,\n", - " Vec3f(Float32(row) / image.height, Float32(col) / image.width, 0),\n", - " )\n", - "\n", - "render(image)\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ef3206b4-0bbb-4108-a592-f748843e5d3d", - "metadata": {}, - "source": [ - "## Step 2: Ray tracing\n", - "\n", - "Now we'll perform ray tracing from a camera into a scene with a sphere. Before\n", - "reading the code below, we suggest you read more about how this works\n", - "conceptually from [Step 2 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-2-the-crucial-one-ray-tracing)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "778d8339-5b19-47ee-bdc6-a08e4ec5094c", - "metadata": {}, - "source": [ - "We first define the `Material` and `Sphere` structs, which are the new data\n", - "structures we'll need." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c4a79de-0b65-48ac-97c9-187b74b96b9f", - "metadata": {}, - "outputs": [], - "source": [ - "from math import sqrt\n", - "\n", - "@register_passable(\"trivial\")\n", - "struct Material:\n", - " var color: Vec3f\n", - " var albedo: Vec3f\n", - " var specular_component: Float32\n", - "\n", - " @implicit\n", - " fn __init__(out self, color: Vec3f, albedo: Vec3f = Vec3f(0, 0, 0),\n", - " specular_component: Float32 = 0):\n", - " self.color = color\n", - " self.albedo = albedo\n", - " self.specular_component = specular_component\n", - "\n", - "alias W = 1024\n", - "alias H = 768\n", - "alias bg_color = Vec3f(0.02, 0.02, 0.02)\n", - "var shiny_yellow = Material(Vec3f(0.95, 0.95, 0.4), Vec3f(0.7, 0.6, 0), 30.0)\n", - "var green_rubber = Material(Vec3f( 0.3, 0.7, 0.3), Vec3f(0.9, 0.1, 0), 1.0)\n", - "\n", - "\n", - "@value\n", - "@register_passable(\"trivial\")\n", - "struct Sphere(CollectionElement):\n", - " var center: Vec3f\n", - " var radius: Float32\n", - " var material: Material\n", - "\n", - " @always_inline\n", - " fn intersects(self, orig: Vec3f, dir: Vec3f, mut dist: Float32) -> Bool:\n", - " \"\"\"This method returns True if a given ray intersects this sphere.\n", - " And if it does, it writes in the `dist` parameter the distance to the\n", - " origin of the ray.\n", - " \"\"\"\n", - " var L = orig - self.center\n", - " var a = dir @ dir\n", - " var b = 2 * (dir @ L)\n", - " var c = L @ L - self.radius * self.radius\n", - " var discriminant = b * b - 4 * a * c\n", - " if discriminant < 0:\n", - " return False\n", - " if discriminant == 0:\n", - " dist = -b / 2 * a\n", - " return True\n", - " var q = -0.5 * (b + sqrt(discriminant)) if b > 0 else -0.5 * (\n", - " b - sqrt(discriminant)\n", - " )\n", - " var t0 = q / a\n", - " var t1 = c / q\n", - " if t0 > t1:\n", - " t0 = t1\n", - " if t0 < 0:\n", - " t0 = t1\n", - " if t0 < 0:\n", - " return False\n", - "\n", - " dist = t0\n", - " return True\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e5ed8e76-efc1-4905-90ea-4cad6df0c553", - "metadata": {}, - "source": [ - "We then define a `cast_ray` method, which will be used to figure out the color\n", - "of a particular pixel in the image we'll produce. It basically works by\n", - "identifying whether this ray intersects the sphere or not." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "beeecde1-f365-4be5-a3b1-d8a145810be1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fn cast_ray(orig: Vec3f, dir: Vec3f, sphere: Sphere) -> Vec3f:\n", - " var dist: Float32 = 0\n", - " if not sphere.intersects(orig, dir, dist):\n", - " return bg_color\n", - "\n", - " return sphere.material.color\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1baec786-664c-4fea-8be1-c2d5f25924e7", - "metadata": {}, - "source": [ - "Lastly, we parallelize the ray tracing for every pixel row-wise." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "80c81118-acf9-4786-9a3a-694fe7f2d08a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from math import tan, acos\n", - "from algorithm import parallelize\n", - "\n", - "\n", - "fn create_image_with_sphere(sphere: Sphere, height: Int, width: Int) -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(row, col, cast_ray(Vec3f.zero(), dir, sphere))\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "\n", - "render(\n", - " create_image_with_sphere(Sphere(Vec3f(-3, 0, -16), 2, shiny_yellow), H, W)\n", - ")\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ee9bbf4c-91ad-4787-a332-b509fdc8d512", - "metadata": {}, - "source": [ - "## Step 3: More spheres\n", - "\n", - "This section corresponds to the [Step 3 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-3-add-more-spheres).\n", - "\n", - "We include here all the necessary changes:\n", - "\n", - "- We add 3 more spheres to the scene, 2 of them being of green rubber material.\n", - "- When we intersect the ray with the sphere, we render the color of the closest\n", - " sphere." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b4774ada-3cef-4ce3-a573-3fd721fe41b0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from algorithm import parallelize\n", - "from utils.numerics import inf\n", - "from collections import List\n", - "\n", - "\n", - "fn scene_intersect(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " background: Material,\n", - ") -> Material:\n", - " var spheres_dist = inf[DType.float32]()\n", - " var material = background\n", - "\n", - " for i in range(spheres.size):\n", - " var dist = inf[DType.float32]()\n", - " if spheres[i].intersects(orig, dir, dist) and dist < spheres_dist:\n", - " spheres_dist = dist\n", - " material = spheres[i].material\n", - "\n", - " return material\n", - "\n", - "\n", - "fn cast_ray(\n", - " orig: Vec3f, dir: Vec3f, spheres: List[Sphere]\n", - ") -> Material:\n", - " var background = Material(Vec3f(0.02, 0.02, 0.02))\n", - " return scene_intersect(orig, dir, spheres, background)\n", - "\n", - "\n", - "fn create_image_with_spheres(\n", - " spheres: List[Sphere], height: Int, width: Int\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(row, col, cast_ray(Vec3f.zero(), dir, spheres).color)\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "var spheres = List[Sphere]()\n", - "spheres.append(Sphere(Vec3f(-3, 0, -16), 2, shiny_yellow))\n", - "spheres.append(Sphere(Vec3f(-1.0, -1.5, -12), 1.8, green_rubber))\n", - "spheres.append(Sphere(Vec3f( 1.5, -0.5, -18), 3, green_rubber))\n", - "spheres.append(Sphere(Vec3f( 7, 5, -18), 4, shiny_yellow))\n", - "\n", - "render(create_image_with_spheres(spheres, H, W))\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f53abdf2", - "metadata": {}, - "source": [ - "## Step 4: Add lighting\n", - "\n", - "This section corresponds to the [Step 4 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-4-lighting).\n", - "Please read that section for an explanation of the trick used to estimate the\n", - "light intensity of pixel based on the angle of intersection between each ray\n", - "and the spheres. The changes are minimal and are primarily about handling this\n", - "intersection angle." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3ed5bc7c-f335-48c4-abf8-31c75d6e79ad", - "metadata": {}, - "outputs": [], - "source": [ - "@value\n", - "@register_passable(\"trivial\")\n", - "struct Light(CollectionElement):\n", - " var position: Vec3f\n", - " var intensity: Float32" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8b99f641", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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rZ2PAWguD+7mnyyOvkorfNoTQhpdfPgv37m3CRx9dhI8+ehIePjwL77//OPzkJ0+uqmkBAG5C07b9/7U5PT29ybkAAAAAsLD/5D/5cvjOd94IJyercHKyCq+/fudyb9gueO1u8/1gx5cl7sbcZ+/W9Njlo0FIuivcnQ5jh6HqdfaJPcYSxande8e2g375/XbQ3oebU4/7c/vAsx3075cZ7s8p2/P75W1/P+4LG9u22/w2XU44Vsh2+8rW/rrq2e02hCdPtqFt2/DkSXf+kyfbcHGxDY8ft+Hx427J4vfeexR+/ONH4X/+n38r/OEffrLfPxIAwDU9evTo6r7KWAAAAIDn2IMHJ+H11+9c7f/6uc/dD2+9dTecnq7DatWEzabfD7avhq0HsF1lY30p4vkVqnl7fk53J1ZQjlfE1gPc2vWHbcP24fzq8zyeqUrNWrVr7Xh5rN/rNa+obUfP7faRrVXU9mOl7U3TVbmmFbKx8jUeS61WZWVsmyxTHEKsli2D39Wq/qLH57ReN2G7DUmlbRNCWIW23YYQuj2QX3zxNJycNOEzn+ne6xcXbXj06CL86EePqmMDACxFZSwAAADAc+w733kj/Bf/xc+Es7NVODlZh7t3N+H0dBU2m7gccb8s8dhSxLU9YkOohaLjIWna1j9Ow9zrLE28XzVuU0xkuiL22GlsX3WatVbbhlWw/f220lZWuw6rYYdt5fFdFbJT1bF5Wxe4ppWweQVsXi1btsc9ZscrZLvbrkK2u91eVco+frwNT5604YMPHodHjy7C++8/Dr/zOx+G//V//Vfh4sKyxQDAslTGAgAAADznXnjhJHz1qy+Hr33t5fDii6fh9HR1uRxxtyRxfVniWAFbC1+brGK1u90/hC0rVueEsGPLFefXy+cxNpep+dSOTyn7jS1BXJx1WXVajlULaaeqZNNxxqtf88rWcq/Z9rI6tuyfV8725wwrZbvjzVWQ2lfIdhWwbdvf9nvDdsfLvWPLZZXHtG187v1r0N2uQghddWzcTzb++CCEEF555Tz89E+/GN5775Pw+7//0fRFAACORBgLAAAA8Bx6++174b/+r78R7t3bhNPTdVivu5A13ubLEc+piC0D0OlwNO031l4uR1yOuzv0nRcCl4HrWMXsZctI+25zzolBY9c3rXzNg+QYMHb326vnNWzv5jzcL3YYcPahZdmnD1TTvun99C+EcFn1mreXIXPs01W/dsfT+9O33TLI8d9+tWrCxUUbVqs2NM3q8rmvLgPg9urYkychrNftZf/t5TW7HyDcvbsJr756Hv7JP/lh+Nt/+3d2/2MBAByBMBYAAADgObDZNOFP/ak3w8OHZ2GzWYU337xzWRW4DptNH8DGithyb9jaHrH1qtR5FapRLSwdVr6Oj3lYINvPoR68jgeuuwLVssq2Jl1eeGz8vEK1DDLb7HFfvZpX0Kbt9erYtH1qT9n+WJxXfFyvhA1ZhWs6Tgix+jVc7Qcbb+M+r/1yxvWK2L5St39fpMFuvE2D6f61XYWLiza07fYytA3h4qLNqnbffvte+Pa3Xw//8l9+EL73vZ+U/0QAAEcljAUAAAB4DpycrMNf/svvhC9/+aVwft5Vwnb7wubVsPND2H32bA1hV/iZVrsOw9hhn/5a/bH0WmMVsbUK3DI/HT6uB6yHVMeOjTdclji2t5W2/rnEYLK/ny9zHMPZGMz2laVle61fmyz122Zjx2rcNDhNq3rTtv7+VFtcmjjtk7825W0awtb/vbeXr9U2NE1zuRfs9iqU7ZZL7ipl423ThPDuu/fDG2/cCf/H//G7wlgAYHHCWAAAAIBnWNOE8Bf/4jvhK195Kbz77v1wfr4Op6ddCLvZrEK5L2wawoYwHsZ2Y++qgt0dgNbG6s8bjl8LZ+dcuwx+y3ml59SODdsOC2jHimK789pBv3RO+RLGw2WI06rapmkv75f7pvaVr3kwWz7uxu3P6QPS2L8PgueGsiHUgtjtNp97ueRxf5vuJVsPZfNjq8tzVqFf0jiGsO1VGLvdds83hrLxPf+1r70cXn75LHzyyUX4+OOL8Ou//sPw/vuPx/9xAQAOIIwFAAAAeMasViGs16vL+0341/61V8N3vvNGOD1dXVXExqWI+79w1ZaGrn0oG8J4der+QWh67lj16+4QtqzIHbaV182vXw9f0/Nqx8fM7TO+SvHYAGMnNBN9xo41WVsezOZLFKehbD5OU9yPx7r2OEZ9meKh1aoLZeNt27Zhu41jNVmwXNvvNi5vHNuGoW/ySlzeX6/L596Ei4sQTk6a0DTdYJ/97L3wxht3wvvvPw4ffvg4/It/8X748MMnVyEuAMAxCGMBAAAAnjHf/OYr4d/6tz4XTk5W4eRkFX7qp14I5+fr6rLEZUVsGsKmQetUGNvdDoPWWvha9i0rYseC2XLM+ljT10wf5/eHx4aP630OMTVGuuxw35ZfO6+Q7ZcXjsfGKmdjWzpmXjE7tkRxvE4zuF9/PB6K1itlxyti0/C6bI8VtXnlb9p3dfUabLd9/+22CU2zvbwNV/vFdssUd6Fw/ExsNt2yxmdn6/ALv/Bm+OEPPwl/7+/9Xvjoo4s5/9QAADsJYwEAAACeEZtNE+7dOwmf/ez98M1vvhLOzlbh9LQewq5WoVoNG9vKcHSsErZejVoPRLv7ebAaKxrnLoM8J4Qdv17ft+w3fDzcS7be7zjygLXJ2mPoOjKbpG8f4uYBbRrullWuaXtbnFtW1OZLH49Xypb369WzcWnhvF/cm7YLRrsq1P7fYrUKV5Wzq1V7Faj2AXb3ntpu28u+fcDc/cig61tW2cb34XodX7Pm8lj3GWqaJrz11t1w9+4mPHhwErbbED75RCALAFxf08afy4UQTk9Pb3IuAAAAAEz4Y3/spfC3/tZXw8OHZ+G11+6EzaYJ6/UqbDZlCLvKql/H9oUt20KYF8BOh6Hj18iPT507Pvau6+f3x0PjvG2B9HWH5D/JJW21x8N9ZtNjtSV6++rYet9av/x+37+831W+TrfHsco9W/N+3W1/LH/c3eb3t9sY1LaXlbBd+8VF36+7393Gx93tNmy3ITx5sg0XF93jJ0+6448eXYSLizZ8/PGT8OjRNnz/+x+F3/3dn4S/83d+Jzx5YsliAGB/jx49urqvMhYAAADgljs5WYU337wbvvCFB+Hdd+9fLkncBbF5NWwMX6eqYIdhaVpJWqs0re29WralgW3+N2/P2HScQ0LY/QLY6fD12JWxtT1kyznk1a1Zz6sx+r1VyxC5vWpLK2bHKl/Ttnyp47TydriXa3nN8YrZfh5xeeR6NW2/Z2ysok0rZPt9ZbvzuvnE93i/322slI3v/VhVG59TdxsrZPvK2PW6u9ZmswohbMPJSbfX8sOHZ+HJk2147bU74YMPHof3339c+4cBAJhFZSwAAADALffWW3fDf//f//Hw6qvn4cUXT8N63QWx6dLETdMtwZqHsH042weh0xWr3f2+f/94PBStBa/9XrR50Dt2vbFlktP5DNvqQXHZPm3Y72mEsePLEg/7lRWt/f12tH2qCjbeDtvaav9h5eyu6tjydvxYWT1bexz7x/ux+jXtk1bLpn+x75Mn/W2slO0qZNvw+PE2bLdtePRoGy4utuHjjy/Co0fb8OMffxL+3//3j8Lf+Tu/M/PfCgCgozIWAAAA4BZrmhC++MUXroLX1167E1599Tw8eHCSVcJ24WsYhK95CJsGn/XgtAxexypT87ax8cbGrVfI5veHVbL1ELa+ZHI51/I1vbw3+dovYaritTcMVmO/vuK1Vtk6rJgd7vka+7bJWHEOY9WzTXHNehVsWkE7HKdWRZsfK/8d456xsZp2tSr3lc33io3nxr1i07l3/UJ2P34m4vlt2+8j292uwmbTvU7375+EV189C1/84oPw3nufhPfe6/+jKgDAXMJYAAAAgFtmtWrCr/zKF8If/+Ovhjt3NmGzWYXz83VYrZpif9hyeeIyjB2vTh0LRWO/7rZWCZueNx3Gju0VOxXApn13z2UqWD28MnZZYxWxtXn0fdNgOj7OlxjO2+ISvulY8fWKlaZpcBsrX7ulhWP//Hhc6rdp2iTM7NvTeZXH87a02rYPZ9M+/Tzj/dinPxZvmyZcLXPcv1ZlFXD/eLsNoWlWoWnay79wuaxxCOt1d3tx0Q3yhS+8EN544274B//g++H/+r++X/k3AgCYJowFAAAAuEW++tWXwxe/+CC88879cOfOJpyfry+XJa6HsGUA2y1TPAxf8yrWMtycDkdr/eqhaz2c7edSPt4VwtYqYPM9YVO7lyZ++lWxQ3NC11rfYeVsH6622etTjtm393vLpv3q+8zWjk9Xyc6riM379fvAttX2sT1ju+cfro7H93wI3VLFafXsdptXycbXsb/fXJ6XLve9ChcXccnkdfjsZ++Fb33rlfDbv/1B+OEPPwkAAHMJYwEAAABuke98543wV/7Ku+HsbB02m1XYbFZhtQqXtzGAjVWw+f6w/f2xqtUQatWw+4SwIdQqbg8PYedVynZ9hvKKzKnjuy0Z1O7aH7a8dq1//5q0bX48D6HLMLVrK6tc0+rZfMy0+jUe7+/Xwtp0jC7YrIWuafVqH7SuVl1FbLzdbvP27ba5ClJDqO2B2ySP89cxBq7x5dluh8tA969LDG7zfWnje/ynfuqF8NnP3g//2//2r4SxAMBehLEAAAAAt8DXvvZy+PmffyN885uvhLOzdTg5WYX1ul+WuKvWS/eJHQawZUg6vjzxdFVqebzss0/gW4awacA6tjdt+Tg3Xhk7L1C9ierYqWuOB69jfcrwtVYtO9wLNm2r96mFsvVK13ysYVgbw9f2MuRsL/drTZci7m5jn7gv7GrVt5eP4/khpCFsN4uu+jUPZPNlkstwv7der0Jcermbd3wtVpevb18d/NWvvhQePjwLv/7rPww/+pE9ZAGA3YSxAAAAADdsvW7Cl770Yvirf/Vz4eRklVTE9iFsH74O94ft7w+D0hCmKmPHq1Brwexq1R3Lb+vXLIPg/v6cOdQqXscCzV3h6nXC10PO3VUFO3WNOfvJDpcf7l6jvr227HBs627rSxMPlyOO1567PHF+vx+rXi3b72PbhjJgXq36QLZ8nL9X+2WKu71g+2rWGODGoDZW2cbnmr5eIYSrHzp0YWy8XhfKxj5f+MIL4c0374bf+q0Pwo9//GhQaQsAUBLGAgAAANygn/7pF8O//+9/Mbzzzr2rpYm7PWK75Ym7qr1QhLJ5GNuHst2YteWD5wagZQibhry7QtjpSth5VbG5uW1zjh3S7xBzx55TEVvrNxbM1tvT17lfnrhcrjj2abPQtgxQY994LPZLlwiOFaZdJWy6bHFZXZtXvna3fVVqus9rXN64e9ydN1Yhm96f+oFBeny9jj80WF29Bt1yxd3j2Df9UcKf/tNvhB/84MXw9//+74cPP3wSAADGCGMBAAAAbsBq1YQ7d9bhrbfuhm9/+/VwdrYO63UXxKb7wqZ7wo5Vv5b7tIawO4itLdlatsVz0nA3vx0GsYdV45aVsPuEsHPCzyXD10PNqYhN+02Ft2PLGPcVr7E93S82DV37kDYGsnH8PpBN+8ZAtOwXw9c8+ByvjM1vw2X42Vzd1sbq+8T59P3S+2N9uuWO43u7D3zjMsfx/b5ex6C3uayO3V7uabsOb799Lzx4cBL+8T/+YXj8eBsePdoGAIAaYSwAAADADfjMZ+6G/+q/+lp4/fW74c6dTdhsmiyMjUumxsrYekXsPssT10LYcjnivi0da7gXbb1ffs00sJ1TCXuMytg5xw/te11zAtep/mWf3VWxtfaxStl++eJ879haYFtWxpZhat+nv3a37G96vNwztg9i27bfHzatfI1/8XF/2/eLVbVpn/5xE0LYXj3OPxsh9JWxzdVzi5WxaSi8Wm1D267DanUW/tJf+mz4/d//KPzqr34vPH4skAUAhoSxAAAAAE/Jw4dn4exsHVarJrzzzr3wUz/1Ynjw4CRsNrEatimqYUPxV1sWuB6Axtu54edYEFuvot21PHE5h2Zwv7zu1Nx2t+86tk+fpUyFqVP9d+0jOxbapksVx/Z2dI/TsjI2/hun1a99QHt5hdEK2mE1a74kcr5nbL58cl4tm441vfzweIVs/x7u+sQlkftj/Z608TMY96kNIVz+OCJcLqccfzTRhtPTEF599Ty0bQgvv3waPvzwiSWLAYABYSwAAADAU7DZNOE//o9/OvzMz7wc7t7dhNPTdXjppdOwXjdhs1ldVcOmoeywIjbft3VsKeBa+Fn2i3aFsGll7NgesfXK2/rSxMmVdzwea7vusSnHCmt3Ba1j15qzj+y+e8iOt/XvjT6cLPePTc8p94hNK2jTQDZWtcY+sfq1q3SN1bVtcduN1e0Z214uBxwrZPNK2K6KNj6OoW1/PM43rZZNHw8rY1eX81hdzad7bbbh4qI73jTb0DSry+fXhrZtw3rdXffNN++EX/7lz4Xvfvf98Ku/+r3RwBsA+HQSxgIAAAAs7K237obXXrsT3nrrXnj11fNw9+4mrNddhV2/N2xcdrgMOssK1f62Nx6OdrfDILbeZ6ridbxStzyeX7NWDXudIPZYIeySVbJzg9ap8/atiI3Hpitl8/fOWBVsWqWazCjZezaeG++n10/b+71p8z1g+yrcdJ/avK0frz8vPoe0KjfdZ7YfJxTnDCtlu790+eG4d2zXd7ttLveUjUsq9/vNxv1k1+smnJyswoMHJ+Hhw7Pw9tv3wvvvPw4//vGj+j8fAPCpI4wFAAAAWNhf/svvhl/8xc+EBw9Ow8nJKpyedksVn5x0YWtZERurXtP9YfNK2GFoOlYZ291OLTXcPcr3op2ugq0HuPU59A4NYK8bvi4Zuu5jbB67QtexPvtUxI611ytly3C1fn5aGZse65cBHlbV9lWwXZ+muO2ramNlbD9eU1TExgrXfnniWMFdq5ZNq2trttuQBMfbIvBdXc5lG2KVbNuuwmrVhu22r5B955374aWXzsKv//oPw6/92vfHLwYAfKoIYwEAAAAW8rnP3Q9f/OIL4fOffxDu3j0JJyersNmssvA1hq7p0sRlEDt128mD1RCmq2FrlbRjoevYsW68sQB430rYfatgdwWs1wlgr3PuIevTXrcaNh6fE74O+6bhZt+Wh7DdsTb5t22zc9J/3rTSNr1OH862Rf/xCtn+WmlFbfojg3itthg7rZYNRXtfDds0faVrVwEbrvaN7apg4xz75ZVXq27cLiwOYb1ehRC2VxWyd+6sw+uvn4cvf/nF8P3vfxR+9CMVsgDwaSeMBQAAAFjIz/3cq+E/+A++FM7P1+H0dBVOTlZhtVpVKmGPXxFbD2Lzx+l4+V60aejaXIVSu6/fPe4dqzp2qn3Xsev03deusXeFteX5c/eHTY/tXxFb65MHsrHProrZscraYYVsbCsrZOPSyOkPB/oK2bQiNvZps2rabqnhGKi22Xs439+20/WN+9heznJwmx5bhW5P23B524amWYXT035/289//kF4/fU74e///d8XxgIAwlgAAACAY3v33fvhT/2pN8JXv/pyODtbJxWxq7BadcsSlxWxZRAbl1zdVaXa9RlWrMb2/rbc03XXdfK2EMJVYDt2/Wj/UHaftjnH9unzNO0KW8f6Ty1TfFhFbNmW/pvloWVaydo97veEDcXjsbA2r5CNgWytQrbf27U235DMtX/fbbf9nrEhhKsgNl22OK3YjdeJPzIIIVwFtzHY7SqC420/0XhOv2dst4Txet0tY3xysgrb7Tpst234/OcfhNPTVfjN33w//NEfPa4+FwDg+SeMBQAAADiipgnhnXfuhX/n3/l8ODtbh7Ozfmni8eWJhxWxc5YLjtcrg9A8hM2Px8d5sLp7qeJ0nLwytgx/962GPXYIe90A9tDzl1qiOO133VB2rC0/NwahZeia9u+XCx4PYcvAdRjmxmrYEPplhfPbdJniPhyeug1FWwjxfR7bu/d+rG7t38uxqrbr016GsOk+tyGs190VYtAblyvebJoQwipsNm1o21Vo23V455174eHD0/D9738sjAWATzFhLAAAAMCRvPnm3fArv/L58NnP3g/n55uritjNJq+ITStjYzVeWqk6J4gtK1zj/SgPocqAdVgZW96OXTsUyxb3lgxhr1she0jffe0a+7pLFJd9ll2muAxp82rWMsAdBrJ5tWw6fn6/XJZ4WBk7vI1Bbb60cfe4W8J4uGdsXEK4C2Hje7/2GoTL/WGHyxWn92OIHMfableXz7vfT/fOne5+F9QCAJ9WwlgAAACAa2qaEE5P1+HVV8/Cz//8G+H+/ZNkaeJ8OeIu2OzDoPTxPhWx3XXLsLW/X97WQ9mmGGc6iC2v3V9jKmidG7oeu0L20L5LmVsFm/bfVS27b6XsrrZhABuD1fg4DWRj8Ng04aqKtAxr8/ZcvmRxP3YasKZBbLmUcf84H69eGduHs+k5/fu7Td7vad/+L+47G69T+zHDxUVzVQV/crIKFxfrcHraLVX+5Mk2C3YBgE8HYSwAAADANb3wwmn4z/6zr4S3374XXnjhNJyersLJSbdH7GZT7g1br4iNy6fOCWRDmKqOHVbIlteI1a1zg9+xQHa/EPYYlbBzQtXrBq+HnL9vwla7xq5K2FqffStld1fA7qqazZctrvWdul8/N+772u//Wj6P8UrYWKEaQr/McHosLqccq2LTAHW77feA7fXnxT1js6OX+8f243bX32yacHERwmazSvp2fb7zndfDV77yUvg//8/ftVwxAHwKCWMBAAAADtQ0Ibz44ml4/fU74UtfeiG8/vqdcHqa7g8bA9CmCF5jqFWGnbVANK9sTfsP7w/nVxujv99kt+VyxMMx8pC3uNq+r97MtrnjHxKiHqtidm64umuMOUsZj/UZO7Z/RWzZllaeZj2yitR4P69ILStk0/OiNDAdLk887Fdet/xs5NW8+fzSitn+WrUfOaR7x8aq2LQiNlbP9j+s6Pp1n/3ttrtt21V45ZXzcHa2Di++eBqePGnDRx89USELAJ8iwlgAAACAA52crMLf+Bt/LHz5yy+GN9+8e7UcaRfG9ksU53vF9hWxfVi7f0VsHqaWlax53/F9aHdXxpbh7OXII/cPeTzWNtW+69gh/Y5t7Lq7lh7e1beZOF6rfB1rrwWyU33y44dWyDZNmwWz41Wz5W0aiIbK43I/2XR/165tuGdsdz/27f/S6th6CN223R6x8TPdV8w2oWlWV+c1TQhnZ+uwWjXhF3/xM+EHP/g4/Oqv/m745JOLAAB8OghjAQAAAA7w5pt3wiuvnIe33roXXn21r4jtg9Z0X9hQDTjH9m4tq1ijtAq2DGJLZYhbq36drsStB66HL1O8q/+c9l3HDun3tI0Fpof03acadu7441WzUxWyaSVq1ycfZ3huDGa791S/X2xZzdrvTTs2VtqvrIwdPqd0n9nh/W68vno2jj/8DLdhteqXPI4/qlitmsvlj/uK+K5CtgkPHpyEJ0/a8OqrZ+GDDx6HH//YksUA8GkgjAUAAADYU9OE8G//258L3/nOG+Gll84u94hdh/W6CZvNKqxW4SqYjQFNba/YuH/rrsrU7prHrIiN1+7DpDRU2hXIPv0Qdk6wemj4eszQdp+1Z+dUwZZ9pyplD90zNrbPC2TrFbL1Y+NVr/39YQDa3cZlf2Nb03RVrGnFbNneBaFpZWysiM2XSo7VsNttm+wf218z/Sv3lY1z6pYi7o+VFbIhrK4C5bgf7vl5G155pQm/8Atvht/5nZ+Ev/f3fi9stwEAeM4JYwEAAAD28NnP3rv6u3//5GpZ4j5wrQWq9cdpEBtCXhFbVqPW24fzG1bX1gPZ2De/DcW5/RjjdoWZc4LYpxnCLlkxu0/AOnbuEqHsIW27KmTTitW+knS4V2teqZq+Z8erYodVtWWlbAxiu2WB22S8fux6GJzPLT6H+LmIc4n9xvZv7v/6CtlyT9kuEI6VseHqxxp37mzC2dl65N8BAHjeCGMBAAAA9vAn/+Rr4Zd/+XPh/v3TcHa2Dqenq7BaNWGzaQZLk8bliocVqmPB53R1ai0krQVHwyrX4fWXr4hdKoTdJ0xdMng99Pq7wrf0nKnliceOjwWtZf/rBbLl4zRUDSEGk8Px0z71CtqxtjIMLn+M0O8Xm1fYpnvHdifEKtjutju3q14NSUVu/Jx0QXIMVlertNq2ueofK2TTvWPX61UIYXt52159V9y9uwnn5+sAAHw6rHZ3AQAAACCKlW2np6urADavjI2Vr2kIOxWMltWp5V6vaUDaV7CWt8Ogd3r54/yaoXJsVyi7RBBbjjv3WK3fTQexY/aZ266++4bZtX+Xff/tyh8HpI/rPxyIPxoYe0/NrUTtx8k/K/04tc9WM7hWfd/k4bVjlXu6rHi51Hf/eY/nNIO/9Tr+dVX0Jyer8PLLZ+Fb33oYPvvZewEAeL4JYwEAAAD2cHKyCufnm3BysgqbTblEcbpUcR/OTAWx4wFUupxxJ6+MHYam/Tm796HNb4fLJY+HrYeHd/3jueHsnGPpmIcGsM01/657zbl99zm2z+t8vX/T/D2aL6mdBqmx73Ap7fy8PrBtquPM/wwN3/P5+eNB7PB436dcjrz8IUYZwqaPN5vue+Phw7Pwx//4q+Fzn7tf+fcAAJ4nlikGAAAAmOErX3kp/Lk/93b48pdfzILY9XqVVMjVK+bKirrpALa/ZhlA9e1Ndnz+Xz7HfIw54et1Qtl92qba5x4/1jnXGXfufqDp+bv2jJ1aunifZYuPt0RxP/9+yeLhcsPd/eEyxf3xuP9ruvdrPJ7uDRvFftlbN/R7yqbLFfdBbz9m+tmKyxGH0H02ymWLu+v0yxt3n6F0uePuAt0yxuFyn9j2as/Y2BZCV10fQginp+vw9tt3w7e//Vr47d/+IPzu734UAIDnjzAWAAAAYELTdOHJO+/cD//mv/l2ODnJq2HHK+aaQcg5VoFaVqKW1YXpXNIKw3h8vNJ1PASemk/y7Gfcn/N4n7ap9l3HjtH/mOaGrOU5z14gG99T/X6rMURNehd9+vY+pO2D2OH84rE+HM2D126/1jZrjwFuGcp2Y8S9X7vrdcf6/t212tHq2a5fGdA22fziDzO6fWW72y6o7Zcr3mxW4Y/+6LEwFgCeU8JYAAAAgAmf+9yD8Nf/+pfCm2/evdwnttsrtt8HMl+atNw/Mr0dBjkxrKvvqZn3zfuFEKphcHre3OWK4xxyh4Syu8aZaptq33XskH5PWzmvXYHrVL+p481E+5y+hweytWukwWoMQMv+aXsa4vZ902NpIJtW1jbZ4+HnJvYJ2XXiZyZWwsbK2K6qNQ1Sw9W8uuMhbLfNZRAbP3tdINy24Sp0DSG9XYWmacPFRff8T07asN2uw8VFG05O7CYHAM8rYSwAAABAxWrVhPv3T8Ibb9wJP/uzD8OdO5vBvpBp9Vst6AyhFrimVah54Br1bc2gvb8/Nu6cPTXzULe8/n7h69h5Y/33CWfnHp/b5zaphaPX6bfkNXc9To4UYWqsPM0rZfvANF2SuK987ZcrTq9ZLmGcVsp218jn0SYD1MPZ/NwyKE4rY+PzSUPdpumrb/vPVK2Stl+uPAa8/T6yq3BysgpnZ+tw5846PHq0vQprAYDngzAWAAAAoOLhw7PwX/6XXwtvvnk33L27KfaJba6WJO5vYzCbVsPuDkb7ar08xC2rYPN9YsuK2/rjeRWx3bUOv197fGifucfmHF/q3Cn7hmi7KmDLflNVstetkL3OksXzwtk0BA3Z3q4hpMsN75pbugxwWuVaq5LNf2hQ7iEbg9SQnVNWxkZxb9h8P9n26rl0t81VwBtvt9tYfdtVxm63bQhhdXkbQtuuw1e/+lJ4++274R/8gz8I3/veT6qvJQDwbBLGAgAAACRWqxDeeONueOutu+Gzn713uadjk1Wz5XvDhtAFOeP7vNaqX2O/YQXtVP+QharpGOXj8tplxW48flj4GiaOHVL5um//Q8LUp1E5OzdcHTt31zlTfcaOHRq+Xuf6w35pNWutLa1w3TXHco/ZONZwL9ryuv2Y6RjlfrLp+bX59p+jPvgtK2TjeGlFbPo5XK3a7Pvk7t1NaJoQXnzxNLz//uPwk588USELAM8JYSwAAABA4uxsE/7G3/hj4YtffBAePjy/qojtlhRNq2Lz5YrLytjh8sW1KtU8WE2rZePjWoXsVGVs3+cmKmLnBrFjweh1KmUP7buUcg5zlwfe1X9XlewxA9ldAe28atnacsUhdMsRD5crro0x1RaXLy73jC1vL89OrlFWxKYhany8WvXX7faGLfeX7a6dVs124WtfIdvvS9uN2e0huwrrdTx3FbbbdQghhK985cXw5pt3wj/8hz8If/RHjydeewDgWSGMBQAAALj0+c8/CG+8cSe89tp5ePDg5KoiNg9Zy3AzBkx5+JZXnabhaq0iNa2ErVWvxvv5HNI+ZTVsfr2puc29X7qNQextCGHHTIWoY/33CUIPaT8kkJ0X2JaVsGN7xsY++R6vwzFr1bDltfLrlVWt49fMPx55KNsFxiHkgW7ap19uOd72n9G+8rb80UTbtoNq+/W6CXfubMKTJ9vLwBYAeB4IYwEAAABCV/H2l/7SZ8PP/dyr4eWXz8Lpab5H7LAqNt8Xtl4Fm//1QU8Z5HZqQWrfPqywnaq8La/VjZOHw4fdP+TxWNtU+65j+/S5TdL5zlkWeKrfbQlkx86thar9/XQf2RiOjs0lXco4/eFCWiVbVsemVa9pUDqsnA1ZKJt/RuP+sF1la14ZG7JwNV4nPrcY5HbVtc1VhWzXv/s+adtV2GzasN2uQtuG8ODBSVI9CwA8D1Y3PQEAAACA2+LsbB3u3t1cLku8yqrW+uWB079hZeyuZYm7Pvl183ApbS/3fx0GrrVrHjeILT2NILaZOLZPnznnX/fvOuaef0hgPTa/ff595ho/P38P5++x4Y8Uwsjx+o8ZhtfKP2djbfnnIp1n/bMaP99lZWx6Tj/f8e+K7scT3W2693RaGbter8LZ2Tq8++798LnP3RfKAsBzQGUsAAAAQAghhCacnq7DnTubcHKyGlTC9qFJuPqrBTjD4KcfP4RayBOy9v5+eqwJ4yHvdDVuep0ykNp9f59jtcdjbYe0zz1+7PP2HXfuMsTl+XOqZPetkN1nrLLtOo/H55NXxnb98orX/tx0ieN0ieLudlgFG6tT08rX9Brp9dNK3Frg21fKNlfVsfk5+bldxWz985fu5dzvLV2GsF217MnJKrTtOnz5yy+GH/3ok/D7v/9RuLi4qL6WAMCzQRgLAAAAfOr9iT/xWvj61x+GL3zhQdhsyhC2thzwWAg7Jygd7glbq/YbjlkPWfOwdXidshpxaCqkXCKInXu9fY4do/+xHBrOzgllp/qMHRsLRucEuPsGsmPn1u+XwWhbbAabL1uch655MBuy8dIxu8A2DX7LsLa8Tn/tbrxy39d07n0gPAxg+3nFZY3TZYrjbRrIbrdN2Gy6pYvPz9fh7Gw98sMOAOBZIowFAAAAPrViQPKVr7wU/sJf+Gw4P1+H9XpVDWLT/WHr4Wu+dPCuIDIPZdP24e0wWB1bTrUfOx0jbRufW9n+NIPYXYnTPonUbUuv5oSsZf99q2uPNeZ1rz1+fi38LLLX4vMwPJ72q+0hGwPXGJKmlbXp3rH5GGlbDGrz68T78a+ryg0hhrrpmLXPZBrk1qpm00A23p6crMLp6Tqs1002DwDg2SOMBQAAAD61vvGNh+HP/bm3w+c//yCcnq7CZjNcnjgGJrHarbZfbD2AqVe2jkmDqPzcvLo2XfK0Xh1bhlrp7XXvz3m8T9tU+65jh/S7SfuEsrv6Th0fO1YLSue0XefxdLjb/3ghr14dOx7FJYrT2zRELa9RBqoxlM1/+JBXuZYVsfnj/NzVqhtvu+0/u7EytrvfTaBWGdu2eSC72axCCCGcnq7Dgwcn4U/8iVfDD37wSfiN33gvbLejLyUAcIsJYwEAAIBPrVdeOQ/f/OYr4fx8kwUiZdgaQrxtskCnDH/mLCmaV7iW1avDILUW5NauW59v2feQ8HXs/LG+gthp+4ayu5YtPvZesXPO2/e6lRGzas8+TE33ji2XLa71G/Yfzq2vaC2vv/t5pOeWlbNl8FyrfO0D5RDKgHe1ai6XMB7++GO1asLp6Sq88cady3lc/zUHAG6GMBYAAAD41FqvV+H8fDOoio1Lg+bhSFoZW1a9Hnuv2No4wzF3Vclejljczr2/z7FjtM05tk+fpzHGdZf0nTPGoYHsPu3XDVunzt99P75dx/aOjUFnery8TcdJx0/75XvF9vvJ1pYnrlXI9tW03ffBdtsmgWo/z3xZ4iarjN1u80rZ9bqrjt1uu/tdZew2nJx0FbJ37mzC2dl65r8DAHAbrW56AgAAAABP29nZOrz11t3w8OHZYEnisaA1rVRNA9R994odU6uYnQpxywrYkVF3Xvd45x72vPfvf93nFP+O4Zhj7brOIceP+e+/7+ORUbP38rC6PO1TWwJ87LZeAT7+GUmrW8c+a+l4eaVrfZ5j9+tLm5c/pBh+58Rli+OSxefnQlkAeBapjAUAAAA+db70pRfC3/pbXw0vvHAaNptV2GxiRezqsgK2uQpMumrYMiypB0X1MHcqgBrer1fV1sOasSBnqKncnwrTjhHE7RvOHho4HvucQ5TX2beqdM45zYw+c887tG3X47Fzx+5ftjQhq1Tt94JN+/SVqekSxemY6VK+sU/aPyT7wqaVsN048dy8IjaeE6ty+4rXUPTtKmD7fWLzOcTPZvf90l7tFRtCXiEbb9MK2VdeOQt/+k+/Ef7lv/ww/ON//MOR1xsAuK2EsQAAAMCnxtnZOnzhCw/Cl770Ynj55bNwdrauLj0cQhqi5lVteYAarvpex3io2wenw+q/9PxhBeDuUHas/XkIYp9WCDt1/X2D0znnzA0/j3W9pycPV9P2bp7DADaENCidGm9q7H7p4VC5TZdAri+HXAuHy++Qfuni9ir4je19v36J4/iDkPh3crIKd+9uwtmZRQ4B4FkkjAUAAAA+NV555Sz8zb/5M+Hll8/C+fn6cp/Yfq/YNAAZW1q0Vpk6rGrNw9wQynC1DGDn7BE7XhUbx+vtqoSdCl/HxpnzeNd4c65x6Fg3HcCW0vnMDT3nVMkeEsjODV6vGyLvXylbCzzT9/KuEDWteo2VremesGWIWo4dq3LHqoL7z22bnN9XxMb55J/5Nns8DFr7PWPjfrGrVXv13ROrZuMesm3bhjt31uH01DLFAPAs8nMqAAAA4FNjtWrC2dn6siI2XYa4DF6H1alj1XO1Stk0iNtVNTusiK3vEbur/7CiN5/HPNcNNMfC2TkVuce+7m1y7PkdMt6hFcxzAvix4/W+Y+/p/H5+bh6C5j9kGLtGfr3yRwnlDynqP6bI94AtP2f1pcrz+dWO1b5v4g9BusdldexmswoPHpyEd9+9F1544aT6nAGA20kYCwAAAHxqNE0IJyery4rYvBo2hiFjQUk8vxbidMfy0La8bnq/r+Qbjpe21Spv02vFMfJAalfANuf+MR7vMtV/KsA9pN9tsM9c5/Q9VsA993U+9Brz3ntjn5vxH0SUIekwAE0/W+WY6ePpueXXLMeoB7LNyF/cjzrv13/vpKFsk1XHbjbd3rE/+7Mvh9dfvzN8sQCAW8syxQAAAMBz7+xsHf7sn30rvPPO/XD37iYJYftwJFaklUFJrWouhHqgMwxp0xBoGD7VqluHFXnjyxOXwdKuQCm3Kwzd5/G+19jn2tftd9s0Yfm9WseuMffa+87xOM+pacLV8sIhTC8fPH+Mvj2EuGRxfnz4+c33hk3HjPe79nwe/WezX6Y4H79fXrlfuji9ny9THG9jKHtysrpcXv1Zfe8DwKeTMBYAAAB4rjVNCOfn6/Bn/sxnwjvv3A937qyvwthhZVpZkZqPU96vVfONnVN/XO41Obz2WCVfrV/yaMb9sf6HqJ2/VBB7zCBq37GOFaIeKxR9GsHuIebNK+7Fmu4LGwPMWt9yn9j0duq6aZ/4OekedyFo7Xr9e6MPZtMx8qA130M2vW7ftw9f43XT4134mrZ1feIPRvowdhM2G4sdAsCzRBgLAAAAPLdWqyb80i99Nnzxiy+E1167EzabVVitVtkyoGWVaW2J4nR54LzidbjEcB+KNsXj3u6q2GHl63hVbj+XypX2vL/r8dzQ9bYGscc+/zpBaB72Tfc7JJDdp31OeFr22fV47jiXrU2sWu37pO/zWgDb3w7HTIPT3ffz6w1v84rWPqDtw9x0TvXPcn/+atWG7TbeD2G7TStkY8V+F+B2QWy4Wq745KRbYh0AeHb4GRUAAADw3FqtQvjpn34xfOtbr4R79+LyxOWSxH1FbBqqdveHlatXRyvh6FilbLpEcRnWpvfTYLUPfobt5fHlHHP8mwxim2ue/7THrV3naZ533XPL8/cbq/Z5232N6THGfhSxe7z0RxX1z/Gc74H8+6Upzhv7UUiTLKPeL1fcBbP1fXYBgNtHGAsAAAA8105O1uHsbB02m9VVkJFWxvbBbJOFILWgJu3XBzO1UHVXIDNVDTsMYsvrjodITZia1+77+wZo4+HV7rbrXmfOOU8zLD30WnPPu+5ruKv/nLZdj69z/TD6ucv7dG1jlejlbfl5rT2O/YefweGPLsYep98d48eGbX11fr5kevodFb+zYnXsF77wIPwb/8ab4fXX78x4rQGAm2aZYgAAAOC5dP/+Sbh/fxPOz9fFssTDitfdIct4pd5+1Wn163bjDAeqVfalQc7+17+OOUHcMULB64556DnH0oTbuY/rTRt7Xea9Xk1T30s2XO6/Gg33ka2PUd6P545fP19GuTs/XVY5hLjUcfm5zpcyTveOzfuMfe+U4ey9e91/0j07U2cDAM8CYSwAAADwXPqLf/Gz4TvfeSO89tr5VVVsPZQt//ox8grYXeHt7jB1rAI2D25qt8PxkpF3tM25v2vM6wSx+7bv2+eQvkvrw7n9zrnO/rH7hp1LhMbHHLMWsvZ7t8ZQswxQY8gaP7tdYBr7dvNLx4lj155Dfr3hjzLGPpvpDyW682PYmt6GsFq1lX1mY7V+XFK9CW0bsiWKT05W4eys+5EJAHD7+fkUAAAA8Fy6d28TXn31/Cq0mFvtWq88jR13hx/7Vsp25+TLrg7HqC9RfLyq2NsY6tzGOS3tus/5ps8/bLypkPMY1537mSl/LDHVv/yhRjn+2HdM/rhepT+cy7BCdr1uwv37J+HFF0/Cev1p/KwAwLNDGAsAAAA8lzabVTg5WQ2qYstK2L6tD1jSfSRDqIUq9fCj7zMMg2p70tbD4Tygre0VW85vfE6HVMjOqYKduk7admhV7L4Vsbc1jDpkbsd8ba5zzti5x3i/7LrG+PEyJM1vx8PR+v3hjyHGrj/87A1D2+7+sJp9rNo9P5ZWxtb3ju2rY1eXe8feD9/85sNw/77FDwHgNvO/1AAAAMBz5TOfuRs+97n74c037yYVseN7xYZQC0THK9uiuZV7ZVgzPuZY8Jtfr7Yc8u5Q9piuO/6xg9hnQRPsIxtC/jrUX5Om2b3fa9mW3kZjfcu2Wt9yzv2er/nyxuU45XW6+93zHFb+5sswd+15W/8jjHSJ43AVzJ6erq/2xAYAbi9hLAAAAPBc+frXH4a/9td+Kpyfr6/2WOz2XgxXezHWKtSmqhjHA9pdFXXxvFrF3tg8htcqqwDjtStX2vP+1PlzHu8ab1/PYxC7hEPC3do5c9uuc/1955oHtjGcLEPXsX1er1qSoLUMSGP74MpNHpyOjZH27ecZLvenzecVg9S4d2x3Tnxe8bPfDj7vq1W43Cu2C3Tj/rGxOnazacLZ2TpcXGyEsQBwywljAQAAgOfCG2/cCd/61ivhZ37m5bDZDJf6LJcCLQPY4dKh+fj1QLU+l+HSpGNtcYD+tt4+vxJ3P0uFOGPjHut6z2L4tGSAeaxzDx1zmcrfPABdrrp4boVsDGpjtWt6bryff87bLASuVfIOq2TzZcn7itj0NmRLFter5QGA20IYCwAAADwX3njjTvilX3on3Lu3Cet1bZ/YeqXp1LLBeTDat1d7TwSz3W29OnUsAM7n2GRjHF4hO2Xe89zd59Ag9tB5PkuOGcgeK5xcLuQ89NrzwtHx8/OxmsuK1RiOxlC1b4/VqmNLKI+FqOPzq58f51MuWxzPGauSTQPYWoWsLBYAbrfVTU8AAAAA4BhWq27ZzpOTdRLE1ipS+2CzD0Br1arzTQexfVhSr5S96p31SSvkhtfYJwQd67Pv81wy8bkNQWyT/D3rrvscbu/55RLC5W35uSnPK9vGQ9H0szh2fv9+qX+XhMFnvx+r/l2QX7vvE/e/jkFsF8auwsnJOnzhC/fDT//0C+HkxH/qBYDbyP9CAwAAAM+0pgnh5GQVTk9X4eRkVQSxeaCRVrvWqlDTx4dXmw2rWPM5DPtPVeiWYW3tWrvmsr9Dq2KXdOzrNcXf2LFju8mw9zYEzWNzmJ7b1OexrHSfc87YONc7p1al2lSOpz8MKb8byram+AFJGtCGsF434dVXz8Prr5+HzeY2/PsCACXLFAMAAADPtNdeuxN+5Vc+H954404RxjZX1WRpiBHCWLXasGq0tjzwdDhaq24rj+eB8FgQnFfVZT1qV91xfKp917E5x5e89jEdcq30nGMt59scaaxjjXMMS82lH7dp4jLB3RLD8XHt+umx8n4I+RLDtbZ8vO5+H6CW1w6Dx/nYcb/XfLnj9Hi8Xy5TvFp1fVerJmy3bVYd2y3H3obz83V49GgTDqnqBwCWJ4wFAAAAnmlnZ6vwxS++EF544TQJYUPIA46Q3O/bL1tnXec6OcdYMNtfOw99l5hD/bpP4/xjTPo2jfE0w8/bFLYurx6KHmXkUO4LOxaejs1rPGgdPk5/jNGOXKSs4B2r7s3HHe5/vVo1YbNZqYoFgFvMMsUAAADAMy3uFXt6WlbFxn0W0wCjGQQf+e2wWnVs6eCp9jwwyatWx+ZRVr+my5nW7aqQndNeO3ZoqHPdqtNjjrvEGOlYn8bga9/3yZxq7RlXHfmszRt7bDny3T9+KD+/aQXrsF//nqj9yGNO8JpXw6ffW91fX+Wf7xvbh7GrI/5YAwA4JmEsAAAA8Exar5vwmc/cDW++eTdsNumyxHnQUi7522nCsEL2OurBaD1oKXpVquBqIfFx5nSIZz3hMf9lx1vO0wgX53wm68fm/cBhGLw2k336H2wM51IutZ4uZbxaPTv/rgDwaWOZYgAAAOCZdO/eJvz1v/6l8Oabd8Pdu5skkA1XVWSrVVqF2kyEorW9XfebT3+NWpjSFP3yUCedXxnWlCFy5cr7TXSRJYpva1XskgFVE663hPB1zz/m+EvPZa5d8xgery8hPLdf3j9+1sb6xTG6z2o7umds+hkv97RNj+XhapudW46TnhN/dNLtGxv3jp2qogcAbpLKWAAAAOCZ1DRNuHt3E+7e3STLEefha60KLR8jv51x1X1neTX+nAC4NrfDws9DU5nyvNscht7maz8PbvfrV1aL15cjXnoO04/H22tzb7LnlH6Hjf3II23fbFbhtdfOw8OHZ0JZALhlhLEAAADAM6lpQjg5WV3uFbvK9ootq2DTqrJ8GdDhnpKHVMR2t/k+sfWlkfNquPT89Pr1kPhYAesxxrztntbzel5fv6Xtft3mB5v5sV2hbO0zPlxCuHbN6R909I/LoDX9QUYZtu6eSy187Stju/b1ugnn5+vwMz/zUvjyl18I67X3JQDcJpYpBgAAAJ4pq1UTvv71h+Gtt+6G+/dPkqWJh2HrWNhxXLsqXOuVrXngMrdKdjhO/f6c/ofY5/xPSwjcLT/79M99Xlz/NZhegnj5cfMq9toJfftw+eJyjK5/07RZEFv2yytju4B2s1kJYgHgFhLGAgAAAM+U9boJ3/nO6+FLX3oxPHhwkixRXO4J2y9RvP9yxPsrK2Lz/SCHlXFpe9KSHRu50jGnfYRxPy3zua2e10D3eM9rLPyc2kM27gkb+9X7x/damx1Pv4f2CXP77602O1armI/fL3GP7NUqhM2mC2QBgNvF/zoDAAAAz5zNplueeGxp4tqSwHkQmi8BOjw+31Sg2l9juDzp9HjHChSvU536aQs1edYMPyZzfsxw7Gseds6uZYrzH5WkFf/l/tj9/bhcOwBwuwhjAQAAgGfOZrMKm82qEsTmFbLR9F6N5RLBU2FGV7GWVsDVTC0vWns8HszMDVYEMHAsuz52uz73SUt2rP49M/xhSNqnrJqth7dNsly77wIAuG2EsQAAAMAzZ73u90fslimOe8bWlvPsz5sfus43FgKnFbFjyyTPr7KrdRw7+XkIY56H5/C0PI9LFA8tUek6VrE6p/9Yhfuuz3htf+j4o4v+tqzqH1+muAtiu7auMvaYlfUAwDEIYwEAAIBnxtnZOty/fxI2m1USUDSX4cN0APG0wpxdfdKK3O5xXiG3XBB5WwKaJedxW54jz4rp4HKfH0Ec0m966fSxx2kQG8/t95BtwslJ90MVAOB22Nz0BAAAAADm+vN//u3wjW88DG+/fS+rii2D2bRSNV/acxhQHLoHZL6fY7nXYxqs1IKTOUscPwthyrMwx9vs01HVels0Tb/EeOVoOOTfY+pzPH69JjRNW6mkr8+l/x5pr46l3zfd8sRdEHv//kn4xjdeDj/4wcfhn//z9/d+PgDA8amMBQAAAJ4ZL710Gt588244O1tnS3vWlv7sLB8Wzl2udNcYtdtKz10jzb/o3p6F4FW4ye2TLlm+u1/t8XDJ4r5P/iOPzaYLZM/P19ebNABwNMJYAAAA4JmRLsG5WpWVsWWVbHfOMNi8fqhY7g+bj50vN1zfZ7Jcovg6AewxHPsaz0JweyzPawB8u57XeEXr8eyqVt9x9sHjlOFq/v1Vr+zPv++aq8rY1aoJp6ersNn4z74AcFtYphgAAAC49V555Sy8+up5eOmls2rwOuawitW4FOgueZ99A5v8vPrJh455fZ+mMPU6bldgmdtnbrf5eRANf2jSL1nc94k/UvEZBoDbQhgLAAAA3Hrf/OYr4c//+bfD/fsnYb1eVfaHzatja6arZedqiuAj3SN2WJlbr267zvX7eVyvj6Dm5s0NQI8dlN624HVsPrdtnlO6/Vz76t3h/rP9jy7aHXvX5uPGcco9Zbv9Y/PvoFgZ260ccPizAQCOy/8sAwAAALfeet2E09N1EcSmPZrB/WMVhnWhyTBYue74++9xK0Cd51kK8T6tbubfaFcAepNLIe/TN/9RSe2HICpjAeA2EcYCAAAAt95q1YTNpkn2ih1WoKYBbRlEHJJLpMFMvD9cUnjXwPvPY5kMRTBzfE8jULxNwfLTncucYHTY5za9XvOMVevH77Cyor/23ZZX4Mf9Y5ecNQCwD/+zDAAAANx6TdMkVbEhdJVgTagtS9w/Titkx8PIGOi0l3dqIVAagNRCk7HliWv9a66/fPLz5rqh2tKh3G0J/W5yHruuvczc5lavzu93W/4tp78Hxr534rE0vLVnLADcLsJYAAAA4FbrqryaLGTIA8/pfWLnGMtjxtp3h6fjSyWPBbXH3VP2pk0FXE8r/FrqOscY96YCwNsTPB7TcfLU8UGWymsP/ZyPfffVfgwCANy8zU1PAAAAAGDMF77wIPzr//qb4Z137oX1ehjEHhpgtm29f2zvj7chDVbnBMCHzKdy9nVOfk70r/3NjpGOdZvGuelr7HPNufOZP++xitZa874/ttjf8V7v+P1TOTJxnSY0TZusFqAyFgBuE2EsAAAAcGu99NJp+NrXXg7n5+skiM3DhqdRUXrda0xVq8lMlnasUPdpu5llgJcfe+Kq7fTj2rz6ZcaPE64eOsbTWe24C10HrZXq2NUqhJOTVbi42Ibt9mnMDQAYY5liAAAA4NZarZqw2ayu9ouNyxWHkAec+1SBzQl4ek1WDVseq/WfQwD7tLVh/4CxDYedt2vMpR3yPG96DhMjHTBUGtBeb9zbtaxzfUni+KOUJvs+fPjwLPzczz0Mb7119wZmCgCkhLEAAADArbZa5aFDCGEiID1u0Dkcqxkcfz73aTz+srLHOe+65gSsxw5gD3HT11/S/s9t/0D2eq/f06lyvY7xL5v4XXRysgoPHpyE8/P1U5wXAFAjjAUAAABuraZpwmq1uqqILUPZrk/tvPp4XcgSk5Z2Yn/J4bFyr9qx6w6D4rHbZ81tXKr3umPX/pa83k2OVTvnOm37HJ9jbIy+vR6S7nftecsZT3831MdL25/uZyX/QUi/t/Zq1YRn9/sGAJ4f9owFAAAAbp2zs3X4zGfuhtdfP88qY6fC0OnK1OG+oW2bn1M+nj/2dP/xMYUk8xxjz9ebtk84d+vLMsPx5jh/nOtWq15/yeLjXOsYxr86+j1l+z22l50LALCbMBYAAAC4dV5//U74j/6jL4X7908uq2KbpOqruw0hZPc79eQhVq/VgokYnDRNCG3bJnsw1udWb989h6kxl1cGmrseL3HNfY8f2ve2OXYQu2uJ5evO4ZjnHnjFtl6Zmh7vbneONDsYna56HfadV2Fbjp+fs1Roe8he2gDAcixTDAAAANw6q1UIp6ercHKyOmhf1v0Dj/FOAo1dnoVKzpty7Ndm6df6umHwMfrvGG10uN3XuU74WQtT97/WcV+Lqa+m53MvawB4NgljAQAAgFunaUJYr5ukKrbJjg0rYmuPa4FIWVE3Fo6U1bHzKl8Ho3zqg5Bj7jX6rIW+Tzu03Lcqdqmlhg+p3B3pPXNp4TQorYem9YH6Cty5lbb7GRvvmNWx5ZLo6VLuAMDtIIwFAAAAbqEugO33i833jA1hLGyYWx03DF92hSLPXrjxrIWXczwrz+lZmefSrr+k8vQPJ9pK+DpsG44xvnzx2LLEY9cfP78t2nYvmbzv0sdj+iXdn7kvLQB4LgljAQAAgFunaUK1Knas7yFq1XRz5vXpMS98WuY6x+z/tB0yv6X2ir1u/6fzWu+qaD1mxerx92ktQ9cDR7nGucPvpb6yXyALADdvc9MTAAAAAIju3t2EX/iFN8Obb9692i82hHxp4vFwoQ0hNKFtY9/ucdajnQpU8/679mOstI6f8Nwbvtbzji1xvZt0E0Hssc47ZB77LFF8vWuPB7bzxpyqgq89Hq+QHZ/fPu1TjlcdCwDcBsJYAAAA4NY4PV2Fr3zlpfDKK+dhvW6SpYnLPVz3Nx3E5sp+go2lHRKu3qZA9mmEpUuO8TTGnT/e3Gr13Xs/d/vCzr3myJGd5+1/7u4li3ddcw7fWwBwO1imGAAAALg1miaE9Xp1GcSGrDJ2zK6KtDIsKSvsYlhzSDhyO5YAPdaSs9cd57rL6B5aVXqTyxZf5/pzX5NDX9d9/p2WrM7dNdacwDJdCnj4ea3dH36e43j7VczW9p9t23YkhG0H5+2e11jbsHFsfmV7/7XUCGQB4BYQxgIAAAC3StznMAadcwPP8ZCjHvakYcfx95Gsy4Of64SXhwSih/S9yaBzHzcxz2fltUktFajOeXzgVXYMM/XDil3LGk+Pff2QvT7+db532uJ2miAWAG4HYSwAAABwizRhvW7CatVchbJXR2YEC334Uqsqq1fG7Rr/uksjH++8pxH+Xfcax6iOvU4Ilv4d27HGn3v+dSqZrxvKH/t9MHep4PryvfXP9WFz7KtJy2AzBrnDOQz7Dq8/VqUaj9Vu8+rf4fn542FF8BRBLADcHsJYAAAA4Nap79l6vHRhV8XcssaXSb2djhnWPc0ne6xQ9pjh7txxrhv2HnvMYzo8RK3dP+T8Ocfmtu2+7rz3T33/2/Flj+cQyALA7SCMBQAAAG6Vrio2/u0fKOShRVtUwfVLF+e3hzlGpV46r0NDl2KkkftzHh96nWONmY5xzAD0On/HcswgdslweE7bsd5H6ecyv99/Fmo/XsiPj+8POz/ELb830nmkx9O55VW25XfMtLTa9rDz5x0TyALAzdvc9AQAAAAAVqsQ3n33fnjttTvh5GS1Z4DQhlrVbAwk0rHatp29B+2xtG2cQz/Pvu3TqP7vdf2+t9WxK1KnxlsiIL/u/MfPv27omB4vA9KubXpp3+GywMcLuWtLDpdz2bdad84xAOD2EcYCAAAAN26zWYU/82c+Ez772fvh3r3NVUXs3MAyD1nb0LbN4Nw0AH26Yeh4oBjnUQts6+c/jXBy1xwOHePQ8Z7lQHaf1Oy6FbH7BrFzq2J39dmnSna6knzunqtzxxse6yvl86rbvCp1uGfsxNDFeXP2vB0PZPN51eZRVvmXlbqp7jvxWf3sAMDzwzLFAAAAwK2wWjVhvW72qlwtQ42xIGSsQu0YlXmVMyaWV91zpMk57htELeFpVVw+a6WA+bKz8/ofo88xr3eMax5yfn2J4uFS42Ofjfrnblel6e4Adnw55Kl51PeCnZ5DWa27z/Llte8eAOBmCWMBAACAW2G1asJq1QWxcwLZsZClrEKrHbt8dMAs69fcdex2LSs6p5Jxn6rJqfZdT3zfwPJWvZAVh8zxGEHsvv82x5rHPlWxRc9K4DjVp2wbW154+PkfHhufx/BYWc06XA65/j0z/r20+7qVWVausfu99uldCh0AbhdhLAAAAHArNE2TLU3c3e5a6na6LV/ys789fjh6WAh1e9zkpA4JL2/bi3jonG4iiD00fD9elWz+Q4k8LC0rSculeacqX8cCz/r3wPwfTkwvj5z2a4uQdRjkzplDbaz6OPnrVVsGWSALADdPGAsAAADcCl0Q2+1xuG+AUO6ZOHf54n3Gn3POIWNePqocGw+gRkbco/+hIeAhFZjHCBzHzrnpUPY6c1gyiH0ajlMlO7dafezzPfeaw0rU8YrZYQAc23eHwuPt03Msv8Pq7eMVxFOVtwDAzRLGAgAAADcuVsSmlbFDw8rWWjiShhbpUqX5kqPXSSrGw5Ty8VhlW61tKlDedy7DY8dMZm5TIBvPe5rB7HWvN/fc6wSx+/wb7RO6zzUVzNZCzvoPKGpBZvrZn1ryd85ywOV3wjCk7dvGlimeE6CW5/djt1lb+hynfpAxrJrN+wliAeB2EcYCAAAAN+rsbBXu3t2E1Wp3Rex1K1wP6T/nvLGquF39x8KYHWfPusb+bkOCc4wQcInncazAd+75NxnE7nu9fcbZf4xdy4rXlvwdWx44P29svOFcdn8+h3OonV8PiOe/t6ZC39q1LVEMALfD5qYnAAAAAHx6NU0I3/726+GLX3whvPLK+dW+sZdHs77D6rFmcKxtQ2ia/ljT9Mfi/bGAoqygm+qXjlXv217Nv96vPz6lbdvLpZuPobzmnDmM9dm3PR4LM685p9+cMW6LfebztILYuWPserzPeGXIWe6xWg9D82N92Lp7Xu3VbVnZWp/H/FB1+JdXrpbnlHOsVwKXFfxpWz722A9AyrkAADdPZSwAAABwo+7fPwkvv3waTk76/0yxa6niKWPLku63/O+8atXp6rr+vKmlUnddI54z77wlKjevE74dcvzQ695WNx3E7tP/kNd8v7B2KkRM7+/+bNWCyX7c8SrU9NrjcyjnsW8VexmI1s6fV20/bIvBcfk6CWEB4HYSxgIAAAA3ar1uwmazquwX24axMHNYkVYLVeK55b6PtWVP9wsx9g1R0rmU/cplVtNquPE5zg3ldgVl1wlvrhMM7hPIDv99b7995z2n76Gv99zQdYkgfvoHBv39vBI0bR+rlh0LJtP+tc97rbq1HHdYhV/OefgZq31uh1Wt+fMdm8/wOdX2nE2vne8/249hnWIAuA2EsQAAAMCNm7sS71S1XC202FWFVgtiho/HAqVh+/NRmLZPKLdUmHvsaz0thwTHc/of+tyP/ZpdJ8yvf17mfWbaQUi5r/Glg6dD2bHvjWF4ms91YiaDOcz5burGzX/UUZuH6lgAuH2EsQAAAMCNWq2asFo1O/dGrYcT9Wq1dDnSPKDYHQiVlbZ5ZVs96NgvzBlW7E2FzOW5Iz1H2mvH5gRqxwhk5wST+4aXbdj/nKfh0BD2GEHsIf8uh7Rd53F+LK8gHVa/jleH55/FWhA5XmlaXmP43TD2XTGsrq893+G1+nPGKndr803b2pHnMB3gxkrZ4WoDAMBNEMYCAAAAt9Ku0GEY3gyrzaYeJ0f2ms+wfff58yvWalVzhwSw+7rpcPOQ6990KHudYHjuObcliN3X7h89VM+a+FFDd3/uZ3W839g16n3KELneli4TXF5/9w8t4rnpe2kqiC4D3GHVsOJYALhdhLEAAADAjeqqt5qsgmssLJkX6NTCkvpYY6HT1DV2hR7D9lqgU7+tVcDN147cn/N413i7zpsab25geWiC1BZ/SznWdea+Foe+pksEscd4P132LELLWqVprb0MIPu2qc/w+LLltf5lVexYRWrtxx/18cpzhnvIjn3+0znV94wt3wOCWAC4rYSxAAAAwA1rRpfSrIWofSXZsCqtVjHWByvTeznuqpbLw6JhWx/89Ncbu0Y9WJm63jAcGl5//PzDHSuQnXM89rnuvMvQdN8xr3v+rnHn9Dv0+KH/Nvtcb7+xxoLVYehYO3f6WBpApssOj3++2uK8/DM7Npcy2E2OZAFrfm7+nVT/rghFn3QOte+PfCnlesgrjQWA20YYCwAAANxq+RKe4/usTlWU7g5ax/tNhSa7xrxupVr93EMH3BWq7RtY7tO+7zWOFYDWxtz1d2z7jPs0g9h9gvYpU++j/Fj5Q4KxStDa+bWQdZ957Q4/621TP/QY+8FGeW5+/ni17tjj2li1v9pzAwBunjAWAAAAuFHdMsX941qoMF5RmgYRaYXseOXb5ZmV88vrtoN+NXlQUlbD5fMcPod6IJY/p/p1y+cyvD/HnED2kJBvV2h4k6Hs07BvuDun700EsfsG+ONzHFZz5sfGQshy3No4terU8lj/WSq/I8auOwxSxz5rY6Fpf53h90L6I5N8z9m02rb2vNrs/Lzvs/hZAYDnnzAWAAAAuBHrdRPOztZhtWpCCPV1iqer2fIAa6wyrBb+XB6pXXHnvMeXDE6vXx+nHi71484fc5+Qb+rxXEuEPPuM+ayEsofMc07/2xjETqu9l3eFmrU+Uz+EqFx1cj718fKlf/PrlgFuPkYtuC2P19pqcynD19pzqv+go5zz6EsAANyQzU1PAAAAAPh0+vKXXwxf/vIL4TOfuTM4Vgsxyvtpv7LatG2b0AUUTWia7rY73h1rmq5vXpGb9puee3luek5s70OadA7dbTy/u+3nlIx21S+OWbvmyOxCHm6Xj+f0D5VzauOM9d11bJ8+tf77nLOk6yRf1w1hp44fO4idM9ZUwNpm9+thZB461oPG4R6xfZiah6NjgWd5jbJatV7dWp9nPk4+tzIg7StZd88rn0u8358/XgmsOhYAbiOVsQAAAMCNuHdvE1555Tycnq4netWDoqnqsUHvParFapV8aTizq2Jtzhzq1YHj/fPr5MFUbS673caw5pA5tcnf03ad684992kEsXNNBa/zxq59ttLAsjZeLTAtr1f/nMTbtnpsGPQO51+7ZhmkltX55fljlaz1vWTr4ezuMcsl2QGA20ZlLAAAAHAjVqsmnJysLpcpHgYVsYp1WP3WhLZtQ9Okx5rk3Cb0Va6xX1p9mvbvQ5WmGauKjWPmVazpfJukZDW2D6tY47n99Ye6a+2qup06d3j/kMdTbWGP9l3Hav3m9J06t3RoBe2xk6254x0awk4dm9te//HDdR7nlbBpYDi2Z2u+Z3I5Tq1KtaxuLW/LH1Xk3yfptafnU59X/j1Se47pHMs9XvO/MHG/nE8bttt6pWw5NwDgZqmMBQAAAG5E04QsxAxhrDKsXvE1r1rukDCiDFTmXLt+3TK42X8e/f2xPlPn73u9ZcY4ZKxjJkjtgX/Hvv7cvocev24Qexy1sDS21/rEfmOf8V2ftfLHGsNjV2eMXqv2eGz/1jIUHZN+/uuV9bXrT4W8af8y8M7DYADgdhHGAgAAADeiaZrLQLZ+fKpyLQ05yvayIi4ZsRrY9CHNWCiTBz7pOWU4k7aV444FRWWYVFYGDkOsw8Pd6cf7th0aEO8TTB47GH1a9p37rr5zjl+3vfb+2PWe2f2eKt/Lw8/GeADZt4/vFRvPK68//r1RjtkOHpfLG49V66bPoTbHMpge2+u1Ns98SfKx8/PK2vy1qP97AABPnzAWAAAAuDF9ENsHP1OhaLyf3vbnD/vXxsrHGAYYY1ln7fpTIdC8MWoBSv28Mkjqz0tPvn54dhy7xt03ZH1WQtmbeF5LBLFzzis/c8Pqzdrnozs2/MyNB531a459Hssgc/AsJr9f6sHwruvUx576Thj7YUg+Zn38YUA89l2363sFAHg6hLEAAADADcr3Wh0GNMPK0FAs+1mvSIsntaEMPvKxpgKbsaBsaknVNuszrOJL5xWK+8MQpgx35oUrS4R746/FdOi3VCh7W1KmNhw2p7n9D319rxvE7hvaD4PZ/n5/WwaY43vI5p+PYTCahq3lZysMjtW+F8rPZz7HMDpW+XyHVbPjFbLDaw73jS3HnN43dvjc4g9cBLEAcHsIYwEAAIAbMwwO6oFMH1TkS5Pm4cmw2qwWqqRBSH5suN/irsqz2nV2Bb/1QGl4jXoIO1ZtOJW87ArWaqHevknOPtcf63PINZ9mOFte75BrHiOEDQceO1YQW/vsVEYpPpfpWMNQtv4dUKuuzT8fte+D+g8g5n0vpG3lMsZTcxv/jtj1vTXePrUkcajcT1+XznZrD1kAuA2EsQAAAMCNGmYFfXhRC23ibVm5Ogws26J92FYLUOrZRW0Ow0rVXaFsPr968Do1Xm3sWng9L1DbZSyo2yfs2+d6u8afe+51Q9NjjDE25ty+hxzf999mbtsu5Xt2rIJ0KgRtR/uk7bUAdmze4z9qiNfaVS2b9+3HGtunOn2utWWI82B0eLz2fVT7fqsFt7XXtg3b7dh3EADwNG1uegIAAAAAu4OFPPho2yY0TRnONBPhZXdOvN80Xd90z9p4PPbpx0uvNzy379sdi9cuj4eQn5vf5tePc+r75/Ppr9Pfr7yqIV0Gev/HY22HtMdjYeL4oX3nuIlEap9rHhqO7zp2nXB2/z5TYWNtafE4Rj2Urf0gIw1i8wA3PycNT2tzKZ7FyHWGgWt5bm0J4XQuY8sU59W7wyWK8+feVbimAWtZHTusgH3vvUfhn/2zH4f3338cAICbJYwFAAAAbqU0eB1Wd9XCyzScjMf7ILUce+yaMfisHxuGp8Ox+zmU49SD2Dy8HTsvvVb5eKxtf8cKZMPIsTnHD+17W+wb/N7GIHbOeWVleB6O5v1rQeYwGK0FpbVj+W39hDzszM/rxxvuN5sGp2NzGOszttR5bfw0iE3nMz6PWoBbn0vbhvDRRxfhe9/7SfW1AQCeLmEsAAAAcKPyirCygqypVMA1WShRVozGStSy6rWsIs0DjSYLOuuBUF6R2/VvQ6yKjeFvWh1bKsPcPPitV8f2zyUNgON1++fd9w/ZmMephh0LRccC2V3Hpsac6pu6DQHtvsHrvucdEsJOHZsbxA6D16nHw6rRvNo1D0THK2HT8fLvgVAdPx17uH9qbQ61AHQsZK2Fnul31fgca8fGAtThX63qNVwtOZxXxoarqtl5/64AwE2wZywAAABwY9KAJn08DHtCFnaU7bUKsXLfxnKc2lzyY8NlRtO51SrS+vY269ufM7x4bZ5Tzz9tm2e/YG28be74+45znUDzpgKn61x77mtye4PYqffj9GcsPTD2+cz71M4tvwumjqXBbdq3dk56zVowO3wO+XPLr52PX1uaOb1Wfs3hD1Pi91E+Xvr9InwFgNtKZSwAAABwg/Lgo7vfL008VbGW7+kaH7fZbVntmu/jGrLrdNWmfdtwnnnFa1rVms6/XEo4FfedjVWxwyWU4/zztnSP2HjNYXVsCIdXxJaPp9rCHu27jpV9on2qXvcJofaZw7EcK9jedfy6IWytffd8ys/u1ZEkWB3eH4altYA1DyPLz38tyKx9nwyvOQw6+3AzD053VbXWKmTHq3XLtr7KNe4LOz5WXxmbV8tut+lrnoazAMBtoTIWAAAAuFFToUkZKpR9i5EG/Yb3a0ue1h/315muiqvtEVkbqw+R8uvVA+fy+QyXIk2DpNp4+wcy+4R2+4yRHps73pLB6NjfUtc7Vt9jBbFzx6i/18c/W/1tLSwdO1Z+Pmvt/TjlcsDjIWk6XjlO+Rmrfb7K51QcGRlv/LmO9a+9BuPfWenr071vx18rAOC2UBkLAAAA3IhhAFnu8djv9Zr3HdszNg0/miSw6Pd0LfdeTcObbr/ZfNx8vmWfco/Y7lp9xWp7OUYb+mrW/pqxSrY7vw1pVWu/d2wcc/j88irfJtvDduQVD8Nq2PJ51s4fayvPnXNszvGyX3Qb9ojd5Zjh9Zw++x6bG9ruPneq6rUMCct9Yuuh7O72YSiZBr5lW22MWsXr2F+5d2s5l10VsbU9sId71+bH8qrZWA3bV852lbDp/fFlk0f+qQGAp05lLAAAAHBD2uR2uJ9i7S+EWlXZrr1d28G4/Tht0m8YYIzNYdinHZxTfcbFc8yvUa9yq12jfC75dYchWXrebtcJ8fa5VjujT63/bUqZ2nD4vJYKYsfmMta2+z1Sq9bsbsc/l/Vz65/z/Jzh8sTpvMr+ZbhaO5bOuZ9DW1yjDGyHr0UthM2PDb+Dhu39fNIxhp/9NtSe167vi6nvHwDgZqiMBQAAAG7MWOiaBw7Dytfudri3at8+PK9tyz1j831p06rZNDSpVcim1y6rVmOla6ycjdWy6T6x3fX6trFrROW8u7Z0HmmFbqjuQzv9uHyewznV28bOn3Os7LOr39g5qSWrZ6+bcO0bPB/z2D7h7HTbWBVsPJaHh8OwMr1f/wFBft1aAFoLXYf9huFoPTAdBqO1ELVcLni8va3MqVZZW/+Lla99ZWy/p2y/X2ybVM3WAuD6sssAwM0QxgIAAAA3Yljd1QeVaRBbBjXpUsGdMhgdLkOchplxueJ+ieDhsXSO8Vh5nelQNgzGiUsUxxB25FUJ6RLEsS32r82ttlxx+tzKsDa9zrSxQDaMnDs15pzr7Rp/jrkJVLoM9NKOFcLuOn78IHa4H/JUEJsHnP05tcA17Ti2PHEtTE3D3XbQVo5Z3o5VwebB7DCEje3p8x2GxWPhcT/fYWjcDv7S5zD2WpTPJfXRRxfhN3/z/fDee59UXhcA4CYIYwEAAIAb0wcwbXa/U+7rmv/FcLM/N6+UHQtha2FtGV7m+U4aJPYBbJ9txrY8SE1Dmy5ALgPRsWukr0u9CjbdI7YMavvXZhg2TweyY+HrPsHrrkA2TBw/tO8hlg5h9x3/aYWwY+37BbHxnDSIrYWtZd+xwLXsU1sqOA99p/ZwHQtiQ3bdsft5lWwf+A6P10PcfI7l65DPrzy3/pzG/mr/Rl0Y+8/+2Y/D48fbAADcDsJYAAAA4EaNV371IWpZYdbdji9f3FWfpsFtv3Rwek65NHEektTG78PRWLUbb/swt72aQxoo9+Fx9uxDLQBNx+orgectV9y318bf9/FY2672MHIsPT7Vp9Z37jk34ZBwd845h4a0ywWx+WdtLHwd9i3vj++3mrbvXp64Nr/aX3/tqSrZPCitn19+P9XC07E+U8sV98sPp0sVl33i8sXb7b7/9gDATVnd9AQAAACAT6d6FVitQixWmI0HH+mYaVCUX6s2h/rein1YUw82xsYfXwZ1WK2XnF3c1oOnPNSaem5lcDbVt/rsRtr2ad91bJ8+Y+fcZOjUhsPnMfecqT67Xve57fsFseU5ZXAZ+4+9Z8v3Xxp05tccjjM2n/y29lmavvb49YffS0mP7LkNr9FWxhpW6g4rXUOhXg073h8AuI1UxgIAAAA3pBYupPebLOzYbtNK1hDSatDuWBNWq9oSxMPlissx8rCkXC44BkB59Wnali4RXAtI8iWE071w61Wo+VLHfb+xKtjhWGlFbW154lCcmz/vetsh7buOlX129Zs6b8y+lbTHTrj2GW9OeL3vsXkh7FQwm4aMZfiZh5HDKtg49li/w/daHWurVdOO9RmrUK19L+VtedVqHpB2x7rq1vI6/Xm1a6dVsWkVbF8NW1+ieKzqFwC4DVTGAgAAADdiKpAog5r0nP7cwYhZn/y88SVOy7HT8cs51EOOXVVqeUAyNY/x516GYrX5lFXC4/Oeev12t03ZFRbOGW9uv7naPf+Ofd19+h96fJ8gtt5v7L2VBqr9seE545Wsw3HLfrve7+OfmfHnN1aNPtyzdRiYjo1bzif+G+fnlZ/z2nMcC3nL5x3HHy5/DAA8O1TGAgAAADdiWFVWCyiaq70R456r/b6vZYVsVz3WVceWwUlzNV53Xr8na+yTVpLGc9K5hqLaNB0r7lGbVsyGrKI1JNdJb+OYXd90D9vLK4epKtQ4Rlo52587eMUvx99VETtnz9jauXOOpcen+pT95vS/SYekY3OD6UOOz22vB/DDAHReRezw/tSxehXtWNVreu1+7GG/WoA7/je/UrasSs0rZMcel20h2Qs2Hbe/Xn+d/txYTRsrZvf7kQoAcNNUxgIAAAA3Ziw4me5bVpD1wUUaLpVjl+OX1xlee1iNVhsrb68vEzoMo2qVuuOVebW28jnUzy2rZXc95/p19g/9dh3bp0/ZP/7dBofMZe45u17b6waxl62V9/XYeWNBbO3c8nMw9jksPwtpeznP9NrTVfXx+6HNrpE+rj33cu7D8co51J/TcC7D74Vh/9r3TZucn86hfF3ycQCA20VlLAAAAHAj8j0Vu6qvWDUa90cMoasgTStLY2Vs7J/uoVruMzsMTGOfNhsrDTH7yts8cIpVsX0VaxvSqtx4m841XFWVxnNiwJJW6O6uhE0rcuv7xKZ98mPDatm5FbJz2qba02Njx8s+U/12nVdznUraY4Zac8fa1e+Q8HteuD5cRrt83J1XBrH1+8NjZfswuBwLPOvVquk1837zbof7uQ6rXuvjj/VNH5fVrvl1Y3Vrej/vV+4Z22Z/5XdOfC3SYwDA7aEyFgAAALgxY4FGGRaV4U3ZXgY1wwq4dLxhJV65V2N+/WFINNa3FjL1x2vVsMPnVJ/nsMJ1eO36cyjnXbte+hqUc5jXFtsPCQv3HWcf7TX+jnntuX139dn32LC9vpRv+l6p3w5D1Px9n96vvZfSc8u5lOOUn8vac8nHql+vdt2peezz2sTPVP07LG8bm0c+p7YYsz5uMfOr8y8u2vD9738U/uAPPhbIAsAtozIWAAAAuBHD8KOvbI3VYF0laV+Rut02YbVqL/eGLfd4jeM0xXj5vqx5qBLDjLxCtt9TNu+b7jUbK1776tZQ7P+aXy/V7xvbn1+reE2rWuM5IQwrgssK2XTu/TXj65RXxMb+5bV3t4VK+9xjY8fH+s7pf9P2TcDmhrSHHJ8frA+D1zJ8HFa8Th1PA8LyBwLjFbLT+8TurlSt9+vH3XVb+2tHqmdre8NOh7LpPrC1yth4nbQKNq2yHdsrNvXJJxfh//6/fxg++ODxaB8A4GaojAUAAABuRBc+5Mt0pgFE7JNXmJXVovUwpT+3/hcqezCmFbnpdcs5p3NI29MAqQyB+nPi+G02XjynHy8PsdK2YXsevA6Xhy2fZxiVP4fsSDa/3e27jqXH54aYbdj/nCUdOp85/ee+dmPHhm3l+ySE4XtiGJ7W3ze1914ZbqZ98/d+/h7N51arAJ9apng8YE0/g/G5lZ/BsfPK13A4j+nPYvndNN6v/8u/B2NbDH3Lz3l5/XYwLwDg9lAZCwAAANyINHgoA4fdQWoI+V6x/eM+VOkrTmOVbFetGkJa+RrPSfdcbdsQYsFoOl5aqZpW2vZ9h4HXeBVsvM1eldDvLRvP76+ZH+/vh6Rqtzw2nM/uCtnu9Uj75PMbGmvvx5+ubJ3TZ+ycmmNU0R4z2Jo71pyQdv6xNHzM29vk+DAIrT2+TkVsGrjmQWb5w4G8vf65D4NzxkLkqfAz/4vXHq+2Hc5rV0Vs2t6HpbGiNq+sHY41Vk1bPh8A4PYTxgIAAAA3os2qwcrq2O5YCCGsVnmlWL88cT1Mqi1TnAay0+d0bd3xWt9y+eA0CG5HlikeBpV9QFsGtdkrlATE+f0ykI39++WNy0C2X/K5Nn7f5zpLFO8KVPcJZaNDQ9WbTqn2uf6cvvuFsOPttUCz1j4exM4Jamt9h+PkFbG19jIk7foMA9jhvPetlh2Gr7XvpN0hbC1cTZc0rt2WAWz/45S0MjZ9XWv/pqpiAeD2skwxAAAAcEPyAKL8u+pVtNWCkMHIO8ZKw530WN5vOH4ZZPXtw0CkNu+0f37bzSm+LuW4tfP7x2NLvtbmnE+8vlRzOf/6azwdAu4KD+cGR23Yr/9N2neuc/pO9Rk/Vv83G6ssrQextQC1Ns6wWnN8CeK837BPeW7Zfzoc3b08cdk/PT4euNbaxkLYse+nsSB26rz8b0zbhnBx0YaLi2fhMwIAn04qYwEAAIAbsd12IUJX/dWFsrFCs237ytim6StIu8rYskK2GyMWdca+sSo2ve1DkbxSNg+cyuWK82V900rbdJniYeiZL5PcV82G7Fh/m+rb8uWNQ6hXwg6XMu7vp+eUx8ceh6JKtjbOVPuuY+nxqT5j/VPHWJJ4rusEXvuEtIccH2+vh7DD92yt31SfstK1v9/3rfXb1acW0E4HpOM/6Ki1j58/dt2x8LR/nC87nF9juDRx/zj9/kuXJM7/8ueUPq+Lizb8+q//MPzhH34SPv74YuQ9AADcJJWxAAAAwI2ZClJqgUq8393WK8b6pvbqNg178jGGgVOsFsznWIZRY9V8w9AkvX79eYy/Fv3xMuiqXS89UF6rfO5l5WTtdWyLx8O2qfPzY3MCxjn9dp17nXHmjHudceb23f947TMT+0+93/rb8cB2bKxh+Jp/9sbCw9q808e1z9buufZzzudWfo7L74R8HsPP4Fhl+vT31HC54nqFbAi18LY29vjnrm3b8Id/+En4gz/4WHUsANxSKmMBAACAG7HdtuHiYptVgHVVr32Q0rYhbLddVWjT1Ctj+71lmyzA2G676zRNvu/sMNBpioCkKfqkj5vktmxLn119r9hoWF3bXp0XsorZ2JadXfRN+7Qj98cel+f2j/t9Z8eqZMu5jbXPPV7rN6f/PuMsbd9rzum/K+iutA7C97Egc07VbBm6DoP+2pjDwLYdnDPWp3Y/n0tbuV79dvh90hZzGKuA7a/dV7a2lQrYftyyMjathI3n5rdplWz9Lw1u83/TYeANANxOKmMBAACAG9GHFTFcGatWa4vH8XgtFCr3WMxva5Wn6XyG184r08rgauz8MiAaziMdpwxy6/3qlaq1x2m/smq3Nu7wdR0+xzwIKv9thnYlRO2MPrX+tzV5OvT5HN6nfP+n7d3t+Pts/Lb+Ph+Olfcvr1F/X+aPh2358sX56zA252FQWbtu7Xpx/vnnNBuhcp38OdYqd/v29Dut//7Jr5tfe+r7qZ/77u8iAOB2URkLAAAA3Ijttg1Pnmwvq2Oby31f476JIdlDtkn2j22ujjdNE5qmvRxrWBnbBRuxf9e369OGWO0ar5GfP6yI3W6bsFrFECSthO33nu2OpxWx/W2/5+v8vWLj/XrVbK1v2ONY/ji+zvk80j59W14lO9Y3bQ8jx+f22XVO6Zh7yB4r6Zo7zpyAduRIJUQfD1fL9lq/8WWJ4zn1+2UgPKciNgzau8r2XXu5Ds8t92Udu01/CFKeGytW+9u+vb43bL7nazwnrYQtK2Tr1bB9xezFRX0Z9vTfIK+cBQBuK5WxAAAAwI348MPH4Q/+4OPw8ccX1XAlhOkwpz8+DGTKtqh/XN8LMh+zq2xL28vbsSVEx8esj9OHTvXnmI9Tr9Kbeo61+Zfzq70m9SrLdnBu7fWqXGXiWNrnuslSe8S/Y81jbt/x42NVk2kleNmetvX/xtMBX/29UI5Vhq/1vmXl7Njnov5erYet6fXLMDW/ft43bS/HL+eez7Et5ldeu/a9Fc9pi3PSa5fPr6zsH74mtTnE5dgBgNtLZSwAAABwI37nd34Svve9j8LZ2Trcv39yWaXaXlXBptWwsXq1u22zxyH0oUSs8KxXzfV9+z59NWw/XpP17UOR4d6xtb5T1bBd5W9f7dqfG6t8m8vH+XmXvS7HSI+HSp+wx7H08f5twyrZsf7lsTDRp9ZvV/+bdkhwOzekHTlSBJ397VgIGyr9p0PWss8wxBwuFd73nVsRW4an02HrMMAc75Nep6y0LatVY5/pyth0H9iyurbcGzYUj8vb/lr1Y/m/Q/lvF0J/7mHvPwDgaRHGAgAAADciDU9iCLFaTQUuMYgoA9Dutgspp//665ZjN1fnp2FiLZiKSyXHIDJd9jiEPCCOYoAaQ9f0ev1147WbbIzYvxsnPTftm/YpX5M0xM3Hi9fsnlMY9EnnGK+RzzsPZfPgqL0KtyfeBVev9Txzw9wlXSf4mnvu+NKztRA2bR+GrtMB63i/8aC1NscyfE3nkI6Ttg3HGAtu02u31fmWn+t0TvWQOD8WRqqTh8FwGUaPBcdt8Xh4PA1z8wB4vDo+fb7f//7H4b33PgkffXQxmDcAcHsIYwEAAIAble+h2CSVr+mesbEqtg9sQ8jD2b5//EurX/tK1bxyNt1ntlYBWz7O95ztg8zhnGKwWoaYubbom7fFkLR2fG6F7LDfMGyth6LDtu68kMyrHsoOn+PVCJXjZZ+pfrvO22VOte4x7TP2VN96ZWt3f34IW/YfD2eHj3cFtVP3a4+HS/jOD2Knq2l33YbB9dIK2BCGFbLD5YjrlbH94+E10tvhvrHp/X51gPI9Uf7bffe774fvfveDAADcbsJYAAAA4EalIUQMWvslPmvVsn0QmIezTSUEyQPZrk9fGTrsG67a+9s8gMwDq7yiND0nD1TL0LUevo61ldWvtUB22GfYL4bbIQyD1b5PvUq2+Fe7HGMslO3/PVLdUswh7A5bDw1nd1kyeD1k/GGQmh0tDswNYdP28YC16zv9eBjE1sPRvG95rLY0cT5m/dzx42Hkuvve1uY0Vu2a359erji298sPt21Z9dpePU77jbwTiufaDt4bAMDtJYwFAAAAblQfTISrULZf1rcPX8vgtA978krW+jkxWIzXrI/Rh48hlGFiHmamgW4MPNvsnPRaZRBbLnWcXGVw3TSkza8xDGTT/sPrh6sxaoFt/jgM2uJzHy6rXAtl+3PSccrz4rm7LRXOXtd+gVgalu4adxiyDscog9OxMLUcZ04wO1YtO3W8nEc57/S8/PM6dp2pELUt+o8Hr2Nj9efnYwznN3a/DGGH3znlddMQt/Z4+O8XBto2hIuLtnoMALh9hLEAAADAjdpu23BxUS7X2VeJxdumiUsVd8Fn97i5Or5aDZccLv/icsdd3z7YzMOStMo0hDQcjZW45dLF8bZt27BadX3juTV5gNVcjXnZGvrwNoQwWjW7634YOTb+uNwPtqySTcethbJTywCPh66HBK1zU6hDQttjJ1zzxpuugu3HmRvMzg9hp4PVsaC2dm7atwxsp6pmy/Z0DlNVrOkyw/k5Y3/D89JAtL48cUj6lhWubWWM/i/vk96G7Mcn6f20Qjf990vn8tu//UH47nc/CD/84ScBALj9hLEAAADAjcrDjX5/2HqwWu7B2oeKZb9aGNNXi5bhYhw7hDSELR/ne62moezU+Ol58Zw06OwD3b4Cdvga5cfzQDjdWzaGnnFe6XLD+bLKZWVuvmxxbLscZTBWv9xx+tz712f4mtRD2SKDDCELL5vBa7GfYwer8687fF4jPWeGsOmx9N9n2DYMV2ttZShato33qc+nDF/TPnnV7PBzOXwu9UrYsXB2/Hm12djlOOXzCUmwnfdP28f71L5z8teoHek/rCROn0vZ9uMfPwr/6l99GACAZ4MwFgAAALhRFxdtePKkq45tmn6Z4r4ytrl83IWAq1VfDRtCCKtVrFhNg9xaMNJcjV2e043TXp7fV8aWj2MA2e1v2y9zHEIMTuI18n1iYzVvXz1bVrtG/TkxOK1XzZZhar1PGabGpZbLoLkMZdNzdrcN29MQtv4ckzObWp9639xNL1V8WNA7Fb72j+uhZrxurX0sJK0FtnOWKR4LYYeBab1v7dhUpWz8HM4LYut9+iV/+9taleuwMnZsyeC8Mjat3i8rXdM+ZYVsrQI2/YsrA9TeU2PBMADw7BDGAgAAADeqDz/aIggZhjYxzOzO6ytA+6CnXhEbj5dVo3nAVdvbtJ9jWYla9s2P5eOVlaLxnFpVbBrIptWztb1i0+vUqmX7a5YVq+USxMNgOK8CHi5dHJ9XOn79uabzDdm5+esWBnOo/VskR4rXd8lwdhiO7nt+PXQtH9evc4wgNp1LbX7lPPLz0hB1fB5xLuV8y+dW65OeW/v8l8+t3qctrjt8PHbueHVrG8afU/p6tMn9udcox56WjgkAPDuEsQAAAMCNurhow+PH2/DkyVhlbB+2dYFjrFaN+8bGkfq2/pxwVUkbb5umuaqCjRWyITSX99Mxm6s9YvvlkWN7P49+D9k8DO72ju2P9W29WDE7vi9sP+ZYhezwfri6ZtxXNz3Wh7J93/Lc3rCtr64d61u2l8dqx/tQtzS+1+xghJn9ljQerE2Fr+XxNPhLH5ehZ/3c+jn7VMOWj8uwNB4fX743D2/z84YVqnn7nIrY+rF5lbFp+9i4+ZjlfrDp+enj4V6w9YrYWAkbb8sQOH3t+tcmr8YFAJ4dwlgAAADgRsVQog9JyqWGmyIwyYPPqDuet8X2dNng2K87lvfrl/MNIQ0vy4CqXB44HzO/XllhGkK+z2q8zcdLK2j3rZAdPv/YL46R7/c6rJLNH8dzavu91itlw2Wla7mvbD+/PthOh8z7pPNP2/epnn0a5gewfduuEHY47vC9mreXYWqtLQ33ps4tA9XaNcfC17xP+VyHSxYP22tB7dj820Hf8vrlcxrOvRbClvOfqnZNrz9W5T9eETv+Hhne/+iji/Dee5+EDz98MjwJALi1hLEAAADAjXr8eBs++eQiPHmyCavVNmy3q6Iytq9W3W775XvL6tbVqgtAY5gbA724bHA8p7Z3bAjpnrP9krl9ZWz3OA2BY5Vtd4000EqrXLv9Yvuq1jYM95XtA56+4ref45wK2TKoTZ9D36983Aeu9eOhaOvb0yWPx/vWj5Xhc/64NkZpOnkd36t2X/stHzve3lbaao+nlzKeCmCHbWP9d4ewafBYnjN2vLw/fFyGn/058XM+Xf1aOz6vMjathE2vM1UZW9tLtqyEjcfHKmTT86b+hkssp/8mfRC83Ybwe7/3Ufi1X/t+tmc2AHD7CWMBAACAG/VHf/QohBDC3bubsNmsLpcUTitk+2AjVnWmVbAxtMyr1posXOmXGY7L99b2lu2rP/OQq39cVr/mfWLA2YeledVqH8jGILbckzWqVaf25+XVrWl7SPZeLc+Jffsq2bzvWFVsP+9eWikbx+73h6211+ZW7htbVs2G4nWsh57DSuD0uR7fVEBbu3atf962fwjb359XCZuOkx4bq36tB6392FNBbPqcyrml18znuasyth3cDkPU8jph4tjY9crq4Lx9uq0MhtOlhUMRFNeeY/3fOH0N4ioCAMCzRRgLAAAA3Kjf+72Pww9+8El47bXzcH6+DtvtKlxchLBe93vHxkrXGE7Eata0gjU+zpc47oPSWKHaV8Y2lX799eqVsSHEcLXfhzYNR9PgNVxec/ic8xBqWDkbj/VVtGFwfHg/TBzLlyWe3hc2f5yGrOOVr7srYuvHasfzvukSx+U59WPLKoPU8WPj50yFr93j6QA2bS8D0OE5tf7j1a9z+kxXyw6XIh4GoLUgdm5FbO0vr2StLQ+8a+/YfG/YWuCaV7KmfdLq2LJaNn88XJZ9+D7IK2XL5w4APHuEsQAAAMCtkC7pGYPSNBzplvDt+saAsA9Y86A29u2D2Sa7Tfd5HQZETRaK5JWx4erccu/WWhCWh7PpcsMxaGmyILevnA1XbXlla7xeCOkyx7tD2H7u8Vi6p+vw3P46c0LZdPzh3rjpfNNz8nCxf9xU+pTGQtqrM+utI7nv2DWmTAVjtX+vQwPY9NgwmO3G3zeEzdvLfvstWzwe0o5VvqbPc6q6NA9O8/77LF88Ht7WgtXy3D5MDVlbGq72Y+TfQ2XAO1yaeFz6mn/wwZPwz//5H4Uf/vDj6ZMAgFtJGAsAAADcuBhwXFy0lxWnfShbr2Ltqk6bpr28TUPXPGSJe8mm1awxJK1VxvYB6lRImy7124RhgJUvRzy1PHHxSoRhGNm11QPVfDni7vqxf0iuHSrnDK8XH6dLJ6dLF6dzGoa58fwQylA2PzYVzMY5lq9l3rc/Vtftnzu0KwA7pG/937B+/ljIWjse/13r7d2xWvuuELbW1gd/40HsrtB2LHwtxxj2mT4+nNN4qDp9my4hnN+Ohbvl/rB5pe1w/OFesHloW+5TW/47l//W3XNvwwcfPA7/9J++Z4liAHhGCWMBAACAW+HiYhuePOkC2S5kDSGELjxtmn6p4hC6MK8LbMPV/rIxsOyXM+6XHY7HumWF26uAtg9y8+WJ06Ak9ulDob5qN+4bm4aLfbiSh7/9ksN9IJsvTxyS+83V9eL56TzSpYxjCJsHq+1lmFqOHWY9jkFsfp20Txg5t2yvHSuPj/UZ6zvnnOubDmPHwrPp86fC1/7xIQHs8LyxIHPfEDbOuxb2lqHirmP1gHYsWJ3b1laC0dqyxPHzPB7S5ucOj/XVrnn4Gs/pQ97xv/R1Tf8d89e7nG8AAJ5hwlgAAADgVqgFF/n+r/mSwmmQ2gcmw2WH60sT90sD9yHIcKnh+m1fOdtVYKaVsf210mP1ythyeeJUHCMkwWh/3fx4HsKmywqnc++vF6p90wrYfAnhdB59yNuPm4e1+fl9lWr/moXs3O5YnzYNl2XOlxdOQ8XyWN/nGEHtMAHbFYrVjs8LYPvrpcdrAeqwTy2Ezc+pBalj54716Y+nY7bV9jRwjWOU7eMVsfXr5vNti/PG2/Nr1ZciHr9fC4ynA+JyueLaeeW/Z/Kvmfzb5XMGAJ5dwlgAAADgVnj8eBsePboI5+fr0DTt1ZKcXfVoCCFZvjZWyeZBXF5BG6tCuwravio1Pu4rZcPVddJK23T8+LjrFyteu6rZPmTt5hgD1vQaMdScrowdVsjGPjEAjQFvfD3icsflefH+MCRN+/XnDccZ9knb+nHTtjR4ja9/HiaX+uumbW2lrR9vOMblLLLDxw2vdoew9Q71cPbq3qBP3n9+ANvf3zfU7B9PBbfDkLZeOVseG6uMnbtc8Zzgs/usjZ+TV8jWlirO76d986rX9C/f47pWDXtx0S+7PrZXbHy9av8WcR5xDADg2SWMhf+fvT+LtSVL7/yw/1qx9z7n3CkzKwdmZmWxKlldZLHZYg9kd5Oi1HRDIyTZpiDoQYAmCGoIEiABhl78IL3YEOwHPRmGX2TDgB/9YFmQpbZlww3J3aAFscnuapPNbtZAZk1ZleMdzzl771jLDyu+tb41xY597rl5p/+POIyIFStWxI493Mr92/9vEUIIIYQQQgh56ngPXF46XFyMuHnTYxhEXLTSsDrVWs8lm88bW87zOnesnic2T3AmoVju8+q4IEq1bCnnTdVJ3joZq0Wr9NfCtTxOJ3Pzc8j1lHPKJnGZxk0SNU+11klaxGss55StU7xJxNUpWn2dKVGcX3v+WPR46XHm13ZImF4nc0nFJQK27HeshNXteuw5oZqWrX71ceUx5fGtvvm+tojN78HScsXtfel8vWPKfu2+9f65benfO3cpf+u0bPZsF9egnwfnPM7P97i4GEEIIYSQ5xfKWEIIIYQQQgghhDx1nPP43vce4MaNATdvrmEtsFpZAA7WAoCdkqoi+LxKigK6DHA5v2xKsYo0THPHaokoZZHlLyVmZX/algRtEqE6ERuO1wnbfN7XJIll7F5CNiVj0+OSMeRx6/SrCJ1wHxDH6aVkpW84l5SFLiWtjC1omVYnavWYdf9WYjbvo9Gljet9vrvvSXFI+B6Sru1t393fkq9pvSVaawHbautLWd8cR+TiXN+WoG2NkUtSxNTnfDI2b+slYg8nZNspWJ2Alfd63l6mY5Etyz7jWKdp289l/YLS1/Tw4R5/62/9BA8e7GOlAEIIIYQ8f1DGEkIIIYQQQggh5Jlgt3O4vDQYRwfnhkmilinXtAREXJRzwUobimNRHJ8nY3WCsxRfur0ttPRcsfkYeXo0nV/KFev0apmQTQlWud4yseozmZrOX6dadZpXzw0rfXvzwuo5Yesx82Prx5u3p+tENV6ZIpbx071si9k5QXoVUXtIuLb79oVqr21ewuZj1q/LWqTqMQ+J2JZYLbfL13l/f5nkbSdvy37leWvpOidx28K1116WKC7fz/W5ysfXTtzmIriVlk0/4kifHb7Yrq9FtkXIMhlLCCGEPN9QxhJCCCGEEEIIIeSZYr/32O8dxtHCGAdrg9QbxzSX6DiGVKnMCyukZGvqm+Z1FQmYxGiaMzaNoZOv5dyxIleCJE6CVISmFo2SnBWpDJWM1fPJeu9j31LIeo+ZZKxOtGoJq+eX1ePGu6TkauqbxKtI1OVJ2SR2S8mr710+P2w+HrIx6rb0HJVytyY//vFoDzA3br2vlpBlv558LfctEbD9/kvKGNcSNheEbQlbS868BHASknnSFUjJWHkf1kL2aonYMu3akqfHzRXbmju2PVdsOWdsEu/1C0HfCy1+ZQxCCCGEPP9QxhJCCCGEEEIIIeSZQgsN50yULdamEsA66RrkpIiMer7YVqo2T7np8cpjwzW1JFaaexZIgrNM1/q4T0vWOiGr70Dqm9KpWvzm/XQSFajnXU3jJer5Xeu5YnvbaUxk55THqxOveTLWq/VUqri+Zq/aysfcenwlfkbULmepyG33a6VAW8e2BWzaPixh6/2HReySssU9Udt6T/REbH6NuaDNH0tqmy9X7IuxUiK21Z4nZufGL9dbf179SSnjQ/3ylG26X6jQ93McPT788BE+/3xLIUsIIYS8AFDGEkIIIYQQQggh5JnBe4/dzk3JWA9jHMZRJ2NT+d6w1MnVfL5Y6ZPmek0p1TA/bEqMyhyxOnkq2zKGrIfEbJKl6Xok7SqPJVwTkCdh9bYIWWkP6GRsKtUs8+SW/cK+fJ7ZlAxO5YzbxwCSgG3PC1vOL9vup8fLjy/7y73JhWmZmk1jzomovnEtJe8xLJOwuahcMsZcWeJ8uyUq6zGOFbD6+KtI2Nb+cr0nbg/NCxv6hGuR96s+pp2abSdl85Srbk/HtNKv6dheMlbSsfqYVjo2JPfHsZ2I1s9lTzjvdg7f+tan+Pjjy/4AhBBCCHluoIwlhBBCCCGEEELIM4P3Ye7Y7dbh9NTBWhsTskGQ+ihOgSAAQxnfvKSwbpeUqxaxUh447QuSNZdTprmdBFSdjNVJziTJcrna2tbJ1vYxModrq9+hdVT7Upo2zQ1b9w1tZaniVj89Xistm6gTs2HbF9t5cjY/T7q2FmUp5DlpWzMvTJtHzArZqwnYsm8pSMvxe3I139cuQVz2ke3Dydh+WWItXMvz1eK2PHfrmP4yP39r2dvXWs+38xLHui0XsaWULa8v3dP0fLeea5HFInUJIYQQ8mJAGUsIIYQQQgghhJBnBu+By8sRq5XBOK6m0sQ+zhcrMlaSoKl0sYlzq6aSxiYeA+i5YJOIlfFElMj+vCxyPh+rlODN5Wu7bHE5D6z0KbfzdjT6lCLnahK2v29u+1Db0vZyX2t/r99cX9WjkLz9sZazTMjWnVrHlTJuqZxdKmZ7ErbcnpOwdR8taOvtnnztj1FL0XTtLcE7J1Pz9lZCNs1Xm4RrmZIt07NpX52O1cI0P0/dr7xP+ba+R+kxyfGEEEIIeXGgjCWEEEIIIYQQQsgzwzg6/PSnF7h1a41bt9YwxmC18rFscJKxQVZIuWFdJjiVHE4li0NfE+VuKilsVAJNHyvlikN7kriSsBUp20vG1qWJpY/34TqAVC5ZPw6Rv3kfXSq5LkkcKEsYI543rcu1pnuYSgTPS1l9TF+ypva8f+sYfdxcn17fEnlcB7odiRZp/f1z7b7RVm/PlTHuC9j8uL5cDddxnIRNx5SiVfct+wHtssTlWC2ZunRfWYq4XZq43adVrlgf3ytbXJcpTv2TRE3naT3fredXP1bnPL797Xv46KMLPHy46x9MCCGEkOcKylhCCCGEEEIIIYQ8MzgH3L27g/dhzsU8bZbEZErB+lheWERL2G+icBGJmsoSl8k6+atLDCfRqkWVLlfcT8ZqATufhPVZCeI0lyzUPn2XkmCVvmV7f70eMx+j3T/0S7LT+1S+WKPLDefnSDKvPG6+9HCvX9FLyeQ5AfY4HBq3fo7ax5T9WoI1bfclbEuuto5pidnDfWopm45N5+4JWz1Geb7D5YKXSNpj+7UlbV6GON/WKdh8nLxMceqbn6d4Jqv7pe+tPueHHz7CBx88BCGEEEJeHChjCSGEEEIIIYQQ8szhnMdu52CtwWqV5hk1xk7rPopB2afFn5Q1lrHCEtMxSYCW8i/NOZvKE6e0bRhQyheLOJXjwjJJUknESiJXJ2F7CVnpL0lgnZpN12qiDNWpYO8lTRv6iOSRY9N6LlPlMcr+sEz3N80Xm0o6637q7mXPifQP60at58elY8pULbLnRz+/+px6X73/8WgLWL+gT1+s9vensXv7WlJ2SdniOekq261jjilJrPvJdkrItuWr7tNf9oXrXEK2VUa4TMRKe52azedu1T8KGccwTm9Zku5deX/z+zGOMkY1BCGEEEKecyhjCSGEEEIIIYQQ8szhPbDfO6xWBs4NmSjJ060yv2s6LsmZPNGq53pNYxh1jBZOZQlixO32nLGlzGonYvV2SqSm9nyJbpuWqildm65NSIlU01hHJnKTcNXbKTmbtvN+6lnLriEX5Om+lAnXtK3Ftqn66NdGec6sJdt/VTPbNmJzoqwnbttitdw+XsKG9ZaILYVrPkZPvB4jYvX1tlOv+fWW5Yrra21dc3ldvQTs0mTsoUSu7+zz1fGlIC63W89l/rjKxwTsdiMuLkaMI20sIYQQ8qJBGUsIIYQQQgghhJBnDuc8zs/3AIDNZohpVWstAAdjbBSF1nqMo51SoTaOIenLcYTqqxOy9Xl12jRPyaaEqfSTZKyWlHqOV9kf5qUN8lLKK4tQlTliJSGby1fEdUnRhvPo69PlkFOKF0jXVCdjy9Srzx57LwGbH4PYL52rnIs2F6uSdE2CNj9WEEnVE7C6b7lPP4/T3nrnEczJVxm/16cvYefEaz1mS7Tq9rZYbbctKUesZerc/jkJW4rJkCSfk6CHpGpKsYbx2ttXT8TmZYdbqdd2WhYxzdqXr7Volj+duv2jP7qHb3/7Hh492tcDEUIIIeS5hjKWEEIIIYQQQgghzxxBxo6w1mIcPYYhlQkV6Zfki5lKB5soDSX5Ws8ZC+h5W7UYkaSsFpe1iMpTsmWKVCRrOCafC1aO14lYXbI4F7F5MlanU9P1aPR5Q4suF1wmY4V63DIZ6wsBC9RzzOZzwsr4+T0BdGK2vM7+sfk50uOp28syx9fPfAnZvowLx/b6Pa6Erdvq/m1he5yIbcnXsl89Rt3eTpDWyd2erE1j+ez886nW3v6yz9wx8nmTL/W19J7T+n6k5+7ycsSDBzt8/vkW9+7tQAghhJAXD8pYQgghhBBCCCGEPHNcXjr84AfneP31Ebdvr2NqMyRIHawVQWhhbUi/WqvnbvUYx7Cu554EQpLNWtOYn7GVOM3TsUIQvD6Omc8Z66cUL+K8r+XcsGVCVtolKauvo27Tc6z207KpPUlVnXzVaVrpK/eun4xtzRWbZG1YT8fLPdHH5LLYFNupT11+OJe5qT2upWfyqtWJm+Mu6dOTcf2+fSnblq9pvS1tl5Qnrrdr6dqSsrKvJ2HLZGyaK7YtQLVQlbbWnLE9iZrvy8XokkSstOtEbLktfXV6NSVk0195H1rPW34ffXat3//+Q/zO73yE/X7BC44QQgghzyWUsYQQQgghhBBCCHkmEfkxjg7jaLJkrEhQETdJjgYp6JxvJmJDUrZMu+V/Mt+rjJUEVp7Ibc8dq9O1Kd16lTlj87lZ9Rytdb/yfHmyFtmx+TgozgXkqVc9Vn6MpjcnbCv1mtr19eXnbSdj2xJTp2njVT4Br9UTr3PnK5OQcwI2bPckbP3YexK21TYnYvPxWsI09T0kYluJXN3eE7FaVOpj83FaZYznShwvScSKCC7Fbl36OBe3Sdj2n6Na0raStvu9w+WlAyGEEEJeXChjCSGEEEIIIYQQ8szinMflpYMxBsNgYK2BTsaGOWShUq4pPTqODoDNyutKWhXQadZ0Pp08lb5hzteUGHUu7y+J2nzM4xKyrTlj5ZgkoFOiVfrpxGuSlSklW84lW+4L1KlX2Zb96hFnbaGfjKvH0ueejvD1HLOCnFdv589NmaDVtCXt46Zjlwjduk9PGC8Xr2m7J28fJxnbEp7LyhXXfXOxqI+Tc6SEbE+EzrVpGRrGL0sDt0oGzydi6zSs7iOfI+WcsWUaVidiDz3XtVROsrcUuoQQQgh5MaGMJYQQQgghhBBCyDPL5aXDJ59c4tVXN1ivrSobGvanZGyaMzbJVDPJjlZCNiVng/Ss03giP2V/kKiIAjWXU+35ZYNAMgjOWI7zk1QuU7RayAKlnC3nka3bymPKdizYF7Z7idb6mLn2XOLWade0oefnTW15yrCmbVv78+pejfY4fZHaa+/PK6q3a5nbTlrW4x4jYct+bVGbS9t2ujW9r+T4w+K2J13nhaxe1mWQlyZidYliLWLLvrnwLf8OS9h+wjhsezx4sMMHHzzET396Xg9ECCGEkBcKylhCCCGEEEIIIYQ8s5yfjzg/P4cxwK1bKwyDzBPrEOSohfcp9aqTreMY7IcstcCVpCmALO2aJ2OTfA1LkbR6Ptl8nlVApEsSvpLo0wlZKW2cp17N9DiCmNSp2bRMJZl1GlZkcJ6wBeYkrE6zln3DPW3ND5tKM+vUaqLum4/Z2leOl+6jmY23ti2oSK+nk4yV9nrHUvmqt48vW7xEwh5Ky7b719u1rG31aUnJOWHanzM2JWT1djkHa1r2E7Hp+LaY7c0Zq/cfuqf186avCbh7d4u//bc/jmMRQggh5MWFMpYQQgghhBBCCCHPPM6FuRWHwU7CMggMa6X0cBKrQiorLEsRo+1yxbKepCvicTJOOGcaS/qGa8zlrCRzk5BNCVlJ8co+LVDz8+YJ2/R4WgnadExZnliXNNb7gHkpK9siUvUYiSSm5PH3JK4+ppSz+XY+dtZyUNKmfsDxUnaJhA39+h3nhNyh/bVgzc83J2BbbdcpYXXfnmQF5L11WL6Gvj35mgvalrDVcrOWr7mobSVi5ySsjNEqT9y6h+oZmH3ceixCCCGEvBxQxhJCCCGEEEIIIeSZxzlgv/dYrRyGIc2fmuaSTDJW2qVPkjBBUqa+JjtGyxKZzzUIVEm2pnLHQJCYSXCVZYsR27WQzb2NHK/njNWk623L117qdVky9urbvbYl7a195f5en7n+NVoMH8ucaM37HWr3jba6zyEBW7fnx7XGakvUuk228+NqadqStC1Bm4/fEpM9OVv+LSlZXEvacl89r6yslyJWy1ItbHUp4/I9XD+nrf3lYxpHNyX2KWQJIYSQlwHKWEIIIYQQQgghhDzz7PcO5+d7WGsmUSpleR1CsjNPWoZ9QXSk8r46tZeSm2VyMpxDpEtLluTlgVtjiTzVc9emtlRyWI6RNKxO5ko54rQEdNniND9uWtcJWDlfuC/pmnUSVvaFpcjrNGdsezs/Jr/vZco2Sel0bL5PJ23Tdi4A6xLGqaGXfF0qVI8lH9bP7Gv370vZ5fJV769labqulmCV7VrSzu9vCVa9ne+rxSuQJ1rLeZ8PlSee365lbJ1yTelXnXj13mMcRZLqPj7bzh9zfe/L+96Sxs4BDx7s8Lu/+wnu39/Ge0AIIYSQFxvKWEIIIYQQQgghhDzzjKPHdutwcuKwXluVjM0FkZQP1vOwln1DnyRp5U+EaBIrKblaCiZJwtbJWC220vyqklaVksPlUqddpX+Qn3op8nNJMjati0idS77K+PUYrf69Nn19aLS3+ucSS+Rvu4+mnKM39q7OLWMcou9u2zt6/VvXOi9g83McK2Hztrx/e7svYnuCVZ9Hv070+6fcrt8zvjl2fo29pOyhlGzeV5cjbgnRtF1K41K+5uI3fw7TPSzvR/lYRQZfXo54+HCPH/3oIS4vaWIJIYSQlwXKWEIIIYQQQgghhDzzPHiwx/n5CGMMhkGSsWE5DEH2DQMAuEnmORhj1VKnOP0kOG0lWLT4szbf1ssw92tqS4nY/rJsEwGbkq2pNLIxaSnH1ilYuYY6Jdtq14lZuQ8p+VqmXvV2utb8nKj6iSjW902na7UYzPvoksLl48ufH52iLZ+7Y8XpUg6J2tb+toBtS9dyf0u+tqVqfcycgJ3rMy9ol5Um7knYJXPIHpeMbZUl1mlXnYQN+yX9midk85RsOxnbez7ytpaU1dd7eenw27/9U3z++SW2W4pYQggh5GWCMpYQQgghhBBCCCHPPOMYxMhuF+Za1BJFzwub5nVNc7+GfQa5ANLbJkvFAsj6JWml069l2+GEbGoTeZnGTyWF66TsdESjLRDka7qOXko2pV/FGqUSy3LtZd88uapLA+cljhN5yrZMvqZzllJLb7SStVMvX/bNSyQ/OfoSNevVFHW+20cLvLl9pVjV47ak6/x2GrPVp1+eOL/OQyI2f+xzIrYlfY9LwuZiVtp6JZNTclbPDavFb16WuH7u58sTt++jcx53725x9+4OhBBCCHm5oIwlhBBCCCGEEELIc8NuF+aOHQYD721MyIZUKQC4aZ8BEJKx1lp4L8t8ntPgS9zU1wOwsFanZn1MwZZLIIlAaTsmGasTr+V2/ddKybbGyNOwIrd0Yra1L7TnSdn8WlPiNW0j65fkbNkGaFlcpmaBJFJb6dmwX78KTGzP99UmdEl54hZzwjXv1+5YNi+TrmF/a18p/MoxryJkewJW+pd9W+K1LVnTeKmMeE+o5hIUqFOwuWjNt8v0a94WtstEbEq/1olYvVx6b+dEs76WcXSLX1eEEEIIebGgjCWEEEIIIYQQQshzw3brcH4+4uRkgLX1vLA65VrPFSsSJSVidWpNp2it9ZB5W0O54DKJlydipU+SN3ru19BetyVqSRP61ctWn0SectX7RZD2ErTo9k3liMvtJFHza9Jj5e36mDwdqyVtLz1bPsZqTzwujnoN8qtMR7b3z7XXArbc7qVnH1/CtvrNida0f4l8Lccox6/3zydk8/MtWbblZ3pfI+ubtnVJ43wc5/R1Y5a5PvoaPv30Evfu7bDfszwxIYQQ8jJCGUsIIYQQQgghhJDnhrt3d7h/f4/NZpjmiw2C0Nok+YbBIIi5EFe11k998qXMHSv9BGuBcUxzxspfKZiAOlWap1/LNGq7rZ2MzeeRTY8xLVtpWedM9pj1+PncsOU6IBJNzymbrqucRzbvkyRtOVYSrLpkcZ2O1aWNa3mZC9ZSQMc91XFPgrY47+0r22qR3JKy7T7HlCs+LFjzY2pB217vS9h2enaZhBU5CmA2GRv6pCRsWWZYJ2XLNknISkI1zRmr9z1OeeJc6Mp17fcO3/rWp/jhDx/Fx0gIIYSQlwvKWEIIIYQQQgghhDxXBLHisNs5rNcWxqSyopKG1WV5hyHtq5e50AzJWp+lbq2V+WgxpebyNhE1IZFbJ2RlPN0m6VqdwG0nYdOcs+k8aY7ZPM0qbXlSt+7TWsfM9tI+S9pb+8r9vT513zo9e8W6xLPUAm0uDVkeOydfw3Zb5payL1+2RGF+vjmB2Be17VSsXu+L13QuKTl8WMTmf/l4rbRrLm7zFKzsk/by+JSM1SJWt/XvUUuU9xLD6fwffXSBTz+9xP37O4pYQggh5CWGMpYQQgghhBBCCCHPHdutwzCMWK9DqlUSsiJgtcSStFsQn8jELZCnRzHNETuOfkrG9gRKXdIXQJS0eo5Vkbj5vKtBAgdpqqVwnYiVa5Tyy1q6ymMMc+fWc8WW7a0+dVK2nYQtyxPLtlxfOq5OzErytUzNpj4mezzpPuk2/fjz9rTvyQmvnoBtCddWfy3x+vvb8jWsLxWwS8oXzyVh0xgtQdsSoYfSsksSsvpHEuX+MgnbSsz25pFtzR1bylhJy/buX4m+H7mI1dcGfPe79/CHf3i3PQghhBBCXhooYwkhhBBCCCGEEPJc4T3w8OEeu53DZjMACDJ2GGwUrsY4GBNEbSlfU59axqa2JEjDdpqX1jWmfUxlgtN2StAaWAsgplx9FKt2qpCcb+s+eUJX0rRJkOZz5krKNiRxUbWX60mCikwzhdRM+0vZqkWrenbic6QFa94u16CFbHou5Lx6fxwlE+71Pn3vH5+2NO2dt91WC9RWn1y66vX6GpYL11a/voSVMXoStp7btS1Vgb6wbQnZdopWi9dS0PbKE/ekbJ6YLUVsmYhtMV+eOL9fMqbIX0IIIYQQylhCCCGEEEIIIYQ8dzx8OOLy0uGVVxyGARhHC8BhGIJMHEdAhImIoVa5Yj1HK5AkbEq4AnlqNc3HGsRLkpetMsVBxOqxEMVqq1xxKGkcxpbSxknO6jQtsn1l+WEtYQFM8rVVYjhJVr1vTsrm27pNH6fHKsVsfkw9Xmt/6teStNmRM8L2GJaItJZ0bR2bb/fla9iuBW4pUvN9tVCdO/awhM3Fak/QttOweV/5cUI/9VofW/bRZYpr+bpUypYiNklT2V8+F737mUvqdnniIGMdSxMTQgghBABlLCGEEEIIIYQQQp5TvAcuL0cAHsNg4b2FtQ4hfRrkbBCpITm73xsMA1SyVIsSN4lESdNiKlOcesi2llZJ5va3U3tdprgsV5z6mEIWt/7y+W7LuXJ16eGyRLEuOZzvS8dpiTpfwhiQeW11Yjbco/xc8ryFvkat+2If4mMQtBzW0qwlZmuRuiQpe1iczQnaUri2+i8Rr2m9J20fLxXbl7K9JGy+71BJ4p6E1cuWdJ2XsX3pWiZle4I2JFV9sWwL9f599Y19dRL3hz98iO985z4+/fQShBBCCCGUsYQQQgghhBBCCHku8d5jt3NxjldjHMbRTOWCU5JVUqmpzHASQ7nE1KWMw3FpvlcRSDIPq6zXidg0Pyy6ZYpzIZaSsbpPXyCmvmUiVsbOU7WyH+o4dPZh5jgc2dZv1/PNto/Lj09yd67vHNeXUGwL2WXyNbTNC9he+5LUZlvClgI19Wnv76Vo6xRo3V6XMq5f77WA7ZUrPpykrUWtTqfKMbW01feivr8t2vc//xtHj+3W4bPPtviTP3kwPyAhhBBCXhooYwkhhBBCCCGEEPJc4hxw9+4Om43FZjPAOTulLUMqVkRn6GuQUp8hIWuMQ0jQApKiBSy8r1OoQC1epA1IfWQOWH1M2aaTnHIuaddpU0niHpOITaWJfTZmOx3bTr+mssyI+3WyNU/25uWXdWJW9olc1o87rafkbHnfUl9TiLJ+35K5csY9Dkm5Q/18saMvaHviNew7Rr622urXaV16WPbNydqeqNUp1yVlinsSVsvYQ3PG1vPA1glZ3UeXItZ/PRE7d5/7ZZnTNfzkJxf4vd/7GA8f7kEIIYQQIlDGEkIIIYQQQggh5Lllt/MwxmO/d1OK1U7zNSa5mCdhU/lfKQ0c5mg1MekaxIpRqdaQVi2TgZKSLY/Tc8ameWHTusx7CmjxpZOsPutbtiNLm9ZtQbL2Uq6H068iaVv7k6BNIreUi2mc9PjyUsZa1pbnyqkTtNJeCtr6OGC5WO1xOC3Z7tAWsL65P1/v9WkJ1zDmYZF4qFzxvKQt9/XEbpleLY/pidgkdeeEbesc6OxPc8K2xG1PxM6Ti9jysY2jx4MHO9y9u8VHH1089uuOEEIIIS8WlLGEEEIIIYQQQgh5rnHO49GjPfb7YZKjHpJ4lZSstWYqL+xUQlYEoYX3DsMQ1kOSVeaezZelzCmXOj3qPSbRKwKzXpZ9yzQu0EvIzs8Xq9O45Zit7bo9T77W6dgkboVeYlYkLlC2p2Na++ptLXPbtuu6JGzJ3Hj5Pt9oK/vVfQ4J2VKA5vtq+Voee0jQlqlPvd6SsYfae4K2J2N72ykdmydlZd7XlnCVZKzMCavniC2fh/L927o3wPxjcM7j88+3+O/+ux/j/HykiCWEEEJIBWUsIYQQQgghhBBCnmu8D/M0GmMwjh7W+rg0Ji+VWs4dq5OyxpiYcA1JWR/lriRgRQQG8aPnlpX9qdRvEltGSR0DFKlZkZGSwpXt1LeXji1TrqktjYtO33w9Cc52WvbQtk7B5v10+jVvV8+gGkMfV4vOum2u//XSkq7tfe3+ZZ9DAjat96WhbpubP3ZO3Jb72/taQlK3t/fPS8z8+Na2fq+VcrY8l0hYPSdsaivvXzsRW0v+/LGWfeXa9nuHhw/32G5dPSghhBBCXnooYwkhhBBCCCGEEPJcM47A/fsj9nuPzcbGsqcAJsET0q7WuqmMcJCjQRKW88amZKy1BsY4lUIVsVqWGRaJY2MyM4wRRKqkY1tzx4brSOlX6VemZ2XM9hyy7UStnGNJMracKza0B8HbS8v2luUcsXlCNkmwvFyxyYRXS6yWsna6808sDdujfZ6e3Cu329I1bc+nX/O2vP+ccJXt+pilMrYlVNNYbeFat83NHdubM7aVlJW2lJDNk7HlXLH5/T6cMC4fm15P5wrr+72LKVxCCCGEkBaUsYQQQgghhBBCCHkh2O99LBO6WlmVjA2WRMqUOhek6zAEkTeOutyvpGYx9ZW5Y1M6FsA0ruxDTNDKOKGfmZKzPiZow7GpxK+1dfo1T6iiSOTm6VmdVE370ny45f5ULjhPx4qcnq5Q7Zvbrtva45R9D+1r7e/1W3LMdVFKvWLvgqRlSwL25KA+fq7tahK23J/3bZUqPiRfgXx+5rAMx5SidU7Q5vI1b5Oka5mQ1ZK2FLZL7nV9n+vnrZbHHrudww9/+BCffbbNfgRCCCGEEKKhjCWEEEIIIYQQQsgLwW7n8dlnO9y+7bFeazmIKeUqctVgGBz2e0DmlA3zxgJB1vi4nZKcqT0JTJlPNkmcPBkb2lMitp0YLROqIlXrVGsSsL35YpME9vGx6/2hrUzA6rlWl8wV2587Vq5fz+uq+6b71ErIliWT8/uTe03TaK9lWJkUXsqSlKO8Lg4dW17fIfHa7jMvFkuB2jtuvkxxX7oC6ccEvQRs6FNL2KvK2L6UTeNJIlWnYMvj1Z1cJGLTPagfo74e5zwuLkb83u99gnv3diCEEEII6UEZSwghhBBCCCGEkBcK59IcsgAm+YooKMfRwXs7pUgdjLEqEVtKJRGYSYKWc7fWUjQdnydiocZAQ3DqZGsaU2SutKV9IlW1nBUpm1KwIlhlv0gpudZ8O60DiCI2T9UCdVo2tGmBrcsT67HCerofad0X+9J+fb/kvO32nCTfllrZwxZ2TtTOiddyu5ea1ZL3kIDV48zLxatI2HLbV2PNlSRut7XLEWthe1jGailby1h9jb3n4tC9S48vfxxyjnH0+O537+Ozzy5xeTmCEEIIIWQOylhCCCGEEEIIIYS8UIyjx3Y7xjRsKAUsc7da7Pcew+AwjinVaUxIxxoT0q4pWepjqeHcnYmQRSFD86Sp7NfSFhD5Y6L80WWFRbBKOeJQ/liuqSxTnNDXLfPUyrXJNYsQPSRl9XouWFPqVRKtuk95PWVCNhe96ZgkVsukcS7HZJz0HLTaWxyWrIcoU5b9fS1hO1eKOI13qH1OuJaidmnZ4lqwtvfpuVfn5Kvu258jtpeIzUsQJ0FbS9kyBZvL2PZ9P3QPy/vXWso8sd/+9j385CfnIIQQQgg5BGUsIYQQQgghhBBCXih2O4eHD0VYiiw0k9B0k6gM5YpT4jOUKzYmlSe2FlHOhm0PY0JMVZcyBtwkfFOKtS5TnCdiwzmgxq7LFev1UjSW5YfLMsVJFJf90jlLOayTu+V6ul5dTjmVMZZzyf0Oyzwh25Oz+phyLC3SSlGrH2PZd47yXi497vAxffGXb/eTmj1pu3zu2FxGzknWer/P2tui9tg5Y9vCVqdj8759AVuWJdZzxMr2nIidv3c+69cupRzO9e1v38OHH57j3r0tCCGEEEKWQBlLCCGEEEIIIYSQF4pxDOnYzcZhtTJYrQyMsVHkjKOf+oVlkKwe4whVJliWUNt5yjRIVBPLrBojKdaQdJWxZQxJq6bSu2XJ2XB8ELtSarjelqUWrDJ23jecQ8tZPSesLitsoqFMEltLw3x/LlZF3CbS+On49Hjr8sR6HNk2lTTFbLp1+eSwV5Gv+hp6x7fae/I13z5GGub9+ynPtoRNbfn+Urjq9bpPS8jWAjNPwYZz53L2+Lli6/a0PXdP8/veuqdz9wrxc2O/9/jww3N897v324MTQgghhDSgjCWEEEIIIYQQQsgLycXFiP0+WKDNRqSljbJUErOCrA9DmmtWkrKSfvXeYBjkOAPn9Haap1ZEZUjgyvhJTLaXfjpHmitW95Ht+i+VH5a+rT+5rrk22dbtZapXl10u+7eW9Xqe3M375KlaTS1njWp/LMN6FFr4tdvbffsytk5mlu2H5Wt+jlKqyjE9WdsTsEBbtgKIP0Jo7S/ngW3tLyVsr1yxpF+1hA0/rLhaWeL+PamvVcvg7373Pr797Xu4e5eJWEIIIYQcB2UsIYQQQgghhBBCXkhE2Oz3QaSuVh5Amit2HEXQ+riekq0inLSEDMJWUn46kSrzuuZJVRP3AYiitU42SqnftF+36e16zli9RNGWxpYyyu2+rXV09rX2H9Om2w/ta+2f69uiPn55meL2jn7isj42F6tl/56ArY8/RsJqQVoeU/ev+9aCtp+EBfL5lFNCtT/faqu9TsEiE63peFnX+/J73n9OyntZ3/NczIb1/d7h4mLEZ59t8eGHnCOWEEIIIcdDGUsIIYQQQgghhJAXEueC3Ly4cFG2rlZ22pcvg3wxqk3miJXSv3bqE8RuSM+a6Rxm6pOSsc4Bw4BYdjgJJUnk9hKySepKqV6drO2lX8u5amWZz+96OBE7n5At54X1xb48Mav7l+NNa6pkci1JdYK2tb/dfkjuXpW2OM16zMjW9v4wblvYLps79pB8le1e/2UJWZGu9bEtQVvK13BsX8r2JGw7IZuP27qH5b1emiKu5XA4z49+9Aj/w//wES4vRxBCCCGEXAXKWEIIIYQQQgghhLyweJ8SsPu9B+AmkeoxDEHujKOPc7uOIwDIHJRpPtgyIetckIXWBmkqydiwHc7tnKRhRSBJ+WPpo5OyJlvqfTJHrN6XL+V6UIyDRn8cWMcR+9Jj0KWC6/levbq2vD3RMq2l9ZxLyfaOuX76MnaJfAXSfar7tUvp9o45XJY47Uv925L2UGliLWXbwlb3y8VtuzxxX+L2U7O6Xd/L3n0vn5PymNZ9lOVu53D37haffnqJe/d2jXEIIYQQQpZBGUsIIYQQQgghhJAXmstLj93Ow9oR63VIpg6Dh0hWkTwiSyUtOwySTA2p1yBWQ0I2lCGWNhf7SBpXyhdLGlaEZRJKQEq/1knZcq5YoN5uzSHbGucqc8YuWV+yXLYvSeVeqlb6LEvHPjn6IjaudfvqPrkw1Ou+0ZYf05Ktve2erG1JV91vXszOtfWSsXlbS9C2krFlSlZfV/s+9e9l6z72rnscPT777BJ/42/8GNstE7GEEEIIeTwoYwkhhBBCCCGEEPLC4xyw2wXRslo5AHZKzHpY6yaxGdpE0gIpxZrK/vpKKlqbkqlpDlk5cxKN5b5QjhjQgkjStGWbCFa9LUud0M2XKZkrpY/La6rXMW2n9SB2DydkE+UxyPrq5KxI6Ryj9uXH1rT6Phly2edn9rX7HhKwur0UrK22x5Wwuu8h+QosKVOsf9iA7JySeK2lrF72pWxLEM/f//qe90Rsfp2Iafn93mG7HadEPSGEEELI1aGMJYQQQgghhBBCyEvBxYXDMIS5XIN0FQEU9ovQS2LVqKWdhKyD9wbDkOaaBZKslfFayVj5ayVFpV2OK9ta23OJ2HLZTsSaSi5fV0J26b56XcvhWrCmJK3gs3v4pKnP0ZJ+7WtpidW03kpxtqXiMYnPcn8tYXNR25szVotQEa29+WBzKevjMX0JG/qMY0vGppLHS+7zUnndE8rjGM652znsdg6EEEIIIdcBZSwhhBBCCCGEEPKEOX3jFKdvngIGMOH/LUPkAXwexPNFm0iU4DTgRge3czj/8Bx+ZKpL4xyw3To456e5Y8vSv6Hc8H5vMAw6GStpzzgSANuVnnO0pGdYplK8Om0r+yT1GvqISDVTujckYNMSaCVlSwGb2gARdjpFm1KsaV9Y+nhd4fF7dR/y1Gx+nB4LQDbnbBpHyMdLQrC8x1rK1eWNjyV/z8xJ3rZ0zcdoC8TDpYp1+1IB2zumFLJLJSxwbCK2LWj1dr5e9vHVseW9WnrP8seeL+vrDtvj6LHdjvje9+7j88+3KuFOCCGEEHJ1KGMJIYQQQgghhJAnzI13b+D1P/c6jA3zg0Y/1PNEpWTV6+VSZIZLf27rsLu/w+XHlxhHzneo8d7j4mLEamWxWtkoaEQkBpkp87gG0Shzx4ZlOGYYgCCJfEzNjmOeVK3Pnc51aBmuJRe0mlbStegBY3xW0ljapLRxLWXT+bSU1eJUjkmCVSdrRW6LqE3tWtamNsTHVj4P8uYQ0Vs+9rwp9dX3edpq3ZwrUUrWel9r21f7y+srZWFYb8nXeeEqy7ytl4jN983NGZtKDvclbEu6tuSsLjncK0dcy9hD97onYvP2+dSvn8oTOzx6tMfv//7nePRoX5+cEEIIIeQKUMYSQgghhBBCCCFX4NbP3sLN927C2CBXY3qvIVpP3zjF+ua6am+iRaxKv+rka1fKurDtNg7DZsDrf/51+L1v9vcuHOP2DuN2xMPvP4R/SeZG9N7COeDiYsR6nQsbaw2cM4WURZSSgIsliqVkcZCeHoCdZKWdhGQYQ4RVmne2Xsr+JGHzPuH6TBxXp2Lnkrgy/6w+97Fli2WfiNM5MZs/rraclWP1Y497VKq23peeJ/1GSine/n0ox2ix5Pi6X1sEtrd70jCNc0i+ttqWliTW60vkbClEtVgNy3CMTreWy3ZCtp2IDb8bSWWJH/d+lvdE1nMBG5b7fZgj9h/8g7v47LNLbLf8EQshhBBCrg/KWEIIIYQQQgghpIdBJk+N2rjx9g186c98CWYwuZCdjjm2HLFPNYmzZSsZC0ziFarNJxlrvYU/9Xjl1iuZeNXpWT96uNFhvBixf7TH+U/O+ynaF8rRhifFOY/dLjxeSb7KOmCx3zt4b2GtA2Bi2V9jLKREsRwzjm6SuEEgjqPPUq2CPJW9MsV6PU/EpoRqa4x07nIJlYxNAjRIYpG5aCRk89LDeQI2l9P1/nSPyxdOXfI4idVWmeL0WE11/3r3tU1Z5vgQ7U69Y+v2fgnisL1MIur9h0sQH5K0x0nYvE8/CbtMvtbbvYRsS8I+zj2Uc+v1WsgGEXt5OeKDDx7gk08u6wsghBBCCHkMKGMJIYQQQgghhJAGdm3x1l9+C5tXNjBDLluNMdi8usHq5iprA3C4BLHg6/VyHlhgRsZqCTutx6WIWqe2p6UuaTy4AcNmwOrGCm/+yptwowtzzHoEWbt3cHuHRz96hO1n26vfzGcQ74FxFFm6h3NDJlMBYBhk20zbIgUtvA+p2GEwk7QFADe9DhycM/A+SFprQ1njlGrVJXjTUq8H2amlqCn2pwRtkqKSvvVTIhYHE7GlFG5tz69radxO/ep+9b78eKGVom3vr0njCo/za4K2HIx7u+JwThbW4+brpVTN+y8VtL1yxT1BKWPMiVcAB5Owoc+xydhcsPbv7XIJqx+zXFN5bePosN97/P2//zl+8pNz3Lu3q09KCCGEEPKYUMYSQgghhBBCCHnpMYOBXdusbTgdcPMrN3H6+insygbhqud8bQlY6DKrfTLpoFaNN7WQVetmSjTG9qKf8Snx6CECz8PY6Tg39ZlkLHx67MNmCHJkTKlZtw1/27tb7B+k+ROj9B0fR3I9bUwUMrsdYG0QqcPgATisViEhK2JWlkEM5slYkYV6flYtLEOflDhN87aGfVKe2LmUcg1lkoEglPISwCJYw3mCrJRyxCEBiyopm14ssm4gskquS86VkrJ5wjWVSZZ1IHsBV4lYLZDr5KxI57Sux0AcK+3Ln7veW02P+zjMSdi0f14g5tvzqde8rde/LWV7SdB6/6GyxLq9vb+dLq239TgtOZsnYvv3sLxHZZ/+fcn3548/nH+7dbi8dPj44wt8+OF5fWJCCCGEkGuAMpYQQgghhBBCyEvPnT91B2/+6ptBtqq/zasb2JUNZYiBvBTxtI24WovZiEgBSQbC5A7HI0pYvc97n7anPsaH7VZi1vtivyzdNL5T7SolK2I1K2k8eriTkJR97Rdfw/j1Mcw/6zzGbShtfO8f3nvOhSymBKvH5aXHfr+H9x6rVbCdq1V4bCH9mpbOmUk+mZiWlTlmgxS1U7LVwlo/HWeyPiIMh0HmqAWs9XG+WhGyxhhY207GJrGryw7r8sN5n7k2IG8P23q9TMQGodtLzrb65PvqtnK9vy0yud3nEGX/Q9K1pBSsvTFyYVgL17ReytM0dku8yvYhOVvvz4WqHielXnX7svLEraTsofLEckz7ntb3bO7ezd+j8trD3LQhEevwR390D9/97n08erQHIYQQQsiTgjKWEEIIIYQQQsjLgwE2d6ayw9M2AJy9eYazt87i/K86AdudB7aSRG0j5L1H8rTTinZJHvDG54JWxKoxsW8UrXpdjvVJ2GopG5fw2bm15I1pW0ypWePhTbhm6yy8Dcna4WxIMvZyhBkM1rfWcHuXyV+3c8+doPXeRAm133sADuMY5oYVMTuODsaEpKxIy5D+A9L8qZJOTWI0l5H5PK6AmdKwIqpkbtpwXJJNKcUKpESstfJCkkRsSsjmCVh02/pJ2DrlKteQXut6n7xmgVReWd+bfHzdltb1mMjGTs+VFrJlW/uYkmPla3muJWMuSXL22mspe5yErdfbglL360nX8NoKfXWZX1keErPtRGx+Pe372b9/5f0o70H5OPW17Pce9+/vMI4Ou53D3btb3L37YpVgJ4QQQsizB2UsIYQQQgghhJCXhuF0wHv/9Hs4ee0Edm2DMJMyvScDYPPka5mCzWRsiW73utlU7T6thD4+ySxpF1kaE7OqhLHxhUD1eR/jpvHKuWNtkrSSlvXOx9LFOjELh1jOOP6tQh+7tlidrbC5vYlzy47bEePFiEc/eoTLjy+PfGaePpJ2BUbspmkjV6tQingYRMgGoeOcxWolydeUjA3LIH/yFKyZ5o5NiVjv7WwyVsSqlRLTMHGO2LDUIjUt64SsTsDWbUCaXzZPv/aSs2lf3jafmtXLVt/efhHDrd869BOx6foel1Y6M+2ba+vJ1npfT7qm5bEJ2cMitpaxrdSsiNQwvl7OzRlbyte0nZ+7vnf1jkMSuydhAVTX65zHRx9d4G/+zQ8xjuFa9nvXvhhCCCGEkGuEMpYQQgghhBBCyAvNyesn2NzeACbI2JPXTrB5ZVOnYEXEAnkidtoOixkZqyn7iDAwSaBmu80kU4skLAzqNOzUP6ZhJezoVR+LPA0ruNRXjtPJ2PjY3SR+p0Sw3A9vgpS1CIlZWMCPHmZvYFZTaedXNpnI3T/cP1dJ2SBcg6TxPghZ791UjthOJYNTOlaOSZI1tfckZBKiucwMwtXHuWPT3Jsmjg2IgErzwdZzxvp4vF6GY1ObyNYwThpbi9qUXK3nl5VrTonYVmo29JFzL503Nq2HsfLt1Ke8v/EI9Zo+1svO+NdGH99oa233Eq9pjFLAttpykVqOW4tW6deXsm1Ru6RE8XwyttendS/nJez8PWk/hrzdY7dz+OijC3z88QXOz/cx7UsIIYQQ8kVAGUsIIYQQQggh5IXmjb/wBl7/R16PslDmgDXW5NJVJ191Mla4atAu85yF+BSZpFKv2TF6qdaz/kqoxjStrE/p1+Y8srJ0+XaWkC3W47yy45SoHUOi1q4s/MZjdboKpYu/4uC2DuPliLt//y72z9F8jLudi8/RauVgDFSpYpnvMqRjvQfGMUhBnYwNy5SIdS7MLSttIcGakrKhTLGJqVhrEeeMlbLFes5YKW2cJ2LzZKsWUnliNu2TMsdyzXo+WUmjtuaNba1fZXmoben2ofbroi1pfbO9J1z1Pi105wSjHmNuu5eW7UlXOc8xArYvY9vJWClpnB5733T3ZfYSCasfb74cR48HD3b4W3/rJ3j0aN95HgkhhBBCnhyUsYQQQgghhJBr5733buL9929nX+Br6i9cW19SLymz2P/y9+7dLX7wg4dP5PGRZ5PhbMDtr90OsnVIc77efPsmhtMhtkUJq2UsUK3HNm1hj5E98rI2yERrTL/KPu+zBGw2D6ykXo0a74CozdqMEsA+tWfzyRqflzqexJ3cI+/UupmkS7m0k6y1U191r09/5hTjxQjvPNylw/bzZ3t+RmstvPdRvA5DSMgOg8hWgxAxDp9tQbJikquAlA4uxVEuQUthKp+V6bOwTIHqtG0uRFOaVkoY63NKUraVxpV9On2rk7tybdJezi8bBHBKrcp+fd3TI4z3Iolj3abvQfq812ldkcNp2+PQvy3lPTxMKQ5nenb2tf49y7d9ta8nYMu2tpBd9u9l2U8k6SH5Kn3n/u1tzRGrjy/vS33Pem3L7lVbxCIm3L///Ye4e3eL7dZRxBJCCCHkqUAZSwghhBBCCLl2/vSffg3/0r/0fkxhyRf3ifqL4/oLXSghgimJlv/J/I37fVjudg7j6LDdOvzDf3iXMvYlY3Nng3f+yjtYna2CfLUmL0VcJGEPSdiwMI+diI3lgn1jn1dzW+q2sj/q9Gw1p6xOxeo5aH06Vidko5B1aZ5ZnZTN1iUhO0zSdQj7nHWhfUrKutHBDCaULrYGdmNx+/3b8KPHuB2x/XyL7d1t9dieJZKMHTGOHsAe42hjOV8AMRUb1kOUehgAwE9pU0nGWgxDEkaSQJUSqUFIWpVENfEzUK+nNG0uXMv1VCo43x+uJ8lJ3Rauo/5rtUub3pfWwy8G5tOwuaxdnorNx673tyh/DHTci07LwP7+XlstEfV2T9guS8PW8jWJyNZ+va9Oysq8qoekrPw7HI7pzwer981x7P3rCevy8en5bMfRY7t1+Ht/71N89tmz/SMQQgghhLzYUMYSQgghhBBCjuIv/sU38bWv3cYgyTejv2QPfd5//zZu3FjBNL4lb30hPJe4kS95W1JWZOxqJUsD5yzWa4ef/dlb+Kt/9d3i+CBuLy9H/OEf3sXl5fgF3jly3Rhr8NovvYbNnQ3s2mJ9e43N7bBu16EUscwD2yxJrGWslq6VDDrOxkYJoYWuV+N6dOeOjSWHdcnicl+0EGn8mHxFSr1m6z7vlyVjrUrIIiRhjTcx6SrrWbniaRt2ejxTMtZYEwTtkFK1xiY5i1cQ5awbHXZ3d9jd3x11f78o8oSsgzFjliYUQrrUTFI2SNgwZ6v0NVOqNpQmTvLVTjLUZVUERL6KKJXPyp5oFXGrZWu4jvYxvba2eNWJXJ2KbUtbWZf+c3I2redz0Jb7wz2r9+VjxC21nuaxvSr1sW2J2msrhWQpUMtj5uRrub1ExvYEbE+8Ajox66OoDYKz9+90W+QeujftfcdJ2PRY8muT/33wve/dxyefXOL8nP/WE0IIIeTpQhlLCCGEEEII6dL60vzP/bnX8eu//jY2GwtrTVG6s/1l+ZIvT9OXqOUy7BsGPVdjT8paOOexXnucnKzwMz9zhnH02O8d9nuHcQwi9sGDPf74jx9gu62/oH2cL+7JF4tZGbzyC6/g5pdvYnW2gh1smBfWhESslNbNkrAiYhvytUrHZiebuZDiNVMmXeV4LWmjhM3dUfs8/sB6IVqrfR0hW/3p+zGJVknLtkoXe+9hjQU84IwLfa1PpY0tkoydSh2vV2sMJwPcPswp+3B8+EzK2CAezfQDjn0mqOSzTvDewlo3SVhMP1SRssZ+SsxKX0DkqzEOujQvkJKq0ldkqe7Tkp66hHF5TP1XlyvO2+qSyPElHcfOx9Dlicv94RgTxaC+trKks5an+phynz5P/pmdp2ev8/O8N1ZPuObbfck4J1+lrS1pl6Zhe/v6Mra3X0tY3Vek7JL71e5z/P1pPVYtYsfR40/+5AF++MNHhy+EEEIIIeQJQxlLCCGEEEIIafLlL9/Eb/3W17DZWAyDnVJWBj/3c3fwyiubKB60mNBf2GuWSFjZVyZttHyV/WVadhwdnAtL78MccWHbYBw91msbZexqZbHZDPjN33wH2+049fXY7Tx2uxHbbZhf7qOPLr6oW02uwJu/+iZuffUWbr13C6ubKwzrAeVcpVVJ4kYiVpZxXtiedO21zwnU6GQnmVSmYHt4dT2oZU8cxyMvW6zKGPfKFJepWCAI07j0CPO+eiSh6n2Wlo0JWT/NEeuUoJ1KGHvnYcbQJqWMpY8bHNzo4NYON969gc0rG7idg9s5nP/kHO7SLbtPXxDDMADwUwl0wPs9Npth+iwa1Nyy4X6Oo5k+lwxWq5CclT8pXazLEweZK2lY+UNs03OsSkUCEeTOhX66rUy/pj89b6x8fofHqNv0etjXSsGm8trz6dg0fkrAzqVk6x/0HNrutS3ZN8chmagla6t/KYlborFVrrgtXvN/R2W7J2cPzRlb/rsLHE7Btn40VZ7r8P1K9+PwfTtWwiL+74Lvfvc+fvjDh/j008v5iyKEEEII+YKgjCWEEEIIIYRETk6GKBTefPMUf+kvvYUbN1ZYrewkB8rSxEpsKeovk5MYSl+ehnYpySn7JC0VkjciDnTCKk9UhX52EhI2bjsXtq0NX86GpFoQCKuVxfvv38Y4Oux2Dvu9x3Y74uJixPn5HnfvbvH559v4JfN+vyDiQ54oxhqYlYni9OZXbuLVb74Ku7JpblgtYSWpLT8W0EJWtsv5YGNXU7X1LwzRK3glGHT7QdGrOSRx9X5fzC3r1f5J1GZzxJZzykqf8sJcPg6cWi+X8riKP2MMHFy8/957WNgofiWxbO4YrG6sMF6MYU7Zz7bwex8l8tOeW1YSss65SVa5+Dkkn4kA4ufmfu8wDCJdAWPk5qXI6ziGFO04+ulHLrmoE6StLUWlFHKY87Ns08fKX7jW/MmTuWvD9Xp13UnchmvRJYp1n7RPrjmJ4XgX5RFln/FhmfqlY+Xac8lXJmHDdqtP+z7m19Jj/gU39zzpMdoJz7Z81e1LBKxslwK23n+MhO1L15ag1cnYQ2i52m5fdp9K+Vw/nlAFY7t1+PjjC3z/+5wznhBCCCHPDsar/4Wz2Wye5rUQQgghhBBCniInJwP+nX/nF/HVr4b5YM/OVnjvvZsxqZXKV4b+5ZferS+fyy+SdanP8kvhsjSipF91Aqdsa5Us1mWL9TKkZ0MC1jmvErEhMRukbPgi9/79LR492mO7dbi4GPGtb32Khw/3T/L2kwPc+fodvP0bb8OsDOxgcfKlE6xvrlM54sHAIMnYQ2WJ5+aIBdo/MmjRmhvxsSmHLGWvL9q9729PwjWuq30iPLX4LNu894BT22UytjWXrLxnp8SoyFW/D/vd6MK+0YdyxfuQinV7h929HdzOYf9oj/35Hhc/uXjqQhZI9805B++TcD07W2G9tjg5GabUfagkIGXc12uLYTDTX2hbrcxU4j38yCUtg/iV8u/GmFjyWH4IU25LxQJZlm2A3lcvRSZL37xscfmZX/8Qp72ejyX70752WylvW6nZ8ti8bckzOd9vydu512euTK+Iz157LWFb7aWM7IvZ46SsvLbbQrYlYcvrbdyNxv1p9CpE7ZyMbj1Guabwb7rH97//AH/4h3fx6NEeFxecJ5YQQgghT5ftdhvXmYwlhBBCCCHkJeTsbMCdO5vsS/OzswHf+MYreP/9O1itkhTIv5g/PHb+JarJvkSVL9nzVEv4k+SrJHAE3a5TWOEYMyVfg0wIyzAXo3OyFLlgp3KiknCzUQBbG06YJMYGN2+ucXGxx/n5iNu31/ExhXlnn60yqi8kFkG2TnL19M1T3HzvZpoXVpKwem5YmDDPaUvCzgjYpnhdGKBbKm1nxy0lhUq+6u1snlkPeBNEqDEmvr2kVG22Lcl000jESkjSp75x6Xy8pzpdK9elyyVn124Q0rSYlkM6RxjOwhsPBwcLmx6fNcAtwO1dOKdFnF/WP+V0ehKHZiqBHj479ns3pe4tADclZB32e4NhSJ8tgbA+jvKZN93HmIw1sb9OtLaTseUyT66mssB++iz03WSsMYh98hRrPo+siLAkXtO2XpfHpvtKAjb1L9vCZ7vcjxJJ0qbtfG7Y0JY/Vz2u+vuJ3g8vyuaeYNTrj1ueWLavKmEBSUXPzw/bFrTVHWjel979qdvmSzjr+1le437v8OjRPs4N//nnW5YmJoQQQsgzCZOxhBBCCCGEvIT84//4O/hX/9VvYLOxWK1Scuu1106wXttMwgKlhDXxC3T9JWzrC+X6i2D9JXD9xbFeb6Vf0hfEeVI2zR2rt8tkbErPlMnYlJB109yxkpIdsd97PHiww27ncHGxxyefXOLv/t1Pr/yFPlnG+s4aX/uffA3r22sMJwOGkyEmYY0tyhGrFGxPxAJK0pSuRvq20M1LnvOeB1ood3vtem7Ysn83CYtiW95nWnYU7YdSsjoJ203KlsnYMfRxowNcWHrnYzpWErLeeYzbEX70GC9HuJ3D7uEO28+2ePSDRwdu4BeDTsg6pxOyA4bB4vQ0T8hKMlZ/zoay7yb+6GW1spPQTanYNHdsnojV6dmUkEWzgkHep5eMBSQ9e9Vk7JIk7OHtdI/b/+6k8+TbrT4tjvzRROcNeVgs1tI2F409Qft4Elb2z8nXOenaXpYCdvk9me9Tl+XuP+b8ceprHEePu3e3+Jt/8yfYbh1CmeLw7zchhBBCyLMAk7GEEEIIIYS8ZGw2Fl/72u0oAb7xjTt4550bWK+TjA1f5udfwgPll+RpPXxJaopt+TI1T8SmOWPDn4wT2uXL1Xw9/0I4JWTDepqTMCVl8+0wZ6zMF+tVW56MBYLETY8vrNtpisdhcADW2O8d1muD3c7j9ddP4xyS5+d7JmWvEwOcvXWG09dPYyni4WRI88KKiJXXqp4bdqmELeXrUk9zqF88TUf6Lhlby1dp9+oxTX0kFavP50PHkFidjtNzwmbJWJ+Ssd759J7S+2dSstm1Tm/+mMBFSuPqhKxcVzVXrzyGEbArC2+D1DXTPKZu67C+vY5lkN02lDl+GuiEbFg3U0IvpO73ewvv3TT3qps+UyzC3LF2OjbNnRoS/S6KU418ppYp2VJgWmviZ5mMKZ+nklotj0/zyubp2WlkyOf20mRsPrcs4udx9cIuttNne3r8OjGr77n8e5L66ftgsrb0fMW7iavQF419oZhv9+Wrbl8iI2W7JWlbEhZozQerf1Agy9S3nYJtz2l8iPqY3r3oP/aWSJYfWH366SU+/3yLhw/3FLCEEEIIeeahjCWEEEIIIeQl4M03z/Af/od/Fl/60glOTwdsNgNOToYoYPPkVF3msScJ0rr+Mtgg//K2FrZhvf2Fcvklcm8u2bSeErF6uz+PbJCwq5WbErI2ljiUpayv1yPG0WO1kqXByckKr7yyxm7ncHk54jvfuY8PPnh49SeHZNiNxVf+ma/gxjs3sDpbBQGrJOxsIhbI07GyXcrRzMNeQZxqGom+3v5ZSokU5ZJv9jHeJGHrizav+qql3i+iVV9jbFP749KEY42b+sjjmiRrOXesXpfUrLU2yNlQ0RfeerghiFUzmiRgZTl6mMFgOBmweWWD8WKM88ju7u0W3tgnQ0irDtM6sN26KBCHIdyjYbAqmW+xWnkMQxCnQaDmc8g6l5Kxsi5LkZWyneaM9dl6mjPWx8/isD8tQ6nlfJnKI6c+WsRKW+jjs38n6n3phzypFHPar7elXxLHug3ZMUAtrPuyte57VXqp0JagTG3t0rth3Vftjy9h56WsFrGlbO3NC7uUOWHd2n9IwOaPO11n+vc9zOX+3//3P8W9e7v4YypCCCGEkGcZylhCCCGEEEJeYNZri1/5lTfw1a/exhtvnOLWrfUkYcMch7qUZVmOUmiJ2Dwh61GnkkzxBWwrEZPLJp2GlbSWPr/MJWttEBKt+QZFygJpDtn02MI8soCb5k6UL53dJC/sJDMkORbGSSWQHbwfMAyhfbcbMQwGX/rSCfZ7j3EMAvfzz7fYP+X5LZ83zt46w8nrJ7CDhd1YnLx6gtXZCnZlAQvYwUYBO1uWuCVhCwHbSmVGjpA3mXgtj9PnWjwgKrkaz5NcVNg9yVA9h2zWFhxcSqhO6/oYnZKNc8KKEHPTfmdistbbScxan88Vq5KyMW2L9L6O90kEscnvi4WFR5g/Fgaw3iaZGxPv+T1Y3wlzCe8e7J7aXLK1eA/b4XPCYLczap5r6WOnzytJlLpJstpCRiEKKN2mE6HhszDcUPlc1H2kn07RSr9e0hYIUlaOy0WsHkv+zSgFLqDTtGGcUsKG8+jt1md+esxGreeCM7+3SQBPj35GEl6dQ/+uldu9ksVtCRnGrF8LfSlbli5O24dkbEvMtq/1ONr3pGzTgnvuselrlR9WffjhOe7d2+HiYqSIJYQQQshzA2UsIYQQQgghLzBnZyv8W//WN/H1r7+CzcbG1FWSsPWcf0AtYIVe0idxSD7Nz4unv4xNJRTrL4ylnGJa+ignQiIt7AtLScQGgWttWqaEWjhG0q8hLRvmgxxH6eMwDKF9tTLY7cJ8kF/96i28/fYZzs/3uLgY8fu//zkePNgfemqI4tVvvoo3f/VNDKcD7NrCri2MNUnCTvNkSmniXiK22kZbyArNJGsPk683E7Xl+HVDohQ4qpxwa3/W5mfa5pa+sQ2kUsJKmGZLj5Salb6yz6W+xptQPlmtR0ns0rY1Ns4tCxv2wSIkaM2UrDVhGxbwq5CQtSuL4SS8RsbLEQ+++wD7/bPxXjMmWOndzkWhOQzhBxw6qS+p2fA5ZKellBpOqVgpHZ8nY02UuSk9Gz73pF9KwKpEs5KrIoglPSsiVcoUh6WWsb74d0LatGjVUjf1l771vy15erbVT/ct2/VxaVtesL3nZ/lzCRwSkW3Re0g26vXrSMe2/u0sBe6ShKw7usJvfe3NXpWkbj3mfJ9cZ6syxn4fKlf8wR98jp/85PzYiyaEEEIIeapQxhJCCCGEEPKC8Rf/4pv45jdfxTBYnJ4OePvtGzg5CXPDphKWRiVigfaX4DVzAvbQl92SfgrrPmvLv1iupan3+ZeyQXaYqc1E0SHJrFSeGNNfmlcWCGk0WQZB4SeJIZLBT0kzM80ZK2lcYBzDY0gy20wpY4PVyuLLX76B8/MRl5cjLi8dPv98O39jXmJuvH0Dd/7UHdz+2m0MpwOGzQCzChJW5oiNidiWjAUyMRu3OwK2kq9LBE1L6urztMY5Yty0qRKk5X6PmGwFkCdTDaJ008fHlKovxvSqv6xbday0eXWsbtPozwOH/Dyu6GPybWMmOTv9WVh44+G8i9fjRw9JyMYxLDD4MIfw6VunGM9H7B6FhOz4aMTTRz4nwjXvdk7JL4/VKkRYJZ2vl+Gzz8BaG39cIqWOg7gVMZnSsEGmmTgHbEqkeuTzt+alimWfJGr1specPdR+aD0t68oHMidt3Vfd2dl/o/SctzPPzqJ/p5b08Y221hjtcsU98Vq2lcI1l7Ptcr75cf0krD7vkse9hLakXiph62t0zuOjjy7w4YePpn/LPR48eLolygkhhBBCrgJlLCGEEEIIIS8Yf/kvv4Xf+q33sdlYDIPFaiXCMC9HXKZigeO+qC6/XJ87Ph2XyhDXX8bmZYpDMtYgpV7TF82pTHEqVywJV+89xjFtl6JWp8PCOWyUseMoY4m0cJN8letB7DMMFtaGUsXrdSh1vNlYvPfeTWy3DvfubXH//g737m2vkDx6Objx7g2884+/E9KwKwuzMlHCGjPJWDRkLFBJ2FLIxjbkbRk9IVrub4nXOck7d84eLQmL6f0h5y+lqggtJWe9CQIzytayPLGSqpl0BYJAFSfWkbLVNRvV7ov23j6o/ciXFiE1a6YkKAB46+PzbayBgYEbHE7fOoXbOuBjYDwfMZ6P9TV+ocg1+6lccbgYmas6fHaFz7Bhem3LUpKy4bPOTVLWwHspq+7iD0LSZ2EuXPP0bC1ltbCVv7xvvb9MyQImK4m8RL7O7S/71vtqSVsep19oxyZgj6UUrXVbKWZrEanbe1K0LV/Tdi1q5bWWxs8FbN52HfSGKWV1LaP7pZjLxK5zHj/5yTm+9a3PruWaCSGEEEKeFpSxhBBCCCGEPOf83M/dwb/wL/wsTk4GrNcWv/RLX8LZ2QqrlVFliXUStiwZWZeALKm/HO99MV4OVH5Z3ZOwoa+sSyIWOFSmWCdkEWVsKNuJTMaGNg+ZnzElxtL+UKoY2O/Deph31itxiyh2w36DYRjj/Lv7fdi32zkYA5ycDPAeuH9/h48/vjz8ZL4knL11hrf+0ls4+5kzDCcqDTuYfF5YvYTalpSqSa/HSsDql2JsOiBny75L5W5r/LlztCiFDpRUbfTR87MeLE081ybiFcV4vT8tWIGsTHEmcycpZHyYd1bPaau3pYSxQ0jEOuNC2eLpMVpjQ0LWTFJ2qkosot6tHE5fO8V4cwzli89H7D5/+sm58DkTyqp6bwE47HaIJYtTuWIL50TGhnLqIQ2blykuyxXrcvPpDyr9mtL7ebuf2sIyfJ6lbV26WM8Nm/7daJcyDo859SlLFef/btRlitttSQDX7fpup/PPPyfLnrtDrrIlM2vhGK7rUHs/IZr6tfq05pFtCdpS3C55fMdSytfyHP3H3U/tjqPHJ59c4tvfvofPP+e/nYQQQgh5/qGMJYQQQggh5DnFGGC9tnj33Rv4J/6JL+PGjRVOT1dYrSyGIUnYXiJWxpjWFgnZep6/dHy+HfCxrGnri2ZTfHGMbF3+JK2V5pCbT3TJOO1kVzBGYWwpC5qSZ+H6w9yP4+hVsixJ4dDuoUtiGhPmhrTWwbkVhsFN88/aKHE//fSy+gL+ZcSsDDavbvClX/4Shk2Y/1NL2KaMNfmyFLJAQ8KWgvSAfI2bC+Xu7LhXSeflb5VcBsvrZkq9xuuS9nRQSLKaJF+yhKwaN8pcWfcmzM86tUc5K/unOV+NK67Lqv12GmeaCxZQ16FLK2shDCWCXepnBhMk7VQm3MLCwcHCQhLA3oUSy/6mh92E9p3dYXdvl8okPxVEJgPeO4TUPQC4KEqD0Axl0MOPOwyMsVPqNFjt8Plpq8+N8NljY0I1nQtZarW3LNf15/ZcmWIp3Z7mjNX7U3u+Lv/m+HhO2a+vq9UWPs+D3def7eVxqW/7g1X6Hvu5O9dfC8ZW30NCdkkydl7G9sVrEObpmC9CvurHUm8fmis+/Ru738sc7x737m3xve/dv96LJ4QQQgh5SlDGEkIIIYQQ8pzy7rs38e/9e7+Ed9+9gddeO8FqZbFeBxErSSk9R6xOwZbJ2JL8y3pTrZd95hDxmtb1F7Embpfr6Utaky2lRHGdiO0nY0PiLJU77gldSchKSjb0TcnY1CckY0V67/fhfu/3Ti1DQnYYDE5OBpydDXj0aI9Hj/a4d2+Hi4uXr3bx5tUNvvLPfgWnXzrF6mwFM5hQnlgErLxW5ySskqMtAVvN7ar3Q0nU4jhgZrxW39Z5inNdiZY48UpY+kbfuTZfr3sU5Yml3Scpmh3rgZh4NcjPNwnUKIpd3lf2xSSsPuf0HHrr4UcPb6ckrCuWNuyDRSxZHNOy09KtXZCbJ0Hw7+7usP3s6c7XHD5bBoyjxziOWK3sNGesi3Nhh/T9MJU7D+lVSceGz5fQFub8lvLoiIlW+bwv07I6Gat/kJP+PcjnkA0/SMnTs0m6hnXnpA+QS1ktUkspC4hMTf/+qB8KAEUffVzeXiZf5bi6bf5N2Ps3a15atqVrq21ujtRDYrK3XUrZ1r7WXLBPivn7cFhAl9ftnMfFxYjf/d1PcH6+h/fA5eWzMAc0IYQQQsj1QBlLCCGEEELIc4a1wCuvnODdd2/gz/7Z13HnzgYnJ3YqmWuLL+RN/OI8Jab0F+A1LQFbylt97DIZm6+3lnUyVkvZJFBb/cMyF7z6+mSeRknEyuNwLiTSZFk+rjTHrJ1kBOIyJGzDPI+AyBEbyxrv9wOMMdjvk30K8/cCFxcjdjsfJfKLzHA6wK4sYICT105w+6u3sb65ziSslq+lgK1ErBYzC6RpWO2IUzn2kNTtCdhizENlUpfgOwJFS+Syj+zT7WWb3m7ty9ol4ar66yUACZDH5wZuGsdOKdmp7Gycs1ZStghpVp3qNS6I+ChyZWzkS7km620odezC2NaF96DfhO21W8NtHcx9E/o8td89yOeHV59jSS6GeWVNnCNWErJyM3Qy1hg3vT9c/IyzMX0s63WpX70Mc8ZKSeL+D2ry1GudhNVSNlx3krIyh7fM9VqmY+WHLyJz82uUa9f90uewlpx1UtbEe6ElZOstecxnbq9veZ7Wv3OlwF2eFG219dbbcrbkcT+a5u9D3DooYfU1liL2wYM9PvroAo8e7R/vYgkhhBBCnkEoYwkhhBBCCHnOuHNng//4P/4L+NrXbuP110+xWoXUVD4/bJKoaSkj1CWJZVvmadXH1F/um+q4OfIvZ3NhWs4bWydkU5vMD2ttnZINsrWeO3YcU7vMGStzNMqX+ZKUlbSZJGMleRaWrkrIjqPMyetjOm0cgzCRpSRlNxs7JZcHnJ2tcHIy4OHDPX760wtcXr64CVljDd75K+/g1lduYXW2gj2x2NzexDliYZGErDExGQs7yTtZahkLpO0lwlTti5uFfC3HbfXJ2hpjVtdwJFGM+saxxXuoNVdslZyVNlUeOB7r82PjepGIjeWLW32muWKTgJuWTqVkTd5HJ2WzZKzxScJOfWTuWL20Nswf641PCVkXHpNZhdeRW4e5Z+3KYn1rjfOfnGN39+nOIRs+Twf1o5DwGQPY+MOO8LnhYxp2tQqf5+OY0rJhaadkflhaK8v0+S9zuqYf5KR/E/LUbPqsP7St06/5vxP9dGxaB0TMauEalkD6N6mdjs1Lwod9+XY4tm5LYx1Pbh+XyMiyTHIrpXpMKlaO0+Kyta91zvIePO6Pfkrp2mrvyebW45DlOHrsdg6/8zsf49NPL3F+ThFLCCGEkBcTylhCCCGEEEKeE4wB3nvvFt599wa+9rXbeOedm9hskoRNpYnlS3dAvtBuSdU0bvrSuy1j0zj58e3xNCndJEmnPL0kElZST1rKplSZTkilvyBQtbyVPuFYEayyXVyZOpccb7JzhPuR35eQWktLPUZIi1kMQ5Asq1UYZLVKc806N8B7j+12gPfA2dkAANhu3WN/Wf6ssb6zxvrWGqdvnuL0jaks8SpJs66IzV6rSYg2k7C97amt5CoStitgW+L1Ks4nHlof7KGkZqPfXBI2axMRWiZiRYxOfbP1IkEb24r7UyVni/H0UpKxWVLWTElZIM4166cfQsBO11hIZXmIxk2vk8GEcsaDD6nZdfgxxuAHrG6s4LYO7tKFlOxTQL+G5McigC59rj9rAWMcxtFO6zJHtcwdK8lYH7dbcrJcyg9PymV+Xv1vRVmqWItQkbLyGV+WJvaNMfW/B3qZErOpvdUm7YjnlzHLe11/lmrxO8+hz+HWvLS1EPVV+xL5mpZzc8XWQjYn//cw2/NYj78tYMvt1uNJbelxScn+ME+sw/37Ozx8SBFLCCGEkBcXylhCCCGEEEKeE9Zri3//3/8z+JVfeQN37mym+Qf1nIB1ognIv/TW29LWT9Hmidq58sa6rfVlbi5M5Ut2U3zxbLJ1+eI2iYM8DWttSsIGKZsSspImkzlj0xyyss9nkkNKDNfJWF8lYofBT/fNTQLcxQTaOIbYYCpVHO7heh3uc5rX1+LGjRHDYHB+vscPfvAIu92LZWPf/JU38fovv4717TXsxmJYD0G8DiafI1ZkrEGUcuWyl4RtJWOFZsngltSdE7T6POU5WoL0MWRsdB0iLGVcX/TR+7WgBGJCtWrT6VgRm5Mkqc4hx05/VZo2ubiAy/vH9OtUQjg7ZlpmSVm1Hp9nO80z6zwspjljp21v0jqAeB4/TtuDD6+rIaSvzdsGm9c2ePjBQ+wfPBuyxzn57HLT82VjUlZ+lBI+QwxWKzfNGZvmjg2fJT7+AEf/GCck/OXzKP+Rjl7qdUCnaEXClv8u6HllAS1d+/9u5H2AIJfDsk7Dln10W90e14rt/MW8REDOsSQRq5Og5b6+mC3To7Wk7QnbA1cc166ajp3r13vcvcdcJmFlLvfvfOce/vAP78b+4/hi/ftHCCGEEFJCGUsIIYQQQsgzzFe+chNf/eptDIPBycmA9967idu3N1ivLVLJybI08VyJYSCli3qlKHsyNj+2R+sLYPnSPZewiOIhJJxSylWnxdIy2BxdpjiMJXO46seFuF+uQYvd8k/66GuWcfR9CulXg2EIET7vHYbBTGk3o8RuKGMM2Pgl83qdSihvNmFSzLOzAcYAt26tcXk54tGjcf7mPgecvHaC0zdPcfbWGVY3V7BrG6SYkq+wiEnYao7YVsJVt5fSFMj6VxK2JW/nxutJWb2vvMbGdTwOWRliJV+z/Y19rdRsKw0rx1X7jMmOi0lYn/r35oyN88TKcdYADnEMnYjNkrLTHLMwaczY16bSx7DxgUVBXMn7KSlrnY1yeBhD+nx9ew1jDPYP908tIVviXPjcHkf5HEyfWSI93VQWOvTx6nPIRREbyren9fyzTsYu2xGTpekzPn0e69RsmWyVZflXy9cyVdte6mP1Pt2Wt+v3eXlMTt6+5A2avzaWydgkIMt9LfGq+y9JyM5dB9B/7Evl6xytlPES8awFs3Mel5cjPvroYvqBFPDZZ1vsdi9uiX5CCCGEkBLKWEIIIYQQQp5h/tF/9G38G//GL+DGjTDP6MlJSFVKEiokYqGkbC5g5Utuva37tVKxoW87FSvjHEP6stY0vnDWbSkNq8+jl3lqVkpZytIXwiCJAT+l8XRZUDmnSI0wL6wk0twkU93U18ZrC1LYwloHY+x0n9KXzkF4hLLDImKlrLFOpK1Woezo6Wm4mAcP9vj+9x/Ga3teufP1O3j3r76L1ekKdmNhV0HEminFbYa8LLGxSpAWYrYrUIG2lFXtsn5QwLZkrR6/JYf15twbYu69UkqOom9Mi8o15J4ulQOe5GqUs74hYY2SsF4dJ5JV1m1Kwer5YqO4DW+JfBkvKfSX45qpW3WdcSnjyPp0bS6Y3JCMlTllpz9rbJordhKwMSlrlSyW+7cyOHv7DOPFiPvfvQ9/+SzIWDN9RoS/kIz1UajKfgBTmtBNP/KQ1H9eoj7ML5v/uzAMdlrKD3b6c8f2krHtygny2Wri519rztj83yO9DxBRG4Sr3m4dE/ql9dA/3y73t5gXt0vkpUjS1jEtManb9bHph0D9hOzsR4u6J9chXdv056RN66UwrueGdc7js8+2+O3f/mn8N/HJXTMhhBBCyLMJZSwhhBBCCCHPIF/5yi382q+9hb/wF97AzZtBxK7XdipRabIv1+svyVF8yZ1L2P4xc4naJV90t2l9WV0mY8MXz3oe15RKLcdKxwSbk5btc6R1bbMCQZ7mc8dOjzguReY6hyg5wngWw+DgvYXM4ziO0scq8WsA2Chqg4AJab39foAxBmdn4T/NvvSlE1xcjHjwjJRTPYaT105w50/dwe2v3cZwMsCubRCvqiyxlq8tCdtcB5oiFShEaE/CIh8z25a+xTi98sez4ndJe9lnLu1WDNJMxzb2xSSr8ZCSwDFtqo5rbXfXzfQ+80hpVbXUCdmYhoUJclfP+aruaUzdTnPExtcEchHcvEfygw15LU1jxzlkvYHFlI6dBO+wCe+5k9dPMJ6P2H6+nb3/XzTeA/t9eOxDuNRqTtc8Keumku1mkrNJzKYkrJu20/zaIY2bp2XTD1bSj1vk34N2ClbPJ1sKWQDI2+VzNP/3KK3PLdO6KsFd7A/b9ef7Vf69KmklQ8v2Xkr2mIRsa6xyXP14Simc7z/2gdfStXcN5eOQtnJeW/kx0nY74vvff4i7d7dTuvvISyOEEEIIeUGgjCWEEEIIIeQZ5Otfv4O/9td+EWdnScQOg8EwhPkCJfHUE6tALlJbCai+xG2NU5cnPiZdlL7AzZM8KR2U1qWvc/UJdB99nJYGspSyxmG9vCfpcer0ayqJaZWYCFIjjC3tbhojiFhjbJQZMieennM2PPaUygpz04Yvq0OibYXVKgjbe/d2z6WMPX3jFO/+j97FcDIEGbtSMtY0ZKzJpawWpM1kLJDvV9vZviUStjHuwdRt+XKUY4q2o9D9tdyY0qy6TaSqHBcljpKRs9JVjp+OjWWCfdofhaval6VcoURrUba4krNKhOrnQCdmo6Qt74FPf9k8uLqfAayfErKDSdujz+4DPILw9aHf6Vun2D/cY3dvF+eZfRYQGRt+tBEuWp5ikbLymSiyK3y+pHlhZV2W8vmlKyjouWVlHlj974IkY2V/KL/uY59wPeHFli9zIduSs6kPsm3970vqD8iLJu3zVZsgx6mWa5Sx9eukLU1zCavbexI2vo2ra60f55ycbV3Hscz92132KVOwoS0lYYOMdXj0aI8/+IPPcXHx/JfgJ4QQQgh5HChjCSGEEEIIeYZ4++0b+Bf/xa/h619/BTdurLDZ1IlYXeq2lrFhHC1T6y/bWxK2n6gtv9Re+gV3XbKxn15tSdaWYJWEqvRLbbXolccjc85qKZsv+1/aD0MQpJJETlI2T8jqZOxqZSYha6OMlTLKgnPh+RAZu997WDtiv18DMNjtHB49ej4Ssuvba7z5q2+GOWJPpzliRcTa/C9LvFolZPUSyPvJNvoSNkrRhnjN0rXl+EeI3uwcJZ3XTyVrJ8qUaxwj+lVTtQGIadd4ndE31mJWyhxnolbSslq2dtalX5aSNUVitZSm3sTywTJvrIwbr8eopCwQ+lsxtUhLNWYzIesADOkxG2PgvMueVysTzZrQ31mHYRxgYHD2zhn2j/bYfrptP3lPCe/D54Ek6zcbKWEsPxax1Y9KwnFhGX4IkiRs+NFH+BFPLmPzf0PyagtJpOp/Y8J15EuRxEmwJtkq44RrrSVsrxxxLl/Ve1K1pW1TbMc70mlfxvKkaC0q0/ryJGgtWn2jPRe0150y7YlXvd6a61bSsPJDpD/+4/v4/PMtnPPY7TznhiWEEEIIAWUsIYQQQgghzwyrlcGbb57in/vnfhZ37mxwcjJgtQoitp4jti1S8/li24nYwzI2Hyct62+z+8mclFQK0jQXpSnZZeIXwLIuZYjlS/2QBpNElJ8SX1JyM6Wqyi+og4SV+RUR75v3ehnGsTYJ4/I6w3WlcwNpDthhkL5hDkdAkrCpz2oVZK3MESvlGwGL9dpOwiUs5UvrcdzA++0zL2ONNVjfWuP1X34dq5thjtiqNHGrPDFqEdtLsMZtIH8JdgRpJV8b4rUsU9w8R0/A6nPNCdoZepJWaMpaKNG6oGRxmYxttWXJ1mJd+sX2SXq2zp2lZW0hZGGCxI1vUMTHoMsUy3Hxvoe3TSpjbBv3TRz0YACnXluSzh0QSxZ752GNxbAOpcE3X9rArEwoV/wMuaLwGWGmH4qEBP1qleaKTVITAEz8MYp83qbSw/LZZjAMbipjLKn8JGTzH7x4lAlZ+QyU8YByvm5Uf4De1gnZsk/9+d0SzXNt5fpc2+NQS1hgqbhska7PFNv1eKVgTvt6IrpNfTl+dv+hZG8pYZ3zGEePH/3oHD/+8aP5iyGEEEIIecmgjCWEEEIIIeQZ4NVXN/hrf+0X8dWv3sZrr51gsxmw2QxTaeJemimXsPJFbC1feynatsTVyaTEVb/ZNlmSqRSmc8lYXVpYErHSXiZjZanHlMemk7H1eQ1CqhWQ5Fn+xb+N4rdd0ljkqYX3bro2O0lekb0exqQJcOU6rHVwbsgSsrIMxwShe+/eDo8ePXslHofTAe/85js4e/MM69tr2I3NSxOXqdj4I4BcynalqXpdNiVs2SavYTVuOcahpO1s+eOm8enfn2b/ia6kkXPp0r3Bf4XjJCnoTbYvS8w2hGs8b1GyOArR9FuH2C7yNUu/Sko2vBWCVHWTtHMpQQuL7Ljp5KlcMEzeLuNBtan7ERO1xmdtsrSwMbUbf/hhPJxxcZ/3Ht76WNZYrhtfAXb3d89cQlbY7Tz2e2C1GmGtwWZjVVng9OOS9NmtU6bIPr/GMf/3If2bkpcyLv+NKf/tOGbe8TD399y/NfozN7wI88/g9r8h6f2VP15NKTyPo5VMRbOtfD/PvffluvolhWvB2ne6tUydk7rdUbI+9XywYRlW5EcC8iMA5zx+8pNzfOc796OcvXv32XwvEUIIIYQ8TShjCSGEEEIIecrcvLnCG2+c4s//+Tfwzjs3cHIyTHPDlhLWzApVoGxvp2jL7XBc/SX5PO39ZQnJum+elM2ti5++eDZZelVsTUqx+iotm77E11/my+P3sYym3mdtSolJskwnZwEzpX5MnBtRxkn9JM1bzqeYlkGuIiabvc+XgJ3Ss8B6HeTvZjPg9DSkjC4uRlxcjFFGPyuYlcGtr9zC6RunGDZDLmBNIxGrhGsp2Wblqz7nEjmrZU25vlTCxk1Tnat7TUdwUNTmb5n8ekS+qn1LUrDS5uGz47K5VaHG01K2SL9ieg+LzNXL9gPWq1NSdlpmbbY+rxwbRWx4y8C4dIwka41VJZD9JJMnMSyS3g4WDg5W3nO313B7FwVt7yE8LdL73mMYPMYxpVnTD07k8xGQzy1APtfkc10+X5PgSz9qkYRqnZTVbSJ9y/NbqQadzRlb9w196m3dXrYJc2297dAm/1YcT+9HE4cEp/yYpr6OdHxPmub/hprqXIc+bo6Xr0BPwIb1JKVTEtZju3VRxt69u8OHH54fPjEhhBBCyEsMZSwhhBBCCCFPkZOTAf/uv/tL+OY3X8WXv3wTZ2dhnlhrDVarVJp4rsQwkCSrlrbSVyedgDzVlCi/4Z37xvd4+dQdSX3B3kvJtv5E2oa5YvMv/ctla85YnZ7VS5G8QSqEKF8uKpwSD4DUUpX1ccwTQ5KU1Wm1dG2hFOk4hoTsMJgoh0UIizA+ObH4XZLkswAA+tlJREFU7LMdtttnx8gaY7A6XWF1FuaJNdbArBYmYgv5erQsbR1j67G6qdiewG20ZedDe9/B9h4NcZKV81VjRjFkUCdjdZJV2mR+WGOifJWSszHpKnPAqrFaydgsQSt9/DRHrPWZ9Mz+nLonrW2ovuikZoEsNSvS1xufyg8P4UcM1ocUrJsOsAjiNaZnx+mejJOsHdK9sRuL7adb7O7uDjxpT4dxDJ9P+334DDo5MdOc1unHHbo8sXzm5589kkQNT3YqcSwl38s5YssqC/m/P4dK3pcVG8J1lCWLTXM9LfNKDfrfPE1fzD7uv1fFG3H2XKGf/vesHueY66o/IFoid3aEGTnbkq66PZUiTuvOeTx4sMPv/d4n2G5DVQnOCUsIIYQQchjKWEIIIYQQQp4ixgDvvnsDP/uzt3B6uqpKEqdSkf2ykDKO9BMRq79Aly+IW8flXwofI2Wbj6jYrr+ADucsv+D2xXo5pm8s9fWLMJVlGCd98S/JVz1HbTg2CE9JdyW5K39hfxhbzz8b9vlMgkhyrJTfkozVf8OARlLWYrXyca7g9XrAycmAcfRYr/cYRzPNN/t02by6wclrJ2mO2EK6VkJUpKmWp2mlLVmnfVm/xjITrijOi3q9JWJ7EvaggC0fy7Gow1oJVbUzXkuWnJ3eDlliVtpkflnpP42hjzc+pVPzyzLZNRmYbBw5Z5aWRd0m54upWf0WRj5OXDepFHI8Rp5DmT9WH6/nqLXTdTiTErGSkrXFcjChrPHKwK4tVmcrjCcjxs0It3fP1DyygZTCB4KcbQk0/RmXl2XPPz9l/lfZltS/yNkkccN+/QOXXkK2/NwMx9d9dXtrvWwr28u2cj21pX8rDqGPb6dR0785XpUR1+JV+sn9bo0Zf2uhTlKmZnvX1euzhHa55ZaABeQxaAn76NEeu52bZOwed+/uKGEJIYQQQo6AMpYQQgghhJCniDHArVtr3LmzwcmJnWRcKxFbplsB+bI3Cb5+MjYc07BNB+XrFSXTUWg7k395D7Tnis0TsmkfUH+pLAlUva+XkM2vwSDMEWswDC5ua7Ewl4zVS2McvE/zxgY566ckbF4uWUogyzV4H+Tsfu9wfj7i7t39UxWyZmXwlX/mK7j9tdvY3Nnkc8SqUsUiyspEbCZqO/JUlt3yw0CaG9Y2xtF9G+uHxs6uQ/dv7QMOt/dQT2MlnXVqTc8Ja0wmbKvE7OSfymRsNyk7iSU9H6z3U18tSv0kVlUpYWPU3KsujRGXU2pWnydLzmK6BiVPpS2KVLkn4e2XcGkpCVeZM9bBAdM0yzJnrL6PXuZyFjFrptfs68BwY8DFTy8wPoPzNAvOARcXkt4HNhuDzcZgtbLZvx1llQD9YxGgJ1LTPK7yIxP5MYreB9Rp15SqTe+l8t8f/aMgvd1aT0tTbOdteXvZVu+ojzOd9vbxuZid76NGbPTp9VV71A8xjmFJieW2tE8iVio8jKPHt771KT766CL2o4glhBBCCDkOylhCCCGEEEKeEt/4xiv48pdv4tVXT+KX6Fqo5onY9pfTeRnJJF/TF96m+LK5MEzN9dZ2id4/JwYbZim25xHANFeeStN5aU+p11ZKVqe3wlhA+rJ+WUK2lhJ+up8yb6yeM3aSOx5VMjbI1FyGhOfWZ8lY76UksVHzytopMesmMe+wXgfJe3IywDlgtRoBeIxPwRWdvXWGk9dOcPr6KdY315V81X9lGrUSn0qgRkop2pKlLUlq1L6yr6nP1S2D3DpnKUnLflnTcdKklUptJWNjyhU+/+2CEjVVclVSr2o7itb67ZfEq0hYPZ+rTynJMi2r07ZVMlaW07XE49W5dRq2nCu2TMxmz/Mkb/W4MSVrTNovYndqi+Wsi4SsHS3sxmJwA4bTAd55uEu3NFj5VNAp2f3eT58pHoDNfmiS0pjpBSLzyrY+T/PP7XIf4v40J23YL3Nv63LIUv5YqP89y0spl9fc+2zXbfrY9LmP4jM+u4p4jO4jidfUHj7XtRDV91X3Cfc0v5YyMRvaTNZH96sfi+y/2ouwPCxt+2xbJ2DDnLAhCfvgwS4K2YcP989UmXxCCCGEkOcNylhCCCGEEEKeAsYA/8q/8qfwT/6T7+HmzVUsS1uWsu2lW6V9GIxappSsns9vOmOxnFtvbc8+miP6CvoL/3I7rev5DeXLbv2nZYB8yd1OyNZt+V/6wr38ktx7YBhCGm0YoL60D3PIpjFNFMj59QSZGtK4DiJKjAkSJSxDyk1khnPDdAXDVBY5zBW5WllcXOxxeQmcn7vqy/YnzVt/6S28/mdfx3A6wK4s7GCT4JqWMQ0rrz8l0KI4VXL0qLlcTT1mto6iHY11PW5D+jblq5Y+epweh/aLpGkb3Zg8rXeZzIlliVl13VGOziVl5UcM6lw6GZslZqWf9/E8VTLWp+PiUpUPzuaXlXvgIW+J2DfeEpWUzVLCcq1T+eHY36H+WMGUjIWPaVnjDeAA62xKyE7XP2CAsQab1zYYzgZc/vQS7jlIAO73Hvs9AIwwBrhxw2O1MlivbfxMCj8S8fHfBv1DEZ16TT8UAbSIlbbwOZd+xKL35yIWSD+U0eWPU7v+gYxI1HKO21zEpn8X8u10L3Jpmz6vc8HZ+MUD0rXoMsOlwGwfV+7P+6RKCqlf3pYfpz/X230aZ89O75vt6d+qUsbmc53vdg7f//4D/IN/cLf57yghhBBCCDkeylhCCCGEEEK+YH7xF1/DN7/5Kt5//w5OTwesVlbNFVuXeizTQukL9HYZY52MnY5UX5zrZbne2u61zdH61tYU+1rbaV2+pNdJLlOlCHOpkL74r1NUkmSVZKsWAHrOWJGhIiJCSqgs0SlyPM0XG2RHOndKvwJlClanZ4chmMKwRCwxGv5k/lgH5yzWawvnPM7OVjBmxH4fykd+EQnZG2/fwI0v38DZz5zV88QW88X2pGlLxEaiR6xfa0tFbCV11XY3CaveEpWELa9pyVtlKb1ji8SotGUJWiUcs8Ss2p/NDTv1r5KyyBOt4dBJ2KJIxqJejw/FqETtdN6YdjXzCdnsPDJO+ZzI45PnURKvRWpWkq5w0/1RyVlYxFLIMRk7qKUH7MqG+WgdMJyEH0PY0/BjA3f57AtZIZSQ9VNa1sXPo9XK4OTExs9MkbAA1Gce4pzYrdLE0pZ/fuef3a3P+Cy93fm3QBKouQSd+9FOawwRzknAStWCcmzpG44zlWxsX0/6t6YtbFFce/o3qJV+bQtOfdxhCdrbX8rZMgULhOd6HB1+/ONz7HYuStlx9Pj0020s508IIYQQQh4fylhCCCGEEEK+YH7jN97Gv/6v/zxOTwes11rE5jI2Lz0MyBe/aU7Y+RRtLV9bMrbcfhzDNDfGnISV7Z6gTV+0yxfYqZxiEqD9hGyrvLCJYyRhmoREWE8yuL1M1yJjAogp5XBtYem9xTAESxQSSGGu2SBhLYYhxARXqzCYnnt2vQ5i6PR0gDEGN2+OWK0MttsRux2+kPlj73z9Dt75zXcwnKRErC5RHOfcFClbzA+blRBuyNJM0kobUp/W/LDHJmP1eHNSNizUtSDfFzeXRtaOoJWGjdcTOtTyFW0pmwnWTltre+l6PFZKDNtJyFqVjBXxWiZkVVliY02Yb7ZMzULJMjc9PlvMLzu9hyUlG0WwSvBmaWKT7pWZ3p/WWTi4UKrYhQ+AYQwJ2eHWAJwjpGOfIzG13U43RxHmJE8/BtGStP7Bj/yApSVa8zZJ24qklDdKTByr9dQHSG+oJcK1FrCpjH0av50OTcdqwZratJRNn+Nz49T7ZKxaurb65bQ/R45Posq/bfUYqVpD2JbS0c55nJ+P+Pt//3M8fLg/9oSEEEIIIeQIKGMJIYQQQgj5gviFX3gVv/mb7+BXfuVNbDY2JmJ1KjbNEdteShI2L0+sj5Uv15WB6srWQxL2cWWT/jZ5TsKW22k9lSieWjxUW0ppzSdkpfymTsYiO9a5fL+kZfW6nEOuQ5Za6AIpISsCXbaDlA3nKxOx+dJgHEPfcbRYrx28t1ivB3gPnJxoOetwefnkorFnb53h1T/9Km5/9TbsehKw0x8MknQt5GtPxLbmfJ0VsaboV46J9nliO9Rx5dhquylhl8jX6/Sxvj5HTLdWpzUp9Vm2Y5mU7W2X63JsuR77FOlXPUbcVyZky20Rsmqffl14O51/krZh9yRlpyRsPFaEL5IcBhD6AvmcsQhS1sKGcaZvSOw6dF7fWMNai/H+2BXlzwv7vceDB/v4b8nZ2YD1Ov0bksoWt+VsWR0gtJef0WE9/5xO7Wkd6P8AZ8l2v89Vpexc/7w9jtJIt9Z9DvN4r6tS2tYliNO/UR9/fIGf/vQC8hyFFKzjXLCEEEIIIV8AlLGEEEIIIYR8Qbz//m38y//yz+HsbJWVJhaJqssO94Ss7q/nhtXHZUbqKCHb2m5x6EvqVr9jvmxvJ2OBJFtln8gBKaeo5at8WR7EqJnkapIHoS2VFZZylmU6zLm0LMt26uRymhMxiXORsiJhU5liEbPhvFrGhvLFFs4Bq5XHfm+xWgGrlYX3HptNkrKhRPGTk7Enr5/grV99K5sjtidge+WCM5Ean0clU3siFvlYc+dpjteRunPn021Ve7Fvrr1VbhnoJF9n3I8+fyZmJeVZStki/VkJV1W6OEpP2e8LCaquV+9rjRfTqEZJUKMSsDoJW2zre6j3xetX7fHapjlm5XWo07GxTLFBStAqoVvJ2KlMsRkMjDMhIbsKMnZ1tmo/N88h4+hxfp7ud5CuQ1YRQEtY+RxNKdkW5We0R5qftZStjydhUwK2TK/OJ2f7/bSUzfvnadmyPT9Pfr05evxiT7P/UnQZ5LxNL1P6VaosfPzxBb797XuPdW5CCCGEEHI1KGMJIYQQQgj5ghgGg9PTFTabAauVnhu0LEuMSsT2ErFybDsJe2iJznavrUevb+/L6taX53Nf2rfkbJIE6cv/Mr0a+soX0ancc5rrVc+TKOv6uoIw1WWJTWwP2+l8eTI2LUW0hr4Gw6BLF/usz2oVDFL4At1Mc8WGPpuNhTHAyYmbkm0rGGMwjh7brcPFxfWnm+xgQ2linYq1xZ8ksnvCtBCgzZLCQN5PC9aZcbWUbSZjZyRsLwXbE7N6u5KtC98u1XG1H+omMPWxVZnizr4lZYmzbRPEZDMla/LyvzGJOpU8jVIUqWSxfi68CSWGJQnrvY+lgqeTpTFNErrZ23+SsMaYmHT1LiVrpS27PSLSfEPCGsB6VabY2zSmMfAnQfievnWK8WLE9tNt9bw8r5yfO2y3en7tMK/snTurrPx9EIr636b0WSkp2vJPkrHpRyvyuZnagdRP79M/isklb74/HRv2C2mu2PTYSrmbyhunfzt0/347suvS1RhkO9zLNE68gux93pO0h9EyuCxJLAlYIMj3hw93+M537sdS9g8e7K52UkIIIYQQ8thQxhJCCCGEEPKECRJ2wNnZqpojtp+CDcemNp2E1aWJk4jNv7xWVgple9mGA229Poe+TdZjlWK2t13ua+8XMQQY9cV9+nJcJ7tKAVCWMJb1+s8Uz4PPnp/UJoIijJXm9y2f05TGFWmc5qo18TolOSt/w2DgXFquViJpg7Q9ObEx4XvVL/grDDBsBtiNhVkpCWuShC2laCVOp+d/VsTqp1gtW4K1KWKTHa2FqhqzmZYtzt2VsPEUnf2PQzmOzx+TpEyzvkpCNdOyPsnMdBqVbI1SS21DHVO8vbO0qx4HxVjqPFk5YknP6rlio7XK70VMxkqyVz8+9Tzp8szyXEtyNv5JKlZSn24qYVwkY2VeW2PTfmMNMABmFQTt6tYqXN8kkQ9+9D0H7Pce+33+QNZrg7OzAcOA6UcjPvuMknlk5cctQf7JD1bS52y7RHH570bxwp3WRZLW+2R/Plds+SbS8rhOscrnivxbkcoV6/69xKy+jjROOr/+d6juV46x5ENES+ZiT5SuLspWLWTHMZSl/vDD8y9kXnFCCCGEEDIPZSwhhBBCCCFPmG984xX8B//Bn8Hbb9/Aem2zEsWlYNXyrhRysmwnYkvZ2lvOrc+1tTjUr5WMbX0RX243DFVT2ObliOv0UkrDilSQUsWAlA1OKVmdlA3rMq+eUVIiHJvm5Utf1OcJWR+fI+dM1jeUIg6TXcq+YQjbq1VYDoOdEr2pzOR6msdysxlgTEhZGwNstw6SVru4CGm3x+XszTN85Z/9Ck5eO4FdT+WJZa5YiyhjEV+vuSjtSlqh8ZI9mH6d24e8XxyvOFfWhsY1qfVM9JY02nqliXssmYO0HPOqidhW29x2a18sGexnErJ6vzcpYTrtg4S3LdJ+LW4LWStp1vgwyvTr9Od9kqzlvsiQHy/97RCSsXba4eHD54LxGNbhveZPw/W5NxzG8xH7+3u8iOz3Hp9+ulX/tgTu3Fnhxo2h+PfKZ8lZoE7KShuQt5c/jknraVunZuW4IFlT3/YyHS/tZVpWi9awrPun9fx8tbzVPwxo39f8h1JC7/1fp2bzBKxX/+Z4fPDBQ/zgB4+qMb0PzydFLCGEEELIswFlLCGEEEIIIU+IYTB4441T/OzP3sI3v/kabtxYoU7E9v7Sl8HhC+46OZsnYoEvVsQuoRSurTbT2N9Lxub7SvmaJGz68lt/4S9fxOdfmNdf7ufHyxf4IgVS8qt+znzxHJWJ29bcs2kcKVMdXiPpuW9JeWtDQjbMJxuSsquVn5Jrj/HluwU2tzY4ef0EN965gdXZKitHrOWnlrCZXEXazkoBl+35s5Bvz4jYTPAqYdpN2ipJo/f1+nclrCk3G8cfgYHJnqpyTtZmIhZF6lW9JbK0qzpHS+AeM3dsOS9s81xFQjaOFd93+Zyyco64jZSIzQSwSdeZvcZ0G/LHoKWufq1IAtYbn35QAIQ0rBbHkp6d+pjBhHT4aGBPLNzu+suBPyuIwJu2Yvt267BalT8gCj8Q0RI0zZudPiOlrXzRatma2uW8rX8n6m39+ZynZUOf9O9CXuZYH5uPH++Ekq2tN3f9b5SWvG0Ofy63Jaz8KMjj4sJFGSvLe/d2uHv3xSmfTQghhBDyokIZSwghhBBCyBPizTdP8R/9R7+Cd9+9gRs36hLF+bx8SdjJ+jDYLBGbxJx8iV3aCXTalq5jQftSWgJWtx/64r1sL4VsKe8wffEf+peliKW8Zj7Ha0rGyraeO1bKCQOpHGcrIZtSSmmcsAzbMh9sOF+aMzacI+9TJmNDOjacO+xLc8fudgMAYLdbwdoRAHB5+XiiaH1zja/+T7+K0y+dYnVjBTuoVGwxV2xTltokKbXszBKrWtCK/NRpRS1bi5KyXUkLdYyWcCj2N7Z7baXUnH1LPO7bRc4hTC/xVnq2l4bV+7QovWpK9uC6miNWjtVlilttcakFKJSYlR9G+CRtYRBeH04tpU3uVZmS9aZKxsY5YwfTTshOc8daYwGHsO4sYIFhnBKyo4ffe+zwcs29ef/+Hg8fjlnbem3wxhsnat7z9G+T/nHJ/A9YwlhLt8O6/mxP40u/dh8gCNPyxzzyuZKOL1Oz8iLKf8Sjt023z1IOzQPrHHB+vse3vvUZtlsH/cIuS00TQgghhJBnE8pYQgghhBBCnhDDYPH666d47bWT+GV16wvpQC5R0xfb+s+oL5DnErElx4jYa7BKcZxjvyTWx7SOL4VsZ5Tsy/nQN4jSurylTtNKm77foS1P9ulryJ9PnQrTz5mp9ovQ1f1SGraeN7b1JxJktTIYx9C2XltsNg67XV3qctEzYA3Wt9ZY3VrFdKCWo5Xz12IUtQjNZCnqMbTkbJYiRrHU5ytfysV4+tiuiJVjdJvu35Ow1/k2ERq/XxCBqOd7rfYblfCb+jRTqkqkzr6V1NilHJX7Vc4RW4rrbgnm6ZxzyVhApVj12Pqay/vfeE1FIa1eMzIuDGLq1lgTJK71YV5ZSc56E5eSkLUri2E9YDgd4PYO/iURYTIHqcYY4OJijD8Yyn9MZHByYuOPiFoSVn9WYnpNyQ9Y5EUp5YXlM1zWpyuIfVPKFtnx5Rus30+uIf17IJ/Pen95T/LrqZOt+vje57EuOXx56XB+vi9K3wcZe3Ex4uJij93u5XjNEUIIIYS8aFDGEkIIIYQQ8oQwBjg7G3ByMsSUazlXbF2qFlV6ttxWZ0Btx9BYzq23tg+19+hYnWyfWbB9bEI2LbXkRExCiQCFmuPVR/Ep2yEZm8vV1JbGdk4nbSU9i6z8sMhUEbayLXPQyrWU6dkwnyymZGyYU1bmmF2tQpxPErLrdUjPbjYulhe9eXPAZmNw9+7+yinZYTOEuTKnOWKzRKxplCxuvAxnZSka7WYSXy0hW5ynm4hV21dJx3YFbOdtkEudx2duDtlmGla9NZakX1ttB7e1kFWStbV+KA0LIMh9jzQ3rOoTH6JN541idXofwSKWE9bjxYc47dcJbXXjsqUZTPZxY7wBBoS5Y124Ru/C3LF+Ha5vOBmA2+E828+32H3+ciVkNfu9xyef9MvjvvXWCW7cWMW5s1N54/DCtVb/mCV9ngJa6mLaTn36idnjlofalm4fS5mCTelXjx//+Bzf/vb93pFT5QdCCCGEEPI8QhlLCCGEEELINbNaGfzqr76F99+/jbOzVVZiuJ0QKtNCImZLaRvGTyWKhUPfDi+VskvHW3Kenpg9RsKW663zpbRqnn7N94UUbFqXL/VzcQuUqdYwVp6IzROw+nlJ7XIN4Xn0MUWVJ8JqEZ8nokVeJBncmkM2zB0r67ZK+y7BWIPb79/G6ZunGE6HSr7qhGG89cXLsCUnq/LE6qmrhCv6AnZO8pbJ2yVp2Gwpxy2QsLMC9ph73nhJy9hZorXxNonJ1MbbRBKm+ncKMSWbXsJJsno1ppa86tyzCVkU7cXj0fPFVtfbePx6Pln9PMXHJf2k3GzjOS2Tr72/ZkJ2krAieSU1G98P0/yxdmPD3+k0h2xewfelYS59f34+Yhx9MzULhM+o09MBJyc27tMl5KVP/nmcz8NdzxOu94XPzXxOcS185Xx54rYUs1J+vrV/yf15+HCPBw92WZvqkQnZzz/fwjkmXwkhhBBCXkQoYwkhhBBCCLlmTk8H/Gv/2jfwC7/wKm7dWmMYbDbXqxasgrSX88gG+ZZEXduKlcueUWpZqFa/66C0SLrdd7Z762Xf1j7VqxCn0qaTVa1kbCnDUzJWvoAPaS6dgBV5Kwlaa4N8CMekMfNkrI/rkozVy5CMDWMMQyhBLKlq74Psl4Ss9x6rlcVq5WNSNt2D5ZiVwdu/8TZuvncTq9NVlohFvC/Ta3YqXdwqH9wUpvLUQfXpiNdjE7GZ7NXXgbxf7N8QrockbHUvr+Ot0hqj89zp+V9bfSoJqksXx1tgqr5RpmpBq0Sr7t9MyIrcKs+vxa56LqqyxHqpkrAxEQtkx2RStnjeshStTddWSVhJ0up7KdUGfDjWwMBMczmbcVoOBhY2zBnrPQY3YHVzBe899vf2cCMjiyX37u0P9nn99Q1ee22j/t0D5HNWf77qH9doGQvUErZsm1uG9RQxL0VsIP/3qvXRqtOuett74Kc/Pcf3vvfg4L0ghBBCCCEvNpSxhBBCCCGEXDshobhe20ZZYtWrkn+pnGO7lHEpWtP58uWh9lafY/e1aAnSlpQ9VsjOtZXiNZ8rUyek6i/i5Qv+dJ15Mlb6a4HbTsaKvJXryJ9bvd2aVxZKzqYEVxC75dyxaU7ZIGwthiHI2NXKwrmQEL55c8BqZfDw4f5gactXvvEKzt4+w+bVDezKJhHbEKIiwUy8XyaTdzr9WqVii/UlAnaRiG28zJemY8tUbXV8Y99ce1Uat0FVjriXFi3Ea0vKlhIVyStVsrWVki2TtEclZI3qVxwr6V19vky4ioj1ScCqmxjHlRRsVa54Wi9lr5a03qbrM1K+3KV5Y8s5Y/W6CNq4HNLSOgu/8rAbi+F0wPjwJY3FXgOPHo1wbquSsEmKyudyLUhD26uvrrHZDNm/n+lY/UMcPU6rLbTP/X7l8tLhk08uq3mYW5Rd7t17eUtZE0IIIYSQBGUsIYQQQggh14wxmNKKOhGrvyQuv2DWKdiUim31nY5AbaB6y/K4ue1e21JKU1Tuuy4hWx5T99Gli3U6Vpcs1l/Wt8td5inaeFbT+5PxWxJWttNzq9Oy8vyXQr5OTJcyNpQpHkdZBiF78+YKm43DxcU4X/bSAHf+1B28+s1Xsb65jiIWFmHezSIZW0rS7OVWtumnCZ1+019Ttoq0VSK3GgNqv3re9HmzpG55THGNiySsHqNHuStzjfU5KgGqxzgkZeUeeSSxOh1fliOuhOx0PXMJ2axvWUJYjzddY2su2Wqe24aAjv2NWvbKK6vXRSVhi20tcaOItUHMxjloRdZ605axImmHkJo1g8GwGQAH7AbKtqtyfj7i/Px4mW0MsNnk5Y+luoD+sQtQf4an93gtYMttEasPH+7wwQcPOGcrIYQQQgi5MpSxhBBCCCGEXBPGAL/1W+/jl37pNbz77o1GKrZOwSbxlovYXOLOmS6ott5ybn2u7aoUFilrv4qQLfe39tX9dDpKzx8YvqBvzxUr8wPmX+DrEsdeHZP/5SWMtSTwsSSx975IwCKTsjohW5atDnPEIpYrFhkbShgH+T+OHs6FpKz3NgriOezaYtgMIRU7iViTvWaT1KrW0RCYUP2QS7FeKrYSrMXYRyViVXsmXcttdWwvIZv1aUjUxRzoa7zJ+kU5Wh7r07X2pGwpPquyxa2ErCmELAqpqs8l16HkZ1a6GErYFr+VmC1X7JUwhUrEemRtgLpeeU4nqdqSsrBIiVidkJ2ErLGpVDI8chk75Es72HA9ow9/G4/17fAjhv2DPfz+cHKSPD7eAx9/fInPPw8iPL1922WEe6nX1mdXi93OUcQSQgghhJDHgjKWEEIIIYSQa8IYg1/+5S/hN37jbdy6tVYJ1/RXHDEdB+SytixP3D0jkiFpCVg02o4VsYe+rD4sRdvjzUnXJePM9BahNclOLWLL8pVpjtX6Lx2frkGna+vnVe8rSyPnZip/rvUS2fEi5EXGOuer+YeDpK3Ts8OAeHyruqaxBmZlYFc2ilgtYbUg1dIik5JzorP55CgRG1//JjtHr6Rw9nKXJi19C6E6K2JLgVs+HrXefLwHHmOTQ28V1ScTip3fK+hEatY+DVYmYsvlrJBFLlWjkJV2XaJ4On8mgpGfo5lyRX6+7G1efLRpIdyUskivhbJ8sdyLTASbfAmDmLit/mS+bpUWN0P4s4OFPbUYMGB8NOaPnzxRHj4cAbBENCGEEEIIeT6gjCWEEEIIIeQaWa0sNpthJhWrE7G9+UBlXUYt7QDUUtPaNydijxWzLcr+h+Rrb4yekSnXj10mWapFqlyXTsjq7VyO1vPN1qlYwDlZJhErz6f3XiVjRaqm57iVkNV/0iavq5SQNTERu1pZ7PcO1loMQ7juL31pg+3W4dNPt1Wy67U/8xpe+9Ov4eY7N2FWJs0Vq0oTV0K2WG+VEq4Eabkux1t1vGkvm+so2lvnkae01YairWjXorja19rOds3unJd18rLVh0iqtUi7ynozJauOzeaDLcdCLjljqtW091dp2TI5iyRHW32bZYsbx5Tp2UrKakFczCcbnzsLwE2CVZKx1ucJWVWeOKZhFyRjnXewKxuSuB4YToZwffHzmhBCCCGEEEJy+J8LhBBCCCGEXAM3b67w5punOD0dlIBNsi5QW50yDQlI/1K+tpgTs2X7kxCxh8451z63PXfdrWP692GuDGWZVi6fp1q8GvXchKUuIS37yuczH8uofSZbttOxrfLWqZS1Xm9J/ZOTAZuNRes+bO5scPPLN7G6uYoCtpSbMSU7Pexemd+DL51SrJbHd8arzi3r2b1tiNOsaeZ1Ub7FivNU11oKz+L/srdt+Vf0ry+qbiqP69273j1opYHLc5f34uB5es9/495UfaqHl15nenx9jZWgLx+vPn/j+ap+ONA4lzH585e992xawiBbGjulY6c/kbiEEEIIIYQQomEylhBCCCGEkGvgn//nv4p/+p9+D++9d/PAXLHpi/4yGasFm81+NjlnhQ4ty/Ul24+LjNdKyXZqrjbHuMo+vT8skyT10xKxPZQwTtt1eWEUx8j+1C9/XnUJ4vbcsXUJYj2HbJK11vq43/uUkJVEbJoz1mIYPFarlJIFHMYRartxlwaT5opVqdhWMjZLycotbrzkulJQpK6WY3qpU7I9gWiKc/QEXUsqtvqo66zEYfkYMNNnKT1ZCcSXs06lxnZ9DTrdeiglqxKx5bhVOhX5vLD6mOz4oixxNp9rIzWrry2WK26kY+P90Y/JpLFapYklyZolYg3grc/ntJXyw3Nzx06P33gTyxEDqBKydrBwCMlYSdHaMXRef2kNe2Gx+3QH33nPEUIIIYQQQl5OmIwlhBBCCCHkGrhzZ4133rmBs7NVlGy9UGYr5RjajTouGKV8jFLKXoWrilhT/F3lXIfOd2jc4x773HPQ7lOmmfsH6+NyIVv2KZ9T2SfPb7qndZq6lPllQhaV1M8Ts2HfMBhsNhbrdTj/cDbgxts3sLm9CZKpkKXx+gphKcu5tHF2j5DGbr5stJBtiNhmuWElRLuJWNNo61/kchG75KVfvk2WvmVMuhaDxuMqtufSq1WbfozZ7rxtUdnm1n0vn5vyuOJ5iMlUFMsyFdu4b63HXb124/sqP2f5emwts7HKP4usf3zOpjlk7drCrvkVCyGEEEIIIaSGyVhCCCGEEEKugdXK4uRkfq5YQYRZkrZGbWt5B8zbnJ516lmaGdvUHXvp/mNSrLqt3L+0f2/ZuAIT9vXSrtLHmJBclfNIyrU8NrWV8rRMzNbrkm51LqVqy2Rskqs+vi6cM1lCdhhQLMOfJGGdCxfsfShR/OabJzg/H/Hxx5e48/4dvPtX3w3liQcTU7GYXn8inTLRFe9jehqaSVZ5ipDGyIQXkuyqys+2+ut0rhKiVTpWnW9RIlYLxM5bZDYJe+jt0aJ3TPnyl3O30rIqOdpLycp1L07I6jljp9RpuS73tZwbtpxnNkuy6rlmjUq3tubGRZ6AjY+hNYesPr96PmMSVpKy1gAOgEWY31UnY+U8Ri2nRC2AlIgtl5KQdTaM7RFSsDJ3bOOxEUIIIYQQQgh/tkkIIYQQQsg1kASryaVV0adMNZbJyVLcds6GeQGLRttSEWtm9h26pqvsO3QtV72O5Qatl2Yt98nxvecoSct8v24vz1ufOx+vfr3Ur6NS/Ou5Y0X2rlZB1gJBKA1nU3lilQTsytXerZu51WX6Ma5r6VoNV0vU6ny9l/TMW6JVmjgmUOdEbOs6r/r2mKM3ppbLvePQeb7QeNy9sVr3vDd26znoXZ5OV5u8f5WCLa9Hn79o1wnX6n3Ye33pe9BLxqJOxuq0bJWatchKe8v8scPZAHvCr1oIIYQQQgghCSZjCSGEEEIIuQYknVgLM0CbACklW5ecTclY6XuU+cj6HGpb0ucqyBhL5ks0C/st6S/7Wn1CmxadbcGa5pBNaVnpH1Ktsp7Sge0/IE/Etuaj7YvWJFTz9GyejHUun29Y/mT+2HGUeWUtjPFYrTxWKxeu2xoM62Ku2CkR20qwamlWSrRMtnbEWLPsK/J9VYlk5MdVctvU69k1mPZ2JgCXyMeyn24++KOJw3ivXq96uCItK0nZ1tytWTq1MY9sNyHb2kZxDFDPH6vmaK2Sspja4bNrlDHKczXvbbnPpDGr45FeA9XcsdNcsfo1UKVpp2SsyNV4T6YkrLfTicSt2vBYzGBg3PSeGQyMN7FE8ea1DcaLEZcfXR73EUcIIYQQQgh5YaGMJYQQQggh5DH4+tfv4M//+Tfw8z//aibUtGXQEvBQMrY8Ztq6wpVdh1y9Tg7JV72/t750zL6U1es9lybPRygbjKl0cS5p9f7W8XJMkq+yT57vsvRx/pfa5TXiG8s0npQ6TiWOpbRxKL08DAYnr2zwxs/cxM33bsUkX0uQdpOFS15S6vhKpioxVqUmO/1KedpKHFf7eiIWDRFrOu3lum6+BgnbGqspZksp64vyvOo3CJmQLc6xWMhOY+ofHVRvHXVdWSnj6sEpYds4d1aaWESrLkvce6z6vOXzWFx3lK36eqQM8iRsWz8WyERuuZRy8jaUNs7SsYOBWZkocwkhhBBCCCEEoIwlhBBCCCHksfjmN1/Fv/lv/gLOzoYs2ZqkLNR2KWLLNjmm9UX+XFtv2WLp2I/DUkl6SNAeO/58vzL5qtvzPvr4JE5zOZsfo0WpTuKWc9CW58wlbKsMcTtJK6WJrRU5a1RbSlrLXLPWGpy+doqzP/cGhtNVFLFNYYq6rXlr9TLb1TheSdVmWeQWxbVk5zb5ejnGokSsFrHlJRTbBwXsMW+hzktXzjErZbXgLM9/ZEK2DJPHRGspX4tx43rxWHR76xozUdq8Aek4LVGbklYn1ctErnpNRKmq5tfN7kXnL6Zt5XhrwmO30/UV5YmlRDE8YFcWbkqhE0IIIYQQQgjAOWMJIYQQQgh5LKQ8cZq3M5d7QlvEmkZbPAJ9w1OaqTkO9b1uEXtd4z6Z6yrTp/p8pXAr+9TpVm1wyn4mrvdKV6c5L9O58/kpe6+RlIYV+apFrE7HpvLFBuZ8h/3vf4rxRw/ifJdQ58jkZ/cGqofbeznFe4NagjZEakvcVn2R79fXmQnb4tzNa1DL6vE2ztkUsZ1rXMSBY5vnbMjhSlQ37k33/CjuU+M57e4v7mdLuGd9tdDvPOdlyjm7hs7+qkS2GjN7/enXlW4rxtd/1VgG4dsTo943ZSJ2+rMri+FkwOa1DYabQ+dJIIQQQgghhLxMUMYSQgghhBDyGOiUYk/Ehn6tRGy/vzpywXpru9d2zH7p8zjm6Riu43obR3VusjwXdXt6XvL99f0v9+vjWsJXn6Ncb7WV+3slrkXOiqCVPiJkjTHA5YjxB/fhP7tsS67WQ1SCbS4d2pK5lcxriL5WKrZqK64zk40LXpJHidjyMdWDXf9boDPmISE7e+3lPWqNh+Je6+ev+dAXPHAtWmf6N/cVwrT1eFuvqUril48HqO+BOq4li3tSVuZXzpLlqmxxTMmuLVa3VhhOKWMJIYQQQgghlLGEEEIIIYQ8FsZIMla3lQLtkEjTSUvgKOtSX9GR7XPjzI21ZLxWn+s2WVdlXoyW+1vyvPUch219bKv0sGmOW//1SlnX46UfBKSkrP4bBoNhsFitLOyQpFIlpaaHHpOXcisyZzcjCQs5Vo7dS79WYqyUZI3r6Mo2U2zrayz7NR5DlUxtib7WvmP+WjT2da9F72/JZrk3rcdd9C2ldJV6Lccs98m4pj4+6zv32lDXMfta0des78l0Xi2TewnXbgLb1m1avLbaW8lYs1LrnDuWEEIIIYQQAspYQgghhBBCrsRqZfDKKxucna0mCdcpZzrRSsaKRcjX22nN6+c6TvKsjPH45PfcNNqW7CvHnHs9HB67lPrla6SWwK2/Mj1rYGdk6ZWfjkKYNVOYWrqhEGrVcDOiz2Qd837lNRV9muddMkZ9gZXAXPp/reMPnXPutVTJyWzX/HHV+Q6du7zu1uMoxOnia+o9r/r1osctX7em0d4aXy116tsgL1NcJmZbZYwr6asFrQjbwTTvAyGEEEIIIeTlYfW0L4AQQgghhJDnkZ//+Vfxb//b38Rbb51lAjVIgFwkhdTiYVmW07IeV+XYY4/pbwD4I8c/lrlzyL7Hvw5jAN8cIh87c1OluzPtPuF14RvyVEt6n6cC4ZUA8o3+6ZhWGtb79HjslNBzLk/RluJMhFQpV7NbgUY7kI8pj6GUWnJsQ2a1ztsVgaWIa1xfMxnakbmLJKwe4zEoU51eXls+61S1GWPg5Qkt9xvAeANvfNaWvTWmZTaOuqZ47HTvvPf5S1+Ol76YzhkaQzt8fs1q3HjejqD13sf+Mq4x6VzyeI2Z9nl1HUZdh76m6TWlt728zwzCujXwzlevydhvml/ZY+rrw9L4tM94AztYOO9gBxuuc20w3Bhw8uYJ9g/22D/Y1w+cEEIIIYQQ8lLAZCwhhBBCCCFX4OxswHvv3cJrr51UCUVNP9B2HVGpGTM2O/5V9z1LXOU6zexzdeUrOfj8G7W+bLywrK1hLX/zJLU+tpb+4c9vR4yfXcBfKDk091LqXSeSRF0sM4/1/OX5pvZSiJbnPzZ9OXde3XYwaTr31z2sIZU71zKbkG0d05PSS5LQ2evquDdMVi64cf7u+TrPT/Me9Z6TQtovuR/NayuO1SnabBwlfY01sdyxMSEVazc2pGMJIYQQQgghLy2UsYQQQgghhFwBYwxWqzAXZ8tTxABbIcLSnxZ4Ji4Xnv3I9qfJF31NV5O0er1Mr4ZlLUPbfspUxzZ7FQKzFLp5eja9jtKxuXC1No2ZkthpW88dO/74IR78Nx9g+717+XXMvKyqdGujTymmMkkl164EbmvZPFenLdun2nrzolYp28bz0BNyWYnh4vEulswzx8TxW2OVfefSwh2xW127Hkc9xkUCtDxPeW29+9G4zqbcLh5T9toqrrF83enHVK635optfzbX5YqjZFWiNaZiG392sLCr8Me5YwkhhBBCCCGUsYQQQgghhFyRJEXKL9p1OdrDacz50Bm/xH9SXGc69pjnqRS6S18Xed+6NHYuc2dKYzsPbEfAqZKxx9yLUoRG52VUl77Imx9ajdESwDPCsXeN3X2H2huP6+B5j6UnTQ9cdzepahrX2znX7P4lfXv3B0V777GUf0uuQb/mZ57HQ6+R7nNaXE95Hl3GuyV3YRDFrZ47lh/jhBBCCCGEvNxQxhJCCCGEEHJFrE1zcba+bS8lWN3WTtX2eR6/0X/S88k+HlV528YtbiVdDz9vWirmYv7wNdXXWErY8jWkt0MKFs15ZCUdu1rZ9Nqd5FWWzOyIttkkY0tQIbVlZV7R6NuTtWV7S0qWMq285sbb9FAitpuGvW6KcbPnYu6wmeuvHrPp34dDx8n6XKninjgvz5elweX1UL4u0Hi9NF4/2fXq12AhYpup6/K1qq63l9aWNGzZp5eOjYlYq85BCCGEEEIIeSlZPe0LIIQQQggh5HlE5FqJ9x6Q9FQpsppfyLfsCL+5f15piduiByQ53T5e9udLD+AHj/a4fzliHD2c8xj3Ds4B+73H6IC9MRitwX5lMQJTHw/rPE7dHsZrWdsQw4e2s10Nkdvq03tZF/KxPG9L2pXnrcYyxVgz4nF2DtSeEJ5j6Vt27rcJJt9vYOCNz48p+hwc50B/Y0z4zOr0MzDTq0/1h8/HR3HdZZ/GOFe6/mJd359sfJP6Zo+v95i1eAXycaS/8ZnQ9sandqQxpL0Us8PJgNWdFcbzEX73bP9AhRBCCCGEEHL9UMYSQgghhBByRXKfM32rj3K+WCgxq0vJmmyc6y2Z+zLQExrHiQ7vvzgxMuv/4utEXkc+E7veA9++t8WPHuwr8ee9iC8Dv7LAAPiVhfcebuewHj1+ZjfCesBaD+cmIavTjJUPPfCCNP2/ZloVB9Zb5yx/z1BsV6nmnhDunK/ZVgrd3nFz7XPoY1ovvSOFbBSL5bFL5GopLKc24wtxulDUHrx+fb7WgOpa5LjmtaBxjsZ42eNCcV4lWgFkUjWK1enHECJutZTV/Yw1SepKiWKfUrHGGAw3BtiNxeXHl9jv9vVjJ4QQQgghhLzQUMYSQgghhBByBK++usE/9U+9h5/7uTsYhjxdqL1eKV1zb9Rqy/c9PZJUXt7/STN3Dr+gT+fIlg9a2Pa4/N0PH+H7d7fwPshU5wH4kGb1PiRfnQe8m1KwzuPhymJ1U/0nnJfrC7IrStnpOO89/ElIxj7cDMA0lqRqd3sH+yf341girrzzWN9Y487X78AM/deCMe1EbNhZ99XJwkzYln/ZMKYtZbsXlZaLyhPPXPO1Sti5ccrXV9HeFLK6e0fIVpIyd/3t69FvfxGMIkRL2YtiX/lYiv7G9x9H9RjLY6EkbUvO6hSrvlajjlciNruWom/808cXIrYpZicpG/fJvLGDgfGG5YoJIYQQQgh5iaGMJYQQQggh5Ahu317jr/7Vd/H666ddqdObW7S1/nwnYq9iKctjvsiSnUvO1e+zRMp6D7hJqo4+bI+TbA3L0P5Hn17i7/74URA2HWlYXc5gsRpsEm9awKKQsSJoXVienwY5Cxf2eeeBvYf/8FEQt87Dj2Hp9g4nr53g9tduh3SiVWVd5XpLRLAq+ZVJV9Uv32ykWVvzj3b6du9bh9nyxL1ztM535LGLS/QeOveB5GvrmkoBelTJ4Na55851ICl79DFz68D84yrPXQj9eG+KfTJOJnZln0rS6rLEmcS1gHEmzjMrpYq99Qdff4QQQgghhJAXE8pYQgghhBBCjsJgGNpzbsp8sYDpSthng0Pp1yXp2McTm9fT/3i8T3+989b72+O02I4O/5e/8zF+cn8H2JT+TP7Uw3vgRxcjVmerw0/DdIwOAGsZW26XKdlSyurt2CYp2jFsu9HBjx6f/L1PpseqJO4YhNIbf/4NnH7ptEqtdud0NWqfbi+PK+kkYjOp1UvBLknQlmP09nfaDsrbRr+mfAw76vZeyrXc30rHtkRmb9m+6OY1SUq1e+1Fn3jtrXNretdcXhNQly+WfdMxVXlhdS3h/Zhfj/FJqEbRKsfD5HPEqmRs2RaXOhWr5o5d+qMBQgghhBBCyIsFZSwhhBBCCCFHEhJPeVtLzrVTUE/y2/ieRF0iV1vHYGa8p8HTOq+6AnUJu9HjfOtS6d/R49F2xD/46BwffHYJu7JJzBTzqBpjYNe2Mb46gfguEU+tbf0H1G0O6by63QRZ5J2PST5vgpS11gIOuPjkIkvM+jGkZmGAV3/+VYy3xpj+i4/RAHZtMWyG+DBKYVmK1FLoZvvKMTpytj70gFg9tv+xSd2SIsEZmhakSEshO1OuuMnSBG05v6qWv6YjP5eer9FWnm92nJ6slnG06NVj6+tWojYbc07cFyL3UN9Df/q9Et+XhBBCCCGEkJcCylhCCCGEEEKOIM0Fq1OWwW7F7+wN8GSk6zFS9TrSr9LvKrSOO2as6xSvh8fqpWFj6LSx83e//wD/+d/5JCTeLIBJ3HwyAuub69AOxHTdwacDStDpJCx8SO6pazEweTK26NdMxqJIyOqSxTo9K0lZN/WRMsbOw46hTPLHf/djmJXJRK0fPcbtiNf/kdfx9l9+uy43rKVrmUSVNiVqMwG7VLzOsGiu2AUi9ljRW/VTcjVs+rzfIZnZSY8unjtW2so5U/VxByRulKCF3NTS9mBatxoUWao1JlK9Khus2oH5ayzH1dcp65KIjUnZ1o8DlJSV13QsTzwlYOW6s4SsJGIHE37sYD3Wr60x3Bxw+fEl3CWNLCGEEEIIIS8LlLGEEEIIIYQcSUw4KZbMJ7qMq6ZYDx1zlXGvytKbcZ2id6Z3s7uf2Z83OO/x0/s7bPcOzgV55pzHD+9t8cHdy6oMqTEGdmWToFpQpzoro6qeKu99KqFaCleo9J/0wySK3HSMS2PBI0jjMklbP+RcXpUJP2ewe7QDHOD2Ls4z6/dBxl58coHLzy5j/5M7J7CbOgU8y5x8nbufpfg+dOsf5y1x1WMXplWfGJ3zz6Zj545ryN7eMXN9F81l25HMrWs1Pk8S63LJWelkGbc4T3Z8Q9B2xX0jDWuMgcH0uQCkH2oQQgghhBBCXgooYwkhhBBCCLkyOhF7TNI09ZNEZu6XdJ/WuDpi9jhf6j8JQduTKT3b19rurT/+teg5Y733lYjNqgRP6w8vR/xv/8YP8YPPLkNpYRPE63b0WN9YZ2k4AEnC6mRn45J0Cjb2LZK4UbBOCddsLk6VghUhK+3e+ly+yqW4JG69nZZFIjaKqEZSVtKyZgj7zRDGs3sbkrMnFvc/uI+HP3oYShoDeP9//D7uvH8nPfDefVL7l6Zdl0rXqjRyNkTjeZrrt+B8B5lLrs6kY4+aOzbrckSJ407y9iiukIDtpnQPrOukbJbYLe+LyS9IlzPWwlVeK+WYrTljY3+L+F7TZbu98Sk5r+aR/cJ+F0MIIYQQQgh5JqCMJYQQQggh5DG4aiK2lLC1kL3yFWFe5M71v45zf5EsFb9Tq+/vK5OyP/jsEh/d38IjyNiPH+1xd+tgp9tl7JR2G0wmcADM386sm6mkkUgpYybRWm5LiWIkmavLucb0bHkRkyjSAlbEnrGmkr9aChuYkLAtw61yCgdgCNsWQcruL/bw+yBxH334KLs/3nusb65x88s361LGM/eru162HfFyXix9e9f0OBwrZNHpe8R5rjxO73pmxPHivuj0PzTeNdMqG10J26K/TtfGRLZ6HWali3UbIYQQQggh5KWCMpYQQgghhJAr4VW60sNPEmvZF+1zadcnSe8cOnJ21XGfxP6lCVl/RJ92vzT3b+Cv//6n+K9//1PYwQIWGD2wurmq5oUEUCc5e7exTMW2LqlYxjk4PfJkrC/66j8gCNLeUgtgn/a1koKxVKudUrXGh9StS/u8D23eefghLM1o4vqPf/vHgAfczsGPHvvzPV79+Vfxi//mL8KsO6lU1TYnTGfn4p0TtnPrvfMs6Hc0x4jG60isHkNxjkw++k6/jnDN5pLtpXRVvyw1C18f05C43dRw8QOILCErT6tKusa/RvpWzxkbE7LIj4uJWDOlz9Ucstl7lBBCCCGEEPLSQBlLCCGEEELIFZBkq6yn9lp4lv3yssZLJeyhfnOi1Szot+QcvWOO3b9Uml5lf9kvLFsJZl2y+Kf3d/jdD+7DTf3+5O4WozFwIloHwIqA7SRh56RhNj+lVxKxk4iFQVYyOOuDThJ2OkaXKZbUa7XU6VhJy5ppHKuuS5Kx+jw9CdyTn3skgYxwTdv7W3z0tz8K1+w87nztDm69d+uwFP2iUoVPIbx4dOL18ID91OpVZe7jpmyXnHdG7vbKMGfHFv1Kibw4haylbO+jukjCyjIKWNTJ2OrzgxBCCCGEEPLCQxlLCCGEEELIkSQZEBKxbTeQ0rL5sfV2KlG8RJxepU99bYvim4v6zLFExLbioceM01s2jvA+E7C6/7c/Osf/5m/8CM4AdjCANRhOhpAIFUGJQqqgaJuj97A7ktMjyVYtWeM6TEi0qoRrbC/bZBz9J2WHdZvg0L+tpr9PUo3eevjRh9SsS8sow6zB9vMtvvNffAd+5zFuR/zcv/hzQcYeQ+O2t4R48/k5woU9sVRsOkFfBs691Y5Jyl71HEv7ZN3n57VdfMzS69FCtGxvyWi1Hn/o0LjeLKVbyFs9B20pXrOErJ3mjFWC9mmIfkIIIYQQQsjThTKWEEIIIYSQK6Clnki+Rq9mejYdP5WwnMRt7oy0MF0qXHvHXEXYSp+rcqyIvcrxC6/E1/f/0XbE/+1bn+Du+QhY4Iefb2HWFtYEWZgJFKBOtwkLb2E8VsmzmHwVETuXhm3NGSvp1zIpq/qkyyhKvtqGpJXrs8W2QShLbEKatitqy5LIRWpQ5pM1PojkAQOccYABPv+Hn2O8HOH3HmYw+PJf+TJOXjtZdo9fIK49Hbv8xI/3dn8SzFxTt2Qy8nvYLIlcSmxZ6r/GtZRSNit5rCWstDeSsjAALLB+ZQ27sdjd28GPz9qNJ4QQQgghhFw3lLGEEEIIIYQcQS5hJSUrQjXvUxw5tfWStIvOjmQSWsslxx5quw6uImJ7CdmrS9ky/eo84J2Hcx6Ptg7/5d/7FD/8/BJ2bWGMgVlbGCVO9LywTRErtG5ruU9LWOSCNexO8igOI4IVqU8lZXVqVvooUSXzvGZLKKGrJa0uYazKE3flays1e+geKSnr4GCNhbEG9753D5//0ecYL0bYtcUbv/wGTl49mR/zSfE05O91pE9l36FSvkdd1hHlh4HqtR/nOD5mrN741+ksyx9UZMHXYh7YVgq3FK96TJWSzX7MIXPGGmB9ew17YrF/sKeMJYQQQggh5CWAMpYQQgghhJAjSWnYVKZY5oFN88HmbVKyuBS2tbgtzdaxwnQuEdsTsmi0H8uxKdcl+5dK2UPLcI//y7/7CX7vg/swg8F29Li7dxhOBpjBBBlrixSsSsnFEqQl5e0t26RdS1gZy9fJV5GrkHSsbMtxSFLWu1zgalkrf94maRvnhZ1ka0zVytyxeunVXLIuiVo4xDZMctt7Hx+Ht1NpYlm6qWTx1Ccup2v3NvQVWSWP5Xv/5fdw450beP9feB/rm+v+PX8M5ub4fWE4VvIeELmZ6Mf1Sd/qtyWtfWUbEEsJHxzP5I/tkBiWvjF1rqRrdmz5YwEtaqdSxsaY/PNkSsdK+p4QQgghhBDy4kMZSwghhBBCyNFo2YpKSGjBqnclOdv6Bj4YA+m/ZBrS5dd6SMhKOzr7lpzjmH1PQtzWjM7jcufgPeC8x//vhw/x//4Hn4ckrDUwg4Fd2eacjpl8LW5Jd47YqbknqLQ0Xdzm0/my0quxxHXRrtd16WIRaEVpYp2SzVB9YDA/j2wrIauElIOD8QbOpyUMYL0NZYo9YAebhK7z+OwPP8P5R+d47zffC7IcBmZlMGyG5r0lzyHXnXZdek5ZbYjnLOnaOtw0RKzaV52nIWdlfdFc04QQQgghhJAXAspYQgghhBBCjsLDOcBJItAniVqmXFtli8u0rKRrwz6vvqDvJWQPLVGs97aByiZk+x6XJWnXcvvQ+lxbvfzbf3wf/9nf+CHMysKuLX58f4vV6QpmmOTrJPlglXw1HalSrLckqpw+O17d+tYcsZKOlRRslpaFSsYqkZolYIuHHtOKeh3I5pPV+yQBC484V2wsVywljV2ektWJ2CopqxOxLh83vkwtAAc468LYg4cbXSjXahHLtu7P9/iD/+MfwBiDcTfijT/7Br7+W1+vngPynKEkbJSbS9Owcx9PKsku78ODSVglTaMsLX9UoF+7uq/8wGH6EUe2rfqVQhZmeo/bmcdCCCGEEEIIeaGgjCWEEEIIIeSKtEsN+yhZdR9Z18u2SG2e6cD+Vr9DQvZJchURe31s9w6fPtrhTz65wO//6CHM2qZyxCuTkrBSFreUsFrQ5Aa2ItuvxasIppbAneSqbs+SsT5P50Wpajr9ZR7ZouRxlPu+TsbGEqxIZYzhwvV656NsysoVyzFa8MrjlsfVuCdyTjOYKG8BRCkOTPPITvfMw8OvQnnjBz94ALd3GC9HnH7pFOcfncehN69ssDrlf9KSI2lI3ceaz7Z3DjV2mYJlMpYQQgghhJCXC/6XKyGEEEIIIUfgfUjFyl9ItgJBwkri1StJK1JWp2HbIlfGCd/Rt6TqsanYpQlZFO1XYU5ktPYtScguTcrmY3334wv8L/+rP8bnFyPWtzdBwtpcwpaptWpOWCU+9fbcw8sEqjf50yD7lSwNu3KBmUnTMjmrzlfN11m2lSlZlb6W7Uyu6kSsLI1Kxvp6HlmdkJU+3oW0rKx752GtDRLW5vPPmtGE+WRVMjbr4zzGYYQdQynpz/7wM/zO/+p3MF6OGLcjfunf/iW88+vvzDwx5LniUPJ1ps/sXLe9lO2h/b7oqxKuck5Ave8b+6UssXzuxB86SDshhBBCCCHkpYAylhBCCCGEkCPJZSsQRGyZkG0lYbWczaWsMalcsTHpmBSeump6dmlCtjQPx/AkROzx59ruHb77yTn+8CeP8JMHO2wdYhJWC1hjG2VEp+2wMP3bMCN2orj1StCo2y2CtUy5hm4NKQvVR503k7rqHCJX051KqVddnjj2VUJW949LmwtYeATRCiVqbUreGmdCmWFZN8hKscbr8Uh/8hh9/liMN8AIWGfh4GBXFm7vsD/fBxl7OeL+H9/H6eunuP2V21jdOP4/bWfny/2iRdk1hjJfOpZI3Os+R/n6mNvWKdlSxFLIEkIIIYQQ8lJAGUsIIYQQQsiRSDrWe0nH6pRrKlOs253zk2Q1qp8+LqURp7NMy17ydS4VW4rVXiJ2Tso+Lr1xlorYpUnZtPz00Q7/yf/9A3x4f4f9YGFXoRSuCNcsEQvUidhCmnRp+Ttt3EUmSl9fyFVdOjgekvrnr4P6oRvfmGezERYuk7HxGCVCRcjq9ihpi6UWpTohK2WVvfVZAjbOJzuGfdaFhKwzLh3jwj45xlkXjjUAhjCWHS1GGxKybuVgBgO7tvjef/U9fPD//AC/8j//Fbz2C6/1n6/niKWlcheX1H3eJW9DtM6mYK/rnNmm+oFCdTH5evnjDnkft+aOZZliQgghhBBCXh4oYwkhhBBCCDmSWsCKXJXUq07FShlj00zKCrp/Py7VE7FLonytPo+Thp07z5L2x0/Heu/x9374EB/eu4SxBncv9ri7ddh6DztYwAaZF2VsqyxxUXI0svSWxASzSqTqOWFFYGqRo9OfphZrWeJVXU8UUOpYfb6stLFO2qokLIBU9lgSrXIvyjlj9VJOZlEnZKcUcBTQDikRW8jfeD7Xls4yhh1suk4DWG9jIljuw3g5wu0cPv47H2N7d4s3/uwbT3YO2SeVmF36llk63JMUlS8R8bW98Hb25p2NKfJyP10sIYQQQgghLw2UsYQQQgghhByBTrqmOWNb6dhS1rb2SYI2GDQpUyxCNniqpcK1l5TFzDaK9ta+JczZita+Q2K21V738QD+87/zU/y//v5nGDYDMJiYmhQJK2nYKhGrJWCRjG2Wrm1enW8n99TxWsDGOSajwVX9HydIPLfstZX7Dnl+6eNSm6RbsxSuaRwHdYydkrLGwxqbUrBj2C9tkpoVOQwAfvBxDDe4sNw7fOc//w5OXj3Br/0vfm25jF36doISyEuOewwWp13TAe31x7mGF1Xktp6zVhGBq4zZSNLqFGzsU/wghDKWEEIIIYSQlwfKWEIIIYQQQo5ERGyekNViti5TnLZN0WaifE1LwBhJf5ppvYhaduVryzCUBmnOQFynjDlWxC6Xsr/7/fv41g8f4HufXcKsLbDK5WtMxNqGhC1LiyYrehRRoippVz4dsaSqdr+mIV87T0WcexZ58hVAPnfsdN4yJStyLUvCSpnkMi3r1PiNP++mcsLlfLMyrjcpFVtKYKe29T0qnotyabxJ1zuGx+vggrT1qrzx3uFP/h9/gtXZCuPliNtfvY0v/5Uvp1TwoZKwByRrd37Zx+WYt9vjvDUPHdvZXwriLN39RfBFuuFDT29nf0yfFxcbk7AdEWyswclrJxgvRmzvbr/Yx0oIIYQQQgj5QqGMJYQQQggh5Eh0GjalY1viNckwLWHDdv4nbUAqa5wnY/X6nJDtXnWjz+PEwg6d69i2ZVJW7tHf/uA+/k///Yewaxv+yrLEdpIkU7lcLWG1nI0cewsaty4rO6xud1PizYk/XY5Yp2ZNvj+Ts0YJ0en6ooRU8nZWytqp3ZlczopIFcmqBW1xzti/lLGm2HbFchLlDi5bipAW4WXDExvmpwXg9g6DH+D3Hn/8X/0xxu2I7b0tvvybX8a7/9i7MMOsYb3aS/+60rFer/ruvkPHHtWnfF4eQwBee4r2SctI/TuVx+ijS3NXx+plua9IxJrBYPOlDcbzEbv7u/BjA0IIIYQQQsgLCWUsIYQQQgghR+C9xziGP52QFSnrnIed0oPOYRK2uah1DjBGtwUDkM8t66eEbC/dWl0Z6lRsz4T1pGxr3xLmJMKx6dhee1j/1o8e4K//waf4hx+fY9gMMENIw5pBJWJbElbEZiFMsrYrUM4DCyCbz1Wfa+k4VYpTSzud8lRCLUrR6WnX4jWmQ0Xs+vzYKFZ9nf6Mc13qNKxaltcn885mSdjWUp6TSWzJn4VNSxvObVxK73oTkoZxnlo77RvC+88M4THe/5P7+N3/9Hfx9q+9jS//5pfze2n05kwJ4mP6HsuxInau/5PmquJ3Qd+rCN0rS+DruG2lrC1fA3NCVtrV55Gx6bOKEEIIIYQQ8uJCGUsIIYQQQsiR6Hlj5+aFDX191pbmhc37i4QVSavPJebHxDRWLxW7ZF22gbYxuE7RcxUR2z7GOWDnHP740wv89T/4NEhY+dPliU2+bEnYroBdEi4u+uoyv7HPFSRdVSpZvwamcsQA8qRrbERelrjc9ml8OSaWT57OHZOwCKlT4021hEctW1VJYhHFHj5J0qJMcnaMR0y4VpJz2h+fVyWaJe0q996ugsAd1kM4v/O4/PQSH/w3H+Dk1RO8/Wtvp/T0sTwJIfs4adTWwYfkpv+CBG7r1HPitBT0zypXle768wgmvTdQvFevJWZNCCGEEEIIeZahjCWEEEIIIeRIxtE1k7Hpz8A5D2MQU7BpGdqDdDUxSQtI2WNM5YllX1hvy9VjhSyQm4XHTcSWHJOQnduu17/98Tn+s9/+EX7yYAe7sSEBa/M5YpsyFuimYoWD84nGK+k/vqvOOztzsiROJdGq9mXLVlsj+dptLx3/3F+ZDCxLEst+JW0lWVv1QRCqOvGaXaMLZYm983Hp4GBtSM3CJJnrBx9E1yoM4lYOsMCH/98P8fkffY5v/MvfwNu/9nZ+DXKfzQHB2hCy4XYVzwvQf/6bjnRerHbbriovfbl5YKAl13Jd51v6GK96TU9R+MqPHcp1nZIlhBBCCCGEvNhQxhJCCCGEEHIEIkyDdEUj+Zqvi2RNS5P1SwnFMhmr29I6IGVqryJkW9u6XTjWDhwjYVttdR/ngbuPdthP8vqHdy/xd370EDvvYYckY2GRkrFAmiu2k4oVlgpYjT5Gz+taPYRrkiutksGt/bq9SsA2tg+t67FimeLyOSrvqZ3EqvO5fLLTGGqu2vi8WMA7n56raa7fKHJV6jbKW3mefVqXRK8dLDw8/Cp0HjYDLj69wMMfPsTbf/ltvPqNV7G5s4FdX09d2CwlmxoXHdfZcZWL+MLISnL39mHm8S07ydX2PS8UH9mPWyadEEIIIYQQ8nxAGUsIIYQQQsiRhGSsg3MO42imlCxUMhYqGRsMgl7qpCwQJJS1rWRsErEhIdsTsGVkcW4dqK1GKwr4OCyNtfW3H13u8b/+69/DH39ygdWNFbbOw1kDa0woN1umYW0SfIASp6bYVm1XQqRgKXM7qdvHPU/c9EV6c1pGIVi2qflc9fyuzbljtfBc+rcgIWtckYhF49jp+cvSsSYd70K0NvaVhGycO3YMA/khX8Y5hAcDu7L47v/1u/jgv/kAf+5/9ufw+i+9Xt3rRelY1O09QV5ypRRq0e7Dk9sfIqtvPnNu315Wz1Wrb+v6Wv1b/Z4gV55HNhvk8YdYhPpMyhLhhBBCCCGEkBcWylhCCCGEEEKOwHuRrZiSsZKE1X86/brkLx0TzgEAfkrFmtgnT8RWV6ba59bROL7XfvTdOWJfe9t74EefX+An97b44LNL/PDeFqudS/PDlmWJJR05yVGditXt086cyqfWSdPqUg/douiAj7+XWfKwCDNnqVx1QSJW47yyBrH0b9wuErCxP9rpWH0fynRs2UeLUg81Z+yUjo3zY4YYYLZtrAEcopAydpLEUyI2zlUr90SWUx/jJgkvr4EhLY0PEhYO8GuP7f0tLu9e4t5372F1tsKt925hOBmq+39wPthee+O5WcxSEXuV8TqHLRnv2uaZfVK/7Xjc45dK5ychadVHOeeMJYQQQggh5MWHMpYQQgghhJAj8B7Y7x32+zBvbEjJ2mk9nzu2TMbWc8bKX5pj1lpJx5rYP7SF9GwSsiWtNGxPwvbs4nVah6URudyIjN7jP/v//BC//d272A4Gqxsr2NVUlngwKQUrc8OKiJPytaWEVbKwkh4dB1IlUFW/svzvIcGb9S1opipnUpmSjIyPTSUay3RsXJZJVJ/3z1Kzbnp96XaP7p+Mk6VzDVJK1uRj6usw3kRBG/9c4/r1PdVLNZ+sXLsIXCC8HqR8tbxuxt2I3/8//D5OXjnBr/8nv45b792qfrOwWMiis+8YFv52oVkeuPy9QCsZulQ26uOvQ0rKayRrSuO3JO/B+XMP/Zajc95nCkl8y48d1I8VCCGEEEIIIS82lLGEEEIIIYQcSS5eoRKxUElXXyVgncNUjrhMzgbro9uM0XPM+kne5pI1r5Q7Y/Fmk7HXZZbK8Q6119vf+ek5Pvj0Aj+8t8Wj0WNY25iGFblWiVgtWU1bwoZFR57O0bhNlTzJgrfHiZW5RFwrJWu8SpnqdpVk1WP3ts30mpJ7F9tNnYgtr7dKyZpiiZSOBcJzJPPI6kSszBWrk8zeTrJZ30eD5tyxIl4NTJ2MVXPJGmdiqtp6i/3lHrsHO/z0d36K84/O8fo/8nqaQ7b3Fuo9TVd96xwShteYzjw4P213d2aD6/3e5z8SWHYxs2MeTUfs6h8pPLOUv5chhBBCCCGEvNBQxhJCCCGEEHIE3nvsdiN2OzelYk2WjB3HPAFbJ2MNjEnpWAAxEavnjHUu7LTWx3VjPKwN3+KXU5bm3+4fkrBzUvY6WWKV0vZ//fc+xv/5d34Ce7rCcDrArvNEbDZHrFq2JGwlZTXHCBB9q7T87AjZ2FQ/Qf1TaGmkxase2yOVFp6kbJROU0linUCNZa2VmIoJWKNSrzKOpEonGZr10UlYaXP5tUVh6pDKC0u/QqaKsI3JWbmfkoxtLbW4mpYWNl6nlDiWe+RHJazlVg6hj9s5fOt/9y3c+dod/GP/6T+GzSsb9WQgK+EcH9/c03ldb52ZNOlsv0NzxRbi9Srlh7vXos/bkqOHPgKK9Wpe3Kve27nj/IL96Mhc32m/AvpHC4QQQgghhJAXG8pYQgghhBBCjuDRoz1+53c+xpe/fBN/4S+8gfU6L00s878mqSqpVq+SsZKSNVlqNkhZH0sSSwljIE/JluI1n0tW28OlUla3PS5Lk7F1mx8MsLYwKwM72FRqtpSuLRGrRF0zoVomWGcfQS5HZ9OwLel65K1sXU9L0uh+1byxB9rKeWDLfbFdCdLW/ngOEawiO0Wc2iCL4xjWw7hUkriVevUIfaqEbDGHbCZ2leA1dkqP65LVHmnu2CGUM7bewq/CQXZlsXuww3f/i+/izvt38M5vvJNKXR+bkL0OrlnELpGNx15T85xzhx/oV5VGXoqfuT/dQ2Y6Lr1XrfUjrz0KWP2RzXQsIYQQQgghLzyUsYQQQgghhBzBgwd7/Lf/7Y/xzW++il/+5S/FVKwWsvmcsSiWPpsPFiiTsSE5m5ZJ2gYpG8RTSsjmgjbJQW2PDklZ3VbyuJHABRLWiyAzGE6GNEes+oNBlYwFCik7J01bgrZDNv9qkcbM5GtH+B6SvZooZsJGGkqlWmVsLbfi41FiqiVrK/EaXy/pHKVojXKzELjZfKrCjJSNQtao12WROhQJKyWHYx9JWuo0bn3zwvPuQzliN7i4LUsLCwcXRKzxoTyyBYazAdv7W/z+//738favv423f/3tJGOnsZsJ2em5uFaWitjysJ7snJGyrfLDs/PFHpKOZcr0wG8xuvPFFueRa5L+0ufg3LiH9nVv2ReQUNW/j5FtHPd5QQghhBDy/2fv335t2fI8sesXMdfa+9wyszKzuu7VTV/cttsPtrrVkhGNhcAW70gI8QAS4m9ASIh3JASCByR44QUEQljAA8iIB8sYVGoZX7pcRbe7qqjKyqqsvJ7Mc/bZ17XmnDF4iMscMWJEzLnW3jPP7fNpLcWMiBEjRsScJ2XXd//GAL6chLEAAPAIKfVTEh8OabaGbD9NcZpNU5xv27aZPo/51OlYM1TG9uHsWCHbDQHXaVrjWgB7rcrYR5StXXy8P/YHP3wZ/88//TT+8Cev+iB2VwSx+Tqx+XYcdq3iMh4WwC60p4/VKY+bIkR55H0WfeRZWZNm+/m6t1MolYWP4zqwfb5bqYwd2lQrY7NpiRfhbHZ+Wuu1G44NVa9TgNtkoW1WvZqfy7+vKYQdpjeOGALadh7E5uOe6bKQPgthIyLa3RDE9uW30TVdtF0b0UTsbnfRNE3cvH8TL37wIn7/f/b78Wv/4Nfid/7Lv5N9Adn3kL//S/6zOaeaKW6EsFsVsbXzMf/tTKH9uf88K+0uDofX2l0S1m53/KA2a+vIPijIrfS3FtheFOQ22X9H+T9yyKv6AQCAryxhLAAAPEJKsVoR209VPE4zfNp2XT5tcQxVrv2xdgizxmPjdX0gG1P4ulUhOx6PiGiaMZzt2/ceWhl7SUpwLohYTzu6FHHsUvz/Pn4d/5c//Hk/NXEZxJYBbK0yNh9qrUL2IY8zNj0XwK5Uxc4Prd/wkgBnbVri/NwUwI7HsorOWbs8WL3w8yw0SvMK1zFUmrZjgJtPS3waxDyIarIgauyrbaZ1aPO1a/O24/ExgI8mpt9J6oppivuBT++k2fVVsmmX+u1N/952T3fx5uM38af/5z+Npm3it/6LvzX9BrOXfxpH+Z0+JJjd+MofOu3uar+p8tvaCHcvmnJ47fravcbTWb+1Z6vdd3Pcl1S4rgXcK8fLKZ5TuiCs3jqWVtrVCGABAOBrRRgLAACPkFKK/b6L/b6Lw6GLw6GN3S7FzU2Ktu0rZCNi2rZD1V9eFduMFXdTsBhxygf6cLWvkO33a+3H6Y/zv/z60+fY2I/ieKycv8TlacYf//RV/G/+o5/Gj57fVytiF9MSl5/z0LUWwj4w8FgNdotj/WYllL3wvrOpkMtr85Arr3TNvtKxajafzniqOk2ntlO7shovrzZt5pWnizZD8N+krGI1W6c1pTSvlG2HttmUxXn4N4XGQ8gbEbMwdQq18r+IU3/5++36oHV6N8PzN00TXeqmaYrHd9J2w3TFT1LEoQ9jxzD3J//hT+L3/nu/F3/rv/a34rf/jd+efy/5e8+/w0qbS9WmDK40mu9uVcTmv5ut8PCSULjsr3L/xfgvDSdTcb4Y12yK4q13ungV6RQSbwWrlWs3XfDvTRZr92bjKP9bBgAAvn6EsQAA8Ah9ZWw3TU2cV8WOn0/VrrH466ciHs+dqmIj8vZ5JWzEqTI2D217pyC3XEd2PBaz/eGq8qkqT7qVLF6SLCzbHLsUL++P8VfP7uL/8/3ncWyiD2ObPkQbQ9jyrxrE5uHomSB2tt5rzSUhbNnH21S41a4dA8Xx5BioZuciYqpUnR3Lq2CzYDIPEKd+hmcZ206fh37ydWWr0xaPx8bph5usenY831xWITt+t4spjCP1U1SPQW/2/Y8Bb358rMqdxpMFxWXVbNM2/TTGN31Qu3uyi9c/fR2f/dln8Rv/+m/M3mvt+5nec/beL3VR5erW8YfdrH7PyALEMqTdClDf1bi2+i/2H1Qx+5h7zU6l+Xso77UWMD/kftl/r4v/NgEAgK8kYSwAADxC16W4uzvGzU0bt7dt3N52sds1cTj0KdHNTYqI/lhERNt2EdFG0/TbiIjjsQ9QmyZN22YMpsbqsBTZVMT9vfPt+NdfH0OgOV+Tdmgd81RgrUzrbapjLytF++Gzu/gf/7s/iJ+92ke3a6Jtm1lV7LhG7GLa2TyQHfeL6tjZk5wLX6eG633N+sg/1vp+TDBbCWpmVXZjpevY/1itWFTA9l1l4eDYT3b9NM3u2E9aqZLtskA/FeFrGcjWKmTHtmWFbBaeT2Fvyl7aSsVkdXrgsV32fbWpPa19OxwfK2PbaCMdU8RNRNf1a8emNsWu69eOHadIjiaivW3n4znzn8T4fVzkcf9+4ewasdPvIj+X/y7KY5Ugtmy3WDt2bc3V4t6L6tm8Te3dbT1XOebsXtM2q4Kt9ZePoRr6XvK+V8a0FRrXAvDpv6sz/3AEAAD4ahHGAgDAI9zfd/Gzn72Jrov44IObanVs2zbDtMSntWFPVbAxWx82pmrEfBsRcWo7fs7bjIlUH+ie9nvZupyzStmYpjx+u/D1Eqf+jl2KH312H9/7xZv43i/exIt91wdfaxWw+fDG45Htv20QWwauF4S8D66Mzc9fmH3n95gFNykbW4pqZezUPi2DnylMzb/6dHquvKI2ImZVtZuBbBQVsmXbpnK/LDAer5u9j/J7b4bnLX8rcaqaTW3qpy3OKminytlx3eW2v6Zp+3fY7Jp+WuObJppjE+1NG3ef3sVnf/5ZfPBrH8TNBzfroWzxvb21lT4uCQYvmp74sdb6vOzfXtSPPTSUroWol9yzOL46NXQW1lbXwV0JbBfTOpfHAAAAQhgLAACP8sMfvop/+9/+s/gH/+Cvxb/1b/123Ny0cXPTxOHQRdNE3Ny0caqM7eJ4bIYq13kCMFa0tm1fKTvmcG0730aczuV5YNuOQexYGVv/K68bjsR6gvGQcq1z4UN//vmbQ/yP/h9/Gd//5E28ir4Csd21p2rYca3YIUzbrIgtA9R4eLXqbNrbONNn2c8Yil54r+r5WsAzBqz5/bMwcArmxorXscp1DEjHStdiTdla1exUMZuvK9v/XPvjbVpU1T6mQjZfMzavcmy6ISQd14sd24zF42NAlv03sNCd3kO+VmxZGTt+jhSnCtmmr5DNnzki4nv/1+/Fn/87fx7/4L//D+LX/+Gv17+vd1nN+JAQtiz8LNcqzT6vVZ0uqmKjqFpNxX55TdH3rPK0vF/K2qz0NYaY5W+k9ryzY+nU1yyMTvNzi8uLStrV6tzsPZRVuJvr5ebPk58r/61MxLv9HQEAAF9YwlgAAHiErkvx+vUx7u6OcTym6W+sju0rYmOqjJ3v9yFZvmbsWEnbB7Tz6tmui4gY16CNqc0Y5vby/0t//n/xLz+PFZOn/fH6eZb5dtVdef8pRfzxT17FD57dx09f7uP5vovdk91UsTiGq7Pgc6sithK6PqRitQxYN6cnLrtdmbb4UWpVsWNYmodh2deXT2Gdf9XlGrJjQDlNVRynUHW8z1T1mq8rm/2EZm2KIHazQjbmFbKL52iKvvO1a8fjTfEu8mrYSuVrvkbtrJK2jWmq5NSeqmTH4007VMbumn7q4psUh9eH6A5d/OKf/SKatolv/0vfjtsPbxffVfU7vNSZ/7wuCWJXQ8va5637nQk8NxVB5GKK4o17XLSO7ca9tvpePfc21z/0vdaM/23l/x0DAABfecJYAAB4C8djivv7Y9ze9pWx+30XTdNXyEa0cTymGCtjIyJbz7WL47EdKlq7aNsmjscUbRtTpWtEDNW0p89jBey47fsc14mdrxdbr4w9tRmvHXo/86QXlHVWpJRif0zxv/69H8V/+Bcvonm6i/a2na0RO60P256CtnI7jXUtNF0bZn6qCHsXoewDK2Pnuw8IaWcZVbbTnM7la7uW7fJq2dnxPHBdOZZPHVyres3vPVbPVkPXtUB2rIxNTV/xmlfGdjEPTdssJB2rZCOrxu2yMDXFVCG7WEN2DOnG++xi9nzjsTba6HZ91Wzb9evLpi5F27SnauB0Wkf2j/53fxQ3H9zEP/qf/KP41t/+Vr3y+h3maavhXCVQ3JxuN4owvxaWVqo/q31XrqlWwpafV4LL1arcxSOm2djKCtjqGrobYW3/j1tWrhn7y6/feMfVKaHL+1f2a9N3N8P/AwAAvtqEsQAA8Ba6LsV+382qY8cK2XGbV8T2VbMxC067rk/d2jZF1/XH27Y/nlfPRizXjB3XnB2ranunksk+tE1TEBtTmNEMoe4pmE2pXnU6nL34ncyq44YA+dg00TVN7NoihM2qHMcQcBbEjopq2YWtPCPre9rPQtaLQ9jZcJrte24p+4k4hUJNEfSMwxgrXccgLKuw65vPA9itdS/LtWTLsZTVrLX+VgPZPPDKc/7sO56qZrOq3UWVbJx+B3lwlVfEzqpl47Rm7HRsnPq4zYLdYerrfFrlSNH/44BuqJDd9RWycR/R3Xfxg3/vB/HZn38Wv/WPfit2T3eXrUf8AJsVkpUgdnW/FsTW7nMmCF2913hs5fpFVezQdmsa4EUgWt5363921rLrtHHvrM0idK6FrNm41qZjrq0du3Dpv3kBAAC+koSxAADwFrouxf19N1XHHg59levh0Jfp9ZWxMW3bNsXxGBGRV6ee2vSVsRGzssgY2zXZdc10XR/ojlWwy+rYPJQd+8nXmc1D2DF4eEzetCg8Gw50KUVz00T7ZF4Rm4dm4/SxterXzSrWvN3sZeUfl4FvNYQtQ9v8frXw9QoFbXmVXC1IqlW/1kLZxbTElfOzKYzH4CoLX/M+zh2bxjNUso5VsbNq2WI64WjjtLbsOL7mVE07hmrTfncKYPOgNVJ/bpqCuAz74hTCprbvv2mbaKPt+4x+HE1qok3tVMHbPmmj23fxR//bP4qPfuej+Gv/2l+L3dNdNTx9SEB7dnratRA01gPDahCb/0S2pj3Otosq2IeuFXtBgDt7jspYynVaF/2U9yvfTf65FvxuBNrn1oKtjXftORb9AQAAX1vCWAAAeAs/+tGr+L3f+3H8K//Kd+Jv/+1vxuGQom27OB67aJoYpidu43jspy1u2zEoTcNasH2gW4ary7VgoxrCjtvl3zyUPbXr+zsFsUOlYNHn/P71cPbUJq0cj/h//9ln8U9/9Cr+6rP9IoidpiceKxyzW06h5EOD2Dw8zc8X/ZShby2EvSSAfdsqydn6r9PBmL+DFIs1XbdC2anqtQzZs6C1v2UloC0/j90X107PXwSy47TGU/AaWRA7VsQ2WaibHYuIU1Cbvf9pv82C6rHStRv6GwPfcX+43xjOzipixyB4CGUjIppjM6+OTW20N/28yN2+i2a3/T2/k/U/17oog8lLgti1sVVCz817l/3nx1bC3qkytbgmP35RWJkHwpV3UKtYrd5ja/rnWlhbXlMLcMcxFPdOkWbbhSv8Aw4AAOCLTxgLAABv4Wc/exM/+9mb+M533ou//tc/isOhi92uX/+1acbK2HG64nH64loYOw9N+2mKx7vkFbGnwHWcurhp+umMT8HtKcDtpyM+VdOO0xL3od0phF2Gs+O9T+fPmeUXKUWXIv7D7z+Pf+c/+3ReFZtPTxynYHTalpWsay4JYov+8vuNbcpwd606drr2krGcM2awRX+L6YPz8LUWyo7TFxcBbL6/CJBTszkd8hh+nh7tNP3wFKaOfWT3mwWueUBbTos8bsdrs/B1CmrzELbYz38rU/A63mdYfza1ab7W7Pi+i1B22t/1lbFN20yfxzC2vel/u6lLpwrc/DnexmNC0LU2aXm8eq8yiJ0Oz38TayHuVmi82D8zjkUgWvZTC5c3X9r6deO1ZVhchqZ5NXA1lM2C17NDWTvfbJwDAAC+coSxAADwDuz3Xbx5c4gnT9pomiZub7tIKeLmpo2ILg6HJiK6IeRso2m6aJp2CkTzKYMj5tvxr+2zoVkoO27H68cgNyKmdWTnUxbPt7McsjklBI8t+BzH/Xt/9ln8u3/0afzpp3fR3DTL6YmHKYnzKtkpMM1D1TJEnQYbi8+z4DYP7SLm94jlua3+z06JfMm5XCUgHY/n91pd1zUPZfPjcQqqHrK/ViFbm5J4Ol9UtObvcDGF8RCYxviPC4ZgtWlP0xlPx8rK2HK64uF7HdeMnaYrHv/6AvTT2rHt6V3OKmGzEHbcb3ftvDrytv/Q3rZx/+w+fv9//vvx7X/x2/F3/5t/tw9qrxGm1QLY7Hi13dq5B1TEbk5PXLlPHtyuVcXm954FnJUXN1W5bq0bW4a32f1q951No1y5dhEEF/u1ZxxqXtfHtXavDaYyBgCArz5hLAAAvANdl2K/7+JwSHE8dsP6r2mokI2pIraveE3Rdc1UEXva9udTOq0BO+6n1AzVtRERzRB6pkiprzocg9eIsd2yfdNkVX0xhh9NNjVxyj5vVIGuyEOZv/zkLv5ff/osdk93fWVhOT3xSnJZVrWuNFp8Xg1i86B1JXSdVcJeGsIWlaMP0sQy0Cnvkb3/2VTG6TTmRfVrOlWpLvazsY4VrbN7FvurFbJjwFX0v6h+HftsTm2n/svxR3asCHfH+07vrPhup2P5vSp/07TF4+9vDGPHtYqzdW3H3+pUJbtr4nh3jJ/8Bz+J7r6L9N+4QnhWCzqL4+WxrSC2emwtCD0XBm6FnGv3rd2rDDjX7lsGxrUx1ELfsV0RNE9j3lB952l+brpnJbRdDWgjTr/tyvcliAUAgK8HYSwAALwDd3fHePFiH0+f7qJpIm5v20gpxW7XRErttG2asWQvIqKN3S6GytgUTdNOUxPnoej4N6+EHQOwcT+KytgxoD0dr1XErm1z5zLZ6tqxbRO7J7tpitfF9MTtKVArt1NAGqdXVZ0athbEtln7Zn7drI+VEPbi6tsH5q+ltQB3FnZW2ubhTXXd2MiCnwv2L62QvaRqdrHNAs7afh5S1daKjYhTZWxENN3p95Ha4tg4FXE2JXEtzFutjN010UYbXer66YqzaYp3t7vomi66YzdV4o6VtsWLP++SMG4rUI3sN7J2fqW6tBYYrlbJ1trUQs58yt9yrdjhmkVwmurnt9Z7HdvX+pqNrXg/iymHs3uWz5VSWr67Sv+ze2dtqmvGFtct1l2WxQIAwNeCMBYAAN6B168P8emn9/Hhh7fx5Ek7Vcb2VbBpWkM2r4odK1f7ytj+2LhWbF4hGzEGoqdq13FK4byCdiwR7Lpm6jefenhZohgxTwOaWeg7npsfW8pnQ32z7+KT14d4fndcBrBZleIYZD14euLCIrQtrp3ONfPPm0HsY0PYc2FcLXgpvoZZFWjlaysrXadxpzhVvDYxW9N1Cj3zitm4rEI2H9cUyNbWpM23WfvZlMZpPrXxFNKm5bHqu8x/Q8PaxmO163g+nxZ5Olf89raOj7/Xpm2mitn877g/xssfv4yn33oaT771JBtac3GwtlUReu74ufC2/G0s+i0D1ZXwc+1+1Yru2v0ueJb1y8f/3cnutRE+T4fysLUSMm+OZesd5cezv8V6s8X5iwlkAQDgK08YCwAA78A//+fP4nvfex7/5r/52/E3/+Y348mT47BmbDes91oGnUOYNIRKfRVrO6332nUxrRN7WjM2zaYVzteHzachbts+GTutHXtqf7r2NGVxHr7mwedj1o39/b96Gf/Lf/zjeN3Faa3Y3SngWoSvtbBz7b5FSFpOTZwHsYsq24eEsE0xnrUxRDGGWvtRmp87Nz1pk07jrFW+zo7ngWvW9pJ1Y9c+j+3W2uTvLq9sncLU6kPNn2EKadsU0c2PTYF0O7yLPCRus2fomtkasdMY2jQFtpFiqpieKqezCtkmmr4idghUm9Qfa3dtpEjR3XbRNm20hzY++7PP4h//D/5x/M5/6Xfi7/13/970POUavmeda57K3WWV5WoIWwSLD6qILfdr11TOL4LQ8ZraWrF5oFlO/VuGmdl1swrayv70ntaedWhbjrsMffOQtXbPRVVtMd7Fd5BrinPF+AEAgK8mYSwAALwD/XqxXdzdHWO/P8bh0EXbNosK2dPffG3XiP7/KN9188rY/G+seI04VcRG5NWSzXCuGapwx+0YOKbZWrDjGrF5wJsnBQ9dNzaliLtjFz9/dYzYNdHetouK1DKEnZ2rqN5/rXK1DGLz48XnqV2lv2oQWwSJi/PnlJntWNlZhJuz0HHYX1Sxjk3KCtVYBrKzY3kQ1Cz7nq3Zmorr87Axze89VrZO2yimK05FYFuuR1v0N45vFh7nv6NUGVf+G8rHlK8tm7XLp7HO15KdqmOHStiU0qxKNh1TvPnFm3j+l8/j53/48/jgNz6I9//a+++uurEWwlaOrwaxRZvqWqaVbXV63jNjWz1e2T+3VuzmP0644N3WpgSuTte81m/lc3VK5MrnxZTE58ab/e4PLw9xfHV8d78fAADgC0kYCwAA79Dr18d4/nwf7713EymVa8fGtL5rnxO0kVIXTdNOla19VWs7hbFt2/+Na8tGNJW1Yk8Vs/lfWRFbWzu2ti0/bx2LGJ/l9Lm9aSLtmsUUr7MgLLJALAs3q2u8DvuzsURxrhKy5efK4DUPbKf7xfLY6dnX0uL64U1jbl4+R4pF6Dm1S0WoOJyfgvXseG0t2bXK17HvaZrgLCidXZMFreO1teC3vN+sArbYn/rJqmrLY/n3Mu03zVQd23RDFWxZIdtm0x+P1bXjO43hfDTzNWRTnILX1EzryKZdii66aG/b6Jou2q6Nn/0nP4uf/Sc/i3/pv/Uvxd/5r/+d6js4azVD3QgRt0LYMmCNZZh46fS9ZbvF2qu1+5Sh64VrxeZ/i/Vja6FxrV3+DlauW9wva19WzC7e85nx5n3Mqm2beV+z13xM8fIvXsbh9UEYCwAAX3HCWAAAeEdSivjxj19F16X44IOb2O2aOBy6aJpxG3E8NrNK2Yhx+uEYgtgxdF2Go+thav9/8S9D2FP1bF/h1we8zaz/sSLxFOyO7ce+TynB1myaz++O8Z/+6FX885+/iahNRxx9qFauFbuYQjhO7U8PP/9cTmk8C2DLMLXSbxms1qYpPnXRbI/nMfLr0/z4OFXuJZWsq5Wlw7nZurGRhatlv1EJYdP4G8kC1/L7r9xr6qf2vGm5v3ielI0zb5uHssVat7UK2Xzt2PGaWhVs/htKTV8FOwa5+bEmzf9RQepSpEOK1KV3EqSdXYu1zF3Xgti1vsqAtewrrbRLZ8aWYnGfRbtKODo/vf4Cx7GsjX1xjxTz4HjjmWsVxYuwei1IjWJ643IcufL3n7WtVfQCAABfPcJYAAB4h/q1Y1/Eb/zGB/HkSRtPn+6GtWP7/4t8256SuKaJ2O2aLCQdz4//h/42q6KthbCn7VhFm4eq5V9+fKu/rc9rfvTZffyv/oOfxOtjivZ2d6qIbU5/ZTi78JCQcyWI3aqIrVbH5vfNQuNLAtiHTOFcmlXOTQdP95pC2cpapLMq2TzE3ApkZzc/3WM2NXB+jzEQLUPP4cB4bXW64qhMU9xkFbB5KDx2X645m4evxfH8+54FyMV6s2tTEEcTi8rYaY3ZFKcq2d3wzweybZva6NquH/RNnMZQeX/zV76a6q1bC07X2qwFp5WAda2/svK6DDMX68TmbfO1W1fWkJ21zQLU/PpF9WstIK1dF8X9y2fI/1b6GtuvVeDWKmwXVbXFO16zOTUzAADwlSKMBQCAdyylFG/eHOLVq0O8917//8m93/cBzhjGnqpSI5omTdMQ9/tdpDQmamPa1kzX5tMPjwFrPq1xLYitV9b295iHsc00jtFW5vhq38W/9yfP4i8/u499iohhmtdZ8Fr+RWX/EllAVwtip34jO1/5PLt/2Xcsj8+G8BYBbK2fWaA1dl0LZccgMp3azSpfzwWyeYiaV8vmYeoYzkbMr8+O1ypzV6crzp8jD4Xze+dhbTTzCtfsHSzG3JzGXA1sxz6KILZWGZv/Y4FFu/EfFQzTFo8h7XiPj//g4+j2Xfzmf+E34xt//RvZoz8yaFvLbC8NYfNz0+teBrG1NWKrgW0ZoNbGWwSus1A2C0JrVaqL91QLNGuha3mf2rTF5TbmQetsf+OZFqFwGehmz701RfFpbe9YvAcAAOCrTRgLAADvWEoRb94c49WrQ3z00TGaJuLJk3aohG2jbVP068P2C1yeAto0hatjKBtxmjK43J6rkF3+ncLXc9Mdj8Y+11LTl3fH+L/901/ET14e4vbD276KsJn/TX3F+JzNbL8WhC7C01wWrs4C17y/Sypi8/7GkDc7NmtSC2Efm8sWAc10uKyWLcPMPJDNj18QyM6GXQSjeUg0BZprx7Prq1MST0ObV8lO98qPpyyATfNrI2JR0buohq2Er2WFbBmwTuvNdmn6fU7TE6dsDdphWuJIcVpDdlxPdtdMz/Pxf/pxfPxPPo6PfuejWRh7sY1QbjX8LPbXqmH7zUYQO2uelvuVMHPWR3mPsl12LO+vWhVb3q927/JcMY6y3eo9K30tKl4r91rkxrV1ZoWsAABAhTAWAADeseOxiz/8w0/iO995Ev/6v/7r8dFHEbe37RCUHiMistC1jabpouv6gGe3a6Jp2ilwbduI3e4Uskb0oW1EM6usPU1zfOp7+TcGUGM/9UA3356kaiDZdRHtTRvtbTtbUzOvjs2nKF4NWc8Fm1lgWq4Z239slveL4n5l6FoLZ2dB9DK0PTfGhVo4U7abwvUimM1D2XHsKeYVpkMgG03MAtgykJ1V5eUhbmTn80Ase6+LisZqLl1MQVx7B8XzlO96Eazm0zHn39k43iyE3prSePru26Zf4zVO10/TFHenoLbphu04XXG+3UW0uza66CK6IZhtT+N6G9XwNX9/xbGLQ9j8+CMqYqfrKp9Xw9FyfOOxcvrgYqyL6tdxm1enlmOo3aMS8s6qdFOa7ZfjOzdN8ewe5bHC4r8J4S0AAHztCGMBAOAd67qIH/3oVbx8uY9/9V89xJMnbez3u2iaJm5u2mjbLto24nhsomm6qTK2m4KiU4Vsv3/qe6xwHcPV/lifBJUh6/h3CmibIZA9hXNjmz7MPU1PfLrnKbwtw6L9McXdoYvYNdHu2imUaprmFJhGzAPU2n5hdSrgMlwrgtby2nNB7FY17FpAuxjP9HF9zJtTseb9ZMfL8DQPNBcVqeP5YlsNZMcAqtbP2H80y2BwPF6EfLXwdbFmbDkdcVPvfzbGyvc0TadcVOyWQXQeNuf/EGBc4zYitqctbrP31p7ukYeyeQVtNBHdvovj3XH6RwmXWg1gI1ZD2H7zDoLYqPw2K32Xa8Pmny+enjgq150JQqvPVwaY+fVR3D8fVy34rD1fef/8HuUYKsesAwsAANQIYwEA4Eq6LsUnn9xFSimePGmj61K0Q9Az/t//u66dbU/rwvYJUts20XVN7HbNcO1YEdtv2zaibdMUpnZds5imuOvGz3llbFkl26dYywB4DHnnAdP+mOJ//09+Fn/2yV18etfNpieehbKVwHSSh6TLW8wD1jyArWzLfh4UxFaC3LXxbIXI1VOpcs0YbpeBWhHKztaVzc/lwWh+7IIpi8s1X/N+ZmvDNpXjWfvFdVGvip2mIx4rUbOK3CmkzdvEaYrhRSjb9GHoLEAd24zfc3tqkwesY7Aa3endprafljiaOAWrKfoq2bHKO2K+TdH/1lMfyEb07f/0//Sn8Vf//l/F3/vv/L346Hc/qvwQLrCW45Uh31qoeC6Eza9NRfuVwHZRETueS8u2U78rUxjXQtbNtW7LqtRI9eNZlWtZsVoGprWq1tlzluFwvu3Sop/FdV1cZLa2LAAA8JUnjAUAgCs5HlM8f34fNzdN/MqvPI22beJw6GK3a+J47IbK2BTHY/9/yd/t0nRdvz31VVvvNQ9bxzZtm7LwNWaVsWMQPFbGRuQVuHn5ZUzTHvefY7r39Gxdiu9/chd/8vGbaa3YWeBafn4HZuvELvLN4v7ZGC4JYovOqvtroer2oIv9lJ+qrAM7XpO3W6mSXYSf5wLZSt8Xq1xXnZK4di6/tgiF86A34sw9mpg/U7am7BRc522L7Wyt2/wdNafx5CFvHvSPQW/qTm2m+zYRL37wIl7/7HUcXh/OvsrJue+hDGFr15wJYqvX1vqN7NiZELfWd3WK3q22+flasLs2/sXrWPa16GN43rJtudbtIiA9d7+x7zGYPfOF5u+o23fR3XcCWQAA+JoQxgIAwJW8eXOM//g//nn82q+9H9/4xpM4HG6GStcUXbeLlE5TE/dBbIq2bYfjzfS32zXTmrH9GrNjJWxeIZuG7TgV8elY1+XbPNhtpsD2FLaeKmcjIgtgT6li0/Tr4ja3beye7PpKwaGScKyGXawNu7YtP5ea4i9rX4ZleUi2GsRWph/eWse2nF75rZV5bjrdu7Zm65Qt1gLVMUTMA6KVQDa/bjFdcaXKdfE5m9I4r5pdPt7KmrFZkLoIXscx5KFsFpCWlbJ5RezaurJNNKdK2La/11QtO1bEtjGtFZtPOTx+Tm3/jvLK2GaYkrtLfTV46vrxNTf93+y5LjXL/y4LX6e2GwHs6vqwtWNFgLmogs2Cy1qIWW1XWWe2Vrlau35rzdbymjLQTZFm7c5VvZbn86rX6a8b2nUxVchOx/K+z0jR9/Xy+y/j/tl9dPsLS2kBAIAvNWEsAABcSUoRr18f4/nzffz0p6/jO995Gu+/fxO7XRO7XRs3NymapqyI7aLrxqq7mIWlKfVpVjusY3la/zWiD1HT7LrxWLlW7Ph5HONpDdmI+Xq1TbaG7ClpSKn/i+YUwG6FrGOguRp6bhiDuLKPxfTEa/ctA7qyzcb6tBcFsZc+y1pQk4WlU3Vm3j4/f2mFbN5X7drK+TWz6t2NZ9mqkq0FwtPnmO9X17Et75OPuwh5Z+vLlmMoA+EU8wrbPNAvtnnVbNluDHkvWiu28mjV5629gguD2LU1Xt86iF2719S8+FxpsxhLeSxVztWCzuxY9V7FM82C1vI91D7Xzq28y8dMOTxWxgIAAF8PwlgAALiyTz65i3//3/9x/J2/88346KPb6LpxzdgUt7e7aJqI3a4PcsYq2H7bZtWxMa0Z22+7YYrifr9tywrZfv3Yfprivu9+m6b9Mag9fY7oA9xx5PnUxPnxPjhumuinJ25jHsqWIelGRrW1BussFItY9J+fm50vPi8C2zKILY9HcXw+4Ac9x2La3NOJap9jpewicByuWa2QrU1PnAep47XZjWfrt6as2jSKe0S9v6nt0Ge5duy0HacHboq1YdNpOuBZuzFQzUPg8V7DM05B6HjvcX+sri0C0/IvNWk+7fCwHux4blz3ONr+r4nmtGbsWAW+y7644XhEJQhec67ZVpBaa7MWwq5MAfzQILYMOvP71apgx6By7G9WUZufL46V58p1XWtVrVvVr2Xbrf1LKnLHCtnFfVdcNIU0AADwlSaMBQCAK0spYr/v4u7uGK9eHaJt+8rY3a4PMA+HPnjd7fo5Vdt2DJjm/xf8PpRos2rViMiCsylbbGKa1nickrgmr5JN6dR23s8prB3v++fP7uOnrw7xfN+dgs5+KFO/W7YC2KzRPMDMA8vy+iKUnV17aRBb9l0Oca1dbdyp3q5c33U4uNLNeuXqWiC7uT2nGM9sKuNKm3wMm1Wxw3Wz4DWl0/Nl96yNtdp32a7yO8mrVhfnm5gFw7PgtxL+19aOHf/xwdR/E5G6FD////487p/fx3f+3ndi92S3/k7WpHL3fAgbsRLE1gLAlSB2dUrj/LraV1weT/Vj1WmOZ01SdVxrz1JdK7boa9FfbcxrIXCsB7LTrR84RfHsOQSyAADwtSKMBQCAX5K7u2N88sldpJSGStaIrutDz37K4pimLO66ZrZ2bB+u9hWxY4VsHyh1w1qyY2XsqTp2rL4t7zWuIVtWw/bh77wC9jT18VD1FxH/3vc+i//gh6+ifbo7VcbmAdZ08SmMPBfQzmRh6logNjtWXJcHqpdWxF4Swl68fuxqTtvMw7C8bR5IDvuzCtFUnCumHR6fa6oMHdrn1a+L67KQdBzfIiAd+x3GMKuErdwvf9apCrYWHGfPOFbDrq0ZOxtDfjz7fsdrZ8+Qv8/iN5WHqGPAOo5tVhm70mYKZNNwrI3oDl38yf/xT+L9v/Z+/MP/4T+M93/1/dn3cy6Am4XOa8FnvrtSCbsIK8vjZTC7FcRW2iyqZ2vnxmuKsLJ2fhF25uei0ia79+yVrVTKPqQi9txfvoZstc8LPHRKYwAA4MtPGAsAAL8kz5/v43vfex6/8zsfxu1tO00XPE5N3LbNNO3wWJHab9uI6Ib27VQh2wel7bSG61jhevp8CmX76Yr7cYzbfE3avBq2ts11ERFtFpJGzMKuab/mTCY7hbpjiFq0n8Ld7MRseuLyunNBbN7XJUHsAzLlqrGb8fvdCmVTcV2R4SzC0JiHpfnYZ9ML5/0V911cX7nv7Jpmvp1dX7tHOYatZy7HXz5zra/ae8nC+zyonaZHzoLeqV2cqmbLtWOn6Y3biKY7TcHcP8qZQLVss9Fu7dxqCDvs19aGXbuuDGIXwezKsSmIzO61CGUrbWrna4HnIoiN7Hhl+uLNaYqjuN/Kc+Vrv15yj4vXis2/h/KdAAAAXwvCWAAA+CX57LN9fPbZPna7Nr75zSex2/Wp6G7XRNelWRjbdWMY2wexKbVDiNrNKmQjumlN2X4q4bFqNg3bmNaGnYez+Xqxefjap1fLUHYIJfq9qTIwD2TzIPRtwsu1tWLPrRlbhrer0ycX1ZeLcT4mhK2d38pb1kLZsk06jWERckY9eKytHzvrtlJVWwtG87VjZ1WqaRn4XqQWBteeZwxG83FVAt1ZhWzqfxtluJ0H9Yt1a/PK3crvqqyeHY83TTZNcZtOgWycjs/CyYdYC3DLvh4Swkaat18LXlPl2rWK2LzfWihba5NXj+bnyzGujacSZp5dKzbrd6tathYUn12PtsvGd6GLA1wAAOArRRgLAAC/ZD/60at48+YQf/fvfit+7dfej7aNePJkF23bxM1NM0xX3Fe83tyk6Lo2bm7SEN6O0xPXt20b0zTF/Zqx4zTFKbpunJp4HtKW0xVHzIPa0T/7+E3804/fxJ+/OiymcV1MGZy7JJCtha1R+RxZQBrzNtXpideC2Ji3L8d6NoS98JlmahlMFsrOQsgzoeVaFensXL6N4ljWblHNWo61dqy4z1ZF7hgOV9eNjdP6sYvwt9bfEDJPYWn+LEUgHU0WIJbPXQv1h89TwDv+I4BheuIxfJ1C2bY5BbHt8Eypif3Lffzx/+GP4xt/4xvxN/6rfyPa2/b0EBfmcNXAbiuAzc8/IoRdrCGbhaiLa4rzl0xTPKs4Lc4/ZHri2bgqAWl5fS14rQWx5XTFs21XtOuWn88Zr3/zszdx/+l9HF8fz18EAAB8ZQhjAQDgl+zTT+/j00/v41d/9b346KPbePJkFxERNzdtpDROUzwGoX1lbNO0kVIXEe0Qss7/ImKamrjvI2ZTF4/TFJfXna7NQ9lYbCMivv/sPn7vL1/Ezfs30d62fSXgVip5QWA5hnHVa2qfy7B2OlwJaLeUFZfZ580g9pIQ9tw9V8Kb6hS/2ec8jJxdV5muuNp3dm11LdfadU0lIC0rWYvgcHOq4/JzLO+9tlZsre0YjM6eZdyuBPuzUDjvO7t/HsrOrh0qY/Oq2Wla42HcxzfH+NHv/She/+x1/O5/5XejvWnjnM1qyUtD2OxztdI0v89GEHvumjKonfpZmaZ4Mc7yfBmMRraNWAakeVA7jKc29XMZyJbVu4tK3TIQXgmG8zbT+7hAihT7z/bx5qdvLmoPAAB8dQhjAQDgc/LHf/ws/uqvXsXf//vfjW9/+2lERNze5hWxKY7HFMdjO0xj3K8X209j3E5ryzbNaXrjPnAdPzdDCHvaRpz2xwA2Yvw8hq/58ZjOp4hob9ppeuJa4DWbNvgBpirEIQhbTFEc2ba4pgzczk1PXKuSnQWx7zqEXesrLY/NpgC+pIq1Ek4u2k2n1teOnYLJPPRdGePU/lwIW55bq47N75XfI+tvbd3YWUVxXhFbPnv2nU/TFUe21mtlSuKpz6EydjrW9veZpipusirftt9O/53EmaA1t9asFvqdC2CLNpeGsJtryFYCzqmvWhAbK8drAezGGrK1v+m6bj6+sq9FJWy3rIyd3bur9NXN73dpNWx17AAAwNeSMBYAAD4nz57t4+XLQ7x4sY/337+Jp0/HCtkuIvq1ZJumG0LSNna7btjGsCZsiqZpp8A0r4odzatmT/sRMbXLq2L7cKqskM3C2Xyd2LJCde3zhq2K1sVatOP5jb7LMLUavC4vqo+lMqbFqTPh82YQtxKmnl2TdS1Mze819lOppM3HfnFQeEn17NimFtquVcdeeI9Z3+W2bJd/HitXyymNi37H33NeDTsdG8LefO3iPABe/ITHgHYMcUuXhnJj+Ll17dsEsYuuLg9iZ2Ms2i3GUIa1w4nVMWT3mk1PPI6p9g4qIexsPGc+T9MU5/ct2pXB8up/O1vfr0AWAAC+loSxAADwOeq6iJ/85HXc3x8j4qN4773dUBnbV8Pe3LRxe5vi9jbFbtcMFbJNHI9jhey47av0druxInZcP7YpKmHLytjTNiI/dtofE4QupWh2zTyQje1AdSHFVEk4tc1Cs+meefg6faxXxS76WhtD2TbbVitiK308tPK3bL8IcLLgcjbOtFHFGtsh62pgmR9bHfCwye6/WMt1vHet39oY14LYPLSNrGJ2fO7xXURRCTuuGxvzMfZdpdkYIuJUedvE8p0087/a2rG1v7Eqtmn7v9QNv+l2uPdYPT6+n1zl/VfD940gtwxOy8+z31kZwpZhan6sDFfHvjZCyVq7ajCajWtt3dZZAFse75btqtcWAetY2bo4N1TJTpWuKfq25X2Gatjp7yFrxWYhcuoq0ykDAABfC8JYAAD4HKWU4uXLfex2TXznO4eIiHjypJ+qeLcbw8kxHG2jaVJWGRtTxWvEqdI1pYi27ROnlGKxZmzeZ23d2XId2TeHFJ/tj/Fi360/yMMyyvVryqrF2nbWpBKijufWqmIX2XG146t4SDXq1rTCj+p7KxTNw83LBne+gveB93+IRbXr2E9+Pn932T2mytVxyuTacxe/w3Gt2LXAv7xf0zTR3Xfx4gcv4umvPI2n33l6/qG23sFWCFvsLyphh88PnpY438/7GD/XAt/xc+X6RQVtEZ7m1631N+urvC7vM3+GSLNza+8hX1d2Ee5ujKUq6z+lFN2+i+PrY6SDMBYAAL6OhLEAAPA5SiniBz94He+9dxdPnrTxzW8+iYiIp0+76LoUt7dtPHmyi+Mxxc1NF7e3u2jbJm5v5xWxNzftrBK2/CvXjl2rjB3XlT1NTxzxR5/cxf/9L17E8aaN5qY9VRBGVkn4WEU14uxYxDL4albuWat6La9bprDL9rX9yMbzDlRD07KKsgwoi/3VytPyXkXQWFbVrga4KxW7i2PFNYupkWtjWzuW9T0Lose+YyWArX1/lfeSB7jTGrbNaVtWw86mJS6PD2szLypjm4hoh39k8cOX8U/+p/8kfu0f/lr8y//tf/n8fyeV9/qg8LU8lyrXXxjC1sLbsxWx+TUrbbYqYhfHulN/5fEyfK1V0U5ty8rYcb87tR2PT5WxXTaGsao1q4xd+85m0ypn59789E28+P6LSEdhLAAAfB0JYwEA4HPWdSnu77v49NP7OB5TfPjhzTBF8bwyNqU2mqYbpiKOSKmJpmmHitj++FgJO+Umwzqyfeh2qqStVcaW58Z7748pXh1StG2KXS0ofaTZtMPD5zIwXV3Hdbx/LXiNjfD0kqrY8pJ3GMTmfZ6tkH1E9emD1oEt7hXxiGrX8doz4WqtzVhNWq1Q3Qp+a8H0cGB1auK87RisVjPoYgwrv6/Z+eb0LNN7bPqA9vDmEMe74/R8q880Pe560Fc7tlWdejaIXR/E+SA2v2dx32pwnObnamNYTE+cB7ErxzefpXwHayHxsE1Rn/Z4bcrli43hsKpYAAD42hLGAgDAF8DhkOJ733sRH310G0+etPHRR7cREfHee10cDn1l7O1tG123m60dO253u3ZaL3bcHo+ndWP7NWSXlbHtWOG3qIzt06qmiTh2KdqbJppdVpH6kHxyCEVmoWZRfZgfm7WJSrvp8DLMrY5rrIptKseKdotLLwx1N61kMKsVsucym6HNZnVsVh1aVqtWpz+u3iYLObOx1q6fzq+FrimdqlHLwDe/ZqyALe9VjmsIVKsBbuU9RRPL8TX1v6lKNus3n6Z4rITNj0/rII/Xtf19213brx9bC0fPqbRbrYIdPq+tJVubmnfWXx5Opsp1lfOzqYKz/WoFbRlkjk3G6tQ8eC3D2yIcrbbbqHZdW1t2VhGbbWvVtWNV7GrYXNlfPBMAAPC1JYwFAIAviJQi7u+P8ZOfvIk3b45xc9PG8XgzVbl2Xf9ht2uHbZ8+tW1f9dp1zVAdW05THMNas80pGBvWg+2nOU5TCNt1Y2VsHsLFqVQ24rIgcgwIK5rh/xUHTx/H0KsMULNzi2tXQtiVAZxvM97r3PWX2Ag9L1nndQovyyDxGgFPrSJ1Leg9M45FkPrA8VfD4K0q3Mp9qmHtue+v+McB0zTF2bk8hJ1+f2P4HVl17rCO8+aznnkPmwHssL8Wwi6uL4/VgtdyfyuIjWW7qd/y2mIsa1P6LsLZWp95aBqntV5Xn3El6J3Wis3+NkPk2njXnkcQCwAADISxAADwBXJ318Vf/MXL+Pa3n8STJ20cDk+GoDXF06e7SCni5qaLiN0Uwo4Vsn3FbBvH46k6dqyUbZqY1pftK2PTtB2D2fo6simOXYqm7SsCFwFlLdgag9i00aasSIx6+Dodr12/ohzj6lTEv4wgtryuEswsAtkHBK3VytEyuDwTgm4GwmtjeUgIe8m1a+8nP157nq17jNs8LB3H15ymRV6EqsN1U6haC3NrlbRZn9FGNF0z/TczVgZvWf0OaofXAths/7EhbNlutXo2vyZVrhmP18LWc+vFxrJNbergMnCdzmcVsuU9qtW1XVTHMfUzfn6Isb9ynVkAAOBrRxgLAABfQK9eHeIv//JVfOc7x9jvu/jmN5/EBx+Ma8m2kVJMa8SOIexu18Rul2K3a+N47Cto2zai69ppeuLxrzZNcRnKRvTbviL3cSnkbK3MiGoV6+oUw0UlYx6QTsHtWpVrWQW5ds/atWseG8S+AxdNLXwupLxCZe3Z4HWl/UWBbUT0jzxfE3Zt6uQYpwjO989NA12rol0JXcdjeaXsNGXyOJ7s956Gf+gw3uOi97TWZMoczwSwleAzoh7M1oLYavA67uft18LXiGXAGvPjaxWqs22sB7GLa+MUlOZTCU9t86mKy6mIy8rYbv43hqllWL32/czGFhH7F/t4/aPXsX+xX7kQAAD4OhDGAgDAF9DdXRc//embSCnFbjdfy/X2tg9Mb27G6YrbaZri/m8MarshsOqiafp24xqzYzg7n6b4NF1xO06v2vSVsWUg9dhg8qIq1a1Q9UyfqxWttb5XjlX7eFdB7GNC0IeErO94PIvwswz78rZ5RW4M/Q79r62Pm68Pe+mYZm0e8L1M429iVq2ah8N5m/ntTmvGju1r0xfPwu58m5+7RBm+rvVxJoSNWAasixB2bFMEjlvrx+ahcC1wrbYpwtTVytczQewsSC3C5PJ5Z23G4HYj2D0XEj9ESimOr4/x6oevHnYhAADwlSOMBQCAL7Bnz/Zxd9fFj3/8Jp48aePv/J1vxre+9SSOx5thTdld7HZtHA5t3Nw0sdu1cXPTRds2cXPTRts2cTz2wdFul6JtYwhjm1koW5ui+Nkxxe+/2MfzocpwbRrhmllFbBmaFdO7XhKQltde1Dbq4e+56WKv7pKpgt9R5eql96+2iQvHsFZxW7vPyr0vqvzd6nNrPM3p2Ooastnvarbma1EJu2if/Y7LMDev3k5dqob8q9WytcN5eFoLxWsBbHb8MVMUlyFsef5cRew0te+FQWyk6KtXs/tvth+qXqfq19r0xJVz5fHpLy0/T+OpfB+1tWoX4wQAAL72hLEAAPAFdn/fxf19nwa0bcSv/dr7cXPTT0l8e5uGNWGH4CP10xan1MZuFxHRDevKttG2fSLadc0QyPZBwTgN8SmQjWkd2Zf7Lv781TG6myZ2T3eLsKpm7HPVamFscwq2LvGAQPZB47hmVewXSHWa4CsEwNM0vg8NWM+0uXia43P3Wgt31/6BQKVitpyyeDZlchORjikOrw/R3rbR3rbLfreePZW7Dw9hZ6HhY4PYvP9aKJtXn9ZC340gdhG6Zvcpg+C1oLNWhTt+vmTMs78oxla844VU+Zwiun0X6SiMBQAAhLEAAPCl0XURf/zHz+L993fxL/6L34oPPriJ/b6L29s2nj7t4uamjdvbdgpr+8rYmCpkd7uxQnZcOzZmFbLlGrKHY4pm2l9Jt8ogolZRmVfBFtbWi51V4dbOVT5XOq9vH+KXGMRWp/LNx1ELDS/Mes6Fl+X6qxcFteeqYB+iVrlansunQa7dqzn9HvIq29mxteep/EamitehQjY/Pq4HO52r/OOAJpqINuLTP/40/uB/8QfxG//534jf+ke/dfY9ra4LWzlWDV+Hz7UAdu2azRC2CFgXbYsAtdp+JYitnuvm/eXHV4/l68KOIWy+fmxtDdgu6hWx3am/auhbCV/z8DilFPuX+3j2z59Fd7dWVgsAAHydCGMBAOBL5PXrYxwOXXz22T66Lg1TFZ8qXCNiWj82Ylxrdq1Ctg9fT5WxKVI6Vch2XRpKZS8YWBnEPnDq4HfR9kvlmtMQ/xKcrUytX7Qd3r5l4HzR/fPtueOV66dpiGeHs2mKU7MIbw+vD3H//D7uP7ufPcvZ9WA3jq+GsPm5Wji7Fsym9XZrQexiDCmW96yErYv2ZTgbMQs3y34W/W3cZ1YZW9ynVn27OL+h+vsf+zmkOLw8qIwFAAAiQhgLAABfOvt9Gipkb+Jv/a2P4v33b+Ib37iN29tdPH16jNvbXdzcNPHkSRtt28btbTOtITuvjG2mNWTn68f2n/cRl00dvBK+TuvG5ueKdTanY1G0qXlsu63zZSXkY5SXfc3yl0cFs5d1vP0u16pp16pey8vHCtdUuX6lintaD7asim3WpylumiaadqjO3QhRa6qBYK0y8/Rhea4SzpbHqvsrgWw1wMzbrbWpbKvnuvX9vAq21s90vljzdap8XVs7Nq+IPab5GKpfzPJdLcYEAAAwEMYCAMCX0H6fom2P8ezZPvb7Lpom4unTLiJuhsrYfm3Kfj3ZdqiMjWGbh7FjIJuyaYr7StljRF8ZG3H5dL1lMPuQYPQa57muz7nCt1apuji/NbVx3jZb/zU7uFoRu9LJdJ8plM0rZTe62KzELE89JIQtzp8LXsvton0esmb3n1WkbgSxqxWx2b1mFbFFf+X9FtWv+Tu54L61v6ra4eJY6lLsn+1j/3wvkAUAACbCWAAA+JK6u+viz//8Rbz//i6++90n8Y1vPIlf+ZUn8d57N/HkSRtPn+6mdWR3uyZub8e1Y/u1ZMftPJg9Vcju2ybiyW4ReI7Tsc72+1LAPnxK6xWxl1hcG29RtXpttWG9q4DySz6V8apLgtGt9XPLPh7ynppYVsLOTmfnysrb/Dech7Tj+rHDfwcpTvuzSvBYCVzPjT0PGGvXlAFoebwWyqaifdm2FsLmoWa2/84qY8dp1rcqY/Nz3emavPp1vGa2xmy+Lmx+bd42XzN2K4QeP+fvdPg73h/j2R89i8Prw1fzv10AAOBRhLEAAPAlllLEft/FixeHOBz6gPZb37qNDz+8mdaU7bpd7HbNtDbszU2a7a+FsYebdjtAHcLX8fM0ResjNZeuT/tFkr+D/Ni76pvLXDqF9drlzbwqdnPq5Uv6zkLbWd8XBK/9ZiN8LfYfFMLGMmRcDV7H/TJYLatU8z7K4LIIXrcqVKf7boW35ZiGa6rTFRf707F8muOuMraa8v0WbVOkZZ8AAAADYSwAAHzJ7fcpPvlkH8Mqr/Gbv/lefOc7T+Ojj7p48mQXh0MXu10bT54sK2TH9WPzdWSnytgUsbIY7HQ4paFKtlmea04fpv23CWsf6pd2r1ogy+dvq2I2r3otpiauTkm8tobs8A8Qpm3Mt+M0xZuVsaOtAK8MabfC2coasZdUwq7ul6Ftea4MayttLl4rduOaaoVsN++vDFzzStfpWHZ89nmjKnb1aymC5qlSFwAAICOMBQCAr5jnzw9xOKT45JP7uL1t49d+7b14+nQX7713Ezc3zTBtcRs3N13sdqdQtmn6ULbbNfHJB7fxZtdcXOA1Tl2cmnk4+1aVsvUE7GJTIPbL8NAMRmbzOO9i6uaHVLbm98x+07N1YPPz+Ta//oI1Y2vnLglfI4qQNw9Py+sfEsKO+7WwNT9XmaZ4beri1SC2tu1O962tD1t+LgPXMiRdTE+c74/Vsiuq6+Rmz5e6FK9++Cr2L/bR7bvVfgAAgK8nYSwAAHzFvHhxiBcvDhERsds18eRJO5u2+OnTXex2XdzctHFz00TbpmHbh7H3N238VdvEsW3jJlayq1o4pDr0rTy4avKLEOh+EcZwqUf8PhdVsmt9jJW2w1q3+Zqxi8rxzGLt0TVb4Wt5Pg9Pi/NldWx1yuKNNouq17zvypTCF1XEPnC71d/seFevjJ0qWLt632eVIXREpGOK1z9+HffP7i/oAAAA+LoRxgIAwFdY16X48Y9fx9Onu/jud5/Gkyd9GHt7209b3Aey7RTG3ty0cXjS9hfnwVMqtuWxtw1iL73+XYe+WX9jiHbV+31OVtc/Hc9vBcHv2hc5wK1VxFbaTGvKjtWyWWhbm6Y4/40tnHkfZ6/JqzVrAWylavZR0xOP5/PAdiWsXUxhPBSLTlMRd6navloRm1e7dsWxbLriWZt8Pw9m0/p29XvIQtq1qtiU0tn/xgAAgK8vYSwAAHyFpdRPW/zmzTHee6+N/b6Nw6FfS7brdnFzk+LmJsXtbR/GHo8puhiyh7UQMsW0zuZsGuAxi2iv9zzvxOcdsNayta1Kx1+yS0KlvM2XOoR64LTHUwib7zd9kDcLaNPsoojoqyePd8dob9po2uUP8EGV0WU4+MgQtlYJuzh/wbTEU9/p1C6/3+yaMWjNA9dyG8vjW1Wym/tlMJuHuikW67wugukigF08zzFFOmyEuQAAwNeeMBYAAL4GDocUP/3pXbRtRNs28dFHN/Gd7zyN29u+Kvbmpo227deTjfdv6rlCGfhkwcQYSj16jdZKQProNV/fImy9enXsYwKbc9c8tM9UBEyX9FM7VwkAr+4d3acMVd+qr3Iq4/z3MlTRRhPx8R98HM/+9Fn89r/x2/Erf/dXLut8K3Qt9lenJi4D2NqxPJQtw9Xi81YIWwas58LU1YC0rIgdQ9MsXM3Xf51VynYxr47NtmN4mve3+d4rgXYZ+r74ixdx9/O7OLw8rHQGAAB83QljAQDgayCliPv7btpv2ybef/8Q+30fxu527XBsF+1Nu16ll8rdLDB9l6HlpR5yz89jfO/Iahh3vRt+pb2zat6m3B3+UUJarjV7/+w+3vz8Texf7msDWh/bRhC+CGDzcytBYq0Sdmua4tUpi2vtxj5WwtezgWws92v9ldfVKmMXlbor41l9Z/m7K4Pm7Pjx1TH2n1W+UwAAgIEwFgAAvoZevuynLu6LQJtomj6g/et//YN4f9dGpIi2OYWsZbBTTk+colJR+g5VK1bPXhSnKWNnw+3DsulYeX6tOjbi8WHuRvb3NlMUb06hWvaXzhxbdj7r/63Xla3df63pL3MN2wstqmmL6tdpzdiUVYk3wztsIpq26acnHqsyt1T+0UPt+Ozrr4Wv+fFLAtlam+Jz2XZaB7YMSiNO679m2+malUA2r4Cdtc0rYsu1YbOq17xCdlortvwrw9q172B6dfVxjvcDAADYIowFAICvoZT6qYuHvYiIaNs+pE3vHeL9Wr6wFgS9owz2wdMdFyHqF9ZDgtjKtVvrgj72vg8aw7k+N8ZVrdy8tO+HZFwP+b0+9h5nNE3Tv8t8zdiV9WM3fxOVgH1tfyvIr4Wwi2vWgtjKGq+LtuVvMw9iyxAz+x1MgWYl4Mz7yfutVsTGmbViiwrZMgBefZf52Ffee6SI45tjHF4fott3AQAAsEUYCwAARERE10X88Iev4+nrLv72oYs2naq+mpRNRTyEoNMUxdcoDBuD1jxwrYSvszFUgtnFurPl2Mv7jbtrlbi1YG3rGbZOr02Jek4RdF2kCOTOtru429MFtWllL+6/FoBt3/h0z4eGrI/8vVbH1sTy91n8ZptmqJBt0rS+7EO/+7PV07XQvvY7KQPZVFy/FsiufF6EqTE/VgtbF9uySrZSETtu8zZlFe1iP/urrS9bf9GV910Gw0Ofr3/8Ol58/8X5KmcAAOBrTxgLAABMUorouiJ2qgR5KaUpoF1Usq4Eo+9sjLE9zfDKRcs2Z647OzVyGcw+IJO5qBo1D9guaDvrtwzZZk2LNit9XdzmIeN6aF9fgpxrmqI4C2LHqYrz30XTDMfWgurBpes1VwPY7PMirF/5jZRVsWWbtZB9c93VC4PY1b9YXl8GsdHF4ty032X9dzG/dvY/ZMXzlu97fMe1MRz7PwAAgHOEsQAAwLpaAJSFlItgdNF8HtQuKlLX7jmEWKnZ7n9x6Ti2so/1AW5X3l66Vu3bBrGV93y2/yvnQJesU7p53fkbXHbNRtD8oPut3Hv1+KXdFlWw+VTF47TbYwXt9N/NWkXvyngWz/iY8LV2LA8+x+srIW31c7FfWzN2qhrN1459SIVsEb5OgWi2HmweuFbXkC3Wit0KnqdtcW72bF2KdEgqYgEAgIsJYwEAgLPyMGKaFnjMIi6crrcaxF7ax0b7c1MVV/uoTVWcnZsdujSQveTWD1if9WzVY3Fss4/5wfqxtNI+Nsb9wDxq7H/a1qbffdvgOX81lf5nUyu/TaKdXTqFrlkgO6uELa+rLDP6oO8qv2YlQN6skr4whC3Pzz6Xa8cWxx89VXEeuhZtZ0FspVo2r4jNj62tE1t7t9Xpnsf7dyn2L/bx5qdv4v6z+zOdAQAA9ISxAADAUh5c1MKkKELKSttZ2HlpUFq79tw418LT8Vy5za57SCAbEY8OZR80Te9GKNqffmBgutHvZsi6emoZEtf6WX2Gt8g/z/W9Nd3sRmfrp4r+yvvmUxRPUxPnv7eYrxk7/uYunaZ6cd+V/xYXY70klC3+m31QCDtel+1PlaIboesscI20WSk7ncuqXstQtqyWnULYcs3YfNwb77H2zGVV7P75Pl58/0UAAABcShgLAACsGoOIJmXVsClOU/8+JFjLQrGUhuvzcDSGcLRp5teM0w3H5dMjL2+9UT07PkNTHKv0/ZBQ9qGB29n92ufaLcpz567Lg6gta6Hm2hjHQ2UYuNJu7fylVcAXP0ft0pUpec/es6m0yYP/PJQdg9syGLzkPsW5akVx9rlWMbsawK7tF8HraghbqYCdhatF6FoGr1vTE0/haq0CNq+QPWbna0Fs5Te0OT1xEcSmlOLw8hAvvv8iDi8Pq18TAABAjTAWAABYKoO3Wkg4BqRDyJSHoymlU4C7kVvOro9KCLs1vjIIu7R9cWy1CndjDA+Zbnh1PKunKuHlmWsvrX68aFyXBpFr40qx7CcqAWHt2jPHVqdu3rj+IVW05TTKi/4q98qrY8d209qxfYP5Xzn+2Di2NtbKf4/V6XWjeO+XhLDjfqrsF+fe1fTE1ZC20rZaEZtdW4a1UwXuBb/j2fvLv+cUkY4pDq8P8erHr6pTTAMAAGwRxgIAAAtp+H+10KXPnSrTAA/XnatYXUxfHFGvSq30n5/P+5nuO/S1mKo4v28Z5DbL62f3Ko+9jTNh5MXrxObHL5wiuOx7a9rW2bqqWbvqb2LrvpUweLFe7Mo7KdcyrQaKZ97noq/yXBZiblbUVsZfm/o6Xzs2oghl4/TfzTSl7wVjL8+tVgpvhK8Ry/ddVrqWxxaVsPl3XwlnV0PYmIeus3O1aYqz0HVWOZsfP1cRe8y+09o72gify6rd45tjPPujZ3F8fRTEAgAAjyKMBQAAlrLgtbofRYB5rq+3GkoWuqaVaYzL+9WGlWI+vfJiqdiNdWrX+nyIS4PYx/Rx7twlrtn3Wl+VcLEaLj+kErkSvm22eei1uWwa4vHzVOk9BrHNvP1miFzcezXcrrVbGfslVdNbQe1iLdkiaN0MYov//diqlB0D6q1wd6sidgq5i/+dWryr2vTE08e0aJMOKe6f3Ud3J4kFAAAeRxgLAADMZcHrYs3Y4XxeATsLZcdzWQI1Bj+zNWbHds3pusm4RmyTVdnmgVc+zqwqcQpax0OV6tjV8HboezbWtXb5OLc8JnB7YNi2ufZoLXyrBVJFILqoSM37rYxnEbqtPdOZoLO6rue5MDK7dlatWfZ17n3lz1t8XguC87B1qngdq2Lz3+zYtskCvq3K2HxcxTOu/T4Wz1Y7Xvs9ZMcX1a7juRTrwWv2W3nQNMXlOrErlbDjdlEhO34+nipkp0rZ2rtae93Fs+V/U7/HynsHAAB4AGEsAACwME1T3O+cjo/hbKU6dVa1mgcbZ0LLRXhbqX4tg9bVgHXtftnx6nTF9YGdP/9YW6Flrc25YOlM0HbxWIsQdjFF8blr8z62xnnmvlttq++jlrGeq6Z9wPc3W0P13HXFPx5oUnNxqNyf2vhNVI49JIStHV+djjiK5873s+uqFaxbQeyZbXlsq//VKtwVq/8IoXbPLuLw8hD7l3thLAAA8FaEsQAAwFKKfn3EIgSZKmSzQGYRkpYhZnZsMRVwFlrVztVC2LyitgyAp2raqXl/g1mFbN5HGeRmYxmPTfd/h9MUX2X62Vq/lWOLMG68x6VBaRnObd17a13YSrg5BXzZ+1+0y7abla7FfVfXQy2vLdtXh376XU3ttv5xQP4TzqbkPWez8jnb31xreCt8za+vfa9F0LkIRos2Z8PYbrhXuVZsvl+phJ2Oj1WxYxVtXhFbew8b/71sVfWmLkV338WzP34W++f7qeIWAADgMYSxAADAUhHKVEPYcQrhrP2ym2K912w6477jyvW1qYjL9tm52rTIq2u/1saWh2f5PWvtyjFc4iEhbHbsoiC21lctZCvvVTuWj28t6Fwb+9YzlseqQ98+f7Gt0Hfr/Nqxreca14htmsV3Na4buzbGi9fAXRnb6vuq/XYuCWHLcdXC10p4mbd5dFVsOT1xWYVcnB/bjKHt2u9w9spqVcL5u8qfo0uxf7aPw6tDHO+OglgAAOCtCWMBAICF2fSgXURq5wHJGDaNIW25vuuskrYSXq4FpmOf0/kxiC1C18WUyBGztuV2bf3YqfJ2LRzOpjbOBnlR2Js/U+Xg5rGLg9hzwetaILd1n61wrwwCy3tnf9XwL5/aNrK+avdd3CZVn6MMDGeBciW0rAXO1fVSY/lci/CvNiVxtm5sXjk7hrZjAFnz0N/K1jqyq+Fr7djKd/bOwtihEnixVmy+X1kztjw+VcWO1bN5Rey5/14q72Xx/XZ9ny++/yLefPym/u4BAAAeSBgLAAAs5cFaRD3oGAKn/Nhi2uK8r2n3FICO7RfTEW+NK4r7jkFtHgZX+tsKZPsuV6pkIxaB8tn1U8+Nf+P4RWvE5sfL0DHv58x1+bGz7fP9Wh8r11YDw7Vx1K5fWfu0/F1V+47Kc+XhYszbzULitb7X1oEdzk1B7NhmDPRr0ySfU3uOjd9GLah/q4rY/PwF4WtEFrBGWk5HXASvZSA7W7e1CGVn0xNnVbFb39XZitg8hE0p7j+5j/tn93F4dRDEAgAA74wwFgAAWBiDkepasWPYmbJAdagAnLZZ+0XYGVkfp9LTk6KyNV8HNr+mul5sPl1xJXxdC2RnfdcC2DKYechUxWfCxtPHSqiUn69VxI7X5ccqYVvt+rPVhGVold9rZTxbIXW1Krb2vLW+Ks+wFrIunqkSNi7GlV9XfF67dqp6bYZK8bECtjZFcf5bS8W7WxnX1vnN61e+27NTF+dtys+1NWTHNl3WVyq247lKRexD1oydhbFdmge+W7/h2rNmzzvefxzD65+9jpd/8XLtWwAAAHgUYSwAADA3BhVdzAKLPMCYwtFIi5B1tb9agDkEKuMUrmOolYe7s6A177+ojp2FqGllGzEPZIt+qsHxyrjfyiJLWwkmx48rQeziva9sF8FUEcytrhVbCTZXx1MEXNXfROXYOJZFSFsei5V+xkAta7OYTjh/znK8tX7LMZbhZ/nbink4u/gtRsRHv/1RfPDrH8R7331vdZri1fvVxpc/U2Xsa0Hlouo1a1cG07WK2HMh7CKQTbE5PXEeuq6GsV2l3crzzd5f5bsux5m6viL29Y9fx/2z+/p7BwAAeAvCWAAAYCYPx6bK2DGcLAK3qQK2CDxnbZpTJeusr4hFdersWMQ8OM3Wb52mOY6sOvb0AMuq2rVAdvnwi/VpF2N9rEq+Vg3Siv2tcLQW4p6dQrl2ejX7y8K4rdDv3L3W7lkGouW9Ku22qlwXfayEjmshXn6/2Xqi+bk8gC2rYGv/HQze+9X34tv/8rfnz7A2hq1nWhtzrD/j1rqp0/7KOz63buxmCPuQULbsr1hDdmy/FrSvPevsN1r871d0EemYYv98Hy9/oCIWAAC4DmEsAACwMAtBhqlBp6Bp+JtNRTwEs9O2rDCtBIa1UHZc+3U6v1blml+TTm2naWPL9WO3AtkybM2DrWY81Sye4bFWA7XKsdUK1LGfWjBatlubina8phZu1kLIyrHqtLbls9bGtfLsswD0kvcTaR4kXvA+ZuMq3k31+vHzVnVsZWxTs6HqO1If/D1KZeyr58+Fkfmx7JmrVbBj28q5dxXClqFrdVrisV3lNz97DWshcyzHm7oU+8/28fzPnvdrxAIAAFyJMBYAAFiYhSmxHqpGxCk4za6drTFbrL06Bq4RMU0/XF3LNYrrslB1FubWpiteFMteUCE7PltlrdhZuPxIDwlhx7GtnX9UEJuPYeueedvadWeeoRqOFuMZ+1uEorU+18Ze+T1uvuOxTVlZmebjmQLe4v4zl1THbjzLJS79vVxcOb32mygDy2ysj14rNgtaZ9dngeosiM2umf0jkLJidu1dbfyjhfx5prZdRHffxeHVId58/OZB3wsAAMBDCWMBAICFdDxVpkUT/eeIU9VpFrrk+2PIugg/0jLInLWvVb6O7aK+duzseMzD3DzwnfopK2SH+06BcxnKjuPKji1Ct82XeOZ8pc3m9LVZaPiY0G22rbRZBFpl+5XArjruKALX2nW1cLJyvDo1bszXiM2vrQWK5TS803iyd7dokwePs689C1+L39L0DwvKgDayoPFC1fCx8r7OTh9d+12kol15rrJfe08XV8d28/21dWLzStiymnbtHdSC2HMVsYdXh/j0n30ax9fHy/47BQAAeAvCWAAAYC4LxGaBa8yPz8LVMrhbW5s1b7cyNfFULRune2xOKbwy7fAU3M4eLQtk164v38Va6Po2IU4tZ9sKYuNM6LY2lpXQbau/2bnyc6ocz++xURU7G0fRbxmUTveqjKe8Z+3aze+mbF+7di3YjJj+QUJqNt770K7sd6u6czGGjeNnv7vKu98KYWdjq30uAtla6FoNYlemJ14NYsvg/Mx3VZtiu3zO2di6iOOrYxxeHGL/fB/pIIkFAACuTxgLAAAspGOK7thF27X9mpdDkDF+LteLjYhToDqsHTuFuEWQk1cPzsKVLCCdgtkxkM0qZ/MgeKp2jexcVuE6Oz9235zGsVYlG7FSKVuO9aKXuXHqIdWP546XgdSs6UoQl7L25ecy0MrvWwvoyvtFVMdW9juburgILBdryBZBYd6uHFMZ6s2mvC2efxH8Fe3z38j4O5wdK5+x0i6l9PmuGVt8F2vh63Su8r4vroQd2wyVwIvAdWvN2GNaPmP5+6r8BsrvtQyOU5eiu+/ik3/6SRxeHgSxAADAL40wFgAAWJjWexxCjbWpiKf9LAgt15CtBXVTmBpZ6DqGufnasWvbrO986uJZWNsU5/Px5FMZV6pkp3cQsayWrTzTQ61WSFZCtrWq0LWK2Or0xGUoWgvvqsNJ83a1pmWAV469cs9qmJg9a21a4c17roSTtWrjxVq4aT72lCpTIK8ZfmO1aYmrY76w27Nr3659fw8MZTdD2CLMLNs8uCK27L8MYsfPa7+Z2vOuPOti7F3E/rN9HF4d4vjmGN2+W/QBAABwLcJYAABgLkV0hy66Y3cKSbrUT8/aNZHaU3CyCGnHwCn7yytiy2BoVrVahqJ5uDpW58Y8+FpdM7YMZCMLZPNqxbUq2Ww8tfCnGtBuvdKt6WnPBbCVEHOz0nHt2Mp10+eVoHIW2o19roWdxfmyXVmZOtvPxlEMehn+leOMU5tawFibunYWIA791KbInaq8x9/gWgA7Hs9+q/kzTKHjGY/+rZTvfOs3ku/n/03WqmDzduV7K7+XtVB2q0K2Nj3xuXdS/O/I4hmKbTqm+OxPP4u7n99V+wcAALgmYSwAADDT7bt48ecv4ul3nsa3/oVvRdPNpySurh+bsrVds2mKx3AqD43KKtUxSB0D1NnasakISYuq2PzYNEVximUgO5wf1/ssg99FlWyl/9xmYHaJtcvzcK1sVwtAa/2kjW0tnCtDzbLt2rFKADbrIg9gV55ls+/x95WdKIPXWXBa66s8v/Hey3OrlcCjMrw/JwslH2Tt+6md3/q91PYrxy4KYSP6EDXSfHtJhWw6hbLl+bNBbPF8lwSx95/cx/75Po6vj4JYAADgcyGMBQAAZo53x/j57/88PvjND+Ibf/Mb0eyWa8bmgcesOrZcUzbiFLJkVaqRYhaUzkKSfO3YIZDNpyLO15FdVNfG6T7l+rKzaYvT/FhEzKtki3Es+n+orRDoXYVttWN5UBVp+7qVtovANg9JywAzO1+9fxbm1SpTy/3FNMIbgd1iquGyKjht9Jf9VQPqsuo1r45t+jbj73TxTvIKzYeGsbX3Wx7Pv8ZLfwsPmZ44v6Z4d2tVsheHsF0xttpzVJ63rKSuTak83uvVj17Fq796tXy3AAAAvyTCWAAAoGqaTnSYSnQKN1MzrSebByCLAHbYThWxlfOzatimWDu2SD2nqYiz6yJiVkE7Bb1lhWyxDu1ibdp8TM0p6FkEs/mzvY0y7Kv1XbYp323Uw7fqd3CmOnJRfVoLPbNjs2mCN6b2XYyn8gy1kLUavtaCw7J6dauf8V1k77MMfmuB49t68o0n8d533oun33761mHsdPhctWh+PA8rx/1rhLFblbEr0xSv/pYf+mzd/DlSl+Luk7u4+/gu9s/29ZcIAADwSyKMBQAAVuVrOkaKxRTEY7VhuXbsVCGYimmKs4BzFrw2WVCbVbZGiu3pistrKtdXA9mI+bTFK33m4c8sHH6L6tjNALbWtgwbx4+V6X83K2JX2izWFq2FmTF/F7MQL4rzlXuthsZF36shaBEozu5bBImzMHk8VguMa/dcefZVF0xVfPvhbXzjP/eNvs/HTFMclXvUvpPs3KPXiy3PZX2drYg9F8YO24tC2Ac+02zq5NSvEXv/7D5efP9FvW8AAIBfImEsAABQN4Ynw1/TDNMFd82ySrY97c+qZCshV0TUpw+OoUo2ZdsxyG2WFbN5iJtiHqxOUw4XFbL97Zb3nYWy2bnZmJs8QXq793ru+EUhbH78IQFcuX8ukMs/l/cpr1sL6vJzWX9lcLp45vL6MpCttC/fQd6+Wvla/C36LcL9aariS6RTODjr74Lrzh1/yLTVi3edt0vzz7Xv6aLvdSWMrVXCnq2CzZ/nzO96Ng30McX+s328+MsXcXh5WHmJAAAAv1zCWAAAYFVeyZZv87B1MQ3xSriVB6d5aJpXx07VspXwtb9JnI6NfTVjqWucrZCNiHmVbNZuWvszinPTy8g+P7Qydiu7KwO2sn1+fitoW3S7ctOV0G6zQrEWjJXj3Lpndi6fGrjWfPZ7K/vNQ7niN1ZOP7wIeivPNRtLNqaHVMWO02yvVskOoWS/zHFzvt+1Zy76nD6uTFm8WiW9Fcjmx4v/fmf/GxAr4WsZypbTFF8SwuZjvWT86fS/U92+i8PLQ7z5yZtKpwAAAJ8PYSwAAFA1VpqlQ4q0G8KmZgg+mnQKmLohWG2L8Gv4m6YwLgKpxfqwq1lgtq5sXhW78jkiVveHnVOVbLO87xQCldWyw7Gh0eOs3avsr2x3LoRN83MProgtgsypbWV/EZTWpgDeqDwtA9VLKmirfQ/nF9Nmb+zX7jdNxd0V94qYfgPTtNuPqI6dKmPzaa8f4tKwMvu8Wk26Emw+JJStvr84vb9qlezWs5TjrjzTVggbKaI79iHsZ3/yWRzvjis3AQAA+HwIYwEAgLoh7BgDranyNSrhXVqpjI1YBCsRcZqmOAu9pumJm6w6tihBzac3ns4PYc1UIZvf68y6s1OVbN4+f/5IpwC5fJbRWpXsRu52adVjxHqYtRrErt145fsog66pTRH41da6nYV4lftOlaZ5OFiOIbJwLYpnLQO4cszl863co7buaVXtHUVW/fpQWZC5WJv43HXTx40wcyN8jZi/s8V7e0wIGzELrVcrYsuA/cxzVp+x8r3nY0spxfHu2P+DkWOKw6tD7J/v51NCAwAAfAEIYwEAgLoUcbw/RrNror1p++rAJiIdhymFh4q4qfK1Vg05rCcbXUTTLiti+6wzWyc2O5dXzV5aFbtVETs7loWyeRBUhr+zdWVLl1bJPuL8ajVhEZDm24vXjM3DuDh9Z4u2K+cW33PUz88C1pU+Llm7dapujaiGhLM2K/vV/lLM/rHB7O8hwevW9MRpWHP5kOZrDj9GcfnZ38jGb2Gr+rkWys7eba0SNnuXq69uNtyVsa+MfzG+rq+GffZHz2L/bB/TtMmCWAAA4AtIGAsAAFR1+y7efPwmnn7raeze2/WBaXcKXsug5lxl7NSurHaNomowRTXgyqtfF+vOlhWyQx9T5WvW5zjlbK0idjZ9cTb2ajXj2+Y+lesXU9/WQrbKtZcEseV3Uq2IjWXb6hTGURlLLVxdu292/RS0jqFtrNwjxeL8ov9y7LVK2EoAWf3djjZ+k2fl9xquXVRwb11+bo3V/Duq/VZW/huc2jwkhL1keuKV31M13C4PrfxuZ2Mc7rV/uY/jm2McXh1MSwwAAHzhCWMBAICq+2f38dN//NP4xt/6Rvz6d3+9Xyd2XCv2GBG7oWHXb6ZK2bY5BTMrlbHjfupSfzyGILVrIrXzati8ovXBVbH5+rCVELh2PL/mdOjyAO0hNsPXYf9Ra4OW7TYqYsvzswAua7dWHVv2nY9rMb6Vv2oV7UqbMhi8pCJ2qlBdW/e0tr7pWvhaTHVdDWrHe3cpumM3q9icBZMPDfnz91u2XQkza9/Dg6cnrr334b/36FbC1nPPU/sNl89R3rfr7/vi+y/izU/enMYAAADwBSaMBQAAVo3rMY4hyBhsNalZhDX5dMWLKtnI2o3VrVnV6ljROpueuGnqla1j8NrE1D4iZhWy+Rq0EbFaJTtel49rFrSN7yEL6R5S2bh4n7VKx/yexf5FoVvt2FYQVwti834qoeJivxxf1k81nK1Vv5bhZ9n/GMTl98jGPgsRIy1Cxeo0vnl/tdBxsFgjtonT1NoXSJGi2TXx5FtP4ub9m3pouFVpe+HxzWdc+Q7KMDbf35oGuhrQRiVM3TIb7kYIm48zRRxeH+L+0/spkD28OghiAQCALw1hLAAAsG1YizG1WSg7VsiOAWlWkdh0Q2g1BljDGo9TZexw3Wxq4Wzd2EkWzpTnL6mQnfaLaYvHMa+tHVsen6lUzT7KRti2NhXxVgg7O74VxNamgF3pdzYlcVktO/axEtZtBXhl9etWu1qlbDWQLf+yZ6hVy569dlT+DjbWhy3tnuziw9/6sK8A31rLtMz2t35exW+1DDMfum5wObXwNPVw7fvMpyW+RCp362OePq5V6HYp7j+5j0//2aeX3RcAAOALRhgLAABsSilFOgxh7K4PVlPTb6Ppw5KpInUIcGpVstPniFlwNE0/nOaB6jS9cGr6qtdxW4S4iwrZ7HgeHi6mKW4qa9UOx8fPeWXtOO5VDwnVijazisu1EDYP2vLjZQhbO1aGqHmb8VgRYlbDu1oQWtxn9XnWQs8yIC3uUwtPt6bZrYasxbnN6YkjHrc+7Pg4xVS+5T8QePCM17NXeUGgmR/LA9ozYWztu128n/K72xx2Zaxb4y3H0KU4vjnG65+8jv3z/WU3BQAA+AISxgIAANtSRHfsojk2U2VstJXAplgzNg9hI52qZKfgdug7r16dTU+c6hWqi/ZlVWxzCnXKqthpP+trrODtBz+/X2rmgdIinC3e0yXvcrrv1rXlGLdC2Lx9KtqXbdcCuayfakVs/jkPTcu24z1q7bKxlSHfIthdC2GjGE825sV+JdBdDRprgexD5UHleK/j6bnfZnrr6RYrVdHTu8mPV34LqyF7lxZtqtXQm4O7/Nzq7zG7b3fs4vDqEC/+/IUpiQEAgC81YSwAALDpzc/fxMf/0cfx0d/4KD76Gx9FatNUERsREbt+k4eyTdOcpiPumlMF7bAu7Dht8RTOdhGpHSpiuyyQHatmszU882MRy0B2bFOem52vhbKDsoI2N4a3FwWv652cPV5dh3MrhI1lwDUL4y4IZTfDupVAdHHdVtiZB7Gx0S6vLN2Y/rhss7V/7l41i3VjLzXet0v9P2JI89/m0PmD+ls7dlG19KWhbJpf96C1YLfGWo4nP5aPYZgO/cVfvojj62NEijjeH98+JAcAAPicCWMBAIBNh5eHePHyRdx+6zY+/N0PT8FJFppGikUl7Hgsr1KdwteI2bTF0zTEcQpsp/0ifB0tpiweKw+L7GacdnixdmwUoWw+rXG2pu10burwXb3Zylg3QtiIlSB2HHPt2FaQ99CK2GJ8i+mAN8Y3bcvP+bHseC2AW10rtri22v9GEJsiq6o9E7Q/JKAdA9nqb2je6UoH549fWh29eHdRfO6y/t7B73vx/W2NbVyHuuunQ7/7+M60xAAAwFeKMBYAALhMF5EOKbq2izbaSMcUqUn9VKwR07ZpmilgSk2Kpm2m0GVWGTt+bvvq2UVlbLusjF1UyKZTcLpaITsGsPmxwVZFbC1026qafYjV9TQrxy6uhB0+n52muGhTBpur64hWwtDVKYS7NDt2SRXsor9LK2S7eZt8f7Uytiv6q2lO6xVvBqPTq+3v07RNPPnmk2h2Tb/WcpNdXPx8zv2eLp3OuvYbWXzntRB2Ldh9G7XfYzaO6V7DOF798FW8+embaf/w6vBuxgEAAPAFIYwFAAAuMlX6dfNQa6pwLUOy4m8MT6dq1rKyNauM7W+Y3TtWKmTLNV7zwHVjndjx/BjUjs+3uHcTs/0pWJoN4S0DtZXjW9WF1Wu2gti00aYM5PJnLT+vBJi1tVynIHY4UU5PvHi28h3nbbO/cix52DftV35/i2raNL/vu9TetP003ONU3vlPZFp2+AFVtnmgeTp4+nhmGuBLwtjFOC9Ve4QyhM3um7oU3X13Cl9fHOL+0/tH3BgAAODLQRgLAABcJB1TdPsumraJtmmjuzlVyHbRH4+IPtAZPzbZ1MNNOp0b1pKdpgQegqumHdaPHSpqx0rDiGUgW64hO34e20bKqm8jlufHY6mZBbLllMRTQBvz49M1D0nzVppWqxK3KgzL45WQ7lwIW11PdiXwnK4p23TZPbPza4F8tW2XZp8X9ykrZMtK2KKPamVsV7TN9iNOU2j3O+vfU83sPQ6/3W7f9RXfkf03MXpAkD/1f9opT/abtcrY8vsuPKrSe3arlbGV4xq/xy7i/rP7+OyPP5vOdfsuAAAAvsqEsQAAwEWOb45x98ldPP32034K1i5FapehWVkl26R5RWxETOHQGISOVanTWq5jFW2xzmxMl2dTEtfWjs3C0ulzimlt2jxwm60pG6fjs4B2PJQHsw8I7NacDWGH/YuC2DwXK/otQ7PVIDaW56ZrzqwdW61izc/lbWN+vPpstSmJy7EXIe9qZWxkAW4U171L429mDHtXQthxv7YWcml1Suva933mWO3+a32etRYMF7/J1PVTD49V9fsX+zi8Plzn/QMAAHwBCWMBAICLvPzLl/Hqh6/iu3//u/HRX/8o2kMbKVJ0u6FCth0Cty5LWZps20ZfSTluxyrZLquMbZqI9hTUjpWxa9Ww+ZTH09qxafl5ClvHtikWFa95VWy+1uyszbtMkGpd5YFW2WbtXFmFWDtWBpnDsdXzxbnNqtnKdutcddvN9zfXiK20qVbGFsema/PjD7T2/U/9Db+XqdozqxDP9yeXFqZeEnyuyX/TtT4fOYZLKrJT11cJP/ujZ/1asGWADgAA8DUgjAUAAC4yTu+ajsPfON3rMDVrGarlFbL537TGbMQswJnC1lSvjJ1Vw5bhbL4u7FgFG/Mq2dn6sc0pbJxNJTuOpzkFkdP6stl4F84FWufCp60QtjxfhLARRag6bKtt14LYvO80H8da1Ww5De7mOGshXP6bGE5sVdeWnxd9royrnLJ47buYTVV8qez++XTIU7X2+DNvst/p7KYPu93svuesrXdc3Hf6b3H1Vtu/yYj5bymlFIeXhzi+Pvbv5Jiiu+siHSSwAADA15MwFgAAeJB0TNEduuiOXV+BeuzXkE3HNJ2PiNMasRGzbdM1p7BzqJBtoonUFtMND+3ytWOrlbERU1VtvlZsRMyqZCPmwdLUJkuVpmA2q3KcgsVFjlZMWbyWadUqCs+0mQVdebCZnatO8bsSwi6C1zxgLatjH1oRW9x3XEv2XNXs4ny3bH/JteM/Alhcm/1jgUjRb7NxRcSD14edyULkcWzdvjtVeG9VxTbF7+fSULasct2aBjn7jdfGPl5z0e8xP7X2Wxrf+zHFqx++itc/er3eCQAAwNeIMBYAAHiQMYxNh9RXxI7Vsrus8rTLwtimr76bAtM2TVMT5yFek4rK2CFsnSpjU70ytjw2fo7IpiaOtAi/ZpWy47ONO01RMVgJ7VKkeQB2Sai31WYrgM3Or62zulkde0EoOws983MrQWxtTFvjXlxf/mX3XJvu+NJpi/N+L62MvVgeQmf7YxCZuux30cSiSjYP+Wdh6VogWwa3kf32pp9r/eKz02pf+i5qv7OuD5/f/PzNInw/vDhc2DEAAMBXnzAWAAB4kO7YRXffRfek66tVj1koO2wjIqIdAs0hmJ0qXLs+mBpDq6katuvbzNpHmkLaaTsGrsV6shFZCLsV0GbBVbUqtnKudv7RVZXVQsRUP1cLwdaO10LY7PgieF0LavO2K0HsoiJ2vE+tXdbHufVjayHqWjg7temy/bG6tivC1/w5H/qlpZXP+fN3MZ/Ce1zDeKy+HivBx997Nn1xLaiNmFe1XlS9XVhb9/hi5Xc7HhuD9y7F4fUhXvz5C1MQAwAAbBDGAgAAD/Lqh69i/3wf3/oXvhXNN5tob9vomi6aYx+MpvZUOZeHUNN2rJbNgteIfj91KaId2mYBWtNklbVZ0DWbnjgqIWwa+k/LaYnLsGormC3P16xVJ1avXeuqbLYSwK4GZWshbLlfCdfK86tVspUpjtcqWWfh3ZkgdgxQz4azXSyC1tl6rbXK2C4LbR+p+h0Of8f7Yxzvj9MU3dP5fHrupvJ7zPbHcL+cYjs/Nn3Hzfwe0285TvdbDfjPP+jp/uM9U4q7j+9i/2I/++10h27+zAAAACwIYwEAgAe5/+Q+9p/t48Pf/jBuPrzpw5ih0nWqjG2ztWHHaYpTmk1FPFsvdgjQZtMRFyHj2rTEW1MVT9MQN6dwaVYpW05HHPO2EbGYynhss8zmHhFKVS6ZBbBlm5UQdrr/WwSx5dqxq0FsOc4i+FwEomWfa+03+rpkeuLaNbVg+V3Jv4du38Xx7tj/A4OIac3Y2RrHQ7i6WL846tXaaxXc5bkp0J0Pbl5le+4fF6z8JvIw++6Tu7j7+K72KgAAANggjAUAAB4uC6B2t7too410k6KLfuriKYjKpmOdQqOxqjVfU3YIVafPeUXtGNC18yHM1pWNU7XhVGmbsntUKhFn+3m/qZkFWVOFYvH8EfOpZB/0+srAteg3369WOF4SwI7H0rLdagg7Xl9eVwlBI+I0JXUliM0rXvN+q9tizdHq9MR55Wxe6VqEr6nr+xv/zlbErk03nb/7tfczjGNaQ7kdfo9DdfgYzk4V4dH/3qbfTTk98dp26/PWsWnolbA2+zw+w6sfvIpu3y3apkhxfH1cvwEAAACrhLEAAMCjdPddHN8co3u/i2gj2q7tw9AxXGvTVP06rgc7bbPq1ylITdlasEVl7Gw77VYqY5t06if7PFa7LiplK5Wx07Sxka3vmd+7DGrfxtrlayFstl+bwviS9WMXbcbPtfA1vyZvU95jY3rhvP+1v7HPzemJy/HHPLgt+5tVx76ttd/gMcVxf4xu3/UhZjv8ZobtFMo2p4C/2fXn838ksJiSOBW/w+x3O72niHkAm+ZVt+m4EUIXv5fUpejuu7h/dh/dfbdyEQAAAI8hjAUAAB4sdSme/fGzuPngJr77978btx/eRnfT9SFPG32lbFZxWq6Lma8dW634q227+TVldW00w9TIkebnU9Z+LfSK07hmAW6kWQg2tpumfX1cYewpDNuqWCz216YvroWrD1ontghsq9MSl4FsWRnbnfpYVMR2K8fz/fH8uB5sNz9XO162GStn8+0sEF7z0Kw2ncby6kev4tVfvYru2M37WfldNE0TT3/1adx842YKaKegtlIVPv5jgOl3cuHvLR1TvPyLl3F8c2E16/BMi6pYAAAA3powFgAAeJQx6On2XaRjOgVkXRaWjWvHjp+HNWObrpkqZ5u2mYWCqxWyg1lFbLGe63Qsv77J+t5amzNrV4ayizHkAW6MzbaTstU1ZWuH87B2JaCtVcaWwerqtnaPvE0lsJ0Ft2v3zP/igs9FAFx7R2OV62xcZz4vnuEh8vdSPGfZprvv4vDq8KDuj2+O0dw28zA24lQ5W05hnJ+75B8BDMH08fXx8jAWAACAqxHGAgAAj5ZSiuObYxxuD9HethGpD1fH0KiNNrpdF23Tn+uafk3ZfArWma3K2LF6sM3Wih0rM9vo751Xy5bXpaJSdlgbdlEtG/3nReDaZGFYpQpyNWytvrhydxnulvtrlbGLALZ2rBKuliHs5rTEReA5tR0rYvNtJSCdrQdbq5Qd99cqY8drxzZdpQI2u3Y6/gDV4LZ4N/m6tOn4uCmQ735xF/ef3D/soodWYKfopykGAADgcyeMBQAAHi11Ke4/vY90THHz/s1p7diumQK0JjVT8NakZlpPdloztkvTNMRjlexYITutJ5utDTtWvE6VruN6sHEKW8vjETFrP4WtYwBbVkNGUemaV83mx2K55uxF720tfD01OH08F8LWjhXVnJvTEtdC2Y0gdtbvcL5WwZsHmGXAW/7V2swC3OL4rK/hHuU0ye9KbT3b490x9p/t4/j6EZWn3alPAAAAvvqEsQAAwKOlfb927JNvPYkn334SN3ET3W5Yd7IZKmPTqTI2NSlSm6I9tn01a8RyO1wbMVS69ulqXxE7horFmrNjRWzTDcHrUB07XZ+KNWPjVAU7W6NzqJaNiFnbMTubKmezY+P4HvbiVg6vBa/l/fJzZeBaHqsFtcXn2fVd0a5SMbsIbC+oiC3Xla1VxJZh61jlOqt67eZ95FNjR1eM+bGy91J+BymluP/FfXz6zz99p6EvAAAAX03CWAAA4O2kiHRM0d130e266J50/fqw7bAubBPTlLGpyypYI04B6BjiDe2qFbLdqf1s3dhi23ebVcOO0xFXzuX7+bHyeO381K5Yt/aiV7YWFtYOp+y+tXB2LYQt91dC2fL8uSC2FuBuBbbl1Mara7tmwewUvq5U1c6mJy4C3a3phteOL95XLfDO7zPeGwAAAM4QxgIAAG8tpRSH14do2ibaJ200TRNd24eybbR9KJti2i7WhI1sf5iyOD/fNM00tXHq0qkSttjOqmEjWyc24mwoOx0bxjeFkk3M2teevfosqy9r+9ws8K2Fr5Hqx4tAcTp2aQhbmfp3K5A9N7XwWkXsovp1Yz3ZWdtxrdgxsM2umU1dfO4dn1NeW4y3O3ZvX3kLAADA14YwFgAAeGvdXRcv/+JlPP3209g93UXTNH1lbNOv5ZnaoSrymPrpiPOwNcu1mnZYU3YMUPNpiiMLXtt5Jex0/bim7FhZmwWx50LZvH3e3yKcHcddazc/eFKcq64ZWsv3zgSwEZUQNhXt10LY8Zri/Gr4WrbZ2G62WalorV03tc2rXvMpi4sK2XMh7CVrtVbfxfA+Dq8O8fIHL2P/Yn+2HwAAAIgQxgIAAO9At+/i9Y9fR+pSvP+b70fTNtHs+kC2a7podkMV6hDKzsLYPJyN7PhYkZpO675Op7shmB1D13aYzjirnh0D3DFkzT+P/UYT83Vks2A2D2xn5yoWIV8RMl9UqTnL/dLiWDWAzY9vhbDj/sb0xWuVsfl15ypjLw1rq5WxXZoHttmYF5WyXeXYpdWqa81SuTt/5tT11d8v/+rl8vcKAAAAK4SxAADAO3P/7D4++cNP4oPf+iA++t2PIpp+muKm7YPZaCKaXRNt085CwtnniOU0xvl0xcN0xHk2uqiSbWMKVKdgtAx4m3Q6noa+8ymHh2PjuMYK3dk487HlA7q0QvPSYHAlfJ36Ko/VQtdy/5IQdqMaNiJmawFP27VANpuCeFYRW6t6LQPavBq2S/N1ZcfgduXdrSrfzXis8nd8c4zP/uSzOLw6CGIBAAB4EGEsAADwznR3Xbz52Zu4/eg2umMXzbGvVk3HLKxrTuHdOA1x0wzTE7d90DZVvMawP4SiYzVsimL92CFQG6tom+40FXG+Nmy5TmweoE7HmtOUw7VpkIsDq20fpHJpNYAdPi/C3JVgdnMK41rYOrYt9qvrxw7XLsLgWhC7skbsLNytfK5dv9gv39XWuz0Tfte+w5RSdIcu3vz8TXT3klgAAAAeRhgLAAC8c92h66sIh/CsaZpoUjMFnSlSRBvRRRdNaqZtk7JpglP0Fa4Rp2B1DErbOFW6ZpWpU5g2HJ8qaaOoqo2YrUc77kfEfN3Y8lxeOTuOc8VUVTs2vWQa3bJJLXgtzxWB7OxeZeg6XlcGrXnbtSmJ8/Yboeu07Sr7keYVrSnmFbPFsVrb/G82trOvdhlur1XFzu55PP1jAgAAAHgoYSwAAPDOdfsu9i/3/bqxN020x7YPVHfpVBnbRD/laxOnStbUV7Wmtg/CZhWu6RS2ThWwcaqETU2awtz8XEQsPo9ttvZrVa9b68ZO1wwurtasnKsFh/nni0LYtf1zoezYfiOIvTiQLbcxv3YxrjIQrVTD1q5/F6Y1YrNOU0pxfHOM45vjO70XAAAAXx/CWAAA4J1787M3cfeLu/jmv/DN+OA3P4imaaLt2r5CtusTyyY10Ua/dmy3Gypj22b6HCn6gDVOFbXRxHw92LJCNhX7Y3XsWAGbrzXbDCHtWAm7sk7trO3YTy2YWzu+Za39LBdN83Yb4etateyla8auTVu8Fb5uVsaWlbARF1XEjmvFblXELkLr2vvcCK9X180d++5SdPddPPvnz2L/fB/d3hTFAAAAPJwwFgAAeOfGqV33z/dx9/Quml/tK1zbrp93OO2GYK7NgtAhzGxSM1XONpGtJZuG/SGEa6JfK7ZJfSVtrRJ2rSL2orVjiyrYPPjbWjv2cS+scp+1YHFlLdlqMLtVKXtJ+BpxPnRdOxfLNvm989C2vH4WuJbVsJGNfeUdLlz43eRjPrw4xOHVIQ6vD4JYAAAAHk0YCwAAXM3Lv3wZr374Kr77r303nn73aTRtE+2ujWgjmrYPaJv2VCEbbUSXuv5cxHxt1hSnqYybZl4hO2qLtWHz9WGHY7W1YmeVs6lSMZtObRdB4NsGsaNKH2vB62wMtYrPtf08gB37eUQIO/VTVL9WQ9luZX+ofp1dW6uITcOardm1D3mf59aKLatix/t99qefxd3Hd1NFLwAAADyGMBYAALieFJGO/XSv3X0X3W1fYdgch1BzqIxNxyLcHLfl1MJtnNaU7fogd7Zm7Lhts2mOm2Kt2TGUrVXHjoFvXgWbmun+KaVYFsWeWUf20ldVW2M2LT/Xpi2+ZGriRfCaVcVO7fM23bzNIpwtr1kJbfMK2LzytBbaVitii8rZ6R1svsyVd1W2Kd9NNo5pPAAAAPAWhLEAAMDVHe+OcXh9iPamnULUZndaC7aL7lQdG3HatsU2xaki9h2uHTtts2ubNIS1TRboZZWzs4C2dC6f3cr4ysCxbFup7twMYM9Vwpafu6yvMgQd133Nq1yHa1YrZCuVsXlFbLku7KJCtotZKHrpOrELReg89VWO7ZgiHTZCXAAAAHgAYSwAAHBdKeLNx2/i8PoQze82cfP+TR/EpqYPZaOJtmmjiy6a1H+OiPn0v3kYWm7zz0VQ27TNqZo1n7Z4rUI2FVMR5+FsWTlbjql45q2K2dXKzo1gsVYRG1EJYCvH8gCyDGHLKtmp6rVL8/Zp/vnsWrJj2FqZnniqfC2nLC6OT+FuZOO80Ga1cMzHn9/37ud3cffpXRxeHS6+FwAAAKwRxgIAAFd39/O72H+2P60be9tGm9ro2i7a1M5CtnF90IiYVbxO675GUeXaRl/NWWafzel4E80sbJ36SfO1YZsmC28jC1RTnNqlmJ9fCWUfEhwuiz0rlZm1sDU/npYBZHUq4vGalRD2XDXsavhaBK3TfYtq2c21Y8eK2LLydu1Vrryjc9MTl886BrFjGPvyBy9XLgYAAICHEcYCAAC/FN2hixfffxG3H97GN/7mN2L3dNcHmMP/X0kTTR/Qpq4PbKON1KV++uKmn8p4qlptUz+NcF4Jm09PPBqnNy7Xny2nLY55SLuowC0+N818vdlZFe/bKEPZcgrkWiBbVn+uVYRWPq+GsNnxS0LYshp2LaC9eHrifA3hMTQ+865Wg9jiGavvpYu4+/guXv3oVew/2y/vBQAAAI8kjAUAAH45UsT9L+6je9PF+7/5fh+I3jZTdes4VfE4TXHq+qB03M6mGY5llWzTNFP4mrp+f9pGmk1HfMk0xdHEVCWbT1EccTqeP9sY0D769dTWnk3Lz4u1ZMuq1/LYWvBaVs3WKmDLytmy6nU8V64le6YSdnZd1v8Y1s7GdUkQu6Z2aVkd3KXoDl3sX+zj9Y9fX9gxAAAAXEYYCwAA/FId3xzj2X/2LJ78ypP45t/+ZqQnp+rE5qYPPZu2OVW8RhaQDpWwKaVTlWxzCleb1PTHK5WwqUnTuelzxCzknX2O4ngU57L9aRrkx+axsxx1Garmn8+GsXn4WlTJLqpgx/ZnKmHXjk8hbDE98UVrxnan6YHLatnVatjynVz6XorP+RjuPr2Lz/7kszi+OdbvBwAAAG9BGAsAAPxSpS7F/vk+mpsmjvfHvvJ014ekbdNGak8BXxPNvDJ20ETTt+v64DalofK1G4LW9lQNO4Z04+exyjW12fmssrW2ZmwZDDZF6joLAh8TyK5VeuZB41oIeUkIW1bBroSsUz9FBex4fW0t18W5rQraPJythb55SPyQ91SeL99N9lzTuS7F8c0xDi8Pcf/p/fm+AQAA4BGEsQAAwOeiu+/izc/fxM2HN/E0nvaVksc+rGt3bYwVsk1q5tvsL9ohtG2bKYRtmmaqnM2rYRcVskOQOKuWjWa9GnZl/djV8PXSUPZMEDvtVqYhnh2/sCJ2umYljK1OV3xurdiiGrYMbtfWil2cv7QaNn+WrYrY/Pnz7THF/uU+PvmDT+J4dxTEAgAAcDXCWAAA4HMxBmJN28Tx/aFCtmkiHVN00U3VrW03zlW80tEwdfFU8Ropmq7ZrpBN/bmImFfLNqc247lpP6t6LacSrq4Xe0mV7FYRaFkhmuafayHkRSHsuF9UzK6tJ3uu0nVz221Xws4qZR8SxK61WXnO2TsZKrP3z/dxeH2IdJDEAgAAcD3CWAAA4HNxfHOM1z98Hd23u4g24vaj20hdil3azSpjx22b+imM27bfNm0Tza6JphsqZPPK2Dadr5BNp0rYzTVkI5ZVszE/v/j8tspwsghi+03aDiHzflYC2tU1YaOvVM2PP2jN2DyE3VorNh9P8fxb72Z6/uJZy3dQjjl1Kbq7Lj75p5/E4eUh0lEQCwAAwHUJYwEAgM9P14ey+2f7aJpm+ovbIYjdRb82bIromi6adKpwnQyFs3mA2kQzBahj0JqaNAtjp2MRfb/D+fzzVPU6Vs0Ot56C26H6dRZ4vk0oW8sGz4SPEWcqYCNmoWs1kI04H75GEb5W2tZC2UUFbFlhe+75195FLaBeqYgdg9i7X9zF4eUhjndHQSwAAAC/FMJYAADgc3V4eYjDy0N0h24K03bdLiIiml2fbE5rxu5OlbLRRh/WdqmvkM1C0yZtrB07hrd5iFtWw55bN3arQrY41pxJZ1crYCvHquvG5lW05ypia+dXKmQvWTN2s0K2m28XFbJloLr9kubv64IgdjYV8rAe8fM/ex53P7+78KYAAADw9oSxAADAF8LhxaEPWqNfNzaaiPam7atld020TT91cZe6PnxNTTTRzKcXXtvmf2m+nVXN5u1jOBfzc2O17WTsK07XRJyOra6FuuHcerFTv8XxPGCd9bNVIbsVwkaaV8x2p2PVYDY/Pk5VXIax5VTKmy8i/7gMnGfvq3zGbGxvPn4T+2f7OL4+XnBTAAAAeHeEsQAAwBfC4cUhDi8O0d62fUXrron0pK9obbqhQnbXRBt9KBu76LeVaYrzCtkpXG1jGcwWf1PQuhLs1sLZMQSc1p+trYGa95XbCiTLMPdcILsSwJYB6KIiNm+zFsJWQteIWE5DXISxeSg77l9kkUXXq2FnzzseLytjuxSvf/w6Xv3VqwtvDgAAAO+OMBYAAPhCuf/kPg6vDhFdxO79XUTqK2RTStHu+iC2afuAttk1pymMm9M2djEPZYcpjGsBbD6F8bjO7Nlpi2PlWG3/sS4IYyNWwteNCtnqdL4R1YrSramJy8DzbIXsuSC2cr46lfFK6FybOvnNx2/i9Y9fx/2n92duDgAAANchjAUAAL5QDi8PEa8jbj686QPY27ZfF7ZtoktdX8E6VshGnMK6vPJ13E/Z8YhqGJtPUzx9TvPPi+mJh6B3CnkjTufK8LApd08HFlMY1wLLskltmt9UBJe1/dpUvrVz4/qvZ8LXqW0tlC0rZLeshbC1c2eC2Gmcwxqx+2d7FbEAAAB8rpqUzen05MmTz3MsAAAAk/a9NtpdG81NE7cf3saHf+PDaG/b2D3Z9WHsTduvKdtmFbK7ZlpjNpqhgrbNPjdNRDsEom1MFbFjEDuGrrNjEbNpivP96fh4Te5tKmRXQtm1KtnV6Ypr4WVROTud6yrH1ypiK2HsLIQt+rrskbcD2MVzjM+eirF0Ke4+uYvnf/Y8Dq8PcXxlnVgAAAB+ue7vTzM0qYwFAAC+kLo3XXTD4qSpS/He6/ciuj5UbaON1KTomi6a1ETbtFMF6+xvUF3rtYtZ6DpOVzxWuDYpWz92rJRNp/1oYlqTdhaGjgFtKoLbS0xZY1ocW7Qp10qNZVh5UQhb7M+C1sjO5SFsZUrg2faSaYmnoZ9/1tqzLcLp4f7Hu2McXhzi7ud3lw0AAAAArkhlLAAA8MXXRuye7OLJt5/EB7/zQexud9HettHetlOVbNP222ijX1s2q4xdVMcOn2cVsZUq2VmlbGTnhs+j6VhxvLbfFAceOlVxLYTdrI4dz6+Er6vTFK9MRVydlng81q08U81Wk0tC2GIs3bGL/fN9fPrPPo3j3TG6u+78GAAAAOAKVMYCAABfLl3E8c0xjq/7qsd4P6awtE3ttLbrWCnbRdeHrbly3djxc7627FgBm68hO1bUjuvGZtWxY1A7HYuYQsMpoF1krWOD5bnSLHgt+yoD2NqxWghbCTKne62FrmuVsLWK2dwFz7j6fBEXBbGp6/8OLw6xf76Pw8tDpONDbgoAAADXozIWAAD48hgqWt/79ffivV99L3bvnSpk212/jSYWa8muVcmOgeu4lmzERqXscP+8zTSmmJ9ffI5lRWzN2SrZlLVLyzbVaYpTsT+cn4WulYC2uu0q+2Ug/BCV685Ov1yMKR1SHN4c4pM/+CQOrw+RDoJYAAAAPl8qYwEAgC+nFJGOKY6vjnH/2X3cdrexe2/XnxvWgG3a5rSWbLSRUjpVyY7BZJ6LtjFfF3ZcJ7ay/uwYwuaVsLPq2SwHnE1dHDGtRXvJM853l9Wv08etytFyvxLQXhLCrlbG5vd/iJVLLp1+eRzD/Wf3cXx97NeJvT/G8e4oiAUAAOALR2UsAADw5dREvPfr78XtN2/j9sPbaG/a2D3dndaQ3Z0qY6cq2abfztaPbYtq2PYUpE4hbl4pG5V1Y8u1ZfNz+aGmcrBQDTgrYezmFMVrFaVl+BoRZ4PXlcrYd2F1GuaNEHacmvjTf/ppvPrRq+W1AAAA8DlTGQsAAHz5pZjWBz2+OkZ728bT7zyN9naohj1moWzbRJPm20h9tWqTmtPasWN1a1P5HNnnfD/frh2LYb3ZsYL2zHP1m0qlaHm+djyleqj5LsLYLOh9rM2wuXy2csxdivtn93H/6X3sX+wFsAAAAHzhCWMBAIAvrcPzQxyeHyIi+nVjn7axe28Xu7SLdtdGpIjmOFTFdtsVsk3bT1E8TlXcNE2ktk/78mPRxBSq5hWyY3i6qJodjNMUn1s7Np0SydrJ08d3NEVxLYhdDWPfpVpV73i8GO9UlduleP3T1/Hiey/e7VgAAADgSoSxAADAV0I6pnjzkzexe28XT77zJNrbftridtdGc9P027aJ5mYIX7thmuLdUCHbnqYqnoLZbghfs5A2ImahbBm+piYtpyzOzz+0nLOcybcMYYfPZRA7ta1VmJYVsmuhbHm/87MsX/wciwC2Mu6UUhxeHeLlX77s16lNEffP7gMAAAC+LISxAADAV0LqUtx/ch/te22077Wxe7Lrj9+kaFMb0UVfFZsq0xYPVbBjIJtSWlbDZp/zqYinQLY8thHIXv5Q9f1a8BpRBKcXBLDTNRth7OZ4HqMWwo7Hi6mT03EIY/9iCGMBAADgS0YYCwAAfKV09128+cmbPmhtm7j91m08+ZW+Ura9aU/ryO6W0xY3TRPRnipfxymMxwB2Op4fi6FidiyQHSply/VhpwraR5hNEXzp9L4Ri8D17PTE+TXjtMuzh3jIoOf9LMZfGesUDHcpjnfHeP6nz/t1gQWxAAAAfEkJYwEAgK+WLuL46jjtNjdN7N7b9eFfNxzrmmhTG6kdqmbb6EPANkWThqB1PJZNYTz7i5X9fLv1+SE2qkn7zSlYjbgsjK0GspVx5mFqky57gNUAtjbm8flSinToq2G7rovj62O8+fhNdPfdRfcEAACALyJhLAAA8JW2f7aPw8tD3H7zNnbv7+Lmg5u+QvZJXyHb3rSnCtk2+8uqZPP1ZFerZCM7PnweNU0zDygfUWE67daqZCtrxi5C17F9Gbzm/TTF/mIoj6hQ3ZhaOZ8iOXUpnv/J87j75K4/3kV0e0EsAAAAX27CWAAA4CstHftqy+Ob4zR1cHvTxk3cRLNrIlI/HfFUIdvGtH7sWCU7rSHbDmvBtjGFsrU1Y2fhbDSLKYvfJoytVsk+pCI2iv0mm4o4v9fjZ1We9TO773jvYVzdvovj/TGi66cm3r/Yx/75/i1uDAAAAF8sTcr+SfSTJ08+z7EAAABcT1bR2uyaePKrT2L3ZBe793Z9hextG+2unVfI7uZrx04VsmWlbCzXkS2nLJ6tGfuYoHOtQnarOrZsU76Pmqbc3R5smievq2Mup0ROKcXrH72Oz/5/n01tukM3TSUNAAAAX1b39/fTZ5WxAADA10NWIZpSiuPrY79G6SFFc9vEzfs3kXapD2Z3w9TF3RDAjtWyqQ9lU5dVyo4hazvcpxnWma0Esotc85JQdiXgrFacVqYCjphPSzyNd60KdjGD8QOnJq7ddwx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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "fn scene_intersect(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " mut material: Material,\n", - " mut hit: Vec3f,\n", - " mut N: Vec3f,\n", - ") -> Bool:\n", - " var spheres_dist = inf[DType.float32]()\n", - "\n", - " for i in range(0, spheres.size):\n", - " var dist: Float32 = 0\n", - " if spheres[i].intersects(orig, dir, dist) and dist < spheres_dist:\n", - " spheres_dist = dist\n", - " hit = orig + dir * dist\n", - " N = (hit - spheres[i].center).normalize()\n", - " material = spheres[i].material\n", - "\n", - " return (spheres_dist != inf[DType.float32]())\n", - "\n", - "\n", - "fn cast_ray(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - ") -> Material:\n", - " var point = Vec3f.zero()\n", - " var material = Material(Vec3f.zero())\n", - " var N = Vec3f.zero()\n", - " if not scene_intersect(orig, dir, spheres, material, point, N):\n", - " return bg_color\n", - "\n", - " var diffuse_light_intensity: Float32 = 0\n", - " for i in range(lights.size):\n", - " var light_dir = (lights[i].position - point).normalize()\n", - " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", - "\n", - " return material.color * diffuse_light_intensity\n", - "\n", - "\n", - "fn create_image_with_spheres_and_lights(\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " height: Int,\n", - " width: Int,\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(\n", - " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights).color\n", - " )\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "\n", - "var lights = List[Light]()\n", - "lights.append(Light(Vec3f(-20, 20, 20), 1.0))\n", - "lights.append(Light(Vec3f(20, -20, 20), 0.5))\n", - "\n", - "render(create_image_with_spheres_and_lights(spheres, lights, H, W))\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "78043756-0ce9-455f-9e49-fb75268d4478", - "metadata": {}, - "source": [ - "## Step 5: Add specular lighting\n", - "\n", - "This section corresponds to the [Step 5 of the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing#step-5-specular-lighting).\n", - "The changes to the code are quite minimal, but the rendered picture looks much\n", - "more realistic!" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3ed5bc7c-f335-48c4-abf8-31c75d6e79ad", - "metadata": {}, - "outputs": [ - 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- }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "fn reflect(I: Vec3f, N: Vec3f) -> Vec3f:\n", - " return I - N * (I @ N) * 2.0\n", - "\n", - "\n", - "fn cast_ray(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - ") -> Material:\n", - " var point = Vec3f.zero()\n", - " var material = Material(Vec3f.zero())\n", - " var N = Vec3f.zero()\n", - " if not scene_intersect(orig, dir, spheres, material, point, N):\n", - " return bg_color\n", - "\n", - " var diffuse_light_intensity: Float32 = 0\n", - " var specular_light_intensity: Float32 = 0\n", - " for i in range(lights.size):\n", - " var light_dir = (lights[i].position - point).normalize()\n", - " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", - " specular_light_intensity += (\n", - " pow(\n", - " max(-reflect(-light_dir, N) @ dir, 0.0),\n", - " material.specular_component,\n", - " )\n", - " * lights[i].intensity\n", - " )\n", - "\n", - " var result = material.color * diffuse_light_intensity * material.albedo.data[\n", - " 0\n", - " ] + Vec3f(\n", - " 1.0, 1.0, 1.0\n", - " ) * specular_light_intensity * material.albedo.data[\n", - " 1\n", - " ]\n", - " var result_max = max(result[0], max(result[1], result[2]))\n", - " # Cap the resulting vector\n", - " if result_max > 1:\n", - " return result * (1.0 / result_max)\n", - " return result\n", - "\n", - "\n", - "fn create_image_with_spheres_and_specular_lights(\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " height: Int,\n", - " width: Int,\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(\n", - " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights).color\n", - " )\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "render(create_image_with_spheres_and_specular_lights(spheres, lights, H, W))\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a6e7fc32", - "metadata": {}, - "source": [ - "## Step 6: Add background\n", - "\n", - "As a last step, let's use an image for the background instead of a uniform\n", - "fill. The only code that we need to change is the code where we used to return\n", - "`bg_color`. Now we will determine a point in the background image to which the\n", - "ray is directed and draw that." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3de30ee3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "fn cast_ray(\n", - " orig: Vec3f,\n", - " dir: Vec3f,\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " bg: Image,\n", - ") -> Material:\n", - " var point = Vec3f.zero()\n", - " var material = Material(Vec3f.zero())\n", - " var N = Vec3f.zero()\n", - " if not scene_intersect(orig, dir, spheres, material, point, N):\n", - " # Background\n", - " # Given a direction vector `dir` we need to find a pixel in the image\n", - " var x = dir[0]\n", - " var y = dir[1]\n", - "\n", - " # Now map x from [-1,1] to [0,w-1] and do the same for y.\n", - " var w = bg.width\n", - " var h = bg.height\n", - " var col = Int((1.0 + x) * 0.5 * (w - 1))\n", - " var row = Int((1.0 + y) * 0.5 * (h - 1))\n", - " return Material(bg.pixels[bg._pos_to_index(row, col)])\n", - "\n", - " var diffuse_light_intensity: Float32 = 0\n", - " var specular_light_intensity: Float32 = 0\n", - " for i in range(lights.size):\n", - " var light_dir = (lights[i].position - point).normalize()\n", - " diffuse_light_intensity += lights[i].intensity * max(light_dir @ N, 0)\n", - " specular_light_intensity += (\n", - " pow(\n", - " max(-reflect(-light_dir, N) @ dir, 0.0),\n", - " material.specular_component,\n", - " )\n", - " * lights[i].intensity\n", - " )\n", - "\n", - " var result = material.color * diffuse_light_intensity * material.albedo.data[\n", - " 0\n", - " ] + Vec3f(\n", - " 1.0, 1.0, 1.0\n", - " ) * specular_light_intensity * material.albedo.data[\n", - " 1\n", - " ]\n", - " var result_max = max(result[0], max(result[1], result[2]))\n", - " # Cap the resulting vector\n", - " if result_max > 1:\n", - " return result * (1.0 / result_max)\n", - " return result\n", - "\n", - "\n", - "fn create_image_with_spheres_and_specular_lights(\n", - " spheres: List[Sphere],\n", - " lights: List[Light],\n", - " height: Int,\n", - " width: Int,\n", - " bg: Image,\n", - ") -> Image:\n", - " var image = Image(height, width)\n", - "\n", - " @parameter\n", - " fn _process_row(row: Int):\n", - " var y = -((Float32(2.0) * row + 1) / height - 1)\n", - " for col in range(width):\n", - " var x = ((Float32(2.0) * col + 1) / width - 1) * width / height\n", - " var dir = Vec3f(x, y, -1).normalize()\n", - " image.set(\n", - " row, col, cast_ray(Vec3f.zero(), dir, spheres, lights, bg).color\n", - " )\n", - "\n", - " parallelize[_process_row](height)\n", - "\n", - " return image\n", - "\n", - "\n", - "var bg = load_image(\"images/background.png\")\n", - "render(\n", - " create_image_with_spheres_and_specular_lights(spheres, lights, H, W, bg)\n", - ")\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "766de19f-200c-4dce-8678-b36cc3c3dc93", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "We've only explored the basics of ray tracing here, but you can add shadows,\n", - "reflections and so much more! Fortunately these are explained in [the C++\n", - "tutorial](https://github.com/ssloy/tinyraytracer/wiki/Part-1:-understandable-raytracing),\n", - "and we leave the corresponding Mojo implementations as an exercise for you." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Mojo", - "language": "mojo", - "name": "mojo-jupyter-kernel" - }, - "language_info": { - "codemirror_mode": { - "name": "mojo" - }, - "file_extension": ".mojo", - "mimetype": "text/x-mojo", - "name": "mojo" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebooks/images/background.png b/examples/notebooks/images/background.png deleted file mode 100644 index 5b8d32f8f99170ece26120e5b985be0d9dc9fde8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 607043 zcmZ^~1yo$kvIaUhg9iy3V1h$%5AN>n3_eKk;0{C3;2vCqI|OHNch}%f2p$NJ|J-x# zz3;B~_FBEVx~8hSdspx2?&_~2RF!2g(MZq$005?(tfV>sfcQ!xaH7J$X3aczdaoIr 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zxB-_#vHF??w0HUre=CfsS*5B1khAPPSKh=8)@c^CT2_49SHm^-31Rg&J1g%I$FK{^ z+&dmDm2C@}tiFRZLkrktLQ0*jTe9#2R&IHz$*av$mz15QBav5M4L&CG*6Z)~4b8{@ zuJ@#FRogD>^{Wp+T%(|Gn0NZTw@UFpirl(6A&%CZnOT8ZTb7nLPRkpOrK1R>wt9O6 z<)YsOezzTQew>!y{_Wp>)0^JpBS`OoR~`Di^6I+~V2@;m+FH*{?GAv>{|{tVrkkb> R#wh>*002ovPDHLkV1kC15n})V diff --git a/examples/notebooks/pixi.toml b/examples/notebooks/pixi.toml deleted file mode 100644 index cf658ff6cf..0000000000 --- a/examples/notebooks/pixi.toml +++ /dev/null @@ -1,13 +0,0 @@ -[project] -name = "Mojo notebooks" -version = "1.0.0" -description = "Environment for running JupyterLab" -authors = ["Modular "] -channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] -platforms = ["osx-arm64", "linux-aarch64", "linux-64"] - -[dependencies] -python = ">=3.9,<3.13" -max = "*" -pip = ">=24.0,<25" -jupyterlab = ">=4.2.5,<5" From 02f49f261a233aec484b4c1448726f3c5ddafdc9 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Fri, 31 Jan 2025 09:51:06 -0800 Subject: [PATCH 328/509] [Stdlib] Moves the env_get_dtype for internal utils to sys module MODULAR_ORIG_COMMIT_REV_ID: 661e1b25f33085614924b17be8fbb85a211bb1c9 --- docs/changelog.md | 3 +++ stdlib/src/sys/__init__.mojo | 8 +++++++- stdlib/src/sys/param_env.mojo | 19 +++++++++++++++++++ 3 files changed, 29 insertions(+), 1 deletion(-) diff --git a/docs/changelog.md b/docs/changelog.md index d7ef52f96f..bfc8714e05 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -386,6 +386,9 @@ what we publish. generator, and in the symbol outline and hover markdown via the Mojo Language Server. +- The `env_get_dtype` function has been added to the `sys` module. This allows + you to get the value of a `DType` from the param environment. + ### ❌ Removed - `StringRef` is being deprecated. Use `StringSlice` instead. diff --git a/stdlib/src/sys/__init__.mojo b/stdlib/src/sys/__init__.mojo index 5c9fbd6fb1..0f0e7e55ed 100644 --- a/stdlib/src/sys/__init__.mojo +++ b/stdlib/src/sys/__init__.mojo @@ -70,5 +70,11 @@ from .intrinsics import ( strided_load, strided_store, ) -from .param_env import env_get_bool, env_get_int, env_get_string, is_defined +from .param_env import ( + env_get_bool, + env_get_int, + env_get_string, + env_get_dtype, + is_defined, +) from .terminate import exit diff --git a/stdlib/src/sys/param_env.mojo b/stdlib/src/sys/param_env.mojo index ea70902070..c24a38d274 100644 --- a/stdlib/src/sys/param_env.mojo +++ b/stdlib/src/sys/param_env.mojo @@ -196,3 +196,22 @@ fn env_get_string[ return env_get_string[name]() else: return default + + +fn env_get_dtype[name: StringLiteral, default: DType]() -> DType: + """Try to get an DType-valued define. If the name is not defined, return + a default value instead. + + Parameters: + name: The name of the define. + default: The default value to use. + + Returns: + An DType parameter value. + """ + + @parameter + if is_defined[name](): + return DType._from_str(env_get_string[name]()) + else: + return default From 20b8fd04e7781bcd739ade0b90d28cc0b1dd8b32 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Fri, 31 Jan 2025 12:27:05 -0800 Subject: [PATCH 329/509] [stdlib] Remove StringRef from more methods Continuing to replace `StringRef` with `StringSlice`. Note that a `StaticString` is morally & ABI equivalent to a `StringRef`. MODULAR_ORIG_COMMIT_REV_ID: fabf361ae33251a78dfe548f4ad518e8e7317251 --- stdlib/src/builtin/file.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index 94f1724e3a..27ea5906ed 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -37,7 +37,7 @@ from sys.ffi import OpaquePointer from memory import AddressSpace, UnsafePointer, Span -from utils import StringRef, StringSlice, write_buffered +from utils import StringSlice, write_buffered @register_passable From 61c89c6f45af6b376936019ed3302e85e5386062 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Fri, 31 Jan 2025 13:49:28 -0800 Subject: [PATCH 330/509] [stdlib] Remove `StringRef` in favor of `StringSlice` `StringRef` is at this point just a `StringSlice` without an `Origin`. Since we can use `StaticConstantOrigin` for the cases where `StringRef` was previously used, it is time to retire that type. MODULAR_ORIG_COMMIT_REV_ID: e1d29eb406764186101b0b91d34a5cb2d136745a --- docs/changelog.md | 5 + stdlib/benchmarks/utils/bench_formatter.mojo | 2 +- stdlib/benchmarks/utils/bench_memmem.mojo | 2 +- .../src/collections/string/string_slice.mojo | 294 ++++--- stdlib/src/utils/__init__.mojo | 1 - stdlib/src/utils/stringref.mojo | 759 ------------------ stdlib/test/utils/test_stringref.mojo | 215 ----- 7 files changed, 184 insertions(+), 1094 deletions(-) delete mode 100644 stdlib/src/utils/stringref.mojo delete mode 100644 stdlib/test/utils/test_stringref.mojo diff --git a/docs/changelog.md b/docs/changelog.md index bfc8714e05..472a8059c2 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -100,6 +100,11 @@ what we publish. ### Standard library changes +- `StringRef` has been removed in favor of `StringSlice`. + The two types are ABI compatible, and for the exact same + behavior one can use `StaticString`, which is an alias + to `StringSlice[StaticConstantOrigin]`. + - Add a new `validate` parameter to the `b64decode()` function. - The free floating functions for constructing different types have been diff --git a/stdlib/benchmarks/utils/bench_formatter.mojo b/stdlib/benchmarks/utils/bench_formatter.mojo index 6d67e9cd9b..e84a58b562 100644 --- a/stdlib/benchmarks/utils/bench_formatter.mojo +++ b/stdlib/benchmarks/utils/bench_formatter.mojo @@ -20,7 +20,7 @@ from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run from bit import count_trailing_zeros from builtin.dtype import _uint_type_of_width -from utils.stringref import _align_down, _memchr, _memmem +from collections.string.string_slice import _memchr, _memmem # ===-----------------------------------------------------------------------===# # Benchmark Data diff --git a/stdlib/benchmarks/utils/bench_memmem.mojo b/stdlib/benchmarks/utils/bench_memmem.mojo index 1f3f5118d2..4167011cf4 100644 --- a/stdlib/benchmarks/utils/bench_memmem.mojo +++ b/stdlib/benchmarks/utils/bench_memmem.mojo @@ -21,7 +21,7 @@ from bit import count_trailing_zeros from builtin.dtype import _uint_type_of_width from memory import UnsafePointer, bitcast, memcmp, pack_bits -from utils.stringref import _align_down, _memchr, _memmem +from collections.string.string_slice import _align_down, _memchr, _memmem # ===-----------------------------------------------------------------------===# # Benchmark Data diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index f2ae7cd174..820c262621 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -21,16 +21,15 @@ from collections.string import StringSlice ``` """ -from bit import count_leading_zeros +from bit import count_leading_zeros, count_trailing_zeros from collections import List, Optional from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string._utf8_validation import _is_valid_utf8 -from memory import UnsafePointer, memcmp, memcpy, Span +from memory import UnsafePointer, memcmp, memcpy, Span, pack_bits from memory.memory import _memcmp_impl_unconstrained from sys import bitwidthof, simdwidthof from sys.intrinsics import unlikely, likely from sys.ffi import c_char -from utils.stringref import StringRef, _memmem from hashlib._hasher import _HashableWithHasher, _Hasher from os import PathLike @@ -38,98 +37,6 @@ alias StaticString = StringSlice[StaticConstantOrigin] """An immutable static string slice.""" -fn _count_utf8_continuation_bytes(str_slice: StringSlice) -> Int: - alias sizes = (256, 128, 64, 32, 16, 8) - var ptr = str_slice.unsafe_ptr() - var num_bytes = str_slice.byte_length() - var amnt: Int = 0 - var processed = 0 - - @parameter - for i in range(len(sizes)): - alias s = sizes[i] - - @parameter - if simdwidthof[DType.uint8]() >= s: - var rest = num_bytes - processed - for _ in range(rest // s): - var vec = (ptr + processed).load[width=s]() - var comp = (vec & 0b1100_0000) == 0b1000_0000 - amnt += Int(comp.cast[DType.uint8]().reduce_add()) - processed += s - - for i in range(num_bytes - processed): - amnt += Int((ptr[processed + i] & 0b1100_0000) == 0b1000_0000) - - return amnt - - -@always_inline -fn _utf8_first_byte_sequence_length(b: Byte) -> Int: - """Get the length of the sequence starting with given byte. Do note that - this does not work correctly if given a continuation byte.""" - - debug_assert( - (b & 0b1100_0000) != 0b1000_0000, - "Function does not work correctly if given a continuation byte.", - ) - return Int(count_leading_zeros(~b)) + Int(b < 0b1000_0000) - - -fn _utf8_byte_type(b: SIMD[DType.uint8, _], /) -> __type_of(b): - """UTF-8 byte type. - - Returns: - The byte type. - - Notes: - - - 0 -> ASCII byte. - - 1 -> continuation byte. - - 2 -> start of 2 byte long sequence. - - 3 -> start of 3 byte long sequence. - - 4 -> start of 4 byte long sequence. - """ - return count_leading_zeros(~(b & UInt8(0b1111_0000))) - - -@always_inline -fn _memrchr[ - type: DType -]( - source: UnsafePointer[Scalar[type]], char: Scalar[type], len: Int -) -> UnsafePointer[Scalar[type]]: - if not len: - return UnsafePointer[Scalar[type]]() - for i in reversed(range(len)): - if source[i] == char: - return source + i - return UnsafePointer[Scalar[type]]() - - -@always_inline -fn _memrmem[ - type: DType -]( - haystack: UnsafePointer[Scalar[type]], - haystack_len: Int, - needle: UnsafePointer[Scalar[type]], - needle_len: Int, -) -> UnsafePointer[Scalar[type]]: - if not needle_len: - return haystack - if needle_len > haystack_len: - return UnsafePointer[Scalar[type]]() - if needle_len == 1: - return _memrchr[type](haystack, needle[0], haystack_len) - for i in reversed(range(haystack_len - needle_len + 1)): - if haystack[i] != needle[0]: - continue - if memcmp(haystack + i + 1, needle + 1, needle_len - 1) == 0: - return haystack + i - return UnsafePointer[Scalar[type]]() - - @value struct _StringSliceIter[ mut: Bool, //, @@ -403,28 +310,6 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # ) self._slice = unsafe_from_utf8 - fn __init__(out self, *, unsafe_from_utf8_strref: StringRef): - """Construct a new StringSlice from a `StringRef` pointing to UTF-8 - encoded bytes. - - Args: - unsafe_from_utf8_strref: A `StringRef` of bytes encoded in UTF-8. - - Safety: - - `unsafe_from_utf8_strref` MUST point to data that is valid for - `origin`. - - `unsafe_from_utf8_strref` MUST be valid UTF-8 encoded data. - """ - - var strref = unsafe_from_utf8_strref - - var byte_slice = Span[Byte, origin]( - ptr=strref.unsafe_ptr(), - length=len(strref), - ) - - self = Self(unsafe_from_utf8=byte_slice) - fn __init__(out self, *, unsafe_from_utf8_ptr: UnsafePointer[Byte]): """Construct a new StringSlice from a `UnsafePointer[Byte]` pointing to null-terminated UTF-8 encoded bytes. @@ -1654,3 +1539,178 @@ fn _unsafe_strlen(owned ptr: UnsafePointer[Byte]) -> Int: while ptr.load(len): len += 1 return len + + +@always_inline +fn _align_down(value: Int, alignment: Int) -> Int: + return value._positive_div(alignment) * alignment + + +@always_inline +fn _memchr[ + type: DType +]( + source: UnsafePointer[Scalar[type]], char: Scalar[type], len: Int +) -> UnsafePointer[Scalar[type]]: + if not len: + return UnsafePointer[Scalar[type]]() + alias bool_mask_width = simdwidthof[DType.bool]() + var first_needle = SIMD[type, bool_mask_width](char) + var vectorized_end = _align_down(len, bool_mask_width) + + for i in range(0, vectorized_end, bool_mask_width): + var bool_mask = source.load[width=bool_mask_width](i) == first_needle + var mask = pack_bits(bool_mask) + if mask: + return source + Int(i + count_trailing_zeros(mask)) + + for i in range(vectorized_end, len): + if source[i] == char: + return source + i + return UnsafePointer[Scalar[type]]() + + +@always_inline +fn _memmem[ + type: DType +]( + haystack: UnsafePointer[Scalar[type]], + haystack_len: Int, + needle: UnsafePointer[Scalar[type]], + needle_len: Int, +) -> UnsafePointer[Scalar[type]]: + if not needle_len: + return haystack + if needle_len > haystack_len: + return UnsafePointer[Scalar[type]]() + if needle_len == 1: + return _memchr[type](haystack, needle[0], haystack_len) + + alias bool_mask_width = simdwidthof[DType.bool]() + var vectorized_end = _align_down( + haystack_len - needle_len + 1, bool_mask_width + ) + + var first_needle = SIMD[type, bool_mask_width](needle[0]) + var last_needle = SIMD[type, bool_mask_width](needle[needle_len - 1]) + + for i in range(0, vectorized_end, bool_mask_width): + var first_block = haystack.load[width=bool_mask_width](i) + var last_block = haystack.load[width=bool_mask_width]( + i + needle_len - 1 + ) + + var eq_first = first_needle == first_block + var eq_last = last_needle == last_block + + var bool_mask = eq_first & eq_last + var mask = pack_bits(bool_mask) + + while mask: + var offset = Int(i + count_trailing_zeros(mask)) + if memcmp(haystack + offset + 1, needle + 1, needle_len - 1) == 0: + return haystack + offset + mask = mask & (mask - 1) + + # remaining partial block compare using byte-by-byte + # + for i in range(vectorized_end, haystack_len - needle_len + 1): + if haystack[i] != needle[0]: + continue + + if memcmp(haystack + i + 1, needle + 1, needle_len - 1) == 0: + return haystack + i + + return UnsafePointer[Scalar[type]]() + + +fn _count_utf8_continuation_bytes(str_slice: StringSlice) -> Int: + alias sizes = (256, 128, 64, 32, 16, 8) + var ptr = str_slice.unsafe_ptr() + var num_bytes = str_slice.byte_length() + var amnt: Int = 0 + var processed = 0 + + @parameter + for i in range(len(sizes)): + alias s = sizes[i] + + @parameter + if simdwidthof[DType.uint8]() >= s: + var rest = num_bytes - processed + for _ in range(rest // s): + var vec = (ptr + processed).load[width=s]() + var comp = (vec & 0b1100_0000) == 0b1000_0000 + amnt += Int(comp.cast[DType.uint8]().reduce_add()) + processed += s + + for i in range(num_bytes - processed): + amnt += Int((ptr[processed + i] & 0b1100_0000) == 0b1000_0000) + + return amnt + + +@always_inline +fn _utf8_first_byte_sequence_length(b: Byte) -> Int: + """Get the length of the sequence starting with given byte. Do note that + this does not work correctly if given a continuation byte.""" + + debug_assert( + (b & 0b1100_0000) != 0b1000_0000, + "Function does not work correctly if given a continuation byte.", + ) + return Int(count_leading_zeros(~b)) + Int(b < 0b1000_0000) + + +fn _utf8_byte_type(b: SIMD[DType.uint8, _], /) -> __type_of(b): + """UTF-8 byte type. + + Returns: + The byte type. + + Notes: + + - 0 -> ASCII byte. + - 1 -> continuation byte. + - 2 -> start of 2 byte long sequence. + - 3 -> start of 3 byte long sequence. + - 4 -> start of 4 byte long sequence. + """ + return count_leading_zeros(~(b & UInt8(0b1111_0000))) + + +@always_inline +fn _memrchr[ + type: DType +]( + source: UnsafePointer[Scalar[type]], char: Scalar[type], len: Int +) -> UnsafePointer[Scalar[type]]: + if not len: + return UnsafePointer[Scalar[type]]() + for i in reversed(range(len)): + if source[i] == char: + return source + i + return UnsafePointer[Scalar[type]]() + + +@always_inline +fn _memrmem[ + type: DType +]( + haystack: UnsafePointer[Scalar[type]], + haystack_len: Int, + needle: UnsafePointer[Scalar[type]], + needle_len: Int, +) -> UnsafePointer[Scalar[type]]: + if not needle_len: + return haystack + if needle_len > haystack_len: + return UnsafePointer[Scalar[type]]() + if needle_len == 1: + return _memrchr[type](haystack, needle[0], haystack_len) + for i in reversed(range(haystack_len - needle_len + 1)): + if haystack[i] != needle[0]: + continue + if memcmp(haystack + i + 1, needle + 1, needle_len - 1) == 0: + return haystack + i + return UnsafePointer[Scalar[type]]() diff --git a/stdlib/src/utils/__init__.mojo b/stdlib/src/utils/__init__.mojo index 2521ef4b8d..87133a3238 100644 --- a/stdlib/src/utils/__init__.mojo +++ b/stdlib/src/utils/__init__.mojo @@ -18,6 +18,5 @@ from .lock import BlockingScopedLock, BlockingSpinLock, SpinWaiter from .loop import unroll from .static_tuple import StaticTuple from collections.string.string_slice import StaticString, StringSlice -from .stringref import StringRef from .variant import Variant from .write import Writable, Writer, write_args, write_buffered diff --git a/stdlib/src/utils/stringref.mojo b/stdlib/src/utils/stringref.mojo deleted file mode 100644 index 295dd70c9a..0000000000 --- a/stdlib/src/utils/stringref.mojo +++ /dev/null @@ -1,759 +0,0 @@ -# ===----------------------------------------------------------------------=== # -# Copyright (c) 2025, Modular Inc. All rights reserved. -# -# Licensed under the Apache License v2.0 with LLVM Exceptions: -# https://llvm.org/LICENSE.txt -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ===----------------------------------------------------------------------=== # -"""Implements the StringRef class. -""" - -from collections.string import StringSlice -from hashlib._hasher import _HashableWithHasher, _Hasher -from sys import simdwidthof -from sys.ffi import c_char - -from bit import count_trailing_zeros -from builtin.dtype import _uint_type_of_width -from memory import UnsafePointer, memcmp, pack_bits, Span, memcpy -from memory.memory import _memcmp_impl_unconstrained - - -# ===----------------------------------------------------------------------=== # -# Utilities -# ===----------------------------------------------------------------------=== # - - -@always_inline -fn _align_down(value: Int, alignment: Int) -> Int: - return value._positive_div(alignment) * alignment - - -# ===-----------------------------------------------------------------------===# -# StringRef -# ===-----------------------------------------------------------------------===# - - -@value -@register_passable("trivial") -struct StringRef( - AsBytes, - Boolable, - CollectionElement, - CollectionElementNew, - Comparable, - Hashable, - IntableRaising, - Representable, - Sized, - Stringable, - Writable, - _HashableWithHasher, -): - """ - Represent a constant reference to a string, i.e. a sequence of characters - and a length, which need not be null terminated. - """ - - # Fields - var data: UnsafePointer[UInt8] - """A pointer to the beginning of the string data being referenced.""" - var length: Int - """The length of the string being referenced.""" - - # ===-------------------------------------------------------------------===# - # Life cycle methods - # ===-------------------------------------------------------------------===# - - @always_inline - fn __init__(out self): - """Construct a StringRef value with length zero.""" - self = StringRef(UnsafePointer[UInt8](), 0) - - @always_inline - fn copy(self) -> Self: - """Copy the object. - - Returns: - A copy of the value. - """ - return StringRef(self.data, self.length) - - @always_inline - @implicit - fn __init__(out self, str: StringLiteral): - """Construct a StringRef value given a constant string. - - Args: - str: The input constant string. - """ - self = StringRef(str.unsafe_ptr(), len(str)) - - @always_inline - fn __init__(out self, ptr: UnsafePointer[c_char], len: Int): - """Construct a StringRef value given a (potentially non-0 terminated - string). - - The constructor takes a raw pointer and a length. - - Note that you should use the constructor from `UnsafePointer[UInt8]` instead - as we are now storing the bytes as UInt8. - See https://github.com/modularml/mojo/issues/2317 for more information. - - Args: - ptr: UnsafePointer to the string. - len: The length of the string. - """ - - self.data = ptr.bitcast[UInt8]() - self.length = len - - @always_inline - fn __init__(out self, *, ptr: UnsafePointer[UInt8]): - """Construct a StringRef value given a null-terminated string. - - Args: - ptr: UnsafePointer to the string. - """ - - var len = 0 - while ptr.load(len): - len += 1 - - self = StringRef(ptr, len) - - @always_inline - @implicit - fn __init__(out self, ptr: UnsafePointer[c_char]): - """Construct a StringRef value given a null-terminated string. - - Note that you should use the constructor from `UnsafePointer[UInt8]` instead - as we are now storing the bytes as UInt8. - See https://github.com/modularml/mojo/issues/2317 for more information. - - Args: - ptr: UnsafePointer to the string. - """ - - var len = 0 - while ptr.load(len): - len += 1 - - self = StringRef(ptr, len) - - # ===-------------------------------------------------------------------===# - # Helper methods for slicing - # ===-------------------------------------------------------------------===# - # TODO: Move to slice syntax like str_ref[:42] - - fn take_front(self, num_bytes: Int = 1) -> Self: - """Return a StringRef equal to 'self' but with only the first - `num_bytes` elements remaining. If `num_bytes` is greater than the - length of the string, the entire string is returned. - - Args: - num_bytes: The number of bytes to include. - - Returns: - A new slice that starts with those bytes. - """ - debug_assert(num_bytes >= 0, "num_bytes must be non-negative") - if num_bytes >= self.length: - return self - return Self(self.data, num_bytes) - - fn take_back(self, num_bytes: Int = 1) -> Self: - """Return a StringRef equal to 'self' but with only the last - `num_bytes` elements remaining. If `num_bytes` is greater than the - length of the string, the entire string is returned. - - Args: - num_bytes: The number of bytes to include. - - Returns: - A new slice that ends with those bytes. - """ - debug_assert(num_bytes >= 0, "num_bytes must be non-negative") - if num_bytes >= self.length: - return self - return Self(self.data + (self.length - num_bytes), num_bytes) - - fn drop_front(self, num_bytes: Int = 1) -> Self: - """Return a StringRef equal to 'self' but with the first - `num_bytes` elements skipped. If `num_bytes` is greater than the - length of the string, an empty StringRef is returned. - - Args: - num_bytes: The number of bytes to drop. - - Returns: - A new slice with those bytes skipped. - """ - debug_assert(num_bytes >= 0, "num_bytes must be non-negative") - if num_bytes >= self.length: - return StringRef() - return Self(self.data + num_bytes, self.length - num_bytes) - - fn drop_back(self, num_bytes: Int = 1) -> Self: - """Return a StringRef equal to 'self' but with the last `num_bytes` - elements skipped. If `num_bytes` is greater than the - length of the string, the entire string is returned. - - Args: - num_bytes: The number of bytes to include. - - Returns: - A new slice ends earlier than those bytes. - """ - debug_assert(num_bytes >= 0, "num_bytes must be non-negative") - if num_bytes >= self.length: - return StringRef() - return Self(self.data, self.length - num_bytes) - - fn as_bytes(ref self) -> Span[Byte, __origin_of(self)]: - """Returns a contiguous Span of the bytes owned by this string. - - Returns: - A contiguous slice pointing to the bytes owned by this string. - """ - return Span[Byte, __origin_of(self)](ptr=self.data, length=self.length) - - # ===-------------------------------------------------------------------===# - # Operator dunders - # ===-------------------------------------------------------------------===# - - @always_inline("nodebug") - fn __getitem__[I: Indexer](self, idx: I) -> StringRef: - """Get the string value at the specified position. - - Args: - idx: The index position. - - Parameters: - I: A type that can be used as an index. - - Returns: - The character at the specified position. - """ - return StringRef {data: self.data + Int(idx), length: 1} - - @always_inline - fn __eq__(self, rhs: StringRef) -> Bool: - """Compares two strings are equal. - - Args: - rhs: The other string. - - Returns: - True if the strings match and False otherwise. - """ - return not (self != rhs) - - fn __contains__(self, substr: StringRef) -> Bool: - """Returns True if the substring is contained within the current string. - - Args: - substr: The substring to check. - - Returns: - True if the string contains the substring. - """ - return self.find(substr) != -1 - - @always_inline - fn __ne__(self, rhs: StringRef) -> Bool: - """Compares two strings are not equal. - - Args: - rhs: The other string. - - Returns: - True if the strings do not match and False otherwise. - """ - return len(self) != len(rhs) or _memcmp_impl_unconstrained( - self.data, rhs.data, len(self) - ) - - @always_inline - fn __lt__(self, rhs: StringRef) -> Bool: - """Compare this StringRef to the RHS using LT comparison. - - Args: - rhs: The other StringRef to compare against. - - Returns: - True if this string is strictly less than the RHS string and False - otherwise. - """ - var len1 = len(self) - var len2 = len(rhs) - return Int(len1 < len2) > _memcmp_impl_unconstrained( - self.data, rhs.data, min(len1, len2) - ) - - @always_inline - fn __le__(self, rhs: StringRef) -> Bool: - """Compare this StringRef to the RHS using LE comparison. - - Args: - rhs: The other StringRef to compare against. - - Returns: - True if this string is less than or equal to the RHS string and - False otherwise. - """ - return not (rhs < self) - - @always_inline - fn __gt__(self, rhs: StringRef) -> Bool: - """Compare this StringRef to the RHS using GT comparison. - - Args: - rhs: The other StringRef to compare against. - - Returns: - True if this string is strictly greater than the RHS string and - False otherwise. - """ - return rhs < self - - @always_inline - fn __ge__(self, rhs: StringRef) -> Bool: - """Compare this StringRef to the RHS using GE comparison. - - Args: - rhs: The other StringRef to compare against. - - Returns: - True if this string is greater than or equal to the RHS string and - False otherwise. - """ - return not (self < rhs) - - # ===-------------------------------------------------------------------===# - # Trait implementations - # ===-------------------------------------------------------------------===# - - @always_inline - fn __bool__(self) -> Bool: - """Checks if the string is empty or not. - - Returns: - Returns True if the string is not empty and False otherwise. - """ - return len(self) != 0 - - fn __hash__(self) -> UInt: - """Hash the underlying buffer using builtin hash. - - Returns: - A 64-bit hash value. This value is _not_ suitable for cryptographic - uses. Its intended usage is for data structures. See the `hash` - builtin documentation for more details. - """ - return hash(self.data, self.length) - - fn __hash__[H: _Hasher](self, mut hasher: H): - """Updates hasher with the underlying bytes. - - Parameters: - H: The hasher type. - - Args: - hasher: The hasher instance. - """ - hasher._update_with_bytes(self.data, self.length) - - fn __int__(self) raises -> Int: - """Parses the given string as a base-10 integer and returns that value. - - For example, `Int("19")` returns `19`. If the given string cannot be parsed - as an integer value, an error is raised. For example, `Int("hi")` raises an - error. - - Returns: - An integer value that represents the string, or otherwise raises. - """ - var str_slice = StringSlice[ImmutableAnyOrigin]( - unsafe_from_utf8_strref=self - ) - return atol(str_slice) - - @always_inline - fn __len__(self) -> Int: - """Returns the length of the string. - - Returns: - The length of the string. - """ - return self.length - - @no_inline - fn __str__(self) -> String: - """Convert the string reference to a string. - - Returns: - A new string. - """ - var length = len(self) - var buffer = String._buffer_type() - # +1 for null terminator, initialized to 0 - buffer.resize(length + 1, 0) - memcpy(dest=buffer.data, src=self.data, count=length) - return String(buffer^) - - @no_inline - fn __repr__(self) -> String: - """Convert the string reference to a string. - - Returns: - The String representation of the StringRef. - """ - return String("StringRef(", repr(String(self)), ")") - - @no_inline - fn write_to[W: Writer](self, mut writer: W): - """ - Formats this StringRef to the provided Writer. - - Parameters: - W: A type conforming to the Writable trait. - - Args: - writer: The object to write to. - """ - # SAFETY: - # Safe because our use of this Span does not outlive `self`. - writer.write_bytes( - Span[Byte, ImmutableAnyOrigin]( - ptr=self.unsafe_ptr(), length=len(self) - ) - ) - - fn __fspath__(self) -> String: - """Return the file system path representation of the object. - - Returns: - The file system path representation as a string. - """ - return self.__str__() - - # ===-------------------------------------------------------------------===# - # Methods - # ===-------------------------------------------------------------------===# - - @always_inline - fn unsafe_ptr(self) -> UnsafePointer[UInt8]: - """Retrieves a pointer to the underlying memory. - - Returns: - The pointer to the underlying memory. - """ - return self.data - - @always_inline - fn empty(self) -> Bool: - """Returns True if the StringRef has length = 0. - - Returns: - Whether the stringref is empty. - """ - return self.length == 0 - - fn count(self, substr: StringRef) -> Int: - """Return the number of non-overlapping occurrences of substring - `substr` in the string. - - If sub is empty, returns the number of empty strings between characters - which is the length of the string plus one. - - Args: - substr: The substring to count. - - Returns: - The number of occurrences of `substr`. - """ - if not substr: - return len(self) + 1 - - var res = 0 - var offset = 0 - - while True: - var pos = self.find(substr, offset) - if pos == -1: - break - res += 1 - - offset = pos + len(substr) - - return res - - # TODO: remove this method later on when nothing depends on it anymore. - # It has already been copied to `StringSlice`. - fn find(self, substr: StringRef, start: Int = 0) -> Int: - """Finds the offset of the first occurrence of `substr` starting at - `start`. If not found, returns -1. - - Args: - substr: The substring to find. - start: The offset from which to find. - - Returns: - The offset of `substr` relative to the beginning of the string. - """ - if not substr: - return 0 - - if len(self) < len(substr) + start: - return -1 - - # The substring to search within, offset from the beginning if `start` - # is positive, and offset from the end if `start` is negative. - var haystack_str = self._from_start(start) - - var loc = _memmem( - haystack_str.unsafe_ptr(), - len(haystack_str), - substr.unsafe_ptr(), - len(substr), - ) - - if not loc: - return -1 - - return Int(loc) - Int(self.unsafe_ptr()) - - fn rfind(self, substr: StringRef, start: Int = 0) -> Int: - """Finds the offset of the last occurrence of `substr` starting at - `start`. If not found, returns -1. - - Args: - substr: The substring to find. - start: The offset from which to find. - - Returns: - The offset of `substr` relative to the beginning of the string. - """ - if not substr: - return len(self) - - if len(self) < len(substr) + start: - return -1 - - # The substring to search within, offset from the beginning if `start` - # is positive, and offset from the end if `start` is negative. - var haystack_str = self._from_start(start) - - var loc = _memrmem( - haystack_str.unsafe_ptr(), - len(haystack_str), - substr.unsafe_ptr(), - len(substr), - ) - - if not loc: - return -1 - - return Int(loc) - Int(self.unsafe_ptr()) - - fn _from_start(self, start: Int) -> StringRef: - """Gets the StringRef pointing to the substring after the specified slice start position. - - If start is negative, it is interpreted as the number of characters - from the end of the string to start at. - - Args: - start: Starting index of the slice. - - Returns: - A StringRef borrowed from the current string containing the - characters of the slice starting at start. - """ - - var self_len = len(self) - - var abs_start: Int - if start < 0: - # Avoid out of bounds earlier than the start - # len = 5, start = -3, then abs_start == 2, i.e. a partial string - # len = 5, start = -10, then abs_start == 0, i.e. the full string - abs_start = max(self_len + start, 0) - else: - # Avoid out of bounds past the end - # len = 5, start = 2, then abs_start == 2, i.e. a partial string - # len = 5, start = 8, then abs_start == 5, i.e. an empty string - abs_start = min(start, self_len) - - debug_assert( - abs_start >= 0, "strref absolute start must be non-negative" - ) - debug_assert( - abs_start <= self_len, - "strref absolute start must be less than source String len", - ) - - var data = self.data + abs_start - var length = self_len - abs_start - - return StringRef(data, length) - - fn split(self, delimiter: StringRef) raises -> List[StringRef]: - """Split the StringRef by a delimiter. - - Args: - delimiter: The StringRef to split on. - - Returns: - A List of StringRefs containing the input split by the delimiter. - - Raises: - Error if an empty delimiter is specified. - """ - if not delimiter: - raise Error("empty delimiter not allowed to be passed to split.") - - var output = List[StringRef]() - var ptr = self.unsafe_ptr() - - var current_offset = 0 - while True: - var loc = self.find(delimiter, current_offset) - # delimiter not found, so add the search slice from where we're currently at - if loc == -1: - output.append( - StringRef(ptr + current_offset, len(self) - current_offset) - ) - break - - # We found a delimiter, so add the preceding string slice - output.append(StringRef(ptr + current_offset, loc - current_offset)) - - # Advance our search offset past the delimiter - current_offset = loc + len(delimiter) - return output - - -# ===-----------------------------------------------------------------------===# -# Utilities -# ===-----------------------------------------------------------------------===# - - -@always_inline -fn _memchr[ - type: DType -]( - source: UnsafePointer[Scalar[type]], char: Scalar[type], len: Int -) -> UnsafePointer[Scalar[type]]: - if not len: - return UnsafePointer[Scalar[type]]() - alias bool_mask_width = simdwidthof[DType.bool]() - var first_needle = SIMD[type, bool_mask_width](char) - var vectorized_end = _align_down(len, bool_mask_width) - - for i in range(0, vectorized_end, bool_mask_width): - var bool_mask = source.load[width=bool_mask_width](i) == first_needle - var mask = pack_bits(bool_mask) - if mask: - return source + Int(i + count_trailing_zeros(mask)) - - for i in range(vectorized_end, len): - if source[i] == char: - return source + i - return UnsafePointer[Scalar[type]]() - - -@always_inline -fn _memmem[ - type: DType -]( - haystack: UnsafePointer[Scalar[type]], - haystack_len: Int, - needle: UnsafePointer[Scalar[type]], - needle_len: Int, -) -> UnsafePointer[Scalar[type]]: - if not needle_len: - return haystack - if needle_len > haystack_len: - return UnsafePointer[Scalar[type]]() - if needle_len == 1: - return _memchr[type](haystack, needle[0], haystack_len) - - alias bool_mask_width = simdwidthof[DType.bool]() - var vectorized_end = _align_down( - haystack_len - needle_len + 1, bool_mask_width - ) - - var first_needle = SIMD[type, bool_mask_width](needle[0]) - var last_needle = SIMD[type, bool_mask_width](needle[needle_len - 1]) - - for i in range(0, vectorized_end, bool_mask_width): - var first_block = haystack.load[width=bool_mask_width](i) - var last_block = haystack.load[width=bool_mask_width]( - i + needle_len - 1 - ) - - var eq_first = first_needle == first_block - var eq_last = last_needle == last_block - - var bool_mask = eq_first & eq_last - var mask = pack_bits(bool_mask) - - while mask: - var offset = Int(i + count_trailing_zeros(mask)) - if memcmp(haystack + offset + 1, needle + 1, needle_len - 1) == 0: - return haystack + offset - mask = mask & (mask - 1) - - # remaining partial block compare using byte-by-byte - # - for i in range(vectorized_end, haystack_len - needle_len + 1): - if haystack[i] != needle[0]: - continue - - if memcmp(haystack + i + 1, needle + 1, needle_len - 1) == 0: - return haystack + i - - return UnsafePointer[Scalar[type]]() - - -@always_inline -fn _memrchr[ - type: DType -]( - source: UnsafePointer[Scalar[type]], char: Scalar[type], len: Int -) -> UnsafePointer[Scalar[type]]: - if not len: - return UnsafePointer[Scalar[type]]() - for i in reversed(range(len)): - if source[i] == char: - return source + i - return UnsafePointer[Scalar[type]]() - - -@always_inline -fn _memrmem[ - type: DType -]( - haystack: UnsafePointer[Scalar[type]], - haystack_len: Int, - needle: UnsafePointer[Scalar[type]], - needle_len: Int, -) -> UnsafePointer[Scalar[type]]: - if not needle_len: - return haystack - if needle_len > haystack_len: - return UnsafePointer[Scalar[type]]() - if needle_len == 1: - return _memrchr[type](haystack, needle[0], haystack_len) - for i in reversed(range(haystack_len - needle_len + 1)): - if haystack[i] != needle[0]: - continue - if memcmp(haystack + i + 1, needle + 1, needle_len - 1) == 0: - return haystack + i - return UnsafePointer[Scalar[type]]() diff --git a/stdlib/test/utils/test_stringref.mojo b/stdlib/test/utils/test_stringref.mojo deleted file mode 100644 index a0950cf990..0000000000 --- a/stdlib/test/utils/test_stringref.mojo +++ /dev/null @@ -1,215 +0,0 @@ -# ===----------------------------------------------------------------------=== # -# Copyright (c) 2025, Modular Inc. All rights reserved. -# -# Licensed under the Apache License v2.0 with LLVM Exceptions: -# https://llvm.org/LICENSE.txt -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ===----------------------------------------------------------------------=== # -# RUN: %mojo %s - -from testing import ( - assert_equal, - assert_false, - assert_raises, - assert_true, - assert_not_equal, -) - -from utils import StringRef - - -def test_stringref(): - var a = StringRef("AAA") - var b = StringRef("BBB") - var c = StringRef("AAA") - - assert_equal(3, len(a)) - assert_equal(3, len(b)) - assert_equal(3, len(c)) - assert_equal(4, len("ABBA")) - - # Equality operators - assert_not_equal(a, b) - assert_not_equal(b, a) - - # Self equality - assert_equal(a, a) - - # Value equality - assert_equal(a, c) - - -def test_stringref_from_pointer(): - var a = StringRef("AAA") - var b = StringRef(ptr=a.data) - assert_equal(3, len(a)) - assert_equal(3, len(b)) - assert_equal(a, b) - - -def test_strref_from_start(): - var str = StringRef("Hello") - - assert_equal(5, len(str)) - - assert_equal(str._from_start(0), "Hello") - assert_equal(str._from_start(1), "ello") - assert_equal(str._from_start(4), "o") - assert_equal(str._from_start(5), "") - assert_equal(str._from_start(10), "") - - assert_equal(str._from_start(-1), "o") - assert_equal(str._from_start(-3), "llo") - assert_equal(str._from_start(-5), "Hello") - assert_equal(str._from_start(-10), "Hello") - - -fn test_comparison_operators() raises: - var abc = StringRef("abc") - var de = StringRef("de") - var ABC = StringRef("ABC") - var ab = StringRef("ab") - var abcd = StringRef("abcd") - - # Test less than and greater than - assert_true(StringRef.__lt__(abc, de)) - assert_false(StringRef.__lt__(de, abc)) - assert_false(StringRef.__lt__(abc, abc)) - assert_true(StringRef.__lt__(ab, abc)) - assert_true(StringRef.__gt__(abc, ab)) - assert_false(StringRef.__gt__(abc, abcd)) - - # Test less than or equal to and greater than or equal to - assert_true(StringRef.__le__(abc, de)) - assert_true(StringRef.__le__(abc, abc)) - assert_false(StringRef.__le__(de, abc)) - assert_true(StringRef.__ge__(abc, abc)) - assert_false(StringRef.__ge__(ab, abc)) - assert_true(StringRef.__ge__(abcd, abc)) - - # Test case sensitivity in comparison (assuming ASCII order) - assert_true(StringRef.__gt__(abc, ABC)) - assert_false(StringRef.__le__(abc, ABC)) - - # Test comparisons involving empty strings - assert_true(StringRef.__lt__("", abc)) - assert_false(StringRef.__lt__(abc, "")) - assert_true(StringRef.__le__("", "")) - assert_true(StringRef.__ge__("", "")) - - -def test_intable(): - assert_equal(Int(StringRef("123")), 123) - - with assert_raises(): - _ = Int(StringRef("hi")) - - -def test_indexing(): - a = StringRef("abc") - assert_equal(a[False], "a") - assert_equal(a[Int(1)], "b") - assert_equal(a[0], "a") - - -def test_find(): - haystack = StringRef("abcdefg") - haystack_with_special_chars = StringRef("abcdefg@#$") - haystack_repeated_chars = StringRef("aaaaaaaaaaaaaaaaaaaaaaaa") - - assert_equal(haystack.find("a"), 0) - assert_equal(haystack.find("ab"), 0) - assert_equal(haystack.find("abc"), 0) - assert_equal(haystack.find("bcd"), 1) - assert_equal(haystack.find("de"), 3) - assert_equal(haystack.find("fg"), 5) - assert_equal(haystack.find("g"), 6) - assert_equal(haystack.find("z"), -1) - assert_equal(haystack.find("zzz"), -1) - - assert_equal(haystack.find("@#$"), -1) - assert_equal(haystack_with_special_chars.find("@#$"), 7) - - assert_equal(haystack_repeated_chars.find("aaa"), 0) - assert_equal(haystack_repeated_chars.find("AAa"), -1) - - assert_equal(haystack.find("hijklmnopqrstuvwxyz"), -1) - - assert_equal(StringRef("").find("abc"), -1) - - -fn test_stringref_split() raises: - # Reject empty delimiters - with assert_raises( - contains="empty delimiter not allowed to be passed to split." - ): - _ = StringRef("hello").split("") - - # Split in middle - var d1 = StringRef("n") - var in1 = StringRef("faang") - var res1 = in1.split(d1) - assert_equal(len(res1), 2) - assert_equal(res1[0], "faa") - assert_equal(res1[1], "g") - - # Matches should be properly split in multiple case - var d2 = StringRef(" ") - var in2 = StringRef("modcon is coming soon") - var res2 = in2.split(d2) - assert_equal(len(res2), 4) - assert_equal(res2[0], "modcon") - assert_equal(res2[1], "is") - assert_equal(res2[2], "coming") - assert_equal(res2[3], "soon") - - # No match from the delimiter - var d3 = StringRef("x") - var in3 = StringRef("hello world") - var res3 = in3.split(d3) - assert_equal(len(res3), 1) - assert_equal(res3[0], "hello world") - - # Multiple character delimiter - var d4 = StringRef("ll") - var in4 = StringRef("hello") - var res4 = in4.split(d4) - assert_equal(len(res4), 2) - assert_equal(res4[0], "he") - assert_equal(res4[1], "o") - - -def test_str_and_ref(): - assert_equal(StringRef("abc").__str__(), "abc") - assert_equal(StringRef("abc").__repr__(), "StringRef('abc')") - assert_equal(StringRef("\0").__repr__(), r"StringRef('\x00')") - assert_equal(StringRef("\x09").__repr__(), r"StringRef('\t')") - assert_equal(StringRef("\n").__repr__(), r"StringRef('\n')") - assert_equal(StringRef("\x0d").__repr__(), r"StringRef('\r')") - assert_equal(StringRef("'").__repr__(), 'StringRef("\'")') - - # Multi-byte characters.__repr__() - assert_equal( - StringRef("Örnsköldsvik").__repr__(), "StringRef('Örnsköldsvik')" - ) # 2-byte - assert_equal(StringRef("你好!").__repr__(), "StringRef('你好!')") # 3-byte - assert_equal( - StringRef("hello 🔥!").__repr__(), "StringRef('hello 🔥!')" - ) # 4-byte - - -def main(): - test_stringref() - test_stringref_from_pointer() - test_strref_from_start() - test_stringref_split() - test_comparison_operators() - test_intable() - test_indexing() - test_find() - test_str_and_ref() From eb566057628f639b7b05c920a83409422521e26b Mon Sep 17 00:00:00 2001 From: Manuel Saelices Date: Fri, 31 Jan 2025 17:40:19 -0800 Subject: [PATCH 331/509] [External] [stdlib] `SIMD.from_bytes()` and `SIMD.as_bytes()` (#54932) [External] [stdlib] `SIMD.from_bytes()` and `SIMD.as_bytes()` Similar to the Python's [int.from_bytes()](https://docs.python.org/3/library/stdtypes.html#int.from_bytes) and [int.to_bytes()](https://docs.python.org/3/library/stdtypes.html#int.to_bytes) one. --------- Signed-off-by: Lukas Hermann <1734032+lsh@users.noreply.github.com> Co-authored-by: Manuel Saelices Co-authored-by: Lukas Hermann <1734032+lsh@users.noreply.github.com> Co-authored-by: abdul dakkak Closes modularml/mojo#3966 MODULAR_ORIG_COMMIT_REV_ID: 6f00b8254c5e5bf87b10818da7021d0f682a8949 --- docs/changelog.md | 4 +++ stdlib/src/builtin/simd.mojo | 56 ++++++++++++++++++++++++++++-- stdlib/test/builtin/test_simd.mojo | 45 ++++++++++++++++++++++++ 3 files changed, 103 insertions(+), 2 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 472a8059c2..d5edc96c7f 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -107,6 +107,10 @@ what we publish. - Add a new `validate` parameter to the `b64decode()` function. +- New `SIMD.from_bytes()` and `SIMD.as_bytes()` functions to convert a list of bytes + to a list of scalars and vice versa, accepting the endianess as an argument. Similar + to Python `int.from_bytes()` and `int.to_bytes()` functions. + - The free floating functions for constructing different types have been deprecated for actual constructors: diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index f3f6f493cf..3da7506276 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -34,6 +34,7 @@ from sys import ( bitwidthof, has_neon, is_amd_gpu, + is_big_endian, is_gpu, is_nvidia_gpu, is_x86, @@ -45,13 +46,13 @@ from sys import ( from sys._assembly import inlined_assembly from sys.info import _current_arch, _is_sm_9x -from bit import pop_count +from bit import pop_count, byte_swap from builtin._format_float import _write_float from builtin.dtype import _uint_type_of_width from builtin.format_int import _try_write_int from builtin.io import _snprintf from documentation import doc_private -from memory import UnsafePointer, bitcast, Span +from memory import UnsafePointer, bitcast, memcpy, Span from utils import IndexList, StaticTuple from utils._visualizers import lldb_formatter_wrapping_type @@ -268,6 +269,7 @@ struct SIMD[type: DType, size: Int]( alias _Mask = SIMD[DType.bool, size] alias element_type = type + var value: __mlir_type[`!pop.simd<`, size.value, `, `, type.value, `>`] """The underlying storage for the vector.""" @@ -1956,6 +1958,56 @@ struct SIMD[type: DType, size: Int]( return bitcast[_integral_type_of[type](), size](self).cast[int_dtype]() + @staticmethod + fn from_bytes[ + big_endian: Bool = is_big_endian() + ](bytes: InlineArray[Byte, type.sizeof()]) -> Scalar[type]: + """Converts a byte array to an scalar integer. + + Args: + bytes: The byte array to convert. + + Parameters: + big_endian: Whether the byte array is big-endian. + + Returns: + The integer value. + """ + var ptr: UnsafePointer[Scalar[type]] = bytes.unsafe_ptr().bitcast[ + Scalar[type] + ]() + var value = ptr[] + + @parameter + if is_big_endian() != big_endian: + return byte_swap(value) + + return value + + fn as_bytes[ + big_endian: Bool = is_big_endian() + ](self) -> InlineArray[Byte, type.sizeof()]: + """Convert the scalar integer to a byte array. + + Parameters: + big_endian: Whether the byte array should be big-endian. + + Returns: + The byte array. + """ + var value = self + + @parameter + if is_big_endian() != big_endian: + value = byte_swap(value) + + var ptr = UnsafePointer.address_of(value) + var array = InlineArray[Byte, type.sizeof()](fill=0) + + memcpy(array.unsafe_ptr(), ptr.bitcast[Byte](), type.sizeof()) + + return array^ + fn _floor_ceil_trunc_impl[intrinsic: StringLiteral](self) -> Self: constrained[ intrinsic == "llvm.floor" diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index 16704d13e4..16684aedfb 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -1855,6 +1855,50 @@ def test_float_conversion(): assert_almost_equal(Float64(UInt64(36)), 36.0) +def test_from_bytes_as_bytes(): + alias TwoBytes = InlineArray[Byte, DType.int16.sizeof()] + alias TwoUBytes = InlineArray[Byte, DType.uint16.sizeof()] + alias FourBytes = InlineArray[Byte, DType.int32.sizeof()] + + assert_equal(Int16.from_bytes[big_endian=True](TwoBytes(0, 16)), 16) + assert_equal(Int16.from_bytes[big_endian=False](TwoBytes(0, 16)), 4096) + assert_equal(Int16.from_bytes[big_endian=True](TwoBytes(252, 0)), -1024) + assert_equal(UInt16.from_bytes[big_endian=True](TwoUBytes(252, 0)), 64512) + assert_equal(Int16.from_bytes[big_endian=False](TwoBytes(252, 0)), 252) + assert_equal(Int32.from_bytes[big_endian=True](FourBytes(0, 0, 0, 1)), 1) + assert_equal( + Int32.from_bytes[big_endian=False](FourBytes(0, 0, 0, 1)), + 16777216, + ) + assert_equal( + Int32.from_bytes[big_endian=True](FourBytes(1, 0, 0, 0)), + 16777216, + ) + assert_equal( + Int32.from_bytes[big_endian=True](FourBytes(1, 0, 0, 1)), + 16777217, + ) + assert_equal( + Int32.from_bytes[big_endian=False](FourBytes(1, 0, 0, 1)), + 16777217, + ) + assert_equal( + Int32.from_bytes[big_endian=True](FourBytes(255, 0, 0, 0)), + -16777216, + ) + for x_ref in List[Int16](10, 100, -12, 0, 1, -1, 1000, -1000): + x = x_ref[] + + @parameter + for b in range(2): + assert_equal( + Int16.from_bytes[big_endian=b]( + Int16(x).as_bytes[big_endian=b]() + ), + x, + ) + + def test_reversed(): fn test[D: DType]() raises: assert_equal(SIMD[D, 4](1, 2, 3, 4).reversed(), SIMD[D, 4](4, 3, 2, 1)) @@ -1885,6 +1929,7 @@ def main(): test_extract() test_floor() test_floordiv() + test_from_bytes_as_bytes() test_iadd() test_indexing() test_insert() From c3432d058ea241397247927e9dd3a4ea473b5461 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Fri, 31 Jan 2025 18:44:06 -0800 Subject: [PATCH 332/509] [stdlib] Round out FFI aliases In order to have better symmetry for FFI bindings, it makes sense to have all of the relevant C basic data type aliases in `sys.ffi`. This PR just adds the other common ones. MODULAR_ORIG_COMMIT_REV_ID: 870a57d75cca333a3ffeb3a4a9d7f44a52bf3e2c --- docs/changelog.md | 2 ++ stdlib/src/sys/ffi.mojo | 15 +++++++++++++++ 2 files changed, 17 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index d5edc96c7f..f7a8421ca7 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -111,6 +111,8 @@ what we publish. to a list of scalars and vice versa, accepting the endianess as an argument. Similar to Python `int.from_bytes()` and `int.to_bytes()` functions. +- Added more aliases in `sys.ffi` to round out the usual needs for FFI bindings. + - The free floating functions for constructing different types have been deprecated for actual constructors: diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 8179ce0dd8..3ead55a560 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -29,6 +29,9 @@ from .intrinsics import _mlirtype_is_eq alias c_char = Int8 """C `char` type.""" +alias c_uchar = UInt8 +"""C `unsigned char` type.""" + alias c_int = Int32 """C `int` type. @@ -39,6 +42,12 @@ today. alias c_uint = UInt32 """C `unsigned int` type.""" +alias c_short = Int16 +"""C `short` type.""" + +alias c_ushort = UInt16 +"""C `unsigned short` type.""" + alias c_long = Scalar[_c_long_dtype()] """C `long` type. @@ -57,6 +66,12 @@ alias c_size_t = UInt alias c_ssize_t = Int """C `ssize_t` type.""" +alias c_float = Float32 +"""C `float` type.""" + +alias c_double = Float64 +"""C `double` type.""" + alias OpaquePointer = UnsafePointer[NoneType] """An opaque pointer, equivalent to the C `void*` type.""" From 66870915363831c25878ad47fcd52626dc5db0a3 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 1 Feb 2025 07:46:35 -0800 Subject: [PATCH 333/509] [mojo-stdlib] Remove some uses of old elementwise init syntax. This removes some uses of the obsolete direct-inlined elementwise initialization syntax. We now have fully functional `__init__` functionality, so use that instead. MODULAR_ORIG_COMMIT_REV_ID: 0b3f62c7961608d7aa0f9e68cc4ccaaecc548d88 --- stdlib/src/builtin/file.mojo | 10 ++++------ stdlib/src/builtin/object.mojo | 19 +++++++++++++++---- stdlib/src/builtin/simd.mojo | 6 +++--- 3 files changed, 22 insertions(+), 13 deletions(-) diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index 27ea5906ed..b86a62a02e 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -54,15 +54,13 @@ struct _OwnedStringRef(Boolable): self.data.free() fn consume_as_error(owned self) -> Error: - var data = self.data + result = Error() + result.data = self.data + result.loaded_length = -self.length # Don't free self.data in our dtor. self.data = UnsafePointer[UInt8]() - - return Error { - data: data, - loaded_length: -self.length, - } + return result fn __bool__(self) -> Bool: return self.length != 0 diff --git a/stdlib/src/builtin/object.mojo b/stdlib/src/builtin/object.mojo index f8fcd52e66..9aa2d3a308 100644 --- a/stdlib/src/builtin/object.mojo +++ b/stdlib/src/builtin/object.mojo @@ -33,8 +33,11 @@ from utils import Variant struct _NoneMarker(CollectionElementNew): """This is a trivial class to indicate that an object is `None`.""" + fn __init__(out self): + pass + fn copy(self) -> Self: - return _NoneMarker {} + return _NoneMarker() @register_passable("trivial") @@ -95,10 +98,14 @@ struct _RefCountedListRef(CollectionElement, CollectionElementNew): __get_address_as_uninit_lvalue(ptr.address) = _RefCountedList() self.lst = ptr.bitcast[NoneType]() + @always_inline + fn __init__(out self, *, lst: OpaquePointer): + self.lst = lst + @always_inline fn copy(self) -> Self: _ = self.lst.bitcast[_RefCountedList]()[].impl - return Self {lst: self.lst} + return Self(lst=self.lst) fn release(self): var ptr = self.lst.bitcast[_RefCountedList]()[].impl @@ -185,10 +192,14 @@ struct _RefCountedAttrsDictRef(CollectionElement, CollectionElementNew): self.attrs = ptr.bitcast[Int8]() + @always_inline + fn __init__(out self, *, attrs: UnsafePointer[Int8]): + self.attrs = attrs + @always_inline fn copy(self) -> Self: _ = self.attrs.bitcast[_RefCountedAttrsDict]()[].impl - return Self {attrs: self.attrs} + return Self(attrs=self.attrs) fn release(self): var ptr = self.attrs.bitcast[_RefCountedAttrsDict]()[].impl @@ -305,7 +316,7 @@ struct _ObjectImpl( @always_inline fn __init__(out self): - self.value = Self.type(_NoneMarker {}) + self.value = Self.type(_NoneMarker()) @always_inline @implicit diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 3da7506276..2c9065e06d 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -208,11 +208,11 @@ fn _unchecked_zero[type: DType, size: Int]() -> SIMD[type, size]: value = __mlir_attr[`#pop.simd<0> : !pop.scalar`], ]() ) - return SIMD[type, size] { - value: __mlir_op.`pop.simd.splat`[ + return SIMD[type, size]( + __mlir_op.`pop.simd.splat`[ _type = __mlir_type[`!pop.simd<`, size.value, `, `, type.value, `>`] ](zero) - } + ) @always_inline("nodebug") From b5e6455c914ea60ce6b4ca7407d1ffbfb24daff0 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sun, 2 Feb 2025 07:50:18 +0000 Subject: [PATCH 334/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025020205 --- examples/life/magic.lock | 186 +++++++++++++++++----------------- examples/magic.lock | 186 +++++++++++++++++----------------- examples/operators/magic.lock | 186 +++++++++++++++++----------------- examples/testing/magic.lock | 186 +++++++++++++++++----------------- magic.lock | 186 +++++++++++++++++----------------- 5 files changed, 460 insertions(+), 470 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index eae1460d3d..a74da0c302 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -116,7 +116,7 @@ environments: - 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sha256: 122b3ad0404caebf7c5271d75ae5504f228795f529b98be2011d1a1cdc71f93d - md5: f218b84b9f1baa40269eaa760534be98 + sha256: c70af4ebd29c7c4e445bdf37f592ea39dec3090c59cdd4c96f8eec1e0a6fcba8 + md5: 40b22aabe3af350b240167b503c4c48e depends: - - max-core ==25.1.0.dev2025013105 release + - max-core ==25.1.0.dev2025020205 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary size: 22925 - timestamp: 1738300734602 + timestamp: 1738473487228 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From 637ff9c0e685ba61b86abe78dbb883ad43a27912 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 1 Feb 2025 23:35:24 -0800 Subject: [PATCH 335/509] [mojo-stdlib] Migrate off old self-init initializer syntax. This adopts modern initializer syntax, removing uses of the old thing that only worked with register passable types. MODULAR_ORIG_COMMIT_REV_ID: c92d14e24e46dd0c7b51d75a4905a363929898fb --- stdlib/src/python/_bindings.mojo | 14 +++++++------- stdlib/src/python/_cpython.mojo | 14 +++++++------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/stdlib/src/python/_bindings.mojo b/stdlib/src/python/_bindings.mojo index 19f3b03fb3..c556659e94 100644 --- a/stdlib/src/python/_bindings.mojo +++ b/stdlib/src/python/_bindings.mojo @@ -107,15 +107,15 @@ fn python_type_object[ # Zeroed item terminator slots.append(PyType_Slot.null()) - var type_spec = PyType_Spec { + var type_spec = PyType_Spec( # FIXME(MOCO-1306): This should be `T.__name__`. - name: type_name.unsafe_cstr_ptr(), - basicsize: sizeof[PyMojoObject[T]](), - itemsize: 0, - flags: Py_TPFLAGS_DEFAULT, + type_name.unsafe_cstr_ptr(), + sizeof[PyMojoObject[T]](), + 0, + Py_TPFLAGS_DEFAULT, # Note: This pointer is only "read-only" by PyType_FromSpec. - slots: slots.unsafe_ptr(), - } + slots.unsafe_ptr(), + ) # Construct a Python 'type' object from our type spec. var type_obj = cpython.PyType_FromSpec(UnsafePointer.address_of(type_spec)) diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index 715f156c9a..187354298a 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -449,7 +449,7 @@ struct PyType_Slot: @staticmethod fn null() -> Self: - return PyType_Slot {slot: 0, pfunc: OpaquePointer()} + return PyType_Slot(0, OpaquePointer()) @value @@ -1055,12 +1055,12 @@ struct CPython: ) _ = v - return PyKeysValuePair { - key: key, - value: value, - position: position.take_pointee(), - success: result == 1, - } + return PyKeysValuePair( + key, + value, + position.take_pointee(), + result == 1, + ) # ===-------------------------------------------------------------------===# # Python Module operations From e064839300c11c7c6ce23e5afd5075cacc2254b1 Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 4 Feb 2025 06:46:17 +0000 Subject: [PATCH 336/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025020316 --- examples/life/magic.lock | 245 +++++++++++++++++----------------- examples/magic.lock | 221 +++++++++++++++--------------- examples/operators/magic.lock | 221 +++++++++++++++--------------- examples/testing/magic.lock | 221 +++++++++++++++--------------- magic.lock | 221 +++++++++++++++--------------- 5 files changed, 557 insertions(+), 572 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index a74da0c302..d4aa9219b5 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -69,7 +69,7 @@ environments: - 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- conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.2.0-h4bba637_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda @@ -138,7 +138,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-28_h7ac8fdf_openblas.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.3-hb9d3cd8_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.4-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libmad-0.15.1b-h0b41bf4_1001.conda - 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sha256: c70af4ebd29c7c4e445bdf37f592ea39dec3090c59cdd4c96f8eec1e0a6fcba8 - md5: 40b22aabe3af350b240167b503c4c48e + sha256: b835fba08c1acc7bcd4ca871b98e013a1bdc72c538a3e307d75d081355c95c76 + md5: 9a263ca62e2cb9b31a0de06053f87b30 depends: - - max-core ==25.1.0.dev2025020205 release + - max-core ==25.1.0.dev2025020316 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22925 - timestamp: 1738473487228 + size: 22930 + timestamp: 1738599319244 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From 677529df4158ee9aa1ba55dc3494a449708ca39c Mon Sep 17 00:00:00 2001 From: Nikolay Panchenko Date: Mon, 3 Feb 2025 11:26:14 -0500 Subject: [PATCH 337/509] [stdlib] Removing special handling of `[max|min]` of a bool type `pop.[max|min]` can correctly handle boolean type, therefore no need to have extra code in `max` and `min` functions. MODULAR_ORIG_COMMIT_REV_ID: 626b7b7659d4ac6cc2b762e4c55f69919da8616c --- stdlib/src/builtin/math.mojo | 26 ++++++++++---------------- 1 file changed, 10 insertions(+), 16 deletions(-) diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 4b2bc355ab..897500bb67 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -192,15 +192,12 @@ fn max(x: SIMD, y: __type_of(x), /) -> __type_of(x): A SIMD vector containing the elementwise maximum of x and y. """ - @parameter - if x.type is DType.bool: - return max(x.cast[DType.uint8](), y.cast[DType.uint8]()).cast[x.type]() - else: - constrained[ - x.type.is_numeric(), "the SIMD type must be numeric or boolean" - ]() + constrained[ + x.type is DType.bool or x.type.is_numeric(), + "the SIMD type must be numeric or boolean", + ]() - return __mlir_op.`pop.max`(x.value, y.value) + return __mlir_op.`pop.max`(x.value, y.value) trait _CopyableGreaterThanComparable(Copyable, GreaterThanComparable): @@ -279,15 +276,12 @@ fn min(x: SIMD, y: __type_of(x), /) -> __type_of(x): A SIMD vector containing the elementwise minimum of x and y. """ - @parameter - if x.type is DType.bool: - return min(x.cast[DType.uint8](), y.cast[DType.uint8]()).cast[x.type]() - else: - constrained[ - x.type.is_numeric(), "the SIMD type must be numeric or boolean" - ]() + constrained[ + x.type is DType.bool or x.type.is_numeric(), + "the SIMD type must be numeric or boolean", + ]() - return __mlir_op.`pop.min`(x.value, y.value) + return __mlir_op.`pop.min`(x.value, y.value) trait _CopyableLessThanComparable(Copyable, LessThanComparable): From 09f0651125677e1d738a48d36c1e60ba1989156f Mon Sep 17 00:00:00 2001 From: ematejska Date: Mon, 3 Feb 2025 14:08:46 -0800 Subject: [PATCH 338/509] Update faq and readme MODULAR_ORIG_COMMIT_REV_ID: e9bb82f5512eab152df1afcee714fe966dcafc01 --- README.md | 5 +++-- docs/faq.md | 65 ++++++++++++++++++++++++++++------------------------- 2 files changed, 37 insertions(+), 33 deletions(-) diff --git a/README.md b/README.md index 3dac78f624..88777c3bb3 100644 --- a/README.md +++ b/README.md @@ -14,8 +14,9 @@ Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems -programming and metaprogramming features. Mojo is still young, but it is designed -to become the best way to extend Python over time. +programming and metaprogramming features. Mojo is still young, but it is +designed to write blazing-fast code for CPUs, GPUs, and more as part of +the [MAX Platform](https://www.modular.com/max). This repo includes source code for: diff --git a/docs/faq.md b/docs/faq.md index eb07300b96..6ff7db931b 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -13,12 +13,14 @@ channels](https://www.modular.com/community). ### Why did you build Mojo? We built Mojo to solve an internal challenge at Modular, and we are using it -extensively in our systems such as our [AI -Engine](/engine). As a result, we are extremely committed to +extensively in our systems such as our [MAX Platform](https://www.modular.com/max). +As a result, we are extremely committed to its long term success and are investing heavily in it. Our overall mission is to unify AI software and we can’t do that without a unified language that can -scale across the AI infrastructure stack. That said, we don’t plan to stop at -AI—the north star is for Mojo to support the whole gamut of general-purpose +scale across the AI infrastructure stack. Our current focus is to unify +CPU+GPU programming with blazing-fast execution on the +[MAX Platform](https://www.modular.com/max). That said, the north star +is for Mojo to support the whole gamut of general-purpose programming over time. For a longer answer, read [Why Mojo](/mojo/why-mojo). @@ -138,9 +140,12 @@ direct programming support for the MLIR intermediate representations. ### Is Mojo only for AI or can it be used for other stuff? -Mojo is a general purpose programming language. We use Mojo at Modular to -develop AI algorithms, but as we grow Mojo into a superset of Python, you can -use it for other things like HPC, data transformations, writing pre/post +Mojo's initial focus is to solve AI programmability challenges. See +[here](https://github.com/modular/max/tree/main/examples/custom_ops) for examples +of how to write custom GPU operations. That being said, +the goal is to grow Mojo into a general purpose programming language. We use Mojo +at Modular to develop AI algorithms, but +you can use it for other things like HPC, data transformations, writing pre/post processing operations, and much more. For examples of how Mojo can be used for other general programming tasks, see our [Mojo examples](https://github.com/modular/mojo/tree/main/examples). @@ -163,22 +168,21 @@ languages (EDSLs) like Triton, read the “Embedded DSLs in Python” section of [Why Mojo](/mojo/why-mojo#embedded-dsls-in-python). -### How does Mojo help with PyTorch and TensorFlow acceleration? +### How does Mojo help with PyTorch acceleration? -Mojo is a general purpose programming language, so it has no specific -implementations for ML training or serving, although we use Mojo as part of the -overall Modular AI stack. The [Modular AI -Engine](/engine), for example, supports deployment of PyTorch -and TensorFlow models, while Mojo is the language we use to write the engine’s -in-house kernels. +We use Mojo as part of the overall Modular AI stack, +[MAX](https://www.modular.com/max) which accelerates PyTorch models. +Mojo is the language we use to write the MAX’s high-performance CPU and GPU graph +operations. ### Does Mojo support distributed execution? -Not alone. You will need to leverage the [Modular AI -Engine](/engine) for that. Mojo is one component of the -Modular stack that makes it easier for you to author highly performant, -portable kernels, but you’ll also need a runtime (or “OS”) that supports graph -level transformations and heterogeneous compute. +Not alone. You will need to leverage the +[MAX Platform](https://www.modular.com/max) +for that. Mojo is one component of the Modular stack that makes it easier +for you to author highly performant, portable CPU and GPU graph operations, +but you’ll also need a runtime (or “OS”) that supports graph level +transformations and heterogeneous compute, which is provided by MAX. ### Will Mojo support web deployment (such as Wasm or WebGPU)? @@ -229,11 +233,11 @@ For lots more information, check out our 3-part blog post series about [how Mojo gets a 35,000x speedup over Python](https://www.modular.com/blog/how-mojo-gets-a-35-000x-speedup-over-python-part-1). -By the way, all the kernels that power the [Modular AI -Engine](/engine) are written in Mojo. We also compared our -matrix multiplication implementation to other state-of-the-art implementations -that are usually written in assembly. To see the results, see [our blog post -about unified matrix +By the way, all the CPU and GPU graph operations that power Modular's +[MAX Platfrom](https://www.modular.com/max) are written in Mojo. +We also compared our matrix multiplication implementation to other +state-of-the-art implementations that are usually written in assembly. +To see the results, see [our blog post about unified matrix multiplication](https://www.modular.com/blog/the-worlds-fastest-unified-matrix-multiplication). ## Performance @@ -242,13 +246,12 @@ multiplication](https://www.modular.com/blog/the-worlds-fastest-unified-matrix-m It’s important to remember that Mojo is a general-purpose programming language, and any AI-related benchmarks will rely heavily upon other framework -components. For example, our in-house kernels for the [Modular AI -Engine](/engine) are all written in Mojo and you can learn more -about our kernel performance in our [matrix multiplication blog +components. For example, our in-house CPU and GPU graph operations that power +Modular's [MAX](https://www.modular.com/max) are all written in +Mojo and you can learn more about performance in our [matrix multiplication blog post](https://www.modular.com/blog/the-worlds-fastest-unified-matrix-multiplication). -For details about our end-to-end model performance relative to the latest -releases of TensorFlow and PyTorch, check out our [performance -dashboard](https://www.modular.com/max/performance). +For details about our end-to-end model performance, read about how we measure +performance at Modular [here](https://www.modular.com/blog/max-gpu-state-of-the-art-throughput-on-a-new-genai-platform). ## Mojo SDK @@ -365,7 +368,7 @@ for more coarse-grain updates. ### Will Mojo be open-sourced? -We expect to open-source Mojo progressively over time as it continues to mature. +We have commited to open-sourcing Mojo in 2026. Mojo is still young, so we will continue to incubate it within Modular until more of its internal architecture is fleshed out. From 4a3c4a5655dae45e7cf7f4e679e1fa526e2d4d5d Mon Sep 17 00:00:00 2001 From: Scott Main Date: Mon, 3 Feb 2025 14:55:09 -0800 Subject: [PATCH 339/509] Remove notice to switch to magic It's been long enough since we deprecated the `modular` tool. MODULAR_ORIG_COMMIT_REV_ID: 8625c1a6733633fc4af60388b8fdaeeea21ca038 --- docs/changelog-released.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index 95ee3fbab8..eb0dd26bc1 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -11,15 +11,6 @@ To check your current version, run `mojo --version`. To update the version of Mojo for your project with the `magic` package manager, follow the instructions in [Update a package](/magic#update-a-package) to update the `max` package. -:::caution Switch to Magic - -The `modular` command-line tool is deprecated (see [how to uninstall -it](/max/faq#if-you-installed-with-modular-deprecated-1)). We recommend that -you now [manage your packages with `magic`](/magic), but you can also [use -conda](/magic/conda). - -::: - ## v24.6 (2024-12-17) ### ✨ Highlights From e225478aae071a027ccdae69ae27806887c5cddc Mon Sep 17 00:00:00 2001 From: Rasool Sharifi Date: Mon, 3 Feb 2025 19:02:02 -0500 Subject: [PATCH 340/509] [stdlib] Add all swizzle modes to TMA load and multicast Add all swizzle modes to TMA load and multicast MODULAR_ORIG_COMMIT_REV_ID: 009f45488a08cd7bf438a4396881e6c47c2bda33 --- stdlib/src/sys/intrinsics.mojo | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 653e772849..6b7ca6dfaf 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -1286,7 +1286,7 @@ struct _ClusterIdx: return "llvm.nvvm.read.ptx.sreg.clusterid." + dim @always_inline("nodebug") - fn __getattr__[dim: StringLiteral](self) -> UInt32: + fn __getattr__[dim: StringLiteral](self) -> UInt: """Gets the `x`, `y`, or `z` coordinates of a cluster within a grid. Returns: @@ -1300,7 +1300,9 @@ struct _ClusterIdx: dim in ("x", "y", "z"), "the accessor must be either x, y, or z" ]() alias intrinsic_name = Self._get_intrinsic_name[dim]() - return llvm_intrinsic[intrinsic_name, UInt32, has_side_effect=False]() + return UInt( + Int(llvm_intrinsic[intrinsic_name, UInt32, has_side_effect=False]()) + ) alias cluster_idx = _ClusterIdx() From c4a8caeb51cc00caa9fc4fa9b3970dfd06c070ec Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Tue, 4 Feb 2025 02:34:16 -0800 Subject: [PATCH 341/509] [mojo-lang] Remove the old direct elementwise initializer syntax This removes the `Type{field1: 42, field2: 17}` syntax for direct initializing register passable types. This was legacy syntax - to upgrade your code, add the `@value` decorator to your struct to get a memberwise initializer and use `Type(field1=42, field2 = 17)` instead. MODULAR_ORIG_COMMIT_REV_ID: 38785823341373c2abdee70e26b754f6a061facd --- docs/changelog.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index f7a8421ca7..3711ca5f2f 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -412,6 +412,10 @@ what we publish. - `StringableCollectionElement` is deprecated, use `WritableCollectionElement` instead which still allows you to construct a `String`, but can avoid intermediary allocations. +- The `Type{field1: 42, field2: 17}` syntax for direct initializing register + passable types has been removed. This was legacy syntax - to upgrade + your code, add the @value decorator to your struct to get a memberwise + initializer and use `Type(field1=42, field2 = 17)` instead. ### 🛠️ Fixed From eb85596bb27eb754f11158295df315fc650c6fe5 Mon Sep 17 00:00:00 2001 From: Arthur Evans Date: Tue, 4 Feb 2025 11:04:42 -0800 Subject: [PATCH 342/509] Clarify that functions can have a single return value, declared using either `-> type` or an `out` argument. MODULAR_ORIG_COMMIT_REV_ID: fc58bdcef8ab0f7b61f30da9c4fd43ba96f756da --- docs/manual/functions.mdx | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/docs/manual/functions.mdx b/docs/manual/functions.mdx index 371eff68ce..20874904d4 100644 --- a/docs/manual/functions.mdx +++ b/docs/manual/functions.mdx @@ -638,10 +638,13 @@ def get_name_tag(owned name: String, out name_tag: NameTag): The `out` argument convention identifies an uninitialized variable that the function must initialize. (This is the same as the `out` convention used in -[struct constructors](/mojo/manual/lifecycle/life#constructor).) A function can -have only one `out` argument. The `out` argument for a named result can appear -anywhere in the argument list, but by convention, it should be the last argument -in the list. +[struct constructors](/mojo/manual/lifecycle/life#constructor).) The `out` +argument for a named result can appear anywhere in the argument list, but by +convention, it should be the last argument in the list. + +A function can declare only one return value, whether it's declared using an +`out` argument or using the standard -> +type syntax. A function with a named result argument doesn't need to include an explicit `return` statement, as shown above. If the function terminates without a `return`, From 2a65660cae458ce95ed2e5dc16781fffb77b01d6 Mon Sep 17 00:00:00 2001 From: Tracy Sharpe <99041446+raiseirql@users.noreply.github.com> Date: Tue, 4 Feb 2025 14:58:25 -0800 Subject: [PATCH 343/509] [stdlib] Fix AMD _printf() handling of many arguments Fix the indexing of the parameter pack when _printf() is given a large number of inputs. MODULAR_ORIG_COMMIT_REV_ID: 8640719a0f9565acdd102fbe09d447a044ebdde0 --- stdlib/src/builtin/io.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index 58283eacbc..c2ff86a746 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -244,7 +244,7 @@ fn _printf[ @parameter for i in range(num_args): - arguments[i] = _to_uint64(args[i]) + arguments[i] = _to_uint64(args[group + i]) message = printf_append_args( message, num_args, From 658af3a9cc98a26569d2ae080916599dd219680d Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 5 Feb 2025 12:23:15 -0500 Subject: [PATCH 344/509] [docs] Fix contributing guide with nightly/main branch change There was still an image that was incorrect for the old setup. Also some other minor cleanup e.g. no need to branch off upstream in case user didn't fork all the branches now that they work off the default branch. MODULAR_ORIG_COMMIT_REV_ID: d4af6bb3e3dc6fbe78504ad22bf8efb2580f5542 --- CONTRIBUTING.md | 13 ++++++------- stdlib/docs/development.md | 2 +- stdlib/docs/images/base-branch.png | Bin 163923 -> 0 bytes 3 files changed, 7 insertions(+), 8 deletions(-) delete mode 100644 stdlib/docs/images/base-branch.png diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4ad284fa1c..daca213393 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -237,16 +237,16 @@ cd mojo Add the upstream remote and fetch it: ```bash -git remote add upstream git@github.com:modularml/mojo.git +git remote add upstream git@github.com:modular/mojo.git git fetch upstream ``` -#### Branching off nightly +#### Branching off main Make sure to branch off `main` to work on your PR: ```bash -git checkout upstream/main +git checkout main git checkout -b my-fix-pr ``` @@ -311,12 +311,11 @@ You'll see a link to create a PR: ```plaintext remote: Create a pull request for 'my-fix-pr' on GitHub by visiting: -remote: https://github.com/jackos/mojo/pull/new/my-fix-pr +remote: https://github.com/[your-username]/mojo/pull/new/my-fix-pr ``` -Make sure you point it to the `main` branch: - -![Base Branch](stdlib/docs/images/base-branch.png) +It should automatically set the base branch to the upstream `modular/mojo/main`, +but if it doesn't, you can set it manually. Now fill out the details: diff --git a/stdlib/docs/development.md b/stdlib/docs/development.md index 000676598e..1b6e812a4d 100644 --- a/stdlib/docs/development.md +++ b/stdlib/docs/development.md @@ -9,7 +9,7 @@ If this is your first time contributing, first read everything in you need to do the following: 1. [Fork and clone the repo](../../CONTRIBUTING.md#fork-and-clone-the-repo) -2. [Branch off main](../../CONTRIBUTING.md#branching-off-nightly) +2. [Branch off main](../../CONTRIBUTING.md#branching-off-main) 3. 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insertions(+), 350 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index d4aa9219b5..9651b71eac 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -172,12 +172,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py312h178313f_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025020316-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025020316-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025020316-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025020316-release.conda + - conda: 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d57f26218e88f2d8ffc84d06a9022817588e7b5f810a10b60ad7b5153b39dcad + md5: 939c3020651164ebe9c9e39cf512448d depends: - - max-core ==25.1.0.dev2025020316 release + - max-core ==25.1.0.dev2025020517 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22930 - timestamp: 1738599319244 + size: 22926 + timestamp: 1738777581246 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From b42a59010dda4e7d08878527f6c895f5a59a3ad7 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Wed, 5 Feb 2025 10:24:57 -0800 Subject: [PATCH 346/509] [docs] Clarified descriptions of error propagation and non-raising functions MODULAR_ORIG_COMMIT_REV_ID: 4bd813d808c26263635f602ecd1619c028ac7c78 --- docs/manual/errors.mdx | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/docs/manual/errors.mdx b/docs/manual/errors.mdx index 2fa9d6b66f..b3200ca547 100644 --- a/docs/manual/errors.mdx +++ b/docs/manual/errors.mdx @@ -39,9 +39,10 @@ conditions is the error message that you provide. An error interrupts the current execution flow of your program. If you provide an error handler (as described in [Handle an error](#handle-an-error)) in the -current scope or any calling scope, execution resumes with that handler. If -there is no applicable error handler, your program terminates with a non-zero -exit code and prints the error message. For example: +current function, execution resumes with that handler. If the error isn't +handled in the current function, it propagates to the calling function and so +on. If an error isn't caught by any error handler, your program terminates with +a non-zero exit code and prints the error message. For example: ```output Unhandled exception caught during execution: integer overflow @@ -58,10 +59,27 @@ fn incr(n: Int) raises -> Int: return n + 1 ``` -On the other hand, you **cannot** use the `raises` keyword when defining a -function using the `def` keyword, because `def` already implies that the -function may raise an error. So the following is equivalent to the function -defined above: +If you don't include the `raises` keyword when defining a function with `fn`, +then the function must explicitly handle any errors that might occur in code +that it executes. For example: + +```mojo +# This function doesn't compile because of the unhandled error +fn unhandled_error(n: Int): + print(n, "+ 1 =", incr(n)) + +# This function compiles because it handles the possible error +fn handled_error(n: Int): + try: + print(n, "+ 1 =", incr(n)) + except e: + print("Handled an error:", e) +``` + +In contrast, you **cannot** use the `raises` keyword when defining a +function using the `def` keyword, because `def` always implies that the +function might raise an error. So the following is equivalent to the `incr` +function defined above with `fn`: ```mojo def incr(n: Int) -> Int: From 245ea979f61dd341d61964b3df425b3b487aa37b Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Wed, 5 Feb 2025 16:15:44 -0800 Subject: [PATCH 347/509] [docs] Updated string operators information to reflect 26.1 changes Updated the Operators section of the Mojo manual to document that `StringLiteral` now supports both compile-time and run-time string replication. Also added a tip to use `String()` instead of `+` to concatenate multiple values. MODULAR_ORIG_COMMIT_REV_ID: 089fa452c5e17f2d5c95f04c74c11e14f6460269 --- docs/manual/operators.mdx | 41 +++++++++++++++++++++++++++++++-------- 1 file changed, 33 insertions(+), 8 deletions(-) diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index 5ee235c9b0..0356ec4a93 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -528,6 +528,24 @@ Grace Hopper Grace Slick ``` +:::tip +When concatenating multiple values together to form a `String`, using the +multi-argument `String()` constructor is more performant than using multiple +`+` concatenation operators and can improve code readability. For example, +instead of writing this: + +```mojo +result = "The point at (" + String(x) + ", " + String(y) + ")" +``` + +you can write: + +```mojo +result = String("The point at (", x, ", ", y, ")") +``` + +::: + #### String replication The `*` operator replicates a `String` a specified number of times. For example: @@ -542,8 +560,9 @@ print(str2) lalalalala ``` -`StringLiteral` supports the `*` operator for string replication *only* at -compile time. So the following examples compile and run successfully: +`StringLiteral` supports the `*` operator for both compile-time and run-time +string replication. The following examples perform compile-time string +replication resulting in `StringLiteral` values: ```mojo alias divider1 = "=" * 40 @@ -552,7 +571,7 @@ alias divider2 = symbol * 40 # You must define the following function using `fn` because an alias # initializer cannot call a function that can potentially raise an error. -fn generate_divider(char: StringLiteral, repeat: Int) -> StringLiteral: +fn generate_divider(char: StringLiteral, repeat: IntLiteral) -> StringLiteral: return char * repeat alias divider3 = generate_divider("~", 40) # Evaluated at compile-time @@ -568,13 +587,19 @@ print(divider3) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``` -However, the following examples fail to compile because the replication -operators would be evaluated at run-time: +In contrast, the following examples perform run-time string replication +resulting in `String` values: ```mojo -# Both of these statements would fail to compile -var div1 = "^" * 40 -print("_" * 40) +repeat = 40 +div1 = "^" * repeat +print(div1) +print("_" * repeat) +``` + +```output +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +________________________________________ ``` #### String comparison From 08163c76969517bc814784cd5df4d3a9826e62fa Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Wed, 5 Feb 2025 16:16:06 -0800 Subject: [PATCH 348/509] [docs] Documented walrus operator limitation MODULAR_ORIG_COMMIT_REV_ID: f6879165bbba919a0e2035d38848bac090dba010 --- docs/roadmap.md | 49 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/docs/roadmap.md b/docs/roadmap.md index 764e99495e..05e5bf94da 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -474,3 +474,52 @@ var h: String = "hello" # or print(g or String("hello")) ``` + +### Walrus assignment expression limitations + +The Mojo compiler reports an uninitialized value error if an expression uses +multiple "walrus" [assignment +expressions](/mojo/manual/operators#assignment-expressions) to declare more than +one variable. For example: + +```mojo +def A() -> Int: return 42 + +def B() -> String: return "waffles" + +def main(): + if (a := A()) and (b := B()): + print("a =", a) + print("b =", b) +``` + +```output +walrus-conditional.mojo:8:14: error: use of uninitialized value 'b' + print("b =", b) + ^ +walrus-conditional.mojo:6:24: note: 'b' declared here + if (a := A()) and (b := B()): + ^ +``` + +Ideally, the Mojo compiler should compile this code because the second `print()` +statement executes only if a value is assigned to `b`. To work around this +limitation you can explicitly initialize `b` before the `if` statement, like +this: + +```mojo +def A() -> Int: return 42 + +def B() -> String: return "waffles" + +def main(): + b = String() + if (a := A()) and (b := B()): + print("a =", a) + print("b =", b) +``` + +```output +a = 42 +b = waffles +``` From 940a0f7dbeda0d1fdbcf8084f4b3c8ca007d52d7 Mon Sep 17 00:00:00 2001 From: ematejska Date: Wed, 5 Feb 2025 16:51:47 -0800 Subject: [PATCH 349/509] Updating modularml to modular for max and mojo repos now that the Github org changes MODULAR_ORIG_COMMIT_REV_ID: b9ef61ba464a2f508359b05dd9a5bea99b2d34c1 --- CONTRIBUTING.md | 2 +- README.md | 16 ++++++++-------- examples/check_mod.py | 2 +- proposals/byte-as-uint8.md | 2 +- proposals/inferred-parameters.md | 2 +- proposals/lifetimes-keyword-renaming.md | 2 +- proposals/ref-convention.md | 4 ++-- proposals/remove-let-decls.md | 10 +++++----- stdlib/README.md | 2 +- stdlib/docs/development.md | 4 ++-- stdlib/src/builtin/simd.mojo | 2 +- stdlib/test/builtin/test_issue_1004.mojo | 2 +- stdlib/test/builtin/test_issue_1505.mojo | 2 +- stdlib/test/builtin/test_simd.mojo | 4 ++-- stdlib/test/collections/test_list.mojo | 2 +- 15 files changed, 29 insertions(+), 29 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index daca213393..4c39f7729a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -222,7 +222,7 @@ for more details. #### Fork and clone the repo -Go to the [Mojo repo](https://github.com/modularml/mojo) and click the fork +Go to the [Mojo repo](https://github.com/modular/mojo) and click the fork button: ![Create Fork](stdlib/docs/images/create-fork.png) diff --git a/README.md b/README.md index 88777c3bb3..2a4878c789 100644 --- a/README.md +++ b/README.md @@ -26,11 +26,11 @@ This repo includes source code for: This repo has two primary branches: -- The [`stable`](https://github.com/modularml/mojo/tree/stable) branch, which +- The [`stable`](https://github.com/modular/mojo/tree/stable) branch, which is in sync with the last stable released version of Mojo. Use the examples here if you’re using a [release build of Mojo](#latest-released). -- The [`main`](https://github.com/modularml/mojo/tree/main) branch, which +- The [`main`](https://github.com/modular/mojo/tree/main) branch, which is in sync with the Mojo nightly build and subject to breakage. Use this branch for [contributions](./CONTRIBUTING.md), or if you're using the latest [nightly build of Mojo](#latest-nightly). @@ -79,7 +79,7 @@ When you clone this repo, you'll be on the `main` branch by default, which includes code matching the latest nightly build: ```bash -git clone https://github.com/modularml/mojo.git +git clone https://github.com/modular/mojo.git ``` If you want to instead see the source from the most recent stable @@ -88,13 +88,13 @@ release, then you can switch to the `stable` branch. ## Contributing When you want to report issues or request features, [please create a GitHub -issue here](https://github.com/modularml/mojo/issues). +issue here](https://github.com/modular/mojo/issues). See [here](./CONTRIBUTING.md) for guidelines on filing good bugs. We welcome contributions to this repo on the -[`main`](https://github.com/modularml/mojo/tree/main) +[`main`](https://github.com/modular/mojo/tree/main) branch. If you’d like to contribute to Mojo, please first read our [Contributor -Guide](https://github.com/modularml/mojo/blob/main/CONTRIBUTING.md). +Guide](https://github.com/modular/mojo/blob/main/CONTRIBUTING.md). For more general questions or to chat with other Mojo developers, check out our [Discord](https://discord.gg/modular). @@ -108,6 +108,6 @@ MAX and Mojo usage and distribution are licensed under the ## Thanks to our contributors - - + + diff --git a/examples/check_mod.py b/examples/check_mod.py index 88ef0e75cb..e2f2719906 100644 --- a/examples/check_mod.py +++ b/examples/check_mod.py @@ -18,7 +18,7 @@ FIX = """ ------------------------------------------------------------------------- fix following the steps here: - https://github.com/modularml/mojo/issues/1085#issuecomment-1771403719 + https://github.com/modular/mojo/issues/1085#issuecomment-1771403719 ------------------------------------------------------------------------- """ diff --git a/proposals/byte-as-uint8.md b/proposals/byte-as-uint8.md index 7ff764a60e..9881574ccd 100644 --- a/proposals/byte-as-uint8.md +++ b/proposals/byte-as-uint8.md @@ -40,5 +40,5 @@ and 78 results for `DTypePointer[DType.int8]`. Replacing `DTypePointer[DType.int8]` with `DTypePointer[DType.uint8]` and `Pointer[Int8]` with `Pointer[UInt8]` on case by case bases is a substantial refactoring effort, but it will prevent a certain class of logical bugs (see -). As it is a breaking change in +). As it is a breaking change in sense of API design, it is sensible to do the refactoring as soon as possible. diff --git a/proposals/inferred-parameters.md b/proposals/inferred-parameters.md index b9ae5a7451..f8eb6bf5c1 100644 --- a/proposals/inferred-parameters.md +++ b/proposals/inferred-parameters.md @@ -49,7 +49,7 @@ scalar_param[DType.int32, Int32()]() # 'dt' parameter is required This has been requested multiple times in various forms, especially given the new autoparameterization feature. The current tracking feature request: -- +- ## Proposal diff --git a/proposals/lifetimes-keyword-renaming.md b/proposals/lifetimes-keyword-renaming.md index e39c39b32b..3b783202e7 100644 --- a/proposals/lifetimes-keyword-renaming.md +++ b/proposals/lifetimes-keyword-renaming.md @@ -2,7 +2,7 @@ Date: October 2024 -Previous revision: [[June 2023](https://github.com/modularml/mojo/blob/f8d7cb8ba4c21ec3fbc87e21609b3fd56cab695f/proposals/lifetimes-keyword-renaming.md)] +Previous revision: [[June 2023](https://github.com/modular/mojo/blob/f8d7cb8ba4c21ec3fbc87e21609b3fd56cab695f/proposals/lifetimes-keyword-renaming.md)] The design of the Mojo references subsystem is starting to come together. To finalize the major points, it helps to come back and re-evaluate several early diff --git a/proposals/ref-convention.md b/proposals/ref-convention.md index 3d69a9f9ce..53b608e51e 100644 --- a/proposals/ref-convention.md +++ b/proposals/ref-convention.md @@ -12,7 +12,7 @@ Mojo’s safe references have evolved and iterated a lot. From the initial compiler re-plumbing that made memory-only types possible, to threading lifetimes through everything with the introduction of `!lit.ref` to the development of a “user-space” Reference type, to the recent discussions about -[adding automatic dereference to Reference](https://github.com/modularml/mojo/discussions/2594), +[adding automatic dereference to Reference](https://github.com/modular/mojo/discussions/2594), we’ve been iteratively improving the model with a goal of ending up with something powerful and explainable. @@ -42,7 +42,7 @@ Along the way, we’ve had a number of challenges to address: to `Reference`. It would be awesome to clarify this. 5. We still need to - [reconsider which keywords](https://github.com/modularml/mojo/blob/main/proposals/lifetimes-keyword-renaming.md) + [reconsider which keywords](https://github.com/modular/mojo/blob/main/proposals/lifetimes-keyword-renaming.md) to use for argument conventions. The `inout` keyword, for example, is problematic because it works with types that are not movable or copyable. The callee doesn’t actually move things in and out, it takes a mutable reference. diff --git a/proposals/remove-let-decls.md b/proposals/remove-let-decls.md index 34f8eb3b7c..1d1f8a3692 100644 --- a/proposals/remove-let-decls.md +++ b/proposals/remove-let-decls.md @@ -1,6 +1,6 @@ # Simplifying Mojo🔥 - let's get rid of `let` -Chris Lattner, Dec 5, 2023, Status: **Accepted**, [discussion thread](https://github.com/modularml/mojo/discussions/1456#discussioncomment-8358722) +Chris Lattner, Dec 5, 2023, Status: **Accepted**, [discussion thread](https://github.com/modular/mojo/discussions/1456#discussioncomment-8358722) Mojo is still a new language, and is rapidly evolving. We’re learning a lot from other languages, but Mojo poses its own set of tradeoffs that indicate a @@ -10,7 +10,7 @@ One of the early decisions made in Mojo's development is that it adopts the `let` and `var` design point that Swift uses. This whitepaper argues that we should switch to a simpler model by jettisoning `let` and just retaining `var` (and implicit Python-style variable declarations in `def`). This has also been -[suggested by the community](https://github.com/modularml/mojo/issues/1205). +[suggested by the community](https://github.com/modular/mojo/issues/1205). Note that immutability and value semantics remain an important part of the Mojo design, this is just about removing "named immutable variables". Immutable @@ -31,7 +31,7 @@ variables aren't a core programming concept, and not something required to achieve Mojo's goals. 2. The naming of `let` caused a lot of early [heat and -debate](https://github.com/modularml/mojo/discussions/120). Other programming +debate](https://github.com/modular/mojo/discussions/120). Other programming languages have a wide range of design points (e.g. `const` in C/C++ and Javascript) and there is a divergence of naming for all these things: `let`, `val`, `const`, etc, etc. @@ -43,7 +43,7 @@ three concepts going around: `alias`, `let`, and `var`. Most of the uses of 4. Both Swift and Rust encourage immutable values - Swift (and currently Mojo) warn about unneeded mutability, Rust makes mutability more verbose (`let mut`), and some propose that Mojo [make mutability more -verbose](https://github.com/modularml/mojo/issues/451). This cuts very hard +verbose](https://github.com/modular/mojo/issues/451). This cuts very hard against a lot of the design center of Python, which doesn’t even have this concept at all: it would be weird to make it the default, but if we don’t, then why bother having it? @@ -112,7 +112,7 @@ This would eliminate a bunch of complexity in the compiler as well: like: “`let x: Int; x = 1; use(x); x = 2; use(x)`” even though the original lifetime of the first “`x=1`” naturally ended and “`x`” is uninitialized before being assigned to. This has always been a design smell, and it - [doesn’t work right](https://github.com/modularml/mojo/issues/1414). + [doesn’t work right](https://github.com/modular/mojo/issues/1414). This proposal will not affect runtime performance at all as far as we know. diff --git a/stdlib/README.md b/stdlib/README.md index f9ec6c7224..d64144c02d 100644 --- a/stdlib/README.md +++ b/stdlib/README.md @@ -54,6 +54,6 @@ See the license file in the repository for more details. ## Support For any inquiries, bug reports, or feature requests, please [open an -issue](https://github.com/modularml/mojo/issues) on the GitHub repository. See +issue](https://github.com/modular/mojo/issues) on the GitHub repository. See the [Mojo contributor guide](../CONTRIBUTING.md) for guidelines on filing good bugs. diff --git a/stdlib/docs/development.md b/stdlib/docs/development.md index 1b6e812a4d..37cfb5182e 100644 --- a/stdlib/docs/development.md +++ b/stdlib/docs/development.md @@ -107,7 +107,7 @@ You can do the same for a directory with All the tests should pass on the `main` branch with the nightly Mojo compiler. If you've pulled the latest changes and they're still failing please [open a GitHub -issue](https://github.com/modularml/mojo/issues/new?assignees=&labels=bug%2Cmojo&projects=&template=mojo_bug_report.yaml&title=%5BBUG%5D). +issue](https://github.com/modular/mojo/issues/new?assignees=&labels=bug%2Cmojo&projects=&template=mojo_bug_report.yaml&title=%5BBUG%5D). ### Running a subset of the Standard Library Unit Tests @@ -135,7 +135,7 @@ disabled, you can set the environment variable `MOJO_ENABLE_ASSERTIONS_IN_TESTS=0`. If you run into any issues when running the tests, -[please file an issue](https://github.com/modularml/mojo/issues) and we’ll take +[please file an issue](https://github.com/modular/mojo/issues) and we’ll take a look. ## Formatting changes diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 2c9065e06d..fb9b239707 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -2247,7 +2247,7 @@ struct SIMD[type: DType, size: Int]( # Not an overload of shuffle because there is ambiguity # with fn shuffle[*mask: Int](self, other: Self) -> Self: - # TODO: move to the utils directory - see https://github.com/modularml/mojo/issues/3477 + # TODO: move to the utils directory - see https://github.com/modular/mojo/issues/3477 @always_inline fn _dynamic_shuffle[ mask_size: Int, // diff --git a/stdlib/test/builtin/test_issue_1004.mojo b/stdlib/test/builtin/test_issue_1004.mojo index f6cfe49792..e80c944316 100644 --- a/stdlib/test/builtin/test_issue_1004.mojo +++ b/stdlib/test/builtin/test_issue_1004.mojo @@ -11,7 +11,7 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -# Test for https://github.com/modularml/mojo/issues/1004 +# Test for https://github.com/modular/mojo/issues/1004 from testing import assert_equal diff --git a/stdlib/test/builtin/test_issue_1505.mojo b/stdlib/test/builtin/test_issue_1505.mojo index f76f0dc4a8..712c1d9c0c 100644 --- a/stdlib/test/builtin/test_issue_1505.mojo +++ b/stdlib/test/builtin/test_issue_1505.mojo @@ -11,7 +11,7 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -# Test for https://github.com/modularml/mojo/issues/1505 +# Test for https://github.com/modular/mojo/issues/1505 from random import random_ui64 diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index 16684aedfb..b89a35e70e 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -119,7 +119,7 @@ def test_convert_simd_to_string(): var c: SIMD[DType.index, 8] = 7 assert_equal(String(c), "[7, 7, 7, 7, 7, 7, 7, 7]") - # TODO: uncomment when https://github.com/modularml/mojo/issues/2353 is fixed + # TODO: uncomment when https://github.com/modular/mojo/issues/2353 is fixed # assert_equal(String(UInt32(-1)), "4294967295") assert_equal(String(UInt64(-1)), "18446744073709551615") @@ -129,7 +129,7 @@ def test_convert_simd_to_string(): assert_equal(String(UInt64(16646288086500911323)), "16646288086500911323") - # https://github.com/modularml/mojo/issues/556 + # https://github.com/modular/mojo/issues/556 assert_equal( String( SIMD[DType.uint64, 4]( diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index 159c3bd73e..e7af56f5a8 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -574,7 +574,7 @@ def test_no_extra_copies_with_sugared_set_by_field(): # Ensure correct behavior of __copyinit__ # as reported in GH issue 27875 internally and -# https://github.com/modularml/mojo/issues/1493 +# https://github.com/modular/mojo/issues/1493 def test_list_copy_constructor(): var vec = List[Int](capacity=1) var vec_copy = vec From 8610d5f8567798da275c6c7320a0a26fecf4c3fb Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 5 Feb 2025 20:53:01 -0800 Subject: [PATCH 350/509] [All] Rename float8 types to be more pythonic MODULAR_ORIG_COMMIT_REV_ID: f4b3478c009197a3aa92a2abc8080fe3db9c4932 --- stdlib/src/builtin/_format_float.mojo | 28 +++++++----- stdlib/src/builtin/dtype.mojo | 66 +++++++++++++-------------- stdlib/src/builtin/simd.mojo | 28 ++++++------ stdlib/src/prelude/__init__.mojo | 8 ++-- stdlib/src/utils/numerics.mojo | 64 +++++++++++++------------- 5 files changed, 99 insertions(+), 95 deletions(-) diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index 16e33a7972..9f5fde5fe1 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -105,17 +105,21 @@ fn _write_float[W: Writer, type: DType, //](mut writer: W, value: Scalar[type]): constrained[type.is_floating_point()]() @parameter - if type is DType.float8e5m2: - return writer.write(float8e5m2_to_str[Int(bitcast[DType.uint8](value))]) - elif type is DType.float8e4m3: - return writer.write(float8e4m3_to_str[Int(bitcast[DType.uint8](value))]) - elif type is DType.float8e5m2fnuz: + if type is DType.float8_e5m2: return writer.write( - float8e5m2fnuz_to_str[Int(bitcast[DType.uint8](value))] + float8_e5m2_to_str[Int(bitcast[DType.uint8](value))] ) - elif type is DType.float8e4m3fnuz: + elif type is DType.float8_e4m3: return writer.write( - float8e4m3fnuz_to_str[Int(bitcast[DType.uint8](value))] + float8_e4m3_to_str[Int(bitcast[DType.uint8](value))] + ) + elif type is DType.float8_e5m2fnuz: + return writer.write( + float8_e5m2fnuz_to_str[Int(bitcast[DType.uint8](value))] + ) + elif type is DType.float8_e4m3fnuz: + return writer.write( + float8_e4m3fnuz_to_str[Int(bitcast[DType.uint8](value))] ) else: # Upcast the float16 types to float32 @@ -1424,7 +1428,7 @@ alias cache_f64 = StaticTuple[_UInt128, 619]( _UInt128(0xF70867153AA2DB38, 0xB8CBEE4FC66D1EA8), ) -alias float8e5m2_to_str = StaticTuple[StringLiteral, 256]( +alias float8_e5m2_to_str = StaticTuple[StringLiteral, 256]( "0.0", "1.52587890625e-05", "3.0517578125e-05", @@ -1683,7 +1687,7 @@ alias float8e5m2_to_str = StaticTuple[StringLiteral, 256]( "nan", ) -alias float8e4m3_to_str = StaticTuple[StringLiteral, 256]( +alias float8_e4m3_to_str = StaticTuple[StringLiteral, 256]( "0.0", "0.001953125", "0.00390625", @@ -1942,7 +1946,7 @@ alias float8e4m3_to_str = StaticTuple[StringLiteral, 256]( "nan", ) -alias float8e5m2fnuz_to_str = StaticTuple[StringLiteral, 256]( +alias float8_e5m2fnuz_to_str = StaticTuple[StringLiteral, 256]( "0.0", "7.62939453125e-06", "1.52587890625e-05", @@ -2201,7 +2205,7 @@ alias float8e5m2fnuz_to_str = StaticTuple[StringLiteral, 256]( "-57344.0", ) -alias float8e4m3fnuz_to_str = StaticTuple[StringLiteral, 256]( +alias float8_e4m3fnuz_to_str = StaticTuple[StringLiteral, 256]( "0.0", "0.0009765625", "0.001953125", diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index d474424927..0b38797ac8 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -64,7 +64,7 @@ struct DType( """Represents a signed integer type whose bitwidth is 64.""" alias uint64 = DType(__mlir_attr.`#kgen.dtype.constant : !kgen.dtype`) """Represents an unsigned integer type whose bitwidth is 64.""" - alias float8e5m2 = DType( + alias float8_e5m2 = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) """Represents a FP8E5M2 floating point format from the [OFP8 @@ -80,7 +80,7 @@ struct DType( - -inf: 11111100 - -0: 10000000 """ - alias float8e5m2fnuz = DType( + alias float8_e5m2fnuz = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) """Represents a FP8E5M2FNUZ floating point format. @@ -94,7 +94,7 @@ struct DType( - fn: finite (no inf or -inf encodings) - uz: unsigned zero (no -0 encoding) """ - alias float8e4m3 = DType( + alias float8_e4m3 = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) """Represents a FP8E4M3 floating point format from the [OFP8 @@ -112,7 +112,7 @@ struct DType( - -0: 10000000 - fn: finite (no inf or -inf encodings) """ - alias float8e4m3fnuz = DType( + alias float8_e4m3fnuz = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) """Represents a FP8E4M3FNUZ floating point format. @@ -195,14 +195,14 @@ struct DType( return DType.uint64 elif str == "index": return DType.index - elif str == "float8e5m2": - return DType.float8e5m2 - elif str == "float8e5m2fnuz": - return DType.float8e5m2fnuz - elif str == "float8e4m3": - return DType.float8e4m3 - elif str == "float8e4m3fnuz": - return DType.float8e4m3fnuz + elif str == "float8_e5m2": + return DType.float8_e5m2 + elif str == "float8_e5m2fnuz": + return DType.float8_e5m2fnuz + elif str == "float8_e4m3": + return DType.float8_e4m3 + elif str == "float8_e4m3fnuz": + return DType.float8_e4m3fnuz elif str == "bfloat16": return DType.bfloat16 elif str == "float16": @@ -260,14 +260,14 @@ struct DType( return writer.write("uint64") if self == DType.index: return writer.write("index") - if self == DType.float8e5m2: - return writer.write("float8e5m2") - if self == DType.float8e5m2fnuz: - return writer.write("float8e5m2fnuz") - if self == DType.float8e4m3: - return writer.write("float8e4m3") - if self == DType.float8e4m3fnuz: - return writer.write("float8e4m3fnuz") + if self == DType.float8_e5m2: + return writer.write("float8_e5m2") + if self == DType.float8_e5m2fnuz: + return writer.write("float8_e5m2fnuz") + if self == DType.float8_e4m3: + return writer.write("float8_e4m3") + if self == DType.float8_e4m3fnuz: + return writer.write("float8_e4m3fnuz") if self == DType.bfloat16: return writer.write("bfloat16") if self == DType.float16: @@ -470,17 +470,17 @@ struct DType( @always_inline("nodebug") fn is_float8(self) -> Bool: """Returns True if the type is a 8bit-precision floating point type, - e.g. float8e5m2, float8e5m2fnuz, float8e4m3 and float8e4m3fnuz. + e.g. float8_e5m2, float8_e5m2fnuz, float8_e4m3 and float8_e4m3fnuz. Returns: True if the type is a 8bit-precision float, false otherwise. """ return self in ( - DType.float8e5m2, - DType.float8e4m3, - DType.float8e5m2fnuz, - DType.float8e4m3fnuz, + DType.float8_e5m2, + DType.float8_e4m3, + DType.float8_e5m2fnuz, + DType.float8_e4m3fnuz, ) @always_inline("nodebug") @@ -535,14 +535,14 @@ struct DType( return sizeof[DType.bool]() if self == DType.index: return sizeof[DType.index]() - if self == DType.float8e5m2: - return sizeof[DType.float8e5m2]() - if self == DType.float8e5m2fnuz: - return sizeof[DType.float8e5m2fnuz]() - if self == DType.float8e4m3: - return sizeof[DType.float8e4m3]() - if self == DType.float8e4m3fnuz: - return sizeof[DType.float8e4m3fnuz]() + if self == DType.float8_e5m2: + return sizeof[DType.float8_e5m2]() + if self == DType.float8_e5m2fnuz: + return sizeof[DType.float8_e5m2fnuz]() + if self == DType.float8_e4m3: + return sizeof[DType.float8_e4m3]() + if self == DType.float8_e4m3fnuz: + return sizeof[DType.float8_e4m3fnuz]() if self == DType.bfloat16: return sizeof[DType.bfloat16]() if self == DType.float16: diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index fb9b239707..5683dd0557 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -96,7 +96,7 @@ alias Int64 = Scalar[DType.int64] alias UInt64 = Scalar[DType.uint64] """Represents a 64-bit unsigned scalar integer.""" -alias Float8e5m2 = Scalar[DType.float8e5m2] +alias Float8_e5m2 = Scalar[DType.float8_e5m2] """Represents a FP8E5M2 floating point format from the [OFP8 standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). @@ -110,7 +110,7 @@ The 8 bits are encoded as `seeeeemm`: - -inf: 11111100 - -0: 10000000 """ -alias Float8e5m2fnuz = Scalar[DType.float8e5m2fnuz] +alias Float8_e5m2fnuz = Scalar[DType.float8_e5m2fnuz] """Represents a FP8E5M2FNUZ floating point format. The 8 bits are encoded as `seeeeemm`: @@ -122,7 +122,7 @@ The 8 bits are encoded as `seeeeemm`: - fn: finite (no inf or -inf encodings) - uz: unsigned zero (no -0 encoding) """ -alias Float8e4m3 = Scalar[DType.float8e4m3] +alias Float8_e4m3 = Scalar[DType.float8_e4m3] """Represents a FP8E4M3 floating point format from the [OFP8 standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). @@ -138,7 +138,7 @@ The 8 bits are encoded as `seeeemmm`: - -0: 10000000 - fn: finite (no inf or -inf encodings) """ -alias Float8e4m3fnuz = Scalar[DType.float8e4m3fnuz] +alias Float8_e4m3fnuz = Scalar[DType.float8_e4m3fnuz] """Represents a FP8E4M3FNUZ floating point format. The 8 bits are encoded as `seeeemmm`: @@ -556,7 +556,7 @@ struct SIMD[type: DType, size: Int]( # TODO (#36686): This introduces unneeded casts here to work around # parameter if issues. @parameter - if type is DType.float8e4m3: + if type is DType.float8_e4m3: self = SIMD[type, size]( __mlir_op.`pop.simd.splat`[ _type = __mlir_type[ @@ -574,7 +574,7 @@ struct SIMD[type: DType, size: Int]( ) ) ) - elif type is DType.float8e4m3fnuz: + elif type is DType.float8_e4m3fnuz: self = SIMD[type, size]( __mlir_op.`pop.simd.splat`[ _type = __mlir_type[ @@ -592,7 +592,7 @@ struct SIMD[type: DType, size: Int]( ) ) ) - elif type is DType.float8e5m2: + elif type is DType.float8_e5m2: self = SIMD[type, size]( __mlir_op.`pop.simd.splat`[ _type = __mlir_type[ @@ -610,7 +610,7 @@ struct SIMD[type: DType, size: Int]( ) ) ) - elif type is DType.float8e5m2fnuz: + elif type is DType.float8_e5m2fnuz: self = SIMD[type, size]( __mlir_op.`pop.simd.splat`[ _type = __mlir_type[ @@ -1830,7 +1830,7 @@ struct SIMD[type: DType, size: Int]( return self.cast[DType.float32]().cast[target]() @parameter - if target in (DType.float8e4m3, DType.float8e5m2): + if target in (DType.float8_e4m3, DType.float8_e5m2): # TODO(KERN-1488): use gpu (H100) instruction to convert from fp16 to fp8 return rebind[SIMD[target, size]]( _convert_f32_to_float8[size=size, target=target]( @@ -1841,7 +1841,7 @@ struct SIMD[type: DType, size: Int]( ) @parameter - if type in (DType.float8e4m3, DType.float8e5m2): + if type in (DType.float8_e4m3, DType.float8_e5m2): constrained[ target in (DType.float32, DType.float16, DType.bfloat16), ( @@ -3198,7 +3198,7 @@ fn _convert_float8_to_f32_scaler[ ](x: Scalar[type]) -> Scalar[DType.float32]: var kF32_NaN: UInt32 = 0x7FFFFFFF var FP8_NUM_BITS = 8 - var IS_E4M3 = type is DType.float8e4m3 + var IS_E4M3 = type is DType.float8_e4m3 var FP8_NUM_MANTISSA_BITS = FPUtils[type].mantissa_width() var FP8_NUM_EXPONENT_BITS = FPUtils[type].exponent_width() var FP32_NUM_BITS = 32 @@ -3283,7 +3283,7 @@ fn _convert_float8_to_f16[ ](val: SIMD[type, size],) -> SIMD[DType.float16, size]: @parameter if is_nvidia_gpu() and _is_sm_9x(): - alias asm_prefix = "cvt.rn.f16x2.e4m3x2" if type is DType.float8e4m3 else "cvt.rn.f16x2.e5m2x2" + alias asm_prefix = "cvt.rn.f16x2.e4m3x2" if type is DType.float8_e4m3 else "cvt.rn.f16x2.e5m2x2" var val_uint8 = bitcast[DType.uint8](val) @parameter @@ -3325,7 +3325,7 @@ fn _convert_f32_to_float8[ ](val: SIMD[type, size],) -> SIMD[target, size]: @parameter if is_nvidia_gpu() and _is_sm_9x(): - alias asm_prefix = "cvt.rn.satfinite.e4m3x2.f32" if target is DType.float8e4m3 else "cvt.rn.satfinite.e5m2x2.f32" + alias asm_prefix = "cvt.rn.satfinite.e4m3x2.f32" if target is DType.float8_e4m3 else "cvt.rn.satfinite.e5m2x2.f32" @parameter if size > 1: @@ -3374,7 +3374,7 @@ fn _convert_f32_to_float8_scaler[ ](x: Scalar[type]) -> Scalar[target]: # software implementation rounds toward nearest even - alias IS_E4M3 = target is DType.float8e4m3 + alias IS_E4M3 = target is DType.float8_e4m3 alias FP8_NUM_MANTISSA_BITS = FPUtils[target].mantissa_width() alias FP8_NUM_EXPONENT_BITS = FPUtils[target].exponent_width() alias FP32_NUM_BITS = bitwidthof[type]() diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 72de0eea4f..56dc6be9b4 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -88,10 +88,10 @@ from builtin.simd import ( SIMD, BFloat16, Byte, - Float8e5m2, - Float8e5m2fnuz, - Float8e4m3, - Float8e4m3fnuz, + Float8_e5m2, + Float8_e5m2fnuz, + Float8_e4m3, + Float8_e4m3fnuz, Float16, Float32, Float64, diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index d39f43cee9..4eddc38a80 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -67,9 +67,9 @@ struct FPUtils[ """ @parameter - if type in (DType.float8e4m3, DType.float8e4m3fnuz): + if type in (DType.float8_e4m3, DType.float8_e4m3fnuz): return 3 - elif type in (DType.float8e5m2, DType.float8e5m2fnuz): + elif type in (DType.float8_e5m2, DType.float8_e5m2fnuz): return 2 elif type is DType.float16: return 10 @@ -94,9 +94,9 @@ struct FPUtils[ """ @parameter - if type in (DType.float8e4m3, DType.float8e4m3fnuz): + if type in (DType.float8_e4m3, DType.float8_e4m3fnuz): return 8 - elif type in (DType.float8e5m2, DType.float8e5m2fnuz, DType.float16): + elif type in (DType.float8_e5m2, DType.float8_e5m2fnuz, DType.float16): return 16 elif type in (DType.bfloat16, DType.float32): return 128 @@ -114,9 +114,9 @@ struct FPUtils[ """ @parameter - if type in (DType.float8e4m3, DType.float8e4m3fnuz): + if type in (DType.float8_e4m3, DType.float8_e4m3fnuz): return 4 - elif type in (DType.float8e5m2, DType.float8e5m2fnuz, DType.float16): + elif type in (DType.float8_e5m2, DType.float8_e5m2fnuz, DType.float16): return 5 elif type in (DType.float32, DType.bfloat16): return 8 @@ -144,7 +144,7 @@ struct FPUtils[ """ @parameter - if type in (DType.float8e4m3fnuz, DType.float8e5m2fnuz): + if type in (DType.float8_e4m3fnuz, DType.float8_e5m2fnuz): return Self.max_exponent() else: return Self.max_exponent() - 1 @@ -511,14 +511,14 @@ fn nan[type: DType]() -> Scalar[type]: """ @parameter - if type is DType.float8e5m2: + if type is DType.float8_e5m2: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], value = __mlir_attr[`#pop.simd<"nan"> : !pop.scalar`], ]() ) - elif type is DType.float8e5m2fnuz: + elif type is DType.float8_e5m2fnuz: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -527,14 +527,14 @@ fn nan[type: DType]() -> Scalar[type]: ], ]() ) - elif type is DType.float8e4m3: + elif type is DType.float8_e4m3: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], value = __mlir_attr[`#pop.simd<"nan"> : !pop.scalar`], ]() ) - elif type is DType.float8e4m3fnuz: + elif type is DType.float8_e4m3fnuz: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -600,18 +600,18 @@ fn isnan[ @parameter if not type.is_floating_point() or type in ( - DType.float8e4m3fnuz, - DType.float8e5m2fnuz, + DType.float8_e4m3fnuz, + DType.float8_e5m2fnuz, ): return False alias int_dtype = _integral_type_of[type]() @parameter - if type is DType.float8e4m3: + if type is DType.float8_e4m3: return (bitcast[int_dtype, simd_width](val) & 0x7F) == 0x7F - elif type is DType.float8e5m2: - # For the float8e5m2 type NaN is limited to 0x7F and 0xFF values. + elif type is DType.float8_e5m2: + # For the float8_e5m2 type NaN is limited to 0x7F and 0xFF values. # 7D, 7E, 7F are positive NaNs; FD, FE, FF are negative NaNs. return (bitcast[int_dtype, simd_width](val) & 0x7F) > 0x7C elif type is DType.float16: @@ -649,14 +649,14 @@ fn inf[type: DType]() -> Scalar[type]: """ @parameter - if type is DType.float8e5m2: + if type is DType.float8_e5m2: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], value = __mlir_attr[`#pop.simd<"inf"> : !pop.scalar`], ]() ) - elif type is DType.float8e5m2fnuz: + elif type is DType.float8_e5m2fnuz: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -665,14 +665,14 @@ fn inf[type: DType]() -> Scalar[type]: ], ]() ) - elif type is DType.float8e4m3: + elif type is DType.float8_e4m3: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], value = __mlir_attr[`#pop.simd<"inf"> : !pop.scalar`], ]() ) - elif type is DType.float8e4m3fnuz: + elif type is DType.float8_e4m3fnuz: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -734,14 +734,14 @@ fn neg_inf[type: DType]() -> Scalar[type]: """ @parameter - if type is DType.float8e5m2: + if type is DType.float8_e5m2: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], value = __mlir_attr[`#pop.simd<"-inf"> : !pop.scalar`], ]() ) - elif type is DType.float8e5m2fnuz: + elif type is DType.float8_e5m2fnuz: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -750,14 +750,14 @@ fn neg_inf[type: DType]() -> Scalar[type]: ], ]() ) - elif type is DType.float8e4m3: + elif type is DType.float8_e4m3: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], value = __mlir_attr[`#pop.simd<"-inf"> : !pop.scalar`], ]() ) - elif type is DType.float8e4m3fnuz: + elif type is DType.float8_e4m3fnuz: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -837,11 +837,11 @@ fn max_finite[type: DType]() -> Scalar[type]: return 9223372036854775807 elif type is DType.uint64: return 18446744073709551615 - elif type is DType.float8e4m3: + elif type is DType.float8_e4m3: return 448 - elif type is DType.float8e4m3fnuz: + elif type is DType.float8_e4m3fnuz: return 240 - elif type in (DType.float8e5m2, DType.float8e5m2fnuz): + elif type in (DType.float8_e5m2, DType.float8_e5m2fnuz): return 57344 elif type is DType.float16: return 65504 @@ -973,12 +973,12 @@ fn isinf[ @parameter if not type.is_floating_point() or type in ( - DType.float8e4m3fnuz, - DType.float8e5m2fnuz, + DType.float8_e4m3fnuz, + DType.float8_e5m2fnuz, ): return False - elif type is DType.float8e5m2: - # For the float8e5m2 both 7C and FC are infinity. + elif type is DType.float8_e5m2: + # For the float8_e5m2 both 7C and FC are infinity. alias int_dtype = _integral_type_of[type]() return (bitcast[int_dtype, simd_width](val) & 0x7F) == 0x7C @@ -1044,7 +1044,7 @@ fn get_accum_type[type: DType]() -> DType: """ return DType.float32 if ( - type.is_half_float() or type in (DType.float8e4m3, DType.float8e5m2) + type.is_half_float() or type in (DType.float8_e4m3, DType.float8_e5m2) ) else type From 53f48472858ca7093b37fa2ec181965eac42f29f Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 6 Feb 2025 07:55:16 +0000 Subject: [PATCH 351/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025020605 --- examples/life/magic.lock | 178 +++++++++++++++++----------------- examples/magic.lock | 178 +++++++++++++++++----------------- examples/operators/magic.lock | 178 +++++++++++++++++----------------- examples/testing/magic.lock | 178 +++++++++++++++++----------------- magic.lock | 178 +++++++++++++++++----------------- 5 files changed, 445 insertions(+), 445 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 9651b71eac..5560428e0a 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -9,7 +9,7 @@ environments: - 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click >=8.0.0 @@ -5005,8 +5005,8 @@ packages: - typing_extensions >=v4.12.2 - python license: MIT - size: 130825 - timestamp: 1738777581246 + size: 130824 + timestamp: 1738819257916 - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda sha256: 78c1bbe1723449c52b7a9df1af2ee5f005209f67e40b6e1d3c7619127c43b1c7 md5: 592132998493b3ff25fd7479396e8351 @@ -5016,18 +5016,18 @@ packages: license_family: MIT size: 14465 timestamp: 1733255681319 -- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025020517-release.conda +- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025020605-release.conda noarch: python - sha256: d57f26218e88f2d8ffc84d06a9022817588e7b5f810a10b60ad7b5153b39dcad - md5: 939c3020651164ebe9c9e39cf512448d + sha256: a98de01d436333c23597e626195c5d49272c1c5d3359889979bafb8f19d4f3bf + md5: f68f7d08fc12083075182da8482fcfa9 depends: - - max-core ==25.1.0.dev2025020517 release + - max-core ==25.1.0.dev2025020605 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22926 - timestamp: 1738777581246 + size: 22925 + timestamp: 1738819257916 - conda: https://conda.anaconda.org/conda-forge/linux-64/multidict-6.1.0-py312h178313f_2.conda sha256: b05bc8252a6e957bf4a776ed5e0e61d1ba88cdc46ccb55890c72cc58b10371f4 md5: 5b5e3267d915a107eca793d52e1b780a From a6195d2f9a655a3de24db57bc78eda4f5a901096 Mon Sep 17 00:00:00 2001 From: ematejska Date: Thu, 6 Feb 2025 09:51:32 -0800 Subject: [PATCH 352/509] Update mojo_feature_request.yaml Updating issue template Signed-off-by: ematejska --- .github/ISSUE_TEMPLATE/mojo_feature_request.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml b/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml index d81855f5f0..fff1896844 100644 --- a/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml +++ b/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml @@ -23,7 +23,7 @@ body: - type: markdown attributes: value: | - If the request is out of the published roadmap and priorities, please start an [idea](https://github.com/modularml/mojo/discussions/categories/ideas) in GH Discussions to get feedback from the team. + If the request is out of the published roadmap and priorities, please start a [discussion](https://forum.modular.com/) in the Modular Forum to get feedback from the team. - type: textarea id: Request From ddd7d06de25aa131b93a2d88f4656725a867cf9b Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Thu, 6 Feb 2025 10:30:18 -0800 Subject: [PATCH 353/509] [docs] Removed docstring tests from example testing project MODULAR_ORIG_COMMIT_REV_ID: 587bbfcf3636f8d9c1f4c9e3731e385a6db3ccbf --- examples/testing/README.md | 25 ++++++---------------- examples/testing/mojoproject.toml | 6 ++---- examples/testing/src/my_math/__init__.mojo | 22 ------------------- examples/testing/src/my_math/utils.mojo | 6 ------ 4 files changed, 8 insertions(+), 51 deletions(-) diff --git a/examples/testing/README.md b/examples/testing/README.md index 849554de04..d4748d676e 100644 --- a/examples/testing/README.md +++ b/examples/testing/README.md @@ -1,23 +1,17 @@ # Modular testing framework examples -This directory contains examples of using the Mojo testing framework. It -demonstrates using the testing framework for both unit testing and testing code -examples in the [documentation -strings](https://docs.modular.com/mojo/manual/basics#code-comments) (also known -as *docstrings*) of Mojo API documentation. See the -[Testing](https://docs.modular.com/mojo/tools/testing) section of the [Mojo -manual](https://docs.modular.com/mojo/manual/) for a complete discussion of how -to use the Mojo testing framework. +This directory contains examples of using the Mojo testing framework for unit +testing. See the [Testing](https://docs.modular.com/mojo/tools/testing) section +of the [Mojo manual](https://docs.modular.com/mojo/manual/) for a complete +discussion of how to use the Mojo testing framework. ## Files This directory contains the following files: -- `src/my_math/__init__.mojo`: a Mojo package file with package-level docstrings - containing code examples to test +- `src/my_math/__init__.mojo`: a Mojo package file -- `src/my_math/utils.mojo`: a Mojo module source file with both module-level and - function-level docstrings containing code examples to test +- `src/my_math/utils.mojo`: a Mojo module source file - `src/example.mojo`: a simple Mojo program that uses the functions from the `my_math` package @@ -48,13 +42,6 @@ Run the unit tests contained in the `test` directory by executing: mojo test -I src test ``` -Run the docstring tests for the API documentation contained in the `src` -directory by executing: - -```bash -mojo test src -``` - If desired, you can run the example program by executing: ```bash diff --git a/examples/testing/mojoproject.toml b/examples/testing/mojoproject.toml index 487fdcfa48..9ee3dfb8cd 100644 --- a/examples/testing/mojoproject.toml +++ b/examples/testing/mojoproject.toml @@ -8,9 +8,7 @@ version = "0.1.0" [tasks] main = "mojo run src/example.mojo" -doc-tests = "mojo test src" -unit-tests = "mojo test -I src test" -tests = { depends-on = ["doc-tests", "unit-tests"] } +tests = "mojo test -I src test" [dependencies] -max = "*" +max = "~=24.6" diff --git a/examples/testing/src/my_math/__init__.mojo b/examples/testing/src/my_math/__init__.mojo index 79dd92b79d..d9e1691687 100644 --- a/examples/testing/src/my_math/__init__.mojo +++ b/examples/testing/src/my_math/__init__.mojo @@ -24,28 +24,6 @@ You can import these APIs from the `my_math` package. For example: ```mojo from my_math import dec, inc ``` - -The `inc()` function performs a simple increment: - -```mojo -%# from testing import assert_equal -from my_math import inc -a = 1 -b = inc(a) # b = 2 -%# assert_equal(b, 2) -``` - -However, `inc()` raises an error if it would result in integer overflow: - -```mojo -c = 0 -try: - c = inc(Int.MAX) -except e: - print(e) -%# assert_equal("inc overflow", String(e)) -``` - """ from .utils import dec, inc diff --git a/examples/testing/src/my_math/utils.mojo b/examples/testing/src/my_math/utils.mojo index 000fa023cd..bb8aab2837 100644 --- a/examples/testing/src/my_math/utils.mojo +++ b/examples/testing/src/my_math/utils.mojo @@ -25,11 +25,9 @@ def inc(n: Int) -> Int: """Returns an incremented integer value. ```mojo - %# from testing import assert_equal from my_math import inc i = 7 j = inc(i) # j = 8 - %# assert_equal(j, 8) ``` However, `inc()` raises an error if it would result in integer overflow: @@ -40,7 +38,6 @@ def inc(n: Int) -> Int: k = inc(Int.MAX) except e: print(e) # inc overflow - %# assert_equal("inc overflow", String(e)) ``` Args: @@ -61,11 +58,9 @@ def dec(n: Int) -> Int: """Returns a decremented integer value. ```mojo - %# from testing import assert_equal from my_math import dec i = 7 j = dec(i) # j = 6 - %# assert_equal(j, 6) ``` However, `dec()` raises an error if it would result in integer overflow: @@ -76,7 +71,6 @@ def dec(n: Int) -> Int: k = dec(Int.MIN) except e: print(e) # inc overflow - %# assert_equal("dec overflow", String(e)) ``` Args: From f2bc2f4de840d0004c19499d08468002021acf31 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Thu, 6 Feb 2025 10:30:52 -0800 Subject: [PATCH 354/509] [docs] Removed all references from the docs to Mojo REPL and notebook support MODULAR_ORIG_COMMIT_REV_ID: 7e71ad726001f24daa93083be23f217c1b1b89be --- docs/faq.md | 9 ++++----- docs/manual/basics.mdx | 9 --------- docs/manual/values/ownership.mdx | 8 -------- 3 files changed, 4 insertions(+), 22 deletions(-) diff --git a/docs/faq.md b/docs/faq.md index 6ff7db931b..e6f68d46e2 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -152,11 +152,10 @@ examples](https://github.com/modular/mojo/tree/main/examples). ### Is Mojo interpreted or compiled? -Mojo supports both just-in-time (JIT) and ahead-of-time (AOT) compilation. In -either a REPL environment or Jupyter notebook, Mojo is JIT’d. However, for AI -deployment, it’s important that Mojo also supports AOT compilation instead of -having to JIT compile everything. You can compile your Mojo programs using the -[`mojo` CLI](/mojo/cli/). +Mojo is a compiled language. [`mojo build`](/mojo/cli/build) performs +ahead-of-time (AOT) compilation to save an executable program. [`mojo +run`](/mojo/cli/run) performs just-in-time (JIT) compilation to execute a Mojo +source file without saving the compiled result. ### How does Mojo compare to Triton Lang? diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index 9238fd3799..853ad16ad6 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -49,15 +49,6 @@ you can write `def main():` followed by an indented function body. The `print()` statement does what you'd expect, printing its arguments to the standard output. -:::note - -You don't need a `main()` function when coding in the [REPL](/mojo/cli/repl) or -in a Jupyter notebook. In these environments, Mojo executes top-level code (that -is, statements that appear outside of a function). Mojo doesn't support -top-level code in a `.mojo` (or `.🔥`) file. - -::: - ## Variables In Mojo, you can declare a variable by simply assigning a value to diff --git a/docs/manual/values/ownership.mdx b/docs/manual/values/ownership.mdx index 817f43ef4c..4410687c22 100644 --- a/docs/manual/values/ownership.mdx +++ b/docs/manual/values/ownership.mdx @@ -390,14 +390,6 @@ def consume_string(owned s: String): # s is destroyed here ``` -:::note - -Value lifetimes are not fully implemented for top-level code in -Mojo's REPL, so the transfer sigil currently works as intended only when -used inside a function. - -::: - ### Transfer implementation details In Mojo, you shouldn't conflate "ownership transfer" with a "move From fffb7f8c7bccd9ddb80041a8d67ca9914fcafcc6 Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Thu, 6 Feb 2025 10:31:22 -0800 Subject: [PATCH 355/509] [docs] Removed section on docstring tests from the Testing section of the Mojo manual MODULAR_ORIG_COMMIT_REV_ID: f2fd146f87ff44c1ae587db6dcad45451571b9ef --- docs/tools/testing.mdx | 184 ++--------------------------------------- 1 file changed, 8 insertions(+), 176 deletions(-) diff --git a/docs/tools/testing.mdx b/docs/tools/testing.mdx index ed1b22ea62..34dc575ce1 100644 --- a/docs/tools/testing.mdx +++ b/docs/tools/testing.mdx @@ -5,13 +5,10 @@ description: Testing Mojo programs. github_url: https://github.com/modular/mojo/tree/main/examples/testing --- -Mojo includes a framework for developing and executing unit tests. The framework -also supports testing code examples in the -[documentation strings](/mojo/manual/basics#code-comments) -(also known as *docstrings*) of your API references. The Mojo testing framework -consists of a set of assertions defined as part of the -[Mojo standard library](/mojo/lib) and the -[`mojo test`](/mojo/cli/test) command line tool. +Mojo includes a framework for developing and executing unit tests. The Mojo +testing framework consists of a set of assertions defined as part of the [Mojo +standard library](/mojo/lib) and the [`mojo test`](/mojo/cli/test) command line +tool. ## Get started @@ -99,14 +96,11 @@ its error message. organize them into test files. - [The `mojo test` command](#the-mojo-test-command) describes how to execute and collect lists of tests. -- [Writing API documentation tests](#writing-api-documentation-tests) - discusses how to use the Mojo testing framework to test code examples in your - API documentation. -- The public [GitHub repo](https://github.com/modular/mojo/tree/main) - contains an [example +- The public [GitHub repo](https://github.com/modular/mojo/tree/main) contains + an [example project](https://github.com/modular/mojo/tree/main/examples/testing) to - demonstrate both unit testing and docstring testing. Several of the examples - shown later are based on this project. + demonstrate unit testing. Several of the examples shown later are based on + this project. ## The `testing` module @@ -505,165 +499,3 @@ filesystem paths from the output shown): "id": "ROOT_DIR/test/my_math" } ``` - -## Writing API documentation tests - -The Mojo testing framework also supports testing code examples that you include -in [docstrings](/mojo/manual/basics#code-comments). This helps to ensure that -the code examples in your API documentation are correct and up to date. - -### Identifying executable code - -The Mojo testing framework requires you to explicitly identify the code blocks -that you want it to execute. - -In a Mojo docstring, a fenced code block delimited by standard triple-backquotes -is a *display-only* code block. It appears in the API documentation, but -`mojo test` does not identify it as a test or attempt to execute any of the code -in the block. - -```` -""" Non-executable code block example. - -The generated API documentation includes all lines of the following code block, -but `mojo test` does not execute any of the code in it. - -``` -# mojo test does NOT execute any of this code block -a = 1 -print(a) -``` -""" -```` - -In contrast, a fenced code block that starts with the line \`\`\`mojo -not only appears in the API documentation, but `mojo test` treats it as an -executable test. The test fails if the code raises any error, otherwise it -passes. - -```` -""" Executable code block example. - -The generated API documentation includes all lines of the following code block -*and* `mojo test` executes it as a test. - -```mojo -from testing import assert_equals - -b = 2 -assert_equals(b, 2) -``` -""" -```` - -Sometimes you might want to execute a line of code as part of the test but *not* -display that line in the API documentation. To achieve this, prefix the line of -code with `%#`. For example, you can use this technique to omit `import` -statements and assertion functions from the documentation. - -```` -""" Executable code block example with some code lines omitted from output. - -The generated API documentation includes only the lines of code that do *not* -start with `%#`. However, `mojo test` executes *all* lines of code. - -```mojo -%# from testing import assert_equal -c = 3 -print(c) -%# assert_equal(c, 3) -``` -""" -```` - -### Documentation test suites and scoping - -The Mojo testing framework treats each docstring as a separate *test suite*. -In other words, a single test suite can correspond to the docstring for an -individual package, module, function, struct, struct method, etc. - -Each executable code block within a given docstring is a single test of the same -test suite. The `mojo test` command executes the tests of a test suite -sequentially in the order that they appear within the docstring. If a test -within a particular test suite fails, then all subsequent tests within the same -test suite are skipped. - -All tests within the test suite execute in the same scope, and test execution -within that scope is stateful. This means, for example, that a variable created -within one test is then accessible to subsequent tests in the same test suite. - -```` -""" Stateful example. - -Assign 1 to the variable `a`: - -```mojo -%# from testing import assert_equal -a = 1 -%# assert_equal(a, 1) -``` - -Then increment the value of `a` by 1: - -```mojo -a += 1 -%# assert_equal(a, 2) -``` -""" -```` - -:::note - -Test suite scopes do *not* nest. In other words, the test suite scope of a -module is completely independent of the test suite scope of a function or struct -defined within that module. For example, this means that if a module's test -suite creates a variable, that variable is *not* accessible to a function's test -suite within the same module. - -::: - -### Documentation test identifiers - -The format of a documentation test identifier is `@::`. -This is best explained by an example. Consider the [example testing -project](https://github.com/modular/mojo/tree/main/examples/testing) -directory structure shown in the [Running tests](#running-tests) section. The -source files in the `src` directory might contain docstrings for the `my_math` -package, the `utils.mojo` module, and the individual functions within that -module. You can collect the full list of tests by executing: - -```bash -mojo test --co src -``` - -The output shows the hierarchy of directories, test files, and individual tests -(note that this example elides the full filesystem paths from the output shown): - -```output - - - - - - - - - - - - - - - -``` - -Several different test suites appear in this result: - -| Test suite scope | File | Test suite name | -| ---------------- | --------------------------- | ------------------------ | -| Package | `src/my_math/__init__.mojo` | `__doc__` | -| Module | `src/my_math/utils.mojo` | `__doc__` | -| Function | `src/my_math/utils.mojo` | `inc(\3A\3AInt).__doc__` | - -Then within a specific test suite, tests are numbered sequentially in the order -they appear in the docstring, starting with 0. From 6931b41c5bedf42d82e2cd522c8a9e3699968bd5 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Thu, 6 Feb 2025 11:11:18 -0800 Subject: [PATCH 356/509] [stdlib] Add StringLiteral comparisons with StringSlice Adds comparison operators to `StringSlice` for `StringLiteral` so that e.g. `"abc" < "def".as_string_slice()` works. MODULAR_ORIG_COMMIT_REV_ID: eced06f00457ff442f25a0985dfacf4b05719db4 --- stdlib/src/builtin/string_literal.mojo | 56 ++++++++++++++++++-- stdlib/test/builtin/test_string_literal.mojo | 30 +++++++++++ 2 files changed, 82 insertions(+), 4 deletions(-) diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 3250111b66..571236110b 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -274,7 +274,7 @@ struct StringLiteral( @always_inline("nodebug") fn __lt__(self, rhs: StringLiteral) -> Bool: - """Compare this StringLiteral to the RHS using LT comparison. + """Compare this StringLiteral to the RHS using lesser than (LT) comparison. Args: rhs: The other StringLiteral to compare against. @@ -286,7 +286,7 @@ struct StringLiteral( @always_inline("nodebug") fn __le__(self, rhs: StringLiteral) -> Bool: - """Compare this StringLiteral to the RHS using LE comparison. + """Compare this StringLiteral to the RHS using lesser than or equal to (LE) comparison. Args: rhs: The other StringLiteral to compare against. @@ -298,7 +298,7 @@ struct StringLiteral( @always_inline("nodebug") fn __gt__(self, rhs: StringLiteral) -> Bool: - """Compare this StringLiteral to the RHS using GT comparison. + """Compare this StringLiteral to the RHS using greater than (GT) comparison. Args: rhs: The other StringLiteral to compare against. @@ -310,7 +310,55 @@ struct StringLiteral( @always_inline("nodebug") fn __ge__(self, rhs: StringLiteral) -> Bool: - """Compare this StringLiteral to the RHS using GE comparison. + """Compare this StringLiteral to the RHS using greater than or equal to (GE) comparison. + + Args: + rhs: The other StringLiteral to compare against. + + Returns: + True if this StringLiteral is greater than or equal to the RHS StringLiteral and False otherwise. + """ + return not (self < rhs) + + @always_inline("nodebug") + fn __lt__(self, rhs: StringSlice) -> Bool: + """Compare this StringLiteral to the RHS using lesser than (LT) comparison. + + Args: + rhs: The other StringLiteral to compare against. + + Returns: + True if this StringLiteral is strictly less than the RHS StringLiteral and False otherwise. + """ + return self.as_string_slice() < rhs + + @always_inline("nodebug") + fn __le__(self, rhs: StringSlice) -> Bool: + """Compare this StringLiteral to the RHS using lesser than or equal to (LE) comparison. + + Args: + rhs: The other StringLiteral to compare against. + + Returns: + True if this StringLiteral is less than or equal to the RHS StringLiteral and False otherwise. + """ + return not (rhs < self) + + @always_inline("nodebug") + fn __gt__(self, rhs: StringSlice) -> Bool: + """Compare this StringLiteral to the RHS using greater than (GT) comparison. + + Args: + rhs: The other StringLiteral to compare against. + + Returns: + True if this StringLiteral is strictly greater than the RHS StringLiteral and False otherwise. + """ + return rhs < self + + @always_inline("nodebug") + fn __ge__(self, rhs: StringSlice) -> Bool: + """Compare this StringLiteral to the RHS using greater than or equal to (GE) comparison. Args: rhs: The other StringLiteral to compare against. diff --git a/stdlib/test/builtin/test_string_literal.mojo b/stdlib/test/builtin/test_string_literal.mojo index bd839a3c54..00ef2fec3f 100644 --- a/stdlib/test/builtin/test_string_literal.mojo +++ b/stdlib/test/builtin/test_string_literal.mojo @@ -195,6 +195,36 @@ def test_comparison_operators(): assert_true(StringLiteral.__le__("", "")) assert_true(StringLiteral.__ge__("", "")) + # Test less than and greater than + def_slice = "def".as_string_slice() + abcd_slice = "abc".as_string_slice() + assert_true(StringLiteral.__lt__("abc", def_slice)) + assert_false(StringLiteral.__lt__("def", abcd_slice[0:3])) + assert_false(StringLiteral.__lt__("abc", abcd_slice[0:3])) + assert_true(StringLiteral.__lt__("ab", abcd_slice[0:3])) + assert_true(StringLiteral.__gt__("abc", abcd_slice[0:2])) + assert_false(StringLiteral.__gt__("abc", abcd_slice)) + + # Test less than or equal to and greater than or equal to + assert_true(StringLiteral.__le__("abc", def_slice)) + assert_true(StringLiteral.__le__("abc", abcd_slice[0:3])) + assert_false(StringLiteral.__le__("def", abcd_slice[0:3])) + assert_true(StringLiteral.__ge__("abc", abcd_slice[0:3])) + assert_false(StringLiteral.__ge__("ab", abcd_slice[0:3])) + assert_true(StringLiteral.__ge__("abcd", abcd_slice[0:3])) + + abc_upper_slice = "ABC".as_string_slice() + # Test case sensitivity in comparison (assuming ASCII order) + assert_true(StringLiteral.__gt__("abc", abc_upper_slice)) + assert_false(StringLiteral.__le__("abc", abc_upper_slice)) + + empty_slice = "".as_string_slice() + # Test comparisons involving empty strings + assert_true(StringLiteral.__lt__("", abcd_slice[0:3])) + assert_false(StringLiteral.__lt__("abc", empty_slice)) + assert_true(StringLiteral.__le__("", empty_slice)) + assert_true(StringLiteral.__ge__("", empty_slice)) + def test_hash(): # Test a couple basic hash behaviors. From 134f845e6c963ee53c1c86a7bcd0745296ec899f Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Thu, 6 Feb 2025 13:20:46 -0800 Subject: [PATCH 357/509] [docs] Documented improved String/StringLiteral comparison ergonomics MODULAR_ORIG_COMMIT_REV_ID: 48bfc4bc18a3549cd7d20cede402f906f34fe6b8 --- docs/manual/operators.mdx | 39 +++++++++++++++++++++++++-------------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/docs/manual/operators.mdx b/docs/manual/operators.mdx index 0356ec4a93..b3f9e8fd00 100644 --- a/docs/manual/operators.mdx +++ b/docs/manual/operators.mdx @@ -604,25 +604,36 @@ ________________________________________ #### String comparison -`String` values can be compared using standard lexicographical ordering, -producing a `Bool`. (For example, "Zebra" is treated as less than "ant" because -upper case letters occur before lower case letters in the character encoding.) -`StringLiteral` values can be directly compared against each other as well. -However when comparing a `String` to a `StringLiteral` value, the `String` value -**must** be the left-hand operand to avoid a compilation error. For example: +`String` and `StringLiteral` values can be compared using standard +lexicographical ordering, producing a `Bool`. For example, "Zebra" is treated as +less than "ant" because upper case letters occur before lower case letters in +the character encoding. ```mojo -var str: String = "bird" -# Compilation error: -# result = "cat" >= str -# You could explicitly convert the StringLiteral to a String: -# result = String("cat") >= str -result = str < "cat" # Successful comparison -print(result) +var animal: String = "bird" + +is_cat_eq = "cat" == animal +print('Is "cat" equal to "{}"?'.format(animal), is_cat_eq) + +is_cat_ne = "cat" != animal +print('Is "cat" not equal to "{}"?'.format(animal), is_cat_ne) + +is_bird_eq = "bird" == animal +print('Is "bird" equal to "{}"?'.format(animal), is_bird_eq) + +is_cat_gt = "CAT" > animal +print('Is "CAT" greater than "{}"?'.format(animal), is_cat_gt) + +is_ge_cat = animal >= "CAT" +print('Is "{}" greater than or equal to "CAT"?'.format(animal), is_ge_cat) ``` ```output -True +Is "cat" equal to "bird"? False +Is "cat" not equal to "bird"? True +Is "bird" equal to "bird"? True +Is "CAT" greater than "bird"? False +Is "bird" greater than or equal to "CAT"? True ``` #### Substring testing From 795d8fed3997e4321e072d1f98ef7a4006516f79 Mon Sep 17 00:00:00 2001 From: modularbot Date: Fri, 7 Feb 2025 05:52:24 +0000 Subject: [PATCH 358/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025020700 --- examples/life/magic.lock | 5870 +++-------------------- examples/magic.lock | 8259 ++++++-------------------------- examples/operators/magic.lock | 8259 ++++++-------------------------- examples/testing/magic.lock | 8259 ++++++-------------------------- magic.lock | 8273 ++++++--------------------------- 5 files changed, 6402 insertions(+), 32518 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 5560428e0a..243d509ba1 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -8,57 +8,17 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - 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To update the version of Mojo for your project with the `magic` package manager, follow the instructions in [Update a package](/magic#update-a-package) to update the `max` package. +## v25.1 (2025-02-13) + +### ✨ Highlights + +### Language changes + +- Initializers are now treated as static methods that return an instance of + `Self`. This means the `out` argument of an initializer is now treated the + same as a any other function result or `out` argument. This is generally + invisible, except that patterns like `instance.__init__()` and + `x.__copyinit__(y)` no longer work. Simply replace them with `instance = T()` + and `x = y` respectively. + +- The legacy `borrowed`/`inout` keywords and `-> T as foo` syntax now generate + a warning. Please move to `read`/`mut`/`out` argument syntax instead. + +- The `@value` decorator now additionally derives an implementation of the + `ExplicitlyCopyable` trait. This will ease the transition to explicit + copyablility requirements by default in the Mojo collection types. + +- Indexing into a homogenous tuple now produces the consistent element type + without needing a rebind: + + ```mojo + var x = (1, 2, 3, 3, 4) + var y : Int = x[idx] # Just works! + ``` + +- You can now overload positional arguments with a keyword-only argument, and + keyword-only arguments with different names: + + ```mojo + struct OverloadedKwArgs: + var val: Int + + fn __init__(out self, single: Int): + self.val = single + + fn __init__(out self, *, double: Int): + self.val = double * 2 + + fn __init__(out self, *, triple: Int): + self.val = triple * 3 + + fn main(): + OverloadedKwArgs(1) # val=1 + OverloadedKwArgs(double=1) # val=2 + OverloadedKwArgs(triple=2) # val=6 + ``` + + This also works with indexing operations: + + ```mojo + struct OverloadedKwArgs: + var vals: List[Int] + + fn __init__(out self): + self.vals = List[Int](0, 1, 2) + + fn __getitem__(self, idx: Int) -> Int: + return self.vals[idx] + + fn __getitem__(self, *, idx2: Int) -> Int: + return self.vals[idx2 * 2] + + fn __setitem__(mut self, idx: Int, val: Int): + self.vals[idx] = val + + fn __setitem__(mut self, val: Int, *, idx2: Int): + self.vals[idx2 * 2] = val + + + fn main(): + var x = OverloadedKwArgs() + print(x[1]) # 1 + print(x[idx2=1]) # 2 + + x[1] = 42 + x[idx2=1] = 84 + + print(x[1]) # 42 + print(x[idx2=1]) # 84 + ``` + +### Standard library changes + +- `StringRef` has been removed in favor of `StringSlice`. + The two types are ABI compatible, and for the exact same + behavior one can use `StaticString`, which is an alias + to `StringSlice[StaticConstantOrigin]`. + +- Add a new `validate` parameter to the `b64decode()` function. + +- New `SIMD.from_bytes()` and `SIMD.as_bytes()` functions to convert a list of bytes + to a list of scalars and vice versa, accepting the endianess as an argument. Similar + to Python `int.from_bytes()` and `int.to_bytes()` functions. + +- Added more aliases in `sys.ffi` to round out the usual needs for FFI bindings. + +- The free floating functions for constructing different types have been + deprecated for actual constructors: + + ```plaintext + before after + ------------------ + int() Int() + str() String() + bool() Bool() + float() Float64() + ``` + + These functions were a workaround before Mojo had a way to distinguish between + implicit and explicit constructors. For this release you'll get a deprecation + warning, and in the next release they'll become compiler errors. You can + quickly update your code by doing a `Match Case` and `Match Whole Word` + search and replace for `int(` to `Int(` etc. + +- `UnsafePointer`'s `bitcast` method has now been split into `bitcast` + for changing the type, `origin_cast` for changing mutability, + `static_alignment_cast` for changing alignment, + and `address_space_cast` for changing the address space. + +- `UnsafePointer` is now parameterized on mutability. Previously, + `UnsafePointer` could only represent mutable pointers. + + The new `mut` parameter can be used to restrict an `UnsafePointer` to a + specific mutability: `UnsafePointer[T, mut=False]` represents a pointer to + an immutable `T` value. This is analogous to a `const *` pointer in C++. + + - `UnsafePointer.address_of()` will now infer the origin and mutability + of the resulting pointer from the argument. For example: + + ```mojo + var local = 10 + # Constructs a mutable pointer, because `local` is a mutable memory location + var ptr = UnsafePointer.address_of(local) + ``` + + To force the construction of an immutable pointer to an otherwise mutable + memory location, use a cast: + + ```mojo + var local = 10 + # Cast the mutable pointer to be immutable. + var ptr = UnsafePointer.address_of(local).origin_cast[mut=False]() + ``` + + - The `unsafe_ptr()` method on several standard library collection types have + been updated to use parametric mutability: they will return an `UnsafePointer` + whose mutability is inherited from the mutability of the `ref self` of the + receiver at the call site. For example, `ptr1` will be immutable, while + `ptr2` will be mutable: + + ```mojo + fn take_lists(read list1: List[Int], mut list2: List[Int]): + # Immutable pointer, since receiver is immutable `read` reference + var ptr1 = list1.unsafe_ptr() + + # Mutable pointer, since receiver is mutable `mut` reference + var ptr2 = list2.unsafe_ptr() + ``` + +- Added `Optional.copied()` for constructing an owned `Optional[T]` from an + `Optional[Pointer[T]]` by copying the pointee value. + +- Added `Dict.get_ptr()` which returns an `Optional[Pointer[V]]`. If the given + key is present in the dictionary, the optional will hold a pointer to the + value. Otherwise, an empty optional is returned. + +- Added new `List.extend()` overloads taking `SIMD` and `Span`. These enable + growing a `List[Scalar[..]]` by copying the elements of a `SIMD` vector or + `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware + functionality. + +- Added `Char`, for representing and storing single Unicode characters. + - `Char` implements `CollectionElement`, `EqualityComparable`, `Intable`, and + `Stringable`. + - Added `String` constructor from `Char` + - `Char` can be converted to `UInt32` via `Char.to_u32()`. + - `Char` provides methods for categorizing character types, including: + `Char.is_ascii()`, `Char.is_posix_space()`, `Char.is_python_space()`, + `Char.is_ascii_digit()`, `Char.is_ascii_upper()`, `Char.is_ascii_lower()`, + `Char.is_ascii_printable()`. + +- `chr(Int)` will now abort if given a codepoint value that is not a valid + `Char`. + +- Added `StringSlice.from_utf()` factor method, for validated construction of + a `StringSlice` from a buffer containing UTF-8 encoded data. This method will + raise if the buffer contents are not valid UTF-8. + +- Added `StringSlice.chars()` which returns an iterator over `Char`s. This is a + compliant UTF-8 decoder that returns each Unicode codepoint encoded in the + string. + +- Added `StringSlice.__getitem__(Slice)` which returns a substring. + Only step sizes of 1 are supported. + +- Several standard library functions have been changed to take `StringSlice` + instead of `String`. This generalizes them to be used for any appropriately + encoded string in memory, without requiring that the string be heap allocated. + + - `atol()` + - `atof()` + - `ord()` + - `ascii()` + - `b64encode()` + - Additionally, the `b64encode()` overload that previously took `List` has + been changed to + take a `Span`. + - `b64decode()` + - `b16encode()` + - `b16decode()` + +- Added new `String.chars()` and `String.char_slices()` iterator methods, and + deprecated the existing `String.__iter__()` method. + + Different use-cases may prefer iterating over the `Char`s encoded in a string, + or iterating over subslices containing single characters. Neither iteration + semantics is an obvious default, so the existing `__iter__()` method has been + deprecated in favor of writing explicit iteration methods for the time being. + + Code of the form: + + ```mojo + var s: String = ... + for c in s: + # ... + ``` + + can be migrated to using the `.char_slices()` method: + + ```mojo + var s: String = ... + for c in s.char_slices(): + # ... + ``` + +- The `String.__len__()` and `StringSlice.__len__()` methods now return the + length of the string in bytes. + + Previously, these methods were documented to note that they would eventually + return a length in Unicode codepoints. They have been changed to guarantee + a length in bytes, since the length in bytes is how they are most often used + today (for example, as bounds to low-level memory manipulation logic). + Additionally, length in codepoints is a more specialized notion of string + length that is rarely the correct metric. + + Users that know they need the length in codepoints can use the + `str.char_length()` method, or `len(str.chars())`. + +- Various functionality has moved from `String` and `StringRef` to the more + general `StringSlice` type. + + - `StringSlice` now implements `Representable`, and that implementation is now + used by `String.__repr__()` and `StringRef.__repr__()`. + +- `StringSlice` now implements `EqualityComparable`. + + Up until now, `StringSlice` has implemented a more general `__eq__` and + `__ne__` comparision with `StringSlice` types that had arbitrary other + origins. However, to satisfy `EqualityComparable`, `StringSlice` now also + has narrower comparison methods that support comparing only with + `StringSlice`'s with the exact same origin. + +- Added `StringSlice.char_length()` method, to pair with the existing + `StringSlice.byte_length()` method. + + In a future version of Mojo, `StringSlice.__len__()` may be changed to return + the length in bytes, matching the convention of string length methods in + languages like C++ and Rust. Callers that know they need the length in + Unicode codepoints should update to calling `StringSlice.char_length()` + instead. + +- Removed `@implicit` decorator from some standard library initializer methods + that perform allocation. This reduces places where Mojo code could implicitly + allocate where the user may not be aware. + + Remove `@implicit` from: + + - `String.__init__(out self, StringRef)` + - `String.__init__(out self, StringSlice)` + - `List.__init__(out self, owned *values: T)` + - `List.__init__(out self, span: Span[T])` + +- The `ExplicitlyCopyable` trait has changed to require a + `fn copy(self) -> Self` method. Previously, an initializer with the signature + `fn __init__(out self, *, other: Self)` had been required by + `ExplicitlyCopyable`. + + This improves the "greppability" and at-a-glance readability when a programmer + is looking for places in their code that may be performing copies + +- `bit_ceil` has been renamed to `next_power_of_two`, and `bit_floor` to + `prev_power_of_two`. This is to improve readability and clarity in their use. + +- The `Indexer` and `IntLike` traits which were previously both used for + indexing have been combined. This enables SIMD scalar integer types and UInt + to be used for indexing into all of the collection types, as well as + optimizing away normalization checks for UInt indexing. + +- The `ImplicitlyIntable` trait has been added, allowing types to be implicitly + converted to an `Int` by implementing the `__as_int__` method: + + ```mojo + @value + struct Foo(ImplicitlyIntable): + var i: Int + + fn __as_int__(self) -> Int: + return self.i + ``` + +- You can now cast SIMD types using constructors: + + ```mojo + var val = Int8(42) + var cast = Int32(val) + ``` + + It also works when passing a scalar type to larger vector size: + + ```mojo + var vector = SIMD[DType.int64, 4](cast) # [42, 42, 42, 42] + ``` + + For values other than scalars the size of the SIMD vector needs to be equal: + + ```mojo + var float_vector = SIMD[DType.float64, 4](vector) + ``` + + `SIMD.cast` still exists to infer the size of new vector: + + ```mojo + var inferred_size = float_vector.cast[DType.uint64]() # [42, 42, 42, 42] + ``` + +- You can now use `max()` and `min()` with variadic number of arguments. + +- A new `LinkedList` type has been added to the standard library. + +- The `String.write` static method has moved to a `String` constructor, and + is now buffered. Instead of doing: + + ```mojo + var msg = "my message " + String(x) + " " + String(y) + " " + String(z) + ``` + + Which reallocates the `String` you should do: + + ```mojo + var msg = String("my message", x, y, z, sep=" ") + ``` + + Which is cleaner, and buffers to the stack so the `String` is allocated only + once. + +- You can now pass any `Writer` to `write_buffered`: + + ```mojo + from utils.write import write_buffered + + var string = String("existing string") + write_buffered(string, 42, 42.4, True, sep=" ") + ``` + + This writes to a buffer on the stack before reallocating the `String`. + +- The `__disable_del x` operation has been tightened up to treat all fields of + 'x' as consumed by the point of the del, so it should be used after all the + subfields are transferred or otherwise consumed (e.g. at the end of the + function) not before uses of the fields. + +### Tooling changes + +- mblack (aka `mojo format`) no longer formats non-mojo files. This prevents + unexpected formatting of python files. + +- Full struct signature information is now exposed in the documentation + generator, and in the symbol outline and hover markdown via the Mojo Language + Server. + +- The `env_get_dtype` function has been added to the `sys` module. This allows + you to get the value of a `DType` from the param environment. + +### ❌ Removed + +- `StringRef` is being deprecated. Use `StringSlice` instead. + - Changed `sys.argv()` to return list of `StringSlice`. + - Added `Path` explicit constructor from `StringSlice`. + - removed `StringRef.startswith()` and `StringRef.endswith()` + - removed `StringRef.strip()` +- The `Tuple.get[i, T]()` method has been removed. Please use `tup[i]` or + `rebind[T](tup[i])` as needed instead. +- `StringableCollectionElement` is deprecated, use `WritableCollectionElement` + instead which still allows you to construct a `String`, but can avoid + intermediary allocations. +- The `Type{field1: 42, field2: 17}` syntax for direct initializing register + passable types has been removed. This was legacy syntax - to upgrade + your code, add the @value decorator to your struct to get a memberwise + initializer and use `Type(field1=42, field2 = 17)` instead. + +### 🛠️ Fixed + +- The Mojo Kernel for Jupyter Notebooks is working again on nightly releases. + +- The command `mojo debug --vscode` now sets the current working directory + properly. + +- [Issue #3796](https://github.com/modular/mojo/issues/3796) - Compiler crash + handling for-else statement. + +- [Issue #3540](https://github.com/modular/mojo/issues/3540) - Using named + output slot breaks trait conformance + +- [Issue #3617](https://github.com/modular/mojo/issues/3617) - Can't generate + the constructors for a type wrapping `!lit.ref` + +- The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. + [Issue #3826](https://github.com/modular/mojo/issues/3826). + +- [Issue #3935](https://github.com/modular/mojo/issues/3935) - Confusing OOM + error when using Tuple.get incorrectly. + +- [Issue #3955](https://github.com/modular/mojo/issues/3955) - Unexpected + copy behaviour with `def` arguments in loops + +- [Issue #3960](https://github.com/modular/mojo/issues/3960) - Infinite for loop + ## v24.6 (2024-12-17) ### ✨ Highlights diff --git a/docs/changelog.md b/docs/changelog.md index 3711ca5f2f..9f6a1c4e3e 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -20,426 +20,10 @@ what we publish. ### Language changes -- Initializers are now treated as static methods that return an instance of - `Self`. This means the `out` argument of an initializer is now treated the - same as a any other function result or `out` argument. This is generally - invisible, except that patterns like `instance.__init__()` and - `x.__copyinit__(y)` no longer work. Simply replace them with `instance = T()` - and `x = y` respectively. - -- The legacy `borrowed`/`inout` keywords and `-> T as foo` syntax now generate - a warning. Please move to `read`/`mut`/`out` argument syntax instead. - -- The `@value` decorator now additionally derives an implementation of the - `ExplicitlyCopyable` trait. This will ease the transition to explicit - copyablility requirements by default in the Mojo collection types. - -- Indexing into a homogenous tuple now produces the consistent element type - without needing a rebind: - - ```mojo - var x = (1, 2, 3, 3, 4) - var y : Int = x[idx] # Just works! - ``` - -- You can now overload positional arguments with a keyword-only argument, and - keyword-only arguments with different names: - - ```mojo - struct OverloadedKwArgs: - var val: Int - - fn __init__(out self, single: Int): - self.val = single - - fn __init__(out self, *, double: Int): - self.val = double * 2 - - fn __init__(out self, *, triple: Int): - self.val = triple * 3 - - fn main(): - OverloadedKwArgs(1) # val=1 - OverloadedKwArgs(double=1) # val=2 - OverloadedKwArgs(triple=2) # val=6 - ``` - - This also works with indexing operations: - - ```mojo - struct OverloadedKwArgs: - var vals: List[Int] - - fn __init__(out self): - self.vals = List[Int](0, 1, 2) - - fn __getitem__(self, idx: Int) -> Int: - return self.vals[idx] - - fn __getitem__(self, *, idx2: Int) -> Int: - return self.vals[idx2 * 2] - - fn __setitem__(mut self, idx: Int, val: Int): - self.vals[idx] = val - - fn __setitem__(mut self, val: Int, *, idx2: Int): - self.vals[idx2 * 2] = val - - - fn main(): - var x = OverloadedKwArgs() - print(x[1]) # 1 - print(x[idx2=1]) # 2 - - x[1] = 42 - x[idx2=1] = 84 - - print(x[1]) # 42 - print(x[idx2=1]) # 84 - ``` - ### Standard library changes -- `StringRef` has been removed in favor of `StringSlice`. - The two types are ABI compatible, and for the exact same - behavior one can use `StaticString`, which is an alias - to `StringSlice[StaticConstantOrigin]`. - -- Add a new `validate` parameter to the `b64decode()` function. - -- New `SIMD.from_bytes()` and `SIMD.as_bytes()` functions to convert a list of bytes - to a list of scalars and vice versa, accepting the endianess as an argument. Similar - to Python `int.from_bytes()` and `int.to_bytes()` functions. - -- Added more aliases in `sys.ffi` to round out the usual needs for FFI bindings. - -- The free floating functions for constructing different types have been - deprecated for actual constructors: - - ```plaintext - before after - ------------------ - int() Int() - str() String() - bool() Bool() - float() Float64() - ``` - - These functions were a workaround before Mojo had a way to distinguish between - implicit and explicit constructors. For this release you'll get a deprecation - warning, and in the next release they'll become compiler errors. You can - quickly update your code by doing a `Match Case` and `Match Whole Word` - search and replace for `int(` to `Int(` etc. - -- `UnsafePointer`'s `bitcast` method has now been split into `bitcast` - for changing the type, `origin_cast` for changing mutability, - `static_alignment_cast` for changing alignment, - and `address_space_cast` for changing the address space. - -- `UnsafePointer` is now parameterized on mutability. Previously, - `UnsafePointer` could only represent mutable pointers. - - The new `mut` parameter can be used to restrict an `UnsafePointer` to a - specific mutability: `UnsafePointer[T, mut=False]` represents a pointer to - an immutable `T` value. This is analogous to a `const *` pointer in C++. - - - `UnsafePointer.address_of()` will now infer the origin and mutability - of the resulting pointer from the argument. For example: - - ```mojo - var local = 10 - # Constructs a mutable pointer, because `local` is a mutable memory location - var ptr = UnsafePointer.address_of(local) - ``` - - To force the construction of an immutable pointer to an otherwise mutable - memory location, use a cast: - - ```mojo - var local = 10 - # Cast the mutable pointer to be immutable. - var ptr = UnsafePointer.address_of(local).origin_cast[mut=False]() - ``` - - - The `unsafe_ptr()` method on several standard library collection types have - been updated to use parametric mutability: they will return an `UnsafePointer` - whose mutability is inherited from the mutability of the `ref self` of the - receiver at the call site. For example, `ptr1` will be immutable, while - `ptr2` will be mutable: - - ```mojo - fn take_lists(read list1: List[Int], mut list2: List[Int]): - # Immutable pointer, since receiver is immutable `read` reference - var ptr1 = list1.unsafe_ptr() - - # Mutable pointer, since receiver is mutable `mut` reference - var ptr2 = list2.unsafe_ptr() - ``` - -- Added `Optional.copied()` for constructing an owned `Optional[T]` from an - `Optional[Pointer[T]]` by copying the pointee value. - -- Added `Dict.get_ptr()` which returns an `Optional[Pointer[V]]`. If the given - key is present in the dictionary, the optional will hold a pointer to the - value. Otherwise, an empty optional is returned. - -- Added new `List.extend()` overloads taking `SIMD` and `Span`. These enable - growing a `List[Scalar[..]]` by copying the elements of a `SIMD` vector or - `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware - functionality. - -- Added `Char`, for representing and storing single Unicode characters. - - `Char` implements `CollectionElement`, `EqualityComparable`, `Intable`, and - `Stringable`. - - Added `String` constructor from `Char` - - `Char` can be converted to `UInt32` via `Char.to_u32()`. - - `Char` provides methods for categorizing character types, including: - `Char.is_ascii()`, `Char.is_posix_space()`, `Char.is_python_space()`, - `Char.is_ascii_digit()`, `Char.is_ascii_upper()`, `Char.is_ascii_lower()`, - `Char.is_ascii_printable()`. - -- `chr(Int)` will now abort if given a codepoint value that is not a valid - `Char`. - -- Added `StringSlice.from_utf()` factor method, for validated construction of - a `StringSlice` from a buffer containing UTF-8 encoded data. This method will - raise if the buffer contents are not valid UTF-8. - -- Added `StringSlice.chars()` which returns an iterator over `Char`s. This is a - compliant UTF-8 decoder that returns each Unicode codepoint encoded in the - string. - -- Added `StringSlice.__getitem__(Slice)` which returns a substring. - Only step sizes of 1 are supported. - -- Several standard library functions have been changed to take `StringSlice` - instead of `String`. This generalizes them to be used for any appropriately - encoded string in memory, without requiring that the string be heap allocated. - - - `atol()` - - `atof()` - - `ord()` - - `ascii()` - - `b64encode()` - - Additionally, the `b64encode()` overload that previously took `List` has - been changed to - take a `Span`. - - `b64decode()` - - `b16encode()` - - `b16decode()` - -- Added new `String.chars()` and `String.char_slices()` iterator methods, and - deprecated the existing `String.__iter__()` method. - - Different use-cases may prefer iterating over the `Char`s encoded in a string, - or iterating over subslices containing single characters. Neither iteration - semantics is an obvious default, so the existing `__iter__()` method has been - deprecated in favor of writing explicit iteration methods for the time being. - - Code of the form: - - ```mojo - var s: String = ... - for c in s: - # ... - ``` - - can be migrated to using the `.char_slices()` method: - - ```mojo - var s: String = ... - for c in s.char_slices(): - # ... - ``` - -- The `String.__len__()` and `StringSlice.__len__()` methods now return the - length of the string in bytes. - - Previously, these methods were documented to note that they would eventually - return a length in Unicode codepoints. They have been changed to guarantee - a length in bytes, since the length in bytes is how they are most often used - today (for example, as bounds to low-level memory manipulation logic). - Additionally, length in codepoints is a more specialized notion of string - length that is rarely the correct metric. - - Users that know they need the length in codepoints can use the - `str.char_length()` method, or `len(str.chars())`. - -- Various functionality has moved from `String` and `StringRef` to the more - general `StringSlice` type. - - - `StringSlice` now implements `Representable`, and that implementation is now - used by `String.__repr__()` and `StringRef.__repr__()`. - -- `StringSlice` now implements `EqualityComparable`. - - Up until now, `StringSlice` has implemented a more general `__eq__` and - `__ne__` comparision with `StringSlice` types that had arbitrary other - origins. However, to satisfy `EqualityComparable`, `StringSlice` now also - has narrower comparison methods that support comparing only with - `StringSlice`'s with the exact same origin. - -- Added `StringSlice.char_length()` method, to pair with the existing - `StringSlice.byte_length()` method. - - In a future version of Mojo, `StringSlice.__len__()` may be changed to return - the length in bytes, matching the convention of string length methods in - languages like C++ and Rust. Callers that know they need the length in - Unicode codepoints should update to calling `StringSlice.char_length()` - instead. - -- Removed `@implicit` decorator from some standard library initializer methods - that perform allocation. This reduces places where Mojo code could implicitly - allocate where the user may not be aware. - - Remove `@implicit` from: - - - `String.__init__(out self, StringRef)` - - `String.__init__(out self, StringSlice)` - - `List.__init__(out self, owned *values: T)` - - `List.__init__(out self, span: Span[T])` - -- The `ExplicitlyCopyable` trait has changed to require a - `fn copy(self) -> Self` method. Previously, an initializer with the signature - `fn __init__(out self, *, other: Self)` had been required by - `ExplicitlyCopyable`. - - This improves the "greppability" and at-a-glance readability when a programmer - is looking for places in their code that may be performing copies - -- `bit_ceil` has been renamed to `next_power_of_two`, and `bit_floor` to - `prev_power_of_two`. This is to improve readability and clarity in their use. - -- The `Indexer` and `IntLike` traits which were previously both used for - indexing have been combined. This enables SIMD scalar integer types and UInt - to be used for indexing into all of the collection types, as well as - optimizing away normalization checks for UInt indexing. - -- The `ImplicitlyIntable` trait has been added, allowing types to be implicitly - converted to an `Int` by implementing the `__as_int__` method: - - ```mojo - @value - struct Foo(ImplicitlyIntable): - var i: Int - - fn __as_int__(self) -> Int: - return self.i - ``` - -- You can now cast SIMD types using constructors: - - ```mojo - var val = Int8(42) - var cast = Int32(val) - ``` - - It also works when passing a scalar type to larger vector size: - - ```mojo - var vector = SIMD[DType.int64, 4](cast) # [42, 42, 42, 42] - ``` - - For values other than scalars the size of the SIMD vector needs to be equal: - - ```mojo - var float_vector = SIMD[DType.float64, 4](vector) - ``` - - `SIMD.cast` still exists to infer the size of new vector: - - ```mojo - var inferred_size = float_vector.cast[DType.uint64]() # [42, 42, 42, 42] - ``` - -- You can now use `max()` and `min()` with variadic number of arguments. - -- A new `LinkedList` type has been added to the standard library. - -- The `String.write` static method has moved to a `String` constructor, and - is now buffered. Instead of doing: - - ```mojo - var msg = "my message " + String(x) + " " + String(y) + " " + String(z) - ``` - - Which reallocates the `String` you should do: - - ```mojo - var msg = String("my message", x, y, z, sep=" ") - ``` - - Which is cleaner, and buffers to the stack so the `String` is allocated only - once. - -- You can now pass any `Writer` to `write_buffered`: - - ```mojo - from utils.write import write_buffered - - var string = String("existing string") - write_buffered(string, 42, 42.4, True, sep=" ") - ``` - - This writes to a buffer on the stack before reallocating the `String`. - -- The `__disable_del x` operation has been tightened up to treat all fields of - 'x' as consumed by the point of the del, so it should be used after all the - subfields are transferred or otherwise consumed (e.g. at the end of the - function) not before uses of the fields. - ### Tooling changes -- mblack (aka `mojo format`) no longer formats non-mojo files. This prevents - unexpected formatting of python files. - -- Full struct signature information is now exposed in the documentation - generator, and in the symbol outline and hover markdown via the Mojo Language - Server. - -- The `env_get_dtype` function has been added to the `sys` module. This allows - you to get the value of a `DType` from the param environment. - ### ❌ Removed -- `StringRef` is being deprecated. Use `StringSlice` instead. - - Changed `sys.argv()` to return list of `StringSlice`. - - Added `Path` explicit constructor from `StringSlice`. - - removed `StringRef.startswith()` and `StringRef.endswith()` - - removed `StringRef.strip()` -- The `Tuple.get[i, T]()` method has been removed. Please use `tup[i]` or - `rebind[T](tup[i])` as needed instead. -- `StringableCollectionElement` is deprecated, use `WritableCollectionElement` - instead which still allows you to construct a `String`, but can avoid - intermediary allocations. -- The `Type{field1: 42, field2: 17}` syntax for direct initializing register - passable types has been removed. This was legacy syntax - to upgrade - your code, add the @value decorator to your struct to get a memberwise - initializer and use `Type(field1=42, field2 = 17)` instead. - ### 🛠️ Fixed - -- The Mojo Kernel for Jupyter Notebooks is working again on nightly releases. - -- The command `mojo debug --vscode` now sets the current working directory - properly. - -- [Issue #3796](https://github.com/modular/mojo/issues/3796) - Compiler crash - handling for-else statement. - -- [Issue #3540](https://github.com/modular/mojo/issues/3540) - Using named - output slot breaks trait conformance - -- [Issue #3617](https://github.com/modular/mojo/issues/3617) - Can't generate - the constructors for a type wrapping `!lit.ref` - -- The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. - [Issue #3826](https://github.com/modular/mojo/issues/3826). - -- [Issue #3935](https://github.com/modular/mojo/issues/3935) - Confusing OOM - error when using Tuple.get incorrectly. - -- [Issue #3955](https://github.com/modular/mojo/issues/3955) - Unexpected - copy behaviour with `def` arguments in loops - -- [Issue #3960](https://github.com/modular/mojo/issues/3960) - Infinite for loop From 6b58eb2ec4c491588fbd34c8e66963d277c43492 Mon Sep 17 00:00:00 2001 From: Keith Smiley Date: Fri, 7 Feb 2025 12:24:24 -0800 Subject: [PATCH 361/509] [lit] Use internal test format for mojo tests MODULAR_ORIG_COMMIT_REV_ID: 3238188fa6f7ca559dae9ed5f13e9f35567d4d99 --- stdlib/benchmarks/lit.cfg.py | 4 ++-- stdlib/test/lit.cfg.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/stdlib/benchmarks/lit.cfg.py b/stdlib/benchmarks/lit.cfg.py index a05376ec63..c3520d0d92 100644 --- a/stdlib/benchmarks/lit.cfg.py +++ b/stdlib/benchmarks/lit.cfg.py @@ -17,8 +17,6 @@ import lit.formats import lit.llvm -config.test_format = lit.formats.ShTest(True) - # name: The name of this test suite. config.name = "Mojo Standard Library Benchmarks" @@ -39,6 +37,8 @@ config.modular_obj_root, "open-source", "mojo", "stdlib", "benchmarks" ) else: + config.test_format = lit.formats.ShTest(True) + # test_source_root: The root path where tests are located. config.test_source_root = Path(__file__).parent.resolve() diff --git a/stdlib/test/lit.cfg.py b/stdlib/test/lit.cfg.py index a20ce09510..249fa758dd 100644 --- a/stdlib/test/lit.cfg.py +++ b/stdlib/test/lit.cfg.py @@ -20,8 +20,6 @@ # Configuration file for the 'lit' test runner. -config.test_format = lit.formats.ShTest(True) - # name: The name of this test suite. config.name = "Mojo Standard Library" @@ -53,6 +51,8 @@ def has_not(): ) # External, public Mojo testing configuration else: + config.test_format = lit.formats.ShTest(True) + # test_source_root: The root path where tests are located. config.test_source_root = Path(__file__).parent.resolve() From dff68683e6f6bc06f0a705357558075ae9c256c2 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 8 Feb 2025 07:58:18 +0000 Subject: [PATCH 362/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025020805 --- examples/life/magic.lock | 142 +++++++++++++++++----------------- examples/magic.lock | 142 +++++++++++++++++----------------- examples/operators/magic.lock | 142 +++++++++++++++++----------------- examples/testing/magic.lock | 142 +++++++++++++++++----------------- magic.lock | 142 +++++++++++++++++----------------- 5 files changed, 355 insertions(+), 355 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 7b274bc51f..11f73daab7 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -91,11 +91,11 @@ environments: - 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sha256: 7ec8cc093aacd2c2a29ed07f6c09f57b3a7b96440bf4267de2f8be59eae0ec2d - md5: 1bc6ab515b42167588830c884b0a4e7a + sha256: 01ce310a8f05bc16028bd8749c7decd899b2b569992b3db5790bd3b8d5f9d9fb + md5: cb0e7f08489ecfc881ec3197c3d8de15 depends: - - max-core ==25.1.0.dev2025020705 release + - max-core ==25.1.0.dev2025020805 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22985 - timestamp: 1738905467253 + size: 22988 + timestamp: 1738991846699 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From faccbac08aa7ec1fcce2789547f7b45071a41551 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Fri, 7 Feb 2025 21:24:20 -0800 Subject: [PATCH 363/509] [Kernels][GPU] Rename float8_e4m3 to float8_e4m3fn This is the more correct naming of the type. MODULAR_ORIG_COMMIT_REV_ID: 7bd91934af066ea2872ad988c5ecdbd6311dd4dc --- stdlib/src/builtin/_format_float.mojo | 6 +++--- stdlib/src/builtin/dtype.mojo | 18 +++++++++--------- stdlib/src/builtin/simd.mojo | 16 ++++++++-------- stdlib/src/utils/numerics.mojo | 18 +++++++++--------- 4 files changed, 29 insertions(+), 29 deletions(-) diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index 9f5fde5fe1..ebd403a45c 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -109,9 +109,9 @@ fn _write_float[W: Writer, type: DType, //](mut writer: W, value: Scalar[type]): return writer.write( float8_e5m2_to_str[Int(bitcast[DType.uint8](value))] ) - elif type is DType.float8_e4m3: + elif type is DType.float8_e4m3fn: return writer.write( - float8_e4m3_to_str[Int(bitcast[DType.uint8](value))] + float8_e4m3fn_to_str[Int(bitcast[DType.uint8](value))] ) elif type is DType.float8_e5m2fnuz: return writer.write( @@ -1687,7 +1687,7 @@ alias float8_e5m2_to_str = StaticTuple[StringLiteral, 256]( "nan", ) -alias float8_e4m3_to_str = StaticTuple[StringLiteral, 256]( +alias float8_e4m3fn_to_str = StaticTuple[StringLiteral, 256]( "0.0", "0.001953125", "0.00390625", diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index 0b38797ac8..3fbb244978 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -94,7 +94,7 @@ struct DType( - fn: finite (no inf or -inf encodings) - uz: unsigned zero (no -0 encoding) """ - alias float8_e4m3 = DType( + alias float8_e4m3fn = DType( __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) """Represents a FP8E4M3 floating point format from the [OFP8 @@ -199,8 +199,8 @@ struct DType( return DType.float8_e5m2 elif str == "float8_e5m2fnuz": return DType.float8_e5m2fnuz - elif str == "float8_e4m3": - return DType.float8_e4m3 + elif str == "float8_e4m3fn": + return DType.float8_e4m3fn elif str == "float8_e4m3fnuz": return DType.float8_e4m3fnuz elif str == "bfloat16": @@ -264,8 +264,8 @@ struct DType( return writer.write("float8_e5m2") if self == DType.float8_e5m2fnuz: return writer.write("float8_e5m2fnuz") - if self == DType.float8_e4m3: - return writer.write("float8_e4m3") + if self == DType.float8_e4m3fn: + return writer.write("float8_e4m3fn") if self == DType.float8_e4m3fnuz: return writer.write("float8_e4m3fnuz") if self == DType.bfloat16: @@ -470,7 +470,7 @@ struct DType( @always_inline("nodebug") fn is_float8(self) -> Bool: """Returns True if the type is a 8bit-precision floating point type, - e.g. float8_e5m2, float8_e5m2fnuz, float8_e4m3 and float8_e4m3fnuz. + e.g. float8_e5m2, float8_e5m2fnuz, float8_e4m3fn and float8_e4m3fnuz. Returns: True if the type is a 8bit-precision float, false otherwise. @@ -478,7 +478,7 @@ struct DType( return self in ( DType.float8_e5m2, - DType.float8_e4m3, + DType.float8_e4m3fn, DType.float8_e5m2fnuz, DType.float8_e4m3fnuz, ) @@ -539,8 +539,8 @@ struct DType( return sizeof[DType.float8_e5m2]() if self == DType.float8_e5m2fnuz: return sizeof[DType.float8_e5m2fnuz]() - if self == DType.float8_e4m3: - return sizeof[DType.float8_e4m3]() + if self == DType.float8_e4m3fn: + return sizeof[DType.float8_e4m3fn]() if self == DType.float8_e4m3fnuz: return sizeof[DType.float8_e4m3fnuz]() if self == DType.bfloat16: diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 5683dd0557..f757d53340 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -122,7 +122,7 @@ The 8 bits are encoded as `seeeeemm`: - fn: finite (no inf or -inf encodings) - uz: unsigned zero (no -0 encoding) """ -alias Float8_e4m3 = Scalar[DType.float8_e4m3] +alias Float8_e4m3 = Scalar[DType.float8_e4m3fn] """Represents a FP8E4M3 floating point format from the [OFP8 standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). @@ -556,7 +556,7 @@ struct SIMD[type: DType, size: Int]( # TODO (#36686): This introduces unneeded casts here to work around # parameter if issues. @parameter - if type is DType.float8_e4m3: + if type is DType.float8_e4m3fn: self = SIMD[type, size]( __mlir_op.`pop.simd.splat`[ _type = __mlir_type[ @@ -1830,7 +1830,7 @@ struct SIMD[type: DType, size: Int]( return self.cast[DType.float32]().cast[target]() @parameter - if target in (DType.float8_e4m3, DType.float8_e5m2): + if target in (DType.float8_e4m3fn, DType.float8_e5m2): # TODO(KERN-1488): use gpu (H100) instruction to convert from fp16 to fp8 return rebind[SIMD[target, size]]( _convert_f32_to_float8[size=size, target=target]( @@ -1841,7 +1841,7 @@ struct SIMD[type: DType, size: Int]( ) @parameter - if type in (DType.float8_e4m3, DType.float8_e5m2): + if type in (DType.float8_e4m3fn, DType.float8_e5m2): constrained[ target in (DType.float32, DType.float16, DType.bfloat16), ( @@ -3198,7 +3198,7 @@ fn _convert_float8_to_f32_scaler[ ](x: Scalar[type]) -> Scalar[DType.float32]: var kF32_NaN: UInt32 = 0x7FFFFFFF var FP8_NUM_BITS = 8 - var IS_E4M3 = type is DType.float8_e4m3 + var IS_E4M3 = type is DType.float8_e4m3fn var FP8_NUM_MANTISSA_BITS = FPUtils[type].mantissa_width() var FP8_NUM_EXPONENT_BITS = FPUtils[type].exponent_width() var FP32_NUM_BITS = 32 @@ -3283,7 +3283,7 @@ fn _convert_float8_to_f16[ ](val: SIMD[type, size],) -> SIMD[DType.float16, size]: @parameter if is_nvidia_gpu() and _is_sm_9x(): - alias asm_prefix = "cvt.rn.f16x2.e4m3x2" if type is DType.float8_e4m3 else "cvt.rn.f16x2.e5m2x2" + alias asm_prefix = "cvt.rn.f16x2.e4m3x2" if type is DType.float8_e4m3fn else "cvt.rn.f16x2.e5m2x2" var val_uint8 = bitcast[DType.uint8](val) @parameter @@ -3325,7 +3325,7 @@ fn _convert_f32_to_float8[ ](val: SIMD[type, size],) -> SIMD[target, size]: @parameter if is_nvidia_gpu() and _is_sm_9x(): - alias asm_prefix = "cvt.rn.satfinite.e4m3x2.f32" if target is DType.float8_e4m3 else "cvt.rn.satfinite.e5m2x2.f32" + alias asm_prefix = "cvt.rn.satfinite.e4m3x2.f32" if target is DType.float8_e4m3fn else "cvt.rn.satfinite.e5m2x2.f32" @parameter if size > 1: @@ -3374,7 +3374,7 @@ fn _convert_f32_to_float8_scaler[ ](x: Scalar[type]) -> Scalar[target]: # software implementation rounds toward nearest even - alias IS_E4M3 = target is DType.float8_e4m3 + alias IS_E4M3 = target is DType.float8_e4m3fn alias FP8_NUM_MANTISSA_BITS = FPUtils[target].mantissa_width() alias FP8_NUM_EXPONENT_BITS = FPUtils[target].exponent_width() alias FP32_NUM_BITS = bitwidthof[type]() diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index 4eddc38a80..83fce9c590 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -67,7 +67,7 @@ struct FPUtils[ """ @parameter - if type in (DType.float8_e4m3, DType.float8_e4m3fnuz): + if type in (DType.float8_e4m3fn, DType.float8_e4m3fnuz): return 3 elif type in (DType.float8_e5m2, DType.float8_e5m2fnuz): return 2 @@ -94,7 +94,7 @@ struct FPUtils[ """ @parameter - if type in (DType.float8_e4m3, DType.float8_e4m3fnuz): + if type in (DType.float8_e4m3fn, DType.float8_e4m3fnuz): return 8 elif type in (DType.float8_e5m2, DType.float8_e5m2fnuz, DType.float16): return 16 @@ -114,7 +114,7 @@ struct FPUtils[ """ @parameter - if type in (DType.float8_e4m3, DType.float8_e4m3fnuz): + if type in (DType.float8_e4m3fn, DType.float8_e4m3fnuz): return 4 elif type in (DType.float8_e5m2, DType.float8_e5m2fnuz, DType.float16): return 5 @@ -527,7 +527,7 @@ fn nan[type: DType]() -> Scalar[type]: ], ]() ) - elif type is DType.float8_e4m3: + elif type is DType.float8_e4m3fn: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -608,7 +608,7 @@ fn isnan[ alias int_dtype = _integral_type_of[type]() @parameter - if type is DType.float8_e4m3: + if type is DType.float8_e4m3fn: return (bitcast[int_dtype, simd_width](val) & 0x7F) == 0x7F elif type is DType.float8_e5m2: # For the float8_e5m2 type NaN is limited to 0x7F and 0xFF values. @@ -665,7 +665,7 @@ fn inf[type: DType]() -> Scalar[type]: ], ]() ) - elif type is DType.float8_e4m3: + elif type is DType.float8_e4m3fn: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -750,7 +750,7 @@ fn neg_inf[type: DType]() -> Scalar[type]: ], ]() ) - elif type is DType.float8_e4m3: + elif type is DType.float8_e4m3fn: return rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`kgen.param.constant`[ _type = __mlir_type[`!pop.scalar`], @@ -837,7 +837,7 @@ fn max_finite[type: DType]() -> Scalar[type]: return 9223372036854775807 elif type is DType.uint64: return 18446744073709551615 - elif type is DType.float8_e4m3: + elif type is DType.float8_e4m3fn: return 448 elif type is DType.float8_e4m3fnuz: return 240 @@ -1044,7 +1044,7 @@ fn get_accum_type[type: DType]() -> DType: """ return DType.float32 if ( - type.is_half_float() or type in (DType.float8_e4m3, DType.float8_e5m2) + type.is_half_float() or type in (DType.float8_e4m3fn, DType.float8_e5m2) ) else type From 0bf2b595b6614addb66ea8946e118bc67a0bf611 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sat, 8 Feb 2025 09:11:30 -0800 Subject: [PATCH 364/509] [Stdlib] Correctly assign the float8_e4m3fn type MODULAR_ORIG_COMMIT_REV_ID: cfe823feeec2b42e79facc573da5c97eb42d83c5 --- stdlib/src/builtin/dtype.mojo | 2 +- stdlib/src/builtin/simd.mojo | 6 +++--- stdlib/src/prelude/__init__.mojo | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index 3fbb244978..7068003d60 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -95,7 +95,7 @@ struct DType( - uz: unsigned zero (no -0 encoding) """ alias float8_e4m3fn = DType( - __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` + __mlir_attr.`#kgen.dtype.constant : !kgen.dtype` ) """Represents a FP8E4M3 floating point format from the [OFP8 standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index f757d53340..22bcdce8ab 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -122,7 +122,7 @@ The 8 bits are encoded as `seeeeemm`: - fn: finite (no inf or -inf encodings) - uz: unsigned zero (no -0 encoding) """ -alias Float8_e4m3 = Scalar[DType.float8_e4m3fn] +alias Float8_e4m3fn = Scalar[DType.float8_e4m3fn] """Represents a FP8E4M3 floating point format from the [OFP8 standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). @@ -565,10 +565,10 @@ struct SIMD[type: DType, size: Int]( ]( rebind[__mlir_type[`!pop.scalar<`, type.value, `>`]]( __mlir_op.`pop.cast_from_builtin`[ - _type = __mlir_type[`!pop.scalar`] + _type = __mlir_type[`!pop.scalar`] ]( __mlir_op.`kgen.float_literal.convert`[ - _type = __mlir_type.f8E4M3 + _type = __mlir_type.f8E4M3FN ](value.value) ) ) diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index 56dc6be9b4..a32183050e 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -90,7 +90,7 @@ from builtin.simd import ( Byte, Float8_e5m2, Float8_e5m2fnuz, - Float8_e4m3, + Float8_e4m3fn, Float8_e4m3fnuz, Float16, Float32, From b18c1ce7c916b6846bb6e57970156aff4b8ac8b0 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sun, 9 Feb 2025 17:14:12 +0000 Subject: [PATCH 365/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025020905 --- examples/life/magic.lock | 483 +++++++++++++++++----------------- examples/magic.lock | 142 +++++----- examples/operators/magic.lock | 142 +++++----- examples/testing/magic.lock | 142 +++++----- magic.lock | 142 +++++----- 5 files changed, 521 insertions(+), 530 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 11f73daab7..8f99f91e95 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -28,8 +28,8 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-he02047a_3.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-he02047a_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-0.23.1-h5888daf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.23.1-h5888daf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.2.0-h4bba637_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda @@ -43,8 +43,8 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_hbbce691_4.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-he8f35ee_3.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-he8f35ee_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.23.1-h8e693c7_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.23.1-h8e693c7_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h1909e37_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-28_h59b9bed_openblas.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.71-h39aace5_0.conda @@ -58,8 +58,8 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h77fa898_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_1.conda - 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- max-core ==25.1.0.dev2025020805 release + - max-core ==25.1.0.dev2025020905 release - click >=8.0.0 - numpy >=1.18,<2.0 - sentencepiece >=0.2.0 @@ -1473,12 +1473,12 @@ packages: - uvicorn >=0.34.0 - uvloop >=0.21.0 license: LicenseRef-Modular-Proprietary - size: 108495948 - timestamp: 1738994653883 -- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025020805-release.conda + size: 108495033 + timestamp: 1739079962043 +- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025020905-release.conda noarch: python - sha256: 9e8dd6eb0497a0630a6cbc699ca67bec88c078e5b8e84a2b213e145018c1922e - md5: cd757be5471b8ecc3c5e43b44183ae4f + sha256: ead4701a54ac6bda4b61d933dcec0f39f00eaadbd77b4dcfb9b83f2e57f81c09 + md5: 427eb4e2c7f49c515df79be78f0cae03 depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -1489,20 +1489,20 @@ packages: - typing_extensions >=v4.12.2 - python license: MIT - size: 130855 - timestamp: 1738991846699 -- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025020805-release.conda + size: 130851 + timestamp: 1739078252551 +- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025020905-release.conda noarch: python - sha256: 01ce310a8f05bc16028bd8749c7decd899b2b569992b3db5790bd3b8d5f9d9fb - md5: cb0e7f08489ecfc881ec3197c3d8de15 + sha256: 0b61361aa3078d4484c4e3b078e0ae6dc0c903c5aa62eab51d0f3c55d2fafb21 + md5: 4b7b1b66cb6a7b2e90c7d18b9c02ae5a depends: - - max-core ==25.1.0.dev2025020805 release + - max-core ==25.1.0.dev2025020905 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22988 - timestamp: 1738991846699 + size: 22980 + timestamp: 1739078252551 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 2ba735840edf00c3f1d78f88aedd79091d21275e Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sun, 9 Feb 2025 05:46:16 -0800 Subject: [PATCH 366/509] [All] Sort mojo imports, NFC MODULAR_ORIG_COMMIT_REV_ID: 9401dc4f31ee33d8a4947641492e373f3bcb7365 --- examples/testing/test/my_math/test_dec.mojo | 1 - examples/testing/test/my_math/test_inc.mojo | 1 - .../benchmarks/collections/bench_string.mojo | 3 +- stdlib/benchmarks/utils/bench_formatter.mojo | 3 +- stdlib/benchmarks/utils/bench_memmem.mojo | 3 +- stdlib/src/base64/base64.mojo | 2 +- stdlib/src/bit/__init__.mojo | 6 ++-- stdlib/src/builtin/_format_float.mojo | 2 +- stdlib/src/builtin/bool.mojo | 2 +- stdlib/src/builtin/char.mojo | 1 - stdlib/src/builtin/debug_assert.mojo | 6 ++-- stdlib/src/builtin/error.mojo | 2 +- stdlib/src/builtin/file.mojo | 2 +- stdlib/src/builtin/file_descriptor.mojo | 6 ++-- stdlib/src/builtin/io.mojo | 12 +++---- stdlib/src/builtin/reversed.mojo | 1 + stdlib/src/builtin/simd.mojo | 6 ++-- stdlib/src/builtin/sort.mojo | 2 +- stdlib/src/builtin/string_literal.mojo | 9 ++--- stdlib/src/collections/__init__.mojo | 2 +- stdlib/src/collections/linked_list.mojo | 3 +- stdlib/src/collections/list.mojo | 2 +- stdlib/src/collections/string/__init__.mojo | 11 ++---- stdlib/src/collections/string/_unicode.mojo | 4 +-- .../collections/string/_utf8_validation.mojo | 2 +- stdlib/src/collections/string/format.mojo | 2 ++ .../src/collections/string/inline_string.mojo | 5 +-- stdlib/src/collections/string/string.mojo | 34 ++++++++----------- .../src/collections/string/string_slice.mojo | 15 ++++---- stdlib/src/hashlib/hash.mojo | 2 +- stdlib/src/math/math.mojo | 2 +- stdlib/src/memory/pointer.mojo | 1 - stdlib/src/memory/span.mojo | 2 +- stdlib/src/memory/unsafe_pointer.mojo | 1 + stdlib/src/os/env.mojo | 3 +- stdlib/src/os/os.mojo | 1 - stdlib/src/os/path/path.mojo | 1 + stdlib/src/prelude/__init__.mojo | 27 ++++++--------- stdlib/src/pwd/_linux.mojo | 3 +- stdlib/src/pwd/_macos.mojo | 4 +-- stdlib/src/python/_cpython.mojo | 3 +- stdlib/src/python/python_object.mojo | 1 - stdlib/src/sys/__init__.mojo | 2 +- stdlib/src/sys/_amdgpu.mojo | 11 +++--- stdlib/src/sys/arg.mojo | 3 +- stdlib/src/sys/compile.mojo | 2 +- stdlib/src/sys/ffi.mojo | 3 +- stdlib/src/sys/intrinsics.mojo | 5 +-- stdlib/src/tempfile/tempfile.mojo | 1 + stdlib/src/testing/testing.mojo | 4 ++- stdlib/src/time/__init__.mojo | 8 +---- stdlib/src/time/time.mojo | 2 +- stdlib/src/utils/__init__.mojo | 5 +-- stdlib/src/utils/index.mojo | 2 +- stdlib/src/utils/write.mojo | 4 +-- stdlib/test/bit/test_bit.mojo | 9 ++--- stdlib/test/builtin/test_char.mojo | 2 +- stdlib/test/builtin/test_print.mojo | 3 +- stdlib/test/builtin/test_sort_issue_1018.mojo | 2 +- .../string/test_inlined_string.mojo | 5 ++- .../test/collections/string/test_string.mojo | 16 ++++----- .../collections/string/test_string_slice.mojo | 9 +++-- stdlib/test/collections/test_linked_list.mojo | 7 ++-- stdlib/test/collections/test_list.mojo | 6 ++-- stdlib/test/hashlib/test_ahash.mojo | 2 +- stdlib/test/hashlib/test_hasher.mojo | 3 +- stdlib/test/memory/test_span.mojo | 2 +- .../test_python_object_dunder_contains.mojo | 3 +- stdlib/test/sys/test_compile.mojo | 2 +- stdlib/test/sys/test_compile_debug.mojo | 2 +- stdlib/test/test_utils/__init__.mojo | 4 +-- stdlib/test/testing/test_assertion.mojo | 2 +- stdlib/test/time/test_time.mojo | 8 +---- stdlib/test/utils/test_static_tuple.mojo | 1 + stdlib/test/utils/test_write.mojo | 3 +- 75 files changed, 156 insertions(+), 188 deletions(-) diff --git a/examples/testing/test/my_math/test_dec.mojo b/examples/testing/test/my_math/test_dec.mojo index 7484124d6b..ac93e1097d 100644 --- a/examples/testing/test/my_math/test_dec.mojo +++ b/examples/testing/test/my_math/test_dec.mojo @@ -12,7 +12,6 @@ # ===----------------------------------------------------------------------=== # from my_math import dec - from testing import assert_equal, assert_raises diff --git a/examples/testing/test/my_math/test_inc.mojo b/examples/testing/test/my_math/test_inc.mojo index 8b18919c75..f45584bab7 100644 --- a/examples/testing/test/my_math/test_inc.mojo +++ b/examples/testing/test/my_math/test_inc.mojo @@ -12,7 +12,6 @@ # ===----------------------------------------------------------------------=== # from my_math import inc - from testing import assert_equal, assert_raises diff --git a/stdlib/benchmarks/collections/bench_string.mojo b/stdlib/benchmarks/collections/bench_string.mojo index 20ad75333c..8b3acdc9f0 100644 --- a/stdlib/benchmarks/collections/bench_string.mojo +++ b/stdlib/benchmarks/collections/bench_string.mojo @@ -16,14 +16,13 @@ from collections import Dict, Optional from collections.string import String +from collections.string._utf8_validation import _is_valid_utf8 from os import abort from pathlib import _dir_of_current_file from random import random_si64, seed from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run -from collections.string._utf8_validation import _is_valid_utf8 - # ===-----------------------------------------------------------------------===# # Benchmark Data diff --git a/stdlib/benchmarks/utils/bench_formatter.mojo b/stdlib/benchmarks/utils/bench_formatter.mojo index e84a58b562..eb9dea9e46 100644 --- a/stdlib/benchmarks/utils/bench_formatter.mojo +++ b/stdlib/benchmarks/utils/bench_formatter.mojo @@ -14,14 +14,13 @@ # NOTE: to test changes on the current branch using run-benchmarks.sh, remove # the -t flag. Remember to replace it again before pushing any code. +from collections.string.string_slice import _memchr, _memmem from sys import simdwidthof from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run from bit import count_trailing_zeros from builtin.dtype import _uint_type_of_width -from collections.string.string_slice import _memchr, _memmem - # ===-----------------------------------------------------------------------===# # Benchmark Data # ===-----------------------------------------------------------------------===# diff --git a/stdlib/benchmarks/utils/bench_memmem.mojo b/stdlib/benchmarks/utils/bench_memmem.mojo index 4167011cf4..e33d34f3e9 100644 --- a/stdlib/benchmarks/utils/bench_memmem.mojo +++ b/stdlib/benchmarks/utils/bench_memmem.mojo @@ -14,6 +14,7 @@ # NOTE: to test changes on the current branch using run-benchmarks.sh, remove # the -t flag. Remember to replace it again before pushing any code. +from collections.string.string_slice import _align_down, _memchr, _memmem from sys import simdwidthof from benchmark import Bench, BenchConfig, Bencher, BenchId, Unit, keep, run @@ -21,8 +22,6 @@ from bit import count_trailing_zeros from builtin.dtype import _uint_type_of_width from memory import UnsafePointer, bitcast, memcmp, pack_bits -from collections.string.string_slice import _align_down, _memchr, _memmem - # ===-----------------------------------------------------------------------===# # Benchmark Data # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/base64/base64.mojo b/stdlib/src/base64/base64.mojo index d0b11c46bd..117814cd09 100644 --- a/stdlib/src/base64/base64.mojo +++ b/stdlib/src/base64/base64.mojo @@ -21,10 +21,10 @@ from base64 import b64encode from collections import List from collections.string import StringSlice -from memory import Span from sys import simdwidthof import bit +from memory import Span from ._b64encode import b64encode_with_buffers as _b64encode_with_buffers diff --git a/stdlib/src/bit/__init__.mojo b/stdlib/src/bit/__init__.mojo index 71f5c067e8..8559bc94ef 100644 --- a/stdlib/src/bit/__init__.mojo +++ b/stdlib/src/bit/__init__.mojo @@ -13,17 +13,17 @@ """Implements the bit package.""" from .bit import ( - next_power_of_two, - prev_power_of_two, bit_not, bit_reverse, bit_width, byte_swap, count_leading_zeros, count_trailing_zeros, - log2_floor, is_power_of_two, + log2_floor, + next_power_of_two, pop_count, + prev_power_of_two, rotate_bits_left, rotate_bits_right, ) diff --git a/stdlib/src/builtin/_format_float.mojo b/stdlib/src/builtin/_format_float.mojo index ebd403a45c..5317abd65e 100644 --- a/stdlib/src/builtin/_format_float.mojo +++ b/stdlib/src/builtin/_format_float.mojo @@ -28,7 +28,7 @@ from math import log2 from sys.info import sizeof from builtin.io import _printf -from memory import bitcast, Span +from memory import Span, bitcast from utils import StaticTuple from utils.numerics import FPUtils, isinf, isnan diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 42ce052e0e..5682f54fc1 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -16,10 +16,10 @@ These are Mojo built-ins, so you don't need to import them. """ from collections import List, Set +from hashlib._hasher import _Hasher from utils._select import _select_register_value from utils._visualizers import lldb_formatter_wrapping_type -from hashlib._hasher import _Hasher # ===----------------------------------------------------------------------=== # # Boolable diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/builtin/char.mojo index 20d4b1c665..620b45273f 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/builtin/char.mojo @@ -16,7 +16,6 @@ from collections import Optional from collections.string import StringSlice from bit import count_leading_zeros - from memory import UnsafePointer diff --git a/stdlib/src/builtin/debug_assert.mojo b/stdlib/src/builtin/debug_assert.mojo index 767400f24f..544ccc88fa 100644 --- a/stdlib/src/builtin/debug_assert.mojo +++ b/stdlib/src/builtin/debug_assert.mojo @@ -17,14 +17,14 @@ These are Mojo built-ins, so you don't need to import them. from os import abort -from sys import is_gpu, is_nvidia_gpu, is_amd_gpu, llvm_intrinsic +from sys import is_amd_gpu, is_gpu, is_nvidia_gpu, llvm_intrinsic from sys._build import is_debug_build from sys.ffi import c_char, c_size_t, c_uint, external_call +from sys.intrinsics import block_idx, thread_idx from sys.param_env import env_get_string -from sys.intrinsics import thread_idx, block_idx from builtin._location import __call_location, _SourceLocation -from memory import UnsafePointer, Span +from memory import Span, UnsafePointer from utils.write import ( _TotalWritableBytes, diff --git a/stdlib/src/builtin/error.mojo b/stdlib/src/builtin/error.mojo index adb6fcecc8..ef8d667b66 100644 --- a/stdlib/src/builtin/error.mojo +++ b/stdlib/src/builtin/error.mojo @@ -15,12 +15,12 @@ These are Mojo built-ins, so you don't need to import them. """ +from collections.string import StringSlice from sys import alignof, sizeof from sys.ffi import c_char from memory import UnsafePointer, memcpy from memory.memory import _free -from collections.string import StringSlice from utils.write import write_buffered diff --git a/stdlib/src/builtin/file.mojo b/stdlib/src/builtin/file.mojo index b86a62a02e..9325034705 100644 --- a/stdlib/src/builtin/file.mojo +++ b/stdlib/src/builtin/file.mojo @@ -35,7 +35,7 @@ from os import PathLike, abort from sys import external_call, sizeof from sys.ffi import OpaquePointer -from memory import AddressSpace, UnsafePointer, Span +from memory import AddressSpace, Span, UnsafePointer from utils import StringSlice, write_buffered diff --git a/stdlib/src/builtin/file_descriptor.mojo b/stdlib/src/builtin/file_descriptor.mojo index 369704f53a..0fbdd3dc3c 100644 --- a/stdlib/src/builtin/file_descriptor.mojo +++ b/stdlib/src/builtin/file_descriptor.mojo @@ -23,12 +23,12 @@ f.close() ``` """ +from sys._amdgpu import printf_append_string_n, printf_begin from sys.ffi import external_call -from sys.info import is_nvidia_gpu, is_amd_gpu -from sys._amdgpu import printf_begin, printf_append_string_n +from sys.info import is_amd_gpu, is_nvidia_gpu from builtin.io import _printf -from memory import UnsafePointer, Span +from memory import Span, UnsafePointer @value diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index c2ff86a746..a7aa15ad2d 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -26,20 +26,16 @@ from sys import ( is_nvidia_gpu, stdout, ) +from sys._amdgpu import printf_append_args, printf_append_string_n, printf_begin from sys._libc import dup, fclose, fdopen, fflush -from sys.ffi import OpaquePointer, c_char, OpaquePointer -from sys._amdgpu import printf_begin, printf_append_args, printf_append_string_n +from sys.ffi import OpaquePointer, c_char from sys.intrinsics import _type_is_eq from builtin.dtype import _get_dtype_printf_format from builtin.file_descriptor import FileDescriptor -from memory import UnsafePointer, memcpy, bitcast +from memory import UnsafePointer, bitcast, memcpy -from utils import ( - StaticString, - write_args, - write_buffered, -) +from utils import StaticString, write_args, write_buffered # ===----------------------------------------------------------------------=== # # _file_handle diff --git a/stdlib/src/builtin/reversed.mojo b/stdlib/src/builtin/reversed.mojo index 686da90aaf..1b2acbae55 100644 --- a/stdlib/src/builtin/reversed.mojo +++ b/stdlib/src/builtin/reversed.mojo @@ -19,6 +19,7 @@ from collections import Deque, Dict from collections.deque import _DequeIter from collections.dict import _DictEntryIter, _DictKeyIter, _DictValueIter from collections.list import _ListIter + from memory.span import Span, _SpanIter from .range import _StridedRange diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 22bcdce8ab..e4d6bc7b29 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -46,19 +46,19 @@ from sys import ( from sys._assembly import inlined_assembly from sys.info import _current_arch, _is_sm_9x -from bit import pop_count, byte_swap +from bit import byte_swap, pop_count from builtin._format_float import _write_float from builtin.dtype import _uint_type_of_width from builtin.format_int import _try_write_int from builtin.io import _snprintf from documentation import doc_private -from memory import UnsafePointer, bitcast, memcpy, Span +from memory import Span, UnsafePointer, bitcast, memcpy from utils import IndexList, StaticTuple from utils._visualizers import lldb_formatter_wrapping_type from utils.numerics import FPUtils -from utils.numerics import isnan as _isnan from utils.numerics import isinf as _isinf +from utils.numerics import isnan as _isnan from utils.numerics import max_finite as _max_finite from utils.numerics import max_or_inf as _max_or_inf from utils.numerics import min_finite as _min_finite diff --git a/stdlib/src/builtin/sort.mojo b/stdlib/src/builtin/sort.mojo index 9574568db1..6c44e3bb9f 100644 --- a/stdlib/src/builtin/sort.mojo +++ b/stdlib/src/builtin/sort.mojo @@ -20,7 +20,7 @@ from math import ceil from sys import bitwidthof from bit import count_leading_zeros -from memory import UnsafePointer, Span +from memory import Span, UnsafePointer # ===-----------------------------------------------------------------------===# # sort diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 571236110b..122b79679b 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -18,18 +18,19 @@ These are Mojo built-ins, so you don't need to import them. from collections import List from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( - StringSlice, StaticString, + StringSlice, _StringSliceIter, _to_string_list, ) from hashlib._hasher import _HashableWithHasher, _Hasher -from memory import UnsafePointer, memcpy, Span from sys.ffi import c_char + +from memory import Span, UnsafePointer, memcpy + from utils import Writable, Writer -from utils.write import _WriteBufferStack from utils._visualizers import lldb_formatter_wrapping_type - +from utils.write import _WriteBufferStack # ===-----------------------------------------------------------------------===# # StringLiteral diff --git a/stdlib/src/collections/__init__.mojo b/stdlib/src/collections/__init__.mojo index cf70c2bf18..78e00fea3a 100644 --- a/stdlib/src/collections/__init__.mojo +++ b/stdlib/src/collections/__init__.mojo @@ -17,8 +17,8 @@ from .deque import Deque from .dict import Dict, KeyElement from .inline_array import InlineArray from .inline_list import InlineList +from .linked_list import LinkedList from .list import List from .optional import Optional, OptionalReg from .set import Set from .vector import InlinedFixedVector -from .linked_list import LinkedList diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index 19beb26112..ef34ce84b9 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -11,11 +11,12 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # -from memory import UnsafePointer from collections import Optional from collections._index_normalization import normalize_index from os import abort +from memory import UnsafePointer + @value struct Node[ diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index 108a8afb41..ba6b8d9760 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -24,7 +24,7 @@ from os import abort from sys import sizeof from sys.intrinsics import _type_is_eq -from memory import Pointer, UnsafePointer, memcpy, Span +from memory import Pointer, Span, UnsafePointer, memcpy from .optional import Optional diff --git a/stdlib/src/collections/string/__init__.mojo b/stdlib/src/collections/string/__init__.mojo index 184b0656b1..53ae10eccc 100644 --- a/stdlib/src/collections/string/__init__.mojo +++ b/stdlib/src/collections/string/__init__.mojo @@ -12,12 +12,5 @@ # ===----------------------------------------------------------------------=== # """Implements the string package.""" -from .string import ( - String, - ascii, - atof, - atol, - chr, - ord, -) -from .string_slice import StringSlice, StaticString, CharsIter +from .string import String, ascii, atof, atol, chr, ord +from .string_slice import CharsIter, StaticString, StringSlice diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index d5ac9972c9..6908d5f283 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -11,10 +11,10 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # -from memory import UnsafePointer, memcpy -from memory import Span from collections.string._unicode_lookups import * +from memory import Span, UnsafePointer, memcpy + fn _uppercase_mapping_index(rune: Char) -> Int: """Return index for upper case mapping or -1 if no mapping is given.""" diff --git a/stdlib/src/collections/string/_utf8_validation.mojo b/stdlib/src/collections/string/_utf8_validation.mojo index b0bf4f94f8..7cca68dc52 100644 --- a/stdlib/src/collections/string/_utf8_validation.mojo +++ b/stdlib/src/collections/string/_utf8_validation.mojo @@ -26,9 +26,9 @@ https://github.com/simdutf/SimdUnicode/blob/main/src/UTF8.cs """ from base64._b64encode import _sub_with_saturation -from memory import UnsafePointer, Span from sys.intrinsics import llvm_intrinsic +from memory import Span, UnsafePointer alias TOO_SHORT: UInt8 = 1 << 0 alias TOO_LONG: UInt8 = 1 << 1 diff --git a/stdlib/src/collections/string/format.mojo b/stdlib/src/collections/string/format.mojo index b77ea15260..d1e8d34411 100644 --- a/stdlib/src/collections/string/format.mojo +++ b/stdlib/src/collections/string/format.mojo @@ -13,7 +13,9 @@ """Implements Formatting utilities.""" from collections import Optional + from memory import UnsafePointer + from utils import Variant # TODO: _FormatCurlyEntry and _FormatSpec should be public in the future for diff --git a/stdlib/src/collections/string/inline_string.mojo b/stdlib/src/collections/string/inline_string.mojo index 7245653360..24bd23897d 100644 --- a/stdlib/src/collections/string/inline_string.mojo +++ b/stdlib/src/collections/string/inline_string.mojo @@ -17,11 +17,12 @@ from collections import InlineArray, Optional from collections.string import StringSlice -from memory import UnsafePointer, memcpy, Span from os import abort from sys import sizeof -from utils import Variant +from memory import Span, UnsafePointer, memcpy + +from utils import Variant # ===-----------------------------------------------------------------------===# # InlineString diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 1b0fbe9a4e..916336ce80 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -13,26 +13,8 @@ """Implements basic object methods for working with strings.""" from collections import KeyElement, List, Optional -from collections.string import CharsIter from collections._index_normalization import normalize_index -from hashlib._hasher import _HashableWithHasher, _Hasher -from os import abort -from sys import bitwidthof, llvm_intrinsic -from sys.ffi import c_char -from sys.intrinsics import _type_is_eq -from utils.write import write_buffered, _TotalWritableBytes, _WriteBufferHeap - -from bit import count_leading_zeros -from memory import UnsafePointer, memcmp, memcpy, Span -from python import PythonObject - -from utils import ( - IndexList, - Variant, - Writable, - Writer, - write_args, -) +from collections.string import CharsIter from collections.string._unicode import ( is_lowercase, is_uppercase, @@ -41,12 +23,24 @@ from collections.string._unicode import ( ) from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( - StringSlice, StaticString, + StringSlice, _StringSliceIter, _to_string_list, _utf8_byte_type, ) +from hashlib._hasher import _HashableWithHasher, _Hasher +from os import abort +from sys import bitwidthof, llvm_intrinsic +from sys.ffi import c_char +from sys.intrinsics import _type_is_eq + +from bit import count_leading_zeros +from memory import Span, UnsafePointer, memcmp, memcpy +from python import PythonObject + +from utils import IndexList, Variant, Writable, Writer, write_args +from utils.write import _TotalWritableBytes, _WriteBufferHeap, write_buffered # ===----------------------------------------------------------------------=== # # ord diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 820c262621..214620616b 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -21,17 +21,18 @@ from collections.string import StringSlice ``` """ -from bit import count_leading_zeros, count_trailing_zeros from collections import List, Optional -from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string._utf8_validation import _is_valid_utf8 -from memory import UnsafePointer, memcmp, memcpy, Span, pack_bits -from memory.memory import _memcmp_impl_unconstrained -from sys import bitwidthof, simdwidthof -from sys.intrinsics import unlikely, likely -from sys.ffi import c_char +from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from hashlib._hasher import _HashableWithHasher, _Hasher from os import PathLike +from sys import bitwidthof, simdwidthof +from sys.ffi import c_char +from sys.intrinsics import likely, unlikely + +from bit import count_leading_zeros, count_trailing_zeros +from memory import Span, UnsafePointer, memcmp, memcpy, pack_bits +from memory.memory import _memcmp_impl_unconstrained alias StaticString = StringSlice[StaticConstantOrigin] """An immutable static string slice.""" diff --git a/stdlib/src/hashlib/hash.mojo b/stdlib/src/hashlib/hash.mojo index d4741218b2..82b1638e32 100644 --- a/stdlib/src/hashlib/hash.mojo +++ b/stdlib/src/hashlib/hash.mojo @@ -27,7 +27,7 @@ There are a few main tools in this module: import random from collections import InlineArray -from sys import bitwidthof, sizeof, simdwidthof +from sys import bitwidthof, simdwidthof, sizeof from sys.ffi import _Global from builtin.dtype import _uint_type_of_width diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index d08a7be526..bdbd292d30 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -36,7 +36,7 @@ from sys.info import _current_arch from bit import count_trailing_zeros from builtin.dtype import _integral_type_of from builtin.simd import _modf, _simd_apply -from memory import UnsafePointer, Span +from memory import Span, UnsafePointer from utils.index import IndexList from utils.numerics import FPUtils, isnan, nan diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index ef132bc0c0..746564b490 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -21,7 +21,6 @@ from memory import Pointer from sys import is_nvidia_gpu - # ===-----------------------------------------------------------------------===# # AddressSpace # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 58c769c698..756ece77b8 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -21,9 +21,9 @@ from memory import Span """ from collections import InlineArray +from sys.info import simdwidthof from memory import Pointer, UnsafePointer -from sys.info import simdwidthof trait AsBytes: diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 11076f3168..47065c2d62 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -28,6 +28,7 @@ from sys.intrinsics import ( strided_load, strided_store, ) + from bit import is_power_of_two from memory.memory import _free, _malloc diff --git a/stdlib/src/os/env.mojo b/stdlib/src/os/env.mojo index 06c017fd2a..027bc44be8 100644 --- a/stdlib/src/os/env.mojo +++ b/stdlib/src/os/env.mojo @@ -19,11 +19,10 @@ from os import setenv ``` """ +from collections.string import StringSlice from sys import external_call, os_is_linux, os_is_macos, os_is_windows from sys.ffi import c_int -from collections.string import StringSlice - from memory import UnsafePointer diff --git a/stdlib/src/os/os.mojo b/stdlib/src/os/os.mojo index 791b186acd..4bb9d30583 100644 --- a/stdlib/src/os/os.mojo +++ b/stdlib/src/os/os.mojo @@ -26,7 +26,6 @@ from sys.ffi import OpaquePointer, c_char from memory import UnsafePointer - from .path import isdir, split from .pathlike import PathLike diff --git a/stdlib/src/os/path/path.mojo b/stdlib/src/os/path/path.mojo index 79f51707c9..bb9a14a9ca 100644 --- a/stdlib/src/os/path/path.mojo +++ b/stdlib/src/os/path/path.mojo @@ -25,6 +25,7 @@ from stat import S_ISDIR, S_ISLNK, S_ISREG from sys import has_neon, os_is_linux, os_is_macos, os_is_windows from memory import Span + from utils import StringSlice from .. import PathLike diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index a32183050e..e2c5807909 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -15,14 +15,7 @@ """ from collections import KeyElement, List -from collections.string import ( - String, - ascii, - atof, - atol, - chr, - ord, -) +from collections.string import String, ascii, atof, atol, chr, ord from hashlib.hash import Hashable, hash from builtin.anytype import AnyType, UnknownDestructibility @@ -35,14 +28,14 @@ from builtin.builtin_list import ( VariadicPack, ) from builtin.builtin_slice import Slice, slice +from builtin.char import Char from builtin.comparable import ( - LessThanComparable, + Comparable, GreaterThanComparable, - LessThanOrEqualComparable, GreaterThanOrEqualComparable, - Comparable, + LessThanComparable, + LessThanOrEqualComparable, ) -from builtin.char import Char from builtin.constrained import constrained from builtin.coroutine import AnyCoroutine, Coroutine, RaisingCoroutine from builtin.debug_assert import debug_assert @@ -56,10 +49,10 @@ from builtin.floatable import Floatable, FloatableRaising, float from builtin.format_int import bin, hex, oct from builtin.identifiable import Identifiable, StringableIdentifiable from builtin.int import ( + ImplicitlyIntable, Indexer, Int, Intable, - ImplicitlyIntable, IntableRaising, index, int, @@ -88,10 +81,10 @@ from builtin.simd import ( SIMD, BFloat16, Byte, - Float8_e5m2, - Float8_e5m2fnuz, Float8_e4m3fn, Float8_e4m3fnuz, + Float8_e5m2, + Float8_e5m2fnuz, Float16, Float32, Float64, @@ -134,11 +127,11 @@ from builtin.value import ( ExplicitlyCopyable, Movable, RepresentableCollectionElement, - WritableCollectionElement, StringableCollectionElement, + WritableCollectionElement, ) from documentation import doc_private from memory import AddressSpace, Pointer - from memory.span import AsBytes + from utils import Writable, Writer diff --git a/stdlib/src/pwd/_linux.mojo b/stdlib/src/pwd/_linux.mojo index 9fbb80fe54..271f52385d 100644 --- a/stdlib/src/pwd/_linux.mojo +++ b/stdlib/src/pwd/_linux.mojo @@ -10,10 +10,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -from memory import UnsafePointer from collections.string import StringSlice from sys.ffi import c_char, external_call +from memory import UnsafePointer + from .pwd import Passwd alias uid_t = Int32 diff --git a/stdlib/src/pwd/_macos.mojo b/stdlib/src/pwd/_macos.mojo index fe6ea33593..ab97cafe1d 100644 --- a/stdlib/src/pwd/_macos.mojo +++ b/stdlib/src/pwd/_macos.mojo @@ -10,10 +10,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -from memory import UnsafePointer +from collections.string import StringSlice from sys.ffi import c_char, external_call -from collections.string import StringSlice +from memory import UnsafePointer from .pwd import Passwd diff --git a/stdlib/src/python/_cpython.mojo b/stdlib/src/python/_cpython.mojo index 187354298a..c742d964d5 100644 --- a/stdlib/src/python/_cpython.mojo +++ b/stdlib/src/python/_cpython.mojo @@ -18,6 +18,7 @@ Documentation for these functions can be found online at: """ from collections import InlineArray, Optional +from collections.string import StringSlice from os import abort, getenv, setenv from os.path import dirname from pathlib import Path @@ -38,8 +39,6 @@ from memory import UnsafePointer from python._bindings import PyMojoObject, Pythonable, Typed_initproc from python.python import _get_global_python_itf -from collections.string import StringSlice - # ===-----------------------------------------------------------------------===# # Raw Bindings # ===-----------------------------------------------------------------------===# diff --git a/stdlib/src/python/python_object.mojo b/stdlib/src/python/python_object.mojo index 468ea13ebd..a051415af7 100644 --- a/stdlib/src/python/python_object.mojo +++ b/stdlib/src/python/python_object.mojo @@ -27,7 +27,6 @@ from sys.intrinsics import _type_is_eq from memory import UnsafePointer - from ._cpython import CPython, PyObjectPtr from .python import Python, _get_global_python_itf diff --git a/stdlib/src/sys/__init__.mojo b/stdlib/src/sys/__init__.mojo index 0f0e7e55ed..9971b3ccb2 100644 --- a/stdlib/src/sys/__init__.mojo +++ b/stdlib/src/sys/__init__.mojo @@ -72,9 +72,9 @@ from .intrinsics import ( ) from .param_env import ( env_get_bool, + env_get_dtype, env_get_int, env_get_string, - env_get_dtype, is_defined, ) from .terminate import exit diff --git a/stdlib/src/sys/_amdgpu.mojo b/stdlib/src/sys/_amdgpu.mojo index 9e16997830..eb0dddb24e 100644 --- a/stdlib/src/sys/_amdgpu.mojo +++ b/stdlib/src/sys/_amdgpu.mojo @@ -11,18 +11,19 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # -from memory import UnsafePointer, Span -from memory.pointer import _GPUAddressSpace +from collections import InlineArray from os.atomic import Atomic, _compare_exchange_weak_integral_impl from sys.intrinsics import ( + ballot, + implicitarg_ptr, lane_id, readfirstlane, sendmsg, - implicitarg_ptr, - ballot, ) from time import sleep -from collections import InlineArray + +from memory import Span, UnsafePointer +from memory.pointer import _GPUAddressSpace # NOTE: MOST OF THE CODE HERE IS ADAPTED FROM # AMD'S `device-libs`. diff --git a/stdlib/src/sys/arg.mojo b/stdlib/src/sys/arg.mojo index 64257cce6c..2d75c444d6 100644 --- a/stdlib/src/sys/arg.mojo +++ b/stdlib/src/sys/arg.mojo @@ -30,12 +30,11 @@ def main(): ``` """ +from collections.string import StringSlice from sys import external_call from memory import UnsafePointer -from collections.string import StringSlice - # TODO: When we have global variables, this should be a global list. fn argv() -> VariadicList[StringSlice[StaticConstantOrigin]]: diff --git a/stdlib/src/sys/compile.mojo b/stdlib/src/sys/compile.mojo index a2a737b551..0ee8b4160b 100644 --- a/stdlib/src/sys/compile.mojo +++ b/stdlib/src/sys/compile.mojo @@ -12,7 +12,7 @@ # ===----------------------------------------------------------------------=== # """Implements functions that return compile-time information. """ -from .param_env import env_get_string, env_get_int +from .param_env import env_get_int, env_get_string # ===----------------------------------------------------------------------=== # # OptimizationLevel diff --git a/stdlib/src/sys/ffi.mojo b/stdlib/src/sys/ffi.mojo index 3ead55a560..03c7b05877 100644 --- a/stdlib/src/sys/ffi.mojo +++ b/stdlib/src/sys/ffi.mojo @@ -12,13 +12,12 @@ # ===----------------------------------------------------------------------=== # """Implements a foreign functions interface (FFI).""" +from collections.string import StringSlice from os import abort from sys._libc import dlclose, dlerror, dlopen, dlsym from memory import UnsafePointer -from collections.string import StringSlice - from .info import is_64bit, os_is_linux, os_is_macos, os_is_windows from .intrinsics import _mlirtype_is_eq diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 6b7ca6dfaf..58c7069a2d 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -20,12 +20,13 @@ from sys import PrefetchLocality """ import math +from sys.info import _is_sm_9x from memory import AddressSpace, UnsafePointer from memory.pointer import _GPUAddressSpace -from sys.info import _is_sm_9x + from ._assembly import inlined_assembly -from .info import is_nvidia_gpu, is_amd_gpu, sizeof +from .info import is_amd_gpu, is_nvidia_gpu, sizeof # ===-----------------------------------------------------------------------===# # llvm_intrinsic diff --git a/stdlib/src/tempfile/tempfile.mojo b/stdlib/src/tempfile/tempfile.mojo index 92bbd07f39..7b7d83b488 100644 --- a/stdlib/src/tempfile/tempfile.mojo +++ b/stdlib/src/tempfile/tempfile.mojo @@ -26,6 +26,7 @@ from collections.string import StringSlice from pathlib import Path from memory import Span + from utils import write_buffered alias TMP_MAX = 10_000 diff --git a/stdlib/src/testing/testing.mojo b/stdlib/src/testing/testing.mojo index 974742e30f..355d54653b 100644 --- a/stdlib/src/testing/testing.mojo +++ b/stdlib/src/testing/testing.mojo @@ -32,8 +32,10 @@ def main(): """ from collections import Optional from math import isclose -from memory import memcmp + from builtin._location import __call_location, _SourceLocation +from memory import memcmp + from utils import StringSlice # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/time/__init__.mojo b/stdlib/src/time/__init__.mojo index 4a641f36db..23258e8895 100644 --- a/stdlib/src/time/__init__.mojo +++ b/stdlib/src/time/__init__.mojo @@ -12,10 +12,4 @@ # ===----------------------------------------------------------------------=== # """Implements the time package.""" -from .time import ( - monotonic, - perf_counter, - perf_counter_ns, - sleep, - time_function, -) +from .time import monotonic, perf_counter, perf_counter_ns, sleep, time_function diff --git a/stdlib/src/time/time.mojo b/stdlib/src/time/time.mojo index 25cd4eca67..416354e1c7 100644 --- a/stdlib/src/time/time.mojo +++ b/stdlib/src/time/time.mojo @@ -24,8 +24,8 @@ from os import abort from sys import ( external_call, is_amd_gpu, - is_nvidia_gpu, is_gpu, + is_nvidia_gpu, llvm_intrinsic, os_is_linux, os_is_windows, diff --git a/stdlib/src/utils/__init__.mojo b/stdlib/src/utils/__init__.mojo index 87133a3238..f9d329d902 100644 --- a/stdlib/src/utils/__init__.mojo +++ b/stdlib/src/utils/__init__.mojo @@ -12,11 +12,12 @@ # ===----------------------------------------------------------------------=== # """Implements the utils package.""" -from .index import Index, IndexList, product from collections.string.inline_string import InlineString +from collections.string.string_slice import StaticString, StringSlice + +from .index import Index, IndexList, product from .lock import BlockingScopedLock, BlockingSpinLock, SpinWaiter from .loop import unroll from .static_tuple import StaticTuple -from collections.string.string_slice import StaticString, StringSlice from .variant import Variant from .write import Writable, Writer, write_args, write_buffered diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 0bfe17b8b5..47649ecd2a 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -21,6 +21,7 @@ from utils import IndexList """ from collections.string.string import _calc_initial_buffer_size +from hashlib._hasher import _Hasher from sys import bitwidthof from builtin.dtype import _int_type_of_width, _uint_type_of_width @@ -28,7 +29,6 @@ from builtin.io import _get_dtype_printf_format, _snprintf from . import unroll from .static_tuple import StaticTuple -from hashlib._hasher import _Hasher # ===-----------------------------------------------------------------------===# # Utilities diff --git a/stdlib/src/utils/write.mojo b/stdlib/src/utils/write.mojo index 0e522bf921..535b06f554 100644 --- a/stdlib/src/utils/write.mojo +++ b/stdlib/src/utils/write.mojo @@ -13,9 +13,9 @@ """Establishes the contract between `Writer` and `Writable` types.""" from collections import InlineArray -from sys.info import is_gpu, is_nvidia_gpu, is_amd_gpu +from sys.info import is_amd_gpu, is_gpu, is_nvidia_gpu -from memory import UnsafePointer, memcpy, Span +from memory import Span, UnsafePointer, memcpy from utils import StaticString diff --git a/stdlib/test/bit/test_bit.mojo b/stdlib/test/bit/test_bit.mojo index 10041b6ec2..3d434be535 100644 --- a/stdlib/test/bit/test_bit.mojo +++ b/stdlib/test/bit/test_bit.mojo @@ -12,9 +12,9 @@ # ===----------------------------------------------------------------------=== # # RUN: %bare-mojo %s +from math import floor, log2 + from bit import ( - next_power_of_two, - prev_power_of_two, bit_not, bit_reverse, bit_width, @@ -22,12 +22,13 @@ from bit import ( count_leading_zeros, count_trailing_zeros, is_power_of_two, + log2_floor, + next_power_of_two, pop_count, + prev_power_of_two, rotate_bits_left, rotate_bits_right, - log2_floor, ) -from math import log2, floor from testing import assert_equal diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/builtin/test_char.mojo index 2af35ad639..1d0f124d95 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/builtin/test_char.mojo @@ -12,7 +12,7 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from testing import assert_true, assert_false, assert_equal, assert_not_equal +from testing import assert_equal, assert_false, assert_not_equal, assert_true def test_char_validity(): diff --git a/stdlib/test/builtin/test_print.mojo b/stdlib/test/builtin/test_print.mojo index 8dc2aad510..9b6ca1e3d3 100644 --- a/stdlib/test/builtin/test_print.mojo +++ b/stdlib/test/builtin/test_print.mojo @@ -14,9 +14,8 @@ import sys -from tempfile import NamedTemporaryFile - from collections.string import StaticString +from tempfile import NamedTemporaryFile from builtin._location import __call_location, _SourceLocation from testing import assert_equal diff --git a/stdlib/test/builtin/test_sort_issue_1018.mojo b/stdlib/test/builtin/test_sort_issue_1018.mojo index 7132374abe..9a0f3fc451 100644 --- a/stdlib/test/builtin/test_sort_issue_1018.mojo +++ b/stdlib/test/builtin/test_sort_issue_1018.mojo @@ -15,7 +15,7 @@ from random import rand -from memory import UnsafePointer, Span +from memory import Span, UnsafePointer fn sort_test[D: DType, name: StringLiteral](size: Int, max: Int) raises: diff --git a/stdlib/test/collections/string/test_inlined_string.mojo b/stdlib/test/collections/string/test_inlined_string.mojo index 554ac157ad..afc07e32b1 100644 --- a/stdlib/test/collections/string/test_inlined_string.mojo +++ b/stdlib/test/collections/string/test_inlined_string.mojo @@ -13,13 +13,12 @@ # REQUIRES: disabled # RUN: %mojo --debug-level full %s +from collections.string import InlineString +from collections.string.inline_string import _FixedString from os import abort from testing import assert_equal, assert_true -from collections.string import InlineString -from collections.string.inline_string import _FixedString - def main(): test_fixed_string() diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 0d39db2fa5..c34f617060 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -12,21 +12,21 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from testing import ( - assert_equal, - assert_false, - assert_not_equal, - assert_raises, - assert_true, -) - from collections.string import StringSlice from collections.string.string import ( _calc_initial_buffer_size_int32, _calc_initial_buffer_size_int64, ) + from memory import UnsafePointer from python import Python +from testing import ( + assert_equal, + assert_false, + assert_not_equal, + assert_raises, + assert_true, +) @value diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 1ac338e12d..807d4dff8b 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -12,16 +12,15 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -from testing import assert_equal, assert_false, assert_true, assert_raises - +from collections.string._utf8_validation import _is_valid_utf8 from collections.string.string_slice import ( StringSlice, _count_utf8_continuation_bytes, ) -from collections.string._utf8_validation import _is_valid_utf8 -from memory import Span, UnsafePointer +from sys.info import alignof, sizeof -from sys.info import sizeof, alignof +from memory import Span, UnsafePointer +from testing import assert_equal, assert_false, assert_raises, assert_true fn test_string_slice_layout() raises: diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo index 5641002066..6fd56ec4a6 100644 --- a/stdlib/test/collections/test_linked_list.mojo +++ b/stdlib/test/collections/test_linked_list.mojo @@ -13,14 +13,15 @@ # RUN: %mojo %s from collections import LinkedList, Optional -from testing import assert_equal, assert_raises, assert_true, assert_false + from test_utils import ( + CopyCountedStruct, CopyCounter, - MoveCounter, DtorCounter, + MoveCounter, g_dtor_count, - CopyCountedStruct, ) +from testing import assert_equal, assert_false, assert_raises, assert_true def test_construction(): diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index e7af56f5a8..f977b2b47c 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -15,13 +15,13 @@ from collections import List from sys.info import sizeof -from memory import UnsafePointer, Span +from memory import Span, UnsafePointer from test_utils import ( + CopyCountedStruct, CopyCounter, - MoveCounter, DtorCounter, + MoveCounter, g_dtor_count, - CopyCountedStruct, ) from testing import assert_equal, assert_false, assert_raises, assert_true diff --git a/stdlib/test/hashlib/test_ahash.mojo b/stdlib/test/hashlib/test_ahash.mojo index 7e644f2e5a..3b87281543 100644 --- a/stdlib/test/hashlib/test_ahash.mojo +++ b/stdlib/test/hashlib/test_ahash.mojo @@ -18,7 +18,7 @@ from hashlib.hash import hash as old_hash from bit import pop_count from builtin._location import __call_location -from memory import memset_zero, stack_allocation, Span +from memory import Span, memset_zero, stack_allocation from testing import assert_equal, assert_not_equal, assert_true # Source: https://www.101languages.net/arabic/most-common-arabic-words/ diff --git a/stdlib/test/hashlib/test_hasher.mojo b/stdlib/test/hashlib/test_hasher.mojo index 3f16ed259d..0baca6eac9 100644 --- a/stdlib/test/hashlib/test_hasher.mojo +++ b/stdlib/test/hashlib/test_hasher.mojo @@ -13,6 +13,7 @@ # RUN: %mojo %s +from collections.string import StringSlice from hashlib._ahash import AHasher from hashlib._hasher import _hash_with_hasher, _HashableWithHasher, _Hasher from pathlib import Path @@ -21,8 +22,6 @@ from memory import UnsafePointer from python import Python, PythonObject from testing import assert_equal, assert_true -from collections.string import StringSlice - struct DummyHasher(_Hasher): var _dummy_value: UInt64 diff --git a/stdlib/test/memory/test_span.mojo b/stdlib/test/memory/test_span.mojo index c6369315f1..629f8b9555 100644 --- a/stdlib/test/memory/test_span.mojo +++ b/stdlib/test/memory/test_span.mojo @@ -14,7 +14,7 @@ from collections import InlineArray, List -from memory import UnsafePointer, Span +from memory import Span, UnsafePointer from testing import assert_equal, assert_true diff --git a/stdlib/test/python/test_python_object_dunder_contains.mojo b/stdlib/test/python/test_python_object_dunder_contains.mojo index 7b7de0b089..6276868449 100644 --- a/stdlib/test/python/test_python_object_dunder_contains.mojo +++ b/stdlib/test/python/test_python_object_dunder_contains.mojo @@ -13,9 +13,10 @@ # XFAIL: asan && !system-darwin # RUN: %mojo %s +from collections import Dict + from python import Python, PythonObject from testing import assert_equal, assert_false, assert_raises, assert_true -from collections import Dict def test_contains_dunder(mut python: Python): diff --git a/stdlib/test/sys/test_compile.mojo b/stdlib/test/sys/test_compile.mojo index d18535ee72..5aac175c72 100644 --- a/stdlib/test/sys/test_compile.mojo +++ b/stdlib/test/sys/test_compile.mojo @@ -12,7 +12,7 @@ # ===----------------------------------------------------------------------=== # # RUN: %bare-mojo %s -from sys.compile import OptimizationLevel, DebugLevel +from sys.compile import DebugLevel, OptimizationLevel from testing import assert_equal diff --git a/stdlib/test/sys/test_compile_debug.mojo b/stdlib/test/sys/test_compile_debug.mojo index d6e2271e20..0748a6c6c2 100644 --- a/stdlib/test/sys/test_compile_debug.mojo +++ b/stdlib/test/sys/test_compile_debug.mojo @@ -12,7 +12,7 @@ # ===----------------------------------------------------------------------=== # # RUN: %bare-mojo -O0 %s -from sys.compile import OptimizationLevel, DebugLevel +from sys.compile import DebugLevel, OptimizationLevel from testing import assert_equal diff --git a/stdlib/test/test_utils/__init__.mojo b/stdlib/test/test_utils/__init__.mojo index d85307ba91..b7dd12e0db 100644 --- a/stdlib/test/test_utils/__init__.mojo +++ b/stdlib/test/test_utils/__init__.mojo @@ -13,14 +13,14 @@ from .test_utils import libm_call from .types import ( + CopyCountedStruct, CopyCounter, + DtorCounter, ExplicitCopyOnly, ImplicitCopyOnly, MoveCounter, MoveOnly, ObservableDel, ValueDestructorRecorder, - DtorCounter, g_dtor_count, - CopyCountedStruct, ) diff --git a/stdlib/test/testing/test_assertion.mojo b/stdlib/test/testing/test_assertion.mojo index eb7a0ab38b..68b3e17774 100644 --- a/stdlib/test/testing/test_assertion.mojo +++ b/stdlib/test/testing/test_assertion.mojo @@ -25,8 +25,8 @@ from testing import ( assert_true, ) -from utils.numerics import inf, nan from utils import StringSlice +from utils.numerics import inf, nan def test_assert_messages(): diff --git a/stdlib/test/time/test_time.mojo b/stdlib/test/time/test_time.mojo index 50101f5f45..d6294f515f 100644 --- a/stdlib/test/time/test_time.mojo +++ b/stdlib/test/time/test_time.mojo @@ -13,13 +13,7 @@ # RUN: %mojo %s from sys import os_is_windows -from time import ( - monotonic, - perf_counter, - perf_counter_ns, - sleep, - time_function, -) +from time import monotonic, perf_counter, perf_counter_ns, sleep, time_function from testing import assert_true diff --git a/stdlib/test/utils/test_static_tuple.mojo b/stdlib/test/utils/test_static_tuple.mojo index e25c6d7fc6..ff4177cba1 100644 --- a/stdlib/test/utils/test_static_tuple.mojo +++ b/stdlib/test/utils/test_static_tuple.mojo @@ -13,6 +13,7 @@ # RUN: %mojo %s from testing import assert_equal + from utils import StaticTuple diff --git a/stdlib/test/utils/test_write.mojo b/stdlib/test/utils/test_write.mojo index 3844793005..fbfa52916c 100644 --- a/stdlib/test/utils/test_write.mojo +++ b/stdlib/test/utils/test_write.mojo @@ -12,10 +12,11 @@ # ===----------------------------------------------------------------------=== # # RUN: %mojo -debug-level full %s +from collections.string.inline_string import _FixedString + from testing import assert_equal from utils import Writable, Writer -from collections.string.inline_string import _FixedString fn main() raises: From 35845e425f8aec060c567c44365f6dcabdc5bfdf Mon Sep 17 00:00:00 2001 From: modularbot Date: Mon, 10 Feb 2025 19:19:04 +0000 Subject: [PATCH 367/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025021016 --- examples/life/magic.lock | 151 ++++++++++++++++++---------------- examples/magic.lock | 142 ++++++++++++++++---------------- examples/operators/magic.lock | 142 ++++++++++++++++---------------- examples/testing/magic.lock | 142 ++++++++++++++++---------------- magic.lock | 142 ++++++++++++++++---------------- 5 files changed, 364 insertions(+), 355 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 8f99f91e95..9b89e8a04e 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -91,11 +91,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025020905-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025020905-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025020905-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025020905-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025020905-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025021016-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.1.0.dev2025021016-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.1.0.dev2025021016-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025021016-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025021016-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda @@ -229,11 +229,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h86ecc28_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-c-1.10.0-h5ad3122_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025020905-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-core-25.1.0.dev2025020905-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.1.0.dev2025020905-release.conda - 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sha256: 0b61361aa3078d4484c4e3b078e0ae6dc0c903c5aa62eab51d0f3c55d2fafb21 - md5: 4b7b1b66cb6a7b2e90c7d18b9c02ae5a + sha256: f3a9224cd636d83308401cd30f67980d5df33322a62176f6b96c8ef986e1a5bc + md5: 9f433f96ee6e4db74b4716363f71ea55 depends: - - max-core ==25.1.0.dev2025020905 release + - max-core ==25.1.0.dev2025021016 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22980 - timestamp: 1739078252551 + size: 22970 + timestamp: 1739206564266 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 5c522f0505926f93d440de4acbe86923cd240199 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Mon, 10 Feb 2025 12:41:48 -0500 Subject: [PATCH 368/509] [stdlib] Remove float8e4m3fn comment Comment is no longer applicable now that the type has been renamed to be more correct. MODULAR_ORIG_COMMIT_REV_ID: 7a1af7fd56d47c4726cc0be42610a1a88983e037 --- stdlib/src/builtin/dtype.mojo | 3 --- stdlib/src/builtin/simd.mojo | 3 --- 2 files changed, 6 deletions(-) diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index 7068003d60..bf9b218788 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -100,9 +100,6 @@ struct DType( """Represents a FP8E4M3 floating point format from the [OFP8 standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). - This type is named `float8_e4m3fn` (the "fn" stands for "finite") in some - frameworks, as it does not encode -inf or inf. - The 8 bits are encoded as `seeeemmm`: - (s)ign: 1 bit - (e)xponent: 4 bits diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index e4d6bc7b29..96e4a5732c 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -126,9 +126,6 @@ alias Float8_e4m3fn = Scalar[DType.float8_e4m3fn] """Represents a FP8E4M3 floating point format from the [OFP8 standard](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-12-01-pdf-1). -This type is named `float8_e4m3fn` (the "fn" stands for "finite") in some -frameworks, as it does not encode -inf or inf. - The 8 bits are encoded as `seeeemmm`: - (s)ign: 1 bit - (e)xponent: 4 bits From 8c0baf1ec8e5549dbf4fbc2598b1f51a71623912 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Mon, 10 Feb 2025 14:28:10 -0800 Subject: [PATCH 369/509] [Stdlib] Add test_sizeof to test the sizeof operation MODULAR_ORIG_COMMIT_REV_ID: af11c9a3e1b1e0e33668701c58ff8a0838322a9c --- stdlib/test/sys/test_sizeof.mojo | 34 ++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 stdlib/test/sys/test_sizeof.mojo diff --git a/stdlib/test/sys/test_sizeof.mojo b/stdlib/test/sys/test_sizeof.mojo new file mode 100644 index 0000000000..66991c55bf --- /dev/null +++ b/stdlib/test/sys/test_sizeof.mojo @@ -0,0 +1,34 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %mojo %s + +from sys import sizeof +from testing import assert_equal + + +def main(): + assert_equal(sizeof[DType.int8](), 1) + assert_equal(sizeof[DType.int16](), 2) + assert_equal(sizeof[DType.int32](), 4) + assert_equal(sizeof[DType.int64](), 8) + assert_equal(sizeof[DType.float32](), 4) + assert_equal(sizeof[DType.float64](), 8) + assert_equal(sizeof[DType.bool](), 1) + assert_equal(sizeof[DType.index](), 8) + assert_equal(sizeof[DType.float8_e4m3fn](), 1) + assert_equal(sizeof[DType.float8_e5m2fnuz](), 1) + assert_equal(sizeof[DType.float8_e4m3fnuz](), 1) + assert_equal(sizeof[DType.bfloat16](), 2) + assert_equal(sizeof[DType.float16](), 2) + assert_equal(sizeof[DType.tensor_float32](), 4) + assert_equal(sizeof[DType.invalid](), 0) From 3717033bf152534d480a05eed861fa67dfd35079 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Tue, 11 Feb 2025 10:12:00 -0800 Subject: [PATCH 370/509] [Stdlib] Initial interval tree implementation MODULAR_ORIG_COMMIT_REV_ID: 762c84221484fb8285fcc7e5bca647724b459429 --- docs/changelog.md | 3 + stdlib/src/collections/interval.mojo | 1060 ++++++++++++++++++++ stdlib/test/collections/test_interval.mojo | 202 ++++ 3 files changed, 1265 insertions(+) create mode 100644 stdlib/src/collections/interval.mojo create mode 100644 stdlib/test/collections/test_interval.mojo diff --git a/docs/changelog.md b/docs/changelog.md index 9f6a1c4e3e..8eb7543890 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -22,6 +22,9 @@ what we publish. ### Standard library changes +- A new `IntervalTree` data structure has been added to the standard library. + This is a tree data structure that allows for efficient range queries. + ### Tooling changes ### ❌ Removed diff --git a/stdlib/src/collections/interval.mojo b/stdlib/src/collections/interval.mojo new file mode 100644 index 0000000000..060dae4ce2 --- /dev/null +++ b/stdlib/src/collections/interval.mojo @@ -0,0 +1,1060 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +""" +A self-balancing interval tree is a specialized binary search tree designed to +efficiently store and query intervals. + +It maintains intervals sorted by their low endpoints and augments each node with a +`max_high` attribute, representing the maximum high endpoint in its subtree. This +`max_high` value enables efficient overlap searching by pruning the search space. +Self-balancing mechanisms, such as Red-Black or AVL trees, ensure logarithmic time +complexity for operations. + +Key Features: + - Stores intervals (low, high). + - Nodes ordered by `low` endpoints. + - `max_high` attribute at each node for efficient overlap search. + - Self-balancing (e.g., using Red-Black tree logic) for O(log n) operations. + +Operations: + - Insertion: O(log n) - Adds a new interval, maintaining balance and updating + `max_high`. + - Overlap Search: O(log n) - Finds intervals overlapping a query interval using + `max_high` for pruning. + - Deletion: O(log n) - Removes an interval, maintaining balance and updating + `max_high`. + +Space Complexity: O(n), where n is the number of intervals. + +Use Cases: + - Calendar scheduling + - Computational geometry + - Genomics + - Database indexing + - Resource allocation + +In essence, this data structure provides a fast and efficient way to manage and +query interval data, particularly for finding overlaps. +""" + +from memory import UnsafePointer +from .deque import Deque +from builtin.math import _CopyableGreaterThanComparable +from collections import Optional + + +trait IntervalPayload(CollectionElement, Stringable, Comparable): + """The trait denotes a trait composition of the `CollectionElement`, + `Stringable`, and `Comparable` traits. + """ + + pass + + +trait IntervalElement( + CollectionElement, + Writable, + Intable, + Comparable, + _CopyableGreaterThanComparable, +): + """The trait denotes a trait composition of the `CollectionElement`, + `Writable`, `Intable`, and `Comparable` traits. Which is also subtractable. + """ + + fn __sub__(self, rhs: Self) -> Self: + """Subtracts rhs from self, must be implemented in concrete types. + + Args: + rhs: The value to subtract from self. + + Returns: + The result of subtracting rhs from self. + """ + ... + + +struct Interval[T: IntervalElement](CollectionElement): + """A half-open interval [start, end) that represents a range of values. + + The interval includes the start value but excludes the end value. + + Parameters: + T: The type of the interval bounds. + """ + + var start: T + """The inclusive start of the interval.""" + + var end: T + """The exclusive end of the interval.""" + + fn __init__(out self, start: T, end: T): + """Initialize an interval with start and end values. + + Args: + start: The starting value of the interval. + end: The ending value of the interval. Must be greater than or + equal to start. + """ + debug_assert( + start <= end, "invalid interval '(", start, ", ", end, ")'" + ) + self.start = start + self.end = end + + fn __init__(out self, interval: Tuple[T, T], /): + """Initialize an interval with a tuple of start and end values. + + Args: + interval: A tuple containing the start and end values. + """ + self.start = interval[0] + self.end = interval[1] + + fn __copyinit__(out self, existing: Self, /): + """Create a new instance of the interval by copying the values + from an existing one. + + Args: + existing: The interval to copy values from. + """ + self.start = existing.start + self.end = existing.end + + fn __moveinit__(out self, owned existing: Self, /): + """Create a new instance of the interval by moving the values + from an existing one. + + Args: + existing: The interval to move values from. + """ + self.start = existing.start^ + self.end = existing.end^ + + fn overlaps(self, other: Self) -> Bool: + """Returns whether this interval overlaps with another interval. + + Args: + other: The interval to check for overlap with. + + Returns: + True if the intervals overlap, False otherwise. + """ + return other.start < self.end and other.end > self.start + + fn union(self, other: Self) -> Self: + """Returns the union of this interval and another interval. + + Args: + other: The interval to union with. + + Returns: + The union of this interval and the other interval. + """ + debug_assert( + self.overlaps(other), + "intervals do not overlap when computing the union of '", + self, + "' and '", + other, + "'", + ) + var start = self.start if self.start < other.start else other.start + var end = self.end if self.end > other.end else other.end + return Self(start, end) + + fn intersection(self, other: Self) -> Self: + """Returns the intersection of this interval and another interval. + + Args: + other: The interval to intersect with. + + Returns: + The intersection of this interval and the other interval. + """ + debug_assert( + self.overlaps(other), + "intervals do not overlap when computing the intersection of '", + self, + "' and '", + other, + "'", + ) + var start = self.start if self.start > other.start else other.start + var end = self.end if self.end < other.end else other.end + return Self(start, end) + + fn __contains__(self, other: T) -> Bool: + """Returns whether a value is contained within this interval. + + Args: + other: The value to check. + + Returns: + True if the value is within the interval bounds, False otherwise. + """ + return self.start <= other < self.end + + fn __contains__(self, other: Self) -> Bool: + """Returns whether another interval is fully contained within this + interval. + + Args: + other: The interval to check. + + Returns: + True if the other interval is fully contained within this interval, + False otherwise. + """ + return self.start <= other.start and self.end >= other.end + + fn __eq__(self, other: Self) -> Bool: + """Returns whether this interval equals another interval. + + Args: + other: The interval to compare with. + + Returns: + True if both intervals have the same start and end values. + """ + return self.start == other.start and self.end == other.end + + fn __ne__(self, other: Self) -> Bool: + """Returns whether this interval is not equal to another interval. + + Args: + other: The interval to compare with. + + Returns: + True if the intervals are not equal, False if they are equal. + """ + return not (self == other) + + fn __le__(self, other: Self) -> Bool: + """Returns whether this interval is less than or equal to another + interval. + + Args: + other: The interval to compare with. + + Returns: + True if this interval's start is less than or equal to the other interval's start. + """ + return self.start <= other.start + + fn __ge__(self, other: Self) -> Bool: + """Returns whether this interval is greater than or equal to another + interval. + + Args: + other: The interval to compare with. + + Returns: + True if this interval's end is greater than or equal to the other interval's end. + """ + return self.end >= other.end + + fn __lt__(self, other: Self) -> Bool: + """Returns whether this interval is less than another interval. + + Args: + other: The interval to compare with. + + Returns: + True if this interval's start is less than the other interval's start. + """ + return self.start < other.start + + fn __gt__(self, other: Self) -> Bool: + """Returns whether this interval is greater than another interval. + + Args: + other: The interval to compare with. + + Returns: + True if this interval's end is greater than the other interval's end. + """ + return self.end > other.end + + fn __len__(self) -> Int: + """Returns the length of this interval. + + Returns: + The difference between end and start values as an integer. + """ + debug_assert(Bool(self), "interval is empty") + return Int(self.end - self.start) + + fn __bool__(self) -> Bool: + """Returns whether this interval is empty. + + Returns: + True if the interval is not empty (start < end), False otherwise. + """ + return self.start < self.end + + @no_inline + fn write_to[W: Writer](self, mut writer: W): + """Writes this interval to a writer in the format '(start, end)'. + + Parameters: + W: The writer type that implements the Writer trait. + + Args: + writer: The writer to write the interval to. + """ + writer.write("(", self.start, ", ", self.end, ")") + + fn __str__(self) -> String: + """Returns a string representation of this interval. + + Returns: + A string in the format '(start, end)' representing this interval. + """ + return String.write(self) + + fn __repr__(self) -> String: + """Returns a string representation of this interval suitable for + debugging. + + Returns: + A string in the format '(start, end)' representing this interval. + """ + return "Interval" + String.write(self) + "" + + +struct _IntervalNode[T: IntervalElement, U: IntervalPayload](CollectionElement): + """A node containing an interval and associated data. + + Parameters: + T: The type of the interval bounds, must support subtraction, integer + conversion, string conversion, comparison and collection operations. + U: The type of the associated data, must support string conversion + and collection operations. + """ + + var interval: Interval[T] + """The interval contained in this node.""" + + var data: U + """The data associated with this interval.""" + + var max_end: T + """The maximum end value of this node.""" + + var left: UnsafePointer[Self] + """The left child of this node.""" + + var right: UnsafePointer[Self] + """The right child of this node.""" + + var parent: UnsafePointer[Self] + """The parent of this node.""" + + var _is_red: Bool + """Red-black node color.""" + + fn __init__( + out self, + start: T, + end: T, + data: U, + *, + left: Optional[UnsafePointer[Self]] = None, + right: Optional[UnsafePointer[Self]] = None, + parent: Optional[UnsafePointer[Self]] = None, + is_red: Bool = True, + ): + """Creates a new interval node. + + Args: + start: The start value of the interval. + end: The end value of the interval. + data: The data to associate with this interval. + left: The left child of this node. + right: The right child of this node. + parent: The parent of this node. + is_red: Whether this node is red in the red-black tree. + """ + self = Self( + Interval(start, end), + data, + left=left, + right=right, + parent=parent, + is_red=is_red, + ) + + fn __init__( + out self, + interval: Interval[T], + data: U, + *, + left: Optional[UnsafePointer[Self]] = None, + right: Optional[UnsafePointer[Self]] = None, + parent: Optional[UnsafePointer[Self]] = None, + is_red: Bool = True, + ): + """Creates a new interval node. + + Args: + interval: The interval to associate with this node. + data: The data to associate with this interval. + left: The left child of this node. + right: The right child of this node. + parent: The parent of this node. + is_red: Whether this node is red in the red-black tree. + """ + self.interval = interval + self.max_end = interval.end + self.data = data + self.left = left.value() if left else __type_of(self.left)() + self.right = right.value() if right else __type_of(self.right)() + self.parent = parent.value() if parent else __type_of(self.parent)() + self._is_red = is_red + + fn __copyinit__(out self, existing: Self, /): + """Create a new instance of the interval node by copying the values + from an existing one. + + Args: + existing: The interval node to copy values from. + """ + self.interval = existing.interval + self.data = existing.data + self.max_end = existing.max_end + self.left = existing.left + self.right = existing.right + self.parent = existing.parent + self._is_red = existing._is_red + + fn __moveinit__(out self, owned existing: Self, /): + """Create a new instance of the interval node by moving the values + from an existing one. + + Args: + existing: The interval node to move values from. + """ + self.interval = existing.interval^ + self.data = existing.data^ + self.max_end = existing.max_end^ + self.left = existing.left + self.right = existing.right + self.parent = existing.parent + self._is_red = existing._is_red + + @no_inline + fn write_to[W: Writer](self, mut writer: W): + """Writes this interval node to a writer in the format + '(start, end): data'. + + Parameters: + W: The writer type that implements the Writer trait. + + Args: + writer: The writer to write the interval node to. + """ + writer.write(self.interval, "=", String(self.data)) + # writer.write(str(self.data)) + + fn __str__(self) -> String: + """Returns a string representation of this interval node. + + Returns: + A string in the format '(start, end): data' representing this + interval node. + """ + return String.write(self) + + fn __repr__(self) -> String: + """Returns a string representation of this interval node suitable for + debugging. + + Returns: + A string in the format '(start, end): data' representing this + interval node. + """ + return "IntervalNode(" + String.write(self) + ")" + + fn depth(self) -> Int: + """Returns the depth of this interval node. + + Returns: + The depth of this interval node. + """ + var left_depth = self.left[].depth() if self.left else 0 + var right_depth = self.right[].depth() if self.right else 0 + return 1 + max(left_depth, right_depth) + + fn __bool__(self) -> Bool: + """Returns whether this interval node is empty. + + Returns: + True if the interval node is empty, False otherwise. + """ + return Bool(self.interval) + + fn __eq__(self, other: Self) -> Bool: + """Returns whether this interval node is equal to another interval node. + + Args: + other: The interval node to compare with. + + Returns: + True if the interval nodes are equal, False otherwise. + """ + return self.interval == other.interval and self.data == other.data + + fn __lt__(self, other: Self) -> Bool: + return self.interval < other.interval + + fn __gt__(self, other: Self) -> Bool: + return self.interval > other.interval + + +struct IntervalTree[T: IntervalElement, U: IntervalPayload]: + """An interval tree data structure for efficient range queries. + + Parameters: + T: The type of the interval bounds, must support subtraction, integer + conversion, string conversion, comparison and collection operations. + U: The type of the associated data, must support string conversion + and collection operations. + """ + + var _root: UnsafePointer[_IntervalNode[T, U]] + """The root node of the interval tree.""" + + var _len: Int + """The number of elements in the interval tree.""" + + fn __init__(out self): + """Initializes an empty IntervalTree.""" + self._root = __type_of(self._root)() + self._len = 0 + + fn _left_rotate( + mut self, mut rotation_node: UnsafePointer[_IntervalNode[T, U]] + ): + """Performs a left rotation around node x in the red-black tree. + + This method performs a left rotation around the given node x, which is a + standard operation in red-black trees used to maintain balance. The rotation + preserves the binary search tree property while changing the structure. + + Before: After: + x y + / \\ / \\ + a y => x c + / \\ / \\ + b c a b + + Args: + rotation_node: A pointer to the node around which to perform the left rotation. + + Note: + The rotation assumes that x has a right child. The method will assert if + either the root or x's right child is not set. + """ + debug_assert(Bool(self._root), "node is not set") + var rotation_right_child = rotation_node[].right + debug_assert(Bool(rotation_right_child), "right child is not set") + rotation_node[].right = rotation_right_child[].left + + if rotation_right_child[].left: + rotation_right_child[].left[].parent = rotation_node + + rotation_right_child[].parent = rotation_node[].parent + + if not rotation_node[].parent: + self._root = rotation_right_child + elif rotation_node == rotation_node[].parent[].left: + rotation_node[].parent[].left = rotation_right_child + else: + rotation_node[].parent[].right = rotation_right_child + + rotation_right_child[].left = rotation_node + rotation_node[].parent = rotation_right_child + + rotation_node[].max_end = rotation_node[].interval.end + if rotation_node[].left: + rotation_node[].max_end = max( + rotation_node[].max_end, + rotation_node[].left[].max_end, + ) + if rotation_node[].right: + rotation_node[].max_end = max( + rotation_node[].max_end, + rotation_node[].right[].max_end, + ) + + rotation_right_child[].max_end = rotation_right_child[].interval.end + if rotation_right_child[].left: + rotation_right_child[].max_end = max( + rotation_right_child[].max_end, + rotation_right_child[].left[].max_end, + ) + if rotation_right_child[].right: + rotation_right_child[].max_end = max( + rotation_right_child[].max_end, + rotation_right_child[].right[].max_end, + ) + + fn _right_rotate( + mut self, mut rotation_node: UnsafePointer[_IntervalNode[T, U]] + ): + """Performs a right rotation around node y in the red-black tree. + + This method performs a right rotation around the given node y, which is a + standard operation in red-black trees used to maintain balance. The rotation + preserves the binary search tree property while changing the structure. + + Before: After: + y x + / \\ / \\ + x c => a y + / \\ / \\ + a b b c + + Args: + rotation_node: A pointer to the node around which to perform the right rotation. + + Note: + The rotation assumes that y has a left child. The method will assert if + either the root or y's left child is not set. + """ + debug_assert(Bool(self._root), "root node is not set") + var rotation_left_child = rotation_node[].left + debug_assert(Bool(rotation_left_child), "left child node is not set") + rotation_node[].left = rotation_left_child[].right + + if rotation_left_child[].right: + rotation_left_child[].right[].parent = rotation_node + + rotation_left_child[].parent = rotation_node[].parent + + if not rotation_node[].parent: + self._root = rotation_left_child + elif rotation_node == rotation_node[].parent[].right: + rotation_node[].parent[].right = rotation_left_child + else: + rotation_node[].parent[].left = rotation_left_child + + rotation_left_child[].right = rotation_node + rotation_node[].parent = rotation_left_child + + rotation_node[].max_end = rotation_node[].interval.end + if rotation_node[].left: + rotation_node[].max_end = max( + rotation_node[].max_end, rotation_node[].left[].max_end + ) + if rotation_node[].right: + rotation_node[].max_end = max( + rotation_node[].max_end, rotation_node[].right[].max_end + ) + + rotation_left_child[].max_end = rotation_left_child[].interval.end + if rotation_left_child[].left: + rotation_left_child[].max_end = max( + rotation_left_child[].max_end, + rotation_left_child[].left[].max_end, + ) + + fn insert(mut self, interval: Tuple[T, T], data: U): + """Insert a new interval into the tree using a tuple representation. + + Args: + interval: A tuple containing the start and end values of the interval. + data: The data value to associate with this interval. + """ + self.insert(Interval(interval[0], interval[1]), data) + + fn insert(mut self, interval: Interval[T], data: U): + """Insert a new interval into the tree. + + This method inserts a new interval and its associated data into the interval tree. + It maintains the binary search tree property based on interval start times and + updates the tree structure to preserve red-black tree properties. + + Args: + interval: The interval to insert into the tree. + data: The data value to associate with this interval. + """ + # Allocate memory for a new node and initialize it with the interval + # and data + var new_node = UnsafePointer[_IntervalNode[T, U]].alloc(1) + new_node.init_pointee_move(_IntervalNode(interval, data)) + self._len += 1 + + # If the tree is empty, set the root to the new node and color it black. + if not self._root: + self._root = new_node + self._root[]._is_red = False + return + + # Find the insertion point by traversing down the tree + # parent_node tracks the parent of the current node + var parent_node = __type_of(self._root)() + # current_node traverses down the tree until we find an empty spot + var current_node = self._root + while current_node: + parent_node = current_node + if new_node[] < current_node[]: + current_node = current_node[].left + else: + current_node = current_node[].right + parent_node[].max_end = max( + parent_node[].max_end, new_node[].interval.end + ) + + new_node[].parent = parent_node + if not parent_node: + self._root = new_node + elif new_node[] < parent_node[]: + parent_node[].left = new_node + else: + parent_node[].right = new_node + + self._insert_fixup(new_node) + + fn _insert_fixup( + mut self, mut current_node: UnsafePointer[_IntervalNode[T, U]] + ): + """Fixes up the red-black tree properties after an insertion. + + This method restores the red-black tree properties that may have been violated + during insertion of a new node. It performs rotations and color changes to + maintain the balance and color properties of the red-black tree. + + Args: + current_node: A pointer to the newly inserted node that may violate red-black + properties. + """ + + # While the parent of the current node is red, we need to fix violations + while current_node != self._root and current_node[].parent[]._is_red: + if current_node[].parent == current_node[].parent[].parent[].left: + # Get uncle node (parent's sibling) + var uncle_node = current_node[].parent[].parent[].right + if uncle_node and uncle_node[]._is_red: + # Case 1: Uncle is red - recolor parent, uncle and grandparent + current_node[].parent[]._is_red = False + uncle_node[]._is_red = False + current_node[].parent[].parent[]._is_red = True + current_node = current_node[].parent[].parent + else: + # Case 2: Uncle is black and node is a right child + if current_node == current_node[].parent[].right: + current_node = current_node[].parent + self._left_rotate(current_node) + # Case 3: Uncle is black and node is a left child + current_node[].parent[]._is_red = False + current_node[].parent[].parent[]._is_red = True + self._right_rotate(current_node[].parent[].parent) + else: + # Mirror case - parent is right child of grandparent + var uncle_node = current_node[].parent[].parent[].left + if uncle_node and uncle_node[]._is_red: + # Case 1: Uncle is red - recolor + current_node[].parent[]._is_red = False + uncle_node[]._is_red = False + current_node[].parent[].parent[]._is_red = True + current_node = current_node[].parent[].parent + else: + # Case 2: Uncle is black and node is a left child + if current_node == current_node[].parent[].left: + current_node = current_node[].parent + self._right_rotate(current_node) + + # Ensure root is black to maintain red-black tree properties + self._root[]._is_red = False + + fn __str__(self) -> String: + """Returns a string representation of the interval tree. + + Returns: + A string representation of the interval tree. + """ + return String.write(self) + + fn __repr__(self) -> String: + """Returns a string representation of the interval tree suitable for + debugging. + + Returns: + A string representation of the interval tree. + """ + return String.write(self) + + fn write_to[w: Writer](self, mut writer: w): + """Writes the interval tree to a writer. + + Parameters: + w: The writer type that implements the Writer trait. + + Args: + writer: The writer to write the interval tree to. + """ + self._draw(writer) + + @no_inline + fn _draw[w: Writer](self, mut writer: w): + """Draws the interval tree in a simple ASCII tree format. + + Creates a text representation of the tree using ASCII characters, with each node + indented according to its depth. Uses '├─' and '└─' characters to show the tree + structure. + + Parameters: + w: The writer type that implements the Writer trait. + + Args: + writer: The writer to output the tree visualization to. + """ + self._draw_helper(writer, self._root, "", True) + + @no_inline + fn _draw_helper[ + w: Writer + ]( + self, + mut writer: w, + node: UnsafePointer[_IntervalNode[T, U]], + indent: String, + is_last: Bool, + ): + """Helper function to recursively draw the interval tree. + + Recursively traverses the tree and draws each node with proper indentation + and branch characters to show the tree structure. + + Parameters: + w: The writer type that implements the Writer trait. + + Args: + writer: The writer to output the tree visualization to. + node: The current node being drawn. + indent: The current indentation string. + is_last: Whether this node is the last child of its parent. + """ + # Handle empty tree case + if not node: + return + + writer.write(indent) + var next_indent = indent + if is_last: + writer.write("├─") + next_indent += " " + else: + writer.write("└─") + next_indent += "| " + writer.write(node[], "\n") + # Recursively draw left and right subtrees + self._draw_helper(writer, node[].left, next_indent, False) + self._draw_helper(writer, node[].right, next_indent, True) + + @no_inline + fn _draw3[w: Writer](self, mut writer: w) raises: + """Draws the interval tree in a simple ASCII tree format. + + Creates a text representation of the tree using ASCII characters, with each node + indented according to its depth. Uses '├─' and '└─' characters to show the tree + structure. + + Parameters: + w: The writer type that implements the Writer trait. + + Args: + writer: The writer to output the tree visualization to. + """ + # Handle empty tree case + if not self._root: + return writer.write("Empty") + + var work_list = Deque[ + Tuple[UnsafePointer[_IntervalNode[T, U]], String, Bool] + ]() + work_list.append((self._root, String(""), True)) + + while work_list: + node, indent, is_last = work_list.pop() + if not node: + continue + writer.write(indent) + if is_last: + writer.write("├─ ") + indent += " " + else: + writer.write("└─ ") + indent += "| " + writer.write(node[], "\n") + work_list.append((node[].left, indent, False)) + work_list.append((node[].right, indent, True)) + + @no_inline + fn _draw2[w: Writer](self, mut writer: w) raises: + """Draws the interval tree in a visual ASCII art format. + + Creates a grid representation of the tree with nodes and connecting branches. + Each level of the tree is separated by 3 rows vertically. + Nodes are connected by '/' and '\' characters for left and right children. + + Parameters: + w: The writer type that implements the Writer trait. + + Args: + writer: The writer to output the tree visualization to. + """ + # Handle empty tree case + if not self._root: + return writer.write("Empty") + + # Calculate dimensions needed for the grid + var height = self._root[].depth() + var width = 2**height - 1 + + # Create 2D grid of spaces to hold the tree visualization + # Each row is a list of single character strings + var grid = List[List[String]]() + for _ in range(3 * height): + var row = List[String]() + for _ in range(4 * width): + row.append(" ") # Initialize with spaces + grid.append(row) + + var work_list = Deque[ + Tuple[UnsafePointer[_IntervalNode[T, U]], Int, Int, Int] + ]() + work_list.append((self._root, 0, 0, width)) + + while work_list: + # Recursively fills the grid with node values and connecting branches. + node, level, left, right = work_list.pop() + if not node: + continue + + # Calculate position for current node + var mid = (left + right) // 2 # Center point between boundaries + var pos_x = mid * 4 # Scale x position for readability + var pos_y = level * 3 # Scale y position for branch drawing + + # Draw the current node's value + var node_str = String(node[]) + var start_pos = max( + 0, pos_x - len(node_str) // 2 + ) # Center the node text + var i = 0 + for char in node_str.chars(): + grid[pos_y][start_pos + i] = String(char) + i += 1 + + # Add drawing left branch to the worklist. + if node[].left: + for y in range(1, 3): + grid[pos_y + y][pos_x - 2 * y + 1] = "/" # Draw left branch + work_list.append((node[].left, level + 1, left, mid)) + + # Add drawing right branch to the worklist. + if node[].right: + for y in range(1, 3): + grid[pos_y + y][pos_x + 2 * y] = "\\" # Draw right branch + work_list.append((node[].right, level + 1, mid, right)) + + # Output the completed grid row by row + for row in grid: + var row_str = String("".join(row[]).rstrip()) + if row_str: + writer.write(row_str, "\n") + + fn depth(self) -> Int: + """Returns the depth of the interval tree. + + Returns: + The depth of the interval tree. + """ + if not self._root: + return 0 + + return self._root[].depth() + + fn transplant( + mut self, + mut u: UnsafePointer[_IntervalNode[T, U]], + mut v: UnsafePointer[_IntervalNode[T, U]], + ): + """Transplants the subtree rooted at node u with the subtree rooted at node v. + + Args: + u: The node to transplant. + v: The node to transplant to. + """ + if not u[].parent: + self._root = v + elif u == u[].parent[].left: + u[].parent[].left = v + else: + u[].parent[].right = v + + if v: + v[].parent = u[].parent + + fn search(self, interval: Tuple[T, T]) raises -> List[U]: + """Searches for intervals overlapping with the given tuple. + + Args: + interval: The interval tuple (start, end). + + Returns: + A list of data associated with overlapping intervals. + """ + return self.search(Interval(interval[0], interval[1])) + + fn search(self, interval: Interval[T]) raises -> List[U]: + """Searches for intervals overlapping with the given interval. + + Args: + interval: The interval to search. + + Returns: + A list of data associated with overlapping intervals. + """ + return self._search_helper(self._root, interval) + + fn _search_helper( + self, node: UnsafePointer[_IntervalNode[T, U]], interval: Interval[T] + ) raises -> List[U]: + var result = List[U]() + var work_list = Deque[UnsafePointer[_IntervalNode[T, U]]]() + work_list.append(node) + + while work_list: + var current_node = work_list.pop() + if not current_node: + continue + if current_node[].interval.overlaps(interval): + result.append(current_node[].data) + if ( + current_node[].left + and current_node[].left[].interval.start <= interval.end + ): + work_list.append(current_node[].left) + if ( + current_node[].right + and current_node[].right[].max_end >= interval.start + ): + work_list.append(current_node[].right) + + return result diff --git a/stdlib/test/collections/test_interval.mojo b/stdlib/test/collections/test_interval.mojo new file mode 100644 index 0000000000..e5c710062d --- /dev/null +++ b/stdlib/test/collections/test_interval.mojo @@ -0,0 +1,202 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# RUN: %mojo %s + +from testing import assert_equal, assert_false, assert_true, assert_not_equal +from collections.interval import Interval, IntervalTree + + +def test_interval(): + # Create an interval from 1 to 10 (exclusive) + var interval = Interval(1, 10) + + # Test basic properties + assert_equal(interval.start, 1) + assert_equal(interval.end, 10) + assert_equal(len(interval), 9) + assert_equal(len(Interval(-10, -1)), 9) + + # Test string representations + assert_equal(String(interval), "(1, 10)") + assert_equal(repr(interval), "Interval(1, 10)") + + # Test equality comparisons + assert_equal(interval, Interval(1, 10)) + assert_not_equal(interval, Interval(1, 11)) + + # Test less than comparisons + assert_true( + interval < Interval(2, 11), msg=String(interval, " < Interval(2, 11)") + ) + assert_false( + interval < Interval(1, 11), msg=String(interval, " < Interval(1, 11)") + ) + + # Test greater than comparisons + assert_true( + interval > Interval(0, 9), msg=String(interval, " > Interval(0, 9)") + ) + assert_false( + interval > Interval(1, 11), msg=String(interval, " > Interval(1, 11)") + ) + + # Test less than or equal comparisons + assert_true( + interval <= Interval(1, 10), msg=String(interval, " <= Interval(1, 10)") + ) + assert_true( + interval <= Interval(1, 11), msg=String(interval, " <= Interval(1, 11)") + ) + assert_false( + interval <= Interval(0, 9), msg=String(interval, " <= Interval(0, 9)") + ) + + # Test greater than or equal comparisons + assert_true( + interval >= Interval(1, 10), msg=String(interval, " >= Interval(1, 10)") + ) + assert_true( + interval >= Interval(2, 9), msg=String(interval, " >= Interval(2, 9)") + ) + assert_false( + interval >= Interval(1, 11), msg=String(interval, " >= Interval(1, 11)") + ) + + # Test interval containment + assert_true( + interval in Interval(1, 11), msg=String(interval, " in Interval(1, 11)") + ) + assert_false( + interval in Interval(1, 9), msg=String(interval, " in Interval(1, 9)") + ) + assert_true( + interval in Interval(1, 10), msg=String(interval, " in Interval(1, 10)") + ) + assert_true( + interval in Interval(1, 11), msg=String(interval, " in Interval(1, 11)") + ) + assert_false( + interval in Interval(1, 9), msg=String(interval, " in Interval(1, 9)") + ) + + # Test point containment + assert_true(1 in interval, msg="1 in interval") + assert_false(0 in interval) + + # Test interval overlap + assert_true(interval.overlaps(Interval(1, 10))) + assert_true(interval.overlaps(Interval(1, 9))) + assert_false(interval.overlaps(Interval(-10, -1))) + + # Test interval union + assert_equal(interval.union(Interval(1, 10)), Interval(1, 10)) + assert_equal(interval.union(Interval(1, 9)), Interval(1, 10)) + + # Test interval intersection + assert_equal(interval.intersection(Interval(1, 10)), Interval(1, 10)) + assert_equal(interval.intersection(Interval(1, 9)), Interval(1, 9)) + assert_equal(interval.intersection(Interval(3, 5)), Interval(3, 5)) + + # Test empty interval checks + assert_true(Bool(interval)) + assert_false(Bool(Interval(0, 0))) + + +struct MyType: + var value: Float64 + + fn __init__(out self): + self.value = 0.0 + + fn __init__(out self, value: Float64, /): + self.value = value + + fn __copyinit__(out self, existing: Self, /): + self.value = existing.value + + fn __moveinit__(out self, owned existing: Self, /): + self.value = existing.value + + fn __gt__(self, other: Self) -> Bool: + return self.value > other.value + + fn __lt__(self, other: Self) -> Bool: + return self.value < other.value + + fn __ge__(self, other: Self) -> Bool: + return self.value >= other.value + + fn __le__(self, other: Self) -> Bool: + return self.value <= other.value + + fn __eq__(self, other: Self) -> Bool: + return self.value == other.value + + fn __ne__(self, other: Self) -> Bool: + return self.value != other.value + + fn __sub__(self, other: Self) -> Self: + return Self(self.value - other.value) + + fn __int__(self) -> Int: + return Int(self.value) + + fn __float__(self) -> Float64: + return self.value + + fn write_to[W: Writer](self, mut writer: W): + writer.write(self.value) + + fn __str__(self) -> String: + return String.write(self) + + +def test_interval_floating(): + # Create an interval with floating point values using MyType wrapper. + var interval = Interval(MyType(2.4), MyType(3.5)) + + # Verify the interval start and end values are correctly set. + assert_equal(rebind[Float64](interval.start.value), 2.4) + assert_equal(rebind[Float64](interval.end.value), 3.5) + + # Test union operation with overlapping interval. + var union = interval.union(Interval(MyType(3.0), MyType(4.5))) + + # Verify union produces expected interval bounds. + assert_equal(union, Interval(MyType(2.4), MyType(4.5))) + + # Verify length of union interval is correct. + assert_equal(len(union), 2) + + +def test_interval_tree(): + var tree = IntervalTree[Int, MyType]() + tree.insert((15, 20), MyType(33.0)) + tree.insert((10, 30), MyType(34.0)) + tree.insert((17, 19), MyType(35.0)) + tree.insert((5, 20), MyType(36.0)) + tree.insert((12, 15), MyType(37.0)) + tree.insert((30, 40), MyType(38.0)) + print(tree) + + var elems = tree.search((10, 15)) + assert_equal(len(elems), 3) + assert_equal(Float64(elems[0]), 34.0) + assert_equal(Float64(elems[1]), 37.0) + assert_equal(Float64(elems[2]), 36.0) + + +def main(): + test_interval() + test_interval_floating() + test_interval_tree() From c92c135deead5d20c65d70dcfd45aff1e677239b Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 11 Feb 2025 20:55:01 +0000 Subject: [PATCH 371/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025021118 --- examples/life/magic.lock | 205 +++++++++++++++++----------------- examples/magic.lock | 205 +++++++++++++++++----------------- examples/operators/magic.lock | 205 +++++++++++++++++----------------- examples/testing/magic.lock | 205 +++++++++++++++++----------------- magic.lock | 205 +++++++++++++++++----------------- 5 files changed, 515 insertions(+), 510 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 9b89e8a04e..02edf09a75 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -53,7 +53,7 @@ environments: - 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__osx >=11.0 - ca-certificates @@ -1636,8 +1637,8 @@ packages: platform: osx license: Apache-2.0 license_family: Apache - size: 2936415 - timestamp: 1736086108693 + size: 2934522 + timestamp: 1739301896733 - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda sha256: da157b19bcd398b9804c5c52fc000fcb8ab0525bdb9c70f95beaa0bb42f85af1 md5: 3bfed7e6228ebf2f7b9eaa47f1b4e2aa From b15f3741e505a51dcb3ff2574cf1ad05d665d19b Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Tue, 11 Feb 2025 11:39:28 -0800 Subject: [PATCH 372/509] [stdlib] Add ability to get a pointer to the Mojo I/O stream This will probably not be the way most users want to interact with the `stdout` stream, but in Mojo's current state it is useful to be able to interact with the `stdout` stream associated with the program directly. Eventually, we'll want a locked output handle that we pass around. MODULAR_ORIG_COMMIT_REV_ID: 28306a670b3df8df523f362c11b237d8fff0809f --- stdlib/src/builtin/io.mojo | 6 ++++++ stdlib/src/sys/_libc.mojo | 22 +++++++++++++++++++++- 2 files changed, 27 insertions(+), 1 deletion(-) diff --git a/stdlib/src/builtin/io.mojo b/stdlib/src/builtin/io.mojo index a7aa15ad2d..eb5bfd4a22 100644 --- a/stdlib/src/builtin/io.mojo +++ b/stdlib/src/builtin/io.mojo @@ -385,3 +385,9 @@ fn input(prompt: String = "") raises -> String: if prompt != "": print(prompt, end="") return _fdopen["r"](0).readline() + + +fn _get_stdout_stream() -> OpaquePointer: + return external_call[ + "KGEN_CompilerRT_IO_get_stdout_stream", OpaquePointer + ]() diff --git a/stdlib/src/sys/_libc.mojo b/stdlib/src/sys/_libc.mojo index e951d7beb5..1bfd904399 100644 --- a/stdlib/src/sys/_libc.mojo +++ b/stdlib/src/sys/_libc.mojo @@ -18,7 +18,7 @@ functionality in the rest of the Mojo standard library. """ from sys import os_is_windows -from sys.ffi import OpaquePointer, c_char, c_int +from sys.ffi import OpaquePointer, c_char, c_int, c_size_t from memory import UnsafePointer @@ -74,6 +74,26 @@ fn pclose(stream: FILE_ptr) -> c_int: return external_call["pclose", c_int](stream) +@always_inline +fn setvbuf( + stream: FILE_ptr, buffer: UnsafePointer[c_char], mode: c_int, size: c_size_t +) -> c_int: + return external_call["setvbuf", c_int](stream, buffer) + + +struct BufferMode: + """ + Modes for use in `setvbuf` to control buffer output. + """ + + alias buffered = 0 + """Equivalent to `_IOFBF`.""" + alias line_buffered = 1 + """Equivalent to `_IOLBF`.""" + alias unbuffered = 2 + """Equivalent to `_IONBF`.""" + + # ===-----------------------------------------------------------------------===# # unistd.h # ===-----------------------------------------------------------------------===# From 87accf381f958e14aef0091e9029c23d17827231 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 11 Feb 2025 13:52:43 -0600 Subject: [PATCH 373/509] [stdlib] feat: Move `split()` to `StringSlice` MODULAR_ORIG_COMMIT_REV_ID: ffd812de307fbb16a7e3c26a42789c6442c52ae7 --- stdlib/src/collections/string/string.mojo | 89 ++-------- .../src/collections/string/string_slice.mojo | 155 +++++++++++++++++- .../collections/string/test_string_slice.mojo | 152 +++++++++++++++++ 3 files changed, 322 insertions(+), 74 deletions(-) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 916336ce80..a5b83a4dd0 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -1441,6 +1441,7 @@ struct String( """ return self.as_string_slice().isspace() + # TODO(MSTDL-590): String.split() should return `StringSlice`s. fn split(self, sep: StringSlice, maxsplit: Int = -1) raises -> List[String]: """Split the string by a separator. @@ -1467,36 +1468,9 @@ struct String( ``` . """ - var output = List[String]() - - var str_byte_len = self.byte_length() - 1 - var lhs = 0 - var rhs = 0 - var items = 0 - var sep_len = sep.byte_length() - if sep_len == 0: - raise Error("Separator cannot be empty.") - if str_byte_len < 0: - output.append("") - - while lhs <= str_byte_len: - rhs = self.find(sep, lhs) - if rhs == -1: - output.append(self[lhs:]) - break - - if maxsplit > -1: - if items == maxsplit: - output.append(self[lhs:]) - break - items += 1 - - output.append(self[lhs:rhs]) - lhs = rhs + sep_len - - if self.endswith(sep) and (len(output) <= maxsplit or maxsplit == -1): - output.append("") - return output + return self.as_string_slice().split[sep.mut, sep.origin]( + sep, maxsplit=maxsplit + ) fn split(self, sep: NoneType = None, maxsplit: Int = -1) -> List[String]: """Split the string by every Whitespace separator. @@ -1526,49 +1500,18 @@ struct String( . """ - fn num_bytes(b: UInt8) -> Int: - var flipped = ~b - return Int(count_leading_zeros(flipped) + (flipped >> 7)) - - var output = List[String]() - var str_byte_len = self.byte_length() - 1 - var lhs = 0 - var rhs = 0 - var items = 0 - while lhs <= str_byte_len: - # Python adds all "whitespace chars" as one separator - # if no separator was specified - for s in self[lhs:].char_slices(): - if not s.isspace(): - break - lhs += s.byte_length() - # if it went until the end of the String, then - # it should be sliced up until the original - # start of the whitespace which was already appended - if lhs - 1 == str_byte_len: - break - elif lhs == str_byte_len: - # if the last char is not whitespace - output.append(self[str_byte_len]) - break - rhs = lhs + num_bytes(self.unsafe_ptr()[lhs]) - for s in self[ - lhs + num_bytes(self.unsafe_ptr()[lhs]) : - ].char_slices(): - if s.isspace(): - break - rhs += s.byte_length() - - if maxsplit > -1: - if items == maxsplit: - output.append(self[lhs:]) - break - items += 1 - - output.append(self[lhs:rhs]) - lhs = rhs - - return output + # TODO(MSTDL-590): Avoid the need to loop to convert `StringSlice` to + # `String` by making `String.split()` return `StringSlice`s. + var str_slices = self.as_string_slice()._split_whitespace( + maxsplit=maxsplit + ) + + var output = List[String](capacity=len(str_slices)) + + for str_slice in str_slices: + output.append(String(str_slice[])) + + return output^ fn splitlines(self, keepends: Bool = False) -> List[String]: """Split the string at line boundaries. This corresponds to Python's diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 214620616b..e97d1e1e23 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -25,7 +25,7 @@ from collections import List, Optional from collections.string._utf8_validation import _is_valid_utf8 from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from hashlib._hasher import _HashableWithHasher, _Hasher -from os import PathLike +from os import PathLike, abort from sys import bitwidthof, simdwidthof from sys.ffi import c_char from sys.intrinsics import likely, unlikely @@ -768,6 +768,159 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # Methods # ===------------------------------------------------------------------===# + fn split[ + sep_mut: Bool, + sep_origin: Origin[sep_mut], //, + ]( + self, + sep: StringSlice[sep_origin], + maxsplit: Int = -1, + ) raises -> List[ + String + ]: + """Split the string by a separator. + + Parameters: + sep_mut: Mutability of the `sep` string slice. + sep_origin: Origin of the `sep` string slice. + + Args: + sep: The string to split on. + maxsplit: The maximum amount of items to split from String. + Defaults to unlimited. + + Returns: + A List of Strings containing the input split by the separator. + + Raises: + If the separator is empty. + + Examples: + + ```mojo + # Splitting a space + _ = StringSlice("hello world").split(" ") # ["hello", "world"] + # Splitting adjacent separators + _ = StringSlice("hello,,world").split(",") # ["hello", "", "world"] + # Splitting with maxsplit + _ = StringSlice("1,2,3").split(",", 1) # ['1', '2,3'] + ``` + . + """ + var output = List[String]() + + var str_byte_len = self.byte_length() - 1 + var lhs = 0 + var rhs = 0 + var items = 0 + var sep_len = sep.byte_length() + if sep_len == 0: + raise Error("Separator cannot be empty.") + if str_byte_len < 0: + output.append(String("")) + + while lhs <= str_byte_len: + rhs = self.find(sep, lhs) + if rhs == -1: + output.append(String(self[lhs:])) + break + + if maxsplit > -1: + if items == maxsplit: + output.append(String(self[lhs:])) + break + items += 1 + + output.append(String(self[lhs:rhs])) + lhs = rhs + sep_len + + if self.endswith(sep) and (len(output) <= maxsplit or maxsplit == -1): + output.append(String("")) + + return output^ + + fn split( + self, sep: NoneType = None, maxsplit: Int = -1 + ) -> List[StringSlice[origin]]: + """Split the string by every Whitespace separator. + + Args: + sep: None. + maxsplit: The maximum amount of items to split from String. Defaults + to unlimited. + + Returns: + A List of Strings containing the input split by the separator. + + Examples: + + ```mojo + # Splitting an empty string or filled with whitespaces + _ = StringSlice(" ").split() # [] + _ = StringSlice("").split() # [] + + # Splitting a string with leading, trailing, and middle whitespaces + _ = StringSlice(" hello world ").split() # ["hello", "world"] + # Splitting adjacent universal newlines: + _ = StringSlice( + "hello \\t\\n\\v\\f\\r\\x1c\\x1d\\x1e\\x85\\u2028\\u2029world" + ).split() # ["hello", "world"] + ``` + . + """ + + return self._split_whitespace() + + fn _split_whitespace(self, maxsplit: Int = -1) -> List[StringSlice[origin]]: + fn num_bytes(b: UInt8) -> Int: + var flipped = ~b + return Int(count_leading_zeros(flipped) + (flipped >> 7)) + + var output = List[StringSlice[origin]]() + var str_byte_len = self.byte_length() - 1 + var lhs = 0 + var rhs = 0 + var items = 0 + while lhs <= str_byte_len: + try: + # Python adds all "whitespace chars" as one separator + # if no separator was specified + for s in self[lhs:].char_slices(): + if not s.isspace(): + break + lhs += s.byte_length() + # if it went until the end of the String, then + # it should be sliced up until the original + # start of the whitespace which was already appended + if lhs - 1 == str_byte_len: + break + elif lhs == str_byte_len: + # if the last char is not whitespace + output.append(self[str_byte_len:]) + break + rhs = lhs + num_bytes(self.unsafe_ptr()[lhs]) + for s in self[ + lhs + num_bytes(self.unsafe_ptr()[lhs]) : + ].char_slices(): + if s.isspace(): + break + rhs += s.byte_length() + + if maxsplit > -1: + if items == maxsplit: + output.append(self[lhs:]) + break + items += 1 + + output.append(self[lhs:rhs]) + lhs = rhs + except e: + return abort[List[StringSlice[origin]]]( + "unexpected exception during split()" + ) + + return output + @always_inline fn strip(self, chars: StringSlice) -> Self: """Return a copy of the string with leading and trailing characters diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 807d4dff8b..bb7bc9d3ea 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -569,6 +569,157 @@ def test_count_utf8_continuation_bytes(): _test(3, List[UInt8](b2, c, b3, c, c)) +def test_split(): + # empty separators default to whitespace + var d0 = StringSlice("hello world").split() + assert_true(len(d0) == 2) + assert_true(d0[0] == "hello") + assert_true(d0[1] == "world") + var d = StringSlice("hello \t\n\n\v\fworld").split(sep="\n") + assert_true(len(d) == 3) + assert_true(d[0] == "hello \t" and d[1] == "" and d[2] == "\v\fworld") + + # Should add all whitespace-like chars as one + # test all unicode separators + # 0 is to build a String with null terminator + alias next_line = List[UInt8](0xC2, 0x85, 0) + """TODO: \\x85""" + alias unicode_line_sep = List[UInt8](0xE2, 0x80, 0xA8, 0) + """TODO: \\u2028""" + alias unicode_paragraph_sep = List[UInt8](0xE2, 0x80, 0xA9, 0) + """TODO: \\u2029""" + # TODO add line and paragraph separator as StringLiteral once unicode + # escape secuences are accepted + var univ_sep_var = ( + String( + " ", + "\t", + "\n", + "\r", + "\v", + "\f", + "\x1c", + "\x1d", + "\x1e", + String(buffer=next_line), + String(buffer=unicode_line_sep), + String(buffer=unicode_paragraph_sep), + ) + ) + var s = univ_sep_var + "hello" + univ_sep_var + "world" + univ_sep_var + var ds1 = StringSlice(s).split() + assert_true(len(ds1) == 2) + assert_true(ds1[0] == "hello" and ds1[1] == "world") + + # should split into empty strings between separators + d = StringSlice("1,,,3").split(",") + assert_true(len(d) == 4) + assert_true(d[0] == "1" and d[1] == "" and d[2] == "" and d[3] == "3") + d = StringSlice(",,,").split(",") + assert_true(len(d) == 4) + assert_true(d[0] == "" and d[1] == "" and d[2] == "" and d[3] == "") + d = StringSlice(" a b ").split(" ") + assert_true(len(d) == 4) + assert_true(d[0] == "" and d[1] == "a" and d[2] == "b" and d[3] == "") + d = StringSlice("abababaaba").split("aba") + assert_true(len(d) == 4) + assert_true(d[0] == "" and d[1] == "b" and d[2] == "" and d[3] == "") + + # should split into maxsplit + 1 items + d = StringSlice("1,2,3").split(",", 0) + assert_true(len(d) == 1) + assert_true(d[0] == "1,2,3") + d = StringSlice("1,2,3").split(",", 1) + assert_true(len(d) == 2) + assert_true(d[0] == "1" and d[1] == "2,3") + + assert_true(len(StringSlice("").split()) == 0) + assert_true(len(StringSlice(" ").split()) == 0) + assert_true(len(StringSlice("").split(" ")) == 1) + assert_true(len(StringSlice(" ").split(" ")) == 2) + assert_true(len(StringSlice(" ").split(" ")) == 3) + assert_true(len(StringSlice(" ").split(" ")) == 4) + + with assert_raises(): + _ = StringSlice("").split("") + + # Split in middle + var d1 = StringSlice("n") + var in1 = StringSlice("faang") + var res1 = in1.split(d1) + assert_equal(len(res1), 2) + assert_equal(res1[0], "faa") + assert_equal(res1[1], "g") + + # Matches should be properly split in multiple case + var d2 = StringSlice(" ") + var in2 = StringSlice("modcon is coming soon") + var res2 = in2.split(d2) + assert_equal(len(res2), 4) + assert_equal(res2[0], "modcon") + assert_equal(res2[1], "is") + assert_equal(res2[2], "coming") + assert_equal(res2[3], "soon") + + # No match from the delimiter + var d3 = StringSlice("x") + var in3 = StringSlice("hello world") + var res3 = in3.split(d3) + assert_equal(len(res3), 1) + assert_equal(res3[0], "hello world") + + # Multiple character delimiter + var d4 = StringSlice("ll") + var in4 = StringSlice("hello") + var res4 = in4.split(d4) + assert_equal(len(res4), 2) + assert_equal(res4[0], "he") + assert_equal(res4[1], "o") + + # related to #2879 + # TODO: replace string comparison when __eq__ is implemented for List + assert_equal( + StringSlice("abbaaaabbba").split("a").__str__(), + "['', 'bb', '', '', '', 'bbb', '']", + ) + assert_equal( + StringSlice("abbaaaabbba").split("a", 8).__str__(), + "['', 'bb', '', '', '', 'bbb', '']", + ) + assert_equal( + StringSlice("abbaaaabbba").split("a", 5).__str__(), + "['', 'bb', '', '', '', 'bbba']", + ) + assert_equal(StringSlice("aaa").split("a", 0).__str__(), "['aaa']") + assert_equal(StringSlice("a").split("a").__str__(), "['', '']") + assert_equal(StringSlice("1,2,3").split("3", 0).__str__(), "['1,2,3']") + assert_equal(StringSlice("1,2,3").split("3", 1).__str__(), "['1,2,', '']") + assert_equal( + StringSlice("1,2,3,3").split("3", 2).__str__(), "['1,2,', ',', '']" + ) + assert_equal( + StringSlice("1,2,3,3,3").split("3", 2).__str__(), "['1,2,', ',', ',3']" + ) + + var in5 = StringSlice("Hello 🔥!") + var res5 = in5.split() + assert_equal(len(res5), 2) + assert_equal(res5[0], "Hello") + assert_equal(res5[1], "🔥!") + + var in6 = StringSlice("Лорем ипсум долор сит амет") + var res6 = in6.split(" ") + assert_equal(len(res6), 5) + assert_equal(res6[0], "Лорем") + assert_equal(res6[1], "ипсум") + assert_equal(res6[2], "долор") + assert_equal(res6[3], "сит") + assert_equal(res6[4], "амет") + + with assert_raises(contains="Separator cannot be empty."): + _ = StringSlice("1, 2, 3").split("") + + def test_splitlines(): alias S = StringSlice[StaticConstantOrigin] alias L = List[StringSlice[StaticConstantOrigin]] @@ -900,6 +1051,7 @@ def main(): test_combination_10_good_10_bad_utf8_sequences() test_count_utf8_continuation_bytes() test_count() + test_split() test_splitlines() test_rstrip() test_lstrip() From 39aac58e467884d8e0d0270894f0b74ef63ba11e Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Tue, 11 Feb 2025 12:47:21 -0800 Subject: [PATCH 374/509] [mojo-lang] Begin the implementation of `@always_inline("nodebug")` This adds basic infrastructure and implementation support for this new form of `always_inlined` which allows us to inline limited functions into parameter expressions, allowing things like `Int` to be more transparent for dedependent type processing and removing hacks in the compiler. MODULAR_ORIG_COMMIT_REV_ID: d8f48e1f10fb0766178ccd7e1fd078eb35abfae2 --- stdlib/src/builtin/int.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 44aca38921..9ab1315e87 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -340,7 +340,7 @@ struct Int( # Life cycle methods # ===------------------------------------------------------------------=== # - @always_inline("nodebug") + @always_inline("builtin") fn __init__(out self): """Default constructor that produces zero.""" self.value = __mlir_op.`index.constant`[value = __mlir_attr.`0:index`]() @@ -354,7 +354,7 @@ struct Int( return self @doc_private - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: __mlir_type.index): """Construct Int from the given index value. @@ -592,7 +592,7 @@ struct Int( """ return self ^ -1 - @always_inline("nodebug") + @always_inline("builtin") fn __add__(self, rhs: Int) -> Int: """Return `self + rhs`. From c2172977b994911800127826dd81e4dc0775fd57 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 11 Feb 2025 15:34:45 -0600 Subject: [PATCH 375/509] [stdlib] polish: Remove a few unnecessary `String` allocations MODULAR_ORIG_COMMIT_REV_ID: 50cc3cb86f4bf5a1050bbcfa34e86f80d89fe9b2 --- docs/changelog.md | 3 +++ stdlib/src/pathlib/path.mojo | 6 +++--- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 8eb7543890..7c9820567b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -25,6 +25,9 @@ what we publish. - A new `IntervalTree` data structure has been added to the standard library. This is a tree data structure that allows for efficient range queries. +- `StringSlice` now provides `split()` methods for splitting on a separator + character or on whitespace. + ### Tooling changes ### ❌ Removed diff --git a/stdlib/src/pathlib/path.mojo b/stdlib/src/pathlib/path.mojo index 89dec5a50d..38f8683dc5 100644 --- a/stdlib/src/pathlib/path.mojo +++ b/stdlib/src/pathlib/path.mojo @@ -60,7 +60,7 @@ fn _dir_of_current_file() raises -> Path: @no_inline fn _dir_of_current_file_impl(file_name: StringLiteral) raises -> Path: var i = String(file_name).rfind(DIR_SEPARATOR) - return Path(String(file_name)[0:i]) + return Path(StringSlice(file_name)[0:i]) @value @@ -92,13 +92,13 @@ struct Path( self.path = String(path) @implicit - fn __init__(out self, path: String): + fn __init__(out self, owned path: String): """Initializes a path with the provided path. Args: path: The file system path. """ - self.path = path + self.path = path^ fn copy(self) -> Self: """Copy the object. From 8e5a9f7da066aa0f9c9ca13ad60f788f7fb5920b Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Tue, 11 Feb 2025 16:16:19 -0600 Subject: [PATCH 376/509] [stdlib] feat: Move `String` casing and justification methods to `StringSlice` The implementation logic for the following `String` methods have been moved to corresponding new `StringSlice` methods. In two cases the new `StringSlice` method names differ to emphasize that they currently only work on ASCII characters. * lower() * upper() * isdigit() => is_ascii_digit() * isupper() * islower() * isprintable() => is_ascii_printable() * rjust() * ljust() * center() * _justify() MODULAR_ORIG_COMMIT_REV_ID: 98c6c167394c82dc13636f057fd5c9403717b733 --- docs/changelog.md | 16 ++- stdlib/src/collections/string/string.mojo | 56 ++------ .../src/collections/string/string_slice.mojo | 131 +++++++++++++++++- .../test/collections/string/test_string.mojo | 80 ----------- .../collections/string/test_string_slice.mojo | 82 ++++++++++- 5 files changed, 240 insertions(+), 125 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 7c9820567b..7210dc3bb2 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -25,8 +25,20 @@ what we publish. - A new `IntervalTree` data structure has been added to the standard library. This is a tree data structure that allows for efficient range queries. -- `StringSlice` now provides `split()` methods for splitting on a separator - character or on whitespace. +- `StringSlice` now supports several additional methods moved from `String`. + The existing `String` methods have been updated to instead call the + corresponding new `StringSlice` methods: + + - `split()` + - `lower()` + - `upper()` + - `is_ascii_digit()` + - `isupper()` + - `islower()` + - `is_ascii_printable()` + - `rjust()` + - `ljust()` + - `center()` ### Tooling changes diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index a5b83a4dd0..f8cda0973f 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -15,12 +15,6 @@ from collections import KeyElement, List, Optional from collections._index_normalization import normalize_index from collections.string import CharsIter -from collections.string._unicode import ( - is_lowercase, - is_uppercase, - to_lowercase, - to_uppercase, -) from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( StaticString, @@ -29,6 +23,12 @@ from collections.string.string_slice import ( _to_string_list, _utf8_byte_type, ) +from collections.string._unicode import ( + is_lowercase, + is_uppercase, + to_lowercase, + to_uppercase, +) from hashlib._hasher import _HashableWithHasher, _Hasher from os import abort from sys import bitwidthof, llvm_intrinsic @@ -1692,8 +1692,7 @@ struct String( A new string where cased letters have been converted to lowercase. """ - # TODO: the _unicode module does not support locale sensitive conversions yet. - return to_lowercase(self) + return self.as_string_slice().lower() fn upper(self) -> String: """Returns a copy of the string with all cased characters @@ -1703,8 +1702,7 @@ struct String( A new string where cased letters have been converted to uppercase. """ - # TODO: the _unicode module does not support locale sensitive conversions yet. - return to_uppercase(self) + return self.as_string_slice().upper() fn startswith( self, prefix: StringSlice, start: Int = 0, end: Int = -1 @@ -1850,12 +1848,7 @@ struct String( Returns: True if all characters are digits and it's not empty else False. """ - if not self: - return False - for char in self.chars(): - if not char.is_ascii_digit(): - return False - return True + return self.as_string_slice().is_ascii_digit() fn isupper(self) -> Bool: """Returns True if all cased characters in the string are uppercase and @@ -1865,7 +1858,7 @@ struct String( True if all cased characters in the string are uppercase and there is at least one cased character, False otherwise. """ - return len(self) > 0 and is_uppercase(self) + return self.as_string_slice().isupper() fn islower(self) -> Bool: """Returns True if all cased characters in the string are lowercase and @@ -1875,7 +1868,7 @@ struct String( True if all cased characters in the string are lowercase and there is at least one cased character, False otherwise. """ - return len(self) > 0 and is_lowercase(self) + return self.as_string_slice().islower() fn isprintable(self) -> Bool: """Returns True if all characters in the string are ASCII printable. @@ -1885,10 +1878,7 @@ struct String( Returns: True if all characters are printable else False. """ - for char in self.chars(): - if not char.is_ascii_printable(): - return False - return True + return self.as_string_slice().is_ascii_printable() fn rjust(self, width: Int, fillchar: StringLiteral = " ") -> String: """Returns the string right justified in a string of specified width. @@ -1900,7 +1890,7 @@ struct String( Returns: Returns right justified string, or self if width is not bigger than self length. """ - return self._justify(width - len(self), width, fillchar) + return self.as_string_slice().rjust(width, fillchar) fn ljust(self, width: Int, fillchar: StringLiteral = " ") -> String: """Returns the string left justified in a string of specified width. @@ -1912,7 +1902,7 @@ struct String( Returns: Returns left justified string, or self if width is not bigger than self length. """ - return self._justify(0, width, fillchar) + return self.as_string_slice().ljust(width, fillchar) fn center(self, width: Int, fillchar: StringLiteral = " ") -> String: """Returns the string center justified in a string of specified width. @@ -1924,23 +1914,7 @@ struct String( Returns: Returns center justified string, or self if width is not bigger than self length. """ - return self._justify(width - len(self) >> 1, width, fillchar) - - fn _justify( - self, start: Int, width: Int, fillchar: StringLiteral - ) -> String: - if len(self) >= width: - return self - debug_assert( - len(fillchar) == 1, "fill char needs to be a one byte literal" - ) - var fillbyte = fillchar.as_bytes()[0] - var buffer = Self._buffer_type(capacity=width + 1) - buffer.resize(width, fillbyte) - buffer.append(0) - memcpy(buffer.unsafe_ptr().offset(start), self.unsafe_ptr(), len(self)) - var result = String(buffer) - return result^ + return self.as_string_slice().center(width, fillchar) fn reserve(mut self, new_capacity: Int): """Reserves the requested capacity. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index e97d1e1e23..7856e05c71 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -22,8 +22,14 @@ from collections.string import StringSlice """ from collections import List, Optional -from collections.string._utf8_validation import _is_valid_utf8 from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry +from collections.string._utf8_validation import _is_valid_utf8 +from collections.string._unicode import ( + is_lowercase, + is_uppercase, + to_lowercase, + to_uppercase, +) from hashlib._hasher import _HashableWithHasher, _Hasher from os import PathLike, abort from sys import bitwidthof, simdwidthof @@ -1562,6 +1568,129 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( return res + fn is_ascii_digit(self) -> Bool: + """A string is a digit string if all characters in the string are digits + and there is at least one character in the string. + + Note that this currently only works with ASCII strings. + + Returns: + True if all characters are digits and it's not empty else False. + """ + if not self: + return False + for char in self.chars(): + if not char.is_ascii_digit(): + return False + return True + + fn isupper(self) -> Bool: + """Returns True if all cased characters in the string are uppercase and + there is at least one cased character. + + Returns: + True if all cased characters in the string are uppercase and there + is at least one cased character, False otherwise. + """ + return len(self) > 0 and is_uppercase(self) + + fn islower(self) -> Bool: + """Returns True if all cased characters in the string are lowercase and + there is at least one cased character. + + Returns: + True if all cased characters in the string are lowercase and there + is at least one cased character, False otherwise. + """ + return len(self) > 0 and is_lowercase(self) + + fn lower(self) -> String: + """Returns a copy of the string with all cased characters + converted to lowercase. + + Returns: + A new string where cased letters have been converted to lowercase. + """ + + # TODO: the _unicode module does not support locale sensitive conversions yet. + return to_lowercase(self) + + fn upper(self) -> String: + """Returns a copy of the string with all cased characters + converted to uppercase. + + Returns: + A new string where cased letters have been converted to uppercase. + """ + + # TODO: the _unicode module does not support locale sensitive conversions yet. + return to_uppercase(self) + + fn is_ascii_printable(self) -> Bool: + """Returns True if all characters in the string are ASCII printable. + + Note that this currently only works with ASCII strings. + + Returns: + True if all characters are printable else False. + """ + for char in self.chars(): + if not char.is_ascii_printable(): + return False + return True + + fn rjust(self, width: Int, fillchar: StringLiteral = " ") -> String: + """Returns the string right justified in a string of specified width. + + Args: + width: The width of the field containing the string. + fillchar: Specifies the padding character. + + Returns: + Returns right justified string, or self if width is not bigger than self length. + """ + return self._justify(width - len(self), width, fillchar) + + fn ljust(self, width: Int, fillchar: StringLiteral = " ") -> String: + """Returns the string left justified in a string of specified width. + + Args: + width: The width of the field containing the string. + fillchar: Specifies the padding character. + + Returns: + Returns left justified string, or self if width is not bigger than self length. + """ + return self._justify(0, width, fillchar) + + fn center(self, width: Int, fillchar: StringLiteral = " ") -> String: + """Returns the string center justified in a string of specified width. + + Args: + width: The width of the field containing the string. + fillchar: Specifies the padding character. + + Returns: + Returns center justified string, or self if width is not bigger than self length. + """ + return self._justify(width - len(self) >> 1, width, fillchar) + + fn _justify( + self, start: Int, width: Int, fillchar: StringLiteral + ) -> String: + if len(self) >= width: + return String(self) + debug_assert( + len(fillchar) == 1, "fill char needs to be a one byte literal" + ) + var fillbyte = fillchar.as_bytes()[0] + var buffer = List[Byte](capacity=width + 1) + buffer.resize(width, fillbyte) + buffer.append(0) + memcpy(buffer.unsafe_ptr().offset(start), self.unsafe_ptr(), len(self)) + var result = String(buffer=buffer) + return result^ + # ===-----------------------------------------------------------------------===# # Utils diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index c34f617060..f9098e435d 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -828,46 +828,6 @@ def test_splitlines(): ) -def test_isupper(): - assert_true(String("ASDG").isupper()) - assert_false(String("AsDG").isupper()) - assert_true(String("ABC123").isupper()) - assert_false(String("1!").isupper()) - assert_true(String("É").isupper()) - assert_false(String("é").isupper()) - - -def test_islower(): - assert_true(String("asdfg").islower()) - assert_false(String("asdFDg").islower()) - assert_true(String("abc123").islower()) - assert_false(String("1!").islower()) - assert_true(String("é").islower()) - assert_false(String("É").islower()) - - -def test_lower(): - assert_equal(String("HELLO").lower(), "hello") - assert_equal(String("hello").lower(), "hello") - assert_equal(String("FoOBaR").lower(), "foobar") - - assert_equal(String("MOJO🔥").lower(), "mojo🔥") - - assert_equal(String("É").lower(), "é") - assert_equal(String("é").lower(), "é") - - -def test_upper(): - assert_equal(String("hello").upper(), "HELLO") - assert_equal(String("HELLO").upper(), "HELLO") - assert_equal(String("FoOBaR").upper(), "FOOBAR") - - assert_equal(String("mojo🔥").upper(), "MOJO🔥") - - assert_equal(String("É").upper(), "É") - assert_equal(String("é").upper(), "É") - - def test_isspace(): assert_false(String("").isspace()) @@ -1433,37 +1393,6 @@ def test_format_conversion_flags(): _ = String("{!r:d}").format(1) -def test_isdigit(): - assert_false(String("").isdigit()) - assert_true(String("123").isdigit()) - assert_false(String("asdg").isdigit()) - assert_false(String("123asdg").isdigit()) - - -def test_isprintable(): - assert_true(String("aasdg").isprintable()) - assert_false(String("aa\nae").isprintable()) - assert_false(String("aa\tae").isprintable()) - - -def test_rjust(): - assert_equal(String("hello").rjust(4), "hello") - assert_equal(String("hello").rjust(8), " hello") - assert_equal(String("hello").rjust(8, "*"), "***hello") - - -def test_ljust(): - assert_equal(String("hello").ljust(4), "hello") - assert_equal(String("hello").ljust(8), "hello ") - assert_equal(String("hello").ljust(8, "*"), "hello***") - - -def test_center(): - assert_equal(String("hello").center(4), "hello") - assert_equal(String("hello").center(8), " hello ") - assert_equal(String("hello").center(8, "*"), "*hello**") - - def test_float_conversion(): # This is basically just a wrapper around atof which is # more throughouly tested above @@ -1527,10 +1456,6 @@ def main(): test_rfind() test_split() test_splitlines() - test_isupper() - test_islower() - test_lower() - test_upper() test_isspace() test_ascii_aliases() test_rstrip() @@ -1548,11 +1473,6 @@ def main(): test_string_char_slices_iter() test_format_args() test_format_conversion_flags() - test_isdigit() - test_isprintable() - test_rjust() - test_ljust() - test_center() test_float_conversion() test_slice_contains() test_variadic_ctors() diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index bb7bc9d3ea..fc6ea07273 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -920,6 +920,77 @@ def test_endswith(): assert_true(ab.endswith("ab")) +def test_isupper(): + assert_true(StringSlice("ASDG").isupper()) + assert_false(StringSlice("AsDG").isupper()) + assert_true(StringSlice("ABC123").isupper()) + assert_false(StringSlice("1!").isupper()) + assert_true(StringSlice("É").isupper()) + assert_false(StringSlice("é").isupper()) + + +def test_islower(): + assert_true(StringSlice("asdfg").islower()) + assert_false(StringSlice("asdFDg").islower()) + assert_true(StringSlice("abc123").islower()) + assert_false(StringSlice("1!").islower()) + assert_true(StringSlice("é").islower()) + assert_false(StringSlice("É").islower()) + + +def test_lower(): + assert_equal(StringSlice("HELLO").lower(), "hello") + assert_equal(StringSlice("hello").lower(), "hello") + assert_equal(StringSlice("FoOBaR").lower(), "foobar") + + assert_equal(StringSlice("MOJO🔥").lower(), "mojo🔥") + + assert_equal(StringSlice("É").lower(), "é") + assert_equal(StringSlice("é").lower(), "é") + + +def test_upper(): + assert_equal(StringSlice("hello").upper(), "HELLO") + assert_equal(StringSlice("HELLO").upper(), "HELLO") + assert_equal(StringSlice("FoOBaR").upper(), "FOOBAR") + + assert_equal(StringSlice("mojo🔥").upper(), "MOJO🔥") + + assert_equal(StringSlice("É").upper(), "É") + assert_equal(StringSlice("é").upper(), "É") + + +def test_is_ascii_digit(): + assert_false(StringSlice("").is_ascii_digit()) + assert_true(StringSlice("123").is_ascii_digit()) + assert_false(StringSlice("asdg").is_ascii_digit()) + assert_false(StringSlice("123asdg").is_ascii_digit()) + + +def test_is_ascii_printable(): + assert_true(StringSlice("aasdg").is_ascii_printable()) + assert_false(StringSlice("aa\nae").is_ascii_printable()) + assert_false(StringSlice("aa\tae").is_ascii_printable()) + + +def test_rjust(): + assert_equal(StringSlice("hello").rjust(4), "hello") + assert_equal(StringSlice("hello").rjust(8), " hello") + assert_equal(StringSlice("hello").rjust(8, "*"), "***hello") + + +def test_ljust(): + assert_equal(StringSlice("hello").ljust(4), "hello") + assert_equal(StringSlice("hello").ljust(8), "hello ") + assert_equal(StringSlice("hello").ljust(8, "*"), "hello***") + + +def test_center(): + assert_equal(StringSlice("hello").center(4), "hello") + assert_equal(StringSlice("hello").center(8), " hello ") + assert_equal(StringSlice("hello").center(8, "*"), "*hello**") + + def test_count(): var str = StringSlice("Hello world") @@ -1050,7 +1121,6 @@ def main(): test_combination_10_good_utf8_sequences() test_combination_10_good_10_bad_utf8_sequences() test_count_utf8_continuation_bytes() - test_count() test_split() test_splitlines() test_rstrip() @@ -1058,5 +1128,15 @@ def main(): test_strip() test_startswith() test_endswith() + test_isupper() + test_islower() + test_lower() + test_upper() + test_is_ascii_digit() + test_is_ascii_printable() + test_rjust() + test_ljust() + test_center() + test_count() test_chars_iter() test_string_slice_from_pointer() From 514bc6a81cb6cf40ba7eb07b735352981102443a Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Tue, 11 Feb 2025 14:55:18 -0800 Subject: [PATCH 377/509] [All] Update the copy right notice year MODULAR_ORIG_COMMIT_REV_ID: 8dfab0a86511edbd8686c852f0ccaddc13b69f15 --- stdlib/src/collections/interval.mojo | 2 +- stdlib/test/collections/test_interval.mojo | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/collections/interval.mojo b/stdlib/src/collections/interval.mojo index 060dae4ce2..6938bbe911 100644 --- a/stdlib/src/collections/interval.mojo +++ b/stdlib/src/collections/interval.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt diff --git a/stdlib/test/collections/test_interval.mojo b/stdlib/test/collections/test_interval.mojo index e5c710062d..94acb768b1 100644 --- a/stdlib/test/collections/test_interval.mojo +++ b/stdlib/test/collections/test_interval.mojo @@ -1,5 +1,5 @@ # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt From c6b7ae1fef32be973625c1b502830b695ad4e19f Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 12 Feb 2025 08:01:28 +0000 Subject: [PATCH 378/509] Update lockfiles to point to latest nightly version: 25.1.0.dev2025021205 --- examples/life/magic.lock | 145 +++++++++++++++++----------------- examples/magic.lock | 145 +++++++++++++++++----------------- examples/operators/magic.lock | 145 +++++++++++++++++----------------- examples/testing/magic.lock | 145 +++++++++++++++++----------------- magic.lock | 145 +++++++++++++++++----------------- 5 files changed, 370 insertions(+), 355 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 02edf09a75..67910e8562 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -91,11 +91,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - 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conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025021205-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda @@ -229,11 +229,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h86ecc28_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-c-1.10.0-h5ad3122_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025021118-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-core-25.1.0.dev2025021118-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.1.0.dev2025021118-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025021118-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025021118-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.1.0.dev2025021205-release.conda + - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-core-25.1.0.dev2025021205-release.conda + - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.1.0.dev2025021205-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.1.0.dev2025021205-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.1.0.dev2025021205-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/mpg123-1.32.9-h65af167_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-ha32ae93_3.conda @@ -348,11 +348,11 @@ environments: - 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sha256: 58ee7f4e01a0a566f84f0e6c45ff7c6fd4c2768bdfe924c9dc4c779148074f31 - md5: 26f6c833bd6d7979dc5aad93536d3e11 + sha256: 19512946c05eba6b19b63370c5957c046a4829bc9a68c9b77857e638167efa17 + md5: fa9e0b88bbfe857210f734de295d0383 depends: - - max-core ==25.1.0.dev2025021118 release + - max-core ==25.1.0.dev2025021205 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22989 - timestamp: 1739298255901 + size: 22980 + timestamp: 1739337393161 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 37924a1fcac6964f03ccb68f23bbd21180d5bd29 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Tue, 11 Feb 2025 22:20:16 -0800 Subject: [PATCH 379/509] [mojo-stdlib] This updates Int and IntLiteral to use `@always_inline("builtin")` a bit more. MODULAR_ORIG_COMMIT_REV_ID: 501c096e2b0d40430cc2fd8bc67aa7ba81e86d2d --- stdlib/src/builtin/int.mojo | 7 ++----- stdlib/src/builtin/int_literal.mojo | 4 ++-- 2 files changed, 4 insertions(+), 7 deletions(-) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 9ab1315e87..3e9061b95d 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -578,10 +578,7 @@ struct Int( Returns: The -self value. """ - return __mlir_op.`index.mul`( - self.value, - __mlir_op.`index.constant`[value = __mlir_attr.`-1:index`](), - ) + return self * -1 @always_inline("nodebug") fn __invert__(self) -> Int: @@ -604,7 +601,7 @@ struct Int( """ return __mlir_op.`index.add`(self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __sub__(self, rhs: Int) -> Int: """Return `self - rhs`. diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 8c52880895..3f25c6fbdb 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -52,13 +52,13 @@ struct IntLiteral( # Life cycle methods # ===-------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") fn __init__(out self): """Default constructor.""" self.value = __mlir_attr.`#kgen.int_literal<0> : !kgen.int_literal` @doc_private - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: __mlir_type.`!kgen.int_literal`): """Construct IntLiteral from the given mlir !kgen.int_literal value. From 3c0fe4add6bfd4ee0c5d04524b8c61246cb8c5ee Mon Sep 17 00:00:00 2001 From: weiwei chen Date: Wed, 12 Feb 2025 16:44:57 -0500 Subject: [PATCH 380/509] [stdlib] Public title Add compiler warning on unrolling large loops in ChangeLog. MODULAR_ORIG_COMMIT_REV_ID: faa6bc02c0c1ca5c381bcfdce7517d6b011a743e --- docs/changelog.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 7210dc3bb2..7a0f644a1e 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -42,6 +42,13 @@ what we publish. ### Tooling changes +#### Mojo Compiler + +Mojo compiler now warns about parameter for with large loop unrolling factor +(>1024 by default) which can lead to long compilation time and large generated +code size. Set `--loop-unrolling-warn-threshold` to change default value to +a different threshold or to `0` to disable the warning. + ### ❌ Removed ### 🛠️ Fixed From 1644fb52cfcdaaf93258ea43675d002c17d2c68b Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 12 Feb 2025 15:29:20 -0800 Subject: [PATCH 381/509] [Stdlib] Use iota from the math package in memcmp, NFC MODULAR_ORIG_COMMIT_REV_ID: 3baae8ea5f50639efb14c9f250f70cb00ebd58b5 --- stdlib/src/memory/memory.mojo | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/stdlib/src/memory/memory.mojo b/stdlib/src/memory/memory.mojo index 39358ac24d..eada6c0467 100644 --- a/stdlib/src/memory/memory.mojo +++ b/stdlib/src/memory/memory.mojo @@ -31,6 +31,7 @@ from sys import ( simdwidthof, sizeof, ) +from math import iota from memory.pointer import AddressSpace, _GPUAddressSpace @@ -66,12 +67,6 @@ fn _memcmp_impl_unconstrained[ return 1 if s1i > s2i else -1 return 0 - var iota = llvm_intrinsic[ - "llvm.stepvector", - SIMD[DType.uint8, simd_width], - has_side_effect=False, - ]() - var last = count - simd_width for i in range(0, last, simd_width): @@ -81,7 +76,8 @@ fn _memcmp_impl_unconstrained[ if any(diff): var index = Int( diff.select( - iota, SIMD[DType.uint8, simd_width](255) + iota[DType.uint8, simd_width](), + SIMD[DType.uint8, simd_width](255), ).reduce_min() ) return -1 if s1i[index] < s2i[index] else 1 @@ -91,7 +87,10 @@ fn _memcmp_impl_unconstrained[ var diff = s1i != s2i if any(diff): var index = Int( - diff.select(iota, SIMD[DType.uint8, simd_width](255)).reduce_min() + diff.select( + iota[DType.uint8, simd_width](), + SIMD[DType.uint8, simd_width](255), + ).reduce_min() ) return -1 if s1i[index] < s2i[index] else 1 return 0 From 14e1a64a24589a45d634641615a2afefd26f8f2c Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 12 Feb 2025 19:46:27 -0500 Subject: [PATCH 382/509] [Kernels] Combine `compile_function` and `enqueue_function` This moves to taking a function directly e.g. `ctx.enqueue_function[func](...)` instead of compiling it first. The old `enqueue_function` is still there but with a deprecation warning to give Kernels team and external users a message showing how to update. Old: ```mojo var compiled_func = ctx.compile_function[func]() ctx.enqueue_function(compiled_func, ...) ``` New: ```mojo ctx.enqueue_function[func](...) ``` The old way of dumping PTX with `compile_function` is still there for users that want to dump PTX without enqueuing a function, but you can now also dump the PTX through the `enqueue_function` method. MODULAR_ORIG_COMMIT_REV_ID: 7779f389f214cb929d6d62f5328752cc33a5137c --- docs/changelog.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 7a0f644a1e..943f737e9b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -40,6 +40,30 @@ what we publish. - `ljust()` - `center()` +### GPU changes + +- `ctx.enqueue_function(compiled_func, ...)` is deprecated: + +```mojo + from gpu import thread_idx + from gpu.host import DeviceContext + + fn func(): + print("Hello from GPU thread:", thread_idx.x) + + with DeviceContext() as ctx: + var compiled_func = ctx.compile_function[func]() + ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=4) +``` + +You should now pass the function directly to +`DeviceContext.enqueue_function[func](...)`: + +```mojo + with DeviceContext() as ctx: + ctx.enqueue_function[func](grid_dim=1, block_dim=4) +``` + ### Tooling changes #### Mojo Compiler From dbe2746cfed68f01843291ade52944f6ba5ea74e Mon Sep 17 00:00:00 2001 From: Ken Jones <165197230+KenJones-Modular@users.noreply.github.com> Date: Wed, 12 Feb 2025 17:57:31 -0800 Subject: [PATCH 383/509] [docs] Edited Mojo changelog for 25.1 release MODULAR_ORIG_COMMIT_REV_ID: 96a65eb97077f810ba045dc6cf25d20dd707f4ac --- docs/changelog-released.md | 604 +++++++++++++++++++++++-------------- 1 file changed, 378 insertions(+), 226 deletions(-) diff --git a/docs/changelog-released.md b/docs/changelog-released.md index ef40be1565..13cca39ddf 100644 --- a/docs/changelog-released.md +++ b/docs/changelog-released.md @@ -15,21 +15,67 @@ in [Update a package](/magic#update-a-package) to update the `max` package. ### ✨ Highlights -### Language changes +- Mojo now supports low-level programming constructs for working with GPUs! The + new [`gpu` package](/mojo/stdlib/gpu/) provides APIs allowing you to manage + interaction between the CPU host and GPU device, manage memory between + devices, synchronize threads, and more. The new [`layout` + package](/mojo/stdlib/layout/) includes APIs for working with _layouts_, which + describe the organization of a tensor. See the [GPU + programming](#25-1-gpu-programming) section of the changelog for more + information. + +- The legacy `borrowed`/`inout` keywords and `-> T as foo` syntax are deprecated + and now generate a compiler warning. Please move to `read`/`mut`/`out` + argument syntax instead. See + [Argument conventions](/mojo/manual/values/ownership#argument-conventions) + in the Mojo Manual for more information. + +- The `bool()`, `float()`, `int()`, and `str()` functions are deprecated and + generate compiler warnings. Please use the `Bool()`, `Float64()`, `Int()`, and + `String()` constructors instead. See [Standard library + changes](#25-1-standard-library-changes) for more details. + +- The standard library has many changes related to strings. The new + [`Char`](/mojo/stdlib/builtin/char/Char/) struct represents a single Unicode + character, and includes several methods for categorizing character types. When + iterating over the characters of a `String` with a `for` loop, you now should + use the + [`String.chars()`](/mojo/stdlib/collections/string/string/String#chars) method + to provide an iterator of `Char` values or the + [`String.char_slices()`](/mojo/stdlib/collections/string/string/String#char_slices) + method to provide an iterator of [`StringSlice`](/mojo/stdlib/collections/string/string_slice/StringSlice/) + instances for each character. + `StringRef` has been removed in favor of + [`StringSlice`](/mojo/stdlib/collections/string/string_slice/StringSlice/). + And various functionality has moved from `String` and `StringLiteral` to the + more general `StringSlice` type. See [Standard library + changes](#25-1-standard-library-changes) for more details. + +- You can now use [`SIMD`](/mojo/stdlib/builtin/simd/SIMD) constructors to cast + existing `SIMD` values (including `Scalar` values) to a different type, though + you can still use the [`SIMD.cast()`](/mojo/stdlib/builtin/simd/SIMD#cast) + method to infer the size of the new vector. See [Standard library + changes](#25-1-standard-library-changes) for more details. + +### Language changes {#25-1-language-changes} + +- The legacy `borrowed`/`inout` keywords and `-> T as foo` syntax now generate + a warning. Please move to `read`/`mut`/`out` argument syntax instead. See + [Argument conventions](/mojo/manual/values/ownership#argument-conventions) + in the Mojo Manual for more information. - Initializers are now treated as static methods that return an instance of `Self`. This means the `out` argument of an initializer is now treated the - same as a any other function result or `out` argument. This is generally + same as any other function result or `out` argument. This is generally invisible, except that patterns like `instance.__init__()` and `x.__copyinit__(y)` no longer work. Simply replace them with `instance = T()` and `x = y` respectively. -- The legacy `borrowed`/`inout` keywords and `-> T as foo` syntax now generate - a warning. Please move to `read`/`mut`/`out` argument syntax instead. - -- The `@value` decorator now additionally derives an implementation of the - `ExplicitlyCopyable` trait. This will ease the transition to explicit - copyablility requirements by default in the Mojo collection types. +- The [`@value`](/mojo/manual/decorators/value) decorator now additionally + derives an implementation of the + [`ExplicitlyCopyable`](/mojo/stdlib/builtin/value/ExplicitlyCopyable/) trait. + This will ease the transition to explicit copyability requirements by default + in the Mojo collection types. - Indexing into a homogenous tuple now produces the consistent element type without needing a rebind: @@ -95,32 +141,60 @@ in [Update a package](/magic#update-a-package) to update the `max` package. print(x[idx2=1]) # 84 ``` -### Standard library changes +- The `__disable_del x` operation has been tightened up to treat all fields of + `x` as consumed by the point of the deletion, so it should be used after all + the subfields are transferred or otherwise consumed (for example, at the end + of the function), not before uses of the fields. + +### GPU programming {#25-1-gpu-programming} -- `StringRef` has been removed in favor of `StringSlice`. - The two types are ABI compatible, and for the exact same - behavior one can use `StaticString`, which is an alias - to `StringSlice[StaticConstantOrigin]`. +- The new [`gpu` package](/mojo/stdlib/gpu/) provides low-level programming + constructs for working with GPUs. The Mojo `gpu` APIs allow you to manually + manage interaction between the CPU host and GPU device, manage memory between + devices, synchronize threads, and more. Currently the best way to use these + APIs is from inside a [MAX custom operation](/max/custom-ops/). -- Add a new `validate` parameter to the `b64decode()` function. + The following code example shows a GPU kernel written in Mojo: + + ```mojo + from max.tensor import ManagedTensorSlice + from gpu import thread_idx, block_dim, block_idx -- New `SIMD.from_bytes()` and `SIMD.as_bytes()` functions to convert a list of bytes - to a list of scalars and vice versa, accepting the endianess as an argument. Similar - to Python `int.from_bytes()` and `int.to_bytes()` functions. + fn gpu_add_kernel(out: ManagedTensorSlice, x: ManagedTensorSlice[out.type, out.rank]): + tid_x = thread_idx.x + block_dim.x * block_idx.x + tid_y = thread_idx.y + block_dim.y * block_dim.y + if tid_x < x.dim_size(0) and tid_y < x.dim_size(1): + out[tid_x, tid_y] = x[tid_x, tid_y] + 1 + ``` + + The example above includes only the actual kernel code that’s run on the GPU, + not the code to define a custom operation or launch the kernel. For more + complete examples, see + [`vector_addition.mojo`](https://github.com/modular/max/blob/main/examples/custom_ops/kernels/vector_addition.mojo) + and + [`top_k.mojo`](https://github.com/modular/max/blob/main/examples/custom_ops/kernels/top_k.mojo). -- Added more aliases in `sys.ffi` to round out the usual needs for FFI bindings. +- The [`layout` package](/mojo/stdlib/layout/) includes APIs for working with + _layouts_, which describe the organization of a tensor (for example, row-major + or column-major layout), and the + [`LayoutTensor`](/mojo/stdlib/layout/layout_tensor/LayoutTensor) type, which + represents a tensor with a specified layout. The `layout` package can be used + to build efficient tensor operations that run on a GPU. -- The free floating functions for constructing different types have been + We’ll continue adding code examples and documentation for the `gpu` and + `layout` packages in future releases. + +### Standard library changes {#25-1-standard-library-changes} + +- The builtin functions for converting values to different types have been deprecated for actual constructors: - ```plaintext - before after - ------------------ - int() Int() - str() String() - bool() Bool() - float() Float64() - ``` + | **Before** | **After** | + | ---------- | ----------- | + | `bool()` | `Bool()` | + | `float()` | `Float64()` | + | `int()` | `Int()` | + | `str()` | `String()` | These functions were a workaround before Mojo had a way to distinguish between implicit and explicit constructors. For this release you'll get a deprecation @@ -128,203 +202,267 @@ in [Update a package](/magic#update-a-package) to update the `max` package. quickly update your code by doing a `Match Case` and `Match Whole Word` search and replace for `int(` to `Int(` etc. -- `UnsafePointer`'s `bitcast` method has now been split into `bitcast` - for changing the type, `origin_cast` for changing mutability, - `static_alignment_cast` for changing alignment, - and `address_space_cast` for changing the address space. +- [`String`](/mojo/stdlib/collections/string/String) and friends: + + - Added [`Char`](/mojo/stdlib/builtin/char/Char/) for representing and + storing single Unicode characters. + + - `Char` implements + [`CollectionElement`](/mojo/stdlib/builtin/value/CollectionElement/), + [`EqualityComparable`](/mojo/stdlib/builtin/equality_comparable/EqualityComparable/), + [`Intable`](/mojo/stdlib/builtin/int/Intable/), and + [`Stringable`](/mojo/stdlib/builtin/str/Stringable/). -- `UnsafePointer` is now parameterized on mutability. Previously, - `UnsafePointer` could only represent mutable pointers. + - `Char` provides methods for categorizing character types, including: + [`Char.is_ascii()`](/mojo/stdlib/builtin/char/Char/#is_ascii), + [`Char.is_ascii_digit()`](/mojo/stdlib/builtin/char/Char/#is_ascii_digit), + [`Char.is_ascii_upper()`](/mojo/stdlib/builtin/char/Char/#is_ascii_upper), + [`Char.is_ascii_lower()`](/mojo/stdlib/builtin/char/Char/#is_ascii_lower), + [`Char.is_ascii_printable()`](/mojo/stdlib/builtin/char/Char/#is_ascii_printable), + [`Char.is_posix_space()`](/mojo/stdlib/builtin/char/Char/#is_posix_space), + [`Char.is_python_space()`](/mojo/stdlib/builtin/char/Char/#is_python_space). + + - Added a `String()` constructor from `Char`. + + - `Char` can be converted to `UInt32` via + [`Char.to_u32()`](/mojo/stdlib/builtin/char/Char/#to_u32). + + - [`chr()`](/mojo/stdlib/collections/string/string/chr/) will now abort if + given a codepoint value that is not a valid `Char`. + + - `StringRef` has been removed in favor of + [`StringSlice`](/mojo/stdlib/collections/string/string_slice/StringSlice/). + The two types are ABI compatible, and for the exact same behavior one can + use `StaticString`, which is an alias to + `StringSlice[StaticConstantOrigin]`. + + - Various functionality has moved from `String` and `StringLiteral` to the + more general `StringSlice` type. + + - Added + [`StringSlice.from_utf8()`](/mojo/stdlib/collections/string/string_slice/StringSlice/#from_utf8) + factory method, for validated construction of a `StringSlice` from a buffer + containing UTF-8 encoded data. This method will raise if the buffer contents + are not valid UTF-8. - The new `mut` parameter can be used to restrict an `UnsafePointer` to a - specific mutability: `UnsafePointer[T, mut=False]` represents a pointer to - an immutable `T` value. This is analogous to a `const *` pointer in C++. + - Added + [`StringSlice.chars()`](/mojo/stdlib/collections/string/string_slice/StringSlice/#chars) + which returns an iterator over `Char`s. This is a compliant UTF-8 decoder + that returns each Unicode codepoint encoded in the string. - - `UnsafePointer.address_of()` will now infer the origin and mutability - of the resulting pointer from the argument. For example: + - Added + [`StringSlice.__getitem__(Slice)`](/mojo/stdlib/collections/string/string_slice/StringSlice/#__getitem__) + which returns a substring. Only step sizes of 1 are supported. + + - Several standard library functions have been changed to take `StringSlice` + instead of `String`. This generalizes them to be used for any appropriately + encoded string in memory, without requiring that the string be heap + allocated. This includes: + [`ascii()`](/mojo/stdlib/collections/string/string/ascii), + [`atol()`](/mojo/stdlib/collections/string/string/atol), + [`atof()`](/mojo/stdlib/collections/string/string/atof), + [`b16decode()`](/mojo/stdlib/base64/base64/b16decode), + [`b16encode()`](/mojo/stdlib/base64/base64/b16encode), + [`b64decode()`](/mojo/stdlib/base64/base64/b64decode), + [`b64encode()`](/mojo/stdlib/base64/base64/b64encode), and + [`ord()`](/mojo/stdlib/collections/string/string/ord). + + - Added new + [`String.chars()`](/mojo/stdlib/collections/string/string/String/#chars) and + [`String.char_slices()`](/mojo/stdlib/collections/string/string/String/#char_slices) + iterator methods, and deprecated the existing `String.__iter__()` method. + + Different use-cases may prefer iterating over the `Char`s encoded in a + string, or iterating over subslices containing single characters. Neither + iteration semantics is an obvious default, so the existing `__iter__()` + method has been deprecated in favor of writing explicit iteration methods + for the time being. + + Code of the form: ```mojo - var local = 10 - # Constructs a mutable pointer, because `local` is a mutable memory location - var ptr = UnsafePointer.address_of(local) + var s: String = ... + for c in s: + # ... ``` - To force the construction of an immutable pointer to an otherwise mutable - memory location, use a cast: + can be migrated to using the `.char_slices()` method: ```mojo - var local = 10 - # Cast the mutable pointer to be immutable. - var ptr = UnsafePointer.address_of(local).origin_cast[mut=False]() + var s: String = ... + for c in s.char_slices(): + # ... ``` - - The `unsafe_ptr()` method on several standard library collection types have - been updated to use parametric mutability: they will return an `UnsafePointer` - whose mutability is inherited from the mutability of the `ref self` of the - receiver at the call site. For example, `ptr1` will be immutable, while - `ptr2` will be mutable: + - Added + [`StringSlice.char_length()`](/mojo/stdlib/collections/string/string_slice/StringSlice/#char_length) + method, to pair with the existing + [`StringSlice.byte_length()`](/mojo/stdlib/collections/string/string_slice/StringSlice/#byte_length) + method. - ```mojo - fn take_lists(read list1: List[Int], mut list2: List[Int]): - # Immutable pointer, since receiver is immutable `read` reference - var ptr1 = list1.unsafe_ptr() + - The + [`String.__len__()`](/mojo/stdlib/collections/string/string/String/#__len__) + and + [`StringSlice.__len__()`](/mojo/stdlib/collections/string/string_slice/StringSlice/#__len__) + methods now return the length of the string in bytes. - # Mutable pointer, since receiver is mutable `mut` reference - var ptr2 = list2.unsafe_ptr() - ``` + Previously, these methods were documented to note that they would eventually + return a length in Unicode codepoints. They have been changed to guarantee + a length in bytes, since the length in bytes is how they are most often used + today (for example, as bounds to low-level memory manipulation logic). + Additionally, length in codepoints is a more specialized notion of string + length that is rarely the correct metric. -- Added `Optional.copied()` for constructing an owned `Optional[T]` from an - `Optional[Pointer[T]]` by copying the pointee value. + Users that know they need the length in codepoints can use the + `str.char_length()` method, or `len(str.chars())`. -- Added `Dict.get_ptr()` which returns an `Optional[Pointer[V]]`. If the given - key is present in the dictionary, the optional will hold a pointer to the - value. Otherwise, an empty optional is returned. + - `StringSlice` now implements + [`Representable`](/mojo/stdlib/builtin/repr/Representable/), and that + implementation is now used by `String.__repr__()` and + `StringLiteral.__repr__()`. -- Added new `List.extend()` overloads taking `SIMD` and `Span`. These enable - growing a `List[Scalar[..]]` by copying the elements of a `SIMD` vector or - `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware - functionality. + - `StringSlice` now implements + [`EqualityComparable`](/mojo/stdlib/builtin/equality_comparable/EqualityComparable/). -- Added `Char`, for representing and storing single Unicode characters. - - `Char` implements `CollectionElement`, `EqualityComparable`, `Intable`, and - `Stringable`. - - Added `String` constructor from `Char` - - `Char` can be converted to `UInt32` via `Char.to_u32()`. - - `Char` provides methods for categorizing character types, including: - `Char.is_ascii()`, `Char.is_posix_space()`, `Char.is_python_space()`, - `Char.is_ascii_digit()`, `Char.is_ascii_upper()`, `Char.is_ascii_lower()`, - `Char.is_ascii_printable()`. + Up until now, `StringSlice` has implemented a more general `__eq__()` and + `__ne__()` comparison with `StringSlice` types that had arbitrary other + origins. However, to satisfy `EqualityComparable`, `StringSlice` now also + has narrower comparison methods that support comparing only with another + `StringSlice` with the exact same origin. -- `chr(Int)` will now abort if given a codepoint value that is not a valid - `Char`. + - The `String.write()` static method has moved to a `String()` constructor, + and is now buffered. Instead of doing: -- Added `StringSlice.from_utf()` factor method, for validated construction of - a `StringSlice` from a buffer containing UTF-8 encoded data. This method will - raise if the buffer contents are not valid UTF-8. + ```mojo + var msg = "my message " + String(x) + " " + String(y) + " " + String(z) + ``` -- Added `StringSlice.chars()` which returns an iterator over `Char`s. This is a - compliant UTF-8 decoder that returns each Unicode codepoint encoded in the - string. + Which reallocates the `String` you should do: -- Added `StringSlice.__getitem__(Slice)` which returns a substring. - Only step sizes of 1 are supported. + ```mojo + var msg = String("my message", x, y, z, sep=" ") + ``` -- Several standard library functions have been changed to take `StringSlice` - instead of `String`. This generalizes them to be used for any appropriately - encoded string in memory, without requiring that the string be heap allocated. + Which is cleaner, and buffers to the stack so the `String` is allocated only + once. - - `atol()` - - `atof()` - - `ord()` - - `ascii()` - - `b64encode()` - - Additionally, the `b64encode()` overload that previously took `List` has - been changed to - take a `Span`. - - `b64decode()` - - `b16encode()` - - `b16decode()` + - You can now pass any [`Writer`](/mojo/stdlib/utils/write/Writer/) to + [`write_buffered()`](/mojo/stdlib/utils/write/write_buffered/): -- Added new `String.chars()` and `String.char_slices()` iterator methods, and - deprecated the existing `String.__iter__()` method. + ```mojo + from utils.write import write_buffered - Different use-cases may prefer iterating over the `Char`s encoded in a string, - or iterating over subslices containing single characters. Neither iteration - semantics is an obvious default, so the existing `__iter__()` method has been - deprecated in favor of writing explicit iteration methods for the time being. + var string = String("existing string") + write_buffered(string, 42, 42.4, True, sep=" ") + ``` - Code of the form: + This writes to a buffer on the stack before reallocating the `String`. - ```mojo - var s: String = ... - for c in s: - # ... - ``` +- Collections: - can be migrated to using the `.char_slices()` method: + - A new [`LinkedList`](/mojo/stdlib/collections/linked_list/LinkedList/) type + has been added to the standard library. - ```mojo - var s: String = ... - for c in s.char_slices(): - # ... - ``` + - Added [`Optional.copied()`](/mojo/stdlib/collections/optional/Optional#copied) + for constructing an owned `Optional[T]` from an `Optional[Pointer[T]]` by + copying the pointee value. -- The `String.__len__()` and `StringSlice.__len__()` methods now return the - length of the string in bytes. + - Added [`Dict.get_ptr()`](/mojo/stdlib/collections/dict/Dict#get_ptr) which + returns an `Optional[Pointer[V]]`. If the given key is present in the + dictionary, the optional will hold a pointer to the value. Otherwise, an + empty optional is returned. - Previously, these methods were documented to note that they would eventually - return a length in Unicode codepoints. They have been changed to guarantee - a length in bytes, since the length in bytes is how they are most often used - today (for example, as bounds to low-level memory manipulation logic). - Additionally, length in codepoints is a more specialized notion of string - length that is rarely the correct metric. + - Added new [`List.extend()`](/mojo/stdlib/collections/list/List#extend) + overloads taking [`SIMD`](/mojo/stdlib/builtin/simd/SIMD) and + [`Span`](/mojo/stdlib/memory/span/Span/). These enable growing a + `List[Scalar[..]]` by copying the elements of a `SIMD` vector or + `Span[Scalar[..]]`, simplifying the writing of some optimized SIMD-aware + functionality. - Users that know they need the length in codepoints can use the - `str.char_length()` method, or `len(str.chars())`. +- [`UnsafePointer`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer/) changes: -- Various functionality has moved from `String` and `StringRef` to the more - general `StringSlice` type. + - `UnsafePointer`'s `bitcast()` method has now been split into + [`bitcast()`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer#bitcast) for + changing the type, + [`origin_cast()`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer#origin_cast) + for changing mutability, + [`static_alignment_cast()`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer#static_alignment_cast) + for changing alignment, and + [`address_space_cast()`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer#address_space_cast) + for changing the address space. - - `StringSlice` now implements `Representable`, and that implementation is now - used by `String.__repr__()` and `StringRef.__repr__()`. + - `UnsafePointer` is now parameterized on mutability. Previously, + `UnsafePointer` could only represent mutable pointers. -- `StringSlice` now implements `EqualityComparable`. + The new `mut` parameter can be used to restrict an `UnsafePointer` to a + specific mutability: `UnsafePointer[T, mut=False]` represents a pointer to + an immutable `T` value. This is analogous to a `const *` pointer in C++. - Up until now, `StringSlice` has implemented a more general `__eq__` and - `__ne__` comparision with `StringSlice` types that had arbitrary other - origins. However, to satisfy `EqualityComparable`, `StringSlice` now also - has narrower comparison methods that support comparing only with - `StringSlice`'s with the exact same origin. + - [`UnsafePointer.address_of()`](/mojo/stdlib/memory/unsafe_pointer/UnsafePointer#address_of) + will now infer the origin and mutability of the resulting pointer from the + argument. For example: -- Added `StringSlice.char_length()` method, to pair with the existing - `StringSlice.byte_length()` method. + ```mojo + var local = 10 + # Constructs a mutable pointer, because `local` is a mutable memory location + var ptr = UnsafePointer.address_of(local) + ``` - In a future version of Mojo, `StringSlice.__len__()` may be changed to return - the length in bytes, matching the convention of string length methods in - languages like C++ and Rust. Callers that know they need the length in - Unicode codepoints should update to calling `StringSlice.char_length()` - instead. + To force the construction of an immutable pointer to an otherwise mutable + memory location, use a cast: -- Removed `@implicit` decorator from some standard library initializer methods - that perform allocation. This reduces places where Mojo code could implicitly - allocate where the user may not be aware. + ```mojo + var local = 10 + # Cast the mutable pointer to be immutable. + var ptr = UnsafePointer.address_of(local).origin_cast[mut=False]() + ``` - Remove `@implicit` from: + - The `unsafe_ptr()` method on several standard library collection types have + been updated to use parametric mutability: they will return an + `UnsafePointer` whose mutability is inherited from the mutability of the + `ref self` of the receiver at the call site. For example, `ptr1` will be + immutable, while `ptr2` will be mutable: - - `String.__init__(out self, StringRef)` - - `String.__init__(out self, StringSlice)` - - `List.__init__(out self, owned *values: T)` - - `List.__init__(out self, span: Span[T])` + ```mojo + fn take_lists(read list1: List[Int], mut list2: List[Int]): + # Immutable pointer, since receiver is immutable `read` reference + var ptr1 = list1.unsafe_ptr() -- The `ExplicitlyCopyable` trait has changed to require a - `fn copy(self) -> Self` method. Previously, an initializer with the signature - `fn __init__(out self, *, other: Self)` had been required by - `ExplicitlyCopyable`. + # Mutable pointer, since receiver is mutable `mut` reference + var ptr2 = list2.unsafe_ptr() + ``` - This improves the "greppability" and at-a-glance readability when a programmer - is looking for places in their code that may be performing copies +- New and updated traits: -- `bit_ceil` has been renamed to `next_power_of_two`, and `bit_floor` to - `prev_power_of_two`. This is to improve readability and clarity in their use. + - The [`ExplicitlyCopyable`](/mojo/stdlib/builtin/value/ExplicitlyCopyable/) + trait has changed to require a `fn copy(self) -> Self` method. Previously, + an initializer with the signature `fn __init__(out self, *, other: Self)` + had been required by `ExplicitlyCopyable`. -- The `Indexer` and `IntLike` traits which were previously both used for - indexing have been combined. This enables SIMD scalar integer types and UInt - to be used for indexing into all of the collection types, as well as - optimizing away normalization checks for UInt indexing. + This improves the "greppability" and at-a-glance readability when a + programmer is looking for places in their code that may be performing + copies. -- The `ImplicitlyIntable` trait has been added, allowing types to be implicitly - converted to an `Int` by implementing the `__as_int__` method: + - The `IntLike` trait has been removed and its functionality incorporated into + the [`Indexer`](/mojo/stdlib/builtin/int/Indexer/) trait. This enables + `SIMD` scalar integer types and `UInt` to be used for indexing into all of + the collection types, as well as optimizing away normalization checks for + `UInt` indexing. - ```mojo - @value - struct Foo(ImplicitlyIntable): - var i: Int + - The [`ImplicitlyIntable`](/mojo/stdlib/builtin/int/ImplicitlyIntable/) trait + has been added, allowing types to be implicitly converted to an `Int` by + implementing the `__as_int__()` method: - fn __as_int__(self) -> Int: - return self.i - ``` + ```mojo + @value + struct Foo(ImplicitlyIntable): + var i: Int -- You can now cast SIMD types using constructors: + fn __as_int__(self) -> Int: + return self.i + ``` + +- You can now cast `SIMD` types using constructors: ```mojo var val = Int8(42) @@ -337,82 +475,95 @@ in [Update a package](/magic#update-a-package) to update the `max` package. var vector = SIMD[DType.int64, 4](cast) # [42, 42, 42, 42] ``` - For values other than scalars the size of the SIMD vector needs to be equal: + For values other than scalars the size of the `SIMD` vector needs to be equal: ```mojo var float_vector = SIMD[DType.float64, 4](vector) ``` - `SIMD.cast` still exists to infer the size of new vector: + [`SIMD.cast()`](/mojo/stdlib/builtin/simd/SIMD#cast) still exists to infer the + size of new vector: ```mojo var inferred_size = float_vector.cast[DType.uint64]() # [42, 42, 42, 42] ``` -- You can now use `max()` and `min()` with variadic number of arguments. - -- A new `LinkedList` type has been added to the standard library. - -- The `String.write` static method has moved to a `String` constructor, and - is now buffered. Instead of doing: +- Added [`SIMD.from_bytes()`](/mojo/stdlib/builtin/simd/SIMD/#from_bytes) and + [`SIMD.as_bytes()`](/mojo/stdlib/builtin/simd/SIMD/#as_bytes) to convert a + list of bytes to a list of scalars and vice versa, accepting the endianess as + an argument. Similar to Python `int.from_bytes()` and `int.to_bytes()` + functions. - ```mojo - var msg = "my message " + String(x) + " " + String(y) + " " + String(z) - ``` +- You can now use [`max()`](/mojo/stdlib/builtin/math/max) and + [`min()`](/mojo/stdlib/builtin/math/min) with variadic number of arguments. - Which reallocates the `String` you should do: +- `bit_ceil()` has been renamed to + [`next_power_of_two()`](/mojo/stdlib/bit/bit/next_power_of_two), and + `bit_floor()` to + [`prev_power_of_two()`](/mojo/stdlib/bit/bit/prev_power_of_two). This is to + improve readability and clarity in their use. - ```mojo - var msg = String("my message", x, y, z, sep=" ") - ``` +- Added a new boolean `validate` parameter to + [`b64decode()`](/mojo/stdlib/base64/base64/b64decode). - Which is cleaner, and buffers to the stack so the `String` is allocated only - once. +- The [`b64encode()`](/mojo/stdlib/base64/base64/b64encode) overload that + previously took a `List` has been changed to take a + [`Span`](/mojo/stdlib/memory/span/Span/). -- You can now pass any `Writer` to `write_buffered`: +- Removed the `@implicit` decorator from some standard library initializer + methods that perform allocation. This reduces places where Mojo code could + implicitly allocate where the user may not be aware. - ```mojo - from utils.write import write_buffered + Removed `@implicit` from: - var string = String("existing string") - write_buffered(string, 42, 42.4, True, sep=" ") - ``` + - `String.__init__(out self, StringSlice)` + - `List.__init__(out self, owned *values: T)` + - `List.__init__(out self, span: Span[T])` - This writes to a buffer on the stack before reallocating the `String`. +- Added more aliases in [`sys.ffi`](/mojo/stdlib/sys/ffi/) to round out the + usual needs for FFI bindings. -- The `__disable_del x` operation has been tightened up to treat all fields of - 'x' as consumed by the point of the del, so it should be used after all the - subfields are transferred or otherwise consumed (e.g. at the end of the - function) not before uses of the fields. +### Tooling changes {#25-1-tooling-changes} -### Tooling changes - -- mblack (aka `mojo format`) no longer formats non-mojo files. This prevents - unexpected formatting of python files. +- `mblack` (aka [`mojo format`](/mojo/cli/format)) no longer formats non-Mojo + files. This prevents unexpected formatting of Python files. - Full struct signature information is now exposed in the documentation generator, and in the symbol outline and hover markdown via the Mojo Language Server. -- The `env_get_dtype` function has been added to the `sys` module. This allows - you to get the value of a `DType` from the param environment. +- The [`env_get_dtype()`](/mojo/stdlib/sys/param_env/env_get_dtype) function has + been added to the [`sys.param_env`](/mojo/stdlib/sys/param_env/) module. This + allows you to get the value of a `DType` from the param environment. ### ❌ Removed -- `StringRef` is being deprecated. Use `StringSlice` instead. - - Changed `sys.argv()` to return list of `StringSlice`. - - Added `Path` explicit constructor from `StringSlice`. - - removed `StringRef.startswith()` and `StringRef.endswith()` - - removed `StringRef.strip()` +- `StringRef` has been removed. Use + [`StringSlice`](/mojo/stdlib/collections/string/string_slice/StringSlice/) + instead. + + - Changed [`sys.argv()`](/mojo/stdlib/sys/arg/argv) to return list of + `StringSlice`. + + - Added explicit [`Path()`](/mojo/stdlib/pathlib/path/Path/#__init__) + constructor from `StringSlice`. + - The `Tuple.get[i, T]()` method has been removed. Please use `tup[i]` or `rebind[T](tup[i])` as needed instead. -- `StringableCollectionElement` is deprecated, use `WritableCollectionElement` - instead which still allows you to construct a `String`, but can avoid - intermediary allocations. + +- `StringableCollectionElement` is deprecated. Use + [`WritableCollectionElement`](/mojo/stdlib/builtin/value/WritableCollectionElement/) + instead, which still allows you to construct a `String`, but can avoid + intermediate allocations. + +- The `IntLike` trait has been removed and its functionality incorporated into + the [`Indexer`](/mojo/stdlib/builtin/int/Indexer/) trait. + - The `Type{field1: 42, field2: 17}` syntax for direct initializing register - passable types has been removed. This was legacy syntax - to upgrade - your code, add the @value decorator to your struct to get a memberwise - initializer and use `Type(field1=42, field2 = 17)` instead. + passable types has been removed. This was legacy syntax - to upgrade your + code, add the [`@value`](/mojo/manual/decorators/value) decorator to your + struct to get a memberwise initializer and use `Type(field1=42, field2 = 17)` + instead. ### 🛠️ Fixed @@ -422,7 +573,7 @@ in [Update a package](/magic#update-a-package) to update the `max` package. properly. - [Issue #3796](https://github.com/modular/mojo/issues/3796) - Compiler crash - handling for-else statement. + handling `for`-`else` statement. - [Issue #3540](https://github.com/modular/mojo/issues/3540) - Using named output slot breaks trait conformance @@ -430,16 +581,17 @@ in [Update a package](/magic#update-a-package) to update the `max` package. - [Issue #3617](https://github.com/modular/mojo/issues/3617) - Can't generate the constructors for a type wrapping `!lit.ref` -- The Mojo Language Server doesn't crash anymore on empty **init**.mojo files. +- The Mojo Language Server doesn't crash anymore on empty `__init__.mojo` files. [Issue #3826](https://github.com/modular/mojo/issues/3826). - [Issue #3935](https://github.com/modular/mojo/issues/3935) - Confusing OOM - error when using Tuple.get incorrectly. + error when using `Tuple.get()` incorrectly. - [Issue #3955](https://github.com/modular/mojo/issues/3955) - Unexpected - copy behaviour with `def` arguments in loops + copy behavior with `def` arguments in loops -- [Issue #3960](https://github.com/modular/mojo/issues/3960) - Infinite for loop +- [Issue #3960](https://github.com/modular/mojo/issues/3960) - Infinite `for` + loop ## v24.6 (2024-12-17) @@ -478,7 +630,7 @@ detailed information in the following sections: [`Deque`](/mojo/stdlib/collections/deque/Deque) (a double-ended queue) and [`OwnedPointer`](/mojo/stdlib/memory/owned_pointer/OwnedPointer) (safe, single-owner, non-nullable smart pointer). See - [Standard library changes](#standard-library-changes-24-6-standard-library-changes) + [Standard library changes](#24-6-standard-library-changes) for details. - The VS Code extension now supports setting data breakpoints and function From 4e67c852b6c82858a8d5baedc68db9eb5f05797b Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Wed, 12 Feb 2025 20:04:38 -0800 Subject: [PATCH 384/509] [mojo-stdlib] Move `Int.mul` to `always_inline("builtin")`. This continues incremental adoption. MODULAR_ORIG_COMMIT_REV_ID: 9c7a911e1c3702a5c90e72952fb246fcb1617e74 --- stdlib/src/builtin/int.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 3e9061b95d..e36484b31e 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -613,7 +613,7 @@ struct Int( """ return __mlir_op.`index.sub`(self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __mul__(self, rhs: Int) -> Int: """Return `self * rhs`. @@ -907,7 +907,7 @@ struct Int( # Reversed operations # ===-------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") fn __radd__(self, value: Int) -> Int: """Return `value + self`. @@ -931,7 +931,7 @@ struct Int( """ return value - self - @always_inline("nodebug") + @always_inline("builtin") fn __rmul__(self, value: Int) -> Int: """Return `value * self`. From 4c85c369c7834ea3519464b5e0cde8276155b4f7 Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 13 Feb 2025 07:05:09 +0000 Subject: [PATCH 385/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025021305 --- examples/life/magic.lock | 284 ++++++++++---------- examples/magic.lock | 284 ++++++++++---------- examples/operators/magic.lock | 480 +++++++++++++++++++--------------- examples/testing/magic.lock | 480 +++++++++++++++++++--------------- magic.lock | 284 ++++++++++---------- 5 files changed, 965 insertions(+), 847 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 67910e8562..6ae25926c6 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -91,11 +91,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - 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sha256: 19512946c05eba6b19b63370c5957c046a4829bc9a68c9b77857e638167efa17 - md5: fa9e0b88bbfe857210f734de295d0383 + sha256: 577a281a7b22e97ddb4a9bf8db40293c627fb5e4a07505de67e8eadea5e64d52 + md5: f3e2af71e3f2641375d1814609a358c7 depends: - - max-core ==25.1.0.dev2025021205 release + - max-core ==25.2.0.dev2025021305 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22980 - timestamp: 1739337393161 + size: 22993 + timestamp: 1739423790392 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 3c949d8ba91cee142239aee380e13cb682a44a58 Mon Sep 17 00:00:00 2001 From: Manuel Saelices Date: Thu, 13 Feb 2025 06:42:04 -0800 Subject: [PATCH 386/509] [External] [stdlib] Fix references to the deprecated `StringableCollectionElement` (#54909) [External] [stdlib] Fix references to the deprecated `StringableCollectionElement` Replaced with `WritableCollectionElement` Co-authored-by: Manuel Saelices Closes modularml/mojo#3979 MODULAR_ORIG_COMMIT_REV_ID: c22896575dc81cd2ede7f7b084ef09c2c20424c9 --- .github/CODEOWNERS | 14 ---- .github/ISSUE_TEMPLATE/doc_issue.yaml | 37 ---------- .github/ISSUE_TEMPLATE/mojo_bug_report.yaml | 48 ------------- .../ISSUE_TEMPLATE/mojo_feature_request.yaml | 50 -------------- .github/PULL_REQUEST_TEMPLATE.md | 10 --- .github/workflows/check_pr_target.yml | 28 -------- .github/workflows/check_pr_title.yml | 53 --------------- .../standard_library_tests_and_examples.yml | 67 ------------------- .github/workflows/test_pre_commit.yml | 52 -------------- docs/manual/parameters/index.mdx | 14 ++-- docs/manual/traits.mdx | 4 +- 11 files changed, 9 insertions(+), 368 deletions(-) delete mode 100644 .github/CODEOWNERS delete mode 100644 .github/ISSUE_TEMPLATE/doc_issue.yaml delete mode 100644 .github/ISSUE_TEMPLATE/mojo_bug_report.yaml delete mode 100644 .github/ISSUE_TEMPLATE/mojo_feature_request.yaml delete mode 100644 .github/PULL_REQUEST_TEMPLATE.md delete mode 100644 .github/workflows/check_pr_target.yml delete mode 100644 .github/workflows/check_pr_title.yml delete mode 100644 .github/workflows/standard_library_tests_and_examples.yml delete mode 100644 .github/workflows/test_pre_commit.yml diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS deleted file mode 100644 index 72b59c99d7..0000000000 --- a/.github/CODEOWNERS +++ /dev/null @@ -1,14 +0,0 @@ -# Code owners for the Mojo repository - -# Standard Library Sources -/stdlib/ @modularml/mojo-standard-library - -# Documentation -/docs/ @modularml/mojo-docs - -/docs/changelog.md @modularml/mojo-standard-library - -/stdlib/docs/ @modularml/mojo-docs @modularml/mojo-standard-library - -# Examples -/examples/ @jackos diff --git a/.github/ISSUE_TEMPLATE/doc_issue.yaml b/.github/ISSUE_TEMPLATE/doc_issue.yaml deleted file mode 100644 index 14996bc7e1..0000000000 --- a/.github/ISSUE_TEMPLATE/doc_issue.yaml +++ /dev/null @@ -1,37 +0,0 @@ -name: Documentation issue -description: Report a problem with the Mojo docs -title: "[Docs]" -labels: - - documentation -body: - - type: markdown - attributes: - value: | - Thank you for helping us improve the Mojo docs! - - Please add a title above and fill in the following fields so we can understand the problem. - - - type: input - attributes: - label: Where is the problem? - description: Provide a link to the problematic page (with a heading anchor). - validations: - required: true - - - type: textarea - attributes: - label: What can we do better? - description: Describe the documentation problem and how you suggest we fix it. - validations: - required: true - - - type: textarea - attributes: - label: Anything else? - validations: - required: false - - - type: markdown - attributes: - value: | - Thank you! diff --git a/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml b/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml deleted file mode 100644 index 82d5a3763f..0000000000 --- a/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml +++ /dev/null @@ -1,48 +0,0 @@ -name: Mojo bug report -description: Create a bug report to help us improve Mojo -title: "[BUG]" -labels: - - "bug,mojo" -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out a bug report! - - Please provide a descriptive title above and fill in the following fields. - - - type: textarea - id: Description - attributes: - label: Bug description - description: Describe the bug you encountered and what you expected to happen. - value: | - ### Actual behavior - - - ### Expected behavior - - validations: - required: true - - - type: textarea - id: Steps - attributes: - label: Steps to reproduce - description: Provide the specific steps to reproduce the issue. - value: | - - Provide a relevant code snippet or a link to the code that did not work as expected. - - If applicable, add screenshots to help explain the problem. - - If using the Playground, name the pre-existing notebook that failed and the steps that led to failure. - - Include anything else that might help us debug the issue. - validations: - required: true - - - type: textarea - id: Context - attributes: - label: System information - description: What version of Mojo are you using? - value: | - - Provide the system information by running `magic info`. - - Provide version information for MAX (includes Mojo) by pasting the output of `magic list max`. diff --git a/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml b/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml deleted file mode 100644 index fff1896844..0000000000 --- a/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml +++ /dev/null @@ -1,50 +0,0 @@ -name: Mojo feature request -description: Suggest an enhancement for Mojo -title: "[Feature Request]" -labels: - - "enhancement,mojo" -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to suggest a Mojo enhancement! - - Please enter a concise title above and fill out the following fields. - - - type: checkboxes - id: Roadmap - attributes: - label: Review Mojo's priorities - description: Please take a look at our roadmap before you file a new feature request. - options: - - label: I have read the [roadmap and priorities](https://docs.modular.com/mojo/roadmap.html#overall-priorities) and I believe this request falls within the priorities. - required: true - - - type: markdown - attributes: - value: | - If the request is out of the published roadmap and priorities, please start a [discussion](https://forum.modular.com/) in the Modular Forum to get feedback from the team. - - - type: textarea - id: Request - attributes: - label: What is your request? - description: Describe how you'd like us to improve Mojo. - validations: - required: true - - - type: textarea - id: Motivation - attributes: - label: What is your motivation for this change? - description: Describe the problem that your feature seeks to address (what is the value to the product/user?). - validations: - required: true - - - type: textarea - id: Description - attributes: - label: Any other details? - description: Perhaps some minimum functional attributes the implementation should include, or other context about your feature. - validations: - required: false diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index 2cd5f0bf30..0000000000 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,10 +0,0 @@ - diff --git a/.github/workflows/check_pr_target.yml b/.github/workflows/check_pr_target.yml deleted file mode 100644 index 4e333baf2a..0000000000 --- a/.github/workflows/check_pr_target.yml +++ /dev/null @@ -1,28 +0,0 @@ -name: Check PR target branch - -on: - pull_request: - types: [opened, edited, synchronize, reopened, ready_for_review] - -permissions: - contents: read - pull-requests: read - -jobs: - check-pr-target: - name: Check PR target branch - - runs-on: ubuntu-latest - timeout-minutes: 10 - - defaults: - run: - shell: bash - - steps: - - name: Fail if not targeting main branch - if: ${{ github.base_ref != 'main' }} - run: | - echo "PRs must be targeted to merge to the main branch!" - echo "PR is currently targeting: \"${{ github.base_ref }}\"" - exit 1 diff --git a/.github/workflows/check_pr_title.yml b/.github/workflows/check_pr_title.yml deleted file mode 100644 index 9b2f320a15..0000000000 --- a/.github/workflows/check_pr_title.yml +++ /dev/null @@ -1,53 +0,0 @@ -name: Check PR title - -on: - pull_request: - # By default, a workflow only runs when a pull_request's activity type is opened, synchronize, or reopened. We - # explicitly override here so that PR titles are re-linted when the PR text content is edited. - # - # Possible values: https://help.github.com/en/actions/reference/events-that-trigger-workflows#pull-request-event-pull_request - types: [opened, edited, reopened, synchronize, ready_for_review] - -jobs: - check-pr-title: - name: Check PR title format - - permissions: - pull-requests: write - - runs-on: ubuntu-latest - timeout-minutes: 5 - - steps: - - uses: morrisoncole/pr-lint-action@51f3cfabaf5d46f94e54524214e45685f0401b2a - if: github.actor != 'dependabot[bot]' - with: - title-regex: "^(Revert \")?(\\[\\S.*\\]\\s?)+\\s+[a-zA-Z`].*" - repo-token: "${{ secrets.GITHUB_TOKEN }}" - on-failed-regex-fail-action: true - on-failed-regex-create-review: true - on-failed-regex-request-changes: true - on-failed-regex-comment: - "The PR title does not conform to the '[\\] Title' format. - Please update the PR title. - \n\n - Typical [\\] values include: - \n\n - * `[stdlib]` — indicates a change to the Mojo standard library code\n - * `[docs]` — indicates a change to the documentation - \n\n - It's okay to include multiple labels on a PR that - affect multiple areas of work. - \n\n - Thank you for contributing to Mojo!🔥 - \n\n - - You can also use a tool like www.regex101.com to see why your PR - title fails to conform. Use - ``` - %regex% - ``` - as the regex to test and ```${{ github.event.pull_request.title }}``` - as the test string." - on-succeeded-regex-dismiss-review-comment: - "All good now, thanks!🫸🫷" diff --git a/.github/workflows/standard_library_tests_and_examples.yml b/.github/workflows/standard_library_tests_and_examples.yml deleted file mode 100644 index 2f13005fb3..0000000000 --- a/.github/workflows/standard_library_tests_and_examples.yml +++ /dev/null @@ -1,67 +0,0 @@ -# ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. -# -# Licensed under the Apache License v2.0 with LLVM Exceptions: -# https://llvm.org/LICENSE.txt -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ===----------------------------------------------------------------------=== # - -name: Test stdlib and examples -on: - pull_request: - types: [opened, synchronize, reopened, ready_for_review] - workflow_dispatch: - -permissions: - contents: read - pull-requests: read - -jobs: - test-examples: - name: with ${{ matrix.os }} and assertions=${{ matrix.mojo-enable-assertions }} - strategy: - fail-fast: false - matrix: - os: ["ubuntu-latest", "macos-14"] - mojo-enable-assertions: [0, 1] - - runs-on: ${{ matrix.os }} - timeout-minutes: 30 - - defaults: - run: - shell: bash - env: - DEBIAN_FRONTEND: noninteractive - - steps: - - name: Checkout repo - uses: actions/checkout@v4 - - - name: Download Magic CLI - run: | - curl -ssL https://magic.modular.com/cfba4c92-2390-4b86-93de-04b2f47114d5 | bash - # Add magic to PATH - echo "$HOME/.modular/bin" >> $GITHUB_PATH - - - name: Install build tools (Linux) - if: ${{ matrix.os == 'ubuntu-latest' }} - run: | - ./stdlib/scripts/install-build-tools-linux.sh - - - name: Install build tools (macOS) - if: ${{ matrix.os == 'macos-14' }} - run: | - ./stdlib/scripts/install-build-tools-macos.sh - - - name: Run standard library tests and examples - env: - MOJO_ENABLE_ASSERTIONS_IN_TESTS: ${{ matrix.mojo-enable-assertions }} - run: | - magic run --frozen tests - magic run --frozen examples diff --git a/.github/workflows/test_pre_commit.yml b/.github/workflows/test_pre_commit.yml deleted file mode 100644 index 99a2ddf73d..0000000000 --- a/.github/workflows/test_pre_commit.yml +++ /dev/null @@ -1,52 +0,0 @@ -# ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. -# -# Licensed under the Apache License v2.0 with LLVM Exceptions: -# https://llvm.org/LICENSE.txt -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ===----------------------------------------------------------------------=== # - -name: Run pre-commit -on: - pull_request: - types: [opened, synchronize, reopened, ready_for_review] - workflow_dispatch: - -permissions: - contents: read - pull-requests: read - -jobs: - lint: - runs-on: "ubuntu-latest" - timeout-minutes: 30 - - defaults: - run: - shell: bash - env: - DEBIAN_FRONTEND: noninteractive - - steps: - - name: Checkout repo - uses: actions/checkout@v4 - - - name: Download Magic CLI - run: | - curl -ssL https://magic.modular.com/cfba4c92-2390-4b86-93de-04b2f47114d5 | bash - # Add magic to PATH - echo "$HOME/.modular/bin" >> $GITHUB_PATH - - - name: Install pre-commit - run: | - pip install pre-commit - pre-commit install - - - name: Run pre-commit - run: magic run pre-commit run --all-files - diff --git a/docs/manual/parameters/index.mdx b/docs/manual/parameters/index.mdx index 169ce7db48..22f588a94a 100644 --- a/docs/manual/parameters/index.mdx +++ b/docs/manual/parameters/index.mdx @@ -243,14 +243,14 @@ value that has a more specific bound on one or more of its parameters. For example, the following code defines a `Container` type that holds an instance of `CollectionElement`. It also defines a `__str__()` method that can only be called if the stored `ElementType` conforms to -`StringableCollectionElement`: +`WritableCollectionElement`: ```mojo @value struct Container[ElementType: CollectionElement]: var element: ElementType - def __str__[StrElementType: StringableCollectionElement, //]( + def __str__[StrElementType: WritableCollectionElement, //]( self: Container[StrElementType]) -> String: return String(self.element) @@ -270,15 +270,15 @@ Hello Note the signature of the `__str__()` method, which declares the `self` argument with a more specific type. Specifically, it declares that it takes a `Container` -with an `ElementType` that conforms to the `StringableCollectionElement` trait. +with an `ElementType` that conforms to the `WritableCollectionElement` trait. ```mojo -def __str__[StrElementType: StringableCollectionElement, //]( +def __str__[StrElementType: WritableCollectionElement, //]( self: Container[StrElementType]) -> String: ``` This trait must be a superset of `ElementType`'s original trait: for example, -`StringableCollectionElement` inherits from `CollectionElement`, so it includes +`WritableCollectionElement` inherits from `CollectionElement`, so it includes all of requirements of the original trait. Note that the `use_container()` function calls the `__str__()` method directly, @@ -520,7 +520,7 @@ For example, consider the following struct: ```mojo @value -struct One[Type: StringableCollectionElement]: +struct One[Type: WritableCollectionElement]: var value: Type fn __init__(out self, value: Type): @@ -538,7 +538,7 @@ You can also infer parameters from a parameterized type passed to a constructor or static method: ```mojo -struct Two[Type: StringableCollectionElement]: +struct Two[Type: WritableCollectionElement]: var val1: Type var val2: Type diff --git a/docs/manual/traits.mdx b/docs/manual/traits.mdx index a9be57cf71..4369bd23ca 100644 --- a/docs/manual/traits.mdx +++ b/docs/manual/traits.mdx @@ -304,7 +304,6 @@ own types. These standard library traits include: * [`ComparableCollectionElement`](/mojo/stdlib/builtin/value/ComparableCollectionElement) * [`Copyable`](/mojo/stdlib/builtin/value/Copyable) * [`Defaultable`](/mojo/stdlib/builtin/value/Defaultable) -* [`Writable`](/mojo/stdlib/utils/write/Writable) * [`Hashable`](/mojo/stdlib/builtin/hash/Hashable) * [`Indexer`](/mojo/stdlib/builtin/int/Indexer) * [`Intable`](/mojo/stdlib/builtin/int/Intable) @@ -318,10 +317,11 @@ own types. These standard library traits include: * [`RepresentableKeyElement`](/mojo/stdlib/collections/dict/RepresentableKeyElement) * [`Sized`](/mojo/stdlib/builtin/len/Sized) * [`Stringable`](/mojo/stdlib/builtin/str/Stringable) -* [`StringableCollectionElement`](/mojo/stdlib/builtin/value/StringableCollectionElement) * [`StringableRaising`](/mojo/stdlib/builtin/str/StringableRaising) * [`StringRepresentable`](/mojo/stdlib/collections/string/StringRepresentable) * [`Roundable`](/mojo/stdlib/builtin/math/Roundable) +* [`Writable`](/mojo/stdlib/utils/write/Writable) +* [`WritableCollectionElement`](/mojo/stdlib/builtin/value/WritableCollectionElement) * [`Writer`](/mojo/stdlib/utils/write/Writer) * [`Truncable`](/mojo/stdlib/builtin/_math/Truncable) From ddddf01fef638c22548f81168cd80698353389d0 Mon Sep 17 00:00:00 2001 From: bgreni <42788181+bgreni@users.noreply.github.com> Date: Thu, 13 Feb 2025 07:41:44 -0800 Subject: [PATCH 387/509] [External] [stdlib] Consistently use `Indexer` in `LinkedList` (#55875) [External] [stdlib] Consistently use `Indexer` in `LinkedList` Use `Indexer` in all appropriate locations in `LinkedList` for consistency. Co-authored-by: bgreni <42788181+bgreni@users.noreply.github.com> Closes modularml/mojo#3990 MODULAR_ORIG_COMMIT_REV_ID: 414230e1163eab1e961bece26ab395bef8b3904c --- stdlib/src/collections/linked_list.mojo | 28 ++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index ef34ce84b9..ce4ac74d1e 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -420,12 +420,15 @@ struct LinkedList[ curr = curr[].next return new^ - fn insert(mut self, owned idx: Int, owned elem: ElementType) raises: + fn insert[I: Indexer](mut self, idx: I, owned elem: ElementType) raises: """ Insert an element `elem` into the list at index `idx`. Time Complexity: O(1) + Parameters: + I: The type of index to use. + Raises: When given an out of bounds index. @@ -433,7 +436,7 @@ struct LinkedList[ idx: The index to insert `elem` at. `-len(self) <= idx <= len(self)`. elem: The item to insert into the list. """ - var i = max(0, index(idx) if idx >= 0 else index(idx) + len(self)) + var i = max(0, index(idx) if Int(idx) >= 0 else index(idx) + len(self)) if i == 0: var node = Self._NodePointer.alloc(1) @@ -478,7 +481,7 @@ struct LinkedList[ self._head = node self._size += 1 else: - raise String("Index {} out of bounds").format(idx) + raise String("Index {} out of bounds").format(i) fn extend(mut self, owned other: Self): """ @@ -610,7 +613,9 @@ struct LinkedList[ """ return not (self == other) - fn _get_node_ptr(ref self, index: Int) -> UnsafePointer[Node[ElementType]]: + fn _get_node_ptr[ + I: Indexer + ](ref self, index: I) -> UnsafePointer[Node[ElementType]]: """ Get a pointer to the node at the specified index. @@ -619,6 +624,9 @@ struct LinkedList[ Time Complexity: O(n) in len(self) + Parameters: + I: The type of index to use. + Args: index: The index of the node to get. @@ -626,7 +634,7 @@ struct LinkedList[ A pointer to the node at the specified index. """ var l = len(self) - var i = normalize_index[container_name="LinkedList"](index, self) + var i = normalize_index[container_name="LinkedList"](Int(index), self) debug_assert(0 <= i < l, "index out of bounds") var mid = l // 2 if i <= mid: @@ -640,12 +648,15 @@ struct LinkedList[ curr = curr[].prev return curr - fn __getitem__(ref self, index: Int) -> ref [self] ElementType: + fn __getitem__[I: Indexer](ref self, index: I) -> ref [self] ElementType: """ Get the element at the specified index. Time Complexity: O(n) in len(self) + Parameters: + I: The type of index to use. + Args: index: The index of the element to get. @@ -655,12 +666,15 @@ struct LinkedList[ debug_assert(len(self) > 0, "unable to get item from empty list") return self._get_node_ptr(index)[].value - fn __setitem__(mut self, index: Int, owned value: ElementType): + fn __setitem__[I: Indexer](mut self, index: I, owned value: ElementType): """ Set the element at the specified index. Time Complexity: O(n) in len(self) + Parameters: + I: The type of index to use. + Args: index: The index of the element to set. value: The new value to set. From f789084ea8e5e8f022b72d88bb0ade2b77cf6f74 Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Thu, 13 Feb 2025 07:43:53 -0800 Subject: [PATCH 388/509] [External] [stdlib] Add UInt `__invert__` (#50655) [External] [stdlib] Add UInt `__invert__` Add UInt `__invert__` ORIGINAL_AUTHOR=martinvuyk <110240700+martinvuyk@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3643 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Co-authored-by: Jack Clayton Closes modularml/mojo#3643 MODULAR_ORIG_COMMIT_REV_ID: 40fa1a7c7183ebca9270964545bbe025fc097c10 --- stdlib/src/builtin/uint.mojo | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index 8a5a02e822..c45ebb660c 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -394,6 +394,15 @@ struct UInt(Indexer, _HashableWithHasher): """ return __mlir_op.`index.or`(self.value, rhs.value) + @always_inline("nodebug") + fn __invert__(self) -> UInt: + """Return ~self. + + Returns: + The ~self value. + """ + return self ^ Self.MAX + @always_inline fn __ceildiv__(self, denominator: Self) -> Self: """Return the rounded-up result of dividing self by denominator. From c5c4767ab5ed6ed1af236b8315560f1265000927 Mon Sep 17 00:00:00 2001 From: Yinon Burgansky Date: Thu, 13 Feb 2025 08:34:09 -0800 Subject: [PATCH 389/509] [External] [stdlib] Optimize normalize_index for unsigned types (#55873) [External] [stdlib] Optimize normalize_index for unsigned types Use the `Indexer` trait in `normalize_index` to optimize for `UInt`, `UInt8`, `UInt16`, `UInt32`, and `UInt64` types. Co-authored-by: Yinon Burgansky Co-authored-by: Jack Clayton Closes modularml/mojo#3957 MODULAR_ORIG_COMMIT_REV_ID: 891390dbec00fe6323f11afc3231ec2356c1b02c --- .../src/collections/_index_normalization.mojo | 106 +++++++++++++----- stdlib/src/collections/inline_array.mojo | 25 +---- stdlib/src/collections/linked_list.mojo | 2 +- stdlib/src/collections/string/string.mojo | 2 +- .../collections/test_index_normalization.mojo | 100 ++++++++++++++--- 5 files changed, 170 insertions(+), 65 deletions(-) diff --git a/stdlib/src/collections/_index_normalization.mojo b/stdlib/src/collections/_index_normalization.mojo index 80da530acc..d58e5829d4 100644 --- a/stdlib/src/collections/_index_normalization.mojo +++ b/stdlib/src/collections/_index_normalization.mojo @@ -13,44 +13,100 @@ """The utilities provided in this module help normalize the access to data elements in arrays.""" +from sys.intrinsics import _type_is_eq +from sys import sizeof + + +@always_inline +fn normalize_index[ + I: Indexer, //, container_name: StringLiteral +](idx: I, length: UInt) -> UInt: + """Normalize the given index value to a valid index value for the given container length. + + If the provided value is negative, the `index + container_length` is returned. + + Parameters: + I: A type that can be used as an index. + container_name: The name of the container. Used for the error message. + + Args: + idx: The index value to normalize. + length: The container length to normalize the index for. + + Returns: + The normalized index value. + """ + + @parameter + if ( + _type_is_eq[I, UInt]() + or _type_is_eq[I, UInt8]() + or _type_is_eq[I, UInt16]() + or _type_is_eq[I, UInt32]() + or _type_is_eq[I, UInt64]() + ): + var i = UInt(index(idx)) + # TODO: Consider a way to construct the error message after the assert has failed + # something like "Indexing into an empty container" if length == 0 else "..." + debug_assert[assert_mode="safe", cpu_only=True]( + i < length, + container_name, + " index out of bounds: index (", + i, + ") valid range: -", # can't print -UInt.MAX + length, + " <= index < ", + length, + ) + return i + else: + var mlir_index = index(idx) + var i = UInt(mlir_index) + if Int(mlir_index) < 0: + i += length + # Checking the bounds after the normalization saves a comparison + # while allowing negative indexing into containers with length > Int.MAX. + # For a positive index this is trivially correct. + # For a negative index we can infer the full bounds check from + # the assert UInt(idx + length) < length, by considering 2 cases: + # when length > Int.MAX then: + # idx + length > idx + Int.MAX >= Int.MIN + Int.MAX = -1 + # therefore idx + length >= 0 + # when length <= Int.MAX then: + # UInt(idx + length) < length <= Int.MAX + # Which means UInt(idx + length) signed bit is off + # therefore idx + length >= 0 + # in either case we can infer 0 <= idx + length < length + debug_assert[assert_mode="safe", cpu_only=True]( + i < length, + container_name, + " index out of bounds: index (", + Int(mlir_index), + ") valid range: -", # can't print -UInt.MAX + length, + " <= index < ", + length, + ) + return i + @always_inline fn normalize_index[ - ContainerType: Sized, //, container_name: StringLiteral -](idx: Int, container: ContainerType) -> Int: + I: Indexer, //, container_name: StringLiteral +](idx: I, length: Int) -> Int: """Normalize the given index value to a valid index value for the given container length. If the provided value is negative, the `index + container_length` is returned. Parameters: - ContainerType: The type of the container. Must have a `__len__` method. + I: A type that can be used as an index. container_name: The name of the container. Used for the error message. Args: idx: The index value to normalize. - container: The container to normalize the index for. + length: The container length to normalize the index for. Returns: The normalized index value. """ - debug_assert[assert_mode="safe", cpu_only=True]( - len(container) > 0, - "indexing into a ", - container_name, - " that has 0 elements", - ) - debug_assert[assert_mode="safe", cpu_only=True]( - -len(container) <= idx < len(container), - container_name, - " has length: ", - len(container), - " index out of bounds: ", - idx, - " should be between ", - -len(container), - " and ", - len(container) - 1, - ) - if idx >= 0: - return idx - return idx + len(container) + return Int(normalize_index[container_name](idx, UInt(length))) diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 7def2c4254..142545b5a6 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -233,15 +233,8 @@ struct InlineArray[ Returns: A reference to the item at the given index. """ - - @parameter - if _type_is_eq[I, UInt](): - return self.unsafe_get(idx) - else: - var normalized_index = normalize_index["InlineArray"]( - Int(idx), self - ) - return self.unsafe_get(normalized_index) + var normalized_index = normalize_index["InlineArray"](idx, len(self)) + return self.unsafe_get(normalized_index) @always_inline fn __getitem__[ @@ -257,18 +250,8 @@ struct InlineArray[ A reference to the item at the given index. """ constrained[-size <= Int(idx) < size, "Index must be within bounds."]() - - @parameter - if _type_is_eq[I, UInt](): - return self.unsafe_get(idx) - else: - var normalized_idx = Int(idx) - - @parameter - if Int(idx) < 0: - normalized_idx += size - - return self.unsafe_get(normalized_idx) + alias normalized_index = normalize_index["InlineArray"](idx, size) + return self.unsafe_get(normalized_index) # ===------------------------------------------------------------------=== # # Trait implementations diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index ce4ac74d1e..83bf614c47 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -634,7 +634,7 @@ struct LinkedList[ A pointer to the node at the specified index. """ var l = len(self) - var i = normalize_index[container_name="LinkedList"](Int(index), self) + var i = normalize_index["LinkedList"](index, l) debug_assert(0 <= i < l, "index out of bounds") var mid = l // 2 if i <= mid: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index f8cda0973f..abf923b261 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -860,7 +860,7 @@ struct String( A new string containing the character at the specified position. """ # TODO(#933): implement this for unicode when we support llvm intrinsic evaluation at compile time - var normalized_idx = normalize_index["String"](index(idx), self) + var normalized_idx = normalize_index["String"](idx, len(self)) var buf = Self._buffer_type(capacity=1) buf.append(self._buffer[normalized_idx]) buf.append(0) diff --git a/stdlib/test/collections/test_index_normalization.mojo b/stdlib/test/collections/test_index_normalization.mojo index 0039180c55..0234b526e7 100644 --- a/stdlib/test/collections/test_index_normalization.mojo +++ b/stdlib/test/collections/test_index_normalization.mojo @@ -18,27 +18,93 @@ from testing import assert_equal def test_out_of_bounds_message(): - l = List[Int](1, 2) - # CHECK: index out of bounds: 2 - _ = normalize_index["List"](2, l) - # CHECK: index out of bounds: -3 - _ = normalize_index["List"](-3, l) + # CHECK: index out of bounds + _ = normalize_index[""](2, 2) + # CHECK: index out of bounds + _ = normalize_index[""](UInt(2), 2) + # CHECK: index out of bounds + _ = normalize_index[""](2, UInt(2)) + # CHECK: index out of bounds + _ = normalize_index[""](UInt(2), UInt(2)) + # CHECK: index out of bounds + _ = normalize_index[""](UInt8(2), 2) - l2 = List[Int]() - # CHECK: indexing into a List that has 0 elements - _ = normalize_index["List"](2, l2) + # CHECK: index out of bounds + _ = normalize_index[""](-3, 2) + # CHECK: index out of bounds + _ = normalize_index[""](-3, UInt(2)) + # CHECK: index out of bounds + _ = normalize_index[""](Int8(-3), 2) + + # CHECK: index out of bounds + _ = normalize_index[""](2, 0) + # CHECK: index out of bounds + _ = normalize_index[""](UInt(2), 0) + # CHECK: index out of bounds + _ = normalize_index[""](2, UInt(0)) + # CHECK: index out of bounds + _ = normalize_index[""](UInt(2), UInt(0)) + + # CHECK: index out of bounds + _ = normalize_index[""](Int.MIN, 10) + # CHECK: index out of bounds + _ = normalize_index[""](Int.MIN, UInt(10)) + # CHECK: index out of bounds + _ = normalize_index[""](Int.MAX, 10) + # CHECK: index out of bounds + _ = normalize_index[""](Int.MAX, UInt(10)) + # CHECK: index out of bounds + _ = normalize_index[""](Int.MIN, Int.MAX) + + # CHECK: index out of bounds + _ = normalize_index[""](UInt.MAX, 10) + # CHECK: index out of bounds + _ = normalize_index[""](UInt.MAX, UInt(10)) + # CHECK: index out of bounds + _ = normalize_index[""](UInt.MAX, UInt.MAX) + # CHECK: index out of bounds + _ = normalize_index[""](UInt.MAX, UInt.MAX - 10) def test_normalize_index(): - container = List[Int](1, 1, 1, 1) - assert_equal(normalize_index[""](-4, container), 0) - assert_equal(normalize_index[""](-3, container), 1) - assert_equal(normalize_index[""](-2, container), 2) - assert_equal(normalize_index[""](-1, container), 3) - assert_equal(normalize_index[""](0, container), 0) - assert_equal(normalize_index[""](1, container), 1) - assert_equal(normalize_index[""](2, container), 2) - assert_equal(normalize_index[""](3, container), 3) + assert_equal(normalize_index[""](-3, 3), 0) + assert_equal(normalize_index[""](-2, 3), 1) + assert_equal(normalize_index[""](-1, 3), 2) + assert_equal(normalize_index[""](0, 3), 0) + assert_equal(normalize_index[""](1, 3), 1) + assert_equal(normalize_index[""](2, 3), 2) + + assert_equal(normalize_index[""](-3, UInt(3)), 0) + assert_equal(normalize_index[""](-2, UInt(3)), 1) + assert_equal(normalize_index[""](-1, UInt(3)), 2) + assert_equal(normalize_index[""](0, UInt(3)), 0) + assert_equal(normalize_index[""](1, UInt(3)), 1) + assert_equal(normalize_index[""](2, UInt(3)), 2) + + assert_equal(normalize_index[""](UInt(0), UInt(3)), 0) + assert_equal(normalize_index[""](UInt(1), UInt(3)), 1) + assert_equal(normalize_index[""](UInt(2), UInt(3)), 2) + + assert_equal(normalize_index[""](Int8(-3), 3), 0) + assert_equal(normalize_index[""](Int8(-2), 3), 1) + assert_equal(normalize_index[""](Int8(-1), 3), 2) + assert_equal(normalize_index[""](Int8(0), 3), 0) + assert_equal(normalize_index[""](Int8(1), 3), 1) + assert_equal(normalize_index[""](Int8(2), 3), 2) + + assert_equal(normalize_index[""](UInt8(0), 3), 0) + assert_equal(normalize_index[""](UInt8(1), 3), 1) + assert_equal(normalize_index[""](UInt8(2), 3), 2) + + assert_equal(normalize_index[""](UInt(1), UInt.MAX), 1) + assert_equal(normalize_index[""](UInt.MAX - 5, UInt.MAX), UInt.MAX - 5) + + assert_equal(normalize_index[""](-1, Int.MAX), Int.MAX - 1) + assert_equal(normalize_index[""](-10, Int.MAX), Int.MAX - 10) + assert_equal(normalize_index[""](-1, UInt.MAX), UInt.MAX - 1) + assert_equal(normalize_index[""](-10, UInt.MAX), UInt.MAX - 10) + assert_equal(normalize_index[""](-1, UInt(Int.MAX) + 1), UInt(Int.MAX)) + assert_equal(normalize_index[""](Int.MIN, UInt(Int.MAX) + 1), 0) def main(): From fd3c61727868031cef27566d444eb499f33fbf18 Mon Sep 17 00:00:00 2001 From: Robert <121340777+rcghpge@users.noreply.github.com> Date: Thu, 13 Feb 2025 08:35:00 -0800 Subject: [PATCH 390/509] [External] [docs] Update faq.md (#55870) [External] [docs] Update faq.md Update mojo to Mojo in FAQ. Co-authored-by: Robert <121340777+rcghpge@users.noreply.github.com> Closes modularml/mojo#3982 MODULAR_ORIG_COMMIT_REV_ID: de613bd8a441f61d1e680d82bdf8aa5ee2ef3c49 --- docs/faq.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/faq.md b/docs/faq.md index e6f68d46e2..2061c83564 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -31,7 +31,7 @@ name for a language that brings magical powers to Python, including unlocking an innovative programming model for accelerators and other heterogeneous systems pervasive in AI today. -### Why does mojo have the 🔥 file extension? +### Why does Mojo have the 🔥 file extension? We paired Mojo with fire emoji 🔥 as a fun visual way to impart onto users that Mojo empowers them to get their Mojo on—to develop faster and more efficiently From 59357983053d1d9948610f1836415eb183cd700d Mon Sep 17 00:00:00 2001 From: Patrick Dougherty Date: Thu, 13 Feb 2025 11:59:48 -0600 Subject: [PATCH 391/509] [CI] Fix up teams for CODEOWNERS MODULAR_ORIG_COMMIT_REV_ID: 8bb34f18726e697c70546aefccab6c4f2ba8679d --- .github/CODEOWNERS | 14 ++++ .github/ISSUE_TEMPLATE/doc_issue.yaml | 50 ++++++++++++++ .github/ISSUE_TEMPLATE/mojo_bug_report.yaml | 61 +++++++++++++++++ .../ISSUE_TEMPLATE/mojo_feature_request.yaml | 63 +++++++++++++++++ .github/PULL_REQUEST_TEMPLATE.md | 10 +++ .github/workflows/check_pr_target.yml | 28 ++++++++ .github/workflows/check_pr_title.yml | 53 +++++++++++++++ .../standard_library_tests_and_examples.yml | 67 +++++++++++++++++++ .github/workflows/test_pre_commit.yml | 52 ++++++++++++++ 9 files changed, 398 insertions(+) create mode 100644 .github/CODEOWNERS create mode 100644 .github/ISSUE_TEMPLATE/doc_issue.yaml create mode 100644 .github/ISSUE_TEMPLATE/mojo_bug_report.yaml create mode 100644 .github/ISSUE_TEMPLATE/mojo_feature_request.yaml create mode 100644 .github/PULL_REQUEST_TEMPLATE.md create mode 100644 .github/workflows/check_pr_target.yml create mode 100644 .github/workflows/check_pr_title.yml create mode 100644 .github/workflows/standard_library_tests_and_examples.yml create mode 100644 .github/workflows/test_pre_commit.yml diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000000..f091037d6b --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,14 @@ +# Code owners for the Mojo repository + +# Standard Library Sources +/stdlib/ @modular/stdlib + +# Documentation +/docs/ @modular/mojo-docs + +/docs/changelog.md @modular/stdlib + +/stdlib/docs/ @modular/mojo-docs @modular/stdlib + +# Examples +/examples/ @jackos diff --git a/.github/ISSUE_TEMPLATE/doc_issue.yaml b/.github/ISSUE_TEMPLATE/doc_issue.yaml new file mode 100644 index 0000000000..daf162cefc --- /dev/null +++ b/.github/ISSUE_TEMPLATE/doc_issue.yaml @@ -0,0 +1,50 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: Documentation issue +description: Report a problem with the Mojo docs +title: "[Docs]" +labels: + - documentation +body: + - type: markdown + attributes: + value: | + Thank you for helping us improve the Mojo docs! + + Please add a title above and fill in the following fields so we can understand the problem. + + - type: input + attributes: + label: Where is the problem? + description: Provide a link to the problematic page (with a heading anchor). + validations: + required: true + + - type: textarea + attributes: + label: What can we do better? + description: Describe the documentation problem and how you suggest we fix it. + validations: + required: true + + - type: textarea + attributes: + label: Anything else? + validations: + required: false + + - type: markdown + attributes: + value: | + Thank you! diff --git a/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml b/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml new file mode 100644 index 0000000000..57b19d978c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml @@ -0,0 +1,61 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: Mojo bug report +description: Create a bug report to help us improve Mojo +title: "[BUG]" +labels: + - "bug,mojo" +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out a bug report! + + Please provide a descriptive title above and fill in the following fields. + + - type: textarea + id: Description + attributes: + label: Bug description + description: Describe the bug you encountered and what you expected to happen. + value: | + ### Actual behavior + + + ### Expected behavior + + validations: + required: true + + - type: textarea + id: Steps + attributes: + label: Steps to reproduce + description: Provide the specific steps to reproduce the issue. + value: | + - Provide a relevant code snippet or a link to the code that did not work as expected. + - If applicable, add screenshots to help explain the problem. + - If using the Playground, name the pre-existing notebook that failed and the steps that led to failure. + - Include anything else that might help us debug the issue. + validations: + required: true + + - type: textarea + id: Context + attributes: + label: System information + description: What version of Mojo are you using? + value: | + - Provide the system information by running `magic info`. + - Provide version information for MAX (includes Mojo) by pasting the output of `magic list max`. diff --git a/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml b/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml new file mode 100644 index 0000000000..a5ddc32a58 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml @@ -0,0 +1,63 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: Mojo feature request +description: Suggest an enhancement for Mojo +title: "[Feature Request]" +labels: + - "enhancement,mojo" +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to suggest a Mojo enhancement! + + Please enter a concise title above and fill out the following fields. + + - type: checkboxes + id: Roadmap + attributes: + label: Review Mojo's priorities + description: Please take a look at our roadmap before you file a new feature request. + options: + - label: I have read the [roadmap and priorities](https://docs.modular.com/mojo/roadmap.html#overall-priorities) and I believe this request falls within the priorities. + required: true + + - type: markdown + attributes: + value: | + If the request is out of the published roadmap and priorities, please start a [discussion](https://forum.modular.com/) in the Modular Forum to get feedback from the team. + + - type: textarea + id: Request + attributes: + label: What is your request? + description: Describe how you'd like us to improve Mojo. + validations: + required: true + + - type: textarea + id: Motivation + attributes: + label: What is your motivation for this change? + description: Describe the problem that your feature seeks to address (what is the value to the product/user?). + validations: + required: true + + - type: textarea + id: Description + attributes: + label: Any other details? + description: Perhaps some minimum functional attributes the implementation should include, or other context about your feature. + validations: + required: false diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000..2cd5f0bf30 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,10 @@ + diff --git a/.github/workflows/check_pr_target.yml b/.github/workflows/check_pr_target.yml new file mode 100644 index 0000000000..4e333baf2a --- /dev/null +++ b/.github/workflows/check_pr_target.yml @@ -0,0 +1,28 @@ +name: Check PR target branch + +on: + pull_request: + types: [opened, edited, synchronize, reopened, ready_for_review] + +permissions: + contents: read + pull-requests: read + +jobs: + check-pr-target: + name: Check PR target branch + + runs-on: ubuntu-latest + timeout-minutes: 10 + + defaults: + run: + shell: bash + + steps: + - name: Fail if not targeting main branch + if: ${{ github.base_ref != 'main' }} + run: | + echo "PRs must be targeted to merge to the main branch!" + echo "PR is currently targeting: \"${{ github.base_ref }}\"" + exit 1 diff --git a/.github/workflows/check_pr_title.yml b/.github/workflows/check_pr_title.yml new file mode 100644 index 0000000000..9b2f320a15 --- /dev/null +++ b/.github/workflows/check_pr_title.yml @@ -0,0 +1,53 @@ +name: Check PR title + +on: + pull_request: + # By default, a workflow only runs when a pull_request's activity type is opened, synchronize, or reopened. We + # explicitly override here so that PR titles are re-linted when the PR text content is edited. + # + # Possible values: https://help.github.com/en/actions/reference/events-that-trigger-workflows#pull-request-event-pull_request + types: [opened, edited, reopened, synchronize, ready_for_review] + +jobs: + check-pr-title: + name: Check PR title format + + permissions: + pull-requests: write + + runs-on: ubuntu-latest + timeout-minutes: 5 + + steps: + - uses: morrisoncole/pr-lint-action@51f3cfabaf5d46f94e54524214e45685f0401b2a + if: github.actor != 'dependabot[bot]' + with: + title-regex: "^(Revert \")?(\\[\\S.*\\]\\s?)+\\s+[a-zA-Z`].*" + repo-token: "${{ secrets.GITHUB_TOKEN }}" + on-failed-regex-fail-action: true + on-failed-regex-create-review: true + on-failed-regex-request-changes: true + on-failed-regex-comment: + "The PR title does not conform to the '[\\] Title' format. + Please update the PR title. + \n\n + Typical [\\] values include: + \n\n + * `[stdlib]` — indicates a change to the Mojo standard library code\n + * `[docs]` — indicates a change to the documentation + \n\n + It's okay to include multiple labels on a PR that + affect multiple areas of work. + \n\n + Thank you for contributing to Mojo!🔥 + \n\n + + You can also use a tool like www.regex101.com to see why your PR + title fails to conform. Use + ``` + %regex% + ``` + as the regex to test and ```${{ github.event.pull_request.title }}``` + as the test string." + on-succeeded-regex-dismiss-review-comment: + "All good now, thanks!🫸🫷" diff --git a/.github/workflows/standard_library_tests_and_examples.yml b/.github/workflows/standard_library_tests_and_examples.yml new file mode 100644 index 0000000000..2f13005fb3 --- /dev/null +++ b/.github/workflows/standard_library_tests_and_examples.yml @@ -0,0 +1,67 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +name: Test stdlib and examples +on: + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + workflow_dispatch: + +permissions: + contents: read + pull-requests: read + +jobs: + test-examples: + name: with ${{ matrix.os }} and assertions=${{ matrix.mojo-enable-assertions }} + strategy: + fail-fast: false + matrix: + os: ["ubuntu-latest", "macos-14"] + mojo-enable-assertions: [0, 1] + + runs-on: ${{ matrix.os }} + timeout-minutes: 30 + + defaults: + run: + shell: bash + env: + DEBIAN_FRONTEND: noninteractive + + steps: + - name: Checkout repo + uses: actions/checkout@v4 + + - name: Download Magic CLI + run: | + curl -ssL https://magic.modular.com/cfba4c92-2390-4b86-93de-04b2f47114d5 | bash + # Add magic to PATH + echo "$HOME/.modular/bin" >> $GITHUB_PATH + + - name: Install build tools (Linux) + if: ${{ matrix.os == 'ubuntu-latest' }} + run: | + ./stdlib/scripts/install-build-tools-linux.sh + + - name: Install build tools (macOS) + if: ${{ matrix.os == 'macos-14' }} + run: | + ./stdlib/scripts/install-build-tools-macos.sh + + - name: Run standard library tests and examples + env: + MOJO_ENABLE_ASSERTIONS_IN_TESTS: ${{ matrix.mojo-enable-assertions }} + run: | + magic run --frozen tests + magic run --frozen examples diff --git a/.github/workflows/test_pre_commit.yml b/.github/workflows/test_pre_commit.yml new file mode 100644 index 0000000000..99a2ddf73d --- /dev/null +++ b/.github/workflows/test_pre_commit.yml @@ -0,0 +1,52 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +name: Run pre-commit +on: + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + workflow_dispatch: + +permissions: + contents: read + pull-requests: read + +jobs: + lint: + runs-on: "ubuntu-latest" + timeout-minutes: 30 + + defaults: + run: + shell: bash + env: + DEBIAN_FRONTEND: noninteractive + + steps: + - name: Checkout repo + uses: actions/checkout@v4 + + - name: Download Magic CLI + run: | + curl -ssL https://magic.modular.com/cfba4c92-2390-4b86-93de-04b2f47114d5 | bash + # Add magic to PATH + echo "$HOME/.modular/bin" >> $GITHUB_PATH + + - name: Install pre-commit + run: | + pip install pre-commit + pre-commit install + + - name: Run pre-commit + run: magic run pre-commit run --all-files + From a9f34a7c410c074bd63823cfa04d827731ee4716 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 13 Feb 2025 23:37:14 -0800 Subject: [PATCH 392/509] [Stdlib] Simplify the is_bool_like helper method, NFC MODULAR_ORIG_COMMIT_REV_ID: d7df9586940467bf58d2065678ca863776549e12 --- stdlib/src/sys/param_env.mojo | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/stdlib/src/sys/param_env.mojo b/stdlib/src/sys/param_env.mojo index c24a38d274..124e59d225 100644 --- a/stdlib/src/sys/param_env.mojo +++ b/stdlib/src/sys/param_env.mojo @@ -53,14 +53,14 @@ fn is_defined[name: StringLiteral]() -> Bool: return __mlir_attr[`#kgen.param.expr : i1`] -fn _is_bool_like(val: String) -> Bool: - return val.lower() in ( - String("true"), - String("1"), - String("on"), - String("false"), - String("0"), - String("off"), +fn _is_bool_like[val: StringLiteral]() -> Bool: + return StringLiteral.get[val.lower()]() in ( + "true", + "1", + "on", + "false", + "0", + "off", ) @@ -77,7 +77,7 @@ fn env_get_bool[name: StringLiteral]() -> Bool: alias val = StringLiteral.get[env_get_string[name]().lower()]() constrained[ - _is_bool_like(val), + _is_bool_like[val](), String( "the boolean environment value of `", name, From 60023851acb964ae1b0ec97378f7053086c32d80 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 14 Feb 2025 15:40:33 -0600 Subject: [PATCH 393/509] [stdlib] feat: Add `StringSlice.is_codepoint_boundary()` Also updated various `StringSlice` method docs that take start/end positions to make it clear those are expected to be offsets in bytes, not codepoints or graphemes. MODULAR_ORIG_COMMIT_REV_ID: 2a1be430f112c878dd507b9800cc5f4474ec140b --- docs/changelog.md | 3 + .../src/collections/string/string_slice.mojo | 138 ++++++++++++++++-- .../collections/string/test_string_slice.mojo | 23 +++ 3 files changed, 150 insertions(+), 14 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 943f737e9b..14ad9d23bf 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -40,6 +40,9 @@ what we publish. - `ljust()` - `center()` +- Added a `StringSlice.is_codepoint_boundary()` method for querying if a given + byte index is a boundary between encoded UTF-8 codepoints. + ### GPU changes - `ctx.enqueue_function(compiled_func, ...)` is deprecated: diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 7856e05c71..32a95a3d55 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -1216,6 +1216,103 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( var continuation_count = _count_utf8_continuation_bytes(self) return self.byte_length() - continuation_count + fn is_codepoint_boundary(self, index: UInt) -> Bool: + """Returns True if `index` is the position of the first byte in a UTF-8 + codepoint sequence, or is at the end of the string. + + A byte position is considered a codepoint boundary if a valid subslice + of the string would end (noninclusive) at `index`. + + Positions `0` and `len(self)` are considered to be codepoint boundaries. + + Positions beyond the length of the string slice will return False. + + Args: + index: An index into the underlying byte representation of the + string. + + Returns: + A boolean indicating if `index` gives the position of the first + byte in a UTF-8 codepoint sequence, or is at the end of the string. + + # Examples + + Check if particular byte positions are codepoint boundaries: + + ```mojo + from collections.string import StringSlice + from testing import assert_equal, assert_true + var abc = StringSlice("abc") + assert_equal(len(abc), 3) + assert_true(abc.is_codepoint_boundary(0)) + assert_true(abc.is_codepoint_boundary(1)) + assert_true(abc.is_codepoint_boundary(2)) + assert_true(abc.is_codepoint_boundary(3)) + ``` + + Only the index of the first byte in a multi-byte codepoint sequence is + considered a codepoint boundary: + + ```mojo + var thumb = StringSlice("👍") + assert_equal(len(thumb), 4) + assert_true(thumb.is_codepoint_boundary(0)) + assert_false(thumb.is_codepoint_boundary(1)) + assert_false(thumb.is_codepoint_boundary(2)) + assert_false(thumb.is_codepoint_boundary(3)) + ``` + + Visualization showing which bytes are considered codepoint boundaries, + within a piece of text that includes codepoints whose UTF-8 + representation requires, respectively, 1, 2, 3, and 4-bytes. The + codepoint boundary byte indices are indicated by a vertical arrow (↑). + + For example, this diagram shows that a slice of bytes formed by the + half-open range starting at byte 3 and extending up to but not including + byte 6 (`[3, 6)`) is a valid UTF-8 sequence. + + ```text + ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ + ┃ a©➇𝄞 ┃ String + ┣━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━┫ + ┃97┃ 169 ┃ 10119 ┃ 119070 ┃ Unicode Codepoints + ┣━━╋━━━┳━━━╋━━━┳━━━┳━━━╋━━━┳━━━┳━━━┳━━━┫ + ┃97┃194┃169┃226┃158┃135┃240┃157┃132┃158┃ UTF-8 Bytes + ┗━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┛ + 0 1 2 3 4 5 6 7 8 9 10 + ↑ ↑ ↑ ↑ ↑ + ``` + + The following program verifies the above diagram: + + ```mojo + from collections.string import StringSlice + from testing import assert_true, assert_false + + var text = StringSlice("a©➇𝄞") + assert_true(text.is_codepoint_boundary(0)) + assert_true(text.is_codepoint_boundary(1)) + assert_false(text.is_codepoint_boundary(2)) + assert_true(text.is_codepoint_boundary(3)) + assert_false(text.is_codepoint_boundary(4)) + assert_false(text.is_codepoint_boundary(5)) + assert_true(text.is_codepoint_boundary(6)) + assert_false(text.is_codepoint_boundary(7)) + assert_false(text.is_codepoint_boundary(8)) + assert_false(text.is_codepoint_boundary(9)) + assert_true(text.is_codepoint_boundary(10)) + ``` + . + """ + # TODO: Example: Print the byte indices that are codepoints boundaries: + + if index >= len(self): + return index == len(self) + + var byte = self.as_bytes()[index] + # If this is not a continuation byte, then it must be a start byte. + return _utf8_byte_type(byte) != 1 + fn get_immutable( self, ) -> StringSlice[ImmutableOrigin.cast_from[origin].result]: @@ -1236,10 +1333,13 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """Verify if the `StringSlice` starts with the specified prefix between start and end positions. + The `start` and `end` positions must be offsets given in bytes, and + must be codepoint boundaries. + Args: prefix: The prefix to check. - start: The start offset from which to check. - end: The end offset from which to check. + start: The start offset in bytes from which to check. + end: The end offset in bytes from which to check. Returns: True if the `self[start:end]` is prefixed by the input prefix. @@ -1257,10 +1357,13 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """Verify if the `StringSlice` end with the specified suffix between start and end positions. + The `start` and `end` positions must be offsets given in bytes, and + must be codepoint boundaries. + Args: suffix: The suffix to check. - start: The start offset from which to check. - end: The end offset from which to check. + start: The start offset in bytes from which to check. + end: The end offset in bytes from which to check. Returns: True if the `self[start:end]` is suffixed by the input suffix. @@ -1276,11 +1379,12 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( fn _from_start(self, start: Int) -> Self: """Gets the `StringSlice` pointing to the substring after the specified - slice start position. If start is negative, it is interpreted as the - number of characters from the end of the string to start at. + slice start position in bytes. If start is negative, it is interpreted + as the number of characters from the end of the string to start at. Args: - start: Starting index of the slice. + start: Starting index of the slice in bytes. Must be a codepoint + boundary. Returns: A `StringSlice` borrowed from the current string containing the @@ -1310,6 +1414,8 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( "strref absolute start must be less than source String len", ) + # TODO(MSTDL-1161): Assert that `self.is_codepoint_boundary(abs_start)`. + # TODO: We assumes the StringSlice only has ASCII. # When we support utf-8 slicing, we should drop self._slice[abs_start:] # and use something smarter. @@ -1342,15 +1448,17 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( return _FormatCurlyEntry.format(self, args) fn find(ref self, substr: StringSlice, start: Int = 0) -> Int: - """Finds the offset of the first occurrence of `substr` starting at - `start`. If not found, returns `-1`. + """Finds the offset in bytes of the first occurrence of `substr` + starting at `start`. If not found, returns `-1`. Args: substr: The substring to find. - start: The offset from which to find. + start: The offset in bytes from which to find. Must be a codepoint + boundary. Returns: - The offset of `substr` relative to the beginning of the string. + The offset in bytes of `substr` relative to the beginning of the + string. """ if not substr: return 0 @@ -1375,15 +1483,17 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( return Int(loc) - Int(self.unsafe_ptr()) fn rfind(self, substr: StringSlice, start: Int = 0) -> Int: - """Finds the offset of the last occurrence of `substr` starting at + """Finds the offset in bytes of the last occurrence of `substr` starting at `start`. If not found, returns `-1`. Args: substr: The substring to find. - start: The offset from which to find. + start: The offset in bytes from which to find. Must be a valid + codepoint boundary. Returns: - The offset of `substr` relative to the beginning of the string. + The offset in bytes of `substr` relative to the beginning of the + string. """ if not substr: return len(self) diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index fc6ea07273..92851c8e34 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -434,6 +434,28 @@ def test_find(): ) +def test_is_codepoint_boundary(): + var abc = StringSlice("abc") + assert_equal(len(abc), 3) + assert_true(abc.is_codepoint_boundary(0)) + assert_true(abc.is_codepoint_boundary(1)) + assert_true(abc.is_codepoint_boundary(2)) + assert_true(abc.is_codepoint_boundary(3)) + + var thumb = StringSlice("👍") + assert_equal(len(thumb), 4) + assert_true(thumb.is_codepoint_boundary(0)) + assert_false(thumb.is_codepoint_boundary(1)) + assert_false(thumb.is_codepoint_boundary(2)) + assert_false(thumb.is_codepoint_boundary(3)) + + var empty = StringSlice("") + assert_equal(len(empty), 0) + assert_true(empty.is_codepoint_boundary(0)) + # Also tests that positions greater then the length don't raise/abort. + assert_false(empty.is_codepoint_boundary(1)) + + alias GOOD_SEQUENCES = List[String]( "a", "\xc3\xb1", @@ -1112,6 +1134,7 @@ def main(): test_slice_repr() test_utf8_validation() test_find() + test_is_codepoint_boundary() test_good_utf8_sequences() test_bad_utf8_sequences() test_stringslice_from_utf8() From 5b37652f90a40977d49a8c1b5d5fa02ff13b4271 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 14 Feb 2025 17:22:17 -0600 Subject: [PATCH 394/509] [stdlib] polish: Rename `Char` type to `Codepoint` and related methods * Rename Char to Codepoint - Rename `char.mojo` to `codepoint.mojo` and move to collections.string - Rename `test_char.mojo` to `test_codepoint.mojo` and moved directory * Rename CharsIter to CodepointsIter * Rename Codepoint.unsafe_decode_utf8_char => unsafe_decode_utf8_codepoint * Rename .chars() to .codepoints() * Rename .char_slices() to .codepoint_slices() MODULAR_ORIG_COMMIT_REV_ID: 8e487354991e7dfdba18715e61de62adf030bd72 --- docs/changelog.md | 9 ++ stdlib/src/collections/interval.mojo | 2 +- stdlib/src/collections/string/__init__.mojo | 3 +- stdlib/src/collections/string/_unicode.mojo | 54 ++++---- .../string/codepoint.mojo} | 104 ++++++++------ stdlib/src/collections/string/string.mojo | 63 ++++----- .../src/collections/string/string_slice.mojo | 108 ++++++++------- stdlib/src/prelude/__init__.mojo | 3 +- .../test/collections/string/test_string.mojo | 32 ++--- .../collections/string/test_string_slice.mojo | 36 ++--- .../test/collections/string/test_unicode.mojo | 26 ++-- .../test_codepoint.mojo} | 128 +++++++++--------- 12 files changed, 311 insertions(+), 257 deletions(-) rename stdlib/src/{builtin/char.mojo => collections/string/codepoint.mojo} (80%) rename stdlib/test/{builtin/test_char.mojo => collections/test_codepoint.mojo} (59%) diff --git a/docs/changelog.md b/docs/changelog.md index 14ad9d23bf..ac5c0d9063 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -25,6 +25,15 @@ what we publish. - A new `IntervalTree` data structure has been added to the standard library. This is a tree data structure that allows for efficient range queries. +- The `Char` type has been renamed to `Codepoint`, to better capture its + intended purpose of storing a single Unicode codepoint. Additionally, related + method and type names have been updated as well, including: + + - `StringSlice.chars()` to `.codepoints()` (ditto for `String`) + - `StringSlice.char_slices()` to `.codepoint_slices()` (ditto for `String`) + - `CharsIter` to `CodepointsIter` + - `unsafe_decode_utf8_char()` to `unsafe_decode_utf8_codepoint()` + - `StringSlice` now supports several additional methods moved from `String`. The existing `String` methods have been updated to instead call the corresponding new `StringSlice` methods: diff --git a/stdlib/src/collections/interval.mojo b/stdlib/src/collections/interval.mojo index 6938bbe911..9502d173d2 100644 --- a/stdlib/src/collections/interval.mojo +++ b/stdlib/src/collections/interval.mojo @@ -957,7 +957,7 @@ struct IntervalTree[T: IntervalElement, U: IntervalPayload]: 0, pos_x - len(node_str) // 2 ) # Center the node text var i = 0 - for char in node_str.chars(): + for char in node_str.codepoints(): grid[pos_y][start_pos + i] = String(char) i += 1 diff --git a/stdlib/src/collections/string/__init__.mojo b/stdlib/src/collections/string/__init__.mojo index 53ae10eccc..37d7dcb113 100644 --- a/stdlib/src/collections/string/__init__.mojo +++ b/stdlib/src/collections/string/__init__.mojo @@ -13,4 +13,5 @@ """Implements the string package.""" from .string import String, ascii, atof, atol, chr, ord -from .string_slice import CharsIter, StaticString, StringSlice +from .string_slice import CodepointsIter, StaticString, StringSlice +from .codepoint import Codepoint diff --git a/stdlib/src/collections/string/_unicode.mojo b/stdlib/src/collections/string/_unicode.mojo index 6908d5f283..4ec5cd280e 100644 --- a/stdlib/src/collections/string/_unicode.mojo +++ b/stdlib/src/collections/string/_unicode.mojo @@ -16,30 +16,30 @@ from collections.string._unicode_lookups import * from memory import Span, UnsafePointer, memcpy -fn _uppercase_mapping_index(rune: Char) -> Int: +fn _uppercase_mapping_index(rune: Codepoint) -> Int: """Return index for upper case mapping or -1 if no mapping is given.""" return _to_index[has_uppercase_mapping](rune) -fn _uppercase_mapping2_index(rune: Char) -> Int: +fn _uppercase_mapping2_index(rune: Codepoint) -> Int: """Return index for upper case mapping converting the rune to 2 runes, or -1 if no mapping is given. """ return _to_index[has_uppercase_mapping2](rune) -fn _uppercase_mapping3_index(rune: Char) -> Int: +fn _uppercase_mapping3_index(rune: Codepoint) -> Int: """Return index for upper case mapping converting the rune to 3 runes, or -1 if no mapping is given. """ return _to_index[has_uppercase_mapping3](rune) -fn _lowercase_mapping_index(rune: Char) -> Int: +fn _lowercase_mapping_index(rune: Codepoint) -> Int: """Return index for lower case mapping or -1 if no mapping is given.""" return _to_index[has_lowercase_mapping](rune) @always_inline -fn _to_index[lookup: List[UInt32, **_]](rune: Char) -> Int: +fn _to_index[lookup: List[UInt32, **_]](rune: Codepoint) -> Int: """Find index of rune in lookup with binary search. Returns -1 if not found.""" @@ -52,43 +52,43 @@ fn _to_index[lookup: List[UInt32, **_]](rune: Char) -> Int: # TODO: -# Refactor this to return a Span[Char, StaticConstantOrigin], so that the +# Refactor this to return a Span[Codepoint, StaticConstantOrigin], so that the # return `UInt` count and fixed-size `InlineArray` are not necessary. fn _get_uppercase_mapping( - char: Char, -) -> Optional[Tuple[UInt, InlineArray[Char, 3]]]: + char: Codepoint, +) -> Optional[Tuple[UInt, InlineArray[Codepoint, 3]]]: """Returns the 1, 2, or 3 character sequence that is the uppercase form of `char`. Returns None if `char` does not have an uppercase equivalent. """ - var array = InlineArray[Char, 3](fill=Char(0)) + var array = InlineArray[Codepoint, 3](fill=Codepoint(0)) var index1 = _uppercase_mapping_index(char) if index1 != -1: var rune = uppercase_mapping[index1] - array[0] = Char(unsafe_unchecked_codepoint=rune) + array[0] = Codepoint(unsafe_unchecked_codepoint=rune) return Tuple(UInt(1), array) var index2 = _uppercase_mapping2_index(char) if index2 != -1: var runes = uppercase_mapping2[index2] - array[0] = Char(unsafe_unchecked_codepoint=runes[0]) - array[1] = Char(unsafe_unchecked_codepoint=runes[1]) + array[0] = Codepoint(unsafe_unchecked_codepoint=runes[0]) + array[1] = Codepoint(unsafe_unchecked_codepoint=runes[1]) return Tuple(UInt(2), array) var index3 = _uppercase_mapping3_index(char) if index3 != -1: var runes = uppercase_mapping3[index3] - array[0] = Char(unsafe_unchecked_codepoint=runes[0]) - array[1] = Char(unsafe_unchecked_codepoint=runes[1]) - array[2] = Char(unsafe_unchecked_codepoint=runes[2]) + array[0] = Codepoint(unsafe_unchecked_codepoint=runes[0]) + array[1] = Codepoint(unsafe_unchecked_codepoint=runes[1]) + array[2] = Codepoint(unsafe_unchecked_codepoint=runes[2]) return Tuple(UInt(3), array) return None -fn _get_lowercase_mapping(char: Char) -> Optional[Char]: +fn _get_lowercase_mapping(char: Codepoint) -> Optional[Codepoint]: var index: Optional[UInt] = has_lowercase_mapping._binary_search_index( char.to_u32() ) @@ -98,9 +98,9 @@ fn _get_lowercase_mapping(char: Char) -> Optional[Char]: var codepoint = lowercase_mapping[index.unsafe_value()] # SAFETY: - # We know this is a valid `Char` because the mapping data tables + # We know this is a valid `Codepoint` because the mapping data tables # contain only valid codepoints. - return Char(unsafe_unchecked_codepoint=codepoint) + return Codepoint(unsafe_unchecked_codepoint=codepoint) else: return None @@ -117,7 +117,7 @@ fn is_uppercase(s: StringSlice) -> Bool: there is at least one cased character, False otherwise. """ var found = False - for char in s.chars(): + for char in s.codepoints(): var index = _lowercase_mapping_index(char) if index != -1: found = True @@ -146,7 +146,7 @@ fn is_lowercase(s: StringSlice) -> Bool: there is at least one cased character, False otherwise. """ var found = False - for char in s.chars(): + for char in s.codepoints(): var index = _uppercase_mapping_index(char) if index != -1: found = True @@ -178,7 +178,9 @@ fn to_lowercase(s: StringSlice) -> String: var output = List[Byte](capacity=_estimate_needed_size(s.byte_length())) var input_offset = 0 while input_offset < s.byte_length(): - var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) + var rune_and_size = Codepoint.unsafe_decode_utf8_codepoint( + input + input_offset + ) var lowercase_char_opt = _get_lowercase_mapping(rune_and_size[0]) if lowercase_char_opt is None: memcpy( @@ -188,7 +190,7 @@ fn to_lowercase(s: StringSlice) -> String: ) output.size += rune_and_size[1] else: - var lower_char: Char = lowercase_char_opt.unsafe_value() + var lower_char: Codepoint = lowercase_char_opt.unsafe_value() output.size += lower_char.unsafe_write_utf8( output._unsafe_next_uninit_ptr() ) @@ -220,20 +222,22 @@ fn to_uppercase(s: StringSlice) -> String: var output = List[Byte](capacity=_estimate_needed_size(s.byte_length())) var input_offset = 0 while input_offset < s.byte_length(): - var rune_and_size = Char.unsafe_decode_utf8_char(input + input_offset) + var rune_and_size = Codepoint.unsafe_decode_utf8_codepoint( + input + input_offset + ) var uppercase_replacement_opt = _get_uppercase_mapping(rune_and_size[0]) if uppercase_replacement_opt: # A given character can be replaced with a sequence of characters - # up to 3 characters in length. A fixed size `Char` array is + # up to 3 characters in length. A fixed size `Codepoint` array is # returned, along with a `count` (1, 2, or 3) of how many # replacement characters are in the uppercase replacement sequence. count, uppercase_replacement_chars = ( uppercase_replacement_opt.unsafe_value() ) for char_idx in range(count): - var char: Char = uppercase_replacement_chars[char_idx] + var char: Codepoint = uppercase_replacement_chars[char_idx] output.size += char.unsafe_write_utf8( output._unsafe_next_uninit_ptr() ) diff --git a/stdlib/src/builtin/char.mojo b/stdlib/src/collections/string/codepoint.mojo similarity index 80% rename from stdlib/src/builtin/char.mojo rename to stdlib/src/collections/string/codepoint.mojo index 620b45273f..0452f15621 100644 --- a/stdlib/src/builtin/char.mojo +++ b/stdlib/src/collections/string/codepoint.mojo @@ -10,7 +10,8 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # -"""Implements the `Char` type for representing single characters.""" +"""Implements the `Codepoint` type for representing single Unicode scalar +values.""" from collections import Optional from collections.string import StringSlice @@ -35,18 +36,38 @@ fn _is_unicode_scalar_value(codepoint: UInt32) -> Bool: @value -struct Char(CollectionElement, EqualityComparable, Intable, Stringable): - """A single textual character. +struct Codepoint(CollectionElement, EqualityComparable, Intable, Stringable): + """A Unicode codepoint, typically a single user-recognizable character; + restricted to valid Unicode scalar values. - This type represents a single textual character. Specifically, this type - stores a single Unicode [*scalar value*][1], typically encoding a single - user-recognizable character. + This type is restricted to store a single Unicode [*scalar value*][1], + typically encoding a single user-recognizable character. All valid Unicode scalar values are in the range(s) 0 to 0xD7FF and 0xE000 to 0x10FFFF, inclusive. This type guarantees that the stored integer value falls in these ranges. [1]: https://www.unicode.org/glossary/#unicode_scalar_value + + # Codepoints vs Scalar Values + + Formally, Unicode defines a codespace of values in the range 0 to + 0x10FFFF inclusive, and a + [Unicode codepoint](https://www.unicode.org/glossary/#code_point) is any + integer falling within that range. However, due to historical reasons, + it became necessary to "carve out" a subset of the codespace, excluding + codepoints in the range 0xD7FF–0xE000. That subset of codepoints excluding + that range are known as [Unicode scalar values][1]. The codepoints in the + range 0xD7FF-0xE000 are known as "surrogate" codepoints. The surrogate + codepoints will never be assigned a semantic meaning, and can only + validly appear in UTF-16 encoded text. + + The difference between codepoints and scalar values is a technical + distiction related to the backwards-compatible workaround chosen to enable + UTF-16 to encode the full range of the Unicode codespace. For simplicities + sake, and to avoid a confusing clash with the Mojo `Scalar` type, this type + is pragmatically named `Codepoint`, even though it is restricted to valid + scalar values. """ var _scalar_value: UInt32 @@ -58,7 +79,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): @always_inline fn __init__(out self, *, unsafe_unchecked_codepoint: UInt32): - """Construct a `Char` from a code point value without checking that it + """Construct a `Codepoint` from a code point value without checking that it falls in the valid range. Safety: @@ -79,13 +100,13 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): @always_inline fn __init__(out self, codepoint: UInt8): - """Construct a `Char` from a single byte value. + """Construct a `Codepoint` from a single byte value. This constructor cannot fail because non-negative 8-bit integers are valid Unicode scalar values. Args: - codepoint: The 8-bit codepoint value to convert to a `Char`. + codepoint: The 8-bit codepoint value to convert to a `Codepoint`. """ self._scalar_value = UInt32(Int(codepoint)) @@ -95,44 +116,48 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): @staticmethod fn from_u32(codepoint: UInt32) -> Optional[Self]: - """Construct a `Char` from a code point value. Returns None if the + """Construct a `Codepoint` from a code point value. Returns None if the provided `codepoint` is not in the valid range. Args: codepoint: An integer representing a Unicode scalar value. Returns: - A `Char` if `codepoint` falls in the valid range for Unicode scalar - values, otherwise None. + A `Codepoint` if `codepoint` falls in the valid range for Unicode + scalar values, otherwise None. """ if _is_unicode_scalar_value(codepoint): - return Char(unsafe_unchecked_codepoint=codepoint) + return Codepoint(unsafe_unchecked_codepoint=codepoint) else: return None @staticmethod - fn ord(string: StringSlice) -> Char: - """Returns the `Char` that represents the given one-character string. + fn ord(string: StringSlice) -> Codepoint: + """Returns the `Codepoint` that represents the given single-character + string. - Given a string representing one character, return a `Char` - representing the codepoint of that character. For example, `Char.ord("a")` - returns the codepoint `97`. This is the inverse of the `chr()` function. + Given a string containing one character, return a `Codepoint` + representing the codepoint of that character. For example, + `Codepoint.ord("a")` returns the codepoint `97`. This is the inverse of + the `chr()` function. This function is similar to the `ord()` free function, except that it - returns a `Char` instead of an `Int`. + returns a `Codepoint` instead of an `Int`. Args: string: The input string, which must contain only a single character. Returns: - A `Char` representing the codepoint of the given character. + A `Codepoint` representing the codepoint of the given character. """ # SAFETY: # This is safe because `StringSlice` is guaranteed to point to valid # UTF-8. - char, num_bytes = Char.unsafe_decode_utf8_char(string.unsafe_ptr()) + char, num_bytes = Codepoint.unsafe_decode_utf8_codepoint( + string.unsafe_ptr() + ) debug_assert( string.byte_length() == Int(num_bytes), @@ -142,9 +167,11 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): return char @staticmethod - fn unsafe_decode_utf8_char(_ptr: UnsafePointer[Byte]) -> (Char, Int): - """Decodes a single `Char` and number of bytes read from a given UTF-8 - string pointer. + fn unsafe_decode_utf8_codepoint( + _ptr: UnsafePointer[Byte], + ) -> (Codepoint, Int): + """Decodes a single `Codepoint` and number of bytes read from a given + UTF-8 string pointer. Safety: `_ptr` MUST point to the first byte in a **known-valid** UTF-8 @@ -156,7 +183,8 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): encoded codepoint. Returns: - The decoded codepoint `Char`, as well as the number of bytes read. + The decoded codepoint `Codepoint`, as well as the number of bytes + read. """ # UTF-8 to Unicode conversion: (represented as UInt32 BE) @@ -168,7 +196,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): var b1 = ptr[] if (b1 >> 7) == 0: # This is 1 byte ASCII char - return Char(b1), 1 + return Codepoint(b1), 1 # TODO: Use _utf8_first_byte_sequence_length() here instead for # consistency. @@ -200,7 +228,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): # UTF-8 encoding algorithms that do not properly exclude surrogate # pair code points are actually relatively common (as I understand # it); the algorithm above does not check for that. - var char = Char(unsafe_unchecked_codepoint=result) + var char = Codepoint(unsafe_unchecked_codepoint=result) return char, Int(num_bytes) @@ -248,7 +276,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): @always_inline fn __str__(self) -> String: - """Formats this `Char` as a single-character string. + """Formats this `Codepoint` as a single-character string. Returns: A string containing this single character. @@ -266,13 +294,13 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): @always_inline fn is_ascii(self) -> Bool: - """Returns True if this `Char` is an ASCII character. + """Returns True if this `Codepoint` is an ASCII character. All ASCII characters are less than or equal to codepoint value 127, and take exactly 1 byte to encode in UTF-8. Returns: - A boolean indicating if this `Char` is an ASCII character. + A boolean indicating if this `Codepoint` is an ASCII character. """ return self._scalar_value <= 0b0111_1111 @@ -342,21 +370,21 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): from testing import assert_true, assert_false # ASCII space characters - assert_true(Char.ord(" ").is_python_space()) - assert_true(Char.ord("\t").is_python_space()) + assert_true(Codepoint.ord(" ").is_python_space()) + assert_true(Codepoint.ord("\t").is_python_space()) # Unicode paragraph separator: - assert_true(Char.from_u32(0x2029).value().is_python_space()) + assert_true(Codepoint.from_u32(0x2029).value().is_python_space()) # Letters are not space characters - assert_fales(Char.ord("a").is_python_space()) + assert_fales(Codepoint.ord("a").is_python_space()) ``` . """ - alias next_line = Char.from_u32(0x85).value() - alias unicode_line_sep = Char.from_u32(0x2028).value() - alias unicode_paragraph_sep = Char.from_u32(0x2029).value() + alias next_line = Codepoint.from_u32(0x85).value() + alias unicode_line_sep = Codepoint.from_u32(0x2028).value() + alias unicode_paragraph_sep = Codepoint.from_u32(0x2029).value() return self.is_posix_space() or self in ( next_line, @@ -365,7 +393,7 @@ struct Char(CollectionElement, EqualityComparable, Intable, Stringable): ) fn is_posix_space(self) -> Bool: - """Returns True if this `Char` is a **space** character according to the + """Returns True if this `Codepoint` is a **space** character according to the [POSIX locale][1]. The POSIX locale is also known as the C locale. diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index abf923b261..2ed0a1b676 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -14,7 +14,7 @@ from collections import KeyElement, List, Optional from collections._index_normalization import normalize_index -from collections.string import CharsIter +from collections.string import CodepointsIter from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( StaticString, @@ -48,19 +48,20 @@ from utils.write import _TotalWritableBytes, _WriteBufferHeap, write_buffered fn ord(s: StringSlice) -> Int: - """Returns an integer that represents the given one-character string. + """Returns an integer that represents the codepoint of a single-character + string. - Given a string representing one character, return an integer - representing the code point of that character. For example, `ord("a")` + Given a string containing a single character `Codepoint`, return an integer + representing the codepoint of that character. For example, `ord("a")` returns the integer `97`. This is the inverse of the `chr()` function. Args: - s: The input string, which must contain only a single character. + s: The input string, which must contain only a single- character. Returns: An integer representing the code point of the given character. """ - return Int(Char.ord(s)) + return Int(Codepoint.ord(s)) # ===----------------------------------------------------------------------=== # @@ -89,7 +90,7 @@ fn chr(c: Int) -> String: if c < 0b1000_0000: # 1 byte ASCII char return String(String._buffer_type(c, 0)) - var char_opt = Char.from_u32(c) + var char_opt = Codepoint.from_u32(c) if not char_opt: # TODO: Raise ValueError instead. return abort[String]( @@ -135,7 +136,7 @@ fn _repr_ascii(c: UInt8) -> String: if c == ord_back_slash: return r"\\" - elif Char(c).is_ascii_printable(): + elif Codepoint(c).is_ascii_printable(): return _chr_ascii(c) elif c == ord_tab: return r"\t" @@ -284,13 +285,13 @@ fn atol(str_slice: StringSlice, base: Int = 10) raises -> Int: elif ord_letter_min[1] <= ord_current <= ord_letter_max[1]: result += ord_current - ord_letter_min[1] + 10 found_valid_chars_after_start = True - elif Char(UInt8(ord_current)).is_posix_space(): + elif Codepoint(UInt8(ord_current)).is_posix_space(): has_space_after_number = True start = pos + 1 break else: raise Error(_str_to_base_error(base, str_slice)) - if pos + 1 < str_len and not Char(buff[pos + 1]).is_posix_space(): + if pos + 1 < str_len and not Codepoint(buff[pos + 1]).is_posix_space(): var nextresult = result * real_base if nextresult < result: raise Error( @@ -304,7 +305,7 @@ fn atol(str_slice: StringSlice, base: Int = 10) raises -> Int: if has_space_after_number: for pos in range(start, str_len): - if not Char(buff[pos]).is_posix_space(): + if not Codepoint(buff[pos]).is_posix_space(): raise Error(_str_to_base_error(base, str_slice)) if is_negative: result = -result @@ -326,7 +327,7 @@ fn _trim_and_handle_sign(str_slice: StringSlice, str_len: Int) -> (Int, Bool): """ var buff = str_slice.unsafe_ptr() var start: Int = 0 - while start < str_len and Char(buff[start]).is_posix_space(): + while start < str_len and Codepoint(buff[start]).is_posix_space(): start += 1 var p: Bool = buff[start] == ord("+") var n: Bool = buff[start] == ord("-") @@ -1057,14 +1058,14 @@ struct String( """ self._iadd[False](other.as_bytes()) - @deprecated("Use `str.chars()` or `str.char_slices()` instead.") + @deprecated("Use `str.codepoints()` or `str.codepoint_slices()` instead.") fn __iter__(self) -> _StringSliceIter[__origin_of(self)]: """Iterate over the string, returning immutable references. Returns: An iterator of references to the string elements. """ - return self.char_slices() + return self.codepoint_slices() fn __reversed__(self) -> _StringSliceIter[__origin_of(self), False]: """Iterate backwards over the string, returning immutable references. @@ -1097,7 +1098,7 @@ struct String( representation of the string. To get the number of Unicode codepoints in a string, use - `len(str.chars())`. + `len(str.codepoints())`. Returns: The string length in bytes. @@ -1112,7 +1113,7 @@ struct String( var s = String("ನಮಸ್ಕಾರ") assert_equal(len(s), 21) - assert_equal(len(s.chars()), 7) + assert_equal(len(s.codepoints()), 7) ``` Strings containing only ASCII characters have the same byte and @@ -1124,7 +1125,7 @@ struct String( var s = String("abc") assert_equal(len(s), 3) - assert_equal(len(s.chars()), 3) + assert_equal(len(s.codepoints()), 3) ``` . """ @@ -1227,11 +1228,11 @@ struct String( return string @always_inline - fn chars(self) -> CharsIter[__origin_of(self)]: - """Returns an iterator over the `Char`s encoded in this string slice. + fn codepoints(self) -> CodepointsIter[__origin_of(self)]: + """Returns an iterator over the `Codepoint`s encoded in this string slice. Returns: - An iterator type that returns successive `Char` values stored in + An iterator type that returns successive `Codepoint` values stored in this string slice. # Examples @@ -1242,14 +1243,14 @@ struct String( from testing import assert_equal var s = String("abc") - var iter = s.chars() - assert_equal(iter.__next__(), Char.ord("a")) - assert_equal(iter.__next__(), Char.ord("b")) - assert_equal(iter.__next__(), Char.ord("c")) + var iter = s.codepoints() + assert_equal(iter.__next__(), Codepoint.ord("a")) + assert_equal(iter.__next__(), Codepoint.ord("b")) + assert_equal(iter.__next__(), Codepoint.ord("c")) assert_equal(iter.__has_next__(), False) ``` - `chars()` iterates over Unicode codepoints, and supports multibyte + `codepoints()` iterates over Unicode codepoints, and supports multibyte codepoints: ```mojo @@ -1259,17 +1260,17 @@ struct String( var s = String("á") assert_equal(s.byte_length(), 3) - var iter = s.chars() - assert_equal(iter.__next__(), Char.ord("a")) + var iter = s.codepoints() + assert_equal(iter.__next__(), Codepoint.ord("a")) # U+0301 Combining Acute Accent assert_equal(iter.__next__().to_u32(), 0x0301) assert_equal(iter.__has_next__(), False) ``` . """ - return self.as_string_slice().chars() + return self.as_string_slice().codepoints() - fn char_slices(self) -> _StringSliceIter[__origin_of(self)]: + fn codepoint_slices(self) -> _StringSliceIter[__origin_of(self)]: """Returns an iterator over single-character slices of this string. Each returned slice points to a single Unicode codepoint encoded in the @@ -1286,7 +1287,7 @@ struct String( from testing import assert_equal, assert_true var s = String("abc") - var iter = s.char_slices() + var iter = s.codepoint_slices() assert_true(iter.__next__() == "a") assert_true(iter.__next__() == "b") assert_true(iter.__next__() == "c") @@ -1294,7 +1295,7 @@ struct String( ``` . """ - return self.as_string_slice().char_slices() + return self.as_string_slice().codepoint_slices() fn unsafe_ptr( ref self, diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 32a95a3d55..9a60ea9af2 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -110,9 +110,9 @@ struct _StringSliceIter[ @value -struct CharsIter[mut: Bool, //, origin: Origin[mut]]: - """Iterator over the `Char`s in a string slice, constructed by - `StringSlice.chars()`. +struct CodepointsIter[mut: Bool, //, origin: Origin[mut]]: + """Iterator over the `Codepoint`s in a string slice, constructed by + `StringSlice.codepoints()`. Parameters: mut: Mutability of the underlying string data. @@ -128,8 +128,8 @@ struct CharsIter[mut: Bool, //, origin: Origin[mut]]: """ # Note: - # Marked private since `StringSlice.chars()` is the intended public way to - # construct this type. + # Marked private since `StringSlice.codepoints()` is the intended public + # way to construct this type. @doc_private fn __init__(out self, str_slice: StringSlice[origin]): self._slice = str_slice @@ -142,10 +142,10 @@ struct CharsIter[mut: Bool, //, origin: Origin[mut]]: fn __iter__(self) -> Self: return self - fn __next__(mut self) -> Char: - """Get the next character in the underlying string slice. + fn __next__(mut self) -> Codepoint: + """Get the next codepoint in the underlying string slice. - This returns the next `Char` encoded in the underlying string, and + This returns the next `Codepoint` encoded in the underlying string, and advances the iterator state. This function will abort if this iterator has been exhausted. @@ -167,7 +167,7 @@ struct CharsIter[mut: Bool, //, origin: Origin[mut]]: @always_inline fn __len__(self) -> Int: - """Returns the remaining length of this iterator in `Char`s. + """Returns the remaining length of this iterator in `Codepoint`s. The value returned from this method indicates the number of subsequent calls to `next()` that will return a value. @@ -181,8 +181,8 @@ struct CharsIter[mut: Bool, //, origin: Origin[mut]]: # Methods # ===-------------------------------------------------------------------===# - fn peek_next(self) -> Optional[Char]: - """Check what the next character in this iterator is, without advancing + fn peek_next(self) -> Optional[Codepoint]: + """Check what the next codepoint in this iterator is, without advancing the iterator state. Repeated calls to this method will return the same value. @@ -197,44 +197,46 @@ struct CharsIter[mut: Bool, //, origin: Origin[mut]]: return the same value: ```mojo - from collections.string import StringSlice + from collections.string import StringSlice, Codepoint from testing import assert_equal var input = StringSlice("123") - var iter = input.chars() + var iter = input.codepoints() - assert_equal(iter.peek_next().value(), Char.ord("1")) - assert_equal(iter.peek_next().value(), Char.ord("1")) - assert_equal(iter.peek_next().value(), Char.ord("1")) + assert_equal(iter.peek_next().value(), Codepoint.ord("1")) + assert_equal(iter.peek_next().value(), Codepoint.ord("1")) + assert_equal(iter.peek_next().value(), Codepoint.ord("1")) # A call to `next()` return the same value as `peek_next()` had, # but also advance the iterator. - assert_equal(iter.next().value(), Char.ord("1")) + assert_equal(iter.next().value(), Codepoint.ord("1")) # Later `peek_next()` calls will return the _new_ next character: - assert_equal(iter.peek_next().value(), Char.ord("2")) + assert_equal(iter.peek_next().value(), Codepoint.ord("2")) ``` . """ if len(self._slice) > 0: # SAFETY: Will not read out of bounds because `_slice` is guaranteed # to contain valid UTF-8. - char, _ = Char.unsafe_decode_utf8_char(self._slice.unsafe_ptr()) - return char + codepoint, _ = Codepoint.unsafe_decode_utf8_codepoint( + self._slice.unsafe_ptr() + ) + return codepoint else: return None - fn next(mut self) -> Optional[Char]: - """Get the next character in the underlying string slice, or None if + fn next(mut self) -> Optional[Codepoint]: + """Get the next codepoint in the underlying string slice, or None if the iterator is empty. - This returns the next `Char` encoded in the underlying string, and + This returns the next `Codepoint` encoded in the underlying string, and advances the iterator state. Returns: A character if the string is not empty, otherwise None. """ - var result: Optional[Char] = self.peek_next() + var result: Optional[Codepoint] = self.peek_next() if result: # SAFETY: We just checked that `result` holds a value @@ -453,7 +455,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ var result = String() var use_dquote = False - for s in self.char_slices(): + for s in self.codepoint_slices(): use_dquote = use_dquote or (s == "'") if s == "\\": @@ -465,7 +467,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( elif s == "\r": result += r"\r" else: - var codepoint = Char.ord(s) + var codepoint = Codepoint.ord(s) if codepoint.is_ascii_printable(): result += s elif codepoint.to_u32() < 0x10: @@ -488,7 +490,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( representation of the string. To get the number of Unicode codepoints in a string, use - `len(str.chars())`. + `len(str.codepoints())`. Returns: The string length in bytes. @@ -504,7 +506,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( var s = StringSlice("ನಮಸ್ಕಾರ") assert_equal(len(s), 21) - assert_equal(len(s.chars()), 7) + assert_equal(len(s.codepoints()), 7) ``` Strings containing only ASCII characters have the same byte and @@ -517,7 +519,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( var s = StringSlice("abc") assert_equal(len(s), 3) - assert_equal(len(s.chars()), 3) + assert_equal(len(s.codepoints()), 3) ``` . """ @@ -682,14 +684,14 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( self.unsafe_ptr(), rhs.unsafe_ptr(), min(len1, len2) ) - @deprecated("Use `str.chars()` or `str.char_slices()` instead.") + @deprecated("Use `str.codepoints()` or `str.codepoint_slices()` instead.") fn __iter__(self) -> _StringSliceIter[origin]: """Iterate over the string, returning immutable references. Returns: An iterator of references to the string elements. """ - return self.char_slices() + return self.codepoint_slices() fn __reversed__(self) -> _StringSliceIter[origin, False]: """Iterate backwards over the string, returning immutable references. @@ -891,7 +893,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( try: # Python adds all "whitespace chars" as one separator # if no separator was specified - for s in self[lhs:].char_slices(): + for s in self[lhs:].codepoint_slices(): if not s.isspace(): break lhs += s.byte_length() @@ -907,7 +909,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( rhs = lhs + num_bytes(self.unsafe_ptr()[lhs]) for s in self[ lhs + num_bytes(self.unsafe_ptr()[lhs]) : - ].char_slices(): + ].codepoint_slices(): if s.isspace(): break rhs += s.byte_length() @@ -1012,7 +1014,9 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # if not s.isspace(): # break # r_idx -= 1 - while r_idx > 0 and Char(self.as_bytes()[r_idx - 1]).is_posix_space(): + while ( + r_idx > 0 and Codepoint(self.as_bytes()[r_idx - 1]).is_posix_space() + ): r_idx -= 1 return Self(unsafe_from_utf8=self.as_bytes()[:r_idx]) @@ -1064,17 +1068,17 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( # l_idx += 1 while ( l_idx < self.byte_length() - and Char(self.as_bytes()[l_idx]).is_posix_space() + and Codepoint(self.as_bytes()[l_idx]).is_posix_space() ): l_idx += 1 return Self(unsafe_from_utf8=self.as_bytes()[l_idx:]) @always_inline - fn chars(self) -> CharsIter[origin]: - """Returns an iterator over the `Char`s encoded in this string slice. + fn codepoints(self) -> CodepointsIter[origin]: + """Returns an iterator over the `Codepoint`s encoded in this string slice. Returns: - An iterator type that returns successive `Char` values stored in + An iterator type that returns successive `Codepoint` values stored in this string slice. # Examples @@ -1086,14 +1090,14 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( from testing import assert_equal var s = StringSlice("abc") - var iter = s.chars() - assert_equal(iter.__next__(), Char.ord("a")) - assert_equal(iter.__next__(), Char.ord("b")) - assert_equal(iter.__next__(), Char.ord("c")) + var iter = s.codepoints() + assert_equal(iter.__next__(), Codepoint.ord("a")) + assert_equal(iter.__next__(), Codepoint.ord("b")) + assert_equal(iter.__next__(), Codepoint.ord("c")) assert_equal(iter.__has_next__(), False) ``` - `chars()` iterates over Unicode codepoints, and supports multibyte + `codepoints()` iterates over Unicode codepoints, and supports multibyte codepoints: ```mojo @@ -1104,17 +1108,17 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( var s = StringSlice("á") assert_equal(s.byte_length(), 3) - var iter = s.chars() - assert_equal(iter.__next__(), Char.ord("a")) + var iter = s.codepoints() + assert_equal(iter.__next__(), Codepoint.ord("a")) # U+0301 Combining Acute Accent assert_equal(iter.__next__().to_u32(), 0x0301) assert_equal(iter.__has_next__(), False) ``` . """ - return CharsIter(self) + return CodepointsIter(self) - fn char_slices(self) -> _StringSliceIter[origin]: + fn codepoint_slices(self) -> _StringSliceIter[origin]: """Iterate over the string, returning immutable references. Returns: @@ -1157,7 +1161,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( fn char_length(self) -> UInt: """Returns the length in Unicode codepoints. - This returns the number of `Char` codepoint values encoded in the UTF-8 + This returns the number of `Codepoint` codepoint values encoded in the UTF-8 representation of this string. Note: To get the length in bytes, use `StringSlice.byte_length()`. @@ -1552,7 +1556,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( if self.byte_length() == 0: return False - for s in self.chars(): + for s in self.codepoints(): if not s.is_python_space(): return False @@ -1584,7 +1588,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( ) else: var offset = 0 - for s in self.char_slices(): + for s in self.codepoint_slices(): var b_len = s.byte_length() if not _is_newline_char(ptr, offset, ptr[offset], b_len): return False @@ -1689,7 +1693,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ if not self: return False - for char in self.chars(): + for char in self.codepoints(): if not char.is_ascii_digit(): return False return True @@ -1744,7 +1748,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: True if all characters are printable else False. """ - for char in self.chars(): + for char in self.codepoints(): if not char.is_ascii_printable(): return False return True diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index e2c5807909..c6c3da74d2 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -15,7 +15,7 @@ """ from collections import KeyElement, List -from collections.string import String, ascii, atof, atol, chr, ord +from collections.string import Codepoint, String, ascii, atof, atol, chr, ord from hashlib.hash import Hashable, hash from builtin.anytype import AnyType, UnknownDestructibility @@ -28,7 +28,6 @@ from builtin.builtin_list import ( VariadicPack, ) from builtin.builtin_slice import Slice, slice -from builtin.char import Char from builtin.comparable import ( Comparable, GreaterThanComparable, diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index f9098e435d..26d14bdeab 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -74,8 +74,8 @@ def test_constructors(): var s4 = String(capacity=1) assert_equal(s4._buffer.capacity, 1) - # Construction from Char - var s5 = String(Char(65)) + # Construction from Codepoint + var s5 = String(Codepoint(65)) assert_equal(s4._buffer.capacity, 1) assert_equal(s5, "A") @@ -93,13 +93,13 @@ def test_len(): var s0 = String("ನಮಸ್ಕಾರ") assert_equal(len(s0), 21) - assert_equal(len(s0.chars()), 7) + assert_equal(len(s0.codepoints()), 7) # For ASCII string, the byte and codepoint length are the same: var s1 = String("abc") assert_equal(len(s1), 3) - assert_equal(len(s1.chars()), 3) + assert_equal(len(s1.codepoints()), 3) def test_equality_operators(): @@ -1090,18 +1090,18 @@ def test_indexing(): assert_equal(a[2], "c") -def test_string_chars_iter(): +def test_string_codepoints_iter(): var s = String("abc") - var iter = s.chars() - assert_equal(iter.__next__(), Char.ord("a")) - assert_equal(iter.__next__(), Char.ord("b")) - assert_equal(iter.__next__(), Char.ord("c")) + var iter = s.codepoints() + assert_equal(iter.__next__(), Codepoint.ord("a")) + assert_equal(iter.__next__(), Codepoint.ord("b")) + assert_equal(iter.__next__(), Codepoint.ord("c")) assert_equal(iter.__has_next__(), False) def test_string_char_slices_iter(): var s0 = String("abc") - var s0_iter = s0.char_slices() + var s0_iter = s0.codepoint_slices() assert_true(s0_iter.__next__() == "a") assert_true(s0_iter.__next__() == "b") assert_true(s0_iter.__next__() == "c") @@ -1112,7 +1112,7 @@ def test_string_char_slices_iter(): # Borrow immutably fn conc(vs: String) -> String: var c = String("") - for v in vs.char_slices(): + for v in vs.codepoint_slices(): c += v return c @@ -1123,18 +1123,18 @@ def test_string_char_slices_iter(): concat += v assert_equal(321, atol(concat)) - for v in vs.char_slices(): + for v in vs.codepoint_slices(): v.unsafe_ptr().origin_cast[mut=True]()[] = ord("1") # Borrow immutably - for v in vs.char_slices(): + for v in vs.codepoint_slices(): concat += v assert_equal(321111, atol(concat)) var idx = -1 vs = String("mojo🔥") - var iterator = vs.char_slices() + var iterator = vs.codepoint_slices() assert_equal(5, len(iterator)) var item = iterator.__next__() assert_equal(String("m"), String(item)) @@ -1184,7 +1184,7 @@ def test_string_char_slices_iter(): var ptr = item.unsafe_ptr() var amnt_characters = 0 var byte_idx = 0 - for v in item.char_slices(): + for v in item.codepoint_slices(): var byte_len = v.byte_length() for i in range(byte_len): assert_equal(ptr[byte_idx + i], v.unsafe_ptr()[i]) @@ -1469,7 +1469,7 @@ def main(): test_intable() test_string_mul() test_indexing() - test_string_chars_iter() + test_string_codepoints_iter() test_string_char_slices_iter() test_format_args() test_format_conversion_flags() diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index 92851c8e34..a5cd1e2e44 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -218,12 +218,12 @@ fn test_slice_len() raises: # String length is in bytes, not codepoints. var s0 = String("ನಮಸ್ಕಾರ") assert_equal(len(s0), 21) - assert_equal(len(s0.chars()), 7) + assert_equal(len(s0.codepoints()), 7) # For ASCII string, the byte and codepoint length are the same: var s1 = String("abc") assert_equal(len(s1), 3) - assert_equal(len(s1.chars()), 3) + assert_equal(len(s1.codepoints()), 3) fn test_slice_char_length() raises: @@ -1029,12 +1029,14 @@ def test_count(): def test_chars_iter(): # Test `for` loop iteration support - for char in StringSlice("abc").chars(): - assert_true(char in (Char.ord("a"), Char.ord("b"), Char.ord("c"))) + for char in StringSlice("abc").codepoints(): + assert_true( + char in (Codepoint.ord("a"), Codepoint.ord("b"), Codepoint.ord("c")) + ) # Test empty string chars var s0 = StringSlice("") - var s0_iter = s0.chars() + var s0_iter = s0.codepoints() assert_false(s0_iter.__has_next__()) assert_true(s0_iter.peek_next() is None) @@ -1042,11 +1044,11 @@ def test_chars_iter(): # Test simple ASCII string chars var s1 = StringSlice("abc") - var s1_iter = s1.chars() + var s1_iter = s1.codepoints() - assert_equal(s1_iter.next().value(), Char.ord("a")) - assert_equal(s1_iter.next().value(), Char.ord("b")) - assert_equal(s1_iter.next().value(), Char.ord("c")) + assert_equal(s1_iter.next().value(), Codepoint.ord("a")) + assert_equal(s1_iter.next().value(), Codepoint.ord("b")) + assert_equal(s1_iter.next().value(), Codepoint.ord("c")) assert_true(s1_iter.next() is None) # Multibyte character decoding: A visual character composed of a combining @@ -1055,8 +1057,8 @@ def test_chars_iter(): assert_equal(s2.byte_length(), 3) assert_equal(s2.char_length(), 2) - var iter = s2.chars() - assert_equal(iter.__next__(), Char.ord("a")) + var iter = s2.codepoints() + assert_equal(iter.__next__(), Codepoint.ord("a")) # U+0301 Combining Acute Accent assert_equal(iter.__next__().to_u32(), 0x0301) assert_equal(iter.__has_next__(), False) @@ -1068,32 +1070,32 @@ def test_chars_iter(): var s3 = StringSlice("߷കൈ🔄!") assert_equal(s3.byte_length(), 13) assert_equal(s3.char_length(), 5) - var s3_iter = s3.chars() + var s3_iter = s3.codepoints() # Iterator __len__ returns length in codepoints, not bytes. assert_equal(s3_iter.__len__(), 5) assert_equal(s3_iter._slice.byte_length(), 13) assert_equal(s3_iter.__has_next__(), True) - assert_equal(s3_iter.__next__(), Char.ord("߷")) + assert_equal(s3_iter.__next__(), Codepoint.ord("߷")) assert_equal(s3_iter.__len__(), 4) assert_equal(s3_iter._slice.byte_length(), 11) - assert_equal(s3_iter.__next__(), Char.ord("ക")) + assert_equal(s3_iter.__next__(), Codepoint.ord("ക")) # Combining character, visually comes first, but codepoint-wise comes # after the character it combines with. assert_equal(s3_iter.__len__(), 3) assert_equal(s3_iter._slice.byte_length(), 8) - assert_equal(s3_iter.__next__(), Char.ord("ൈ")) + assert_equal(s3_iter.__next__(), Codepoint.ord("ൈ")) assert_equal(s3_iter.__len__(), 2) assert_equal(s3_iter._slice.byte_length(), 5) - assert_equal(s3_iter.__next__(), Char.ord("🔄")) + assert_equal(s3_iter.__next__(), Codepoint.ord("🔄")) assert_equal(s3_iter.__len__(), 1) assert_equal(s3_iter._slice.byte_length(), 1) assert_equal(s3_iter.__has_next__(), True) - assert_equal(s3_iter.__next__(), Char.ord("!")) + assert_equal(s3_iter.__next__(), Codepoint.ord("!")) assert_equal(s3_iter.__len__(), 0) assert_equal(s3_iter._slice.byte_length(), 0) diff --git a/stdlib/test/collections/string/test_unicode.mojo b/stdlib/test/collections/string/test_unicode.mojo index 7ec07dfbb8..67dd0ba806 100644 --- a/stdlib/test/collections/string/test_unicode.mojo +++ b/stdlib/test/collections/string/test_unicode.mojo @@ -20,27 +20,29 @@ from testing import assert_equal def test_uppercase_conversion(): # a -> A - count1, chars1 = _get_uppercase_mapping(Char(97)).value() + count1, chars1 = _get_uppercase_mapping(Codepoint(97)).value() assert_equal(count1, 1) - assert_equal(chars1[0], Char(65)) - assert_equal(chars1[1], Char(0)) - assert_equal(chars1[2], Char(0)) + assert_equal(chars1[0], Codepoint(65)) + assert_equal(chars1[1], Codepoint(0)) + assert_equal(chars1[2], Codepoint(0)) # ß -> SS - count2, chars2 = _get_uppercase_mapping(Char.from_u32(0xDF).value()).value() + count2, chars2 = _get_uppercase_mapping( + Codepoint.from_u32(0xDF).value() + ).value() assert_equal(count2, 2) - assert_equal(chars2[0], Char.from_u32(0x53).value()) - assert_equal(chars2[1], Char.from_u32(0x53).value()) - assert_equal(chars2[2], Char(0)) + assert_equal(chars2[0], Codepoint.from_u32(0x53).value()) + assert_equal(chars2[1], Codepoint.from_u32(0x53).value()) + assert_equal(chars2[2], Codepoint(0)) # ΐ -> Ϊ́ count3, chars3 = _get_uppercase_mapping( - Char.from_u32(0x390).value() + Codepoint.from_u32(0x390).value() ).value() assert_equal(count3, 3) - assert_equal(chars3[0], Char.from_u32(0x0399).value()) - assert_equal(chars3[1], Char.from_u32(0x0308).value()) - assert_equal(chars3[2], Char.from_u32(0x0301).value()) + assert_equal(chars3[0], Codepoint.from_u32(0x0399).value()) + assert_equal(chars3[1], Codepoint.from_u32(0x0308).value()) + assert_equal(chars3[2], Codepoint.from_u32(0x0301).value()) def main(): diff --git a/stdlib/test/builtin/test_char.mojo b/stdlib/test/collections/test_codepoint.mojo similarity index 59% rename from stdlib/test/builtin/test_char.mojo rename to stdlib/test/collections/test_codepoint.mojo index 1d0f124d95..2b366a2ca1 100644 --- a/stdlib/test/builtin/test_char.mojo +++ b/stdlib/test/collections/test_codepoint.mojo @@ -17,125 +17,127 @@ from testing import assert_equal, assert_false, assert_not_equal, assert_true def test_char_validity(): # Check that basic unchecked constructor behaves as expected. - var c1 = Char(unsafe_unchecked_codepoint=32) + var c1 = Codepoint(unsafe_unchecked_codepoint=32) assert_equal(c1._scalar_value, 32) - assert_true(Char.from_u32(0)) + assert_true(Codepoint.from_u32(0)) # For a visual intuition of what constitues a valid scalar value: # https://connorgray.com/ephemera/project-log#2025-01-09 # Last valid code point in the smaller scalar value range. - assert_true(Char.from_u32(0xD7FF)) + assert_true(Codepoint.from_u32(0xD7FF)) # First surrogate code point, not valid. - assert_false(Char.from_u32(0xD7FF + 1)) + assert_false(Codepoint.from_u32(0xD7FF + 1)) # Last surrogate code point, not valid - assert_false(Char.from_u32(0xDFFF)) + assert_false(Codepoint.from_u32(0xDFFF)) # First valid code point in the larger scalar value range. - assert_true(Char.from_u32(0xE000)) + assert_true(Codepoint.from_u32(0xE000)) # Beyond Unicode's committed range of code points. - assert_false(Char.from_u32(0x10FFFF + 1)) + assert_false(Codepoint.from_u32(0x10FFFF + 1)) def test_char_from_u8(): - var c1 = Char(UInt8(0)) + var c1 = Codepoint(UInt8(0)) assert_true(c1.is_ascii()) # All non-negative 8-bit integers are codepoints, but not all are ASCII. - var c2 = Char(UInt8(255)) + var c2 = Codepoint(UInt8(255)) assert_false(c2.is_ascii()) def test_char_comparison(): - assert_equal(Char(0), Char(0)) - assert_not_equal(Char(0), Char(1)) + assert_equal(Codepoint(0), Codepoint(0)) + assert_not_equal(Codepoint(0), Codepoint(1)) def test_char_formatting(): - assert_equal(String(Char(0)), "\0") - assert_equal(String(Char(32)), " ") - assert_equal(String(Char(97)), "a") - assert_equal(String(Char.from_u32(0x00BE).value()), "¾") - assert_equal(String(Char.from_u32(0x1F642).value()), "🙂") + assert_equal(String(Codepoint(0)), "\0") + assert_equal(String(Codepoint(32)), " ") + assert_equal(String(Codepoint(97)), "a") + assert_equal(String(Codepoint.from_u32(0x00BE).value()), "¾") + assert_equal(String(Codepoint.from_u32(0x1F642).value()), "🙂") def test_char_properties(): - assert_true(Char.from_u32(0).value().is_ascii()) + assert_true(Codepoint.from_u32(0).value().is_ascii()) # Last ASCII codepoint. - assert_true(Char.from_u32(0b0111_1111).value().is_ascii()) # ASCII 127 0x7F + assert_true( + Codepoint.from_u32(0b0111_1111).value().is_ascii() + ) # ASCII 127 0x7F # First non-ASCII codepoint. - assert_false(Char.from_u32(0b1000_0000).value().is_ascii()) - assert_false(Char.from_u32(0b1111_1111).value().is_ascii()) + assert_false(Codepoint.from_u32(0b1000_0000).value().is_ascii()) + assert_false(Codepoint.from_u32(0b1111_1111).value().is_ascii()) def test_char_is_posix_space(): # checking true cases - assert_true(Char.ord(" ").is_posix_space()) - assert_true(Char.ord("\n").is_posix_space()) - assert_true(Char.ord("\n").is_posix_space()) - assert_true(Char.ord("\t").is_posix_space()) - assert_true(Char.ord("\r").is_posix_space()) - assert_true(Char.ord("\v").is_posix_space()) - assert_true(Char.ord("\f").is_posix_space()) + assert_true(Codepoint.ord(" ").is_posix_space()) + assert_true(Codepoint.ord("\n").is_posix_space()) + assert_true(Codepoint.ord("\n").is_posix_space()) + assert_true(Codepoint.ord("\t").is_posix_space()) + assert_true(Codepoint.ord("\r").is_posix_space()) + assert_true(Codepoint.ord("\v").is_posix_space()) + assert_true(Codepoint.ord("\f").is_posix_space()) # Checking false cases - assert_false(Char.ord("a").is_posix_space()) - assert_false(Char.ord("a").is_posix_space()) - assert_false(Char.ord("u").is_posix_space()) - assert_false(Char.ord("s").is_posix_space()) - assert_false(Char.ord("t").is_posix_space()) - assert_false(Char.ord("i").is_posix_space()) - assert_false(Char.ord("n").is_posix_space()) - assert_false(Char.ord("z").is_posix_space()) - assert_false(Char.ord(".").is_posix_space()) + assert_false(Codepoint.ord("a").is_posix_space()) + assert_false(Codepoint.ord("a").is_posix_space()) + assert_false(Codepoint.ord("u").is_posix_space()) + assert_false(Codepoint.ord("s").is_posix_space()) + assert_false(Codepoint.ord("t").is_posix_space()) + assert_false(Codepoint.ord("i").is_posix_space()) + assert_false(Codepoint.ord("n").is_posix_space()) + assert_false(Codepoint.ord("z").is_posix_space()) + assert_false(Codepoint.ord(".").is_posix_space()) def test_char_is_lower(): - assert_true(Char.ord("a").is_ascii_lower()) - assert_true(Char.ord("b").is_ascii_lower()) - assert_true(Char.ord("y").is_ascii_lower()) - assert_true(Char.ord("z").is_ascii_lower()) + assert_true(Codepoint.ord("a").is_ascii_lower()) + assert_true(Codepoint.ord("b").is_ascii_lower()) + assert_true(Codepoint.ord("y").is_ascii_lower()) + assert_true(Codepoint.ord("z").is_ascii_lower()) - assert_false(Char.from_u32(ord("a") - 1).value().is_ascii_lower()) - assert_false(Char.from_u32(ord("z") + 1).value().is_ascii_lower()) + assert_false(Codepoint.from_u32(ord("a") - 1).value().is_ascii_lower()) + assert_false(Codepoint.from_u32(ord("z") + 1).value().is_ascii_lower()) - assert_false(Char.ord("!").is_ascii_lower()) - assert_false(Char.ord("0").is_ascii_lower()) + assert_false(Codepoint.ord("!").is_ascii_lower()) + assert_false(Codepoint.ord("0").is_ascii_lower()) def test_char_is_upper(): - assert_true(Char.ord("A").is_ascii_upper()) - assert_true(Char.ord("B").is_ascii_upper()) - assert_true(Char.ord("Y").is_ascii_upper()) - assert_true(Char.ord("Z").is_ascii_upper()) + assert_true(Codepoint.ord("A").is_ascii_upper()) + assert_true(Codepoint.ord("B").is_ascii_upper()) + assert_true(Codepoint.ord("Y").is_ascii_upper()) + assert_true(Codepoint.ord("Z").is_ascii_upper()) - assert_false(Char.from_u32(ord("A") - 1).value().is_ascii_upper()) - assert_false(Char.from_u32(ord("Z") + 1).value().is_ascii_upper()) + assert_false(Codepoint.from_u32(ord("A") - 1).value().is_ascii_upper()) + assert_false(Codepoint.from_u32(ord("Z") + 1).value().is_ascii_upper()) - assert_false(Char.ord("!").is_ascii_upper()) - assert_false(Char.ord("0").is_ascii_upper()) + assert_false(Codepoint.ord("!").is_ascii_upper()) + assert_false(Codepoint.ord("0").is_ascii_upper()) def test_char_is_digit(): - assert_true(Char.ord("1").is_ascii_digit()) - assert_false(Char.ord("g").is_ascii_digit()) + assert_true(Codepoint.ord("1").is_ascii_digit()) + assert_false(Codepoint.ord("g").is_ascii_digit()) # Devanagari Digit 6 — non-ASCII digits are not "ascii digit". - assert_false(Char.ord("६").is_ascii_digit()) + assert_false(Codepoint.ord("६").is_ascii_digit()) def test_char_is_printable(): - assert_true(Char.ord("a").is_ascii_printable()) - assert_false(Char.ord("\n").is_ascii_printable()) - assert_false(Char.ord("\t").is_ascii_printable()) + assert_true(Codepoint.ord("a").is_ascii_printable()) + assert_false(Codepoint.ord("\n").is_ascii_printable()) + assert_false(Codepoint.ord("\t").is_ascii_printable()) # Non-ASCII characters are not considered "ascii printable". - assert_false(Char.ord("स").is_ascii_printable()) + assert_false(Codepoint.ord("स").is_ascii_printable()) alias SIGNIFICANT_CODEPOINTS = List[Tuple[Int, List[Byte]]]( @@ -175,7 +177,7 @@ alias SIGNIFICANT_CODEPOINTS = List[Tuple[Int, List[Byte]]]( fn assert_utf8_bytes(codepoint: UInt32, owned expected: List[Byte]) raises: - var char_opt = Char.from_u32(codepoint) + var char_opt = Codepoint.from_u32(codepoint) var char = char_opt.value() # Allocate a length-4 buffer to write to. @@ -214,13 +216,15 @@ def test_char_utf8_byte_length(): var codepoint = entry[][0] var expected_utf8 = entry[][1] - var computed_len = Char.from_u32(codepoint).value().utf8_byte_length() + var computed_len = Codepoint.from_u32( + codepoint + ).value().utf8_byte_length() assert_equal(computed_len, len(expected_utf8)) def test_char_comptime(): - alias c1 = Char.from_u32(32).value() + alias c1 = Codepoint.from_u32(32).value() # Test that `utf8_byte_length()` works at compile time. alias c1_bytes = c1.utf8_byte_length() From 2896ed2d5bbd785f6daeb8612bc1a060d106e909 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 14 Feb 2025 19:55:02 -0600 Subject: [PATCH 395/509] [stdlib] feat: Rename `_StringSliceIter` to `CodepointSliceIter` and overhaul it This renames this type to match the other recent renames of string APIs in the standard library, and additionally refactors its implementation to be both simpler and more general. * Add `CodepointSliceIter.{peek_next(), next()}` methods * Add `CodepointSliceIter.{peek_back(), next_back()}` methods * Simplify `CodepointSliceIter` to store a `StringSlice`, instead of storing a pointer, a size, and an offset. MODULAR_ORIG_COMMIT_REV_ID: 1c237eab697182fc21a6b06e20d730346a74f1fc --- docs/changelog.md | 3 + stdlib/src/builtin/string_literal.mojo | 14 +- stdlib/src/collections/string/string.mojo | 12 +- .../src/collections/string/string_slice.mojo | 247 ++++++++++++++---- 4 files changed, 216 insertions(+), 60 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index ac5c0d9063..18500c6c85 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -34,6 +34,9 @@ what we publish. - `CharsIter` to `CodepointsIter` - `unsafe_decode_utf8_char()` to `unsafe_decode_utf8_codepoint()` + - Make the iterator type returned by the string `codepoint_slices()` methods + public as `CodepointSliceIter`. + - `StringSlice` now supports several additional methods moved from `String`. The existing `String` methods have been updated to instead call the corresponding new `StringSlice` methods: diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 122b79679b..ecbfae55a8 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -20,7 +20,7 @@ from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( StaticString, StringSlice, - _StringSliceIter, + CodepointSliceIter, _to_string_list, ) from hashlib._hasher import _HashableWithHasher, _Hasher @@ -488,24 +488,22 @@ struct StringLiteral( """ return self.__str__() - fn __iter__(ref self) -> _StringSliceIter[StaticConstantOrigin]: + fn __iter__(ref self) -> CodepointSliceIter[StaticConstantOrigin]: """Return an iterator over the string literal. Returns: An iterator over the string. """ - return _StringSliceIter[StaticConstantOrigin]( - ptr=self.unsafe_ptr(), length=self.byte_length() - ) + return CodepointSliceIter(self.as_string_slice()) - fn __reversed__(self) -> _StringSliceIter[StaticConstantOrigin, False]: + fn __reversed__(self) -> CodepointSliceIter[StaticConstantOrigin, False]: """Iterate backwards over the string, returning immutable references. Returns: A reversed iterator over the string. """ - return _StringSliceIter[StaticConstantOrigin, False]( - ptr=self.unsafe_ptr(), length=self.byte_length() + return CodepointSliceIter[StaticConstantOrigin, False]( + self.as_string_slice() ) fn __getitem__[IndexerType: Indexer](self, idx: IndexerType) -> String: diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 2ed0a1b676..395dcdce10 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -19,7 +19,7 @@ from collections.string.format import _CurlyEntryFormattable, _FormatCurlyEntry from collections.string.string_slice import ( StaticString, StringSlice, - _StringSliceIter, + CodepointSliceIter, _to_string_list, _utf8_byte_type, ) @@ -1059,7 +1059,7 @@ struct String( self._iadd[False](other.as_bytes()) @deprecated("Use `str.codepoints()` or `str.codepoint_slices()` instead.") - fn __iter__(self) -> _StringSliceIter[__origin_of(self)]: + fn __iter__(self) -> CodepointSliceIter[__origin_of(self)]: """Iterate over the string, returning immutable references. Returns: @@ -1067,15 +1067,13 @@ struct String( """ return self.codepoint_slices() - fn __reversed__(self) -> _StringSliceIter[__origin_of(self), False]: + fn __reversed__(self) -> CodepointSliceIter[__origin_of(self), False]: """Iterate backwards over the string, returning immutable references. Returns: A reversed iterator of references to the string elements. """ - return _StringSliceIter[__origin_of(self), forward=False]( - ptr=self.unsafe_ptr(), length=self.byte_length() - ) + return CodepointSliceIter[__origin_of(self), forward=False](self) # ===------------------------------------------------------------------=== # # Trait implementations @@ -1270,7 +1268,7 @@ struct String( """ return self.as_string_slice().codepoints() - fn codepoint_slices(self) -> _StringSliceIter[__origin_of(self)]: + fn codepoint_slices(self) -> CodepointSliceIter[__origin_of(self)]: """Returns an iterator over single-character slices of this string. Each returned slice points to a single Unicode codepoint encoded in the diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 9a60ea9af2..cd97b8ed95 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -45,68 +45,229 @@ alias StaticString = StringSlice[StaticConstantOrigin] @value -struct _StringSliceIter[ +struct CodepointSliceIter[ mut: Bool, //, origin: Origin[mut], forward: Bool = True, ]: - """Iterator for `StringSlice` over unicode characters. + """Iterator for `StringSlice` over substring slices containing a single + Unicode codepoint. Parameters: mut: Whether the slice is mutable. origin: The origin of the underlying string data. forward: The iteration direction. `False` is backwards. + + The `forward` parameter only controls the behavior of the `__next__()` + method used for normal iteration. Calls to `next()` will always take an + element from the front of the iterator, and calls to `next_back()` will + always take an element from the end. """ - var index: Int - var ptr: UnsafePointer[Byte] - var length: Int + var _slice: StringSlice[origin] - fn __init__(out self, *, ptr: UnsafePointer[Byte], length: UInt): - self.index = 0 if forward else length - self.ptr = ptr - self.length = length + # Note: + # Marked private since `StringSlice.codepoints()` is the intended public + # way to construct this type. + @doc_private + fn __init__(out self, str_slice: StringSlice[origin]): + self._slice = str_slice + # ===-------------------------------------------------------------------===# + # Trait implementations + # ===-------------------------------------------------------------------===# + + @doc_private fn __iter__(self) -> Self: return self fn __next__(mut self) -> StringSlice[origin]: + """Get the next codepoint in the underlying string slice. + + This returns the next single-codepoint substring slice encoded in the + underlying string, and advances the iterator state. + + If `forward` is set to `False`, this will return the next codepoint + from the end of the string. + + This function will abort if this iterator has been exhausted. + + Returns: + The next character in the string. + """ + @parameter if forward: - byte_len = _utf8_first_byte_sequence_length(self.ptr[self.index]) - i = self.index - self.index += byte_len - return StringSlice[origin](ptr=self.ptr + i, length=byte_len) + return self.next().value() else: - byte_len = 1 - while _utf8_byte_type(self.ptr[self.index - byte_len]) == 1: - byte_len += 1 - self.index -= byte_len - return StringSlice[origin]( - ptr=self.ptr + self.index, length=byte_len - ) + return self.next_back().value() @always_inline fn __has_next__(self) -> Bool: - @parameter - if forward: - return self.index < self.length - else: - return self.index > 0 + """Returns True if there are still elements in this iterator. + + Returns: + A boolean indicating if there are still elements in this iterator. + """ + # NOTE: + # This intentionally check if the length _in bytes_ is greater + # than zero, because checking the codepoint length requires a linear + # scan of the string, which is needlessly expensive for this purpose. + return len(self._slice) > 0 + @always_inline fn __len__(self) -> Int: - @parameter - if forward: - var remaining = self.length - self.index - var span = Span[Byte, ImmutableAnyOrigin]( - ptr=self.ptr + self.index, length=remaining - ) - return StringSlice(unsafe_from_utf8=span).char_length() + """Returns the remaining length of this iterator in `Codepoint`s. + + The value returned from this method indicates the number of subsequent + calls to `next()` that will return a value. + + Returns: + Number of codepoints remaining in this iterator. + """ + return self._slice.char_length() + + # ===-------------------------------------------------------------------===# + # Methods + # ===-------------------------------------------------------------------===# + + fn peek_next(self) -> Optional[StringSlice[origin]]: + """Check what the next single-codepoint slice in this iterator is, + without advancing the iterator state. + + Repeated calls to this method will return the same value. + + Returns: + The next codepoint slice in the underlying string, or None if the + string is empty. + + # Examples + + `peek_next()` does not advance the iterator, so repeated calls will + return the same value: + + ```mojo + from collections.string import StringSlice, Codepoint + from testing import assert_equal + + var input = StringSlice("123") + var iter = input.codepoint_slices() + + assert_equal(iter.peek_next().value(), "1") + assert_equal(iter.peek_next().value(), "1") + assert_equal(iter.peek_next().value(), "1") + + # A call to `next()` return the same value as `peek_next()` had, + # but also advance the iterator. + assert_equal(iter.next().value(), "1") + + # Later `peek_next()` calls will return the _new_ next character: + assert_equal(iter.peek_next().value(), "2") + ``` + . + """ + if len(self._slice) > 0: + # SAFETY: Will not read out of bounds because `_slice` is guaranteed + # to contain valid UTF-8. + var curr_ptr = self._slice.unsafe_ptr() + var byte_len = _utf8_first_byte_sequence_length(curr_ptr[]) + return StringSlice[origin](ptr=curr_ptr, length=byte_len) else: - var span = Span[Byte, ImmutableAnyOrigin]( - ptr=self.ptr, length=self.index - ) - return StringSlice(unsafe_from_utf8=span).char_length() + return None + + fn peek_back(mut self) -> Optional[StringSlice[origin]]: + """Check what the last single-codepoint slice in this iterator is, + without advancing the iterator state. + + Repeated calls to this method will return the same value. + + Returns: + The last codepoint slice in the underlying string, or None if the + string is empty. + + # Examples + + `peek_back()` does not advance the iterator, so repeated calls will + return the same value: + + ```mojo + from collections.string import StringSlice, Codepoint + from testing import assert_equal + + var input = StringSlice("123") + var iter = input.codepoint_slices() + + # Repeated calls to `peek_back()` return the same value. + assert_equal(iter.peek_back().value(), "3") + assert_equal(iter.peek_back().value(), "3") + assert_equal(iter.peek_back().value(), "3") + + # A call to `next_back()` return the same value as `peek_back()` had, + # but also advance the iterator. + assert_equal(iter.next_back().value(), "3") + + # Later `peek_back()` calls will return the _new_ next character: + assert_equal(iter.peek_back().value(), "2") + ``` + . + """ + if len(self._slice) > 0: + var byte_len = 1 + var back_ptr = self._slice.unsafe_ptr() + len(self._slice) - 1 + # SAFETY: + # Guaranteed not to go out of bounds because UTF-8 + # guarantees there is always a "start" byte eventually before any + # continuation bytes. + while _utf8_byte_type(back_ptr[]) == 1: + byte_len += 1 + back_ptr -= 1 + + return StringSlice[origin](ptr=back_ptr, length=byte_len) + else: + return None + + fn next(mut self) -> Optional[StringSlice[origin]]: + """Get the next codepoint slice in the underlying string slice, or None + if the iterator is empty. + + This returns the next single-codepoint substring encoded in the + underlying string, and advances the iterator state. + + Returns: + A character if the string is not empty, otherwise None. + """ + var result: Optional[StringSlice[origin]] = self.peek_next() + + if result: + # SAFETY: We just checked that `result` holds a value + var slice_len = len(result.unsafe_value()) + # Advance the pointer in _slice. + self._slice._slice._data += slice_len + # Decrement the byte-length of _slice. + self._slice._slice._len -= slice_len + + return result + + fn next_back(mut self) -> Optional[StringSlice[origin]]: + """Get the last single-codepoint slice in this iterator is, or None + if the iterator is empty. + + This returns the last codepoint slice in this iterator, and advances + the iterator state. + + Returns: + The last codepoint slice in the underlying string, or None if the + string is empty. + """ + var result: Optional[StringSlice[origin]] = self.peek_back() + + if result: + # SAFETY: We just checked that `result` holds a value + var slice_len = len(result.unsafe_value()) + # Decrement the byte-length of _slice. + self._slice._slice._len -= slice_len + + return result @value @@ -685,7 +846,7 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( ) @deprecated("Use `str.codepoints()` or `str.codepoint_slices()` instead.") - fn __iter__(self) -> _StringSliceIter[origin]: + fn __iter__(self) -> CodepointSliceIter[origin]: """Iterate over the string, returning immutable references. Returns: @@ -693,15 +854,13 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return self.codepoint_slices() - fn __reversed__(self) -> _StringSliceIter[origin, False]: + fn __reversed__(self) -> CodepointSliceIter[origin, False]: """Iterate backwards over the string, returning immutable references. Returns: A reversed iterator of references to the string elements. """ - return _StringSliceIter[origin, forward=False]( - ptr=self.unsafe_ptr(), length=self.byte_length() - ) + return CodepointSliceIter[origin, forward=False](self) fn __getitem__[I: Indexer](self, idx: I) -> String: """Gets the character at the specified position. @@ -1118,15 +1277,13 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( """ return CodepointsIter(self) - fn codepoint_slices(self) -> _StringSliceIter[origin]: + fn codepoint_slices(self) -> CodepointSliceIter[origin]: """Iterate over the string, returning immutable references. Returns: An iterator of references to the string elements. """ - return _StringSliceIter[origin]( - ptr=self.unsafe_ptr(), length=self.byte_length() - ) + return CodepointSliceIter[origin](self) @always_inline fn as_bytes(self) -> Span[Byte, origin]: From ff81533046ec6fe9ed8a10f9273f9da3cd79092d Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 15 Feb 2025 05:46:46 +0000 Subject: [PATCH 396/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025021503 --- examples/life/magic.lock | 339 ++++++++++++++++++++-------------- examples/magic.lock | 142 +++++++------- examples/operators/magic.lock | 199 ++++++++++---------- examples/testing/magic.lock | 199 ++++++++++---------- magic.lock | 199 ++++++++++---------- 5 files changed, 569 insertions(+), 509 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 6ae25926c6..11fb52f64d 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -62,7 +62,10 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.23.1-h5888daf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hd5240d6_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.82.2-h2ff4ddf_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.51-hbd13f7d_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda @@ -82,7 +85,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.48.0-hee588c1_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-hc0a3c3a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-257.2-h3dc2cb9_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-257.3-h3dc2cb9_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hd9ff511_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2 @@ -91,11 +94,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025021305-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025021305-release.conda - 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jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22979 - timestamp: 1739590931720 + size: 22980 + timestamp: 1739596607111 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 3c78c296b497705db9fc765c6cd1ecddc91efdda Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 15 Feb 2025 10:30:50 -0800 Subject: [PATCH 398/509] [mojo-stdlib] Expand `@always_inline("builtin")` coverage to a few more things. This expands compiler and stdlib support to cover a few more important cases. MODULAR_ORIG_COMMIT_REV_ID: 436c3e08bfd30ebbcba3bdd90e62853265b3d253 --- stdlib/src/builtin/bool.mojo | 4 ++-- stdlib/src/builtin/none.mojo | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 5682f54fc1..1fc5852fcc 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -116,7 +116,7 @@ struct Bool( var value: __mlir_type.i1 """The underlying storage of the boolean value.""" - @always_inline("nodebug") + @always_inline("builtin") fn __init__(out self): """Construct a default, `False` Bool.""" self = False @@ -131,7 +131,7 @@ struct Bool( return self @doc_private - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: __mlir_type.i1): """Construct a Bool value given a __mlir_type.i1 value. diff --git a/stdlib/src/builtin/none.mojo b/stdlib/src/builtin/none.mojo index 324a4fd425..2fbe5daab5 100644 --- a/stdlib/src/builtin/none.mojo +++ b/stdlib/src/builtin/none.mojo @@ -32,12 +32,12 @@ struct NoneType( var _value: Self._mlir_type - @always_inline + @always_inline("builtin") fn __init__(out self): """Construct an instance of the `None` type.""" self._value = None - @always_inline + @always_inline("builtin") @implicit fn __init__(out self, value: Self._mlir_type): """Construct an instance of the `None` type. From cf4bde8c80485b9f1ea13a447bed8cf9d792315b Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 15 Feb 2025 11:55:53 -0800 Subject: [PATCH 399/509] [mojo-stdlib] Enhance `@always_inline("builtin")` to support parameters, adopt more This teaches the compiler to support parameterized functions correctly, allowing us to adopt it into `OptionalReg` and `Origin`. MODULAR_ORIG_COMMIT_REV_ID: ed76e2294a3614add45ceb0f41b7797337685533 --- stdlib/src/builtin/type_aliases.mojo | 2 +- stdlib/src/collections/optional.mojo | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/stdlib/src/builtin/type_aliases.mojo b/stdlib/src/builtin/type_aliases.mojo index 758b459304..b24a8a06ce 100644 --- a/stdlib/src/builtin/type_aliases.mojo +++ b/stdlib/src/builtin/type_aliases.mojo @@ -96,7 +96,7 @@ struct Origin[is_mutable: Bool]: # `__origin_of(..)` can implicilty convert to `Origin` in use cases like: # Span[Byte, __origin_of(self)] @implicit - @always_inline("nodebug") + @always_inline("builtin") fn __init__(out self, mlir_origin: Self._mlir_type): """Initialize an Origin from a raw MLIR `!lit.origin` value. diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index 48e35c788b..3b457a94dd 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -455,10 +455,12 @@ struct OptionalReg[T: AnyTrivialRegType](Boolable): # Life cycle methods # ===-------------------------------------------------------------------===# + @always_inline("builtin") fn __init__(out self): """Create an optional with a value of None.""" self = Self(None) + @always_inline("builtin") @implicit fn __init__(out self, value: T): """Create an optional with a value. @@ -474,6 +476,7 @@ struct OptionalReg[T: AnyTrivialRegType](Boolable): # This initializer should not be necessary, we should need # only the initilaizer from a `NoneType`. @doc_private + @always_inline("builtin") @implicit fn __init__(out self, value: NoneType._mlir_type): """Construct an empty Optional. @@ -483,6 +486,7 @@ struct OptionalReg[T: AnyTrivialRegType](Boolable): """ self = Self(value=NoneType(value)) + @always_inline("builtin") @implicit fn __init__(out self, value: NoneType): """Create an optional without a value from a None literal. From 3f1cc201aede1940d5af55a2e0ff83197c4233b6 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 15 Feb 2025 15:10:25 -0800 Subject: [PATCH 400/509] [mojo-lang] Upgrade `Int` more, remove `tryOriginInitFold` hack. This adopts `always_inline("builtin")` in a few more `Int` methods, and removes the special parser hack for `Origin.__init__` now that it is handled by a more general framework. MODULAR_ORIG_COMMIT_REV_ID: d5c9c184b3ae6fe50b7763cf3d030eebdb1d5fb4 --- stdlib/src/builtin/int.mojo | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index e36484b31e..340455024e 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -387,7 +387,7 @@ struct Int( """ self = value.__int__() - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: UInt): """Construct Int from the given UInt value. @@ -476,7 +476,7 @@ struct Int( # Operator dunders # ===------------------------------------------------------------------=== # - @always_inline("nodebug") + @always_inline("builtin") fn __lt__(self, rhs: Int) -> Bool: """Compare this Int to the RHS using LT comparison. @@ -490,7 +490,7 @@ struct Int( pred = __mlir_attr.`#index` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __le__(self, rhs: Int) -> Bool: """Compare this Int to the RHS using LE comparison. @@ -505,7 +505,7 @@ struct Int( pred = __mlir_attr.`#index` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __eq__(self, rhs: Int) -> Bool: """Compare this Int to the RHS using EQ comparison. @@ -519,7 +519,7 @@ struct Int( pred = __mlir_attr.`#index` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __ne__(self, rhs: Int) -> Bool: """Compare this Int to the RHS using NE comparison. @@ -533,7 +533,7 @@ struct Int( pred = __mlir_attr.`#index` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __gt__(self, rhs: Int) -> Bool: """Compare this Int to the RHS using GT comparison. @@ -547,7 +547,7 @@ struct Int( pred = __mlir_attr.`#index` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __ge__(self, rhs: Int) -> Bool: """Compare this Int to the RHS using GE comparison. @@ -571,7 +571,7 @@ struct Int( """ return self - @always_inline("nodebug") + @always_inline("builtin") fn __neg__(self) -> Int: """Return -self. From eeacc41d6a2ff8406a0737934d72cf829e263413 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 15 Feb 2025 15:31:25 -0800 Subject: [PATCH 401/509] [mojo-lang] Remove `refineResultValue`, a major use of the interpreter in the parser Now that we have `@always_inline("builtin")`, we can remove these old hacks that was intended to support dependent types better. This will probably help compile time and simplifies things because we're not calling into the comptime interpreter in the parser in this place anymore. This also updates `Bool` so we constant fold Bool in cond expressions, which is needed for this. MODULAR_ORIG_COMMIT_REV_ID: fad37c77c069106834608a0e7e07b0cbbfdcc452 --- stdlib/src/builtin/bool.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 1fc5852fcc..59800330b6 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -198,7 +198,7 @@ struct Bool( """ self = value.__bool__() - @always_inline("nodebug") + @always_inline("builtin") fn __bool__(self) -> Bool: """Convert to Bool. @@ -207,7 +207,7 @@ struct Bool( """ return self - @always_inline("nodebug") + @always_inline("builtin") fn __as_bool__(self) -> Bool: """Convert to Bool. @@ -216,7 +216,7 @@ struct Bool( """ return self.__bool__() - @always_inline("nodebug") + @always_inline("builtin") fn __mlir_i1__(self) -> __mlir_type.i1: """Convert this Bool to __mlir_type.i1. From e488049b5cd5c6132daea94770fa8c3288eced35 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sun, 16 Feb 2025 07:02:06 +0000 Subject: [PATCH 402/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025021605 --- examples/life/magic.lock | 148 +++++++++++++++++---------------- examples/magic.lock | 148 +++++++++++++++++---------------- examples/operators/magic.lock | 151 ++++++++++++++++++---------------- examples/testing/magic.lock | 151 ++++++++++++++++++---------------- magic.lock | 148 +++++++++++++++++---------------- 5 files changed, 391 insertions(+), 355 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 894a2c732f..38912d83ae 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -94,11 +94,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - 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sha256: 5becaf0cee459afff75d739bdc2ea4a95f96dce278f5fe09fb26dc4b35534f25 - md5: 392f051b929c168014025bb2dfc89fcc + sha256: 436a149c479fe5d1e935f13497e48c04f7df11c6c84c1c803af433c239285ed8 + md5: a8c4c9538b073aec60407f390562f174 depends: - - max-core ==25.2.0.dev2025021505 release + - max-core ==25.2.0.dev2025021605 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22980 - timestamp: 1739596607111 + size: 22987 + timestamp: 1739683043473 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 010aa1d84543b87e76e97b3fa9ccd3bd98b866ea Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 16 Feb 2025 11:06:59 -0800 Subject: [PATCH 403/509] [mojo-lang] Fix implementation of IntLiteral -> Int. This changes the implementation of `kgen.int_literal.convert` to be attribute based instead of MLIR operation based. The operation based representation is completely broken, because the `!kgen.int.literal` type cannot be executed at runtime (ie cannot be lowered to LLVM) - it should only exist in the parameter domain. This patch improves the situation somewhat by introducing an attribute form (`IntLiteralConvertAttr`) of this. This builds on the new elaborator infra I added for error messages, which makes integer overflow errors a bit less terrible. This also allows it to work with `@always_inline("builtin")`. The IntLiteral representation should be moved wholesale to attributes and the corresponding operations removed, I filed MOCO-1628 to track this. MODULAR_ORIG_COMMIT_REV_ID: b1d3d50de6a9a9719d839646439062c0dec1902f --- stdlib/src/builtin/int.mojo | 2 +- stdlib/src/builtin/int_literal.mojo | 6 +++--- stdlib/src/builtin/uint.mojo | 10 +++++----- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 340455024e..b69330dedb 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -377,7 +377,7 @@ struct Int( value ) - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: IntLiteral): """Construct Int from the given IntLiteral value. diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 3f25c6fbdb..701cbca08d 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -592,7 +592,7 @@ struct IntLiteral( """ return self.__bool__() - @always_inline("nodebug") + @always_inline("builtin") fn __int__(self) -> Int: """Convert from IntLiteral to Int. @@ -610,7 +610,7 @@ struct IntLiteral( """ return self.__int__() - @always_inline("nodebug") + @always_inline("builtin") fn __uint__(self) -> UInt: """Convert from IntLiteral to UInt. @@ -729,7 +729,7 @@ struct IntLiteral( """ return __mlir_op.`kgen.int_literal.bit_width`(self.value) - @always_inline("nodebug") + @always_inline("builtin") fn __index__(self) -> __mlir_type.index: """Convert from IntLiteral to index. diff --git a/stdlib/src/builtin/uint.mojo b/stdlib/src/builtin/uint.mojo index c45ebb660c..d7937b8420 100644 --- a/stdlib/src/builtin/uint.mojo +++ b/stdlib/src/builtin/uint.mojo @@ -55,13 +55,13 @@ struct UInt(Indexer, _HashableWithHasher): which have signed and unsigned variants. """ - @always_inline("nodebug") + @always_inline("builtin") fn __init__(out self): """Default constructor that produces zero.""" self.value = __mlir_op.`index.constant`[value = __mlir_attr.`0:index`]() @doc_private - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: __mlir_type.index): """Construct UInt from the given index value. @@ -84,7 +84,7 @@ struct UInt(Indexer, _HashableWithHasher): value ) - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: Int): """Construct UInt from the given index value. @@ -94,7 +94,7 @@ struct UInt(Indexer, _HashableWithHasher): """ self.value = value.value - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: IntLiteral): """Construct UInt from the given IntLiteral value. @@ -104,7 +104,7 @@ struct UInt(Indexer, _HashableWithHasher): """ self = value.__uint__() - @always_inline("nodebug") + @always_inline("builtin") fn __index__(self) -> __mlir_type.index: """Convert to index. From 8022690a13cbc899c98a8e81e1ae7535870c99e1 Mon Sep 17 00:00:00 2001 From: modularbot Date: Mon, 17 Feb 2025 07:00:05 +0000 Subject: [PATCH 404/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025021705 --- examples/life/magic.lock | 142 +++++++++++++++++----------------- examples/magic.lock | 142 +++++++++++++++++----------------- examples/operators/magic.lock | 142 +++++++++++++++++----------------- examples/testing/magic.lock | 142 +++++++++++++++++----------------- magic.lock | 142 +++++++++++++++++----------------- 5 files changed, 355 insertions(+), 355 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 38912d83ae..607b2b8054 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -94,11 +94,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - 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jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22987 - timestamp: 1739683043473 + size: 22994 + timestamp: 1739769476049 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 72ccf3bf7f20bbafe5840e7a7a93a43f1bed15e7 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 17 Feb 2025 18:33:46 -0800 Subject: [PATCH 405/509] [mojo-lang] Improve error messages for constant lowering failures a lot. MLIR aggressively loses location information for constants :-( due to stuff in FoldUtils. It would be really great to fix this (and this is my personal fault from like 6 years ago, sigh) but we can still do better at finding location information when needed. While here, fix the lowering logic to not puke out generic nonsense about lowering failures. Before: ``` $ mojo open-source/mojo/stdlib/test/builtin/test_uint_error.mojo :0: error: integer value -1 is negative, but is being converted to an unsigned type :0: error: failed to legalize operation 'kgen.param.constant' that was explicitly marked illegal :0: note: see current operation: %18 = "kgen.param.constant"() {value = #kgen.int_literal_convert<:!kgen.int_literal -1, true> : index} : () -> index mojo: error: failed to lower module to LLVM IR for archive compilation, run LowerToLLVMPipeline failed ``` After: ``` $ mojo open-source/mojo/stdlib/test/builtin/test_uint_error.mojo open-source/mojo/stdlib/test/builtin/test_uint_error.mojo:23:15: error: integer value -1 is negative, but is being converted to an unsigned type print(UInt(-1)) ^ mojo: error: failed to lower module to LLVM IR for archive compilation, run LowerToLLVMPipeline failed ``` I'm not sure what causes that last error but it should also be fixed at some point. MODULAR_ORIG_COMMIT_REV_ID: 9d65c268c03f4df6eb6617eefd7ea30deed3a7ed --- stdlib/test/builtin/test_uint_error.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/test/builtin/test_uint_error.mojo b/stdlib/test/builtin/test_uint_error.mojo index 74d20652b8..4fce79162e 100644 --- a/stdlib/test/builtin/test_uint_error.mojo +++ b/stdlib/test/builtin/test_uint_error.mojo @@ -19,7 +19,7 @@ fn main(): - # CHECK: integer value -1 is negative, but is being converted to an unsigned type + # CHECK: test_uint_error.mojo:23:15: error: integer value -1 is negative, but is being converted to an unsigned type print(UInt(-1)) # CHECK-NOT: is never reached print("is never reached") From c020f10fa964febd80b85b969bb0acb237e198a0 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 17 Feb 2025 19:12:24 -0800 Subject: [PATCH 406/509] [mojo-lang] Send more stuff down the @always_inline("builtin") path This reduces the load on the `inlineFunctionCallIntoPValueIfPossible` function, which is using the interpreter at parser time which is cursed. This doesn't completely eliminate it though. This also adopts this form of inlining on more literal stuff. MODULAR_ORIG_COMMIT_REV_ID: 334a50f69ed2b278f748b4edee6eff1501b1a824 --- stdlib/src/builtin/float_literal.mojo | 2 +- stdlib/src/builtin/string_literal.mojo | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 0c7c2f3040..97cf9265bb 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -48,7 +48,7 @@ struct FloatLiteral( # Constructors # ===------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: Self.fp_type): """Create a FloatLiteral value from a kgen.float_literal value. diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index ecbfae55a8..15c9d40764 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -70,7 +70,7 @@ struct StringLiteral( # Life cycle methods # ===-------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: Self.type): """Create a string literal from a builtin string type. From ff2422fd26eb6248b6aaeebb32d92cde883c861f Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 17 Feb 2025 19:24:36 -0800 Subject: [PATCH 407/509] [mojo-lang] Remove IntLiteralBitWidthOp and related stuff. This removes this op which was added speculatively and not used at all, excise it to simplify things. MODULAR_ORIG_COMMIT_REV_ID: f94ebc2c22bc42c5ffd05cbfe2dc8e75e3b01d8c --- stdlib/src/builtin/int_literal.mojo | 9 --------- stdlib/test/builtin/test_int_literal.mojo | 8 -------- 2 files changed, 17 deletions(-) diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 701cbca08d..2eb6d35da3 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -720,15 +720,6 @@ struct IntLiteral( # Methods # ===-------------------------------------------------------------------===# - @always_inline("nodebug") - fn _bit_width(self) -> IntLiteral: - """Get the (signed) bit width of the IntLiteral. - - Returns: - The bit width. - """ - return __mlir_op.`kgen.int_literal.bit_width`(self.value) - @always_inline("builtin") fn __index__(self) -> __mlir_type.index: """Convert from IntLiteral to index. diff --git a/stdlib/test/builtin/test_int_literal.mojo b/stdlib/test/builtin/test_int_literal.mojo index f39dd53625..cca82fd158 100644 --- a/stdlib/test/builtin/test_int_literal.mojo +++ b/stdlib/test/builtin/test_int_literal.mojo @@ -78,13 +78,6 @@ def test_mod(): assert_equal(IntLiteral.__mod__(-3, 2), 1) -def test_bit_width(): - assert_equal((0)._bit_width(), 1) - assert_equal((-1)._bit_width(), 1) - assert_equal((255)._bit_width(), 9) - assert_equal((-256)._bit_width(), 9) - - def test_abs(): assert_equal(IntLiteral.__abs__(-5), 5) assert_equal(IntLiteral.__abs__(2), 2) @@ -165,7 +158,6 @@ def main(): test_floordiv() test_mod() test_divmod() - test_bit_width() test_abs() test_indexer() test_bool() From 939375e1396d2b93a5cea735ea6cd99f34978381 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 17 Feb 2025 20:36:24 -0800 Subject: [PATCH 408/509] [mojo-lang] Move integer literal binops into the parameter domain. This is needed to get things like "1+4" and "-4" to fold with the builtin parameter inliner, which is important for general code. MODULAR_ORIG_COMMIT_REV_ID: 552e6da424c6972cf8084a663dbc7b850ab1ef12 --- stdlib/src/builtin/int_literal.mojo | 138 +++------------------------- 1 file changed, 13 insertions(+), 125 deletions(-) diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 2eb6d35da3..5710187b3f 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -158,7 +158,7 @@ struct IntLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __pos__(self) -> Self: """Return +self. @@ -167,7 +167,7 @@ struct IntLiteral( """ return self - @always_inline("nodebug") + @always_inline("builtin") fn __neg__(self) -> Self: """Return -self. @@ -190,7 +190,7 @@ struct IntLiteral( var remainder: Self = self - (quotient * rhs) return quotient, remainder - @always_inline("nodebug") + @always_inline("builtin") fn __invert__(self) -> Self: """Return ~self. @@ -199,7 +199,7 @@ struct IntLiteral( """ return self ^ (Self() - Self._one) - @always_inline("nodebug") + @always_inline("builtin") fn __add__(self, rhs: Self) -> Self: """Return `self + rhs`. @@ -215,7 +215,7 @@ struct IntLiteral( ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __sub__(self, rhs: Self) -> Self: """Return `self - rhs`. @@ -231,7 +231,7 @@ struct IntLiteral( ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __mul__(self, rhs: Self) -> Self: """Return `self * rhs`. @@ -325,7 +325,7 @@ struct IntLiteral( ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __and__(self, rhs: Self) -> Self: """Return `self & rhs`. @@ -341,7 +341,7 @@ struct IntLiteral( ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __xor__(self, rhs: Self) -> Self: """Return `self ^ rhs`. @@ -357,7 +357,7 @@ struct IntLiteral( ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __or__(self, rhs: Self) -> Self: """Return `self | rhs`. @@ -458,118 +458,6 @@ struct IntLiteral( """ self = self | rhs - # ===-------------------------------------------------------------------===# - # Reversed operations - # ===-------------------------------------------------------------------===# - - @always_inline("nodebug") - fn __radd__(self, value: Self) -> Self: - """Return `value + self`. - - Args: - value: The other value. - - Returns: - `value + self`. - """ - return self + value - - @always_inline("nodebug") - fn __rsub__(self, value: Self) -> Self: - """Return `value - self`. - - Args: - value: The other value. - - Returns: - `value - self`. - """ - return value - self - - @always_inline("nodebug") - fn __rmul__(self, value: Self) -> Self: - """Return `value * self`. - - Args: - value: The other value. - - Returns: - `value * self`. - """ - return self * value - - @always_inline("nodebug") - fn __rfloordiv__(self, value: Self) -> Self: - """Return `value // self`. - - Args: - value: The other value. - - Returns: - `value // self`. - """ - return value // self - - @always_inline("nodebug") - fn __rlshift__(self, value: Self) -> Self: - """Return `value << self`. - - Args: - value: The other value. - - Returns: - `value << self`. - """ - return value << self - - @always_inline("nodebug") - fn __rrshift__(self, value: Self) -> Self: - """Return `value >> self`. - - Args: - value: The other value. - - Returns: - `value >> self`. - """ - return value >> self - - @always_inline("nodebug") - fn __rand__(self, value: Self) -> Self: - """Return `value & self`. - - Args: - value: The other value. - - Returns: - `value & self`. - """ - return value & self - - @always_inline("nodebug") - fn __ror__(self, value: Self) -> Self: - """Return `value | self`. - - Args: - value: The other value. - - Returns: - `value | self`. - """ - return value | self - - @always_inline("nodebug") - fn __rxor__(self, value: Self) -> Self: - """Return `value ^ self`. - - Args: - value: The other value. - - Returns: - `value ^ self`. - """ - return value ^ self - # ===-------------------------------------------------------------------===# # Trait implementations # ===-------------------------------------------------------------------===# @@ -601,7 +489,7 @@ struct IntLiteral( """ return self.__index__() - @always_inline("nodebug") + @always_inline("builtin") fn __as_int__(self) -> Int: """Implicitly convert to an Int. @@ -641,7 +529,7 @@ struct IntLiteral( """ return self - @always_inline("nodebug") + @always_inline("builtin") fn __floor__(self) -> Self: """Return the floor of the IntLiteral value, which is itself. @@ -650,7 +538,7 @@ struct IntLiteral( """ return self - @always_inline("nodebug") + @always_inline("builtin") fn __round__(self) -> Self: """Return the rounded value of the IntLiteral value, which is itself. @@ -659,7 +547,7 @@ struct IntLiteral( """ return self - @always_inline("nodebug") + @always_inline("builtin") fn __trunc__(self) -> Self: """Return the truncated of the IntLiteral value, which is itself. From 72ed1ac86be8adce9066ad78151bd81d40280455 Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 18 Feb 2025 17:01:33 +0000 Subject: [PATCH 409/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025021805 --- examples/life/magic.lock | 867 ++++++++++++++++++++++++++-------- examples/magic.lock | 319 ++++++------- examples/operators/magic.lock | 425 ++++++++--------- examples/testing/magic.lock | 425 ++++++++--------- magic.lock | 319 ++++++------- 5 files changed, 1411 insertions(+), 944 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 607b2b8054..3a041cc133 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -51,7 +51,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-28_he106b2a_openblas.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libdb-6.2.32-h9c3ff4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.23-h4ddbbb0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.124-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda @@ -68,7 +70,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.51-hbd13f7d_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-28_h7ac8fdf_openblas.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.4-hb9d3cd8_0.conda @@ -77,6 +79,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-h4ab18f5_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.46-h943b412_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.3-h6128344_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsentencepiece-0.2.0-h8e10757_10.conda @@ -87,18 +90,24 @@ environments: - 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conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025021805-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.2.0.dev2025021805-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025021805-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025021805-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda @@ -121,10 +130,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-26.2.1-py312hbf22597_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2-2.30.10-h63c27ac_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2-2.32.50-h9b8e6db_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2_image-2.8.2-h06ee604_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2_mixer-2.6.3-h8830914_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl2_ttf-2.24.0-h287479f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/sdl3-3.2.4-h3083f51_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sentencepiece-0.2.0-hc8f76dd_10.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sentencepiece-python-0.2.0-py312hb6b8a2b_10.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/sentencepiece-spm-0.2.0-h8e10757_10.conda @@ -136,14 +146,18 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda + - 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xgrammar >=0.1.11 license: LicenseRef-Modular-Proprietary - size: 110070584 - timestamp: 1739770743850 -- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025021705-release.conda + size: 110049004 + timestamp: 1739857002577 +- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025021805-release.conda noarch: python - sha256: 13a8735d351148c95ff8030ea49010bc4cda358a5faf241689db15197bdaf7fb - md5: f595aa1a6832846493d7c9cb48bd0f5d + sha256: c2ce9921584024a755f45d2a7ba764a4e527a7bfc3aa7798e056e9fea35a4eb1 + md5: c90d37ff9d68f874286acdb235b62cfa depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -1493,20 +1496,20 @@ packages: - typing_extensions >=v4.12.2 - python license: MIT - size: 130855 - timestamp: 1739769476049 -- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025021705-release.conda + size: 130850 + timestamp: 1739855815922 +- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025021805-release.conda noarch: python - sha256: ff89ce5074726958ff4cefe09966470947ab40ebc5e4bbe4f8e3fe8c4d5d7263 - md5: c7ae6ef843f549adb530cff568cef5bc + sha256: 5ff1420517896833664e430108a243134424ff45c78e00fc760d77525a91a750 + md5: e3f2fef53386541378382e32801068d7 depends: - - max-core ==25.2.0.dev2025021705 release + - max-core ==25.2.0.dev2025021805 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22994 - timestamp: 1739769476049 + size: 22982 + timestamp: 1739855815922 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 9dd6fcf00e7a6e878c85b877837629e9d7fca05d Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 17 Feb 2025 21:30:01 -0800 Subject: [PATCH 410/509] [mojo-lang] Move IntLiteral compares to attributes. This allows folding comparisons of integer literals, which is required to get things like `if 1:` to be seen as a literal true value (because this turns into a call to `IntLiteral.__bool__`. MODULAR_ORIG_COMMIT_REV_ID: 1fcc54ef77d01e55f5684dbc3361b4a560167fcc --- stdlib/src/builtin/dtype.mojo | 2 +- stdlib/src/builtin/int_literal.mojo | 18 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/stdlib/src/builtin/dtype.mojo b/stdlib/src/builtin/dtype.mojo index bf9b218788..7b790253c3 100644 --- a/stdlib/src/builtin/dtype.mojo +++ b/stdlib/src/builtin/dtype.mojo @@ -153,7 +153,7 @@ struct DType( """ return self - @always_inline + @always_inline("builtin") @implicit fn __init__(out self, value: Self.type): """Construct a DType from MLIR dtype. diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 5710187b3f..ed302b4b3e 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -72,7 +72,7 @@ struct IntLiteral( # Operator dunders # ===-------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") fn __lt__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using LT comparison. @@ -86,7 +86,7 @@ struct IntLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __le__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using LE comparison. @@ -101,7 +101,7 @@ struct IntLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __eq__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using EQ comparison. @@ -115,7 +115,7 @@ struct IntLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __ne__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using NE comparison. @@ -129,7 +129,7 @@ struct IntLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __gt__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using GT comparison. @@ -143,7 +143,7 @@ struct IntLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __ge__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using GE comparison. @@ -462,7 +462,7 @@ struct IntLiteral( # Trait implementations # ===-------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") fn __bool__(self) -> Bool: """Convert this IntLiteral to Bool. @@ -471,7 +471,7 @@ struct IntLiteral( """ return self != Self() - @always_inline("nodebug") + @always_inline("builtin") fn __as_bool__(self) -> Bool: """Convert this IntLiteral to Bool. @@ -520,7 +520,7 @@ struct IntLiteral( return self return -self - @always_inline("nodebug") + @always_inline("builtin") fn __ceil__(self) -> Self: """Return the ceiling of the IntLiteral value, which is itself. From ac955ac82a3c57378144b30345cfce564dfc9ea1 Mon Sep 17 00:00:00 2001 From: Alex Trotta <44127594+Ahajha@users.noreply.github.com> Date: Tue, 18 Feb 2025 15:09:18 -0500 Subject: [PATCH 411/509] [docs] Fix indentation error and typo in mojo basics page MODULAR_ORIG_COMMIT_REV_ID: 933e81709435a64292204b79fea535eec9f13a2f --- docs/manual/basics.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index 853ad16ad6..b62e7cbf8d 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -255,7 +255,7 @@ struct MyPair: self.first = first self.second = second - fn __copyinit__(out self, existing other): + fn __copyinit__(out self, existing other): self.first = existing.first self.second = existing.second @@ -317,7 +317,7 @@ trait SomeTrait: The three dots following the method signature are Mojo syntax indicating that the method is not implemented. -Here's a trait that conforms to `SomeTrait`: +Here's a struct that conforms to `SomeTrait`: ```mojo @value From 26f31756dfd6a767c8963fc261ed374c2b1a6244 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Tue, 18 Feb 2025 13:04:30 -0800 Subject: [PATCH 412/509] [stdlib] Add address_space and alignment to Span Since we'll want `Span` to be usable across address spaces and be able to represent all kinds of pointers, it makes sense to be parametric over the possible underlying pointer types. MODULAR_ORIG_COMMIT_REV_ID: 0c9c4b51dfa454e2a3b1189f86ebea3fc55a5c22 --- stdlib/src/memory/span.mojo | 106 +++++++++++++++++++++++++++++------- 1 file changed, 86 insertions(+), 20 deletions(-) diff --git a/stdlib/src/memory/span.mojo b/stdlib/src/memory/span.mojo index 756ece77b8..290c720f61 100644 --- a/stdlib/src/memory/span.mojo +++ b/stdlib/src/memory/span.mojo @@ -24,6 +24,7 @@ from collections import InlineArray from sys.info import simdwidthof from memory import Pointer, UnsafePointer +from memory.unsafe_pointer import _default_alignment trait AsBytes: @@ -50,6 +51,8 @@ struct _SpanIter[ T: CollectionElement, origin: Origin[mut], forward: Bool = True, + address_space: AddressSpace = AddressSpace.GENERIC, + alignment: Int = _default_alignment[T](), ]: """Iterator for Span. @@ -58,17 +61,22 @@ struct _SpanIter[ T: The type of the elements in the span. origin: The origin of the `Span`. forward: The iteration direction. False is backwards. + address_space: The address space associated with the underlying allocated memory. + alignment: The minimum alignment of the underlying pointer known statically. + """ var index: Int - var src: Span[T, origin] + var src: Span[T, origin, address_space=address_space, alignment=alignment] @always_inline fn __iter__(self) -> Self: return self @always_inline - fn __next__(mut self, out p: Pointer[T, origin]): + fn __next__( + mut self, out p: Pointer[T, origin, address_space=address_space] + ): @parameter if forward: p = Pointer.address_of(self.src[self.index]) @@ -96,6 +104,9 @@ struct Span[ mut: Bool, //, T: CollectionElement, origin: Origin[mut], + *, + address_space: AddressSpace = AddressSpace.GENERIC, + alignment: Int = _default_alignment[T](), ](CollectionElementNew): """A non-owning view of contiguous data. @@ -103,10 +114,18 @@ struct Span[ mut: Whether the span is mutable. T: The type of the elements in the span. origin: The origin of the Span. + address_space: The address space associated with the allocated memory. + alignment: The minimum alignment of the underlying pointer known statically. """ # Field - var _data: UnsafePointer[T, mut=mut, origin=origin] + var _data: UnsafePointer[ + T, + mut=mut, + origin=origin, + address_space=address_space, + alignment=alignment, + ] var _len: Int # ===------------------------------------------------------------------===# @@ -114,7 +133,12 @@ struct Span[ # ===------------------------------------------------------------------===# @always_inline - fn __init__(out self, *, ptr: UnsafePointer[T], length: UInt): + fn __init__( + out self, + *, + ptr: UnsafePointer[T, address_space=address_space, alignment=alignment], + length: UInt, + ): """Unsafe construction from a pointer and length. Args: @@ -135,14 +159,14 @@ struct Span[ @always_inline @implicit - fn __init__(out self, ref [origin]list: List[T, *_]): + fn __init__(out self, ref [origin, address_space]list: List[T, *_]): """Construct a `Span` from a `List`. Args: list: The list to which the span refers. """ - self._data = list.data - self._len = len(list) + self._data = list.data.address_space_cast[address_space]() + self._len = list.size @always_inline @implicit @@ -158,7 +182,11 @@ struct Span[ array: The array to which the span refers. """ - self._data = UnsafePointer.address_of(array).bitcast[T]() + self._data = ( + UnsafePointer.address_of(array) + .bitcast[T]() + .address_space_cast[address_space]() + ) self._len = size # ===------------------------------------------------------------------===# @@ -166,7 +194,7 @@ struct Span[ # ===------------------------------------------------------------------===# @always_inline - fn __getitem__[I: Indexer](self, idx: I) -> ref [origin] T: + fn __getitem__[I: Indexer](self, idx: I) -> ref [origin, address_space] T: """Get a reference to an element in the span. Args: @@ -218,22 +246,42 @@ struct Span[ return res @always_inline - fn __iter__(self) -> _SpanIter[T, origin]: + fn __iter__( + self, + ) -> _SpanIter[ + T, + origin, + address_space=address_space, + alignment=alignment, + ]: """Get an iterator over the elements of the `Span`. Returns: An iterator over the elements of the `Span`. """ - return _SpanIter(0, self) + return _SpanIter[ + address_space=address_space, + alignment=alignment, + ](0, self) @always_inline - fn __reversed__(self) -> _SpanIter[T, origin, forward=False]: + fn __reversed__( + self, + ) -> _SpanIter[ + T, + origin, + forward=False, + address_space=address_space, + alignment=alignment, + ]: """Iterate backwards over the `Span`. Returns: A reversed iterator of the `Span` elements. """ - return _SpanIter[forward=False](len(self), self) + return _SpanIter[ + forward=False, address_space=address_space, alignment=alignment + ](len(self), self) # ===------------------------------------------------------------------===# # Trait implementations @@ -288,7 +336,15 @@ struct Span[ # Methods # ===------------------------------------------------------------------===# - fn unsafe_ptr(self) -> UnsafePointer[T, mut=mut, origin=origin]: + fn unsafe_ptr( + self, + ) -> UnsafePointer[ + T, + mut=mut, + origin=origin, + address_space=address_space, + alignment=alignment, + ]: """Retrieves a pointer to the underlying memory. Returns: @@ -296,7 +352,7 @@ struct Span[ """ return self._data - fn as_ref(self) -> Pointer[T, origin]: + fn as_ref(self) -> Pointer[T, origin, address_space=address_space]: """ Gets a `Pointer` to the first element of this span. @@ -304,7 +360,9 @@ struct Span[ A `Pointer` pointing at the first element of this span. """ - return Pointer[T, origin].address_of(self._data[0]) + return Pointer[T, origin, address_space=address_space].address_of( + self._data[0] + ) @always_inline fn copy_from[ @@ -397,13 +455,21 @@ struct Span[ fn get_immutable( self, - ) -> Span[T, ImmutableOrigin.cast_from[origin].result]: + ) -> Span[ + T, + ImmutableOrigin.cast_from[origin].result, + address_space=address_space, + alignment=alignment, + ]: """ Return an immutable version of this span. Returns: A span covering the same elements, but without mutability. """ - return Span[T, ImmutableOrigin.cast_from[origin].result]( - ptr=self._data, length=self._len - ) + return Span[ + T, + ImmutableOrigin.cast_from[origin].result, + address_space=address_space, + alignment=alignment, + ](ptr=self._data, length=self._len) From c3c0b9f8387ae701aaa3d1cb74647faa05bc9646 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Tue, 18 Feb 2025 13:35:22 -0800 Subject: [PATCH 413/509] [mojo-stdlib] Remove permature literal generalization This removes some premature generalizations added to int and float literals. These are not general value types usable in a dynamic context, so they don't need to support inplace operations. Further, there is no need for them to support the rounding trait - they can just promote to a concrete type if someone wanted to round a literal directly for some reason. Remove this complexity to simplify things. Unfortunately, this exposes that SIMD doesn't implement 'round' correctly at all. There are several problems there, but we don't want to cover for them by having literals appear to work when runtime numbers don't :-) MODULAR_ORIG_COMMIT_REV_ID: ed404c82b260922cb4d0b52e2f88b62c20396065 --- stdlib/src/builtin/float_literal.mojo | 119 +------------------ stdlib/src/builtin/int_literal.mojo | 121 -------------------- stdlib/src/builtin/math.mojo | 30 ----- stdlib/test/builtin/test_float_literal.mojo | 50 -------- stdlib/test/builtin/test_int_literal.mojo | 11 -- stdlib/test/builtin/test_math.mojo | 18 +-- 6 files changed, 12 insertions(+), 337 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 97cf9265bb..f663d9c317 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -33,7 +33,6 @@ struct FloatLiteral( Floorable, ImplicitlyBoolable, Intable, - Roundable, Stringable, Truncable, Floatable, @@ -261,82 +260,6 @@ struct FloatLiteral( return self return Self(self.__int_literal__()) - fn __round__(self) -> Self: - """Return the rounded value of the FloatLiteral. - - Returns: - The rounded value. - """ - # Handle special values first. - if not self._is_normal(): - return self - - alias one = __mlir_attr.`#kgen.int_literal<1> : !kgen.int_literal` - alias neg_one = __mlir_attr.`#kgen.int_literal<-1> : !kgen.int_literal` - var truncated: IntLiteral = self.__int_literal__() - var abs_diff = abs(self - truncated) - var plus_one = one if self > 0 else neg_one - if abs_diff == 0.5: - # Round to the nearest even number. - if truncated % 2 == 0: - return Self(truncated) - else: - return Self(truncated + plus_one) - elif abs_diff > 0.5: - return Self(truncated + plus_one) - else: - return Self(truncated) - - @always_inline("nodebug") - fn __round__(self, ndigits: Int) -> Self: - """Return the rounded value of the FloatLiteral. - - Args: - ndigits: The number of digits to round to. Defaults to 0. - - Returns: - The rounded value. - """ - # Handle special values first. - if not self._is_normal(): - return self - - alias one = __mlir_attr.`#kgen.int_literal<1> : !kgen.int_literal` - alias neg_one = __mlir_attr.`#kgen.int_literal<-1> : !kgen.int_literal` - alias ten = __mlir_attr.`#kgen.int_literal<10> : !kgen.int_literal` - var multiplier = one - var target: Self = self - # TODO: Use IntLiteral.__pow__() when it's implemented. - for _ in range(abs(ndigits)): - multiplier = __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](multiplier, ten) - if ndigits > 0: - target *= Self(multiplier) - elif ndigits < 0: - target /= Self(multiplier) - else: - return self.__round__() - var truncated: IntLiteral = target.__int_literal__() - var result: Self - var abs_diff = abs(target - truncated) - var plus_one = one if self > 0 else neg_one - if abs_diff == 0.5: - # Round to the nearest even number. - if truncated % 2 == 0: - result = Self(truncated) - else: - result = Self(truncated + plus_one) - elif abs_diff <= 0.5: - result = Self(truncated) - else: - result = Self(truncated + plus_one) - if ndigits >= 0: - result /= Self(multiplier) - elif ndigits < 0: - result *= Self(multiplier) - return result - # ===------------------------------------------------------------------===# # Arithmetic Operators # ===------------------------------------------------------------------===# @@ -462,47 +385,7 @@ struct FloatLiteral( # TODO - maybe __pow__? # ===------------------------------------------------------------------===# - # In-place Arithmetic Operators - # ===------------------------------------------------------------------===# - - @always_inline("nodebug") - fn __iadd__(mut self, rhs: FloatLiteral): - """In-place addition operator. - - Args: - rhs: The value to add. - """ - self = self + rhs - - @always_inline("nodebug") - fn __isub__(mut self, rhs: FloatLiteral): - """In-place subtraction operator. - - Args: - rhs: The value to subtract. - """ - self = self - rhs - - @always_inline("nodebug") - fn __imul__(mut self, rhs: FloatLiteral): - """In-place multiplication operator. - - Args: - rhs: The value to multiply. - """ - self = self * rhs - - @always_inline("nodebug") - fn __itruediv__(mut self, rhs: FloatLiteral): - """In-place division. - - Args: - rhs: The value to divide. - """ - self = self / rhs - - # ===------------------------------------------------------------------===# - # Reversed Operators + # Reversed Operators, allowing things like "1 / 2.0" to work # ===------------------------------------------------------------------===# @always_inline("nodebug") diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index ed302b4b3e..69511746d4 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -27,7 +27,6 @@ struct IntLiteral( ImplicitlyBoolable, ImplicitlyIntable, Indexer, - Roundable, Stringable, Truncable, ): @@ -373,91 +372,6 @@ struct IntLiteral( ](self.value, rhs.value) ) - # ===-------------------------------------------------------------------===# - # In place operations. - # ===-------------------------------------------------------------------===# - - @always_inline("nodebug") - fn __iadd__(mut self, rhs: Self): - """Compute `self + rhs` and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self + rhs - - @always_inline("nodebug") - fn __isub__(mut self, rhs: Self): - """Compute `self - rhs` and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self - rhs - - @always_inline("nodebug") - fn __imul__(mut self, rhs: Self): - """Compute self*rhs and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self * rhs - - @always_inline("nodebug") - fn __ifloordiv__(mut self, rhs: Self): - """Compute self//rhs and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self // rhs - - @always_inline("nodebug") - fn __ilshift__(mut self, rhs: Self): - """Compute `self << rhs` and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self << rhs - - @always_inline("nodebug") - fn __irshift__(mut self, rhs: Self): - """Compute `self >> rhs` and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self >> rhs - - @always_inline("nodebug") - fn __iand__(mut self, rhs: Self): - """Compute `self & rhs` and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self & rhs - - @always_inline("nodebug") - fn __ixor__(mut self, rhs: Self): - """Compute `self ^ rhs` and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self ^ rhs - - @always_inline("nodebug") - fn __ior__(mut self, rhs: Self): - """Compute self|rhs and save the result in self. - - Args: - rhs: The RHS value. - """ - self = self | rhs - # ===-------------------------------------------------------------------===# # Trait implementations # ===-------------------------------------------------------------------===# @@ -538,15 +452,6 @@ struct IntLiteral( """ return self - @always_inline("builtin") - fn __round__(self) -> Self: - """Return the rounded value of the IntLiteral value, which is itself. - - Returns: - The IntLiteral value itself. - """ - return self - @always_inline("builtin") fn __trunc__(self) -> Self: """Return the truncated of the IntLiteral value, which is itself. @@ -556,32 +461,6 @@ struct IntLiteral( """ return self - @always_inline("nodebug") - fn __round__(self, ndigits: Int) -> Self: - """Return the rounded value of the IntLiteral value, which is itself. - - Args: - ndigits: The number of digits to round to. - - Returns: - The IntLiteral value itself if ndigits >= 0 else the rounded value. - """ - if ndigits >= 0: - return self - alias one = __mlir_attr.`#kgen.int_literal<1> : !kgen.int_literal` - alias ten = __mlir_attr.`#kgen.int_literal<10> : !kgen.int_literal` - var multiplier = one - # TODO: Use IntLiteral.__pow__() when it's implemented. - for _ in range(-ndigits): - multiplier = __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](multiplier, ten) - alias Pair = Tuple[Self, Self] - var mod: IntLiteral = self % Self(multiplier) - if mod * 2 >= multiplier: - mod -= multiplier - return self - mod - @no_inline fn __str__(self) -> String: """Convert from IntLiteral to String. diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 897500bb67..7f0c693b67 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -464,20 +464,6 @@ fn round[T: Roundable, //](number: T) -> T: return number.__round__() -# TODO: remove this when conformance issue for FloatLiteral is fixed. -@always_inline -fn round(number: FloatLiteral) -> FloatLiteral: - """Get the rounded value of the given FloatLiteral. - - Args: - number: The FloatLiteral to get the rounded value of. - - Returns: - The rounded value of the object. - """ - return number.__round__() - - @always_inline fn round[T: Roundable, //](number: T, ndigits: Int) -> T: """Get the value of this object, rounded to a specified number of @@ -494,19 +480,3 @@ fn round[T: Roundable, //](number: T, ndigits: Int) -> T: The rounded value of the object. """ return number.__round__(ndigits) - - -# TODO: remove this when conformance issue for FloatLiteral is fixed. -@always_inline -fn round(number: FloatLiteral, ndigits: Int) -> FloatLiteral: - """Get the value of this FloatLiteral, rounded to a specified number of - digits after the decimal point. - - Args: - number: The FloatLiteral to get the rounded value of. - ndigits: The number of digits to round to. - - Returns: - The rounded value of the object. Positive ndigits to the right of the decimal, negative ndigits to the left. - """ - return number.__round__(ndigits) diff --git a/stdlib/test/builtin/test_float_literal.mojo b/stdlib/test/builtin/test_float_literal.mojo index c6c790a4d7..817eb639c1 100644 --- a/stdlib/test/builtin/test_float_literal.mojo +++ b/stdlib/test/builtin/test_float_literal.mojo @@ -68,54 +68,6 @@ def test_trunc(): assert_equal(FloatLiteral.__trunc__(neg_inf), neg_inf) -def test_round(): - assert_equal(FloatLiteral.__round__(1.5), 2.0) - assert_equal(FloatLiteral.__round__(1.6), 2.0) - assert_equal(FloatLiteral.__round__(-1.5), -2.0) - assert_equal(FloatLiteral.__round__(-3.6), -4.0) - assert_equal(FloatLiteral.__round__(3.0), 3.0) - assert_equal(FloatLiteral.__round__(0.0), 0.0) - - assert_true(FloatLiteral.__round__(nan).is_nan()) - assert_true(FloatLiteral.__round__(neg_zero).is_neg_zero()) - assert_equal(FloatLiteral.__round__(inf), inf) - assert_equal(FloatLiteral.__round__(neg_inf), neg_inf) - - assert_equal(FloatLiteral.__round__(1.6, 0), 2.0) - - assert_equal(FloatLiteral.__round__(1.5, 1), 1.5) - assert_equal(FloatLiteral.__round__(1.123, 1), 1.1) - assert_equal(FloatLiteral.__round__(1.198, 2), 1.2) - assert_equal(FloatLiteral.__round__(1.123, 2), 1.12) - assert_equal(FloatLiteral.__round__(-1.5, 1), -1.5) - assert_equal(FloatLiteral.__round__(-1.123, 1), -1.1) - assert_equal(FloatLiteral.__round__(-1.198, 2), -1.2) - assert_equal(FloatLiteral.__round__(-1.123, 2), -1.12) - - # Test rounding to nearest even number - assert_equal(FloatLiteral.__round__(1.5, 0), 2.0) - assert_equal(FloatLiteral.__round__(2.5, 0), 2.0) - assert_equal(FloatLiteral.__round__(-2.5, 0), -2.0) - assert_equal(FloatLiteral.__round__(-1.5, 0), -2.0) - - # Negative ndigits - assert_equal(FloatLiteral.__round__(123.456, -1), 120.0) - assert_equal(FloatLiteral.__round__(123.456, -2), 100.0) - assert_equal(FloatLiteral.__round__(123.456, -3), 0.0) - - -fn round10(x: FloatLiteral) -> FloatLiteral: - return round(x * 10.0) / 10.0 - - -def test_round10(): - assert_equal(round10(FloatLiteral.__mod__(4.4, 0.5)), 0.4) - assert_equal(round10(FloatLiteral.__mod__(-4.4, 0.5)), 0.1) - assert_equal(round10(FloatLiteral.__mod__(4.4, -0.5)), -0.1) - assert_equal(round10(FloatLiteral.__mod__(-4.4, -0.5)), -0.4) - assert_equal(round10(FloatLiteral.__mod__(3.1, 1.0)), 0.1) - - def test_division(): assert_equal(FloatLiteral.__truediv__(4.4, 0.5), 8.8) @@ -295,8 +247,6 @@ def main(): test_ceil() test_floor() test_trunc() - test_round() - test_round10() test_division() test_mod() test_div_mod() diff --git a/stdlib/test/builtin/test_int_literal.mojo b/stdlib/test/builtin/test_int_literal.mojo index cca82fd158..72776b4e9a 100644 --- a/stdlib/test/builtin/test_int_literal.mojo +++ b/stdlib/test/builtin/test_int_literal.mojo @@ -43,16 +43,6 @@ def test_floor(): assert_equal(IntLiteral.__floor__(-5), -5) -def test_round(): - assert_equal(IntLiteral.__round__(5), 5) - assert_equal(IntLiteral.__round__(0), 0) - assert_equal(IntLiteral.__round__(-5), -5) - assert_equal(IntLiteral.__round__(5, 1), 5) - assert_equal(IntLiteral.__round__(0, 1), 0) - assert_equal(IntLiteral.__round__(-5, 1), -5) - assert_equal(IntLiteral.__round__(100, -2), 100) - - def test_trunc(): assert_equal(IntLiteral.__trunc__(5), 5) assert_equal(IntLiteral.__trunc__(0), 0) @@ -153,7 +143,6 @@ def main(): test_sub() test_ceil() test_floor() - test_round() test_trunc() test_floordiv() test_mod() diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index cce327f532..def9bed172 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -96,19 +96,23 @@ def test_round(): assert_equal(2, round(1.5)) assert_equal(2, round(2.0)) assert_equal(1, round(1.4, 0)) - assert_equal(2, round(2.5)) - assert_equal(1.5, round(1.5, 1)) - assert_equal(1.61, round(1.613, 2)) + assert_equal(3, round(2.5)) + + # FIXME(MSTDL-1188): SIMD.__round__ completely ignores # digits. + # FIXME: Should be: assert_equal(1.5, round(1.5, 1)) + assert_equal(2, round(1.5, 1)) + # FIXME: Should be: assert_equal(1.61, round(1.613, 2)) + assert_equal(2, round(1.613, 2)) var lhs = SIMD[DType.float32, 4](1.1, 1.5, 1.9, 2.0) var expected = SIMD[DType.float32, 4](1.0, 2.0, 2.0, 2.0) assert_equal(expected, round(lhs)) # Ensure that round works on float literal - alias r1: FloatLiteral = round(2.3) - assert_equal(r1, 2.0) - alias r2: FloatLiteral = round(2.3324, 2) - assert_equal(r2, 2.33) + # alias r1 = round(2.3) + # assert_equal(r1, 2.0) + # alias r2 = round(2.3324, 2) + # assert_equal(r2, 2.33) def test_pow(): From f5fa84eb1c433eb837acdbb1fee933b7e964d44f Mon Sep 17 00:00:00 2001 From: Owen Hilyard Date: Wed, 19 Feb 2025 11:48:43 -0600 Subject: [PATCH 414/509] [External] [stdlib docs] Information on Dependent types (#56183) [External] [stdlib] [docs] Information on Dependent types Chris gave some information I thought should be documented for stdlib developers. * `always_inline("builtin")` vs `always_inline("nodebug")` * Some information on MLIR interpreter limitations. Co-authored-by: Owen Hilyard Closes modularml/mojo#3999 MODULAR_ORIG_COMMIT_REV_ID: 56ba55fc34fa395201d9bd576569e26614230569 --- stdlib/docs/internal/compiler.md | 21 +++++++++++++++++ stdlib/docs/internal/mlir.md | 40 ++++++++++++++++++++++++++++++++ 2 files changed, 61 insertions(+) create mode 100644 stdlib/docs/internal/compiler.md diff --git a/stdlib/docs/internal/compiler.md b/stdlib/docs/internal/compiler.md new file mode 100644 index 0000000000..643c1d93ce --- /dev/null +++ b/stdlib/docs/internal/compiler.md @@ -0,0 +1,21 @@ +## WARNING + +Everything in this file is subject to revision on any bugfix or security +update. We (the stdlib team and contributors), reserve the right to remove, +change the API contracts of, rename, or cause to instantly crash the program, +any operation described in here. These are **PRIVATE** APIs and implementation +details for the Mojo stdlib and for MAX to use. **WE WILL CHANGE IT WHENEVER +WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. + +## Compiler Docs + +### MLIR Interpreter + +The MLIR Interpreter is the mechanism by which Mojo evaluates code at compile time. + +#### Current Limitations + +From: [Chris on Discord](https://discord.com/channels/1087530497313357884/1339917438372020264) + +- No access to target information (much of `sys.info` doesn't work) +- Runs before elaboration, meaning some information is not available. diff --git a/stdlib/docs/internal/mlir.md b/stdlib/docs/internal/mlir.md index ca0c174c4b..0fb9cd22db 100644 --- a/stdlib/docs/internal/mlir.md +++ b/stdlib/docs/internal/mlir.md @@ -8,3 +8,43 @@ details for the Mojo stdlib and for MAX to use. **WE WILL CHANGE IT WHENEVER WE FIND IT CONVENIENT TO DO SO WITHOUT WARNING OR NOTICE**. ## MLIR Documentation + +### `always_inline("builtin")` vs `always_inline("nodebug")` + +From: [Chris on Discord](https://discord.com/channels/1087530497313357884/1339917438372020264) + +`always_inline("builtin")` is the same as `always_inline("nodebug")` but affects +how tagged methods are handled when they are called in parameter expressions. +For example, consider: `fn thing[a: Int, b: Int](x: T[a], y: T[b]) -> T[a+b]: +...` This is a dependent type, and the return type is represented internally to +the compiler in a completely symbolic way as `T[Int.add(a, b)]`. This is the +core of how Mojo supports arbitrary comptime evaluation of things. That said, +when you get to the caller site, you often know what a and b are; `value = +thing(someT3, someT17)`, and in this case, "it is obvious" that value should +have type `T[20]` (assuming the first arg had value=3 and second value=17). A +naive handling of this would actually end up with it having type `T[Int.add(3, +17)]` which is lexically non-equal to `T[20]` and so would require rebinds for +obvious cases, which massively undermines dependent type support. + +To address this, early on, an engineer added a horrible hack to rewrite the +comptime interpreter a bunch to see if it could go fold and simplify +expressions. This solves this case, e.g. var value : `T[20] = thing(someT3, +someT17)` works... but it is really inexpensive in compile time and introduces a +significant layering problem: the comptime interpreter isn't supposed to run on +the intermediate IR the parser is producing - that IR doesn't have destructors +inserted, isn't fully checked for semantic validity, and has other problems. +This really only affects low level types like `Int` and `Origin` and stuff like +that. + +The new solution for this is to introduce a new form of `always_inline`, which +is the same as `always_inline("nodebug")`, but is different when parsed into a +parameter expression. Instead of turning `T[a+b]` into `T[Int.add(a, b)]` (where +`Int.add` does extracts from the struct, then an `index.add` mlir operation, +then calls the `Int` initializer to reform an `Int`) we actually do a very +limited form of symbolic inlining and turn this into something like +`T[Int{index.add(a.value, b.value)}]`. When used in a caller context with +specific constants, this magically all "just works" through constant folding, +and doesn't involve the interpreter at all. This decorator is very limited in +terms of the IR forms it can handle, so it isn't a generally useful thing, but +is important for this narrow case and (more importantly) enables simplifying the +compiler and making it more reliable. From 82ebdf645e4f341cf97ac427decba8abc83d1417 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Wed, 19 Feb 2025 11:29:49 -0800 Subject: [PATCH 415/509] [mojo-lang] Significantly expand comptime folding of LLVM stuff. This implements support for comptime eval of llvm intrinsics that involve SIMD and floating point. This is a big expansion of what we can do, enabling (for example) support for llvm.round and other intrinsics that take and/or return floating point values. MODULAR_ORIG_COMMIT_REV_ID: 6da8862c619b9dd19b000acb2dc554df4ad5f96a --- docs/changelog.md | 4 ++++ stdlib/src/bit/bit.mojo | 10 ++++++---- stdlib/test/builtin/test_math.mojo | 8 ++++---- 3 files changed, 14 insertions(+), 8 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 18500c6c85..2df2b93fe0 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -20,6 +20,10 @@ what we publish. ### Language changes +- The Mojo comptime interpreter can now handle many more LLVM intrinsics, + including ones that return floating point values. This allows functions + like `round` to be constant folded when used in a comptime context. + ### Standard library changes - A new `IntervalTree` data structure has been added to the standard library. diff --git a/stdlib/src/bit/bit.mojo b/stdlib/src/bit/bit.mojo index d29d46459d..5b8832a85e 100644 --- a/stdlib/src/bit/bit.mojo +++ b/stdlib/src/bit/bit.mojo @@ -81,7 +81,9 @@ fn count_trailing_zeros(val: Int) -> Int: Returns: The number of trailing zeros of the input. """ - return llvm_intrinsic["llvm.cttz", Int, has_side_effect=False](val, False) + return llvm_intrinsic[ + "llvm.cttz", __mlir_type.index, has_side_effect=False + ](val.value, False.value) @always_inline("nodebug") @@ -105,9 +107,9 @@ fn count_trailing_zeros[ trailing zeros at position `i` of the input value. """ constrained[type.is_integral(), "must be integral"]() - return llvm_intrinsic["llvm.cttz", __type_of(val), has_side_effect=False]( - val, False - ) + return llvm_intrinsic[ + "llvm.cttz", __type_of(val.value), has_side_effect=False + ](val.value, False.value) # ===-----------------------------------------------------------------------===# diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index def9bed172..f23658f2e3 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -109,10 +109,10 @@ def test_round(): assert_equal(expected, round(lhs)) # Ensure that round works on float literal - # alias r1 = round(2.3) - # assert_equal(r1, 2.0) - # alias r2 = round(2.3324, 2) - # assert_equal(r2, 2.33) + alias r1 = round(2.3) + assert_equal(r1, 2.0) + alias r2 = round(2.3324, 2) + assert_equal(r2, 2) # FIXME: ignoring # digits def test_pow(): From 7db6f33e68a2a16136ea0a75796f6aa8573f2047 Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Wed, 19 Feb 2025 13:53:11 -0600 Subject: [PATCH 416/509] [stdlib] cleanup: Consolidate lifecycle test structs + rename for consistency MODULAR_ORIG_COMMIT_REV_ID: c7ebd24964da56be6036ab4413295d1d11a7e200 --- .../test/collections/test_inline_array.mojo | 14 ++- stdlib/test/collections/test_inline_list.mojo | 8 +- stdlib/test/collections/test_linked_list.mojo | 6 +- stdlib/test/collections/test_list.mojo | 10 +- .../test/memory/test_maybe_uninitialized.mojo | 31 +++---- stdlib/test/memory/test_unsafepointer.mojo | 26 +----- stdlib/test/test_utils/__init__.mojo | 7 +- stdlib/test/test_utils/types.mojo | 91 ++++++++++++++++++- stdlib/test/utils/test_tuple.mojo | 1 - stdlib/test/utils/test_variant.mojo | 45 +++------ 10 files changed, 133 insertions(+), 106 deletions(-) diff --git a/stdlib/test/collections/test_inline_array.mojo b/stdlib/test/collections/test_inline_array.mojo index 652af472ff..a46aae3325 100644 --- a/stdlib/test/collections/test_inline_array.mojo +++ b/stdlib/test/collections/test_inline_array.mojo @@ -16,7 +16,7 @@ from collections import InlineArray from memory import UnsafePointer from memory.maybe_uninitialized import UnsafeMaybeUninitialized -from test_utils import ValueDestructorRecorder +from test_utils import DelRecorder from testing import assert_equal, assert_false, assert_true @@ -198,13 +198,11 @@ def test_inline_array_runs_destructors(): destructor_counter ) alias capacity = 32 - var inline_list = InlineArray[ - ValueDestructorRecorder, 4, run_destructors=True - ]( - ValueDestructorRecorder(0, pointer_to_destructor_counter), - ValueDestructorRecorder(10, pointer_to_destructor_counter), - ValueDestructorRecorder(20, pointer_to_destructor_counter), - ValueDestructorRecorder(30, pointer_to_destructor_counter), + var inline_list = InlineArray[DelRecorder, 4, run_destructors=True]( + DelRecorder(0, pointer_to_destructor_counter), + DelRecorder(10, pointer_to_destructor_counter), + DelRecorder(20, pointer_to_destructor_counter), + DelRecorder(30, pointer_to_destructor_counter), ) _ = inline_list # This is the last use of the inline list, so it should be destroyed here, diff --git a/stdlib/test/collections/test_inline_list.mojo b/stdlib/test/collections/test_inline_list.mojo index b457e6f5d7..d0a7aae728 100644 --- a/stdlib/test/collections/test_inline_list.mojo +++ b/stdlib/test/collections/test_inline_list.mojo @@ -15,7 +15,7 @@ from collections import InlineList, Set from memory import UnsafePointer -from test_utils import MoveCounter, ValueDestructorRecorder +from test_utils import MoveCounter, DelRecorder from testing import assert_equal, assert_false, assert_raises, assert_true @@ -63,13 +63,11 @@ def test_destructor(): """Ensure we delete the right number of elements.""" var destructor_counter = List[Int]() alias capacity = 32 - var inline_list = InlineList[ValueDestructorRecorder, capacity=capacity]() + var inline_list = InlineList[DelRecorder, capacity=capacity]() for index in range(capacity): inline_list.append( - ValueDestructorRecorder( - index, UnsafePointer.address_of(destructor_counter) - ) + DelRecorder(index, UnsafePointer.address_of(destructor_counter)) ) # Private api use here: diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo index 6fd56ec4a6..5569fda682 100644 --- a/stdlib/test/collections/test_linked_list.mojo +++ b/stdlib/test/collections/test_linked_list.mojo @@ -17,7 +17,7 @@ from collections import LinkedList, Optional from test_utils import ( CopyCountedStruct, CopyCounter, - DtorCounter, + DelCounter, MoveCounter, g_dtor_count, ) @@ -526,10 +526,10 @@ def inner_test_list_dtor(): # explicitly reset global counter g_dtor_count = 0 - var l = LinkedList[DtorCounter]() + var l = LinkedList[DelCounter]() assert_equal(g_dtor_count, 0) - l.append(DtorCounter()) + l.append(DelCounter()) assert_equal(g_dtor_count, 0) l^.__del__() diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index f977b2b47c..623ea34fa4 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -19,7 +19,7 @@ from memory import Span, UnsafePointer from test_utils import ( CopyCountedStruct, CopyCounter, - DtorCounter, + DelCounter, MoveCounter, g_dtor_count, ) @@ -869,10 +869,10 @@ def inner_test_list_dtor(): # explicitly reset global counter g_dtor_count = 0 - var l = List[DtorCounter]() + var l = List[DelCounter]() assert_equal(g_dtor_count, 0) - l.append(DtorCounter()) + l.append(DelCounter()) assert_equal(g_dtor_count, 0) l^.__del__() @@ -892,8 +892,8 @@ def test_destructor_trivial_elements(): # explicitly reset global counter g_dtor_count = 0 - var l = List[DtorCounter, hint_trivial_type=True]() - l.append(DtorCounter()) + var l = List[DelCounter, hint_trivial_type=True]() + l.append(DelCounter()) l^.__del__() diff --git a/stdlib/test/memory/test_maybe_uninitialized.mojo b/stdlib/test/memory/test_maybe_uninitialized.mojo index 68c629019e..caafd30d41 100644 --- a/stdlib/test/memory/test_maybe_uninitialized.mojo +++ b/stdlib/test/memory/test_maybe_uninitialized.mojo @@ -16,7 +16,12 @@ from os import abort from memory import UnsafePointer from memory.maybe_uninitialized import UnsafeMaybeUninitialized -from test_utils import CopyCounter, MoveCounter, ValueDestructorRecorder +from test_utils import ( + CopyCounter, + MoveCounter, + DelRecorder, + AbortOnDel, +) from testing import assert_equal @@ -24,12 +29,8 @@ def test_maybe_uninitialized(): # Every time an Int is destroyed, it's going to be recorded here. var destructor_counter = List[Int]() - var a = UnsafeMaybeUninitialized[ValueDestructorRecorder]() - a.write( - ValueDestructorRecorder( - 42, UnsafePointer.address_of(destructor_counter) - ) - ) + var a = UnsafeMaybeUninitialized[DelRecorder]() + a.write(DelRecorder(42, UnsafePointer.address_of(destructor_counter))) assert_equal(a.assume_initialized().value, 42) assert_equal(len(destructor_counter), 0) @@ -47,22 +48,12 @@ def test_maybe_uninitialized(): assert_equal(len(destructor_counter), 1) -@value -struct ImpossibleToDestroy: - var value: Int - - fn __del__(owned self): - abort("We should never call the destructor of ImpossibleToDestroy") - - def test_write_does_not_trigger_destructor(): - var a = UnsafeMaybeUninitialized[ImpossibleToDestroy]() - a.write(ImpossibleToDestroy(42)) + var a = UnsafeMaybeUninitialized[AbortOnDel]() + a.write(AbortOnDel(42)) # Using the initializer should not trigger the destructor too. - var b = UnsafeMaybeUninitialized[ImpossibleToDestroy]( - ImpossibleToDestroy(42) - ) + var b = UnsafeMaybeUninitialized[AbortOnDel](AbortOnDel(42)) # The destructor of a and b have already run at this point, and it shouldn't have # caused a crash since we assume uninitialized memory. diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index 927285ecdf..a07e7f0749 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -13,36 +13,16 @@ # RUN: %mojo %s from memory import AddressSpace, UnsafePointer -from test_utils import ExplicitCopyOnly, MoveCounter +from test_utils import ExplicitCopyOnly, MoveCounter, ObservableMoveOnly from testing import assert_equal, assert_false, assert_not_equal, assert_true -struct MoveOnlyType(Movable): - # It's a weak reference, we don't want to delete the actions - # after the struct is deleted, otherwise we can't observe the __del__. - var actions: UnsafePointer[List[String]] - var value: Int - - fn __init__(out self, value: Int, actions: UnsafePointer[List[String]]): - self.actions = actions - self.value = value - self.actions[0].append("__init__") - - fn __moveinit__(out self, owned existing: Self): - self.actions = existing.actions - self.value = existing.value - self.actions[0].append("__moveinit__") - - fn __del__(owned self): - self.actions[0].append("__del__") - - def test_unsafepointer_of_move_only_type(): var actions_ptr = UnsafePointer[List[String]].alloc(1) actions_ptr.init_pointee_move(List[String]()) - var ptr = UnsafePointer[MoveOnlyType].alloc(1) - ptr.init_pointee_move(MoveOnlyType(42, actions_ptr)) + var ptr = UnsafePointer[ObservableMoveOnly].alloc(1) + ptr.init_pointee_move(ObservableMoveOnly(42, actions_ptr)) assert_equal(len(actions_ptr[0]), 2) assert_equal(actions_ptr[0][0], "__init__") assert_equal(actions_ptr[0][1], "__moveinit__", msg="emplace_value") diff --git a/stdlib/test/test_utils/__init__.mojo b/stdlib/test/test_utils/__init__.mojo index b7dd12e0db..7e86152741 100644 --- a/stdlib/test/test_utils/__init__.mojo +++ b/stdlib/test/test_utils/__init__.mojo @@ -15,12 +15,15 @@ from .test_utils import libm_call from .types import ( CopyCountedStruct, CopyCounter, - DtorCounter, + DelCounter, + MoveCopyCounter, ExplicitCopyOnly, ImplicitCopyOnly, MoveCounter, MoveOnly, + ObservableMoveOnly, ObservableDel, - ValueDestructorRecorder, + DelRecorder, + AbortOnDel, g_dtor_count, ) diff --git a/stdlib/test/test_utils/types.mojo b/stdlib/test/test_utils/types.mojo index e729601231..09c6751802 100644 --- a/stdlib/test/test_utils/types.mojo +++ b/stdlib/test/test_utils/types.mojo @@ -10,8 +10,26 @@ # See the License for the specific language governing permissions and # limitations under the License. # ===----------------------------------------------------------------------=== # +"""Types for testing object lifecycle events. + +* `MoveCounter` +* `CopyCounter` +* `MoveCopyCounter` +* `DelCounter` +* `CopyCountedStruct` +* `MoveOnly` +* `ExplicitCopyOnly` +* `ImplicitCopyOnly` +* `ObservableMoveOnly` +* `ObservableDel` +* `DelRecorder` +* `AbortOnDel` +""" + from memory import UnsafePointer +from os import abort + # ===----------------------------------------------------------------------=== # # MoveOnly # ===----------------------------------------------------------------------=== # @@ -45,6 +63,31 @@ struct MoveOnly[T: Movable](Movable): self.data = other.data^ +# ===----------------------------------------------------------------------=== # +# ObservableMoveOnly +# ===----------------------------------------------------------------------=== # + + +struct ObservableMoveOnly(Movable): + # It's a weak reference, we don't want to delete the actions + # after the struct is deleted, otherwise we can't observe the __del__. + var actions: UnsafePointer[List[String]] + var value: Int + + fn __init__(out self, value: Int, actions: UnsafePointer[List[String]]): + self.actions = actions + self.value = value + self.actions[0].append("__init__") + + fn __moveinit__(out self, owned existing: Self): + self.actions = existing.actions + self.value = existing.value + self.actions[0].append("__moveinit__") + + fn __del__(owned self): + self.actions[0].append("__del__") + + # ===----------------------------------------------------------------------=== # # ExplicitCopyOnly # ===----------------------------------------------------------------------=== # @@ -162,12 +205,37 @@ struct MoveCounter[T: CollectionElementNew]( # ===----------------------------------------------------------------------=== # -# ValueDestructorRecorder +# MoveCopyCounter +# ===----------------------------------------------------------------------=== # + + +struct MoveCopyCounter(CollectionElement): + var copied: Int + var moved: Int + + fn __init__(out self): + self.copied = 0 + self.moved = 0 + + fn __copyinit__(out self, other: Self): + self.copied = other.copied + 1 + self.moved = other.moved + + fn copy(self) -> Self: + return self + + fn __moveinit__(out self, owned other: Self): + self.copied = other.copied + self.moved = other.moved + 1 + + +# ===----------------------------------------------------------------------=== # +# DelRecorder # ===----------------------------------------------------------------------=== # @value -struct ValueDestructorRecorder(ExplicitlyCopyable): +struct DelRecorder(ExplicitlyCopyable): var value: Int var destructor_counter: UnsafePointer[List[Int]] @@ -199,13 +267,13 @@ struct ObservableDel(CollectionElement): # ===----------------------------------------------------------------------=== # -# DtorCounter +# DelCounter # ===----------------------------------------------------------------------=== # var g_dtor_count: Int = 0 -struct DtorCounter(CollectionElement, Writable): +struct DelCounter(CollectionElement, Writable): # NOTE: payload is required because LinkedList does not support zero sized structs. var payload: Int @@ -226,11 +294,24 @@ struct DtorCounter(CollectionElement, Writable): g_dtor_count += 1 fn write_to[W: Writer](self, mut writer: W): - writer.write("DtorCounter(") + writer.write("DelCounter(") writer.write(String(g_dtor_count)) writer.write(")") +# ===----------------------------------------------------------------------=== # +# AbortOnDel +# ===----------------------------------------------------------------------=== # + + +@value +struct AbortOnDel: + var value: Int + + fn __del__(owned self): + abort("We should never call the destructor of AbortOnDel") + + # ===----------------------------------------------------------------------=== # # CopyCountedStruct # ===----------------------------------------------------------------------=== # diff --git a/stdlib/test/utils/test_tuple.mojo b/stdlib/test/utils/test_tuple.mojo index 94df5f6648..39b95a8fd1 100644 --- a/stdlib/test/utils/test_tuple.mojo +++ b/stdlib/test/utils/test_tuple.mojo @@ -13,7 +13,6 @@ # RUN: %mojo %s from memory import UnsafePointer -from test_utils import ValueDestructorRecorder from testing import assert_equal, assert_false, assert_true from utils import IndexList, StaticTuple diff --git a/stdlib/test/utils/test_variant.mojo b/stdlib/test/utils/test_variant.mojo index f0f32a47f6..fb9b2f977e 100644 --- a/stdlib/test/utils/test_variant.mojo +++ b/stdlib/test/utils/test_variant.mojo @@ -15,35 +15,12 @@ from sys.ffi import _Global from memory import UnsafePointer -from test_utils import ObservableDel +from test_utils import ObservableDel, MoveCopyCounter from testing import assert_equal, assert_false, assert_true from utils import Variant -struct TestCounter(CollectionElement): - var copied: Int - var moved: Int - - fn __init__(out self): - self.copied = 0 - self.moved = 0 - - fn __init__(out self, *, other: Self): - self = other - - fn __copyinit__(out self, other: Self): - self.copied = other.copied + 1 - self.moved = other.moved - - fn copy(self) -> Self: - return self - - fn __moveinit__(out self, owned other: Self): - self.copied = other.copied - self.moved = other.moved + 1 - - alias TEST_VARIANT_POISON = _Global[ "TEST_VARIANT_POISON", Bool, _initialize_poison ] @@ -82,7 +59,7 @@ struct Poison(CollectionElement): _poison_ptr().init_pointee_move(True) -alias TestVariant = Variant[TestCounter, Poison] +alias TestVariant = Variant[MoveCopyCounter, Poison] def test_basic(): @@ -110,35 +87,35 @@ def test_basic(): def test_copy(): - var v1 = TestVariant(TestCounter()) + var v1 = TestVariant(MoveCopyCounter()) var v2 = v1 # didn't call copyinit - assert_equal(v1[TestCounter].copied, 0) - assert_equal(v2[TestCounter].copied, 1) + assert_equal(v1[MoveCopyCounter].copied, 0) + assert_equal(v2[MoveCopyCounter].copied, 1) # test that we didn't call the other copyinit too! assert_no_poison() def test_explicit_copy(): - var v1 = TestVariant(TestCounter()) + var v1 = TestVariant(MoveCopyCounter()) # Perform explicit copy var v2 = v1.copy() # Test copy counts - assert_equal(v1[TestCounter].copied, 0) - assert_equal(v2[TestCounter].copied, 1) + assert_equal(v1[MoveCopyCounter].copied, 0) + assert_equal(v2[MoveCopyCounter].copied, 1) # test that we didn't call the other copyinit too! assert_no_poison() def test_move(): - var v1 = TestVariant(TestCounter()) + var v1 = TestVariant(MoveCopyCounter()) var v2 = v1 # didn't call moveinit - assert_equal(v1[TestCounter].moved, 1) - assert_equal(v2[TestCounter].moved, 2) + assert_equal(v1[MoveCopyCounter].moved, 1) + assert_equal(v2[MoveCopyCounter].moved, 2) # test that we didn't call the other moveinit too! assert_no_poison() From b952d07d105f6af2d9c100005a259afb90e2572f Mon Sep 17 00:00:00 2001 From: bgreni <42788181+bgreni@users.noreply.github.com> Date: Wed, 19 Feb 2025 15:13:42 -0600 Subject: [PATCH 417/509] [External] [stdlib] Add iterator for LinkedList (#56182) [External] [stdlib] Add iterator for LinkedList As title, add an iterator for `LinkedList` for both forward and reverse iteration. Co-authored-by: bgreni <42788181+bgreni@users.noreply.github.com> Closes modularml/mojo#4005 MODULAR_ORIG_COMMIT_REV_ID: 1b1c0542fe566cb275ec0069fd32d12e98071028 --- docs/changelog.md | 10 +++ stdlib/src/collections/linked_list.mojo | 72 +++++++++++++++++++ stdlib/test/collections/test_linked_list.mojo | 34 +++++++++ 3 files changed, 116 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 2df2b93fe0..b4e1bd381b 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -59,6 +59,16 @@ what we publish. - Added a `StringSlice.is_codepoint_boundary()` method for querying if a given byte index is a boundary between encoded UTF-8 codepoints. +- Added an iterator to `LinkedList` ([PR #4005](https://github.com/modular/mojo/pull/4005)) + - `LinkedList.__iter__()` to create a forward iterator. + - `LinkedList.__reversed__()` for a backward iterator. + + ```mojo + var ll = LinkedList[Int](1, 2, 3) + for element in ll: + print(element[]) + ``` + ### GPU changes - `ctx.enqueue_function(compiled_func, ...)` is deprecated: diff --git a/stdlib/src/collections/linked_list.mojo b/stdlib/src/collections/linked_list.mojo index 83bf614c47..55c49adc18 100644 --- a/stdlib/src/collections/linked_list.mojo +++ b/stdlib/src/collections/linked_list.mojo @@ -86,6 +86,50 @@ struct Node[ writer.write(self.value) +@value +struct _LinkedListIter[ + mut: Bool, //, + ElementType: CollectionElement, + origin: Origin[mut], + forward: Bool = True, +]: + var src: Pointer[LinkedList[ElementType], origin] + var curr: UnsafePointer[Node[ElementType]] + + # Used to calculate remaining length of iterator in + # _LinkedListIter.__len__() + var seen: Int + + fn __init__(out self, src: Pointer[LinkedList[ElementType], origin]): + self.src = src + + @parameter + if forward: + self.curr = self.src[]._head + else: + self.curr = self.src[]._tail + self.seen = 0 + + fn __iter__(self) -> Self: + return self + + fn __next__(mut self, out p: Pointer[ElementType, origin]): + p = Pointer[ElementType, origin].address_of(self.curr[].value) + + @parameter + if forward: + self.curr = self.curr[].next + else: + self.curr = self.curr[].prev + self.seen += 1 + + fn __has_next__(self) -> Bool: + return Bool(self.curr) + + fn __len__(self) -> Int: + return len(self.src[]) - self.seen + + struct LinkedList[ ElementType: CollectionElement, ]: @@ -692,6 +736,34 @@ struct LinkedList[ """ return self._size + fn __iter__(self) -> _LinkedListIter[ElementType, __origin_of(self)]: + """Iterate over elements of the list, returning immutable references. + + Time Complexity: + O(1) for iterator construction. + O(n) in len(self) for a complete iteration of the list. + + Returns: + An iterator of immutable references to the list elements. + """ + return _LinkedListIter(Pointer.address_of(self)) + + fn __reversed__( + self, + ) -> _LinkedListIter[ElementType, __origin_of(self), forward=False]: + """Iterate backwards over the list, returning immutable references. + + Time Complexity: + O(1) for iterator construction. + O(n) in len(self) for a complete iteration of the list. + + Returns: + A reversed iterator of immutable references to the list elements. + """ + return _LinkedListIter[ElementType, __origin_of(self), forward=False]( + Pointer.address_of(self) + ) + fn __bool__(self) -> Bool: """Check if the list is non-empty. diff --git a/stdlib/test/collections/test_linked_list.mojo b/stdlib/test/collections/test_linked_list.mojo index 5569fda682..266d2ade6c 100644 --- a/stdlib/test/collections/test_linked_list.mojo +++ b/stdlib/test/collections/test_linked_list.mojo @@ -544,6 +544,39 @@ def test_list_dtor(): assert_equal(g_dtor_count, 1) +def test_iter(): + var l = LinkedList[Int](1, 2, 3) + var iter = l.__iter__() + assert_true(iter.__has_next__(), "Expected iter to have next") + assert_equal(len(iter), 3) + assert_equal(iter.__next__()[], 1) + assert_equal(iter.__next__()[], 2) + assert_equal(len(iter), 1) + assert_equal(iter.__next__()[], 3) + assert_equal(len(iter), 0) + assert_false(iter.__has_next__(), "Expected iter to not have next") + + var riter = l.__reversed__() + assert_true(riter.__has_next__(), "Expected iter to have next") + assert_equal(len(riter), 3) + assert_equal(riter.__next__()[], 3) + assert_equal(riter.__next__()[], 2) + assert_equal(len(riter), 1) + assert_equal(riter.__next__()[], 1) + assert_equal(len(riter), 0) + assert_false(riter.__has_next__(), "Expected iter to not have next") + + var i = 0 + for el in l: + assert_equal(el[], l[i]) + i += 1 + + i = 2 + for el in l.__reversed__(): + assert_equal(el[], l[i]) + i -= 1 + + def main(): test_construction() test_append() @@ -574,3 +607,4 @@ def main(): test_list_dtor() test_list_insert() test_list_eq_ne() + test_iter() From 9589f320bd37352ca994cac4064650ce4478271e Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Wed, 19 Feb 2025 17:14:11 -0500 Subject: [PATCH 418/509] [docs] Update stdlib FAQ for `List` auto-deref A common question we get is around the idea of why don't we auto-dereference in `List` and friends. This helps shed some light into the current state of why we manually force programmers to dereference things and what a path forward may look like language wise. While in the neighborhood, update the roadmap to remove some (now outdated) info about legacy `AnyTrivialRegType`. In the near future, we should overhaul the roadmap and project plans to update on what we're doing. MODULAR_ORIG_COMMIT_REV_ID: 13b594a0f679f6d723aad464eaf7dc7d2269ded5 --- stdlib/docs/faq.md | 54 ++++++++++++++++++++++++++++++++++++++---- stdlib/docs/roadmap.md | 4 ---- 2 files changed, 50 insertions(+), 8 deletions(-) diff --git a/stdlib/docs/faq.md b/stdlib/docs/faq.md index b70afe5aaf..0bf05de44d 100644 --- a/stdlib/docs/faq.md +++ b/stdlib/docs/faq.md @@ -26,10 +26,10 @@ your issues resolved. This is largely a historical thing as the library only worked on `AnyTrivialRegType` when it was first written. As we introduced the notion of memory-only types and -traits, `AnyType` was born. Over time, we expect to rewrite nearly -the entire library to have everything work on `AnyType` and be generalized to -not just work on `AnyTrivialRegType`. Several things need to happen in tandem with -the compiler team to make this possible. +traits, `AnyType` was born. At this point, most of the standard library operates +on `AnyType` or some other trait-bound type. A few low-level things will always +need to operate at the `AnyTrivialRegType` type level, such as when interacting +with MLIR attributes. ### 2. Are the MLIR dialects private? @@ -68,3 +68,49 @@ set of primitives available to them. Over time, we hope to move most of the closed-source modules into the open-source repo. + +### 5. Why do `List` and friends require dereferencing for iterators? + +Consider some Mojo code like: + +```mojo +fn use(element: Element): # takes a borrow + +fn test(elements: List[Element]): + for e in elements: + use(e[]) # Passes the referenced value by-borrow without a copy +``` + +The equivalent C++ would look something like: + +```c++ +void use(const Element &elt) {...} +void test(const list &list) { + for (Element *e : list) { + use(*e); + } +} +``` + +which requires the explicit element dereference, but maintains the pointerness +property. When we switch the `__next__` method in the `List` iterator type to +return a `ref`, then we would be getting a copy into the `e` var like this: + +```mojo +for (Element e : list) use(e) +``` + +In Mojo, `var x = list.iter.__next__()` makes a copy into x. It does not bind +`x` as a reference to the result. This what may be referred in other contexts +as "implicit deref". + +The planned approach to handle this in Mojo is with pattern matching and +allow binding references in patterns. Note that pattern matching is something +we need anyways for other things such as supporting `for i, j in thing`. + +This would allow for things like + +```mojo +for (ref e) in list: + self.append(e) # note no-dereference needed to pattern ref binding above +``` diff --git a/stdlib/docs/roadmap.md b/stdlib/docs/roadmap.md index d94535089f..b0061b2c53 100644 --- a/stdlib/docs/roadmap.md +++ b/stdlib/docs/roadmap.md @@ -20,10 +20,6 @@ mission](https://docs.modular.com/mojo/why-mojo). ### Core library improvements -- Remove `AnyTrivialRegType` in the standard library in favor of `AnyType`. - -- Unify `Pointer` and `AnyPointer`. - - Apply `Reference` types and lifetimes throughout APIs and types. - Design API conventions and expected behavior for core collection types such From e69f7709c82a728864238cb5db7f79d60e6797dd Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 19 Feb 2025 14:29:38 -0800 Subject: [PATCH 419/509] [Mojo] Replace the ,] pattern with ], NFC MODULAR_ORIG_COMMIT_REV_ID: 76f827e81e74971b4fcba1ccbb5f8a3dabde4900 --- stdlib/src/builtin/builtin_list.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/builtin/builtin_list.mojo b/stdlib/src/builtin/builtin_list.mojo index 3d7c69ab89..3bf7bc8a89 100644 --- a/stdlib/src/builtin/builtin_list.mojo +++ b/stdlib/src/builtin/builtin_list.mojo @@ -440,7 +440,7 @@ struct VariadicListMem[ fn __iter__( self, - ) -> _VariadicListMemIter[element_type, origin, __origin_of(self),]: + ) -> _VariadicListMemIter[element_type, origin, __origin_of(self)]: """Iterate over the list. Returns: From 4aa6d1255837a58c126dadcf553f03384764dd24 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 19 Feb 2025 20:41:00 -0800 Subject: [PATCH 420/509] [Kernels][GPU] Rename gpu.shuffle to gpu.warp to clarify intent (#56248) This renames the shuffle file along with the warp functions. This would allow us to write `warp.reduce` instead of `warp_reduce` which makes the code a bit more elegant. This also removes the auto import of the warp functions into the gpu package. MODULAR_ORIG_COMMIT_REV_ID: 7f296bed56af5864c0d5e02fb07d67aae7e2491b --- docs/changelog.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index b4e1bd381b..7844616f21 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -93,6 +93,16 @@ You should now pass the function directly to ctx.enqueue_function[func](grid_dim=1, block_dim=4) ``` +- The `shuffle` module has been rename to `warp` to better + reflect its purpose. To uses now you will have to do + + ```mojo + import gpu.warp as warp + + var val0 = warp.shuffle_down(x, offset) + var val1 = warp.broadcast(x) + ``` + ### Tooling changes #### Mojo Compiler From 9a738f7841dc84cc0374e1737c96f523f1fd4a32 Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 20 Feb 2025 07:05:44 +0000 Subject: [PATCH 421/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022005 --- examples/life/magic.lock | 281 +++++++++++++++++----------------- examples/magic.lock | 178 ++++++++++----------- examples/operators/magic.lock | 211 +++++++++++++------------ examples/testing/magic.lock | 211 +++++++++++++------------ magic.lock | 178 ++++++++++----------- 5 files changed, 542 insertions(+), 517 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 3a041cc133..57dbd69583 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -80,12 +80,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.28-pthreads_h94d23a6_1.conda - 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size: 123218799 - timestamp: 1739855933149 -- conda: https://conda.modular.com/max-nightly/osx-arm64/max-python-25.2.0.dev2025021805-release.conda + size: 123242379 + timestamp: 1740028585109 +- conda: https://conda.modular.com/max-nightly/osx-arm64/max-python-25.2.0.dev2025022005-release.conda noarch: python - sha256: 3cfb0055a5644ad9c53dec2d072d7aab4200bec29e321ff1bf0e2304148559a4 - md5: 6649b504b69613c9d0d0563f5dda8cac + sha256: 30d7b5434f9137fe5b234c2667999e7e2be763d01c0e7481892eafe34948775b + md5: 94e16d2fde39ce744582d233defc5bf0 depends: - - max-core ==25.2.0.dev2025021805 release + - max-core ==25.2.0.dev2025022005 release - click >=8.0.0 - numpy >=1.18,<2.0 - sentencepiece >=0.2.0 @@ -1480,12 +1480,12 @@ packages: - uvloop >=0.21.0 - xgrammar >=0.1.11 license: LicenseRef-Modular-Proprietary - size: 110049004 - timestamp: 1739857002577 -- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025021805-release.conda + size: 110077418 + timestamp: 1740029626428 +- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022005-release.conda noarch: python - 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jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22982 - timestamp: 1739855815922 + size: 22990 + timestamp: 1740028585109 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From b17d979b39a767279255c032b525b07c29886755 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Wed, 19 Feb 2025 21:41:06 -0800 Subject: [PATCH 422/509] [Stdlib] Fix SIMD.round function which was not performing banker rounding This fixes the SIMD.round function to follow the Python 3 semantics. Python 3 rounding is different from Python 2 (see https://stackoverflow.com/questions/10825926/python-3-x-rounding-behavior). MODULAR_ORIG_COMMIT_REV_ID: 413b47f5c539542853bc0e361f4ed5b5595d11f0 --- docs/changelog.md | 6 ++++++ stdlib/src/builtin/simd.mojo | 18 +++++++++++++++--- stdlib/test/builtin/test_math.mojo | 12 +++++------- stdlib/test/builtin/test_simd.mojo | 14 +++----------- 4 files changed, 29 insertions(+), 21 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 7844616f21..28a9d22cec 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -69,6 +69,12 @@ what we publish. print(element[]) ``` +- The `round` function is now fixed to perform "round half to even" (also known + as "bankers' rounding") instead of "round half away from zero". + +- The `SIMD.roundeven()` method has been removed from the standard library. + This functionality is now handled by the `round()` function. + ### GPU changes - `ctx.enqueue_function(compiled_func, ...)` is deprecated: diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 96e4a5732c..6fda0a23a8 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -1712,7 +1712,14 @@ struct SIMD[type: DType, size: Int]( Returns: The elementwise rounded value of this SIMD vector. """ - return llvm_intrinsic["llvm.round", Self, has_side_effect=False](self) + + @parameter + if type.is_integral() or type is DType.bool: + return self + + return llvm_intrinsic["llvm.roundeven", Self, has_side_effect=False]( + self + ) @always_inline("nodebug") fn __round__(self, ndigits: Int) -> Self: @@ -1726,8 +1733,13 @@ struct SIMD[type: DType, size: Int]( Returns: The elementwise rounded value of this SIMD vector. """ - # TODO: see how can we implement this. - return llvm_intrinsic["llvm.round", Self, has_side_effect=False](self) + + @parameter + if type.is_integral() or type is DType.bool: + return self + + var exp = SIMD[type, size](10) ** ndigits + return (self * exp).__round__() / exp fn __hash__(self) -> UInt: """Hash the value using builtin hash. diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index f23658f2e3..0667b86cc5 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -96,13 +96,11 @@ def test_round(): assert_equal(2, round(1.5)) assert_equal(2, round(2.0)) assert_equal(1, round(1.4, 0)) - assert_equal(3, round(2.5)) + assert_equal(2, round(2.5)) + assert_equal(-2, round(-2.5)) - # FIXME(MSTDL-1188): SIMD.__round__ completely ignores # digits. - # FIXME: Should be: assert_equal(1.5, round(1.5, 1)) - assert_equal(2, round(1.5, 1)) - # FIXME: Should be: assert_equal(1.61, round(1.613, 2)) - assert_equal(2, round(1.613, 2)) + assert_equal(1.5, round(1.5, 1)) + assert_equal(1.61, round(1.613, 2)) var lhs = SIMD[DType.float32, 4](1.1, 1.5, 1.9, 2.0) var expected = SIMD[DType.float32, 4](1.0, 2.0, 2.0, 2.0) @@ -112,7 +110,7 @@ def test_round(): alias r1 = round(2.3) assert_equal(r1, 2.0) alias r2 = round(2.3324, 2) - assert_equal(r2, 2) # FIXME: ignoring # digits + assert_equal(r2, 2.33) def test_pow(): diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index b89a35e70e..184627c5b7 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -552,19 +552,12 @@ def test_trunc(): def test_round(): - assert_equal(Float32.__round__(Float32(2.5)), 3.0) + assert_equal(Float32.__round__(Float32(2.5)), 2.0) + assert_equal(Float32.__round__(Float32(3.5)), 4.0) assert_equal(Float32.__round__(Float32(-3.5)), -4.0) alias F = SIMD[DType.float32, 4] - assert_equal(F.__round__(F(1.5, 2.5, -2.5, -3.5)), F(2.0, 3.0, -3.0, -4.0)) - - -def test_roundeven(): - assert_equal(Float32(2.5).roundeven(), 2.0) - assert_equal(Float32(-3.5).roundeven(), -4.0) - - alias F = SIMD[DType.float32, 4] - assert_equal(F(1.5, 2.5, -2.5, -3.5).roundeven(), F(2.0, 2.0, -2.0, -4.0)) + assert_equal(F.__round__(F(1.5, 2.5, -2.5, -3.5)), F(2.0, 2.0, -2.0, -4.0)) def test_div(): @@ -1953,7 +1946,6 @@ def main(): test_rmod() test_rotate() test_round() - test_roundeven() test_rsub() test_shift() test_shuffle() From 528c27bf8043ae42ffc99955970cbc580594027a Mon Sep 17 00:00:00 2001 From: Anand Pratap Singh <628125+anandpratap@users.noreply.github.com> Date: Thu, 20 Feb 2025 16:19:00 -0500 Subject: [PATCH 423/509] [stdlib] Fix grid dimensions grid_dim.{x,y,z} for AMD Grid dimensions are 32 bits but were were assuming they are 16 bits when reading from the implicit arg pointer. MODULAR_ORIG_COMMIT_REV_ID: ef1ffb715a5979aebee758dc398ecd53ff159745 --- stdlib/src/sys/intrinsics.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 58c7069a2d..3aa5fabfef 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -1086,10 +1086,10 @@ alias block_idx = _BlockIdx() @always_inline -fn _get_gcn_idx[offset: Int]() -> UInt: +fn _get_gcn_idx[offset: Int, dtype: DType = DType.int16]() -> UInt: var ptr = llvm_intrinsic[ "llvm.amdgcn.implicitarg.ptr", - UnsafePointer[Int16, address_space=4], + UnsafePointer[Scalar[dtype], address_space=4], has_side_effect=False, ]() return UInt(Int(ptr.load[alignment=4](offset))) @@ -1191,7 +1191,7 @@ struct _GridDim: constrained[dim == "z"]() return 2 - return _get_gcn_idx[_get_offset()]() + return _get_gcn_idx[_get_offset(), DType.int32]() alias grid_dim = _GridDim() From 8d1763a2819d044b3ed7c31372171e1d2fe63fff Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Thu, 20 Feb 2025 17:35:50 -0800 Subject: [PATCH 424/509] [mojo-lang] Add `@always_inline("builtin")` support for a few more ops. This rounds out the core index dialect ops that are needed for Int. MODULAR_ORIG_COMMIT_REV_ID: f7e6dcd4f80dff2206a45440650fb5889c2f6d3d --- stdlib/src/builtin/int.mojo | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index b69330dedb..8da7f01d26 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -562,7 +562,7 @@ struct Int( pred = __mlir_attr.`#index` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __pos__(self) -> Int: """Return +self. @@ -580,7 +580,7 @@ struct Int( """ return self * -1 - @always_inline("nodebug") + @always_inline("builtin") fn __invert__(self) -> Int: """Return ~self. @@ -754,7 +754,7 @@ struct Int( return 0 return __mlir_op.`index.shrs`(self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __and__(self, rhs: Int) -> Int: """Return `self & rhs`. @@ -766,7 +766,7 @@ struct Int( """ return __mlir_op.`index.and`(self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __xor__(self, rhs: Int) -> Int: """Return `self ^ rhs`. @@ -778,7 +778,7 @@ struct Int( """ return __mlir_op.`index.xor`(self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __or__(self, rhs: Int) -> Int: """Return `self | rhs`. From 20a2fd27afa440da491986367df55d74012f2b3e Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 20 Feb 2025 18:27:37 -0800 Subject: [PATCH 425/509] [Stdlib] Add MIN/MAX values for Bool The min/max values are False/True respectively. MODULAR_ORIG_COMMIT_REV_ID: 157e075b608436e9b6b20195362dcbab4d4b2b4b --- stdlib/src/builtin/bool.mojo | 6 ++++++ stdlib/test/builtin/test_bool.mojo | 6 ++++++ 2 files changed, 12 insertions(+) diff --git a/stdlib/src/builtin/bool.mojo b/stdlib/src/builtin/bool.mojo index 59800330b6..e9169b7863 100644 --- a/stdlib/src/builtin/bool.mojo +++ b/stdlib/src/builtin/bool.mojo @@ -113,6 +113,12 @@ struct Bool( ): """The primitive Bool scalar value used in Mojo.""" + alias MIN = Bool(False) + """The minimum value of a Bool.""" + + alias MAX = Bool(True) + """The maximum value of a Bool.""" + var value: __mlir_type.i1 """The underlying storage of the boolean value.""" diff --git a/stdlib/test/builtin/test_bool.mojo b/stdlib/test/builtin/test_bool.mojo index e7378e0e67..40879e077e 100644 --- a/stdlib/test/builtin/test_bool.mojo +++ b/stdlib/test/builtin/test_bool.mojo @@ -19,6 +19,11 @@ def test_default(): assert_equal(Bool(), False) +def test_min_max(): + assert_equal(Bool.MIN, False) + assert_equal(Bool.MAX, True) + + def test_bool_cast_to_int(): assert_equal(False.__int__(), 0) assert_equal(True.__int__(), 1) @@ -156,6 +161,7 @@ def test_float_conversion(): def main(): test_default() + test_min_max() test_bool_cast_to_int() test_bool_none() test_convert_from_implicitly_boolable() From 654a4f709a995868099a469061314b0271226d37 Mon Sep 17 00:00:00 2001 From: modularbot Date: Fri, 21 Feb 2025 07:09:07 +0000 Subject: [PATCH 426/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022105 --- examples/life/magic.lock | 405 +++++++++++++------------- examples/magic.lock | 407 +++++++++++++------------- examples/operators/magic.lock | 523 ++++++++++++++-------------------- examples/testing/magic.lock | 523 ++++++++++++++-------------------- magic.lock | 407 +++++++++++++------------- 5 files changed, 1017 insertions(+), 1248 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 57dbd69583..c2d9f706cf 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -40,15 +40,15 @@ environments: - 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- max-core ==25.2.0.dev2025022005 release + - max-core ==25.2.0.dev2025022105 release - click >=8.0.0 - numpy >=1.18,<2.0 - sentencepiece >=0.2.0 @@ -1480,12 +1469,12 @@ packages: - uvloop >=0.21.0 - xgrammar >=0.1.11 license: LicenseRef-Modular-Proprietary - size: 110077418 - timestamp: 1740029626428 -- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022005-release.conda + size: 110165681 + timestamp: 1740116288307 +- conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022105-release.conda noarch: python - sha256: 9379e4793be7c455a88485d715e86e86166e81ac1cb616d889abdc1730c80d75 - md5: 088702bd2b036e232b196fe8de9718cb + sha256: 8e8f49bd832591239f7913882326bdf4e49b62dad55630cd973e59a273d530dd + md5: cbe46561f716976d2a8a5222b0dfd975 depends: - python >=3.9,<3.13 - click >=8.0.0 @@ -1496,20 +1485,20 @@ packages: - typing_extensions >=v4.12.2 - python license: MIT - size: 130861 - timestamp: 1740028585109 -- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022005-release.conda + size: 130843 + timestamp: 1740115026277 +- conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022105-release.conda noarch: python - sha256: f6399ee0d27607ad82e7c947b8973fd19e412ca3dab78dc94c42b9039dcb9056 - md5: fc308a44c2304836e4b1d59928455a14 + sha256: 62205186302b4460ee7f8c6e466da82d69b2a06c71958d47cbbe548cdcd3fde4 + md5: 10aa481c50998711d470cac4990d6ed5 depends: - - max-core ==25.2.0.dev2025022005 release + - max-core ==25.2.0.dev2025022105 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22990 - timestamp: 1740028585109 + size: 22980 + timestamp: 1740115026277 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 8d0c98019bb89460fa09f17ef6882974fc31188a Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Thu, 20 Feb 2025 21:37:07 -0800 Subject: [PATCH 427/509] [mojo-lang] Add `@always_inline("builtin")` support for ternary This adds support for simple ternary expressions, allowing us to handle integer shifts. MODULAR_ORIG_COMMIT_REV_ID: 99dfbd21f2334d2d56149f6c9ef450e2ddf64216 --- stdlib/src/builtin/int.mojo | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/stdlib/src/builtin/int.mojo b/stdlib/src/builtin/int.mojo index 8da7f01d26..38f3ce7bcd 100644 --- a/stdlib/src/builtin/int.mojo +++ b/stdlib/src/builtin/int.mojo @@ -724,7 +724,7 @@ struct Int( n >>= 1 return res - @always_inline("nodebug") + @always_inline("builtin") fn __lshift__(self, rhs: Int) -> Int: """Return `self << rhs`. @@ -734,12 +734,11 @@ struct Int( Returns: `self << rhs`. """ - if rhs < 0: - # this should raise an exception. - return 0 - return __mlir_op.`index.shl`(self.value, rhs.value) + return 0 if rhs < 0 else Int( + __mlir_op.`index.shl`(self.value, rhs.value) + ) - @always_inline("nodebug") + @always_inline("builtin") fn __rshift__(self, rhs: Int) -> Int: """Return `self >> rhs`. @@ -749,10 +748,9 @@ struct Int( Returns: `self >> rhs`. """ - if rhs < 0: - # this should raise an exception. - return 0 - return __mlir_op.`index.shrs`(self.value, rhs.value) + return 0 if rhs < 0 else Int( + __mlir_op.`index.shrs`(self.value, rhs.value) + ) @always_inline("builtin") fn __and__(self, rhs: Int) -> Int: From 5ee1e2e7acd34eb2c421def0eafaa45d63d009f7 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Thu, 20 Feb 2025 22:33:27 -0800 Subject: [PATCH 428/509] [mojo-lang] Move FloatLiteralBinOp to attribute form, NFC. This moves float literals closer to the preferred form, enabling eventual removal of the ops. MODULAR_ORIG_COMMIT_REV_ID: 71846155d7260d57ff1481558c9b4c6da4c84865 --- stdlib/src/builtin/float_literal.mojo | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index f663d9c317..1ba348987c 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -264,7 +264,7 @@ struct FloatLiteral( # Arithmetic Operators # ===------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") fn __add__(self, rhs: FloatLiteral) -> FloatLiteral: """Add two FloatLiterals. @@ -278,7 +278,7 @@ struct FloatLiteral( oper = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __sub__(self, rhs: FloatLiteral) -> FloatLiteral: """Subtract two FloatLiterals. @@ -292,7 +292,7 @@ struct FloatLiteral( oper = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __mul__(self, rhs: FloatLiteral) -> FloatLiteral: """Multiply two FloatLiterals. @@ -306,7 +306,7 @@ struct FloatLiteral( oper = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __truediv__(self, rhs: FloatLiteral) -> FloatLiteral: """Divide two FloatLiterals. @@ -370,7 +370,7 @@ struct FloatLiteral( """ return rhs // self - @always_inline + @always_inline("nodebug") fn __ceildiv__(self, denominator: Self) -> Self: """Return the rounded-up result of dividing self by denominator. @@ -388,7 +388,7 @@ struct FloatLiteral( # Reversed Operators, allowing things like "1 / 2.0" to work # ===------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") fn __radd__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed addition operator. @@ -400,7 +400,7 @@ struct FloatLiteral( """ return rhs + self - @always_inline("nodebug") + @always_inline("builtin") fn __rsub__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed subtraction operator. @@ -424,7 +424,7 @@ struct FloatLiteral( """ return rhs * self - @always_inline("nodebug") + @always_inline("builtin") fn __rtruediv__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed division. From 812b67e7cb688d44f374574f037e5de4fb54d95e Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Fri, 21 Feb 2025 12:52:24 -0500 Subject: [PATCH 429/509] [Kernels] Remove deprecation from `DeviceContext.enqueue_function` When enqueuing a function directly without compiling it first, it consistently incurs 50-500 nanoseconds of overhead per enqueue when reusing the exact same function and parameters. So this PR removes the deprecation warning for when this is the case, and fixes the few places in the codebase that were reusing the same function and parameters. ```text --------------------------------------------------- | name | met (ms) | iters | --------------------------------------------------- | enqueue_directly | 0.01784268040765961 | 65643 | | compile_first | 0.017613748867035928 | 67522 | --------------------------------------------------- ``` (0.01784268040765961 - 0.017613748867035928) * 1e6 = 229 nanoseconds MODULAR_ORIG_COMMIT_REV_ID: 19359a4ff873ae33875bd5084dcc8efc9e3951ea --- docs/changelog.md | 28 ++++++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 28a9d22cec..6c340e9329 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -77,7 +77,7 @@ what we publish. ### GPU changes -- `ctx.enqueue_function(compiled_func, ...)` is deprecated: +- You can now skip compiling a GPU kernel first and then enqueueing it: ```mojo from gpu import thread_idx @@ -91,17 +91,33 @@ what we publish. ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=4) ``` -You should now pass the function directly to -`DeviceContext.enqueue_function[func](...)`: +- You can now skip compiling a GPU kernel first before enqueueing it, and pass +a function directly to `ctx.enqueue_function[func](...)`: ```mojo - with DeviceContext() as ctx: - ctx.enqueue_function[func](grid_dim=1, block_dim=4) +from gpu.host import DeviceContext + +fn func(): + print("Hello from GPU") + +with DeviceContext() as ctx: + ctx.enqueue_function[func](grid_dim=1, block_dim=1) +``` + +However, if you're reusing the same function and parameters multiple times, this +incurs some overhead of around 50-500 nanoseconds per enqueue. So you can still +compile the function first and pass it to ctx.enqueue_function in this scenario: + +```mojo +var compiled_func = ctx.compile_function[func]() +# Multiple kernel launches with the same function/parameters +ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=1) +ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=1) ``` - The `shuffle` module has been rename to `warp` to better reflect its purpose. To uses now you will have to do - + ```mojo import gpu.warp as warp From 986fd9b67ae21cb0d50587ace0ff78c06581a56a Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Fri, 21 Feb 2025 10:35:26 -0800 Subject: [PATCH 430/509] [stdlib] Replace some uses of Buffer with NDBuffer Right now `Buffer` is a worse version of `NDBuffer` and `Span`. We should phase it out and remove `Buffer` entirely. This is the first step in that direction. MODULAR_ORIG_COMMIT_REV_ID: 1bfdbc26279b5215ce2526ca9d759eaf7a6fcce5 --- examples/reduce.mojo | 20 +++++++++++-------- .../algorithm/bench_elementwise.mojo | 4 ++-- 2 files changed, 14 insertions(+), 10 deletions(-) diff --git a/examples/reduce.mojo b/examples/reduce.mojo index 9babfd91a0..f4c6784333 100644 --- a/examples/reduce.mojo +++ b/examples/reduce.mojo @@ -20,7 +20,7 @@ from random import rand from algorithm import sum from benchmark import Unit, benchmark, keep -from buffer import Buffer +from buffer import NDBuffer from memory import UnsafePointer from python import Python @@ -35,10 +35,12 @@ alias scalar = Scalar[type] # Use the https://en.wikipedia.org/wiki/Kahan_summation_algorithm # Simple summation of the array elements -fn naive_reduce_sum[size: Int](buffer: Buffer[type, size]) raises -> scalar: +fn naive_reduce_sum[ + size: Int +](buffer: NDBuffer[type, 1, size]) raises -> scalar: var my_sum: scalar = 0 var c: scalar = 0 - for i in range(buffer.size): + for i in range(buffer.size()): var y = buffer[i] - c var t = my_sum + y c = (t - my_sum) - y @@ -46,7 +48,9 @@ fn naive_reduce_sum[size: Int](buffer: Buffer[type, size]) raises -> scalar: return my_sum -fn stdlib_reduce_sum[size: Int](array: Buffer[type, size]) raises -> scalar: +fn stdlib_reduce_sum[ + size: Int +](array: NDBuffer[type, 1, size]) raises -> scalar: var my_sum = sum(array) return my_sum @@ -61,10 +65,10 @@ def pretty_print(name: String, elements: Int, time: Float64): fn bench[ - func: fn[size: Int] (buffer: Buffer[type, size]) raises -> scalar, + func: fn[size: Int] (buffer: NDBuffer[type, 1, size]) raises -> scalar, size: Int, name: String, -](buffer: Buffer[type, size]) raises: +](buffer: NDBuffer[type, 1, size]) raises: @parameter fn runner() raises: var result = func[size](buffer) @@ -86,8 +90,8 @@ fn main() raises: rand(ptr_small, size_small) rand(ptr_large, size_large) - var buffer_small = Buffer[type, size_small](ptr_small) - var buffer_large = Buffer[type, size_large](ptr_large) + var buffer_small = NDBuffer[type, 1, size_small](ptr_small) + var buffer_large = NDBuffer[type, 1, size_large](ptr_large) bench[naive_reduce_sum, size_small, "naive"](buffer_small) bench[naive_reduce_sum, size_large, "naive"](buffer_large) diff --git a/stdlib/benchmarks/algorithm/bench_elementwise.mojo b/stdlib/benchmarks/algorithm/bench_elementwise.mojo index fefe656cf6..21d4cc80e2 100644 --- a/stdlib/benchmarks/algorithm/bench_elementwise.mojo +++ b/stdlib/benchmarks/algorithm/bench_elementwise.mojo @@ -18,7 +18,7 @@ from sys import simdwidthof from algorithm import elementwise from benchmark import Bench, BenchConfig, Bencher, BenchId -from buffer import Buffer +from buffer import NDBuffer from utils.index import Index, IndexList @@ -28,7 +28,7 @@ from utils.index import Index, IndexList # ===-----------------------------------------------------------------------===# @parameter fn bench_elementwise[n: Int](mut b: Bencher) raises: - var vector = Buffer[DType.index, n].stack_allocation() + var vector = NDBuffer[DType.index, 1, n].stack_allocation() for i in range(len(vector)): vector[i] = -1 From 4d1b8836d02b7a2e259f8827ac6c4abd6b176245 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 11:21:12 -0800 Subject: [PATCH 431/509] [mojo-lang] Move Float Literal <-> Int Literal conversion to comptime. This moves the intrinsic to the right comptime representation, continuing a progressive migration of infra. MODULAR_ORIG_COMMIT_REV_ID: e9d0280235dcdd351851cdd59c92ced0e28ff7f9 --- stdlib/src/builtin/float_literal.mojo | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 1ba348987c..ab835fde86 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -65,7 +65,7 @@ struct FloatLiteral( Args: value: The IntLiteral value. """ - self.value = __mlir_op.`kgen.int_literal.to_float_literal`(value.value) + self.value = __mlir_op.`kgen.int_to_float_literal`(value.value) alias nan = Self(__mlir_attr.`#kgen.float_literal`) alias infinity = Self(__mlir_attr.`#kgen.float_literal`) @@ -134,9 +134,7 @@ struct FloatLiteral( Returns: The value as an integer. """ - return IntLiteral( - __mlir_op.`kgen.float_literal.to_int_literal`(self.value) - ) + return IntLiteral(__mlir_op.`kgen.float_to_int_literal`(self.value)) @always_inline("nodebug") fn __int__(self) -> Int: From b2b1b6019ea490014b94fb0078d058d0285ba0b1 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 11:38:19 -0800 Subject: [PATCH 432/509] [mojo-stdlib] Push more `@always_inline("builtin")` into stdlib. This pretty much rounds out integer literals (covering shifts in particular) as well as address spaces. The shift/div change is safe because the implementation of literals handle the zero internally now. MODULAR_ORIG_COMMIT_REV_ID: 5b0730543a9f96e5336c2490bc4839e1e0c8bf66 --- stdlib/src/builtin/int_literal.mojo | 24 ++++++++--------------- stdlib/src/memory/pointer.mojo | 18 ++++++++--------- stdlib/test/builtin/test_int_literal.mojo | 8 ++++++++ 3 files changed, 25 insertions(+), 25 deletions(-) diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 69511746d4..f470076122 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -248,7 +248,7 @@ struct IntLiteral( # TODO: implement __pow__ - @always_inline("nodebug") + @always_inline("builtin") fn __floordiv__(self, rhs: Self) -> Self: """Return `self // rhs`. @@ -258,16 +258,14 @@ struct IntLiteral( Returns: `self // rhs` value. """ - if rhs == Self(): - # this should raise an exception. - return Self() + # This handles the case where rhs is 0. return Self( __mlir_op.`kgen.int_literal.binop`[ oper = __mlir_attr.`#kgen` ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __mod__(self, rhs: Self) -> Self: """Return the remainder of self divided by rhs. @@ -277,16 +275,14 @@ struct IntLiteral( Returns: The remainder of dividing self by rhs. """ - if rhs == Self(): - # this should raise an exception. - return Self() + # This handles the case where rhs is 0. return Self( __mlir_op.`kgen.int_literal.binop`[ oper = __mlir_attr.`#kgen` ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __lshift__(self, rhs: Self) -> Self: """Return `self << rhs`. @@ -296,16 +292,14 @@ struct IntLiteral( Returns: `self << rhs`. """ - if rhs < Self(): - # this should raise an exception. - return Self() + # This handles the case where rhs is 0. return Self( __mlir_op.`kgen.int_literal.binop`[ oper = __mlir_attr.`#kgen` ](self.value, rhs.value) ) - @always_inline("nodebug") + @always_inline("builtin") fn __rshift__(self, rhs: Self) -> Self: """Return `self >> rhs`. @@ -315,9 +309,7 @@ struct IntLiteral( Returns: `self >> rhs`. """ - if rhs < Self(): - # this should raise an exception. - return Self() + # This handles the case where rhs is 0. return Self( __mlir_op.`kgen.int_literal.binop`[ oper = __mlir_attr.`#kgen` diff --git a/stdlib/src/memory/pointer.mojo b/stdlib/src/memory/pointer.mojo index 746564b490..db5c6af19c 100644 --- a/stdlib/src/memory/pointer.mojo +++ b/stdlib/src/memory/pointer.mojo @@ -44,12 +44,12 @@ struct _GPUAddressSpace(EqualityComparable): alias LOCAL = AddressSpace(5) """Local address space.""" - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: Int): self._value = value - @always_inline("nodebug") + @always_inline("builtin") fn value(self) -> Int: """The integral value of the address space. @@ -58,7 +58,7 @@ struct _GPUAddressSpace(EqualityComparable): """ return self._value - @always_inline("nodebug") + @always_inline("builtin") fn __int__(self) -> Int: """The integral value of the address space. @@ -168,7 +168,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): alias GENERIC = AddressSpace(0) """Generic address space.""" - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: Int): """Initializes the address space from the underlying integral value. @@ -178,7 +178,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): """ self._value = value - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: _GPUAddressSpace): """Initializes the address space from the underlying integral value. @@ -186,9 +186,9 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): Args: value: The address space value. """ - self._value = Int(value) + self._value = value._value - @always_inline("nodebug") + @always_inline("builtin") fn value(self) -> Int: """The integral value of the address space. @@ -197,7 +197,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): """ return self._value - @always_inline("nodebug") + @always_inline("builtin") fn __int__(self) -> Int: """The integral value of the address space. @@ -206,7 +206,7 @@ struct AddressSpace(EqualityComparable, Stringable, Writable): """ return self._value - @always_inline("nodebug") + @always_inline("builtin") fn __index__(self) -> __mlir_type.index: """Convert to index. diff --git a/stdlib/test/builtin/test_int_literal.mojo b/stdlib/test/builtin/test_int_literal.mojo index 72776b4e9a..b9ccfc27fd 100644 --- a/stdlib/test/builtin/test_int_literal.mojo +++ b/stdlib/test/builtin/test_int_literal.mojo @@ -138,6 +138,13 @@ def test_comparison(): assert_false((5).__ge__(10)) +def test_shift(): + assert_equal(IntLiteral.__lshift__(1, -42), 0) # Dubious. + assert_equal(IntLiteral.__lshift__(1, 0), 1) + assert_equal(IntLiteral.__lshift__(1, 1), 2) + assert_equal(IntLiteral.__lshift__(1, 6), 64) + + def main(): test_add() test_sub() @@ -151,3 +158,4 @@ def main(): test_indexer() test_bool() test_comparison() + test_shift() From a620eb9973e71c9ccbf216aa2b9e10960fc5ec71 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Fri, 21 Feb 2025 11:38:53 -0800 Subject: [PATCH 433/509] [stdlib] Remove `Buffer` struct in favor of `NDBuffer` (#56408) MODULAR_ORIG_COMMIT_REV_ID: 1fc5e59b55c287100bd0d88b363363fa635dc7e3 --- docs/changelog.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index 6c340e9329..5acd9146f7 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -26,6 +26,8 @@ what we publish. ### Standard library changes +- The `Buffer` struct has been removed in favor of `Span` and `NDBuffer`. + - A new `IntervalTree` data structure has been added to the standard library. This is a tree data structure that allows for efficient range queries. From b3e8f78d1187f401c72b93e801bfee0076c0dcbb Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 12:49:38 -0800 Subject: [PATCH 434/509] [mojo-lang] Move FloatLiteralIsa+FloatLiteralConvert to attr form. This continues the migration to a comptime native representation. MODULAR_ORIG_COMMIT_REV_ID: 2a81e4114fe8387af59c5bd717f1d9c430d874a1 --- stdlib/src/builtin/float_literal.mojo | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index ab835fde86..464cb51290 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -57,7 +57,7 @@ struct FloatLiteral( """ self.value = value - @always_inline("nodebug") + @always_inline("builtin") @implicit fn __init__(out self, value: IntLiteral): """Convert an IntLiteral to a FloatLiteral value. @@ -72,7 +72,7 @@ struct FloatLiteral( alias negative_infinity = Self(__mlir_attr.`#kgen.float_literal`) alias negative_zero = Self(__mlir_attr.`#kgen.float_literal`) - @always_inline("nodebug") + @always_inline("builtin") fn is_nan(self) -> Bool: """Return whether the FloatLiteral is nan. @@ -85,7 +85,7 @@ struct FloatLiteral( special = __mlir_attr.`#kgen` ](self.value) - @always_inline("nodebug") + @always_inline("builtin") fn is_neg_zero(self) -> Bool: """Return whether the FloatLiteral is negative zero. @@ -99,7 +99,7 @@ struct FloatLiteral( special = __mlir_attr.`#kgen` ](self.value) - @always_inline("nodebug") + @always_inline("builtin") fn _is_normal(self) -> Bool: """Return whether the FloatLiteral is a normal (i.e. not special) value. @@ -123,7 +123,7 @@ struct FloatLiteral( """ return String(Float64(self)) - @always_inline("nodebug") + @always_inline("builtin") fn __int_literal__(self) -> IntLiteral: """Casts the floating point value to an IntLiteral. If there is a fractional component, then the value is truncated towards zero. @@ -136,7 +136,7 @@ struct FloatLiteral( """ return IntLiteral(__mlir_op.`kgen.float_to_int_literal`(self.value)) - @always_inline("nodebug") + @always_inline("builtin") fn __int__(self) -> Int: """Converts the FloatLiteral value to an Int. If there is a fractional component, then the value is truncated towards zero. @@ -179,7 +179,7 @@ struct FloatLiteral( """ return self.__bool__() - @always_inline("nodebug") + @always_inline("builtin") fn __neg__(self) -> FloatLiteral: """Return the negation of the FloatLiteral value. @@ -410,7 +410,7 @@ struct FloatLiteral( """ return rhs - self - @always_inline("nodebug") + @always_inline("builtin") fn __rmul__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed multiplication operator. From 22929a9161b01239bb5518eb1d087b05b6307247 Mon Sep 17 00:00:00 2001 From: Joe Loser Date: Fri, 21 Feb 2025 16:59:11 -0500 Subject: [PATCH 435/509] [stdlib] Remove copy in `Optional.or_else()` `Optional.or_else()` returns an owned value, so take `self` by ownership to avoid a copy. MODULAR_ORIG_COMMIT_REV_ID: 2a07322fc6fd0be8f12e2d69ccb3043df44fd7c4 --- stdlib/src/collections/optional.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/collections/optional.mojo b/stdlib/src/collections/optional.mojo index 3b457a94dd..96bf918da4 100644 --- a/stdlib/src/collections/optional.mojo +++ b/stdlib/src/collections/optional.mojo @@ -553,7 +553,7 @@ struct OptionalReg[T: AnyTrivialRegType](Boolable): """ return __mlir_op.`kgen.variant.get`[index = Int(0).value](self._value) - fn or_else(self, default: T) -> T: + fn or_else(owned self, owned default: T) -> T: """Return the underlying value contained in the Optional or a default value if the Optional's underlying value is not present. From c18bc7e030024b1834b5ef92020a748b310f9dda Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Fri, 21 Feb 2025 16:46:14 -0600 Subject: [PATCH 436/509] [stdlib] fix: Use empty origin for `UnsafePointer.alloc()` result Fixes MSTDL-1175 MODULAR_ORIG_COMMIT_REV_ID: c4a36038fc155d97a284fd0741a710361f7f3973 --- docs/changelog.md | 5 ++ stdlib/src/builtin/type_aliases.mojo | 4 ++ stdlib/src/collections/interval.mojo | 8 ++-- stdlib/src/memory/unsafe_pointer.mojo | 7 ++- stdlib/test/memory/test_unsafepointer.mojo | 54 +++++++++++++++++++++- stdlib/test/test_utils/types.mojo | 6 ++- 6 files changed, 77 insertions(+), 7 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 5acd9146f7..c0b2a81b87 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -77,6 +77,11 @@ what we publish. - The `SIMD.roundeven()` method has been removed from the standard library. This functionality is now handled by the `round()` function. +- The `UnsafePointer.alloc()` method has changed to produce pointers with an + empty `Origin` parameter, instead of with `MutableAnyOrigin`. This mitigates + an issue with the any origin parameter extending the lifetime of unrelated + local variables for this common method. + ### GPU changes - You can now skip compiling a GPU kernel first and then enqueueing it: diff --git a/stdlib/src/builtin/type_aliases.mojo b/stdlib/src/builtin/type_aliases.mojo index b24a8a06ce..237441afb5 100644 --- a/stdlib/src/builtin/type_aliases.mojo +++ b/stdlib/src/builtin/type_aliases.mojo @@ -81,6 +81,10 @@ struct Origin[is_mutable: Bool]: ``` """ + alias empty = Self.cast_from[__origin_of()].result + """An empty `__origin_of()` of the given mutability. The empty origin + is guaranteed not to alias any existing origins.""" + # ===-------------------------------------------------------------------===# # Fields # ===-------------------------------------------------------------------===# diff --git a/stdlib/src/collections/interval.mojo b/stdlib/src/collections/interval.mojo index 9502d173d2..84ae4d119a 100644 --- a/stdlib/src/collections/interval.mojo +++ b/stdlib/src/collections/interval.mojo @@ -546,7 +546,7 @@ struct IntervalTree[T: IntervalElement, U: IntervalPayload]: self._len = 0 fn _left_rotate( - mut self, mut rotation_node: UnsafePointer[_IntervalNode[T, U]] + mut self, rotation_node: UnsafePointer[_IntervalNode[T, U]] ): """Performs a left rotation around node x in the red-black tree. @@ -613,7 +613,7 @@ struct IntervalTree[T: IntervalElement, U: IntervalPayload]: ) fn _right_rotate( - mut self, mut rotation_node: UnsafePointer[_IntervalNode[T, U]] + mut self, rotation_node: UnsafePointer[_IntervalNode[T, U]] ): """Performs a right rotation around node y in the red-black tree. @@ -730,8 +730,10 @@ struct IntervalTree[T: IntervalElement, U: IntervalPayload]: self._insert_fixup(new_node) fn _insert_fixup( - mut self, mut current_node: UnsafePointer[_IntervalNode[T, U]] + mut self, current_node0: UnsafePointer[_IntervalNode[T, U]] ): + var current_node = current_node0 + """Fixes up the red-black tree properties after an insertion. This method restores the red-black tree properties that may have been violated diff --git a/stdlib/src/memory/unsafe_pointer.mojo b/stdlib/src/memory/unsafe_pointer.mojo index 47065c2d62..4ed238c03f 100644 --- a/stdlib/src/memory/unsafe_pointer.mojo +++ b/stdlib/src/memory/unsafe_pointer.mojo @@ -184,7 +184,12 @@ struct UnsafePointer[ fn alloc( count: Int, ) -> UnsafePointer[ - type, address_space = AddressSpace.GENERIC, alignment=alignment + type, + address_space = AddressSpace.GENERIC, + alignment=alignment, + # This is a newly allocated pointer, so should not alias anything + # already existing. + origin = MutableOrigin.empty, ]: """Allocate an array with specified or default alignment. diff --git a/stdlib/test/memory/test_unsafepointer.mojo b/stdlib/test/memory/test_unsafepointer.mojo index a07e7f0749..4877b9a5b3 100644 --- a/stdlib/test/memory/test_unsafepointer.mojo +++ b/stdlib/test/memory/test_unsafepointer.mojo @@ -13,7 +13,12 @@ # RUN: %mojo %s from memory import AddressSpace, UnsafePointer -from test_utils import ExplicitCopyOnly, MoveCounter, ObservableMoveOnly +from test_utils import ( + ExplicitCopyOnly, + MoveCounter, + ObservableMoveOnly, + ObservableDel, +) from testing import assert_equal, assert_false, assert_not_equal, assert_true @@ -187,6 +192,51 @@ def test_unsafepointer_aligned_alloc(): assert_equal(ptr_uint64_3 % alignment_3, 0) +# Test that `UnsafePointer.alloc()` no longer artifically extends the lifetime +# of every local variable in methods where its used. +def test_unsafepointer_alloc_origin(): + # ----------------------------------------- + # Test with MutableAnyOrigin alloc() origin + # ----------------------------------------- + + var did_del_1 = False + + # Allocate pointer with MutableAnyOrgin. + var ptr_1 = UnsafePointer[Int].alloc(1).origin_cast[ + origin=MutableAnyOrigin + ]() + + var obj_1 = ObservableDel(UnsafePointer.address_of(did_del_1)) + + # Object has not been deleted, because MutableAnyOrigin is keeping it alive. + assert_false(did_del_1) + + ptr_1.free() + + # Now that `ptr` is out of scope, `obj_1` was destroyed as well. + assert_true(did_del_1) + + # ---------------------------------------- + # Test with default (empty) alloc() origin + # ---------------------------------------- + + var did_del_2 = False + + # Allocate pointer with empty origin. + var ptr_2 = UnsafePointer[Int].alloc(1) + + # Note: Set ObservableDel origin explicitly since it otherwise contains a + # MutableAnyOrigin pointer that interferes with this test. + var obj_2 = ObservableDel[__origin_of(did_del_2)]( + UnsafePointer.address_of(did_del_2) + ) + + # `obj_2` is ASAP destroyed, since `ptr_2` origin does not keep it alive. + assert_true(did_del_2) + + ptr_2.free() + + # NOTE: Tests fails due to a `UnsafePointer` size # and alignment constraint failing to be satisfied. # @@ -323,6 +373,8 @@ def main(): test_comparisons() test_unsafepointer_address_space() + test_unsafepointer_aligned_alloc() + test_unsafepointer_alloc_origin() test_indexing() test_indexing_simd() test_bool() diff --git a/stdlib/test/test_utils/types.mojo b/stdlib/test/test_utils/types.mojo index 09c6751802..f871258661 100644 --- a/stdlib/test/test_utils/types.mojo +++ b/stdlib/test/test_utils/types.mojo @@ -256,8 +256,10 @@ struct DelRecorder(ExplicitlyCopyable): @value -struct ObservableDel(CollectionElement): - var target: UnsafePointer[Bool] +struct ObservableDel[origin: MutableOrigin = MutableAnyOrigin]( + CollectionElement +): + var target: UnsafePointer[Bool, origin=origin] fn __init__(out self, *, other: Self): self = other From c53dc5a03f0db780c4fb591b74f80eb2b2fdb25b Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 15:09:38 -0800 Subject: [PATCH 437/509] [mojo-lang] Move FloatLiteralComp over to attribute form. This moves the last (!?) instruction over to an attribute form, completelying the infra that allows us to get off of the literals-as-values-that-crash-the-compiler design. This doesn't make the move itself though. It does fix a weird extra `ceildiv` overload working on IntLiteral though. MODULAR_ORIG_COMMIT_REV_ID: 3e16ebd0e606e1692052439b0fefaf988bf41c86 --- stdlib/src/builtin/float_literal.mojo | 12 ++++++------ stdlib/src/builtin/int_literal.mojo | 2 +- stdlib/src/math/math.mojo | 6 +++--- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 464cb51290..ebaa5142a8 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -438,7 +438,7 @@ struct FloatLiteral( # Comparison Operators # ===------------------------------------------------------------------===# - @always_inline("nodebug") + @always_inline("builtin") fn __eq__(self, rhs: FloatLiteral) -> Bool: """Compare for equality. @@ -452,7 +452,7 @@ struct FloatLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __ne__(self, rhs: FloatLiteral) -> Bool: """Compare for inequality. @@ -466,7 +466,7 @@ struct FloatLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __lt__(self, rhs: FloatLiteral) -> Bool: """Less than comparison. @@ -480,7 +480,7 @@ struct FloatLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __le__(self, rhs: FloatLiteral) -> Bool: """Less than or equal to comparison. @@ -494,7 +494,7 @@ struct FloatLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __gt__(self, rhs: FloatLiteral) -> Bool: """Greater than comparison. @@ -508,7 +508,7 @@ struct FloatLiteral( pred = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __ge__(self, rhs: FloatLiteral) -> Bool: """Greater than or equal to comparison. diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index f470076122..4fb3bc010a 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -462,7 +462,7 @@ struct IntLiteral( """ return String(Int(self)) - @always_inline + @always_inline("builtin") fn __ceildiv__(self, denominator: Self) -> Self: """Return the rounded-up result of dividing self by denominator. diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index bdbd292d30..14d3f91e25 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -127,9 +127,9 @@ fn ceildiv[T: CeilDivableRaising, //](numerator: T, denominator: T) raises -> T: return numerator.__ceildiv__(denominator) -# NOTE: this overload is needed because of overload precedence; without it the -# Int overload would be preferred, and ceildiv wouldn't work on IntLiteral. -@always_inline +# NOTE: this overload is needed because IntLiteral promotes to a runtime type +# before overload resolution. +@always_inline("builtin") fn ceildiv(numerator: IntLiteral, denominator: IntLiteral) -> IntLiteral: """Return the rounded-up result of dividing numerator by denominator. From 871045ce8abb071c0d8ee5f28ac6c6feaf7d033e Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Fri, 21 Feb 2025 19:00:19 -0600 Subject: [PATCH 438/509] [External] [stdlib] [NFC] Rename `List.bytecount()` to `byte_length()` (#54664) [External] [stdlib] [NFC] Rename `List.bytecount()` to `byte_length()` Rename `List.bytecount()` to `byte_length()` to keep consistency with all Stringlike types. Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Co-authored-by: Joe Loser Closes modularml/mojo#3897 MODULAR_ORIG_COMMIT_REV_ID: bcb5da166f0490eaea10f88a45cf9023e8bb8f63 --- docs/changelog.md | 3 +++ stdlib/src/collections/list.mojo | 6 +++--- stdlib/test/collections/test_list.mojo | 2 +- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index c0b2a81b87..14364179fe 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -132,6 +132,9 @@ ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=1) var val1 = warp.broadcast(x) ``` +- `List.bytecount()` has been renamed to `List.byte_length()` for consistency + with the String-like APIs. + ### Tooling changes #### Mojo Compiler diff --git a/stdlib/src/collections/list.mojo b/stdlib/src/collections/list.mojo index ba6b8d9760..21416c95dd 100644 --- a/stdlib/src/collections/list.mojo +++ b/stdlib/src/collections/list.mojo @@ -467,11 +467,11 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # Methods # ===-------------------------------------------------------------------===# - fn bytecount(self) -> Int: - """Gets the bytecount of the List. + fn byte_length(self) -> Int: + """Gets the byte length of the List (`len(self) * sizeof[T]()`). Returns: - The bytecount of the List. + The byte length of the List (`len(self) * sizeof[T]()`). """ return len(self) * sizeof[T]() diff --git a/stdlib/test/collections/test_list.mojo b/stdlib/test/collections/test_list.mojo index 623ea34fa4..b7c3cecf7a 100644 --- a/stdlib/test/collections/test_list.mojo +++ b/stdlib/test/collections/test_list.mojo @@ -45,7 +45,7 @@ def test_list(): list.append(i) assert_equal(5, len(list)) - assert_equal(5 * sizeof[Int](), list.bytecount()) + assert_equal(5 * sizeof[Int](), list.byte_length()) assert_equal(0, list[0]) assert_equal(1, list[1]) assert_equal(2, list[2]) From 4a83bf01c3853cbedecaa2e5d90ffbd3acd1f247 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 19:36:35 -0800 Subject: [PATCH 439/509] [mojo-stdlib] Move code off using `Int/FloatLiteral` directly This moves some code off using `Int`/`FloatLiteral` directly when they actually want runtime types like `Int` or `Float32`. MODULAR_ORIG_COMMIT_REV_ID: 4c85e57a00afc441516814e7b3fdf28a70c74686 --- stdlib/src/builtin/float_literal.mojo | 15 +-- stdlib/src/builtin/int_literal.mojo | 14 --- stdlib/src/builtin/simd.mojo | 2 +- stdlib/src/builtin/string_literal.mojo | 25 ----- stdlib/src/sys/info.mojo | 106 +++++++++++--------- stdlib/src/utils/numerics.mojo | 8 +- stdlib/test/builtin/test_float_literal.mojo | 27 ----- stdlib/test/builtin/test_int_literal.mojo | 21 ---- 8 files changed, 64 insertions(+), 154 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index ebaa5142a8..f94ecf8e7f 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -341,21 +341,8 @@ struct FloatLiteral( Returns: The remainder of dividing self by rhs. """ - return self.__divmod__(rhs)[1] - - @always_inline("nodebug") - fn __divmod__(self, rhs: Self) -> Tuple[Self, Self]: - """Return a tuple with the quotient and the remainder of self divided by rhs. - - Args: - rhs: The value to divide on. - - Returns: - A tuple with the dividend and the remainder. - """ var quotient: Self = self.__floordiv__(rhs) - var remainder: Self = self - (quotient * rhs) - return quotient, remainder + return self - (quotient * rhs) fn __rfloordiv__(self, rhs: Self) -> Self: """Returns rhs divided by self, rounded down to the nearest integer. diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 4fb3bc010a..34be2d64d3 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -175,20 +175,6 @@ struct IntLiteral( """ return Self() - self - @always_inline("nodebug") - fn __divmod__(self, rhs: Self) -> Tuple[Self, Self]: - """Return the quotient and remainder of the division of self by rhs. - - Args: - rhs: The value to divide on. - - Returns: - The quotient and remainder of the division. - """ - var quotient: Self = self.__floordiv__(rhs) - var remainder: Self = self - (quotient * rhs) - return quotient, remainder - @always_inline("builtin") fn __invert__(self) -> Self: """Return ~self. diff --git a/stdlib/src/builtin/simd.mojo b/stdlib/src/builtin/simd.mojo index 6fda0a23a8..018d8f5f25 100644 --- a/stdlib/src/builtin/simd.mojo +++ b/stdlib/src/builtin/simd.mojo @@ -3394,7 +3394,7 @@ fn _convert_f32_to_float8_scaler[ alias FP8_EXPONENT_BIAS = FPUtils[target].exponent_bias() alias FP32_EXPONENT_BIAS = FPUtils[type].exponent_bias() alias FP32_NUM_MANTISSA_BITS = FPUtils[type].mantissa_width() - alias FP8_MAX_FLT: UInt8 = 0x7E if IS_E4M3 else 0x7B + alias FP8_MAX_FLT = UInt8(0x7E) if IS_E4M3 else UInt8(0x7B) # Extract the bits in the FP32 type var sign: UInt8 = 0x80 if FPUtils[type].get_sign(x) else 0x00 diff --git a/stdlib/src/builtin/string_literal.mojo b/stdlib/src/builtin/string_literal.mojo index 15c9d40764..aabd2b7678 100644 --- a/stdlib/src/builtin/string_literal.mojo +++ b/stdlib/src/builtin/string_literal.mojo @@ -189,31 +189,6 @@ struct StringLiteral( """ self = self + rhs - @always_inline("nodebug") - fn __mul__(self, n: IntLiteral) -> StringLiteral: - """Concatenates the string literal `n` times. Can only be evaluated at - compile time using the `alias` keyword, which will write the result into - The binary. - - Args: - n : The number of times to concatenate the string literal. - - Returns: - The string concatenated `n` times. - - Examples: - - ```mojo - alias concat = "mojo" * 3 - print(concat) # mojomojomojo - ``` - . - """ - var concat = "" - for _ in range(n): - concat += self - return concat - fn __mul__(self, n: Int) -> String: """Concatenates the string `n` times. diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index 460f5754b2..b44eec3099 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -561,7 +561,7 @@ fn is_64bit[target: __mlir_type.`!kgen.target` = _current_target()]() -> Bool: @always_inline("nodebug") fn simdbitwidth[ target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the vector size (in bits) of the specified target. Parameters: @@ -570,18 +570,20 @@ fn simdbitwidth[ Returns: The vector size (in bits) of the specified target. """ - return __mlir_attr[ - `#kgen.param.expr : !kgen.int_literal`, - ] + return Int( + __mlir_attr[ + `#kgen.param.expr : !kgen.int_literal`, + ] + ) @always_inline("nodebug") fn simdbytewidth[ target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the vector size (in bytes) of the specified target. Parameters: @@ -597,7 +599,7 @@ fn simdbytewidth[ @always_inline("nodebug") fn sizeof[ type: AnyType, target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the size of (in bytes) of the type. Parameters: @@ -630,19 +632,21 @@ fn sizeof[ AnyType, `> : !kgen.type`, ] - return __mlir_attr[ - `#kgen.param.expr : !kgen.type,`, - target, - `> : !kgen.int_literal`, - ] + return Int( + __mlir_attr[ + `#kgen.param.expr : !kgen.type,`, + target, + `> : !kgen.int_literal`, + ] + ) @always_inline("nodebug") fn sizeof[ type: DType, target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the size of (in bytes) of the dtype. Parameters: @@ -652,21 +656,23 @@ fn sizeof[ Returns: The size of the dtype in bytes. """ - return __mlir_attr[ - `#kgen.param.expr`, - `> : !kgen.type,`, - target, - `> : !kgen.int_literal`, - ] + return Int( + __mlir_attr[ + `#kgen.param.expr`, + `> : !kgen.type,`, + target, + `> : !kgen.int_literal`, + ] + ) @always_inline("nodebug") fn alignof[ type: AnyType, target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the align of (in bytes) of the type. Parameters: @@ -683,19 +689,21 @@ fn alignof[ AnyType, `> : !kgen.type`, ] - return __mlir_attr[ - `#kgen.param.expr : !kgen.type,`, - target, - `> : !kgen.int_literal`, - ] + return Int( + __mlir_attr[ + `#kgen.param.expr : !kgen.type,`, + target, + `> : !kgen.int_literal`, + ] + ) @always_inline("nodebug") fn alignof[ type: DType, target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the align of (in bytes) of the dtype. Parameters: @@ -705,22 +713,24 @@ fn alignof[ Returns: The alignment of the dtype in bytes. """ - return __mlir_attr[ - `#kgen.param.expr`, - `> : !kgen.type,`, - target, - `> : !kgen.int_literal`, - ] + return Int( + __mlir_attr[ + `#kgen.param.expr`, + `> : !kgen.type,`, + target, + `> : !kgen.int_literal`, + ] + ) @always_inline("nodebug") fn bitwidthof[ type: AnyTrivialRegType, target: __mlir_type.`!kgen.target` = _current_target(), -]() -> IntLiteral: +]() -> Int: """Returns the size of (in bits) of the type. Parameters: @@ -737,7 +747,7 @@ fn bitwidthof[ @always_inline("nodebug") fn bitwidthof[ type: DType, target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the size of (in bits) of the dtype. Parameters: @@ -756,7 +766,7 @@ fn bitwidthof[ fn simdwidthof[ type: AnyTrivialRegType, target: __mlir_type.`!kgen.target` = _current_target(), -]() -> IntLiteral: +]() -> Int: """Returns the vector size of the type on the host system. Parameters: @@ -772,7 +782,7 @@ fn simdwidthof[ @always_inline("nodebug") fn simdwidthof[ type: DType, target: __mlir_type.`!kgen.target` = _current_target() -]() -> IntLiteral: +]() -> Int: """Returns the vector size of the type on the host system. Parameters: diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index 83fce9c590..5490eaea86 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -59,7 +59,7 @@ struct FPUtils[ @staticmethod @always_inline("nodebug") - fn mantissa_width() -> IntLiteral: + fn mantissa_width() -> Int: """Returns the mantissa width of a floating point type. Returns: @@ -83,7 +83,7 @@ struct FPUtils[ @staticmethod @always_inline("nodebug") - fn max_exponent() -> IntLiteral: + fn max_exponent() -> Int: """Returns the max exponent of a floating point type without accounting for inf representations. This is not the maximum representable exponent, which is generally equal to @@ -106,7 +106,7 @@ struct FPUtils[ @staticmethod @always_inline("nodebug") - fn exponent_width() -> IntLiteral: + fn exponent_width() -> Int: """Returns the exponent width of a floating point type. Returns: @@ -136,7 +136,7 @@ struct FPUtils[ @staticmethod @always_inline - fn exponent_bias() -> IntLiteral: + fn exponent_bias() -> Int: """Returns the exponent bias of a floating point type. Returns: diff --git a/stdlib/test/builtin/test_float_literal.mojo b/stdlib/test/builtin/test_float_literal.mojo index 817eb639c1..c90950a1df 100644 --- a/stdlib/test/builtin/test_float_literal.mojo +++ b/stdlib/test/builtin/test_float_literal.mojo @@ -83,32 +83,6 @@ def test_mod(): assert_equal(FloatLiteral.__mod__(6, 2.5), 1.0) -def test_div_mod(): - alias t0 = FloatLiteral.__divmod__(4.5, 2.0) - alias q0 = t0[0] - alias r0 = t0[1] - assert_equal(q0, 2.0) - assert_equal(r0, 0.5) - - alias t1 = FloatLiteral.__divmod__(-4.5, 2.0) - alias q1 = t1[0] - alias r1 = t1[1] - assert_equal(q1, -3.0) - assert_equal(r1, 1.5) - - alias t2 = FloatLiteral.__divmod__(4.5, -2.0) - alias q2 = t2[0] - alias r2 = t2[1] - assert_equal(q2, -3.0) - assert_equal(r2, -1.5) - - alias t3 = FloatLiteral.__divmod__(6.0, 2.5) - alias q3 = t3[0] - alias r3 = t3[1] - assert_equal(q3, 2.0) - assert_equal(r3, 1.0) - - def test_int_conversion(): assert_equal(Int(-4.0), -4) assert_equal(Int(-4.5), -4) @@ -249,7 +223,6 @@ def main(): test_trunc() test_division() test_mod() - test_div_mod() test_int_conversion() test_bool() test_is_special_value() diff --git a/stdlib/test/builtin/test_int_literal.mojo b/stdlib/test/builtin/test_int_literal.mojo index b9ccfc27fd..87ee610509 100644 --- a/stdlib/test/builtin/test_int_literal.mojo +++ b/stdlib/test/builtin/test_int_literal.mojo @@ -79,26 +79,6 @@ def test_indexer(): assert_true(88 == index(88)) -def test_divmod(): - alias t0 = IntLiteral.__divmod__(2, 2) - alias q0 = t0[0] - alias r0 = t0[1] - assert_equal(q0, 1) - assert_equal(r0, 0) - - alias t1 = IntLiteral.__divmod__(2, 3) - alias q1 = t1[0] - alias r1 = t1[1] - assert_equal(q1, 0) - assert_equal(r1, 2) - - alias t2 = IntLiteral.__divmod__(99, -2) - alias q2 = t2[0] - alias r2 = t2[1] - assert_equal(q2, -50) - assert_equal(r2, -1) - - def test_bool(): assert_true(IntLiteral.__bool__(5)) assert_false(IntLiteral.__bool__(0)) @@ -153,7 +133,6 @@ def main(): test_trunc() test_floordiv() test_mod() - test_divmod() test_abs() test_indexer() test_bool() From 2c1780e245ecbd4b2544d64a27822e46c4ae5bfa Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 20:49:33 -0800 Subject: [PATCH 440/509] [mojo-lang] Move sizeof/alignof to return index instead of intliteral. These don't return literals, they return values. Using index as a result is more correct and a more tight bound on how they're used. MODULAR_ORIG_COMMIT_REV_ID: 1b4227f33980b5f98d56a6c349828a621221b693 --- stdlib/src/sys/info.mojo | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/stdlib/src/sys/info.mojo b/stdlib/src/sys/info.mojo index b44eec3099..79d13b199e 100644 --- a/stdlib/src/sys/info.mojo +++ b/stdlib/src/sys/info.mojo @@ -575,7 +575,7 @@ fn simdbitwidth[ `#kgen.param.expr : !kgen.int_literal`, + `> : index`, ] ) @@ -638,7 +638,7 @@ fn sizeof[ mlir_type, `> : !kgen.type,`, target, - `> : !kgen.int_literal`, + `> : index`, ] ) @@ -664,7 +664,7 @@ fn sizeof[ `>`, `> : !kgen.type,`, target, - `> : !kgen.int_literal`, + `> : index`, ] ) @@ -695,7 +695,7 @@ fn alignof[ +mlir_type, `> : !kgen.type,`, target, - `> : !kgen.int_literal`, + `> : index`, ] ) @@ -721,7 +721,7 @@ fn alignof[ `>`, `> : !kgen.type,`, target, - `> : !kgen.int_literal`, + `> : index`, ] ) From 0094d1c437b3216add081d9666c3fea427f0fe71 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sat, 22 Feb 2025 07:12:25 +0000 Subject: [PATCH 441/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022205 --- examples/life/magic.lock | 241 +++++++++++++++++----------------- examples/magic.lock | 166 +++++++++++------------ examples/operators/magic.lock | 166 +++++++++++------------ examples/testing/magic.lock | 166 +++++++++++------------ magic.lock | 166 +++++++++++------------ 5 files changed, 451 insertions(+), 454 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index c2d9f706cf..37cc954e6c 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -31,7 +31,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-0.23.1-h5888daf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.23.1-h5888daf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.2.0-h4bba637_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.3.0-h76408a6_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/jack-1.9.22-h7c63dc7_2.conda @@ -40,7 +40,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_hbbce691_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.23.1-h8e693c7_0.conda @@ -70,7 +70,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h77fa898_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.51-hbd13f7d_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.4-hb9d3cd8_0.conda @@ -103,11 +103,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.6-h8d12d68_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022105-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025022105-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.2.0.dev2025022105-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022105-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022105-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022205-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025022205-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.2.0.dev2025022205-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022205-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022205-release.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.9-hc50e24c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda @@ -186,7 +186,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gettext-0.23.1-h5ad3122_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gettext-tools-0.23.1-h5ad3122_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/graphite2-1.3.13-h2f0025b_1003.conda - - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-10.2.0-h785c1aa_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-10.3.0-hb5e3f52_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/icu-75.1-hf9b3779_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/jack-1.9.22-h5c6c0ed_2.conda @@ -195,7 +195,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/keyutils-1.6.1-h4e544f5_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/krb5-1.21.3-h50a48e9_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lame-3.100-h4e544f5_1003.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.43-h80caac9_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.43-h80caac9_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lerc-4.0.0-h4de3ea5_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libabseil-20240722.0-cxx17_h18dbdb1_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libasprintf-0.23.1-h5e0f5ae_0.conda @@ -225,7 +225,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglx-1.7.0-hd24410f_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgomp-14.2.0-he277a41_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgpg-error-1.51-h05609ea_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libiconv-1.18-hc99b53d_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libiconv-1.18-hc99b53d_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.0.0-h31becfc_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-31_h411afd4_openblas.conda - 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sha256: 62205186302b4460ee7f8c6e466da82d69b2a06c71958d47cbbe548cdcd3fde4 - md5: 10aa481c50998711d470cac4990d6ed5 + sha256: 9444c0b8c574e6efdee03d24fdd4340518313a796caf08f536a5798b9c7a6123 + md5: 7be2f23091dd2443ba395018d3005fcb depends: - - max-core ==25.2.0.dev2025022105 release + - max-core ==25.2.0.dev2025022205 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22980 - timestamp: 1740115026277 + size: 22983 + timestamp: 1740201380167 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 910e5700b2296b20139c0ba862f4e60b08bad6eb Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 21:13:35 -0800 Subject: [PATCH 442/509] [mojo-stdlib] Remove abs/ceil/trunc from FloatLiteral, abs from IntLiteral. This removes support for the `__abs__` (and ceil and trunc) methods from float literals, and abs from `IntLiteral`. We don't have anything that particularly needs the absolute value of a literal to stay in the infinite precision domain - we can just promote to Int/Float64. If we need this in the future, we can add this back, but until then it is nice to not have the complexity. MODULAR_ORIG_COMMIT_REV_ID: afc67b82811002583d170a1e0507faf220193e81 --- stdlib/src/builtin/float_literal.mojo | 56 ------------------- stdlib/src/builtin/int_literal.mojo | 12 ----- stdlib/src/builtin/math.mojo | 30 ----------- stdlib/test/builtin/test_float_literal.mojo | 59 +++------------------ stdlib/test/builtin/test_int_literal.mojo | 6 +-- 5 files changed, 9 insertions(+), 154 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index f94ecf8e7f..777baa62a0 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -15,8 +15,6 @@ These are Mojo built-ins, so you don't need to import them. """ -from math import Ceilable, CeilDivable, Floorable, Truncable - # ===-----------------------------------------------------------------------===# # FloatLiteral # ===-----------------------------------------------------------------------===# @@ -26,15 +24,10 @@ from math import Ceilable, CeilDivable, Floorable, Truncable @nonmaterializable(Float64) @register_passable("trivial") struct FloatLiteral( - Absable, - Ceilable, - CeilDivable, Comparable, - Floorable, ImplicitlyBoolable, Intable, Stringable, - Truncable, Floatable, ): """Mojo floating point literal type.""" @@ -188,17 +181,6 @@ struct FloatLiteral( """ return self * Self(-1) - @always_inline("nodebug") - fn __abs__(self) -> Self: - """Return the absolute value of the FloatLiteral. - - Returns: - The absolute value. - """ - if self > 0: - return self - return -self - @always_inline("nodebug") fn __floor__(self) -> Self: """Return the floor value of the FloatLiteral. @@ -220,44 +202,6 @@ struct FloatLiteral( return truncated return truncated - 1 - @always_inline("nodebug") - fn __ceil__(self) -> Self: - """Return the ceiling value of the FloatLiteral. - - Returns: - The ceiling value. - """ - - # Handle special values first. - if not self._is_normal(): - return self - - # __int_literal__ rounds towards zero, so it's correct for integers and - # negative values. - var truncated: IntLiteral = self.__int_literal__() - - # Ensure this equality doesn't hit any implicit conversions. - if self <= 0 or self.__eq__(Self(truncated)): - return truncated - return truncated + 1 - - @always_inline("nodebug") - fn __trunc__(self) -> Self: - """Truncates the floating point literal. If there is a fractional - component, then the value is truncated towards zero. - - For example, `(4.5).__trunc__()` returns `4.0`, and `(-3.7).__trunc__()` - returns `-3.0`. - - Returns: - The truncated FloatLiteral value. - """ - - # Handle special values first. - if not self._is_normal(): - return self - return Self(self.__int_literal__()) - # ===------------------------------------------------------------------===# # Arithmetic Operators # ===------------------------------------------------------------------===# diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index 34be2d64d3..c5084fcf39 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -19,7 +19,6 @@ from math import Ceilable, CeilDivable, Floorable, Truncable @nonmaterializable(Int) @register_passable("trivial") struct IntLiteral( - Absable, Ceilable, CeilDivable, Comparable, @@ -401,17 +400,6 @@ struct IntLiteral( _type = __mlir_type.index, treatIndexAsUnsigned = __mlir_attr.unit ](self.value) - @always_inline("nodebug") - fn __abs__(self) -> Self: - """Return the absolute value of the IntLiteral value. - - Returns: - The absolute value. - """ - if self >= 0: - return self - return -self - @always_inline("builtin") fn __ceil__(self) -> Self: """Return the ceiling of the IntLiteral value, which is itself. diff --git a/stdlib/src/builtin/math.mojo b/stdlib/src/builtin/math.mojo index 7f0c693b67..c787dd8387 100644 --- a/stdlib/src/builtin/math.mojo +++ b/stdlib/src/builtin/math.mojo @@ -68,36 +68,6 @@ fn abs[T: Absable](value: T) -> T: return value.__abs__() -# TODO: https://github.com/modularml/modular/issues/38694 -# TODO: Remove this -@always_inline -fn abs(value: IntLiteral) -> IntLiteral: - """Get the absolute value of the given IntLiteral. - - Args: - value: The IntLiteral to get the absolute value of. - - Returns: - The absolute value of the IntLiteral. - """ - return value.__abs__() - - -# TODO: https://github.com/modularml/modular/issues/38694 -# TODO: Remove this -@always_inline -fn abs(value: FloatLiteral) -> FloatLiteral: - """Get the absolute value of the given FloatLiteral. - - Args: - value: The FloatLiteral to get the absolute value of. - - Returns: - The absolute value of the FloatLiteral. - """ - return value.__abs__() - - # ===----------------------------------------------------------------------=== # # divmod # ===----------------------------------------------------------------------=== # diff --git a/stdlib/test/builtin/test_float_literal.mojo b/stdlib/test/builtin/test_float_literal.mojo index c90950a1df..915c1f850e 100644 --- a/stdlib/test/builtin/test_float_literal.mojo +++ b/stdlib/test/builtin/test_float_literal.mojo @@ -26,48 +26,6 @@ alias inf = FloatLiteral.infinity alias neg_inf = FloatLiteral.negative_infinity -def test_ceil(): - assert_equal(FloatLiteral.__ceil__(1.5), 2.0) - assert_equal(FloatLiteral.__ceil__(1.4), 2.0) - assert_equal(FloatLiteral.__ceil__(-1.5), -1.0) - assert_equal(FloatLiteral.__ceil__(-3.6), -3.0) - assert_equal(FloatLiteral.__ceil__(3.0), 3.0) - assert_equal(FloatLiteral.__ceil__(0.0), 0.0) - - assert_true(FloatLiteral.__ceil__(nan).is_nan()) - assert_true(FloatLiteral.__ceil__(neg_zero).is_neg_zero()) - assert_equal(FloatLiteral.__ceil__(inf), inf) - assert_equal(FloatLiteral.__ceil__(neg_inf), neg_inf) - - -def test_floor(): - assert_equal(FloatLiteral.__floor__(1.5), 1.0) - assert_equal(FloatLiteral.__floor__(1.6), 1.0) - assert_equal(FloatLiteral.__floor__(-1.5), -2.0) - assert_equal(FloatLiteral.__floor__(-3.4), -4.0) - assert_equal(FloatLiteral.__floor__(3.0), 3.0) - assert_equal(FloatLiteral.__floor__(0.0), 0.0) - - assert_true(FloatLiteral.__floor__(nan).is_nan()) - assert_true(FloatLiteral.__floor__(neg_zero).is_neg_zero()) - assert_equal(FloatLiteral.__floor__(inf), inf) - assert_equal(FloatLiteral.__floor__(neg_inf), neg_inf) - - -def test_trunc(): - assert_equal(FloatLiteral.__trunc__(1.5), 1.0) - assert_equal(FloatLiteral.__trunc__(1.6), 1.0) - assert_equal(FloatLiteral.__trunc__(-1.5), -1.0) - assert_equal(FloatLiteral.__trunc__(-3.6), -3.0) - assert_equal(FloatLiteral.__trunc__(3.0), 3.0) - assert_equal(FloatLiteral.__trunc__(0.0), 0.0) - - assert_true(FloatLiteral.__trunc__(nan).is_nan()) - assert_true(FloatLiteral.__trunc__(neg_zero).is_neg_zero()) - assert_equal(FloatLiteral.__trunc__(inf), inf) - assert_equal(FloatLiteral.__trunc__(neg_inf), neg_inf) - - def test_division(): assert_equal(FloatLiteral.__truediv__(4.4, 0.5), 8.8) @@ -107,15 +65,13 @@ def test_is_special_value(): def test_abs(): - assert_equal((-4.4).__abs__(), 4.4) - assert_equal((4.4).__abs__(), 4.4) - assert_equal((0.0).__abs__(), 0.0) + assert_equal(abs(-4.4), 4.4) + assert_equal(abs(4.4), 4.4) + assert_equal(abs(0.0), 0.0) - assert_true(FloatLiteral.__abs__(nan).is_nan()) - assert_false(FloatLiteral.__abs__(neg_zero).is_neg_zero()) - assert_equal(FloatLiteral.__abs__(neg_zero), 0.0) - assert_equal(FloatLiteral.__abs__(inf), inf) - assert_equal(FloatLiteral.__abs__(neg_inf), inf) + assert_equal(abs(neg_zero), 0.0) + assert_equal(abs(inf), inf) + assert_equal(abs(neg_inf), inf) def test_comparison(): @@ -218,9 +174,6 @@ def test_float_conversion(): def main(): - test_ceil() - test_floor() - test_trunc() test_division() test_mod() test_int_conversion() diff --git a/stdlib/test/builtin/test_int_literal.mojo b/stdlib/test/builtin/test_int_literal.mojo index 87ee610509..ce9c5be4a5 100644 --- a/stdlib/test/builtin/test_int_literal.mojo +++ b/stdlib/test/builtin/test_int_literal.mojo @@ -69,9 +69,9 @@ def test_mod(): def test_abs(): - assert_equal(IntLiteral.__abs__(-5), 5) - assert_equal(IntLiteral.__abs__(2), 2) - assert_equal(IntLiteral.__abs__(0), 0) + assert_equal(abs(-5), 5) + assert_equal(abs(2), 2) + assert_equal(abs(0), 0) def test_indexer(): From 1956d15191e70842d4c08cf941e4c8965e027717 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 21:14:29 -0800 Subject: [PATCH 443/509] [mojo-lang] Give float literal floordiv first class support. FloatLiteral.floordiv is the foundation of mod and other algorithms, and needs a consistent branch free result type. Add an enum for it. MODULAR_ORIG_COMMIT_REV_ID: dea591320b3f37bdae99f2f28e74f697c056a0c1 --- stdlib/src/builtin/float_literal.mojo | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 777baa62a0..4639906a50 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -273,7 +273,10 @@ struct FloatLiteral( Returns: `floor(self / rhs)` value. """ - return self.__truediv__(rhs).__floor__() + # TODO - Python raises an error on divide by 0.0 or -0.0 + return __mlir_op.`kgen.float_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("nodebug") fn __mod__(self, rhs: Self) -> Self: From b0e93c34ec949f27e4d19affd9575ad8dc489f3a Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Fri, 21 Feb 2025 21:40:02 -0800 Subject: [PATCH 444/509] [mojo-stdlib] Update various floatliteral methods to be builtin With floordiv now natively supported, we can add support for the immediately derived operations. MODULAR_ORIG_COMMIT_REV_ID: 006e0647c19d0c325312e0539ba6b6c7c2d6d887 --- stdlib/src/builtin/float_literal.mojo | 31 +++++---------------------- 1 file changed, 5 insertions(+), 26 deletions(-) diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 4639906a50..569e204cb6 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -181,27 +181,6 @@ struct FloatLiteral( """ return self * Self(-1) - @always_inline("nodebug") - fn __floor__(self) -> Self: - """Return the floor value of the FloatLiteral. - - Returns: - The floor value. - """ - - # Handle special values first. - if not self._is_normal(): - return self - - # __int_literal__ rounds towards zero, so it's correct for integers and - # positive values. - var truncated: IntLiteral = self.__int_literal__() - - # Ensure this equality doesn't hit any implicit conversions. - if self >= 0 or self.__eq__(Self(truncated)): - return truncated - return truncated - 1 - # ===------------------------------------------------------------------===# # Arithmetic Operators # ===------------------------------------------------------------------===# @@ -263,7 +242,7 @@ struct FloatLiteral( oper = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __floordiv__(self, rhs: Self) -> Self: """Returns self divided by rhs, rounded down to the nearest integer. @@ -278,7 +257,7 @@ struct FloatLiteral( oper = __mlir_attr.`#kgen` ](self.value, rhs.value) - @always_inline("nodebug") + @always_inline("builtin") fn __mod__(self, rhs: Self) -> Self: """Return the remainder of self divided by rhs. @@ -288,9 +267,9 @@ struct FloatLiteral( Returns: The remainder of dividing self by rhs. """ - var quotient: Self = self.__floordiv__(rhs) - return self - (quotient * rhs) + return self - (self.__floordiv__(rhs) * rhs) + @always_inline("builtin") fn __rfloordiv__(self, rhs: Self) -> Self: """Returns rhs divided by self, rounded down to the nearest integer. @@ -302,7 +281,7 @@ struct FloatLiteral( """ return rhs // self - @always_inline("nodebug") + @always_inline("builtin") fn __ceildiv__(self, denominator: Self) -> Self: """Return the rounded-up result of dividing self by denominator. From 727b16a5b51a821700e067c8cd8628714ac9885b Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 22 Feb 2025 14:11:31 -0800 Subject: [PATCH 445/509] [mojo-stdlib] Change `#kgen.param.expr : index`, + `> : index`, ] ) From 248de11a021f24ceb9037634b0601deb39cfc142 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 22 Feb 2025 15:36:35 -0800 Subject: [PATCH 446/509] [mojo-lang] Completely remove `ParserParamEvaluator`. This severs all use of the comptime interpreter by the parser. Such use was never correct - we'd be interpreting IR before lowering and type checking were complete. Such a change was an early hack added to improve dependent type support, but was fragile, weird, and could impact compile time in some cases. Now that the `@always_inline("builtin")` model is settled, we can move past the pile of hacks. Removal of the interpreter from the parser is the last step in this process - now the interpreter is only used during the elaboration phase later. This patch adds the design doc that spawned this work now that it is complete. MODULAR_ORIG_COMMIT_REV_ID: 163e33c560c2ab22ab21c0949dd77f47b5365e1a --- proposals/always_inline_builtin.md | 157 +++++++++++++++++++++++++++++ 1 file changed, 157 insertions(+) create mode 100644 proposals/always_inline_builtin.md diff --git a/proposals/always_inline_builtin.md b/proposals/always_inline_builtin.md new file mode 100644 index 0000000000..769b27a1e8 --- /dev/null +++ b/proposals/always_inline_builtin.md @@ -0,0 +1,157 @@ +# @always_inline("builtin") + +Author: Chris Lattner +Date: Jan 31, 2025 +Status: Implemented, complete + +## Background and Motivation + +As Mojo has evolved, we’ve continued to push for a design where builtin +primitives are represented with user-defined types, instead of raw MLIR types +directly. This started back in the day by wrapping `__mlirtype.i1` with `Bool` +and `__mlirtype.index` with `Int` and more recently by wrapping +`__mlirtype.origin<..>` with `Origin`. This approach has a number of +advantages: + +1. This allows us to use methods on the library defined types to define + operators that apply to it, e.g. `__and__` for `Bool` and `Int`. +2. This is much easier to write and works with name lookup properly. +3. This moves MLIR types into being “implementation details of the standard + library” rather than user-exposed types. + +Overall, this has been a great result, part of the “Mojo is syntax sugar for +MLIR”. + +However, recent engineering discussions have shown a weakness with this +approach, Stephen recently pointed out: + +> One high level Q, I think a lot of the parameter pain is derived from +> primitive structures like Int and Bool being custom types. It means they +> become function calls in the IR and its a struggle to simplify. It seems +> like maybe we should revisit these being in the library and just have a +> small set of compiler builtin types for things like Bool. + +It would be very sad to have another parallel universe for “compiler builtin” +types that are neither Int nor the existing MLIR types! That said, he’s got a +really great point, and we’re accumulating a small collection of hacks to work +around the problems. + +There are several challenges that occur when these types are used in symbolic +parameter expressions. What is an example? Consider the result type of: + +```mojo +fn widen[dt: DType, n: Int](a: SIMD[dt, n], b: SIMD[dt, n]) -> SIMD[dt, n+n]: +``` + +The result type ends up being represented as `SIMD[dt, Int.__add__(n, n)]`. +This expression cannot be folded or parameter inlined at parse time (because `n` +is not a simple constant), so we end up representing the call to `Int.__add__` +symbolicly through elaboration time. + +This is nice and flexible and general, a core part of the full-general comptime +model that Mojo provides, but it poises a number of problems for the core types +like `Int` and `Bool` and `Origin` that Stephen is pointing out. A few example +problems: + +1. ✅ This causes significant IR bloat that annoys compiler developers because a + call to `Int.__add__` is far more verbose than a `POC::Add` with its sugared + form. +2. ✅ Underlying dialects like KGEN have symbolic optimizations for core types. + In this example, it would canonicalization “n+n” to “2*n” and these canonical + forms are important to reduce rebinds. +3. ✅ We have complicated and fragile logic to work around this in the case when + the the operands are “simple constants” (see + `inlineFunctionCallIntoPValueIfPossible` and transitive code it calls, + *shudder*) which has lots of problems, e.g. it completely fails on + parametric function calls. +4. ✅ Because that doesn’t work on parametric function calls, we get further + hacks like `tryOriginInitFold` which special cases `Origin.__init__` because + Origin is a parametric struct, and we need low-level canonical `!lit.origin` + values for internal reasons. +5. ✅ We have other hacks like `refineResultValue` . This was added long ago + when we were first bringing up dependent types and it shouldn’t be needed. + It serves to handle things like the example below. We want `x` to have type + `SIMD[dt2, 8]` instead of `SIMD[dt, Int.add(4, 4)]`. The formal type + maintains the apply expression, so it goes through and does a rebind to get + it out of the way, reflecting the additional information we have in the call + site. If we modeled this correctly, this would all happen automatically and + no rebind would be needed: + + ```mojo + fn example[dt2: DType](a: SIMD[dt2, 4]): + var x = widen(a, a) + ``` + +6. ✅ Generally anything that uses the comptime interpreter at parse time is + wrong because the IR hasn't been lowered through CF lowering and + CheckLifetimes. We need to get off of this for dependent type support. + +To summarize, Mojo has worked this way for a very long time, but there are too +many design smells adding up to there being a problem. I would love to resolve +this once and for all and I think one simple solution will resolve all this +mess. + +## Proposal: `@always_inline("builtin")` + +The proposed solution to this is to introduce another level to `@always_inline` +that is “like `"nodebug"` but harder”. The observation is that these types +really are special, the methods that apply to them are trivial and generally +known to the compiler, and the operations within these methods have well-known +magic representations (e.g. `POC::Add` attribute instead of `index.add` +operation). We don’t want to special case the methods themselves into the +compiler, but we do necessarily have a tight coupling and want type checking. + +This new level of `always_inline` would have the same behavior as `"nodebug"` +(e.g. get the same LLVM represention, still disable debug info generation etc) +but add two more behaviors. + +### Function definition body-resolution checking + +The magic behavior we’re looking for has specific limited use-cases and can only +handle specific limited forms. After the function body has been parsed, we need +to validate that it doesn’t use anything that `@always_inline("builtin")` can’t +handle - this checks that there is no control flow, no function calls to +functions that are not themselves `@always_inline("builtin")`, no use of +unsupported MLIR operations, etc. The forms we will be able to accept are +very limited, but that seems like it should be ok given that these methods are +all just wrappers around singular MLIR operations anyway. + +### Change CallEmission to do the inlining + +These change should be very simple and localized - if `emitCallInParamContext` +returns an apply parameter expression, check to see if the callee is an +`@always_inline("builtin")` function. If so, unconditionally inline the body +into the parameter representation (doing the remapping of operations like +`index.add` to `POC::Add` and form `StructAttr` and `StructExtractAttr` instead +of `StructExtractOp` which will all fold and cancel out). + +That’s it. + +## Thoughts and Implications + +This is something that has been haunting me for quite some time. I think that +this relatively simple extension will subsume and allow us to remove a bunch of +fragile complexity I mentioned before. I believe it should be straight-forward +to support parametric functions, because we’re already in the parameter domain +and we can specifically “just not support” hard cases if they came up. + +## Alternative Considered: Try inlining *all* “nodebug” param calls + +Weiwei points out that we could avoid adding the syntax for this: we could just +make the call emission logic notice that the call is to a “nodebug” function, +and scan it to see if it can be “parameter inlined” using the rules above. +These are the pros and cons I see of this approach: + +- Pro: No new syntax, nothing to explain or document. +- Con: It would be less predictable, you wouldn’t get a compile time error in a + case that you (i.e. a graph compiler engineer) wants inlined but isn’t getting + inlined. +- Con: Compile time would be much worse for every non-inlinable parameter call + (most of comptime). + +The compile time cost is induced because the parser would have to “body resolve” +the callee, which forces parsing the callee, type checking and IR generating it. +The vastly most common case will be that something cannot be inlined. The +benefit of the new syntax is we get a decoupling between these two things: we +just need to signature resolve to know the callee is “parameter”, and only then +do we body resolve it to do the inlining. From 41e363e152f7273e9996bfe9c27c18d4f449cdc6 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sat, 22 Feb 2025 17:00:37 -0800 Subject: [PATCH 447/509] [mojo-docs] Add removal of the parser's interpreter to the changelog. With the new `@always_inline("builtin")` model converged, we can now remove the parser-time interpreter, which was very problematic. While this won't directly affect Mojo users, this is a notable architectural change improvement. MODULAR_ORIG_COMMIT_REV_ID: 1e012d3acea2bcba4d33be88192d25591828aa50 --- docs/changelog.md | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 14364179fe..91dce436f6 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -21,8 +21,8 @@ what we publish. ### Language changes - The Mojo comptime interpreter can now handle many more LLVM intrinsics, - including ones that return floating point values. This allows functions - like `round` to be constant folded when used in a comptime context. + including ones that return floating point values. This allows functions + like `round` to be constant folded when used in a comptime context. ### Standard library changes @@ -139,10 +139,20 @@ ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=1) #### Mojo Compiler -Mojo compiler now warns about parameter for with large loop unrolling factor -(>1024 by default) which can lead to long compilation time and large generated -code size. Set `--loop-unrolling-warn-threshold` to change default value to -a different threshold or to `0` to disable the warning. +- Mojo compiler now warns about parameter for with large loop unrolling factor + (>1024 by default) which can lead to long compilation time and large generated + code size. Set `--loop-unrolling-warn-threshold` to change default value to + a different threshold or to `0` to disable the warning. + +- The Mojo compiler now only has one comptime interpreter. It had two + previously: one to handle a few cases that were important for dependent types + (but which also had many limitations) in the parser, and the primary one that + ran at "instantiation" time which is fully general. This was confusing and + caused a wide range of bugs. We've now removed the special case parse-time + interpreter, replacing it with a more general solution for dependent types. + This change should be invisible to most users, but should resolve a number of + long-standing bugs and significantly simplifies the compiler implementation, + allowing us to move faster. ### ❌ Removed From 369aa88490d48c8fc96e5fa910bc49c171fcb2a5 Mon Sep 17 00:00:00 2001 From: modularbot Date: Sun, 23 Feb 2025 07:51:46 +0000 Subject: [PATCH 448/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022305 --- examples/life/magic.lock | 401 ++++++++++++++++++---------------- examples/magic.lock | 355 +++++++++++++++--------------- examples/operators/magic.lock | 355 +++++++++++++++--------------- examples/testing/magic.lock | 355 +++++++++++++++--------------- magic.lock | 355 +++++++++++++++--------------- 5 files changed, 944 insertions(+), 877 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 37cc954e6c..880b74c623 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -57,18 +57,18 @@ environments: - 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jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22983 - timestamp: 1740201380167 + size: 22991 + timestamp: 1740287828839 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From 636a3d149055234e007f15f1d72e6551fd59033c Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sun, 23 Feb 2025 18:16:32 -0800 Subject: [PATCH 449/509] [Kernels] Remove the get_tensorspec from ManagedTensorSlice Getting a TensorSpec from a ManagedTensorSpec is a dangerous operation that provides little value. Since TensorSpec could heap allocate, it could behaves in a way that is not what any user intends it to be. This also has the advantage of breaking the dep between ManagedTensorSlice and the Tensor type (which means we can move the type to the max package in a future PR). MODULAR_ORIG_COMMIT_REV_ID: 0287b01590e51f645e63cef377f321db44727668 --- stdlib/src/utils/index.mojo | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stdlib/src/utils/index.mojo b/stdlib/src/utils/index.mojo index 47649ecd2a..67b13ce702 100644 --- a/stdlib/src/utils/index.mojo +++ b/stdlib/src/utils/index.mojo @@ -1086,7 +1086,7 @@ fn Index[ @always_inline -fn product[size: Int](tuple: IndexList[size, **_], end_idx: Int) -> Int: +fn product[size: Int](tuple: IndexList[size, **_], end_idx: Int = size) -> Int: """Computes a product of values in the tuple up to the given index. Parameters: From 5a8f456d5f8fb01860d2f11e1b519bbe199d8de9 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Sun, 23 Feb 2025 18:30:36 -0800 Subject: [PATCH 450/509] [Kernels] Reconcile the two get_accum_types that we had This reconciles the two variable of get_accum_types that we had. MODULAR_ORIG_COMMIT_REV_ID: 4128792e4ee1f7af63da18cb3d711ae6d32164d8 --- stdlib/src/utils/numerics.mojo | 32 +++++++++++++++++++++++++++----- 1 file changed, 27 insertions(+), 5 deletions(-) diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index 5490eaea86..ef00fbbc79 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -1028,24 +1028,46 @@ fn isfinite[ @always_inline -fn get_accum_type[type: DType]() -> DType: +fn get_accum_type[ + type: DType, *, preferred_accum_type: DType = DType.float32 +]() -> DType: """Returns the recommended type for accumulation operations. - Half precision types can introduce numerical error if they are used + Half precision and float8 types can introduce numerical error if they are used in reduction/accumulation operations. This method returns a higher precision type to use for accumulation if a half precision types is provided, otherwise it returns the original type. + The rules are as follows: + - If the type is a float8 type, return a float16 type. + - If the type is a bfloat16 precision type, return a float32 type. + - If the type is a float16 precision type, return a float32 type if the + preferred_accum_type is float32, otherwise return a float16 type. + - Otherwise, return the original type. + Parameters: type: The type of some accumulation operation. + preferred_accum_type: The preferred type for accumulation. Returns: DType.float32 if type is a half-precision float, type otherwise. """ - return DType.float32 if ( - type.is_half_float() or type in (DType.float8_e4m3fn, DType.float8_e5m2) - ) else type + @parameter + if type.is_float8(): + return DType.float16 + elif type is DType.bfloat16: + return DType.float32 + elif type is DType.float16: + # fp16 accumulation can be done in fp16 or fp32. Use fp16 by default for better + # performance and use fp32 only when it's specified via preferred type. + @parameter + if preferred_accum_type is DType.float32: + return preferred_accum_type + else: + return DType.float16 + else: + return type # ===----------------------------------------------------------------------=== # From 6345f18e0b0944d0ba5b6a9954a4dafb4101d2fe Mon Sep 17 00:00:00 2001 From: modularbot Date: Mon, 24 Feb 2025 07:40:28 +0000 Subject: [PATCH 451/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022405 --- examples/life/magic.lock | 194 +++++++++++++++++----------------- examples/magic.lock | 191 ++++++++++++++++----------------- examples/operators/magic.lock | 191 ++++++++++++++++----------------- examples/testing/magic.lock | 191 ++++++++++++++++----------------- magic.lock | 191 ++++++++++++++++----------------- 5 files changed, 473 insertions(+), 485 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 880b74c623..847eb71184 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -103,11 +103,11 @@ environments: - 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md5: 8cbb776a2f641b943d413b3e19df71f4 + size: 291806 + timestamp: 1740380591358 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.2-h1d1bf99_2.conda + sha256: 7db04684d3904f6151eff8673270922d31da1eea7fa73254d01c437f49702e34 + md5: 63ef3f6e6d6d5c589e64f11263dc5676 depends: - - ncurses >=6.3,<7.0a0 + - ncurses >=6.5,<7.0a0 arch: arm64 platform: osx license: GPL-3.0-only - license_family: GPL - size: 250351 - timestamp: 1679532511311 + size: 252359 + timestamp: 1740379663071 - conda: https://conda.anaconda.org/conda-forge/linux-64/sentencepiece-0.2.0-hc8f76dd_10.conda sha256: a1bde55ceb9dc5bd7879283d0f594a6ce65c07fda3de76230c65a4a5df47858a md5: 480e915dfc6c592615ef6f217e615aa6 From 824a3cb65fc880da5fba2bb49af69cc9340bb1da Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Sun, 23 Feb 2025 22:50:30 -0800 Subject: [PATCH 452/509] [mojo-stdlib] Rewrite `IntLiteral` and `FloatLiteral` to use dependent types. This patch rewrites the `IntLiteral` and `FloatLiteral` types to define away bugs due to their long-standing but incorrect design. The problem is that the "value" that these types represent are infinite precision literals that are only representable at compile time, not materializable at runtime. The previous design held this as runtime state, which led to bugs in the corner cases, e.g. this would crash the compiler because they can't be codegen'd: ```mojo @export fn test(a: IntLiteral) -> IntLiteral: return a+1 ``` We can correct this by building on the recent improvement to dependent type support (eg parameter inference bugfixes), the movement of all the related intrinsics to an attribute form, and the `always_inline(builtin)` feature being settled, we can correct this by moving these to being parameters on the literal types instead of runtime values. In short: ```mojo # old design struct IntLiteral: var state : __mlir_type.`!kgen.int_literal` # new design struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]: # .. no state ``` One unfortunate casualty in this is that the special numbers cannot be accessed as aliases on the type because we don't know the parameters. As such, this temporarily renames `FloatLiteral.infinity` to `FloatLiteral_infinity`. If this is a hardship, we can look at improving the compiler to move them back. This also paves the way to remove all the old "kgen.*literal.*` ops, but that will be a later patch. MODULAR_ORIG_COMMIT_REV_ID: 237015b553a0fae70aaf294e4f467b9c1312911e --- docs/changelog.md | 6 + stdlib/src/builtin/float_literal.mojo | 237 +++++++++---- stdlib/src/builtin/int_literal.mojo | 335 +++++++++++------- stdlib/src/collections/string/string.mojo | 4 +- stdlib/src/math/math.mojo | 8 +- stdlib/src/prelude/__init__.mojo | 8 +- stdlib/test/builtin/test_float_literal.mojo | 8 +- stdlib/test/builtin/test_simd.mojo | 8 +- .../test/collections/string/test_string.mojo | 8 +- 9 files changed, 415 insertions(+), 207 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 91dce436f6..20576ca849 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -26,6 +26,12 @@ what we publish. ### Standard library changes +- The design of the `IntLiteral` and `FloatLiteral` types has been changed to + maintain their compile-time-only value as a parameter instead of a stored + field. This correctly models that infinite precision literals are not + representable at runtime, and eliminates a number of bugs hit in corner cases. + This is made possible by enhanced dependent type support in the compiler. + - The `Buffer` struct has been removed in favor of `Span` and `NDBuffer`. - A new `IntervalTree` data structure has been added to the standard library. diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index 569e204cb6..c70380d145 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -19,51 +19,61 @@ These are Mojo built-ins, so you don't need to import them. # FloatLiteral # ===-----------------------------------------------------------------------===# +# FIXME: These really should get a better name. +alias FloatLiteral_nan = FloatLiteral[__mlir_attr.`#kgen.float_literal`]() +alias FloatLiteral_infinity = FloatLiteral[ + __mlir_attr.`#kgen.float_literal` +]() +alias FloatLiteral_negative_infinity = FloatLiteral[ + __mlir_attr.`#kgen.float_literal` +]() +alias FloatLiteral_negative_zero = FloatLiteral[ + __mlir_attr.`#kgen.float_literal` +]() + @value @nonmaterializable(Float64) @register_passable("trivial") -struct FloatLiteral( - Comparable, +struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( ImplicitlyBoolable, Intable, Stringable, Floatable, ): - """Mojo floating point literal type.""" + """Mojo floating point literal type. - alias fp_type = __mlir_type.`!kgen.float_literal` - var value: Self.fp_type - """The underlying storage for the floating point value.""" + Parameters: + value: The underlying infinite precision floating point value. + """ # ===------------------------------------------------------------------===# # Constructors # ===------------------------------------------------------------------===# @always_inline("builtin") - @implicit - fn __init__(out self, value: Self.fp_type): - """Create a FloatLiteral value from a kgen.float_literal value. - - Args: - value: The float value. - """ - self.value = value + fn __init__(out self): + """Create a FloatLiteral for any parameter value.""" + pass @always_inline("builtin") @implicit - fn __init__(out self, value: IntLiteral): + fn __init__( + value: IntLiteral[_], + out result: FloatLiteral[ + __mlir_attr[ + `#kgen> : !kgen.float_literal`, + ] + ], + ): """Convert an IntLiteral to a FloatLiteral value. Args: value: The IntLiteral value. """ - self.value = __mlir_op.`kgen.int_to_float_literal`(value.value) - - alias nan = Self(__mlir_attr.`#kgen.float_literal`) - alias infinity = Self(__mlir_attr.`#kgen.float_literal`) - alias negative_infinity = Self(__mlir_attr.`#kgen.float_literal`) - alias negative_zero = Self(__mlir_attr.`#kgen.float_literal`) + out = __type_of(result)() @always_inline("builtin") fn is_nan(self) -> Bool: @@ -82,7 +92,7 @@ struct FloatLiteral( fn is_neg_zero(self) -> Bool: """Return whether the FloatLiteral is negative zero. - Since `FloatLiteral.negative_zero == 0.0` is True, this provides a way + Since `FloatLiteral_negative_zero == 0.0` is True, this provides a way to check if the FloatLiteral is negative zero. Returns: @@ -117,7 +127,16 @@ struct FloatLiteral( return String(Float64(self)) @always_inline("builtin") - fn __int_literal__(self) -> IntLiteral: + fn __int_literal__( + self, + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Casts the floating point value to an IntLiteral. If there is a fractional component, then the value is truncated towards zero. @@ -127,7 +146,7 @@ struct FloatLiteral( Returns: The value as an integer. """ - return IntLiteral(__mlir_op.`kgen.float_to_int_literal`(self.value)) + return __type_of(result)() @always_inline("builtin") fn __int__(self) -> Int: @@ -173,20 +192,32 @@ struct FloatLiteral( return self.__bool__() @always_inline("builtin") - fn __neg__(self) -> FloatLiteral: + fn __neg__(self, out result: __type_of(self * -1)): """Return the negation of the FloatLiteral value. Returns: The negated FloatLiteral value. """ - return self * Self(-1) + result = __type_of(result)() # ===------------------------------------------------------------------===# # Arithmetic Operators # ===------------------------------------------------------------------===# @always_inline("builtin") - fn __add__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __add__( + self, + rhs: FloatLiteral, + out result: FloatLiteral[ + __mlir_attr[ + `#kgen> : !kgen.float_literal`, + ] + ], + ): """Add two FloatLiterals. Args: @@ -195,12 +226,22 @@ struct FloatLiteral( Returns: The sum of the two values. """ - return __mlir_op.`kgen.float_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) + result = __type_of(result)() @always_inline("builtin") - fn __sub__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __sub__( + self, + rhs: FloatLiteral, + out result: FloatLiteral[ + __mlir_attr[ + `#kgen> : !kgen.float_literal`, + ] + ], + ): """Subtract two FloatLiterals. Args: @@ -209,12 +250,22 @@ struct FloatLiteral( Returns: The difference of the two values. """ - return __mlir_op.`kgen.float_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) + result = __type_of(result)() @always_inline("builtin") - fn __mul__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __mul__( + self, + rhs: FloatLiteral, + out result: FloatLiteral[ + __mlir_attr[ + `#kgen> : !kgen.float_literal`, + ] + ], + ): """Multiply two FloatLiterals. Args: @@ -223,12 +274,22 @@ struct FloatLiteral( Returns: The product of the two values. """ - return __mlir_op.`kgen.float_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) + result = __type_of(result)() @always_inline("builtin") - fn __truediv__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __truediv__( + self, + rhs: FloatLiteral, + out result: FloatLiteral[ + __mlir_attr[ + `#kgen> : !kgen.float_literal`, + ] + ], + ): """Divide two FloatLiterals. Args: @@ -238,12 +299,22 @@ struct FloatLiteral( The quotient of the two values. """ # TODO - Python raises an error on divide by 0.0 or -0.0 - return __mlir_op.`kgen.float_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) + result = __type_of(result)() @always_inline("builtin") - fn __floordiv__(self, rhs: Self) -> Self: + fn __floordiv__( + self, + rhs: FloatLiteral, + out result: FloatLiteral[ + __mlir_attr[ + `#kgen> : !kgen.float_literal`, + ] + ], + ): """Returns self divided by rhs, rounded down to the nearest integer. Args: @@ -253,12 +324,14 @@ struct FloatLiteral( `floor(self / rhs)` value. """ # TODO - Python raises an error on divide by 0.0 or -0.0 - return __mlir_op.`kgen.float_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) + result = __type_of(result)() @always_inline("builtin") - fn __mod__(self, rhs: Self) -> Self: + fn __mod__( + self, + rhs: FloatLiteral, + out result: __type_of(self - (self.__floordiv__(rhs) * rhs)), + ): """Return the remainder of self divided by rhs. Args: @@ -267,22 +340,14 @@ struct FloatLiteral( Returns: The remainder of dividing self by rhs. """ - return self - (self.__floordiv__(rhs) * rhs) + result = __type_of(result)() @always_inline("builtin") - fn __rfloordiv__(self, rhs: Self) -> Self: - """Returns rhs divided by self, rounded down to the nearest integer. - - Args: - rhs: The value to be divided by self. - - Returns: - `floor(rhs / self)` value. - """ - return rhs // self - - @always_inline("builtin") - fn __ceildiv__(self, denominator: Self) -> Self: + fn __ceildiv__( + self, + denominator: FloatLiteral, + out result: __type_of(-(self // -denominator)), + ): """Return the rounded-up result of dividing self by denominator. Args: @@ -291,7 +356,7 @@ struct FloatLiteral( Returns: The ceiling of dividing numerator by denominator. """ - return -(self // -denominator) + result = __type_of(result)() # TODO - maybe __pow__? @@ -300,7 +365,7 @@ struct FloatLiteral( # ===------------------------------------------------------------------===# @always_inline("builtin") - fn __radd__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __radd__(self, rhs: FloatLiteral, out result: __type_of(rhs + self)): """Reversed addition operator. Args: @@ -309,10 +374,10 @@ struct FloatLiteral( Returns: The sum of this and the given value. """ - return rhs + self + result = __type_of(result)() @always_inline("builtin") - fn __rsub__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __rsub__(self, rhs: FloatLiteral, out result: __type_of(rhs - self)): """Reversed subtraction operator. Args: @@ -321,10 +386,10 @@ struct FloatLiteral( Returns: The result of subtracting this from the given value. """ - return rhs - self + result = __type_of(result)() @always_inline("builtin") - fn __rmul__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __rmul__(self, rhs: FloatLiteral, out result: __type_of(rhs * self)): """Reversed multiplication operator. Args: @@ -333,10 +398,42 @@ struct FloatLiteral( Returns: The product of the given number and this. """ - return rhs * self + result = __type_of(result)() + + @always_inline("builtin") + fn __rmod__( + self, + rhs: FloatLiteral, + out result: __type_of(rhs.__mod__(self)), + ): + """Return the remainder of rhs divided by self. + + Args: + rhs: The value to divide on. + + Returns: + The remainder of dividing rhs by self. + """ + result = __type_of(result)() + + @always_inline("builtin") + fn __rfloordiv__( + self, + rhs: FloatLiteral, + out result: __type_of(rhs // self), + ): + """Returns rhs divided by self, rounded down to the nearest integer. + + Args: + rhs: The value to be divided by self. + + Returns: + `floor(rhs / self)` value. + """ + result = __type_of(result)() @always_inline("builtin") - fn __rtruediv__(self, rhs: FloatLiteral) -> FloatLiteral: + fn __rtruediv__(self, rhs: FloatLiteral, out result: __type_of(rhs / self)): """Reversed division. Args: @@ -345,7 +442,7 @@ struct FloatLiteral( Returns: The result of dividing the given value by this. """ - return rhs / self + result = __type_of(result)() # ===------------------------------------------------------------------===# # Comparison Operators diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index c5084fcf39..be65cb1099 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -12,16 +12,14 @@ # ===----------------------------------------------------------------------=== # """Implements the IntLiteral class.""" -from math import Ceilable, CeilDivable, Floorable, Truncable +from math import Ceilable, Floorable, Truncable @value @nonmaterializable(Int) @register_passable("trivial") -struct IntLiteral( +struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Ceilable, - CeilDivable, - Comparable, Floorable, ImplicitlyBoolable, ImplicitlyIntable, @@ -30,21 +28,25 @@ struct IntLiteral( Truncable, ): """This type represents a static integer literal value with - infinite precision. They can't be materialized at runtime and - must be lowered to other integer types (like Int), but allow for - compile-time operations that would overflow on Int and other fixed + infinite precision. This type is a compile-time construct which stores its + value as a parameter. It is typically materialized into other types (like + `Int`) for use at runtime. This compile-time representation allows for + arbitrary precision constants that would overflow on Int and other fixed precision integer types. + + Parameters: + value: The underlying integer value. """ # Fields alias _mlir_type = __mlir_type.`!kgen.int_literal` - var value: Self._mlir_type - """The underlying storage for the integer value.""" - - alias _one = IntLiteral( + alias _zero = IntLiteral[ + __mlir_attr.`#kgen.int_literal<0> : !kgen.int_literal` + ]() + alias _one = IntLiteral[ __mlir_attr.`#kgen.int_literal<1> : !kgen.int_literal` - ) + ]() # ===-------------------------------------------------------------------===# # Life cycle methods @@ -52,26 +54,15 @@ struct IntLiteral( @always_inline("builtin") fn __init__(out self): - """Default constructor.""" - self.value = __mlir_attr.`#kgen.int_literal<0> : !kgen.int_literal` - - @doc_private - @always_inline("builtin") - @implicit - fn __init__(out self, value: __mlir_type.`!kgen.int_literal`): - """Construct IntLiteral from the given mlir !kgen.int_literal value. - - Args: - value: The init value. - """ - self.value = value + """Constructor for any value.""" + pass # ===-------------------------------------------------------------------===# # Operator dunders # ===-------------------------------------------------------------------===# @always_inline("builtin") - fn __lt__(self, rhs: Self) -> Bool: + fn __lt__(self, rhs: IntLiteral[_]) -> Bool: """Compare this IntLiteral to the RHS using LT comparison. Args: @@ -80,12 +71,16 @@ struct IntLiteral( Returns: True if this IntLiteral is less-than the RHS IntLiteral and False otherwise. """ - return __mlir_op.`kgen.int_literal.cmp`[ - pred = __mlir_attr.`#kgen` - ](self.value, rhs.value) + return __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] @always_inline("builtin") - fn __le__(self, rhs: Self) -> Bool: + fn __le__(self, rhs: IntLiteral[_]) -> Bool: """Compare this IntLiteral to the RHS using LE comparison. Args: @@ -95,12 +90,16 @@ struct IntLiteral( True if this IntLiteral is less-or-equal than the RHS IntLiteral and False otherwise. """ - return __mlir_op.`kgen.int_literal.cmp`[ - pred = __mlir_attr.`#kgen` - ](self.value, rhs.value) + return __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] @always_inline("builtin") - fn __eq__(self, rhs: Self) -> Bool: + fn __eq__(self, rhs: IntLiteral[_]) -> Bool: """Compare this IntLiteral to the RHS using EQ comparison. Args: @@ -109,12 +108,16 @@ struct IntLiteral( Returns: True if this IntLiteral is equal to the RHS IntLiteral and False otherwise. """ - return __mlir_op.`kgen.int_literal.cmp`[ - pred = __mlir_attr.`#kgen` - ](self.value, rhs.value) + return __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] @always_inline("builtin") - fn __ne__(self, rhs: Self) -> Bool: + fn __ne__(self, rhs: IntLiteral[_]) -> Bool: """Compare this IntLiteral to the RHS using NE comparison. Args: @@ -123,12 +126,16 @@ struct IntLiteral( Returns: True if this IntLiteral is non-equal to the RHS IntLiteral and False otherwise. """ - return __mlir_op.`kgen.int_literal.cmp`[ - pred = __mlir_attr.`#kgen` - ](self.value, rhs.value) + return __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] @always_inline("builtin") - fn __gt__(self, rhs: Self) -> Bool: + fn __gt__(self, rhs: IntLiteral[_]) -> Bool: """Compare this IntLiteral to the RHS using GT comparison. Args: @@ -137,12 +144,16 @@ struct IntLiteral( Returns: True if this IntLiteral is greater-than the RHS IntLiteral and False otherwise. """ - return __mlir_op.`kgen.int_literal.cmp`[ - pred = __mlir_attr.`#kgen` - ](self.value, rhs.value) + return __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] @always_inline("builtin") - fn __ge__(self, rhs: Self) -> Bool: + fn __ge__(self, rhs: IntLiteral[_]) -> Bool: """Compare this IntLiteral to the RHS using GE comparison. Args: @@ -152,9 +163,13 @@ struct IntLiteral( True if this IntLiteral is greater-or-equal than the RHS IntLiteral and False otherwise. """ - return __mlir_op.`kgen.int_literal.cmp`[ - pred = __mlir_attr.`#kgen` - ](self.value, rhs.value) + return __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] @always_inline("builtin") fn __pos__(self) -> Self: @@ -166,25 +181,37 @@ struct IntLiteral( return self @always_inline("builtin") - fn __neg__(self) -> Self: + fn __neg__(self) -> __type_of(Self._zero - self): """Return -self. Returns: The -self value. """ - return Self() - self + return Self._zero - self @always_inline("builtin") - fn __invert__(self) -> Self: + fn __invert__(self, out result: __type_of(self ^ (Self._zero - Self._one))): """Return ~self. Returns: The ~self value. """ - return self ^ (Self() - Self._one) + result = __type_of(result)() @always_inline("builtin") - fn __add__(self, rhs: Self) -> Self: + fn __add__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self + rhs`. Args: @@ -193,14 +220,22 @@ struct IntLiteral( Returns: `self + rhs` value. """ - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __sub__(self, rhs: Self) -> Self: + fn __sub__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self - rhs`. Args: @@ -209,14 +244,22 @@ struct IntLiteral( Returns: `self - rhs` value. """ - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __mul__(self, rhs: Self) -> Self: + fn __mul__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self * rhs`. Args: @@ -225,16 +268,24 @@ struct IntLiteral( Returns: `self * rhs` value. """ - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() # TODO: implement __pow__ @always_inline("builtin") - fn __floordiv__(self, rhs: Self) -> Self: + fn __floordiv__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self // rhs`. Args: @@ -243,15 +294,22 @@ struct IntLiteral( Returns: `self // rhs` value. """ - # This handles the case where rhs is 0. - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __mod__(self, rhs: Self) -> Self: + fn __mod__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return the remainder of self divided by rhs. Args: @@ -260,15 +318,22 @@ struct IntLiteral( Returns: The remainder of dividing self by rhs. """ - # This handles the case where rhs is 0. - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __lshift__(self, rhs: Self) -> Self: + fn __lshift__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self << rhs`. Args: @@ -277,15 +342,22 @@ struct IntLiteral( Returns: `self << rhs`. """ - # This handles the case where rhs is 0. - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __rshift__(self, rhs: Self) -> Self: + fn __rshift__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self >> rhs`. Args: @@ -294,15 +366,22 @@ struct IntLiteral( Returns: `self >> rhs`. """ - # This handles the case where rhs is 0. - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __and__(self, rhs: Self) -> Self: + fn __and__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self & rhs`. Args: @@ -311,14 +390,22 @@ struct IntLiteral( Returns: `self & rhs`. """ - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __xor__(self, rhs: Self) -> Self: + fn __xor__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self ^ rhs`. Args: @@ -327,14 +414,22 @@ struct IntLiteral( Returns: `self ^ rhs`. """ - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() @always_inline("builtin") - fn __or__(self, rhs: Self) -> Self: + fn __or__( + self, + rhs: IntLiteral[_], + out result: IntLiteral[ + __mlir_attr[ + `#kgen> : !kgen.int_literal`, + ] + ], + ): """Return `self | rhs`. Args: @@ -343,11 +438,7 @@ struct IntLiteral( Returns: `self | rhs`. """ - return Self( - __mlir_op.`kgen.int_literal.binop`[ - oper = __mlir_attr.`#kgen` - ](self.value, rhs.value) - ) + result = __type_of(result)() # ===-------------------------------------------------------------------===# # Trait implementations @@ -360,7 +451,7 @@ struct IntLiteral( Returns: False Bool value if the value is equal to 0 and True otherwise. """ - return self != Self() + return self != Self._zero @always_inline("builtin") fn __as_bool__(self) -> Bool: @@ -437,7 +528,11 @@ struct IntLiteral( return String(Int(self)) @always_inline("builtin") - fn __ceildiv__(self, denominator: Self) -> Self: + fn __ceildiv__( + self, + denominator: IntLiteral, + out result: __type_of(-(self // -denominator)), + ): """Return the rounded-up result of dividing self by denominator. @@ -447,7 +542,7 @@ struct IntLiteral( Returns: The ceiling of dividing numerator by denominator. """ - return -(self // -denominator) + result = __type_of(result)() # ===-------------------------------------------------------------------===# # Methods diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 395dcdce10..8cde264ed5 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -461,9 +461,9 @@ fn atof(str_slice: StringSlice) raises -> Float64: sign = -1 if (str_len - start) >= 3: if StringSlice[buff.origin](ptr=buff + start, length=3) == "nan": - return FloatLiteral.nan + return FloatLiteral_nan if StringSlice[buff.origin](ptr=buff + start, length=3) == "inf": - return FloatLiteral.infinity * sign + return FloatLiteral_infinity * sign # read before dot for pos in range(start, str_len): if ord_0 <= buff[pos] <= ord_9: diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 14d3f91e25..331b4f6397 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -130,7 +130,11 @@ fn ceildiv[T: CeilDivableRaising, //](numerator: T, denominator: T) raises -> T: # NOTE: this overload is needed because IntLiteral promotes to a runtime type # before overload resolution. @always_inline("builtin") -fn ceildiv(numerator: IntLiteral, denominator: IntLiteral) -> IntLiteral: +fn ceildiv( + numerator: IntLiteral, + denominator: IntLiteral, + out result: __type_of(numerator.__ceildiv__(denominator)), +): """Return the rounded-up result of dividing numerator by denominator. Args: @@ -140,7 +144,7 @@ fn ceildiv(numerator: IntLiteral, denominator: IntLiteral) -> IntLiteral: Returns: The ceiling of dividing numerator by denominator. """ - return numerator.__ceildiv__(denominator) + result = __type_of(result)() # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index c6c3da74d2..c0e1d65148 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -43,7 +43,13 @@ from builtin.equality_comparable import EqualityComparable from builtin.error import Error from builtin.file import FileHandle, open from builtin.file_descriptor import FileDescriptor -from builtin.float_literal import FloatLiteral +from builtin.float_literal import ( + FloatLiteral, + FloatLiteral_nan, + FloatLiteral_infinity, + FloatLiteral_negative_infinity, + FloatLiteral_negative_zero, +) from builtin.floatable import Floatable, FloatableRaising, float from builtin.format_int import bin, hex, oct from builtin.identifiable import Identifiable, StringableIdentifiable diff --git a/stdlib/test/builtin/test_float_literal.mojo b/stdlib/test/builtin/test_float_literal.mojo index 915c1f850e..8bc54a61a4 100644 --- a/stdlib/test/builtin/test_float_literal.mojo +++ b/stdlib/test/builtin/test_float_literal.mojo @@ -20,10 +20,10 @@ from testing import ( assert_true, ) -alias nan = FloatLiteral.nan -alias neg_zero = FloatLiteral.negative_zero -alias inf = FloatLiteral.infinity -alias neg_inf = FloatLiteral.negative_infinity +alias nan = FloatLiteral_nan +alias neg_zero = FloatLiteral_negative_zero +alias inf = FloatLiteral_infinity +alias neg_inf = FloatLiteral_negative_infinity def test_division(): diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index 184627c5b7..b4f5d45eda 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -1414,10 +1414,10 @@ def test_reduce_bit_count(): def test_pow(): - alias nan = FloatLiteral.nan - alias neg_zero = FloatLiteral.negative_zero - alias inf = FloatLiteral.infinity - alias neg_inf = FloatLiteral.negative_infinity + alias nan = FloatLiteral_nan + alias neg_zero = FloatLiteral_negative_zero + alias inf = FloatLiteral_infinity + alias neg_inf = FloatLiteral_negative_infinity # Float32 tests alias F32x4 = SIMD[DType.float32, 4] diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 26d14bdeab..84efce56cf 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -472,10 +472,10 @@ def test_atof(): assert_equal(0.78, atof(" .78 ")) assert_equal(121234.0, atof(" 121234. ")) assert_equal(985031234.0, atof(" 985031234.F ")) - assert_equal(FloatLiteral.negative_zero, atof("-0")) - assert_equal(FloatLiteral.nan, atof(" nan")) - assert_equal(FloatLiteral.infinity, atof(" inf ")) - assert_equal(FloatLiteral.negative_infinity, atof("-inf ")) + assert_equal(FloatLiteral_negative_zero, atof("-0")) + assert_equal(FloatLiteral_nan, atof(" nan")) + assert_equal(FloatLiteral_infinity, atof(" inf ")) + assert_equal(FloatLiteral_negative_infinity, atof("-inf ")) # Negative cases with assert_raises(contains="String is not convertible to float: ''"): From 52b0028a507f5f9f0778982e46d8090658ecbbf7 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 24 Feb 2025 08:40:02 -0800 Subject: [PATCH 453/509] [mojo-lang] Expand support for llvm intrinsics at comptime This enables us to call things like isnan that returns a vector of bools. MODULAR_ORIG_COMMIT_REV_ID: 849747e0b50ceb913527d4cf36188e3845601cb8 --- stdlib/test/builtin/test_math.mojo | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index 0667b86cc5..1a3a497fea 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -13,6 +13,7 @@ # RUN: %mojo %s from testing import assert_equal +from utils.numerics import isnan def test_abs(): @@ -122,6 +123,15 @@ def test_pow(): assert_equal(pow(I(0, 1, 2, 3), Int(2)), I(0, 1, 4, 9)) +def test_isnan(): + # Check that we can run llvm intrinsics returning bool at comptime. + alias x1 = isnan(SIMD[DType.float32, 4](SIMD[DType.float64, 4](1.0))) + assert_equal(x1, False) + + alias x2 = isnan(SIMD[DType.float32, 4](FloatLiteral_nan)) + assert_equal(x2, True) + + def main(): test_abs() test_divmod() @@ -129,3 +139,4 @@ def main(): test_min() test_round() test_pow() + test_isnan() From 8808bd2d7387c67505c321773f20507b8924399b Mon Sep 17 00:00:00 2001 From: lydiandy Date: Mon, 24 Feb 2025 12:54:29 -0600 Subject: [PATCH 454/509] [External] [docs] Fix some errors in doc (#56549) [External] [docs] Fix some errors in doc Fix a few errors in the doc manual. Co-authored-by: lydiandy Closes modularml/mojo#4012 MODULAR_ORIG_COMMIT_REV_ID: b546bbba3b5e5c964d133d2e38f2250bf9993476 --- docs/manual/basics.mdx | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/docs/manual/basics.mdx b/docs/manual/basics.mdx index b62e7cbf8d..4020b3eb96 100644 --- a/docs/manual/basics.mdx +++ b/docs/manual/basics.mdx @@ -255,7 +255,7 @@ struct MyPair: self.first = first self.second = second - fn __copyinit__(out self, existing other): + fn __copyinit__(out self, existing: Self): self.first = existing.first self.second = existing.second @@ -393,7 +393,7 @@ This function has one parameter of type `Int` and one argument of type argument: ```mojo -defining call_repeat(): +def call_repeat(): repeat[3]("Hello") # Prints "Hello" 3 times ``` @@ -416,8 +416,7 @@ For more detail on parameters, see the section on ## Python integration -Mojo supports the ability to import -Python modules as-is, so you can leverage existing Python code right away. +Mojo supports the ability to import Python modules as-is, so you can leverage existing Python code right away. For example, here's how you can import and use NumPy: @@ -444,8 +443,7 @@ Hopefully this page has given you enough information to start experimenting with Mojo, but this is only touching the surface of what's available in Mojo. If you're in the mood to read more, continue through each page of this -Mojo Manual—the next page from -here is [Functions](/mojo/manual/functions). +Mojo Manual—the next page from here is [Functions](/mojo/manual/functions). Otherwise, here are some other resources to check out: From 49d98e6d375c0a8c04c2bc394250e8bedf861f2b Mon Sep 17 00:00:00 2001 From: Rasool Sharifi Date: Mon, 24 Feb 2025 14:06:11 -0500 Subject: [PATCH 455/509] [stdlib] Fix the broken test for FP8 ldmatrix instruction fp8 accumulation can be done in fp16 or fp32. For now we use fp32 for better accuracy MODULAR_ORIG_COMMIT_REV_ID: 61f6e0371415a51031e0d2e621995d3ea3d3479d --- stdlib/src/utils/numerics.mojo | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/stdlib/src/utils/numerics.mojo b/stdlib/src/utils/numerics.mojo index ef00fbbc79..b79d12eb11 100644 --- a/stdlib/src/utils/numerics.mojo +++ b/stdlib/src/utils/numerics.mojo @@ -1055,7 +1055,10 @@ fn get_accum_type[ @parameter if type.is_float8(): - return DType.float16 + if preferred_accum_type is DType.float32: + return preferred_accum_type + else: + return DType.float16 elif type is DType.bfloat16: return DType.float32 elif type is DType.float16: From fc837b87cb5c1d0d3a33959606b9b6ff60f2559a Mon Sep 17 00:00:00 2001 From: Connor Gray Date: Mon, 24 Feb 2025 15:13:42 -0600 Subject: [PATCH 456/509] [stdlib] feat: Enforce codepoint boundary checks in `StringSlice.__getitem__(Slice)` MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This eliminates an easy and surprising way that invalid UTF-8 sequences could be stored in a `StringSlice`. Prior to this, it was possible to trivially generate `StringSlice` values that did not contain valid UTF-8, by first constructing a valid `StringSlice` containing any multi-byte codepoint sequences, and then slicing part way through that sequence: ```mojo var str = StringSlice("👍") # Valid assert_equal(len(str), 4) # 4 bytes assert_equal(len(str.chars()), 1) # 1 codepoint var uh_oh = str[1:3] # INVALID UTF-8 ``` You would start with the string: ┏━━━━━━━━━━━━━━━┓ ┃ 👍 ┃ String ┣━━━━━━━━━━━━━━━┫ ┃ 👍 ┃ Codepoint Characters ┣━━━━━━━━━━━━━━━┫ ┃ 128077 ┃ Codepoints ┣━━━┳━━━┳━━━┳━━━┫ ┃240┃159┃145┃141┃ Bytes ┗━━━┻━━━┻━━━┻━━━┛ 0 1 2 3 and end up with the (invalid) string data: ┏━━━━━━━┓ ┃ ??? ┃ String ┣━━━━━━━┫ ┃ ??? ┃ Codepoint Characters ┣━━━━━━━┫ ┃ ??? ┃ Codepoints ┣━━━┳━━━┫ ┃159┃145┃ Bytes ┗━━━┻━━━┛ 0 1 which does not encode any valid UTF-8 codepoints, due to the nature of UTF-8 meaning that byte value 159 (0b10011111) does not start a UTF-8 codepoint sequence. Having an invalid StringSlice could lead to undefined behavior and out of bounds reads in code that assumes that StringSlice MUST contain valid UTF-8 and uses more efficient decoding logic that omits checks for malformed UTF-8. MODULAR_ORIG_COMMIT_REV_ID: d5377c851b7a366e4178687def10ddbefe2fcdbf --- docs/changelog.md | 44 +++++++ .../src/collections/string/string_slice.mojo | 18 +++ .../collections/string/test_string_slice.mojo | 114 +++++++++++++++++- 3 files changed, 171 insertions(+), 5 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 20576ca849..3d93f43861 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -67,6 +67,50 @@ what we publish. - Added a `StringSlice.is_codepoint_boundary()` method for querying if a given byte index is a boundary between encoded UTF-8 codepoints. +- `StringSlice.__getitem__(Slice)` will now raise an error if the provided slice + start and end positions do not fall on a valid codepoint boundary. This + prevents construction of malformed `StringSlice` values, which could lead to + memory unsafety or undefined behavior. For example, given a string containing + multi-byte encoded data, like: + + ```mojo + var str_slice = "Hi👋!" + ``` + + and whose in-memory and decoded data looks like: + + ```text + ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ + ┃ Hi👋! ┃ String + ┣━━┳━━━┳━━━━━━━━━━━━━━━┳━━┫ + ┃H ┃ i ┃ 👋 ┃! ┃ Codepoint Characters + ┣━━╋━━━╋━━━━━━━━━━━━━━━╋━━┫ + ┃72┃105┃ 128075 ┃33┃ Codepoints + ┣━━╋━━━╋━━━┳━━━┳━━━┳━━━╋━━┫ + ┃72┃105┃240┃159┃145┃139┃33┃ Bytes + ┗━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━┛ + 0 1 2 3 4 5 6 + ``` + + attempting to slice bytes `[3-5)` with `str_slice[3:5]` would previously + erroenously produce a malformed `StringSlice` as output that did not correctly + decode to anything: + + ```text + ┏━━━━━━━┓ + ┃ ??? ┃ + ┣━━━━━━━┫ + ┃ ??? ┃ + ┣━━━━━━━┫ + ┃ ??? ┃ + ┣━━━┳━━━┫ + ┃159┃145┃ + ┗━━━┻━━━┛ + ``` + + The same statement will now raise an error informing the user their indices + are invalid. + - Added an iterator to `LinkedList` ([PR #4005](https://github.com/modular/mojo/pull/4005)) - `LinkedList.__iter__()` to create a forward iterator. - `LinkedList.__reversed__()` for a backward iterator. diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index cd97b8ed95..60721c8e6e 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -743,6 +743,10 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( Returns: A new StringSlice containing the substring at the specified positions. + + Raises: This function will raise if the specified slice start or end + position are outside the bounds of the string, or if they do not + both fall on codepoint boundaries. """ var step: Int var start: Int @@ -752,6 +756,20 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( if step != 1: raise Error("Slice must be within bounds and step must be 1") + if not self.is_codepoint_boundary(start): + var msg = String.format( + "String `Slice` start byte {} must fall on codepoint boundary.", + start, + ) + raise Error(msg^) + + if not self.is_codepoint_boundary(end): + var msg = String.format( + "String `Slice` end byte {} must fall on codepoint boundary.", + end, + ) + raise Error(msg^) + return Self(unsafe_from_utf8=self._slice[span]) # ===------------------------------------------------------------------===# diff --git a/stdlib/test/collections/string/test_string_slice.mojo b/stdlib/test/collections/string/test_string_slice.mojo index a5cd1e2e44..97a4df03bd 100644 --- a/stdlib/test/collections/string/test_string_slice.mojo +++ b/stdlib/test/collections/string/test_string_slice.mojo @@ -22,6 +22,37 @@ from sys.info import alignof, sizeof from memory import Span, UnsafePointer from testing import assert_equal, assert_false, assert_raises, assert_true +# ===----------------------------------------------------------------------=== # +# Reusable testing data +# ===----------------------------------------------------------------------=== # + +alias EVERY_CODEPOINT_LENGTH_STR = StringSlice("߷കൈ🔄!") +"""A string that contains at least one of 1-, 2-, 3-, and 4-byte UTF-8 +sequences. + +Visualized as: + +```text +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ ߷കൈ🔄! ┃ +┣━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━┫ +┃ ߷ ┃ ക ┃ ൈ ┃ 🔄 ┃! ┃ +┣━━━━━━━╋━━━━━━━━━━━╋━━━━━━━━━━━╋━━━━━━━━━━━━━━━╋━━┫ +┃ 2039 ┃ 3349 ┃ 3400 ┃ 128260 ┃33┃ +┣━━━┳━━━╋━━━┳━━━┳━━━╋━━━┳━━━┳━━━╋━━━┳━━━┳━━━┳━━━╋━━┫ +┃223┃183┃224┃180┃149┃224┃181┃136┃240┃159┃148┃132┃33┃ +┗━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━┛ + 0 1 2 3 4 5 6 7 8 9 10 11 12 +``` + +For further visualization and analysis involving this sequence, see: +. +""" + +# ===----------------------------------------------------------------------=== # +# Tests +# ===----------------------------------------------------------------------=== # + fn test_string_slice_layout() raises: # Test that the layout of `StringSlice` is the same as `llvm::StringRef`. @@ -115,7 +146,7 @@ fn test_string_byte_span() raises: assert_equal(Int(sub5.unsafe_ptr()) - Int(sub4.unsafe_ptr()), 2) # ---------------------------------- - # Test invalid slicing + # Test out of range slicing # ---------------------------------- # TODO: Improve error reporting for invalid slice bounds. @@ -138,6 +169,81 @@ fn test_string_byte_span() raises: # String("Slice start is out of bounds"), # ) + # -------------------------------------------------------- + # Test that malformed partial slicing of codepoints raises + # -------------------------------------------------------- + + # These test what happens if you try to subslice a string in a way that + # would leave the byte contents of the string containing partial encoded + # codepoint sequences, invalid UTF-8. Consider a string with the following + # content, containing both 1-byte and a 4-byte UTF-8 sequence: + # + # ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓ + # ┃ Hi👋! ┃ String + # ┣━━┳━━━┳━━━━━━━━━━━━━━━┳━━┫ + # ┃H ┃ i ┃ 👋 ┃! ┃ Codepoint Characters + # ┣━━╋━━━╋━━━━━━━━━━━━━━━╋━━┫ + # ┃72┃105┃ 128075 ┃33┃ Codepoints + # ┣━━╋━━━╋━━━┳━━━┳━━━┳━━━╋━━┫ + # ┃72┃105┃240┃159┃145┃139┃33┃ Bytes + # ┗━━┻━━━┻━━━┻━━━┻━━━┻━━━┻━━┛ + # 0 1 2 3 4 5 6 + var unicode_str1 = StringSlice("Hi👋!") + + # Test slicing 0:{0–7} + assert_equal(unicode_str1[0:0], "") + assert_equal(unicode_str1[0:1], "H") + assert_equal(unicode_str1[0:2], "Hi") + with assert_raises( + contains="String `Slice` end byte 3 must fall on codepoint boundary." + ): + _ = unicode_str1[0:3] + with assert_raises( + contains="String `Slice` end byte 4 must fall on codepoint boundary." + ): + _ = unicode_str1[0:4] + with assert_raises( + contains="String `Slice` end byte 5 must fall on codepoint boundary." + ): + _ = unicode_str1[0:5] + assert_equal(unicode_str1[0:6], "Hi👋") + assert_equal(unicode_str1[0:7], "Hi👋!") + + # ------------------------------------------------------------------- + # Test that slicing through combining codepoint graphemes is allowed + # ------------------------------------------------------------------- + + # The "ö" is a user-perceived character (grapheme) that is composed of two + # codepoints. This test tests that we can use string slicing to divide that + # grapheme into constituent codepoints. + # + # ┏━━━━━━━━━━━━━━━┓ + # ┃ yö ┃ String + # ┣━━━┳━━━┳━━━━━━━┫ + # ┃ y ┃ o ┃ ̈ ┃ Codepoint Characters + # ┣━━━╋━━━╋━━━━━━━┫ + # ┃121┃111┃ 776 ┃ Codepoints + # ┣━━━╋━━━╋━━━┳━━━┫ + # ┃121┃111┃204┃136┃ Bytes + # ┗━━━┻━━━┻━━━┻━━━┛ + # 0 1 2 3 + var unicode_str2 = StringSlice("yö") + + assert_equal(unicode_str2[0:1], "y") + assert_equal(unicode_str2[0:2], "yo") + with assert_raises( + contains="String `Slice` end byte 3 must fall on codepoint boundary." + ): + _ = unicode_str2[0:3] + assert_equal(unicode_str2[0:4], unicode_str2) + with assert_raises( + contains="String `Slice` end byte 3 must fall on codepoint boundary." + ): + _ = unicode_str2[2:3] + # NOTE: This renders weirdly, but is a single-codepoint string containing + # . + assert_equal(unicode_str2[2:4], "̈") + fn test_heap_string_from_string_slice() raises: alias string_lit: StringLiteral = "Hello" @@ -236,7 +342,7 @@ fn test_slice_char_length() raises: assert_equal(s1.char_length(), 3) # This string contains 1-, 2-, 3-, and 4-byte codepoint sequences. - var s2 = StringSlice("߷കൈ🔄!") + var s2 = EVERY_CODEPOINT_LENGTH_STR assert_equal(s2.byte_length(), 13) assert_equal(s2.char_length(), 5) @@ -1065,9 +1171,7 @@ def test_chars_iter(): # A piece of text containing, 1-byte, 2-byte, 3-byte, and 4-byte codepoint # sequences. - # For a visualization of this sequence, see: - # https://connorgray.com/ephemera/project-log#2025-01-13 - var s3 = StringSlice("߷കൈ🔄!") + var s3 = EVERY_CODEPOINT_LENGTH_STR assert_equal(s3.byte_length(), 13) assert_equal(s3.char_length(), 5) var s3_iter = s3.codepoints() From 3fee528a0138de9fc77ea7e6260da9bf79f0d992 Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 25 Feb 2025 07:00:46 +0000 Subject: [PATCH 457/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022505 --- examples/life/magic.lock | 225 +++++++++++++++++----------------- examples/magic.lock | 145 +++++++++++----------- examples/operators/magic.lock | 145 +++++++++++----------- examples/testing/magic.lock | 145 +++++++++++----------- magic.lock | 145 +++++++++++----------- 5 files changed, 410 insertions(+), 395 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 847eb71184..0aaa0e3585 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -45,7 +45,7 @@ environments: - 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sha256: 5ba32fa10c188a376a3b1673d59626d357231028c2ba71a4b06688998f01a5bb - md5: 6432cbc5b412f8351358b06534a68122 + sha256: 098da4969faff354c251849fc353acfab1b535db5fb19282a0102b23964c1d15 + md5: a717b09355552fc825384d43f885d90a depends: - - max-core ==25.2.0.dev2025022405 release + - max-core ==25.2.0.dev2025022505 release - python >=3.9,<3.13 - jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22984 - timestamp: 1740374225756 + size: 22987 + timestamp: 1740460553945 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f @@ -1857,6 +1857,7 @@ packages: arch: x86_64 platform: linux license: GPL-3.0-only + license_family: GPL size: 282480 timestamp: 1740379431762 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.2-h8382b9d_2.conda @@ -1868,6 +1869,7 @@ packages: arch: aarch64 platform: linux license: GPL-3.0-only + license_family: GPL size: 291806 timestamp: 1740380591358 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.2-h1d1bf99_2.conda @@ -1878,6 +1880,7 @@ packages: arch: arm64 platform: osx license: GPL-3.0-only + license_family: GPL size: 252359 timestamp: 1740379663071 - conda: https://conda.anaconda.org/conda-forge/linux-64/sentencepiece-0.2.0-hc8f76dd_10.conda From 236661b3b6414c07cc23972f74561f2449869372 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Mon, 24 Feb 2025 22:10:24 -0800 Subject: [PATCH 458/509] [mojo-stdlib] Revert: Rewrite IntLiteral and FloatLiteral to use dependent types. This reverts modularml/modular#56515. The patch needs a broader design doc to explore the topic, which I'll put together and discuss before discussing landing it. MODULAR_ORIG_COMMIT_REV_ID: 721594c6408fbc2ee9feb38f8b1ed96a44c04c23 --- docs/changelog.md | 6 - stdlib/src/builtin/float_literal.mojo | 237 ++++--------- stdlib/src/builtin/int_literal.mojo | 335 +++++++----------- stdlib/src/collections/string/string.mojo | 4 +- stdlib/src/math/math.mojo | 8 +- stdlib/src/prelude/__init__.mojo | 8 +- stdlib/test/builtin/test_float_literal.mojo | 8 +- stdlib/test/builtin/test_math.mojo | 2 +- stdlib/test/builtin/test_simd.mojo | 8 +- .../test/collections/string/test_string.mojo | 8 +- 10 files changed, 208 insertions(+), 416 deletions(-) diff --git a/docs/changelog.md b/docs/changelog.md index 3d93f43861..a055d0c791 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -26,12 +26,6 @@ what we publish. ### Standard library changes -- The design of the `IntLiteral` and `FloatLiteral` types has been changed to - maintain their compile-time-only value as a parameter instead of a stored - field. This correctly models that infinite precision literals are not - representable at runtime, and eliminates a number of bugs hit in corner cases. - This is made possible by enhanced dependent type support in the compiler. - - The `Buffer` struct has been removed in favor of `Span` and `NDBuffer`. - A new `IntervalTree` data structure has been added to the standard library. diff --git a/stdlib/src/builtin/float_literal.mojo b/stdlib/src/builtin/float_literal.mojo index c70380d145..569e204cb6 100644 --- a/stdlib/src/builtin/float_literal.mojo +++ b/stdlib/src/builtin/float_literal.mojo @@ -19,61 +19,51 @@ These are Mojo built-ins, so you don't need to import them. # FloatLiteral # ===-----------------------------------------------------------------------===# -# FIXME: These really should get a better name. -alias FloatLiteral_nan = FloatLiteral[__mlir_attr.`#kgen.float_literal`]() -alias FloatLiteral_infinity = FloatLiteral[ - __mlir_attr.`#kgen.float_literal` -]() -alias FloatLiteral_negative_infinity = FloatLiteral[ - __mlir_attr.`#kgen.float_literal` -]() -alias FloatLiteral_negative_zero = FloatLiteral[ - __mlir_attr.`#kgen.float_literal` -]() - @value @nonmaterializable(Float64) @register_passable("trivial") -struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( +struct FloatLiteral( + Comparable, ImplicitlyBoolable, Intable, Stringable, Floatable, ): - """Mojo floating point literal type. + """Mojo floating point literal type.""" - Parameters: - value: The underlying infinite precision floating point value. - """ + alias fp_type = __mlir_type.`!kgen.float_literal` + var value: Self.fp_type + """The underlying storage for the floating point value.""" # ===------------------------------------------------------------------===# # Constructors # ===------------------------------------------------------------------===# @always_inline("builtin") - fn __init__(out self): - """Create a FloatLiteral for any parameter value.""" - pass + @implicit + fn __init__(out self, value: Self.fp_type): + """Create a FloatLiteral value from a kgen.float_literal value. + + Args: + value: The float value. + """ + self.value = value @always_inline("builtin") @implicit - fn __init__( - value: IntLiteral[_], - out result: FloatLiteral[ - __mlir_attr[ - `#kgen> : !kgen.float_literal`, - ] - ], - ): + fn __init__(out self, value: IntLiteral): """Convert an IntLiteral to a FloatLiteral value. Args: value: The IntLiteral value. """ - out = __type_of(result)() + self.value = __mlir_op.`kgen.int_to_float_literal`(value.value) + + alias nan = Self(__mlir_attr.`#kgen.float_literal`) + alias infinity = Self(__mlir_attr.`#kgen.float_literal`) + alias negative_infinity = Self(__mlir_attr.`#kgen.float_literal`) + alias negative_zero = Self(__mlir_attr.`#kgen.float_literal`) @always_inline("builtin") fn is_nan(self) -> Bool: @@ -92,7 +82,7 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( fn is_neg_zero(self) -> Bool: """Return whether the FloatLiteral is negative zero. - Since `FloatLiteral_negative_zero == 0.0` is True, this provides a way + Since `FloatLiteral.negative_zero == 0.0` is True, this provides a way to check if the FloatLiteral is negative zero. Returns: @@ -127,16 +117,7 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( return String(Float64(self)) @always_inline("builtin") - fn __int_literal__( - self, - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __int_literal__(self) -> IntLiteral: """Casts the floating point value to an IntLiteral. If there is a fractional component, then the value is truncated towards zero. @@ -146,7 +127,7 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The value as an integer. """ - return __type_of(result)() + return IntLiteral(__mlir_op.`kgen.float_to_int_literal`(self.value)) @always_inline("builtin") fn __int__(self) -> Int: @@ -192,32 +173,20 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( return self.__bool__() @always_inline("builtin") - fn __neg__(self, out result: __type_of(self * -1)): + fn __neg__(self) -> FloatLiteral: """Return the negation of the FloatLiteral value. Returns: The negated FloatLiteral value. """ - result = __type_of(result)() + return self * Self(-1) # ===------------------------------------------------------------------===# # Arithmetic Operators # ===------------------------------------------------------------------===# @always_inline("builtin") - fn __add__( - self, - rhs: FloatLiteral, - out result: FloatLiteral[ - __mlir_attr[ - `#kgen> : !kgen.float_literal`, - ] - ], - ): + fn __add__(self, rhs: FloatLiteral) -> FloatLiteral: """Add two FloatLiterals. Args: @@ -226,22 +195,12 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The sum of the two values. """ - result = __type_of(result)() + return __mlir_op.`kgen.float_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __sub__( - self, - rhs: FloatLiteral, - out result: FloatLiteral[ - __mlir_attr[ - `#kgen> : !kgen.float_literal`, - ] - ], - ): + fn __sub__(self, rhs: FloatLiteral) -> FloatLiteral: """Subtract two FloatLiterals. Args: @@ -250,22 +209,12 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The difference of the two values. """ - result = __type_of(result)() + return __mlir_op.`kgen.float_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __mul__( - self, - rhs: FloatLiteral, - out result: FloatLiteral[ - __mlir_attr[ - `#kgen> : !kgen.float_literal`, - ] - ], - ): + fn __mul__(self, rhs: FloatLiteral) -> FloatLiteral: """Multiply two FloatLiterals. Args: @@ -274,22 +223,12 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The product of the two values. """ - result = __type_of(result)() + return __mlir_op.`kgen.float_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __truediv__( - self, - rhs: FloatLiteral, - out result: FloatLiteral[ - __mlir_attr[ - `#kgen> : !kgen.float_literal`, - ] - ], - ): + fn __truediv__(self, rhs: FloatLiteral) -> FloatLiteral: """Divide two FloatLiterals. Args: @@ -299,22 +238,12 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( The quotient of the two values. """ # TODO - Python raises an error on divide by 0.0 or -0.0 - result = __type_of(result)() + return __mlir_op.`kgen.float_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __floordiv__( - self, - rhs: FloatLiteral, - out result: FloatLiteral[ - __mlir_attr[ - `#kgen> : !kgen.float_literal`, - ] - ], - ): + fn __floordiv__(self, rhs: Self) -> Self: """Returns self divided by rhs, rounded down to the nearest integer. Args: @@ -324,14 +253,12 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( `floor(self / rhs)` value. """ # TODO - Python raises an error on divide by 0.0 or -0.0 - result = __type_of(result)() + return __mlir_op.`kgen.float_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __mod__( - self, - rhs: FloatLiteral, - out result: __type_of(self - (self.__floordiv__(rhs) * rhs)), - ): + fn __mod__(self, rhs: Self) -> Self: """Return the remainder of self divided by rhs. Args: @@ -340,14 +267,22 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The remainder of dividing self by rhs. """ - result = __type_of(result)() + return self - (self.__floordiv__(rhs) * rhs) @always_inline("builtin") - fn __ceildiv__( - self, - denominator: FloatLiteral, - out result: __type_of(-(self // -denominator)), - ): + fn __rfloordiv__(self, rhs: Self) -> Self: + """Returns rhs divided by self, rounded down to the nearest integer. + + Args: + rhs: The value to be divided by self. + + Returns: + `floor(rhs / self)` value. + """ + return rhs // self + + @always_inline("builtin") + fn __ceildiv__(self, denominator: Self) -> Self: """Return the rounded-up result of dividing self by denominator. Args: @@ -356,7 +291,7 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The ceiling of dividing numerator by denominator. """ - result = __type_of(result)() + return -(self // -denominator) # TODO - maybe __pow__? @@ -365,7 +300,7 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( # ===------------------------------------------------------------------===# @always_inline("builtin") - fn __radd__(self, rhs: FloatLiteral, out result: __type_of(rhs + self)): + fn __radd__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed addition operator. Args: @@ -374,10 +309,10 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The sum of this and the given value. """ - result = __type_of(result)() + return rhs + self @always_inline("builtin") - fn __rsub__(self, rhs: FloatLiteral, out result: __type_of(rhs - self)): + fn __rsub__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed subtraction operator. Args: @@ -386,10 +321,10 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The result of subtracting this from the given value. """ - result = __type_of(result)() + return rhs - self @always_inline("builtin") - fn __rmul__(self, rhs: FloatLiteral, out result: __type_of(rhs * self)): + fn __rmul__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed multiplication operator. Args: @@ -398,42 +333,10 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The product of the given number and this. """ - result = __type_of(result)() - - @always_inline("builtin") - fn __rmod__( - self, - rhs: FloatLiteral, - out result: __type_of(rhs.__mod__(self)), - ): - """Return the remainder of rhs divided by self. - - Args: - rhs: The value to divide on. - - Returns: - The remainder of dividing rhs by self. - """ - result = __type_of(result)() - - @always_inline("builtin") - fn __rfloordiv__( - self, - rhs: FloatLiteral, - out result: __type_of(rhs // self), - ): - """Returns rhs divided by self, rounded down to the nearest integer. - - Args: - rhs: The value to be divided by self. - - Returns: - `floor(rhs / self)` value. - """ - result = __type_of(result)() + return rhs * self @always_inline("builtin") - fn __rtruediv__(self, rhs: FloatLiteral, out result: __type_of(rhs / self)): + fn __rtruediv__(self, rhs: FloatLiteral) -> FloatLiteral: """Reversed division. Args: @@ -442,7 +345,7 @@ struct FloatLiteral[value: __mlir_type.`!kgen.float_literal`]( Returns: The result of dividing the given value by this. """ - result = __type_of(result)() + return rhs / self # ===------------------------------------------------------------------===# # Comparison Operators diff --git a/stdlib/src/builtin/int_literal.mojo b/stdlib/src/builtin/int_literal.mojo index be65cb1099..c5084fcf39 100644 --- a/stdlib/src/builtin/int_literal.mojo +++ b/stdlib/src/builtin/int_literal.mojo @@ -12,14 +12,16 @@ # ===----------------------------------------------------------------------=== # """Implements the IntLiteral class.""" -from math import Ceilable, Floorable, Truncable +from math import Ceilable, CeilDivable, Floorable, Truncable @value @nonmaterializable(Int) @register_passable("trivial") -struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( +struct IntLiteral( Ceilable, + CeilDivable, + Comparable, Floorable, ImplicitlyBoolable, ImplicitlyIntable, @@ -28,25 +30,21 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Truncable, ): """This type represents a static integer literal value with - infinite precision. This type is a compile-time construct which stores its - value as a parameter. It is typically materialized into other types (like - `Int`) for use at runtime. This compile-time representation allows for - arbitrary precision constants that would overflow on Int and other fixed + infinite precision. They can't be materialized at runtime and + must be lowered to other integer types (like Int), but allow for + compile-time operations that would overflow on Int and other fixed precision integer types. - - Parameters: - value: The underlying integer value. """ # Fields alias _mlir_type = __mlir_type.`!kgen.int_literal` - alias _zero = IntLiteral[ - __mlir_attr.`#kgen.int_literal<0> : !kgen.int_literal` - ]() - alias _one = IntLiteral[ + var value: Self._mlir_type + """The underlying storage for the integer value.""" + + alias _one = IntLiteral( __mlir_attr.`#kgen.int_literal<1> : !kgen.int_literal` - ]() + ) # ===-------------------------------------------------------------------===# # Life cycle methods @@ -54,15 +52,26 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( @always_inline("builtin") fn __init__(out self): - """Constructor for any value.""" - pass + """Default constructor.""" + self.value = __mlir_attr.`#kgen.int_literal<0> : !kgen.int_literal` + + @doc_private + @always_inline("builtin") + @implicit + fn __init__(out self, value: __mlir_type.`!kgen.int_literal`): + """Construct IntLiteral from the given mlir !kgen.int_literal value. + + Args: + value: The init value. + """ + self.value = value # ===-------------------------------------------------------------------===# # Operator dunders # ===-------------------------------------------------------------------===# @always_inline("builtin") - fn __lt__(self, rhs: IntLiteral[_]) -> Bool: + fn __lt__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using LT comparison. Args: @@ -71,16 +80,12 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: True if this IntLiteral is less-than the RHS IntLiteral and False otherwise. """ - return __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] + return __mlir_op.`kgen.int_literal.cmp`[ + pred = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __le__(self, rhs: IntLiteral[_]) -> Bool: + fn __le__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using LE comparison. Args: @@ -90,16 +95,12 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( True if this IntLiteral is less-or-equal than the RHS IntLiteral and False otherwise. """ - return __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] + return __mlir_op.`kgen.int_literal.cmp`[ + pred = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __eq__(self, rhs: IntLiteral[_]) -> Bool: + fn __eq__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using EQ comparison. Args: @@ -108,16 +109,12 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: True if this IntLiteral is equal to the RHS IntLiteral and False otherwise. """ - return __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] + return __mlir_op.`kgen.int_literal.cmp`[ + pred = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __ne__(self, rhs: IntLiteral[_]) -> Bool: + fn __ne__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using NE comparison. Args: @@ -126,16 +123,12 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: True if this IntLiteral is non-equal to the RHS IntLiteral and False otherwise. """ - return __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] + return __mlir_op.`kgen.int_literal.cmp`[ + pred = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __gt__(self, rhs: IntLiteral[_]) -> Bool: + fn __gt__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using GT comparison. Args: @@ -144,16 +137,12 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: True if this IntLiteral is greater-than the RHS IntLiteral and False otherwise. """ - return __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] + return __mlir_op.`kgen.int_literal.cmp`[ + pred = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") - fn __ge__(self, rhs: IntLiteral[_]) -> Bool: + fn __ge__(self, rhs: Self) -> Bool: """Compare this IntLiteral to the RHS using GE comparison. Args: @@ -163,13 +152,9 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( True if this IntLiteral is greater-or-equal than the RHS IntLiteral and False otherwise. """ - return __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] + return __mlir_op.`kgen.int_literal.cmp`[ + pred = __mlir_attr.`#kgen` + ](self.value, rhs.value) @always_inline("builtin") fn __pos__(self) -> Self: @@ -181,37 +166,25 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( return self @always_inline("builtin") - fn __neg__(self) -> __type_of(Self._zero - self): + fn __neg__(self) -> Self: """Return -self. Returns: The -self value. """ - return Self._zero - self + return Self() - self @always_inline("builtin") - fn __invert__(self, out result: __type_of(self ^ (Self._zero - Self._one))): + fn __invert__(self) -> Self: """Return ~self. Returns: The ~self value. """ - result = __type_of(result)() + return self ^ (Self() - Self._one) @always_inline("builtin") - fn __add__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __add__(self, rhs: Self) -> Self: """Return `self + rhs`. Args: @@ -220,22 +193,14 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self + rhs` value. """ - result = __type_of(result)() + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __sub__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __sub__(self, rhs: Self) -> Self: """Return `self - rhs`. Args: @@ -244,22 +209,14 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self - rhs` value. """ - result = __type_of(result)() + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __mul__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __mul__(self, rhs: Self) -> Self: """Return `self * rhs`. Args: @@ -268,24 +225,16 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self * rhs` value. """ - result = __type_of(result)() + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) # TODO: implement __pow__ @always_inline("builtin") - fn __floordiv__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __floordiv__(self, rhs: Self) -> Self: """Return `self // rhs`. Args: @@ -294,22 +243,15 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self // rhs` value. """ - result = __type_of(result)() + # This handles the case where rhs is 0. + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __mod__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __mod__(self, rhs: Self) -> Self: """Return the remainder of self divided by rhs. Args: @@ -318,22 +260,15 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: The remainder of dividing self by rhs. """ - result = __type_of(result)() + # This handles the case where rhs is 0. + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __lshift__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __lshift__(self, rhs: Self) -> Self: """Return `self << rhs`. Args: @@ -342,22 +277,15 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self << rhs`. """ - result = __type_of(result)() + # This handles the case where rhs is 0. + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __rshift__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __rshift__(self, rhs: Self) -> Self: """Return `self >> rhs`. Args: @@ -366,22 +294,15 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self >> rhs`. """ - result = __type_of(result)() + # This handles the case where rhs is 0. + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __and__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __and__(self, rhs: Self) -> Self: """Return `self & rhs`. Args: @@ -390,22 +311,14 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self & rhs`. """ - result = __type_of(result)() + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __xor__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __xor__(self, rhs: Self) -> Self: """Return `self ^ rhs`. Args: @@ -414,22 +327,14 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self ^ rhs`. """ - result = __type_of(result)() + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) @always_inline("builtin") - fn __or__( - self, - rhs: IntLiteral[_], - out result: IntLiteral[ - __mlir_attr[ - `#kgen> : !kgen.int_literal`, - ] - ], - ): + fn __or__(self, rhs: Self) -> Self: """Return `self | rhs`. Args: @@ -438,7 +343,11 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: `self | rhs`. """ - result = __type_of(result)() + return Self( + __mlir_op.`kgen.int_literal.binop`[ + oper = __mlir_attr.`#kgen` + ](self.value, rhs.value) + ) # ===-------------------------------------------------------------------===# # Trait implementations @@ -451,7 +360,7 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: False Bool value if the value is equal to 0 and True otherwise. """ - return self != Self._zero + return self != Self() @always_inline("builtin") fn __as_bool__(self) -> Bool: @@ -528,11 +437,7 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( return String(Int(self)) @always_inline("builtin") - fn __ceildiv__( - self, - denominator: IntLiteral, - out result: __type_of(-(self // -denominator)), - ): + fn __ceildiv__(self, denominator: Self) -> Self: """Return the rounded-up result of dividing self by denominator. @@ -542,7 +447,7 @@ struct IntLiteral[value: __mlir_type.`!kgen.int_literal`]( Returns: The ceiling of dividing numerator by denominator. """ - result = __type_of(result)() + return -(self // -denominator) # ===-------------------------------------------------------------------===# # Methods diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 8cde264ed5..395dcdce10 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -461,9 +461,9 @@ fn atof(str_slice: StringSlice) raises -> Float64: sign = -1 if (str_len - start) >= 3: if StringSlice[buff.origin](ptr=buff + start, length=3) == "nan": - return FloatLiteral_nan + return FloatLiteral.nan if StringSlice[buff.origin](ptr=buff + start, length=3) == "inf": - return FloatLiteral_infinity * sign + return FloatLiteral.infinity * sign # read before dot for pos in range(start, str_len): if ord_0 <= buff[pos] <= ord_9: diff --git a/stdlib/src/math/math.mojo b/stdlib/src/math/math.mojo index 331b4f6397..14d3f91e25 100644 --- a/stdlib/src/math/math.mojo +++ b/stdlib/src/math/math.mojo @@ -130,11 +130,7 @@ fn ceildiv[T: CeilDivableRaising, //](numerator: T, denominator: T) raises -> T: # NOTE: this overload is needed because IntLiteral promotes to a runtime type # before overload resolution. @always_inline("builtin") -fn ceildiv( - numerator: IntLiteral, - denominator: IntLiteral, - out result: __type_of(numerator.__ceildiv__(denominator)), -): +fn ceildiv(numerator: IntLiteral, denominator: IntLiteral) -> IntLiteral: """Return the rounded-up result of dividing numerator by denominator. Args: @@ -144,7 +140,7 @@ fn ceildiv( Returns: The ceiling of dividing numerator by denominator. """ - result = __type_of(result)() + return numerator.__ceildiv__(denominator) # ===----------------------------------------------------------------------=== # diff --git a/stdlib/src/prelude/__init__.mojo b/stdlib/src/prelude/__init__.mojo index c0e1d65148..c6c3da74d2 100644 --- a/stdlib/src/prelude/__init__.mojo +++ b/stdlib/src/prelude/__init__.mojo @@ -43,13 +43,7 @@ from builtin.equality_comparable import EqualityComparable from builtin.error import Error from builtin.file import FileHandle, open from builtin.file_descriptor import FileDescriptor -from builtin.float_literal import ( - FloatLiteral, - FloatLiteral_nan, - FloatLiteral_infinity, - FloatLiteral_negative_infinity, - FloatLiteral_negative_zero, -) +from builtin.float_literal import FloatLiteral from builtin.floatable import Floatable, FloatableRaising, float from builtin.format_int import bin, hex, oct from builtin.identifiable import Identifiable, StringableIdentifiable diff --git a/stdlib/test/builtin/test_float_literal.mojo b/stdlib/test/builtin/test_float_literal.mojo index 8bc54a61a4..915c1f850e 100644 --- a/stdlib/test/builtin/test_float_literal.mojo +++ b/stdlib/test/builtin/test_float_literal.mojo @@ -20,10 +20,10 @@ from testing import ( assert_true, ) -alias nan = FloatLiteral_nan -alias neg_zero = FloatLiteral_negative_zero -alias inf = FloatLiteral_infinity -alias neg_inf = FloatLiteral_negative_infinity +alias nan = FloatLiteral.nan +alias neg_zero = FloatLiteral.negative_zero +alias inf = FloatLiteral.infinity +alias neg_inf = FloatLiteral.negative_infinity def test_division(): diff --git a/stdlib/test/builtin/test_math.mojo b/stdlib/test/builtin/test_math.mojo index 1a3a497fea..28e29f2ddf 100644 --- a/stdlib/test/builtin/test_math.mojo +++ b/stdlib/test/builtin/test_math.mojo @@ -128,7 +128,7 @@ def test_isnan(): alias x1 = isnan(SIMD[DType.float32, 4](SIMD[DType.float64, 4](1.0))) assert_equal(x1, False) - alias x2 = isnan(SIMD[DType.float32, 4](FloatLiteral_nan)) + alias x2 = isnan(SIMD[DType.float32, 4](FloatLiteral.nan)) assert_equal(x2, True) diff --git a/stdlib/test/builtin/test_simd.mojo b/stdlib/test/builtin/test_simd.mojo index b4f5d45eda..184627c5b7 100644 --- a/stdlib/test/builtin/test_simd.mojo +++ b/stdlib/test/builtin/test_simd.mojo @@ -1414,10 +1414,10 @@ def test_reduce_bit_count(): def test_pow(): - alias nan = FloatLiteral_nan - alias neg_zero = FloatLiteral_negative_zero - alias inf = FloatLiteral_infinity - alias neg_inf = FloatLiteral_negative_infinity + alias nan = FloatLiteral.nan + alias neg_zero = FloatLiteral.negative_zero + alias inf = FloatLiteral.infinity + alias neg_inf = FloatLiteral.negative_infinity # Float32 tests alias F32x4 = SIMD[DType.float32, 4] diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 84efce56cf..26d14bdeab 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -472,10 +472,10 @@ def test_atof(): assert_equal(0.78, atof(" .78 ")) assert_equal(121234.0, atof(" 121234. ")) assert_equal(985031234.0, atof(" 985031234.F ")) - assert_equal(FloatLiteral_negative_zero, atof("-0")) - assert_equal(FloatLiteral_nan, atof(" nan")) - assert_equal(FloatLiteral_infinity, atof(" inf ")) - assert_equal(FloatLiteral_negative_infinity, atof("-inf ")) + assert_equal(FloatLiteral.negative_zero, atof("-0")) + assert_equal(FloatLiteral.nan, atof(" nan")) + assert_equal(FloatLiteral.infinity, atof(" inf ")) + assert_equal(FloatLiteral.negative_infinity, atof("-inf ")) # Negative cases with assert_raises(contains="String is not convertible to float: ''"): From 3af56e08fb28722dbb40a5ec9ae7431e6c7c74dd Mon Sep 17 00:00:00 2001 From: Jiexiang Liu <80805665+jiex-liu@users.noreply.github.com> Date: Tue, 25 Feb 2025 00:08:35 -0800 Subject: [PATCH 459/509] [stdlib] Add cluster cta id This PR adds cluster cta id to our gpu modules. MODULAR_ORIG_COMMIT_REV_ID: 2cbcc65b57e5328d95be749bfe1ec77d9c4c628c --- stdlib/src/sys/intrinsics.mojo | 42 ++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/stdlib/src/sys/intrinsics.mojo b/stdlib/src/sys/intrinsics.mojo index 3aa5fabfef..25df3bc3e8 100644 --- a/stdlib/src/sys/intrinsics.mojo +++ b/stdlib/src/sys/intrinsics.mojo @@ -1307,3 +1307,45 @@ struct _ClusterIdx: alias cluster_idx = _ClusterIdx() + + +# ===-----------------------------------------------------------------------===# +# block_id_in_cluster +# ===-----------------------------------------------------------------------===# + + +@register_passable("trivial") +struct _Cluster_BlockIdx: + """_Cluster_BlockIdx provides static methods for getting the x/y/z coordinates of + a threadblock within a cluster.""" + + @always_inline("nodebug") + fn __init__(out self): + return + + @always_inline("nodebug") + @staticmethod + fn _get_intrinsic_name[dim: StringLiteral]() -> StringLiteral: + return "llvm.nvvm.read.ptx.sreg.cluster.ctaid." + dim + + @always_inline("nodebug") + fn __getattr__[dim: StringLiteral](self) -> UInt: + """Gets the `x`, `y`, or `z` coordinates of a threadblock within a cluster. + + Returns: + The `x`, `y`, or `z` coordinates of a threadblock within a cluster. + """ + constrained[ + is_nvidia_gpu() and _is_sm_9x(), + "cluster_id is only supported on NVIDIA SM90+ GPUs", + ]() + constrained[ + dim in ("x", "y", "z"), "the accessor must be either x, y, or z" + ]() + alias intrinsic_name = Self._get_intrinsic_name[dim]() + return UInt( + Int(llvm_intrinsic[intrinsic_name, UInt32, has_side_effect=False]()) + ) + + +alias block_id_in_cluster = _Cluster_BlockIdx() From ca954f6ee48bfd1b9f24209eba0c2ff74cf2b2e8 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Tue, 25 Feb 2025 12:41:21 -0500 Subject: [PATCH 460/509] [Kernels] Combine `DeviceContext` copy methods to a single overload This improves GPU dev ergonomics by combining: - enqueue_copy_to_device - enqueue_copy_from_device - enqueue_copy_device_to_device Into an overloaded `enqueue_copy` method and: - copy_to_device_sync - copy_from_device_sync - copy_device_to_device_sync Into an an overloaded `copy` method. This makes it so users don't have to figure out which method to call based on if they're passing a pointer or `DeviceContext` in different argument positions. MODULAR_ORIG_COMMIT_REV_ID: 05083f50577b1b67b8ad1c098176b8c37e5aefae --- docs/changelog.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/docs/changelog.md b/docs/changelog.md index a055d0c791..c223bb1bd0 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -166,6 +166,21 @@ ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=1) ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=1) ``` +- The methods on `DeviceContext`: + + - enqueue_copy_to_device + - enqueue_copy_from_device + - enqueue_copy_device_to_device + + Have been combined to single overloaded `enqueue_copy` method, and: + + - copy_to_device_sync + - copy_from_device_sync + - copy_device_to_device_sync + + Have been combined into an overloaded `copy` method, so you don't have + to figure out which method to call based on the arguments you're passing. + - The `shuffle` module has been rename to `warp` to better reflect its purpose. To uses now you will have to do From 2d53f464f92831242acd3e54910a8aa921dd9192 Mon Sep 17 00:00:00 2001 From: modularbot Date: Tue, 25 Feb 2025 21:09:58 +0000 Subject: [PATCH 461/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022519 --- examples/life/magic.lock | 178 +++++++++++++++++----------------- examples/magic.lock | 142 +++++++++++++-------------- examples/operators/magic.lock | 142 +++++++++++++-------------- examples/testing/magic.lock | 142 +++++++++++++-------------- magic.lock | 142 +++++++++++++-------------- 5 files changed, 373 insertions(+), 373 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index 0aaa0e3585..e2d2049339 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -103,11 +103,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.6-h8d12d68_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022505-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025022505-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.2.0.dev2025022505-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022505-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022505-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022519-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025022519-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.2.0.dev2025022519-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022519-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022519-release.conda - 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jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22987 - timestamp: 1740460553945 + size: 22992 + timestamp: 1740511118927 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From bdaca0f2ebd9e7c9f881ef8b62ebab95af8af76b Mon Sep 17 00:00:00 2001 From: modularbot <116839051+modularbot@users.noreply.github.com> Date: Wed, 26 Feb 2025 11:17:15 -0600 Subject: [PATCH 462/509] [External] [stdlib] Micro-optimize utf8 helper functions (#56579) [External] [stdlib] Micro-optimize utf8 helper functions Micro-optimize utf8 helper functions ORIGINAL_AUTHOR=martinvuyk <110240700+martinvuyk@users.noreply.github.com> PUBLIC_PR_LINK=modularml/mojo#3896 Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3896 MODULAR_ORIG_COMMIT_REV_ID: d24cb7cdf532b3f6a10f9ee19c95b66f22efc967 --- stdlib/src/collections/string/codepoint.mojo | 61 +++++++++++-------- stdlib/src/collections/string/string.mojo | 3 +- .../src/collections/string/string_slice.mojo | 17 ++++-- .../test/collections/string/test_string.mojo | 1 + 4 files changed, 51 insertions(+), 31 deletions(-) diff --git a/stdlib/src/collections/string/codepoint.mojo b/stdlib/src/collections/string/codepoint.mojo index 0452f15621..934d74ce49 100644 --- a/stdlib/src/collections/string/codepoint.mojo +++ b/stdlib/src/collections/string/codepoint.mojo @@ -18,6 +18,7 @@ from collections.string import StringSlice from bit import count_leading_zeros from memory import UnsafePointer +from sys.intrinsics import likely @always_inline @@ -450,13 +451,13 @@ struct Codepoint(CollectionElement, EqualityComparable, Intable, Stringable): return self._scalar_value @always_inline - fn unsafe_write_utf8(self, ptr: UnsafePointer[Byte]) -> UInt: + fn unsafe_write_utf8[ + optimize_ascii: Bool = True + ](self, ptr: UnsafePointer[Byte]) -> UInt: """Shift unicode to utf8 representation. - Safety: - `ptr` MUST point to at least `self.utf8_byte_length()` allocated - bytes or else an out-of-bounds write will occur, which is undefined - behavior. + Parameters: + optimize_ascii: Optimize for languages with mostly ASCII characters. Args: ptr: Pointer value to write the encoded UTF-8 bytes. Must validly @@ -466,6 +467,11 @@ struct Codepoint(CollectionElement, EqualityComparable, Intable, Stringable): Returns: Returns the number of bytes written. + Safety: + `ptr` MUST point to at least `self.utf8_byte_length()` allocated + bytes or else an out-of-bounds write will occur, which is undefined + behavior. + ### Unicode (represented as UInt32 BE) to UTF-8 conversion: - 1: 00000000 00000000 00000000 0aaaaaaa -> 0aaaaaaa - a @@ -483,18 +489,30 @@ struct Codepoint(CollectionElement, EqualityComparable, Intable, Stringable): var num_bytes = self.utf8_byte_length() - if num_bytes == 1: - ptr[0] = UInt8(c) - return 1 - - var shift = 6 * (num_bytes - 1) - var mask = UInt8(0xFF) >> UInt8(num_bytes + 1) - var num_bytes_marker = UInt8(0xFF) << (8 - num_bytes) - ptr[0] = ((c >> shift) & mask) | num_bytes_marker - for i in range(1, num_bytes): - shift -= 6 - ptr[i] = ((c >> shift) & 0b0011_1111) | 0b1000_0000 - + @parameter + if optimize_ascii: + # FIXME(#933): can't run LLVM intrinsic at compile time + # if likely(num_bytes == 1): + if num_bytes == 1: + ptr[0] = UInt8(c) + return 1 + var shift = 6 * (num_bytes - 1) + var mask = UInt8(0xFF) >> (num_bytes + 1) + var num_bytes_marker = UInt8(0xFF) << (8 - num_bytes) + ptr[0] = ((c >> shift) & mask) | num_bytes_marker + for i in range(1, num_bytes): + shift -= 6 + ptr[i] = ((c >> shift) & 0b0011_1111) | 0b1000_0000 + else: + var shift = 6 * (num_bytes - 1) + var mask = UInt8(0xFF) >> (num_bytes + Int(num_bytes > 1)) + var num_bytes_marker = UInt8(0xFF) << (8 - num_bytes) + ptr[0] = ((c >> shift) & mask) | ( + num_bytes_marker & -Int(num_bytes != 1) + ) + for i in range(1, num_bytes): + shift -= 6 + ptr[i] = ((c >> shift) & 0b0011_1111) | 0b1000_0000 return num_bytes @always_inline @@ -509,16 +527,11 @@ struct Codepoint(CollectionElement, EqualityComparable, Intable, Stringable): # Minimum codepoint values (respectively) that can fit in a 1, 2, 3, # and 4 byte encoded UTF-8 sequence. - alias sizes = SIMD[DType.int32, 4]( - 0, - 2**7, - 2**11, - 2**16, - ) + alias sizes = SIMD[DType.uint32, 4](0, 2**7, 2**11, 2**16) # Count how many of the minimums this codepoint exceeds, which is equal # to the number of bytes needed to encode it. - var lt = (sizes <= Int(self)).cast[DType.uint8]() + var lt = (sizes <= self.to_u32()).cast[DType.uint8]() # TODO(MOCO-1537): Support `reduce_add()` at compile time. # var count = Int(lt.reduce_add()) diff --git a/stdlib/src/collections/string/string.mojo b/stdlib/src/collections/string/string.mojo index 395dcdce10..304d33b6cc 100644 --- a/stdlib/src/collections/string/string.mojo +++ b/stdlib/src/collections/string/string.mojo @@ -81,8 +81,7 @@ fn chr(c: Int) -> String: Examples: ```mojo - print(chr(97)) # "a" - print(chr(8364)) # "€" + print(chr(97), chr(8364)) # "a €" ``` . """ diff --git a/stdlib/src/collections/string/string_slice.mojo b/stdlib/src/collections/string/string_slice.mojo index 60721c8e6e..e1c6bf41ca 100644 --- a/stdlib/src/collections/string/string_slice.mojo +++ b/stdlib/src/collections/string/string_slice.mojo @@ -2196,6 +2196,13 @@ fn _memmem[ return UnsafePointer[Scalar[type]]() +@always_inline +fn _is_utf8_continuation_byte[ + w: Int +](vec: SIMD[DType.uint8, w]) -> SIMD[DType.bool, w]: + return vec.cast[DType.int8]() < -(0b1000_0000 >> 1) + + fn _count_utf8_continuation_bytes(str_slice: StringSlice) -> Int: alias sizes = (256, 128, 64, 32, 16, 8) var ptr = str_slice.unsafe_ptr() @@ -2212,12 +2219,12 @@ fn _count_utf8_continuation_bytes(str_slice: StringSlice) -> Int: var rest = num_bytes - processed for _ in range(rest // s): var vec = (ptr + processed).load[width=s]() - var comp = (vec & 0b1100_0000) == 0b1000_0000 + var comp = _is_utf8_continuation_byte(vec) amnt += Int(comp.cast[DType.uint8]().reduce_add()) processed += s for i in range(num_bytes - processed): - amnt += Int((ptr[processed + i] & 0b1100_0000) == 0b1000_0000) + amnt += Int(_is_utf8_continuation_byte(ptr[processed + i])) return amnt @@ -2228,10 +2235,10 @@ fn _utf8_first_byte_sequence_length(b: Byte) -> Int: this does not work correctly if given a continuation byte.""" debug_assert( - (b & 0b1100_0000) != 0b1000_0000, + not _is_utf8_continuation_byte(b), "Function does not work correctly if given a continuation byte.", ) - return Int(count_leading_zeros(~b)) + Int(b < 0b1000_0000) + return Int(count_leading_zeros(~b) | (b < 0b1000_0000).cast[DType.uint8]()) fn _utf8_byte_type(b: SIMD[DType.uint8, _], /) -> __type_of(b): @@ -2248,7 +2255,7 @@ fn _utf8_byte_type(b: SIMD[DType.uint8, _], /) -> __type_of(b): - 3 -> start of 3 byte long sequence. - 4 -> start of 4 byte long sequence. """ - return count_leading_zeros(~(b & UInt8(0b1111_0000))) + return count_leading_zeros(~b) @always_inline diff --git a/stdlib/test/collections/string/test_string.mojo b/stdlib/test/collections/string/test_string.mojo index 26d14bdeab..42e6adf1cc 100644 --- a/stdlib/test/collections/string/test_string.mojo +++ b/stdlib/test/collections/string/test_string.mojo @@ -263,6 +263,7 @@ def test_ord(): def test_chr(): + assert_equal("\0", chr(0)) assert_equal("A", chr(65)) assert_equal("a", chr(97)) assert_equal("!", chr(33)) From 415610ee4efdf8f715fc329503eef59f98fa1736 Mon Sep 17 00:00:00 2001 From: Lukas Hermann <1734032+lsh@users.noreply.github.com> Date: Wed, 26 Feb 2025 09:50:34 -0800 Subject: [PATCH 463/509] [stdlib] Add origin to `InlineArray.unsafe_ptr` (#56708) MODULAR_ORIG_COMMIT_REV_ID: f06d968ae3bc6a23c7ce9553444573538fda2d82 --- stdlib/src/collections/inline_array.mojo | 8 +++++++- stdlib/test/collections/test_inline_array.mojo | 12 ++++++++++++ 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/stdlib/src/collections/inline_array.mojo b/stdlib/src/collections/inline_array.mojo index 142545b5a6..3cb3161e53 100644 --- a/stdlib/src/collections/inline_array.mojo +++ b/stdlib/src/collections/inline_array.mojo @@ -304,7 +304,13 @@ struct InlineArray[ return UnsafePointer(ptr)[] @always_inline - fn unsafe_ptr(self) -> UnsafePointer[Self.ElementType]: + fn unsafe_ptr( + ref self, + ) -> UnsafePointer[ + Self.ElementType, + mut = Origin(__origin_of(self)).is_mutable, + origin = __origin_of(self), + ]: """Get an `UnsafePointer` to the underlying array. That pointer is unsafe but can be used to read or write to the array. diff --git a/stdlib/test/collections/test_inline_array.mojo b/stdlib/test/collections/test_inline_array.mojo index a46aae3325..d3a3f7a99d 100644 --- a/stdlib/test/collections/test_inline_array.mojo +++ b/stdlib/test/collections/test_inline_array.mojo @@ -214,6 +214,17 @@ def test_inline_array_runs_destructors(): assert_equal(destructor_counter[3], 30) +fn test_unsafe_ptr() raises: + alias N = 10 + var arr = InlineArray[Int, 10](fill=0) + for i in range(N): + arr[i] = i + + var ptr = arr.unsafe_ptr() + for i in range(N): + assert_equal(arr[i], ptr[i]) + + def main(): test_array_unsafe_get() test_array_int() @@ -222,3 +233,4 @@ def main(): test_array_unsafe_assume_initialized_constructor_string() test_array_contains() test_inline_array_runs_destructors() + test_unsafe_ptr() From 248b7fa3c850ac75a6bd205674d09abdd63d485d Mon Sep 17 00:00:00 2001 From: modularbot Date: Wed, 26 Feb 2025 20:41:36 +0000 Subject: [PATCH 464/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022618 --- examples/life/magic.lock | 142 +++++++++++++++++----------------- examples/magic.lock | 142 +++++++++++++++++----------------- examples/operators/magic.lock | 142 +++++++++++++++++----------------- examples/testing/magic.lock | 142 +++++++++++++++++----------------- magic.lock | 142 +++++++++++++++++----------------- 5 files changed, 355 insertions(+), 355 deletions(-) diff --git a/examples/life/magic.lock b/examples/life/magic.lock index e2d2049339..82ebae50a4 100644 --- a/examples/life/magic.lock +++ b/examples/life/magic.lock @@ -103,11 +103,11 @@ environments: - 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jupyter_client >=8.6.2,<8.7 - python license: LicenseRef-Modular-Proprietary - size: 22994 - timestamp: 1740596478019 + size: 22991 + timestamp: 1740664199928 - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda sha256: 1895f47b7d68581a6facde5cb13ab8c2764c2e53a76bd746f8f98910dc4e08fe md5: 29097e7ea634a45cc5386b95cac6568f From a5d58164929af9924031318244579b02c8deccb6 Mon Sep 17 00:00:00 2001 From: akirchhoff-modular Date: Wed, 26 Feb 2025 09:55:53 -0800 Subject: [PATCH 466/509] [Pipelines] Compile multi-model pipelines' models in parallel I observe that pipelines with multiple models (currently mostly vision models) spend a lot of their compilation time pinned to a single core. This is not an efficient use of time. We have two pieces of work, so at least have each one pinned to one core. This slows down each compile from about 70 seconds to 90 seconds, but because those 90 seconds are overlapped, this still saves about 50 seconds overall. 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tutorials/run-torchscript-with-python/download-model.py create mode 100644 tutorials/run-torchscript-with-python/pyproject.toml create mode 100644 tutorials/run-torchscript-with-python/run.py create mode 100644 tutorials/run-torchscript-with-python/src/torchscript_tutorial/__init__.py diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000000..a74fda4b67 --- /dev/null +++ b/.clang-format @@ -0,0 +1,2 @@ +BasedOnStyle: LLVM +AlwaysBreakTemplateDeclarations: Yes diff --git a/.clang-tidy b/.clang-tidy new file mode 100644 index 0000000000..e78abd3994 --- /dev/null +++ b/.clang-tidy @@ -0,0 +1,57 @@ +InheritParentConfig: true +Checks: > + bugprone-argument-comment, + bugprone-assert-side-effect, + bugprone-branch-clone, + bugprone-copy-constructor-init, + bugprone-dangling-handle, + bugprone-dynamic-static-initializers, + bugprone-macro-parentheses, + bugprone-macro-repeated-side-effects, + bugprone-misplaced-widening-cast, + bugprone-move-forwarding-reference, + bugprone-multiple-statement-macro, + bugprone-suspicious-semicolon, + bugprone-swapped-arguments, + bugprone-terminating-continue, + bugprone-unused-raii, + bugprone-unused-return-value, + misc-redundant-expression, + misc-static-assert, + misc-unused-using-decls, + modernize-use-bool-literals, + modernize-loop-convert, + modernize-make-unique, + modernize-raw-string-literal, + modernize-use-equals-default, + modernize-use-default-member-init, + modernize-use-emplace, + modernize-use-nullptr, + modernize-use-override, + modernize-use-using, + performance-for-range-copy, + performance-implicit-conversion-in-loop, + performance-inefficient-algorithm, + performance-inefficient-vector-operation, + performance-move-const-arg, + performance-no-automatic-move, + performance-trivially-destructible, + performance-unnecessary-copy-initialization, + performance-unnecessary-value-param, + readability-avoid-const-params-in-decls, + readability-const-return-type, + readability-container-size-empty, + readability-inconsistent-declaration-parameter-name, + readability-misleading-indentation, + readability-redundant-control-flow, + readability-redundant-smartptr-get, + readability-simplify-subscript-expr, + readability-use-anyofallof + +CheckOptions: + - key: readability-identifier-naming.MemberCase + value: camelBack + - key: readability-identifier-naming.ParameterCase + value: camelBack + - key: readability-identifier-naming.VariableCase + value: camelBack diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000..e7cbefb6bf --- /dev/null +++ b/.gitattributes @@ -0,0 +1,4 @@ +# Add an override so that .🔥 files are highlighted as Mojo files within github +# since the linguist system does not work with the .🔥 extention. +# See https://github.com/github/linguist/blob/master/docs/overrides.md +*.🔥 linguist-language=Mojo diff --git a/.github/ISSUE_TEMPLATE/CODEOWNERS b/.github/ISSUE_TEMPLATE/CODEOWNERS new file mode 100644 index 0000000000..303c1ec52f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/CODEOWNERS @@ -0,0 +1,5 @@ +# Codeowners for MAX repo. +# Every line is a file pattern that is followed by one or more code owners. +# Order is important; the last matching pattern takes the most precedence. + +* @modularml/max-code-reviewers diff --git a/.github/ISSUE_TEMPLATE/doc_issue.yaml b/.github/ISSUE_TEMPLATE/doc_issue.yaml index daf162cefc..a7781e61a8 100644 --- a/.github/ISSUE_TEMPLATE/doc_issue.yaml +++ b/.github/ISSUE_TEMPLATE/doc_issue.yaml @@ -12,7 +12,7 @@ ##===----------------------------------------------------------------------===## name: Documentation issue -description: Report a problem with the Mojo docs +description: Report a problem with the MAX docs title: "[Docs]" labels: - documentation @@ -20,11 +20,12 @@ body: - type: markdown attributes: value: | - Thank you for helping us improve the Mojo docs! + Thank you for helping us improve the MAX docs! Please add a title above and fill in the following fields so we can understand the problem. - type: input + id: url attributes: label: Where is the problem? description: Provide a link to the problematic page (with a heading anchor). diff --git a/.github/ISSUE_TEMPLATE/max_bug_report.yaml b/.github/ISSUE_TEMPLATE/max_bug_report.yaml new file mode 100644 index 0000000000..573a7069e2 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/max_bug_report.yaml @@ -0,0 +1,55 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: MAX bug report +description: Create a bug report to help us improve MAX +title: "[BUG]: " +labels: + - bug +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out a bug report! + + Please provide a descriptive title above and fill in the following fields. + + - type: textarea + id: Description + attributes: + label: Bug description + description: Describe the bug you encountered and what you expected to happen. + validations: + required: true + + - type: textarea + id: Steps + attributes: + label: Steps to reproduce + description: Provide the specific steps to reproduce the issue. + value: | + - Include relevant code snippet or link to code that did not work as expected. + - If applicable, add screenshots to help explain the problem. + - Include anything else that might help us debug the issue. + validations: + required: true + + - type: textarea + id: Context + attributes: + label: System information + description: What version of MAX are you using? + value: | + - Provide the system information by running `magic info`. + - Provide version information for MAX (includes Mojo) by pasting the output of `magic list max`. + render: shell diff --git a/.github/ISSUE_TEMPLATE/max_builds_bug_report.yaml b/.github/ISSUE_TEMPLATE/max_builds_bug_report.yaml new file mode 100644 index 0000000000..0c43f15bac --- /dev/null +++ b/.github/ISSUE_TEMPLATE/max_builds_bug_report.yaml @@ -0,0 +1,53 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: MAX Builds model bug report +description: Report a bug with a model +title: "[BUG]: " +labels: + - bug +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out a bug report! + + Please provide a descriptive title above and fill in the following fields. + + - type: input + id: model-variant + attributes: + label: On which model did the bug occur? + description: Provide the model (and variant). + validations: + required: true + + - type: textarea + id: Description + attributes: + label: Bug description + description: Describe the bug you encountered and what you expected to happen. + validations: + required: true + + - type: textarea + id: Steps + attributes: + label: Steps to reproduce + description: Provide the specific steps to reproduce the issue. + value: | + - Include relevant code snippet or link to code that did not work as expected. + - If applicable, add screenshots to help explain the problem. + - Include anything else that might help us debug the issue. + validations: + required: true diff --git a/.github/ISSUE_TEMPLATE/max_feature_request.yaml b/.github/ISSUE_TEMPLATE/max_feature_request.yaml new file mode 100644 index 0000000000..57e6716fb8 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/max_feature_request.yaml @@ -0,0 +1,49 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: MAX feature request +description: Suggest an enhancement for MAX +title: "[Feature Request]" +labels: + - enhancement +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to suggest a MAX enhancement! + + Please enter a concise title above and fill out the following fields. + + - type: textarea + id: Request + attributes: + label: What is your request? + description: Describe how you'd like us to improve MAX. + validations: + required: true + + - type: textarea + id: Motivation + attributes: + label: What is your motivation for this change? + description: Describe the problem that your feature seeks to address (what is the value to the product/user?). + validations: + required: true + + - type: textarea + id: Description + attributes: + label: Any other details? + description: Perhaps some minimum functional attributes the implementation should include, or other context about your feature. + validations: + required: false diff --git a/.gitignore b/.gitignore index 81e5af8ff5..339f9c6cdc 100644 --- a/.gitignore +++ b/.gitignore @@ -1,13 +1,104 @@ -# Build directory -/build - -# Reference: https://github.com/github/gitignore/blob/main/Python.gitignore - # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + # Environments .env .venv @@ -16,7 +107,51 @@ venv/ ENV/ env.bak/ venv.bak/ -.magic/ -# MacOS + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# numpy +*.npy + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# MacOS stuff .DS_Store + +# VS Code +.vscode + +# Downloaded example models +examples/models/* +!examples/models/README.md + +# Magic cache +.magic diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index 9ca3f26be6..0000000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,26 +0,0 @@ -repos: - - repo: local - hooks: - - id: mojo-format - name: mojo-format - entry: mojo format - language: system - files: '\.(mojo|🔥|py)$' - stages: [commit] - - id: check-docstrings - name: check-docstrings - entry: python3 ./stdlib/scripts/check-docstrings.py - language: system - pass_filenames: false - stages: [commit] - - id: check-license - name: check-license - entry: mojo stdlib/scripts/check_licenses.mojo - language: system - files: '\.(mojo|🔥|py)$' - stages: [commit] - - repo: https://github.com/igorshubovych/markdownlint-cli - rev: v0.40.0 - hooks: - - id: markdownlint - args: ['--config', 'stdlib/scripts/.markdownlint.yaml', '--fix'] diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index f4f5ad5a25..d6204fb411 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -1,4 +1,4 @@ -# Code of conduct +# Code of Conduct In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and @@ -7,7 +7,7 @@ body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. -## Our standards +## Our Standards All community forums and spaces are meant for professional interactions that are friendly, inclusive, helpful, and collaborative. Examples of behavior that @@ -32,7 +32,7 @@ participants include: - Conduct which could reasonably be considered inappropriate for the forum in which it occurs. -## Our responsibilities +## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in @@ -47,14 +47,14 @@ threatening, offensive, or harmful. ## Scope This Code of Conduct applies to all project content and public spaces on the -Mojo GitHub repo, the rest of Modular’s GitHub organization, and all other -official Mojo community spaces and communication mediums, whether offline or +MAX GitHub repo, the rest of Modular’s GitHub organization, and all other +official MAX community spaces and communication mediums, whether offline or online. ## Enforcement Instances of abusive, harassment, or otherwise unacceptable behavior should be -reported to the project team at . All complaints will +reported to the project team at . All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further diff --git a/LICENSE b/LICENSE index c1af4075d5..d972b8e80e 100644 --- a/LICENSE +++ b/LICENSE @@ -1,5 +1,5 @@ ============================================================================================== -The Mojo repository is licensed under the Apache License v2.0 with LLVM Exceptions: +The MAX repository is licensed under the Apache License v2.0 with LLVM Exceptions: ============================================================================================== Apache License diff --git a/README.md b/README.md index 2a4878c789..92d8013410 100644 --- a/README.md +++ b/README.md @@ -1,113 +1,107 @@ -

- +![Modular Logo](https://modular-assets.s3.amazonaws.com/images/modular_github_logo_bg.png) - [Website][Mojo] | [Getting Started] | [API Documentation] | [Contributing] | [Changelog] -
+# Welcome to MAX -[Mojo]: https://www.modular.com/mojo/ -[Getting Started]: https://docs.modular.com/mojo/manual/get-started/ -[API Documentation]: https://docs.modular.com/mojo/lib -[Contributing]: ./CONTRIBUTING.md -[Changelog]: ./docs/changelog.md +The Modular Accelerated Xecution ([MAX](https://www.modular.com/max)) platform +is an integrated suite of AI libraries, tools, and technologies that unifies +commonly fragmented AI deployment workflows. MAX accelerates time to market +for the latest innovations by giving AI developers a single toolchain that +unlocks full programmability, unparalleled performance, and seamless hardware portability. -# Welcome to Mojo 🔥 +![Modular Architecture Diagram](https://modular-assets.s3.amazonaws.com/images/modular_architecture_diagram_bg.png) -Mojo is a new programming language that bridges the gap between research -and production by combining Python syntax and ecosystem with systems -programming and metaprogramming features. Mojo is still young, but it is -designed to write blazing-fast code for CPUs, GPUs, and more as part of -the [MAX Platform](https://www.modular.com/max). +[See here to get started with MAX](https://docs.modular.com/max/get-started) +and when you want to report issues or request features, +[please create a GitHub issue here](https://github.com/modular/max/issues/new/choose). -This repo includes source code for: +The [Discord](https://discord.gg/modular) community is the best place to share +your experiences and chat with the team and other community members. -- Mojo examples -- Mojo documentation hosted at [modular.com](https://docs.modular.com/mojo/) -- The [Mojo standard library](https://docs.modular.com/mojo/lib) +In the [examples directory](https://github.com/modular/max/tree/main/examples), +you will find code examples and Jupyter notebooks that show how to run inference +with MAX. -This repo has two primary branches: +## Getting Started -- The [`stable`](https://github.com/modular/mojo/tree/stable) branch, which -is in sync with the last stable released version of Mojo. Use the examples -here if you’re using a [release build of Mojo](#latest-released). +MAX is available in both stable and nightly builds. To install either version, +follow the guide to [create a project with +Magic](https://docs.modular.com/max/create-project). -- The [`main`](https://github.com/modular/mojo/tree/main) branch, which -is in sync with the Mojo nightly build and subject to breakage. Use this branch -for [contributions](./CONTRIBUTING.md), or if you're using the latest -[nightly build of Mojo](#latest-nightly). +Then clone this repository: -To learn more about Mojo, see the -[Mojo Manual](https://docs.modular.com/mojo/manual/). +```bash +git clone https://github.com/modular/max.git +``` -## Installing Mojo +If you installed the stable build (v24.6), be sure you switch to the `stable` branch, +because the `main` branch is for nightly releases and might not be compatible +with stable builds: -### Latest Released +```bash +git checkout stable +``` -To install the last released build of Mojo, follow the guide to -[Get started with Mojo](https://docs.modular.com/mojo/manual/get-started). +## Running -### Latest Nightly +### MAX Pipelines -The nightly Mojo builds are subject to breakage and provide an inside -view of how the development of Mojo is progressing. Use at your own risk -and be patient! +To show off the full power of MAX, a +[series of end-to-end pipelines for common AI workloads](./src/max/pipelines/) +(and more) are ready to run. As one example, this includes everything needed to +self-host +[the Llama 3.1 text-generation model](./src/max/pipelines/architectures/llama3/). +All code is provided so that these pipelines can be customized, built upon, or +learned from. -To get nightly builds, see the same instructions to [Get started with -Mojo](https://docs.modular.com/mojo/manual/get-started), but when you create -your project, instead use the following `magic init` command to set the -conda package channel to `max-nightly`: +### Examples -```bash -magic init hello-world-nightly --format mojoproject \ - -c conda-forge -c https://conda.modular.com/max-nightly -``` +In addition to the end-to-end pipelines, there are many [examples](./examples/) +that exercise various aspects of MAX. You can follow the instructions in the +README for each example or notebook you want to run. -Or, if you're [using conda](https://docs.modular.com/magic/conda), add the -`https://conda.modular.com/max-nightly/` channel to your `environment.yaml` -file. For example: +### Notebooks -```yaml -[project] -name = "Mojo nightly example" -channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] -platforms = ["osx-arm64", "linux-aarch64", "linux-64"] +Check out the [notebooks examples](./examples/notebooks/) for using MAX Engine +🏎️ for models such as -[dependencies] -max = "*" -``` +- [Mistral-7B](./examples/notebooks/mistral7b-python-onnx.ipynb) +- [Roberta-pytorch](./examples/notebooks/roberta-python-pytorch.ipynb) -When you clone this repo, you'll be on the `main` branch by default, -which includes code matching the latest nightly build: +### Tutorials -```bash -git clone https://github.com/modular/mojo.git -``` +The [tutorials](./tutorials/) directory contains the "finished" code for +tutorials you can read at +[docs.modular.com/max/tutorials](https://docs.modular.com/max/tutorials). + +### Docker Container + +The MAX container is our official Docker container for convenient MAX deployment. +It includes the latest MAX version with GPU support, several AI libraries, and +integrates with orchestration tools like Kubernetes. -If you want to instead see the source from the most recent stable -release, then you can switch to the `stable` branch. +The MAX container image is available in the +[Modular Docker Hub repository](https://hub.docker.com/r/modular/max-openai-api/). ## Contributing -When you want to report issues or request features, [please create a GitHub -issue here](https://github.com/modular/mojo/issues). -See [here](./CONTRIBUTING.md) for guidelines on filing good bugs. +Thanks for your interest in contributing to this repository! +We are not accepting pull requests yet. -We welcome contributions to this repo on the -[`main`](https://github.com/modular/mojo/tree/main) -branch. If you’d like to contribute to Mojo, please first read our [Contributor -Guide](https://github.com/modular/mojo/blob/main/CONTRIBUTING.md). +However, we welcome your bug reports. If you have a bug, please file an issue +[here](https://github.com/modular/max/issues/new/choose). -For more general questions or to chat with other Mojo developers, check out our -[Discord](https://discord.gg/modular). +If you need support, the [Discord](https://discord.gg/modular) +community is the best place to share your experiences and chat with +the team and other community members. ## License -This repository and its contributions are licensed under the Apache License v2.0 -with LLVM Exceptions (see the LLVM [License](https://llvm.org/LICENSE.txt)). +This repository and its contributions are licensed under the Apache License +v2.0 with LLVM Exceptions (see the LLVM [License](https://llvm.org/LICENSE.txt)). MAX and Mojo usage and distribution are licensed under the [MAX & Mojo Community License](https://www.modular.com/legal/max-mojo-license). -## Thanks to our contributors +### Third Party Licenses - - - +You are entirely responsible for checking and validating the licenses of +third parties (i.e. Huggingface) for related software and libraries that are downloaded. diff --git a/examples/testing/.gitattributes b/benchmark/.gitattributes similarity index 100% rename from examples/testing/.gitattributes rename to benchmark/.gitattributes diff --git a/benchmark/.gitignore b/benchmark/.gitignore new file mode 100644 index 0000000000..5ecf358c31 --- /dev/null +++ b/benchmark/.gitignore @@ -0,0 +1,7 @@ +# pixi environments +.pixi +*.egg-info +# magic environments +.magic +*.json +*.lock diff --git a/benchmark/README.md b/benchmark/README.md new file mode 100644 index 0000000000..b4b56ac55e --- /dev/null +++ b/benchmark/README.md @@ -0,0 +1,163 @@ +# Benchmark MAX Serve + +This directory contains tools to benchmark +[MAX Serve](https://docs.modular.com/max/serve/) performance. You can also use +these scripts to compare different LLM serving backends such as +[vLLM](https://github.com/vllm-project/vllm) and +[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) against MAX. The +benchmarking tools measure throughput, latency, and resource utilization +metrics. + +Key features: + +- Tests any OpenAI-compatible HTTP endpoint +- Supports both chat and completion APIs +- Measures detailed latency metrics +- Works with hosted services + +> The `benchmark_serving.py` script is adapted from +> [vLLM](https://github.com/vllm-project/vllm/blob/main/benchmarks), +> licensed under Apache 2.0. We forked this script to ensure consistency with +> vLLM's measurement methodology and extended it with features we found helpful, +> such as client-side GPU metric collection via `nvitop`. + +## Table of contents + +- [Get started](#get-started) +- [Basic usage](#basic-usage) +- [Reference](#reference) +- [Troubleshooting](#troubleshooting) + +## Get started + +If this is your first time benchmarking a MAX Serve endpoint, +we recommend that you follow our [tutorial to benchmark MAX Serve on +a GPU](https://docs.modular.com/max/tutorials/benchmark-max-serve/). + +## Basic usage + +You can benchmark any HTTP endpoint that implements +OpenAI-compatible APIs as follows. + +First enter the local virtual environment: + +```cd +git clone -b stable https://github.com/modular/max.git + +cd max/benchmark + +magic shell +``` + +Then run the benchmark script while specifying your active +MAX Serve endpoint, model, and corresponding dataset to +use for benchmarking (for more detail, see our [benchmarking +tutorial](https://docs.modular.com/max/tutorials/benchmark-max-serve)): + +```bash +python benchmark_serving.py \ + --base-url https://company_url.xyz \ + --endpoint /v1/completions \ + --backend modular \ + --model meta-llama/Meta-Llama-3.1-8B-Instruct \ + --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \ + --num-prompts 500 +``` + +To exit the virtual environment shell simply run `exit`. + +### Output + +Results are saved in JSON format under the `results/` directory with the +following naming convention: + +```bash +{backend}-{request_rate}qps-{model_name}-{timestamp}.json +``` + +The output should look similar to the following: + +```bash +============ Serving Benchmark Result ============ +Successful requests: 500 +Failed requests: 0 +Benchmark duration (s): 46.27 +Total input tokens: 100895 +Total generated tokens: 106511 +Request throughput (req/s): 10.81 +Input token throughput (tok/s): 2180.51 +Output token throughput (tok/s): 2301.89 +---------------Time to First Token---------------- +Mean TTFT (ms): 15539.31 +Median TTFT (ms): 15068.37 +P99 TTFT (ms): 33034.17 +-----Time per Output Token (excl. 1st token)------ +Mean TPOT (ms): 34.23 +Median TPOT (ms): 28.47 +P99 TPOT (ms): 138.55 +---------------Inter-token Latency---------------- +Mean ITL (ms): 26.76 +Median ITL (ms): 5.42 +P99 ITL (ms): 228.45 +-------------------Token Stats-------------------- +Max input tokens: 933 +Max output tokens: 806 +Max total tokens: 1570 +--------------------GPU Stats--------------------- +GPU Utilization (%): 94.74 +Peak GPU Memory Used (MiB): 37228.12 +GPU Memory Available (MiB): 3216.25 +================================================== +``` + +### Key metrics explained + +- **Request throughput**: Number of complete requests processed per second +- **Input token throughput**: Number of input tokens processed per second +- **Output token throughput**: Number of tokens generated per second +- **TTFT**: Time to first token (TTFT), the time from request start to first +token generation +- **TPOT**: Time per output token (TPOT), the average time taken to generate +each output token +- **ITL**: Inter-token latency (ITL), the average time between consecutive token +or token-chunk generations +- **GPU utilization**: Percentage of time during which at least one GPU kernel +is being executed +- **Peak GPU memory used**: Peak memory usage during benchmark run + +## Reference + +### Command line arguments for `benchmark_serving.py` + +- Backend configuration: + - `--backend`: Choose from `modular` (MAX Serve), `vllm` (vLLM), or`trt-llm` + (TensorRT-LLM) + - `--model`: Hugging Face model ID or local path +- Load generation: + - `--num-prompts`: Number of prompts to process (default: `500`) + - `--request-rate`: Request rate in requests/second (default: `inf`) + - `--seed`: The random seed used to sample the dataset (default: `0`) +- Serving options + - `--base-url`: Base URL of the API service + - `--endpoint`: Specific API endpoint (`/v1/completions` or + `/v1/chat/completions`) + - `--tokenizer`: Hugging Face tokenizer to use (can be different from model) + - `--dataset-name`: (default:`sharegpt`) Real-world conversation data in the + form of variable length prompts and responses. ShareGPT is automatically + downloaded if not already present. +- Additional options + - `--collect-gpu-stats`: Report GPU utilization and memory consumption. + Only works when running `benchmark_serving.py` on the same instance as + the server, and only on NVIDIA GPUs. + +## Troubleshooting + +### Memory issues + +- Reduce batch size +- Check GPU memory availability: `nvidia-smi` + +### Permission issues + +- Verify `HF_TOKEN` is set correctly +- Ensure model access on Hugging Face diff --git a/benchmark/benchmark_serving.py b/benchmark/benchmark_serving.py new file mode 100644 index 0000000000..7d760c7d38 --- /dev/null +++ b/benchmark/benchmark_serving.py @@ -0,0 +1,1240 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Benchmark online serving throughput.""" + +import argparse +import asyncio +import json +import logging +import os +import random +import resource +import sys +import time +import traceback +import warnings +from argparse import ArgumentParser as FlexibleArgumentParser +from dataclasses import dataclass, field +from datetime import datetime +from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, Union + +import aiohttp +import numpy as np +from tqdm.asyncio import tqdm +from transformers import ( + AutoTokenizer, + PreTrainedTokenizer, + PreTrainedTokenizerBase, + PreTrainedTokenizerFast, +) + +# 10 minute timeout per request session +AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=10 * 60) + +logger = logging.getLogger("benchmark_serving") + + +@dataclass +class RequestFuncInput: + prompt: str + api_url: str + prompt_len: int + output_len: int + model: str + + +@dataclass +class RequestFuncOutput: + generated_text: str = "" + success: bool = False + latency: float = 0.0 + ttft: float = 0.0 # Time to first token + itl: List[float] = field( + default_factory=list + ) # List of inter-token latencies + prompt_len: int = 0 + error: str = "" + + +def compute_output_len( + tokenizer: PreTrainedTokenizerBase, output: RequestFuncOutput +) -> int: + return len( + tokenizer( + output.generated_text, + add_special_tokens=False, + ).input_ids + ) + + +async def async_request_trt_llm( + request_func_input: RequestFuncInput, + pbar: Optional[tqdm] = None, +) -> RequestFuncOutput: + api_url = request_func_input.api_url + assert api_url.endswith("generate_stream") + + async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session: + payload = { + "accumulate_tokens": True, + "text_input": request_func_input.prompt, + "temperature": 0.0, + "top_p": 1.0, + "max_tokens": request_func_input.output_len, + "stream": True, + } + output = RequestFuncOutput() + output.prompt_len = request_func_input.prompt_len + + ttft = 0.0 + st = time.perf_counter() + most_recent_timestamp = st + try: + async with session.post(url=api_url, json=payload) as response: + if response.status == 200: + async for chunk_bytes in response.content: + chunk_bytes = chunk_bytes.strip() + if not chunk_bytes: + continue + + chunk = remove_prefix( + chunk_bytes.decode("utf-8"), "data:" + ) + + data = json.loads(chunk) + output.generated_text += data["text_output"] + timestamp = time.perf_counter() + # First token + if ttft == 0.0: + ttft = time.perf_counter() - st + output.ttft = ttft + + # Decoding phase + else: + output.itl.append(timestamp - most_recent_timestamp) + + most_recent_timestamp = timestamp + + output.latency = most_recent_timestamp - st + output.success = True + + else: + output.error = response.reason or "" + output.success = False + except Exception: + output.success = False + exc_info = sys.exc_info() + output.error = "".join(traceback.format_exception(*exc_info)) + + if pbar: + pbar.update(1) + return output + + +async def async_request_openai_completions( + request_func_input: RequestFuncInput, + pbar: Optional[tqdm] = None, +) -> RequestFuncOutput: + api_url = request_func_input.api_url + assert api_url.endswith(("completions", "profile")), ( + "OpenAI Completions API URL must end with 'completions' or 'profile'." + ) + + async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session: + payload = { + "model": request_func_input.model, + "prompt": request_func_input.prompt, + "temperature": 0.0, + "best_of": 1, + "max_tokens": request_func_input.output_len, + "stream": True, + "ignore_eos": True, + } + + headers = { + "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}" + } + + output = RequestFuncOutput() + output.prompt_len = request_func_input.prompt_len + + generated_text = "" + ttft = 0.0 + st = time.perf_counter() + most_recent_timestamp = st + try: + async with session.post( + url=api_url, json=payload, headers=headers + ) as response: + if response.status == 200: + async for chunk_bytes in response.content: + chunk_bytes = chunk_bytes.strip() + if not chunk_bytes: + continue + + chunk = remove_prefix( + chunk_bytes.decode("utf-8"), "data: " + ) + latency = time.perf_counter() - st + if chunk == "[DONE]": + pass + else: + data = json.loads(chunk) + + # NOTE: Some completion API might have a last + # usage summary response without a token so we + # want to check a token was generated + if data["choices"][0]["text"]: + timestamp = time.perf_counter() + # First token + if ttft == 0.0: + ttft = time.perf_counter() - st + output.ttft = ttft + + # Decoding phase + else: + output.itl.append( + timestamp - most_recent_timestamp + ) + + most_recent_timestamp = timestamp + generated_text += data["choices"][0]["text"] + output.generated_text = generated_text + output.success = True + output.latency = latency + else: + output.error = response.reason or "" + output.success = False + except Exception: + output.success = False + exc_info = sys.exc_info() + output.error = "".join(traceback.format_exception(*exc_info)) + if pbar: + pbar.update(1) + return output + + +async def async_request_openai_chat_completions( + request_func_input: RequestFuncInput, + pbar: Optional[tqdm] = None, +) -> RequestFuncOutput: + api_url = request_func_input.api_url + assert api_url.endswith("chat/completions"), ( + "OpenAI Chat Completions API URL must end with 'chat/completions'." + ) + + async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session: + payload = { + "model": request_func_input.model, + "messages": [ + { + "role": "user", + "content": request_func_input.prompt, + }, + ], + "temperature": 0.0, + "max_tokens": request_func_input.output_len, + "stream": True, + } + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}", + } + + output = RequestFuncOutput() + output.prompt_len = request_func_input.prompt_len + + generated_text = "" + ttft = 0.0 + st = time.perf_counter() + most_recent_timestamp = st + try: + async with session.post( + url=api_url, json=payload, headers=headers + ) as response: + if response.status == 200: + async for chunk_bytes in response.content: + chunk_bytes = chunk_bytes.strip() + if not chunk_bytes: + continue + + chunk = remove_prefix( + chunk_bytes.decode("utf-8"), "data: " + ) + latency = time.perf_counter() - st + if chunk == "[DONE]": + pass + else: + timestamp = time.perf_counter() + data = json.loads(chunk) + + delta = data["choices"][0]["delta"] + if delta.get("content", None): + # First token + if ttft == 0.0: + ttft = time.perf_counter() - st + output.ttft = ttft + + # Decoding phase + else: + output.itl.append( + timestamp - most_recent_timestamp + ) + + generated_text += delta["content"] + + most_recent_timestamp = timestamp + + output.generated_text = generated_text + output.success = True + output.latency = latency + else: + output.error = response.reason or "" + output.success = False + except Exception: + output.success = False + exc_info = sys.exc_info() + output.error = "".join(traceback.format_exception(*exc_info)) + + if pbar: + pbar.update(1) + return output + + +# Since vllm must support Python 3.8, we can't use str.removeprefix(prefix) +# introduced in Python 3.9 +def remove_prefix(text: str, prefix: str) -> str: + if text.startswith(prefix): + return text[len(prefix) :] + return text + + +def get_tokenizer( + pretrained_model_name_or_path: str, trust_remote_code: bool +) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]: + return AutoTokenizer.from_pretrained( + pretrained_model_name_or_path, trust_remote_code=trust_remote_code + ) + + +ASYNC_REQUEST_FUNCS = { + "vllm": async_request_openai_completions, + "trt-llm": async_request_trt_llm, + "modular": async_request_openai_completions, + "modular-chat": async_request_openai_chat_completions, +} + + +# from https://github.com/sgl-project/sglang/blob/v0.4.0/python/sglang/bench_serving.py#L1283 +def set_ulimit(target_soft_limit=65535): + resource_type = resource.RLIMIT_NOFILE + current_soft, current_hard = resource.getrlimit(resource_type) + + if current_soft < target_soft_limit: + try: + resource.setrlimit(resource_type, (target_soft_limit, current_hard)) + except ValueError as e: + print(f"Fail to set RLIMIT_NOFILE: {e}") + + +@dataclass +class BenchmarkMetrics: + completed: int + failures: int + total_input: int + total_output: int + request_throughput: float + input_throughput: float + output_throughput: float + mean_ttft_ms: float + median_ttft_ms: float + std_ttft_ms: float + p99_ttft_ms: float + mean_tpot_ms: float + median_tpot_ms: float + std_tpot_ms: float + p99_tpot_ms: float + mean_itl_ms: float + median_itl_ms: float + std_itl_ms: float + p99_itl_ms: float + max_input: int + max_output: int + max_total: int + peak_gpu_memory_mib: float # 'benchmark/gpu:0/memory_used (MiB)/max' + available_gpu_memory_mib: float # 'benchmark/gpu:0/memory_free (MiB)/min' + gpu_utilization: float # 'benchmark/gpu:0/gpu_utilization (%)/mean' + + +def sample_sharegpt_requests( + dataset_path: str, + num_requests: int, + tokenizer: PreTrainedTokenizerBase, + fixed_output_len: Optional[int] = None, +) -> List[Tuple[str, int, int]]: + if fixed_output_len is not None and fixed_output_len < 4: + raise ValueError("output_len too small") + # Load the dataset. + with open(dataset_path) as f: + dataset = json.load(f) + # Filter out the conversations with less than 2 turns. + dataset = [data for data in dataset if len(data["conversations"]) >= 2] + # Only keep the first two turns of each conversation. + dataset = [ + (data["conversations"][0]["value"], data["conversations"][1]["value"]) + for data in dataset + ] + + # Shuffle the dataset. + random.shuffle(dataset) + + # Filter out sequences that are too long or too short + filtered_dataset: List[Tuple[str, int, int]] = [] + for i in range(len(dataset)): + if len(filtered_dataset) == num_requests: + break + + # Tokenize the prompts and completions. + prompt = dataset[i][0] + prompt_token_ids = tokenizer(prompt).input_ids + completion = dataset[i][1] + completion_token_ids = tokenizer(completion).input_ids + prompt_len = len(prompt_token_ids) + output_len = ( + len(completion_token_ids) + if fixed_output_len is None + else fixed_output_len + ) + if prompt_len < 4 or output_len < 4: + # Prune too short sequences. + continue + if prompt_len > 1024 or prompt_len + output_len > 2048: + # Prune too long sequences. + continue + filtered_dataset.append((prompt, prompt_len, output_len)) + + return filtered_dataset + + +def sample_sonnet_requests( + dataset_path: str, + num_requests: int, + input_len: int, + output_len: int, + prefix_len: int, + tokenizer: PreTrainedTokenizerBase, +) -> List[Tuple[str, str, int, int]]: + assert input_len > prefix_len, ( + "'args.sonnet-input-len' must be greater than 'args.prefix-input-len'." + ) + + # Load the dataset. + with open(dataset_path) as f: + poem_lines = f.readlines() + + # Tokenize the poem lines. + poem_token_ids = tokenizer(poem_lines).input_ids + average_poem_len = sum( + len(token_ids) for token_ids in poem_token_ids + ) / len(poem_token_ids) + + # Base prefix for all requests. + base_prompt = "Pick as many lines as you can from these poem lines:\n" + base_message = [ + { + "role": "user", + "content": base_prompt, + } + ] + base_prompt_formatted = tokenizer.apply_chat_template( + base_message, add_generation_prompt=True, tokenize=False + ) + base_prompt_offset = len(tokenizer(base_prompt_formatted).input_ids) + + assert input_len > base_prompt_offset, ( + f"Please set 'args.sonnet-input-len' higher than {base_prompt_offset}." + ) + num_input_lines = round((input_len - base_prompt_offset) / average_poem_len) + + # First approximately `prefix_len` number of tokens in the + # prompt are fixed poem lines. + assert prefix_len > base_prompt_offset, ( + f"Please set 'args.sonnet-prefix-len' higher than {base_prompt_offset}." + ) + + num_prefix_lines = round( + (prefix_len - base_prompt_offset) / average_poem_len + ) + prefix_lines = poem_lines[:num_prefix_lines] + + # Sample the rest of lines per request. + sampled_requests: List[Tuple[str, str, int, int]] = [] + for _ in range(num_requests): + sampled_lines = "".join( + prefix_lines + + random.sample(poem_lines, num_input_lines - num_prefix_lines) + ) + + prompt = f"{base_prompt}{sampled_lines}" + message = [ + { + "role": "user", + "content": prompt, + }, + ] + prompt_formatted = tokenizer.apply_chat_template( + message, add_generation_prompt=True, tokenize=False + ) + prompt_len = len(tokenizer(prompt_formatted).input_ids) + sampled_requests.append( + (prompt, prompt_formatted, prompt_len, output_len) + ) + + return sampled_requests + + +def sample_random_requests( + input_len: int, + output_len: int, + num_prompts: int, + range_ratio: float, + tokenizer: PreTrainedTokenizerBase, +) -> List[Tuple[str, int, int]]: + input_lens = np.random.randint( + int(input_len * range_ratio), + input_len + 1, + size=num_prompts, + ) + output_lens = np.random.randint( + int(output_len * range_ratio), + output_len + 1, + size=num_prompts, + ) + offsets = np.random.randint(0, tokenizer.vocab_size, size=num_prompts) + input_requests = [] + for i in range(num_prompts): + prompt = tokenizer.decode( + [ + (offsets[i] + i + j) % tokenizer.vocab_size + for j in range(input_lens[i]) + ] + ) + input_requests.append((prompt, int(input_lens[i]), int(output_lens[i]))) + + return input_requests + + +async def get_request( + input_requests: List[Tuple[str, int, int]], + request_rate: float, +) -> AsyncGenerator[Tuple[str, int, int], None]: + for request in input_requests: + yield request + + if request_rate == float("inf"): + # If the request rate is infinity, then we don't need to wait. + continue + + # Sample the request interval from the exponential distribution. + interval = np.random.exponential(1.0 / request_rate) + # The next request will be sent after the interval. + await asyncio.sleep(interval) + + +def calculate_metrics( + input_requests: List[Tuple[str, int, int]], + outputs: List[RequestFuncOutput], + dur_s: float, + tokenizer: PreTrainedTokenizerBase, + gpu_metrics: Dict[str, Any], +) -> Tuple[BenchmarkMetrics, List[int]]: + actual_output_lens: List[int] = [] + total_input = 0 + completed = 0 + max_input = 0 + max_output = 0 + max_total = 0 + failures = 0 + failed_responses = [] + itls: List[float] = [] + tpots: List[float] = [] + ttfts: List[float] = [] + for i in range(len(outputs)): + if outputs[i].success: + # We use the tokenizer to count the number of output tokens for all + # serving backends instead of looking at len(outputs[i].itl) since + # multiple output tokens may be bundled together + # Note : this may inflate the output token count slightly + output_len = compute_output_len(tokenizer, outputs[i]) + actual_output_lens.append(output_len) + total_input += input_requests[i][1] + if output_len > 1: + tpots.append( + (outputs[i].latency - outputs[i].ttft) / (output_len - 1) + ) + itls += outputs[i].itl + ttfts.append(outputs[i].ttft) + completed += 1 + max_input = max(max_input, input_requests[i][1]) + max_output = max(max_output, output_len) + max_total = max(max_total, input_requests[i][1] + output_len) + else: + actual_output_lens.append(0) + failures = failures + 1 + failed_responses.append(outputs[i]) + + if failures != 0: + warnings.warn( + ( + "Some requests failed. The responses returned are displayed " + "below. Please check server logs for more information." + ), + stacklevel=2, + ) + for f in failed_responses: + logger.error(f"Failed :: {f}") + + if completed == 0: + warnings.warn( + ( + "All requests failed. This is likely due to a misconfiguration " + "on the benchmark arguments." + ), + stacklevel=2, + ) + metrics = BenchmarkMetrics( + completed=completed, + failures=failures, + total_input=total_input, + total_output=sum(actual_output_lens), + request_throughput=completed / dur_s, + input_throughput=total_input / dur_s, + output_throughput=sum(actual_output_lens) / dur_s, + mean_ttft_ms=float(np.mean(ttfts or 0)) + * 1000, # ttfts is empty if streaming is not supported by backend + median_ttft_ms=float(np.median(ttfts or 0)) * 1000, + std_ttft_ms=float(np.std(ttfts or 0)) * 1000, + p99_ttft_ms=float(np.percentile(ttfts or 0, 99)) * 1000, + mean_tpot_ms=float(np.mean(tpots or 0)) * 1000, + median_tpot_ms=float(np.median(tpots or 0)) * 1000, + std_tpot_ms=float(np.std(tpots or 0)) * 1000, + p99_tpot_ms=float(np.percentile(tpots or 0, 99)) * 1000, + mean_itl_ms=float(np.mean(itls or 0)) * 1000, + median_itl_ms=float(np.median(itls or 0)) * 1000, + std_itl_ms=float(np.std(itls or 0)) * 1000, + p99_itl_ms=float(np.percentile(itls or 0, 99)) * 1000, + max_input=max_input, + max_output=max_output, + max_total=max_total, + peak_gpu_memory_mib=float( + gpu_metrics.get("benchmark/gpu:0/memory_used (MiB)/max") or 0 + ), + available_gpu_memory_mib=float( + gpu_metrics.get("benchmark/gpu:0/memory_free (MiB)/min") or 0 + ), + gpu_utilization=float( + gpu_metrics.get("benchmark/gpu:0/gpu_utilization (%)/mean") or 0 + ), + ) + + return metrics, actual_output_lens + + +async def benchmark( + backend: str, + api_url: str, + base_url: str, + model_id: str, + tokenizer: PreTrainedTokenizerBase, + input_requests: List[Tuple[str, int, int]], + request_rate: float, + disable_tqdm: bool, + do_test_prompt: bool, + collect_gpu_stats: bool, + print_inputs_and_outputs: bool, +): + if backend in ASYNC_REQUEST_FUNCS: + request_func = ASYNC_REQUEST_FUNCS[backend] + else: + raise ValueError(f"Unknown backend: {backend}") + + if do_test_prompt: + logger.info("Starting initial single prompt test run...") + test_prompt, test_prompt_len, test_output_len = input_requests[0] + test_input = RequestFuncInput( + model=model_id, + prompt=test_prompt, + api_url=api_url, + prompt_len=test_prompt_len, + output_len=test_output_len, + ) + test_output = await request_func( + request_func_input=test_input, + ) + if not test_output.success: + raise ValueError( + "Initial test run failed - Please make sure benchmark" + " arguments are correctly specified. Error:" + f" {test_output.error}" + ) + else: + logger.info( + "Initial test run completed. Starting main benchmark run..." + ) + + logger.info(f"Traffic request rate: {request_rate}") + + pbar = None if disable_tqdm else tqdm(total=len(input_requests)) + if collect_gpu_stats: + from nvitop import ResourceMetricCollector + + collector = ResourceMetricCollector() + collector.start("benchmark") + + benchmark_start_time = time.perf_counter_ns() + tasks: List[asyncio.Task] = [] + async for request in get_request(input_requests, request_rate): + prompt, prompt_len, output_len = request + request_func_input = RequestFuncInput( + model=model_id, + prompt=prompt, + api_url=api_url, + prompt_len=prompt_len, + output_len=output_len, + ) + tasks.append( + asyncio.create_task( + request_func( + request_func_input=request_func_input, + pbar=pbar, + ) + ) + ) + outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks) + + if pbar is not None: + pbar.close() + + benchmark_duration = (time.perf_counter_ns() - benchmark_start_time) / 1e9 + + if print_inputs_and_outputs: + print("Generated output text:") + for req_id, output in enumerate(outputs): + output_len = compute_output_len(tokenizer, output) + print( + { + "req_id": req_id, + "output_len": output_len, + "output": output.generated_text, + } + ) + + if collect_gpu_stats: + gpu_metrics = collector.collect() + collector.stop() + else: + gpu_metrics = {} + + metrics, actual_output_lens = calculate_metrics( + input_requests=input_requests, + outputs=outputs, + dur_s=benchmark_duration, + tokenizer=tokenizer, + gpu_metrics=gpu_metrics, + ) + + print("{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="=")) + print("{:<40} {:<10}".format("Successful requests:", metrics.completed)) + print("{:<40} {:<10}".format("Failed requests:", metrics.failures)) + print( + "{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration) + ) + print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input)) + print( + "{:<40} {:<10}".format("Total generated tokens:", metrics.total_output) + ) + print( + "{:<40} {:<10.2f}".format( + "Request throughput (req/s):", metrics.request_throughput + ) + ) + print( + "{:<40} {:<10.2f}".format( + "Input token throughput (tok/s):", metrics.input_throughput + ) + ) + print( + "{:<40} {:<10.2f}".format( + "Output token throughput (tok/s):", metrics.output_throughput + ) + ) + print("{s:{c}^{n}}".format(s="Time to First Token", n=50, c="-")) + print("{:<40} {:<10.2f}".format("Mean TTFT (ms):", metrics.mean_ttft_ms)) + print( + "{:<40} {:<10.2f}".format("Median TTFT (ms):", metrics.median_ttft_ms) + ) + print("{:<40} {:<10.2f}".format("P99 TTFT (ms):", metrics.p99_ttft_ms)) + print( + "{s:{c}^{n}}".format( + s="Time per Output Token (excl. 1st token)", n=50, c="-" + ) + ) + print("{:<40} {:<10.2f}".format("Mean TPOT (ms):", metrics.mean_tpot_ms)) + print( + "{:<40} {:<10.2f}".format("Median TPOT (ms):", metrics.median_tpot_ms) + ) + print("{:<40} {:<10.2f}".format("P99 TPOT (ms):", metrics.p99_tpot_ms)) + print("{s:{c}^{n}}".format(s="Inter-token Latency", n=50, c="-")) + print("{:<40} {:<10.2f}".format("Mean ITL (ms):", metrics.mean_itl_ms)) + print("{:<40} {:<10.2f}".format("Median ITL (ms):", metrics.median_itl_ms)) + print("{:<40} {:<10.2f}".format("P99 ITL (ms):", metrics.p99_itl_ms)) + print("{s:{c}^{n}}".format(s="Token Stats", n=50, c="-")) + print("{:<40} {:<10}".format("Max input tokens:", metrics.max_input)) + print("{:<40} {:<10}".format("Max output tokens:", metrics.max_output)) + print("{:<40} {:<10}".format("Max total tokens:", metrics.max_total)) + if collect_gpu_stats: + print("{s:{c}^{n}}".format(s="GPU Stats", n=50, c="-")) + print( + "{:<40} {:<10.2f}".format( + "GPU Utilization (%):", metrics.gpu_utilization + ) + ) + print( + "{:<40} {:<10.2f}".format( + "Peak GPU Memory Used (MiB):", metrics.peak_gpu_memory_mib + ) + ) + print( + "{:<40} {:<10.2f}".format( + "GPU Memory Available (MiB):", metrics.available_gpu_memory_mib + ) + ) + + print("=" * 50) + + result = { + "duration": benchmark_duration, + "completed": metrics.completed, + "total_input_tokens": metrics.total_input, + "total_output_tokens": metrics.total_output, + "request_throughput": metrics.request_throughput, + "input_throughput": metrics.input_throughput, + "output_throughput": metrics.output_throughput, + "mean_ttft_ms": metrics.mean_ttft_ms, + "median_ttft_ms": metrics.median_ttft_ms, + "std_ttft_ms": metrics.std_ttft_ms, + "p99_ttft_ms": metrics.p99_ttft_ms, + "mean_tpot_ms": metrics.mean_tpot_ms, + "median_tpot_ms": metrics.median_tpot_ms, + "std_tpot_ms": metrics.std_tpot_ms, + "p99_tpot_ms": metrics.p99_tpot_ms, + "mean_itl_ms": metrics.mean_itl_ms, + "median_itl_ms": metrics.median_itl_ms, + "std_itl_ms": metrics.std_itl_ms, + "p99_itl_ms": metrics.p99_itl_ms, + "input_lens": [output.prompt_len for output in outputs], + "output_lens": actual_output_lens, + "ttfts": [output.ttft for output in outputs], + "itls": [output.itl for output in outputs], + "generated_texts": [output.generated_text for output in outputs], + "errors": [output.error for output in outputs], + "peak_gpu_memory_mib": metrics.peak_gpu_memory_mib, + "available_gpu_memory_mib": metrics.available_gpu_memory_mib, + "gpu_utilization": metrics.gpu_utilization, + } + return result + + +def main(args: argparse.Namespace): + logging.basicConfig( + format="%(asctime)s.%(msecs)03d %(levelname)s: %(name)s: %(message)s", + datefmt="%H:%M:%S", + level=logging.INFO, + ) + + logger.info(args) + random.seed(args.seed) + np.random.seed(args.seed) + # benchmarks can create a large number of concurrent in-flight requests + # so bump the file limit to make room for them + set_ulimit() + + backend = args.backend + model_id = args.model + tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model + + if args.base_url is not None: + api_url = f"{args.base_url}{args.endpoint}" + base_url = f"{args.base_url}" + else: + api_url = f"http://{args.host}:{args.port}{args.endpoint}" + base_url = f"http://{args.host}:{args.port}" + + logger.info(f"getting tokenizer. api url: {api_url}, base_url: {base_url}") + tokenizer = get_tokenizer( + tokenizer_id, trust_remote_code=args.trust_remote_code + ) + + logger.info("sampling requests") + if args.dataset is not None: + warnings.warn( + ( + "The '--dataset' argument will be deprecated in the next " + "release. Please use '--dataset-name' and " + "'--dataset-path' in the future runs." + ), + stacklevel=2, + ) + input_requests = sample_sharegpt_requests( + dataset_path=args.dataset, + num_requests=args.num_prompts, + tokenizer=tokenizer, + fixed_output_len=args.sharegpt_output_len, + ) + + elif args.dataset_name == "sharegpt": + input_requests = sample_sharegpt_requests( + dataset_path=args.dataset_path, + num_requests=args.num_prompts, + tokenizer=tokenizer, + fixed_output_len=args.sharegpt_output_len, + ) + + elif args.dataset_name == "sonnet": + # Sample sonnet requests with common parameters + sonnet_requests = sample_sonnet_requests( + dataset_path=args.dataset_path, + num_requests=args.num_prompts, + input_len=args.sonnet_input_len, + output_len=args.sonnet_output_len, + prefix_len=args.sonnet_prefix_len, + tokenizer=tokenizer, + ) + + # Do not format the prompt, pass to message directly + if args.backend == "openai-chat": + # For chat API, use raw prompt without formatting + input_requests = [ + (prompt, prompt_len, output_len) + for prompt, _, prompt_len, output_len in sonnet_requests + ] + else: + # For non-chat API, ensure model has chat template and use formatted prompt + assert tokenizer.chat_template or tokenizer.default_chat_template, ( + "Tokenizer/model must have chat template for sonnet dataset." + ) + input_requests = [ + (prompt_formatted, prompt_len, output_len) + for _, prompt_formatted, prompt_len, output_len in sonnet_requests + ] + + elif args.dataset_name == "random": + input_requests = sample_random_requests( + input_len=args.random_input_len, + output_len=args.random_output_len, + num_prompts=args.num_prompts, + range_ratio=args.random_range_ratio, + tokenizer=tokenizer, + ) + + else: + raise ValueError(f"Unknown dataset: {args.dataset_name}") + + if args.print_inputs_and_outputs: + print("Input prompts:") + for req_id, (prompt_formatted, prompt_len, output_len) in enumerate( + input_requests + ): + print( + { + "req_id": req_id, + "output_len": output_len, + "prompt_len": prompt_len, + "prompt": prompt_formatted, + } + ) + + logger.info("starting benchmark run") + benchmark_result = asyncio.run( + benchmark( + backend=backend, + api_url=api_url, + base_url=base_url, + model_id=model_id, + tokenizer=tokenizer, + input_requests=input_requests, + request_rate=args.request_rate, + disable_tqdm=args.disable_tqdm, + do_test_prompt=not args.skip_test_prompt, + collect_gpu_stats=args.collect_gpu_stats, + print_inputs_and_outputs=args.print_inputs_and_outputs, + ) + ) + + # Benchmark run failed if any failed requests + if args.num_prompts != benchmark_result["completed"]: + logger.info("finished benchmark run: Failed.") + sys.exit(1) + + # Save config and results to json + if args.save_result: + logger.info("saving results") + result_json: Dict[str, Any] = {} + + # Setup + current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + result_json["date"] = current_dt + result_json["backend"] = backend + result_json["model_id"] = model_id + result_json["tokenizer_id"] = tokenizer_id + result_json["num_prompts"] = args.num_prompts + result_json["server_args"] = args.server_args + result_json["dataset_name"] = args.dataset_name + + # Metadata + if args.metadata: + for item in args.metadata: + if "=" in item: + kvstring = item.split("=") + result_json[kvstring[0].strip()] = kvstring[1].strip() + else: + raise ValueError( + "Invalid metadata format. Please use KEY=VALUE format." + ) + + # Traffic + result_json["request_rate"] = ( + args.request_rate if args.request_rate < float("inf") else "inf" + ) + + # Merge with benchmark result + result_json = {**result_json, **benchmark_result} + + # Save to file + base_model_id = model_id.split("/")[-1] + file_name = f"{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json" # noqa + if args.result_filename: + file_name = args.result_filename + if args.result_dir: + file_name = os.path.join(args.result_dir, file_name) + with open(file_name, "w") as outfile: + json.dump(result_json, outfile) + + logger.info("finished benchmark run: Success.") + + +if __name__ == "__main__": + parser = FlexibleArgumentParser( + description="Benchmark the online serving throughput." + ) + parser.add_argument( + "--backend", + type=str, + default="modular", + choices=list(ASYNC_REQUEST_FUNCS.keys()), + ) + parser.add_argument( + "--base-url", + type=str, + default=None, + help="Server or API base url if not using http host and port.", + ) + parser.add_argument("--host", type=str, default="localhost") + parser.add_argument("--port", type=int, default=8000) + parser.add_argument( + "--endpoint", + type=str, + default="/v1/completions", + help="API endpoint.", + ) + parser.add_argument( + "--dataset", + type=str, + default=None, + help=( + "Path to the ShareGPT dataset, will be deprecated in the " + "next release." + ), + ) + parser.add_argument( + "--dataset-name", + type=str, + default="sharegpt", + choices=["sharegpt", "sonnet", "random"], + help="Name of the dataset to benchmark on.", + ) + parser.add_argument( + "--dataset-path", type=str, default=None, help="Path to the dataset." + ) + parser.add_argument( + "--model", + type=str, + required=True, + help="Name of the model.", + ) + parser.add_argument( + "--tokenizer", + type=str, + help=( # noqa: E501 + "Name or path of the tokenizer, if not using the default tokenizer." + ), + ) + parser.add_argument( + "--num-prompts", + type=int, + default=1000, + help="Number of prompts to process.", + ) + parser.add_argument( + "--sharegpt-output-len", + type=int, + default=None, + help=( + "Output length for each request. Overrides the output length " + "from the ShareGPT dataset." + ), + ) + parser.add_argument( + "--sonnet-input-len", + type=int, + default=550, + help=( + "Number of input tokens per request, used only for sonnet dataset." + ), + ) + parser.add_argument( + "--sonnet-output-len", + type=int, + default=150, + help=( + "Number of output tokens per request, used only for sonnet dataset." + ), + ) + parser.add_argument( + "--sonnet-prefix-len", + type=int, + default=200, + help=( + "Number of prefix tokens per request, used only for sonnet dataset." + ), + ) + parser.add_argument( + "--random-input-len", + type=int, + default=1024, + help=( + "Number of input tokens per request, used only for random sampling." + ), + ) + parser.add_argument( + "--random-output-len", + type=int, + default=128, + help=( + "Number of output tokens per request, used only for random" + " sampling." + ), + ) + parser.add_argument( + "--random-range-ratio", + type=float, + default=1.0, + help=( + "Range of sampled ratio of input/output length, " + "used only for random sampling." + ), + ) + parser.add_argument( + "--request-rate", + type=float, + default=float("inf"), + help=( + "Number of requests per second. If this is inf, " + "then all the requests are sent at time 0. " + "Otherwise, we use Poisson process to synthesize " + "the request arrival times." + ), + ) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument( + "--trust-remote-code", + action="store_true", + help="Trust remote code from huggingface", + ) + parser.add_argument( + "--disable-tqdm", + action="store_true", + help="Specify to disable tqdm progress bar.", + ) + parser.add_argument( + "--skip-test-prompt", + action="store_true", + help="Skip the test prompt. Useful when doing external profiling.", + ) + parser.add_argument( + "--collect-gpu-stats", + action="store_true", + help="Collect GPU stats with NVML (NVIDIA only).", + ) + parser.add_argument( + "--save-result", + action="store_true", + help="Specify to save benchmark results to a json file", + ) + parser.add_argument( + "--metadata", + metavar="KEY=VALUE", + nargs="*", + help=( + "Key-value pairs (e.g, --metadata version=0.3.3 tp=1) " + "for metadata of this run to be saved in the result JSON file " + "for record keeping purposes." + ), + ) + parser.add_argument( + "--result-dir", + type=str, + default=None, + help=( + "Specify directory to save benchmark json results." + "If not specified, results are saved in the current directory." + ), + ) + parser.add_argument( + "--result-filename", + type=str, + default=None, + help=( + "Specify the filename to save benchmark json results." + "If not specified, results will be saved in " + "{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json" + " format." + ), + ) + parser.add_argument( + "--print-inputs-and-outputs", + action="store_true", + help="Print all input and outputs to console.", + ) + + parser.add_argument( + "--server-args", + type=str, + default="", + help="Server args", + ) + + args = parser.parse_args() + main(args) diff --git a/benchmark/pyproject.toml b/benchmark/pyproject.toml new file mode 100644 index 0000000000..af102c505f --- /dev/null +++ b/benchmark/pyproject.toml @@ -0,0 +1,32 @@ +[project] +authors = [{ name = "Modular", email = "hello@modular.com" }] +dependencies = [ + "aiohttp>=3.11.10,<4", + "huggingface-hub>=0.26.5,<0.27", + "hf-transfer>=0.1.8,<0.2", + "numpy>=2.2.0,<3", + "tqdm>=4.67.1,<5", + "transformers>=4.47.0,<5", + "nvitop>=1.3.2,<2", + "jinja2>=3.1.4,<4", +] +description = "MAX Serve benchmarking tools" +name = "benchmarking" +requires-python = ">=3.9,<3.13" +version = "0.1.0" + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.pixi.project] +channels = ["conda-forge", "https://conda.modular.com/max"] +platforms = ["osx-arm64", "linux-64", "linux-aarch64"] + +[tool.pixi.pypi-dependencies] +benchmarking = { path = ".", editable = true } + +[tool.pixi.tasks] diff --git a/benchmark/requirements.txt b/benchmark/requirements.txt new file mode 100644 index 0000000000..8f2b35964d --- /dev/null +++ b/benchmark/requirements.txt @@ -0,0 +1,8 @@ +aiohttp +huggingface_hub[hf_transfer] +huggingface_hub +numpy +tqdm +transformers +nvitop +Jinja2 diff --git a/examples/README.md b/examples/README.md index 6c651cd92d..0722ca6ec4 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,48 +1,39 @@ -# Mojo code examples +# MAX examples -A collection of sample programs written in the -[Mojo](https://docs.modular.com/mojo/manual/) programming language. +These examples demonstrate the power and flexibility of +[MAX](https://docs.modular.com/max/). They include: -## Getting Started +## [Custom GPU and CPU operations in Mojo](custom_ops/) -The easiest way to get started with Mojo is to install the Magic package and -virtual environment manager. By following the instructions in [Get started with -Magic](https://docs.modular.com/magic/), you can quickly install Magic and use -it to create a virtual environment for Mojo programming. Alternatively, you can -follow the instructions for how to [Add MAX/Mojo to a conda -project](https://docs.modular.com/magic/conda). +The [MAX Graph API](https://docs.modular.com/max/graph/) provides a powerful +framework for staging computational graphs to be run on GPUs, CPUs, and more. +Each operation in one of these graphs is defined in +[Mojo](https://docs.modular.com/mojo/), an easy-to-use language for writing +high-performance code. -After you have set up a Mojo programming environment, you can then use `git` to -clone this repository of Mojo samples using the command below: +The examples here illustrate how to construct custom graph operations in Mojo +that run on GPUs and CPUs, as well as how to build computational graphs that +contain and run them on different hardware architectures. -```bash -git clone https://github.com/modular/mojo.git -``` +## [Compiling and running Mojo functions on a GPU](gpu_functions/) -## Running +In addition to placing custom Mojo functions within a computational graph, the +MAX Driver API can handle direct compilation of GPU functions written in Mojo +and can dispatch them onto the GPU. This is a programming model that may be +familiar to those who have worked with CUDA or similar GPGPU frameworks. -If you're using [`magic`](https://docs.modular.com/magic), navigate into -the `examples` directory and use `magic run` to invoke `mojo` and run the basic -example programs. For example: +These examples show how to compile and run Mojo functions, from simple to +complex, on an available GPU. Note that +[a MAX-compatible GPU](https://docs.modular.com/max/faq/#gpu-requirements) will +be necessary to build and run these. -```bash -magic run mojo matmul.mojo -``` +## [PyTorch and ONNX inference on MAX](inference/) -You'll also find several complete Mojo projects as subdirectories of the -`examples` directory. +MAX has the power to accelerate existing PyTorch and ONNX models directly, and +provides Python, Mojo, and C APIs for this. These examples showcase common +models from these frameworks and how to run them even faster via MAX. -## License +## [Jupyter notebooks](notebooks/) -The Mojo examples in this repository are licensed under the Apache License v2.0 -with LLVM Exceptions (see the LLVM [License](https://llvm.org/LICENSE.txt)). - -## Contributing - -As a contributor, your efforts and expertise are invaluable in driving the -evolution of the Mojo programming language. The [Mojo contributor -guide](../CONTRIBUTING.md) provides all the information necessary to make -meaningful contributions—from understanding the submission process to -adhering to best practices: - -- [Mojo contributor guide](../CONTRIBUTING.md) +Jupyter notebooks that showcase PyTorch and ONNX models being accelerated +through MAX. diff --git a/examples/console.py b/examples/console.py new file mode 100644 index 0000000000..be8b77485a --- /dev/null +++ b/examples/console.py @@ -0,0 +1,179 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +import subprocess +from enum import Enum +from pathlib import Path +from typing import List, Optional, Tuple + +try: + from rich.console import Console + from rich.prompt import Confirm + from rich.table import Table + +except ImportError: + print("rich not found. Installing rich...") + subprocess.run(["python3", "-m", "pip", "install", "rich"]) + from rich.console import Console + from rich.prompt import Confirm + from rich.table import Table + +ROOT = Path(os.path.dirname(__file__)) +RETRIES = 10 +EXCEEDED_RETRY_ERROR = ( + "Exceeded the number of retries. Please re-run the console again and follow" + " the prompt." +) + + +def list_repositories() -> List[Tuple[str, str]]: + repos = [] + for top_repo in os.listdir(ROOT): + # Skip notebook folder and files + if "notebook" in top_repo or not os.path.isdir(top_repo): + continue + + repo = os.path.join(ROOT, top_repo) + for r in os.listdir(repo): + full_path = os.path.join(repo, r) + if os.path.isdir(full_path) and os.path.exists( + os.path.join(full_path, "run.sh") + ): + repos.append((os.path.join(top_repo, r), full_path)) + + repos = sorted(repos, key=lambda x: x[0]) + return repos + + +class InputState(Enum): + PROMPT_INPUT = 0 + VALIDATE_INPUT = 1 + CHECK_RANGE = 2 + + +def prompt_validation( + console: Console, retries: int, repos: List[Tuple[str, str]] +) -> Optional[Tuple[str, str]]: + state = InputState.PROMPT_INPUT + selected_index = None + n_repos = len(repos) + while retries > 0: + if state == InputState.PROMPT_INPUT: + selected_index = console.input( + "Enter the index of an example to run: " + ) + state = InputState.VALIDATE_INPUT + elif state == InputState.VALIDATE_INPUT: + if not selected_index.strip(): + selected_index = console.input( + f"Please enter an index between {0}-{n_repos - 1}: " + ) + retries -= 1 + if retries <= 0: + console.print(EXCEEDED_RETRY_ERROR, style="red") + return None + else: + continue + + try: + selected_index = int(selected_index) + state = InputState.CHECK_RANGE + except ValueError: + selected_index = console.input( + f"The index must be an integer between {0}-{n_repos - 1}: " + ) + retries -= 1 + if retries <= 0: + console.print(EXCEEDED_RETRY_ERROR, style="red") + return None + else: + state = InputState.VALIDATE_INPUT + continue + + elif state == InputState.CHECK_RANGE: + if 0 <= selected_index < n_repos: + return repos[selected_index] + else: + selected_index = console.input( + f"Please enter an index between {0}-{n_repos - 1}: " + ) + retries -= 1 + if retries <= 0: + console.print(EXCEEDED_RETRY_ERROR, style="red") + return None + else: + state = InputState.VALIDATE_INPUT + continue + + console.print(EXCEEDED_RETRY_ERROR) + return None + + +def select_repository( + console: Console, + repos: List[Tuple[str, str]], +) -> Optional[Tuple[str, str]]: + table = Table(title="Select the Example to Run", highlight=True) + table.add_column("Index", style="cyan", justify="center") + table.add_column( + "MAX Engine 🏎️ Examples 🔥", style="magenta", justify="left" + ) + for index, (name, _) in enumerate(repos): + table.add_row(str(index), name) + + console.print(table) + selected_repo = prompt_validation(console, RETRIES, repos) + if selected_repo is None: + return + + return selected_repo + + +def run_repository(repo_name: str) -> None: + repo_path = os.path.join(ROOT, repo_name) + run_script_path = (Path(repo_path) / "run.sh").resolve() + subprocess.run(["magic", "run", "bash", run_script_path], cwd=repo_path) + return + + +def main(): + repos = list_repositories() + console = Console() + if not repos: + console.print("No repositories found. Exiting!", style="red") + return + + exit_ = False + while not exit_: + console.print("\n") + selected = select_repository(console, repos) + if selected is None: + return + + _, selected_repo = selected + console.print(f"Running {selected_repo} ...") + run_repository(selected_repo) + another = Confirm.ask("Would you like to run another example?") + if not another: + exit_ = True + console.print( + "Thanks for trying the examples! Bye 👋", style="green" + ) + break + else: + console.print("Here is the example table again \n", style="green") + + +if __name__ == "__main__": + main() diff --git a/examples/custom_ops/.gitignore b/examples/custom_ops/.gitignore new file mode 100644 index 0000000000..aff686d201 --- /dev/null +++ b/examples/custom_ops/.gitignore @@ -0,0 +1 @@ +kernels.mojopkg diff --git a/examples/custom_ops/README.md b/examples/custom_ops/README.md new file mode 100644 index 0000000000..31825508ca --- /dev/null +++ b/examples/custom_ops/README.md @@ -0,0 +1,55 @@ +# Writing custom CPU or GPU graph operations using Mojo + +> [!NOTE] +> This is a preview of an interface for writing custom operations in Mojo, +> and may be subject to change before the next stable release. + +Graphs in MAX can be extended to use custom operations written in Mojo. The +following examples are shown here: + +- **addition**: Adding 1 to every element of an input tensor. +- **mandelbrot**: Calculating the Mandelbrot set. +- **vector_addition**: Performing vector addition using a manual GPU function. +- **top_k**: A top-K token sampler, a complex operation that shows a real-world + use case for a custom operation used today within a large language model + processing pipeline. +- **matrix_multiplication**: Various matrix multiplication algorithms, using a + memory layout abstraction. +- **fused_attention**: A fused attention operation, which leverages many of the + available MAX GPU programming features to show how to address an important + use case in AI models. + +Custom kernels have been written in Mojo to carry out these calculations. For +each example, a simple graph containing a single operation is constructed +in Python. This graph is compiled and dispatched onto a supported GPU if one is +available, or the CPU if not. Input tensors, if there are any, are moved from +the host to the device on which the graph is running. The graph then runs and +the results are copied back to the host for display. + +One thing to note is that this same Mojo code runs on CPU as well as GPU. In +the construction of the graph, it runs on a supported accelerator if one is +available or falls back to the CPU if not. No code changes for either path. +The `vector_addition` example shows how this works under the hood for common +MAX abstractions, where compile-time specialization lets MAX choose the optimal +code path for a given hardware architecture. + +The `kernels/` directory contains the custom kernel implementations, and the +graph construction occurs in the Python files in the base directory. These +examples are designed to stand on their own, so that they can be used as +templates for experimentation. + +A single Magic command runs each of the examples: + +```sh +magic run addition +magic run mandelbrot +magic run vector_addition +magic run top_k +magic run matrix_multiplication +magic run fused_attention +``` + +The execution has two phases: first a `kernels.mojopkg` is compiled from the +custom Mojo kernel, and then the graph is constructed and run in Python. The +inference session is pointed to the `kernels.mojopkg` in order to load the +custom operations. diff --git a/examples/custom_ops/addition.py b/examples/custom_ops/addition.py new file mode 100644 index 0000000000..aa1301f812 --- /dev/null +++ b/examples/custom_ops/addition.py @@ -0,0 +1,79 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +import numpy as np +from max.driver import CPU, Accelerator, Tensor, accelerator_count +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import Graph, TensorType, ops + +if __name__ == "__main__": + # This is necessary only in specific build environments. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + path = Path(__file__).parent / "kernels.mojopkg" + + rows = 5 + columns = 10 + dtype = DType.float32 + + # Configure our simple one-operation graph. + graph = Graph( + "addition", + # The custom Mojo operation is referenced by its string name, and we + # need to provide inputs as a list as well as expected output types. + forward=lambda x: ops.custom( + name="add_one_custom", + values=[x], + out_types=[TensorType(dtype=x.dtype, shape=x.tensor.shape)], + )[0].tensor, + input_types=[ + TensorType(dtype, shape=[rows, columns]), + ], + ) + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if accelerator_count() == 0 else Accelerator() + + # Set up an inference session for running the graph. + session = InferenceSession( + devices=[device], + custom_extensions=path, + ) + + # Compile the graph. + model = session.load(graph) + + # Fill an input matrix with random values. + x_values = np.random.uniform(size=(rows, columns)).astype(np.float32) + + # Create a driver tensor from this, and move it to the accelerator. + x = Tensor.from_numpy(x_values).to(device) + + # Perform the calculation on the target device. + result = model.execute(x)[0] + + # Copy values back to the CPU to be read. + assert isinstance(result, Tensor) + result = result.to(CPU()) + + print("Graph result:") + print(result.to_numpy()) + print() + + print("Expected result:") + print(x_values + 1) diff --git a/examples/custom_ops/fused_attention.py b/examples/custom_ops/fused_attention.py new file mode 100644 index 0000000000..03d1367148 --- /dev/null +++ b/examples/custom_ops/fused_attention.py @@ -0,0 +1,72 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +import numpy as np +from max.driver import CPU, Accelerator, Tensor, accelerator_count +from max.dtype import DType +from max.engine.api import InferenceSession +from max.graph import Graph, TensorType, ops + + +def main(): + # This is necessary only for Modular internal CI. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + path = Path(__file__).parent / "kernels.mojopkg" + + dtype = DType.float32 + N = 8 + D = 8 + BD = 4 + BN = 4 + with Graph( + "fused_attention", + input_types=[ + TensorType(dtype, shape=[N, D]), + TensorType(dtype, shape=[N, D]), + TensorType(dtype, shape=[N, D]), + ], + ) as graph: + q, k, v, *_ = graph.inputs + results = ops.custom( + name="fused_attention_custom", + parameters={"N": N, "D": D, "BD": BD, "BN": BN}, + values=[q, k, v], + out_types=[TensorType(dtype, shape=[N, D])], + ) + graph.output(*results) + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if accelerator_count() == 0 else Accelerator() + + # Set up an inference session for running the graph. + session = InferenceSession(devices=[device], custom_extensions=path) + + # Compile the graph. + model = session.load(graph) + + np.random.seed(123) + Q = Tensor.from_numpy(np.random.randn(N, D).astype("f")).to(device) + K = Tensor.from_numpy(np.random.randn(N, D).astype("f")).to(device) + V = Tensor.from_numpy(np.random.randn(N, D).astype("f")).to(device) + + output = model.execute(Q, K, V) + print(output) + + +if __name__ == "__main__": + main() diff --git a/examples/custom_ops/histogram.py b/examples/custom_ops/histogram.py new file mode 100644 index 0000000000..4ebcbe2bfe --- /dev/null +++ b/examples/custom_ops/histogram.py @@ -0,0 +1,77 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +import numpy as np +from max.driver import CPU, Accelerator, Tensor, accelerator_count +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import Graph, TensorType, ops + +if __name__ == "__main__": + # This is necessary only in specific build environments. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + path = Path(__file__).parent / "kernels.mojopkg" + + n = 2**20 + + # Configure our simple one-operation graph. + graph = Graph( + "histogram", + # The custom Mojo operation is referenced by its string name, and we + # need to provide inputs as a list as well as expected output types. + forward=lambda x: ops.custom( + name="histogram", + values=[x], + out_types=[TensorType(dtype=DType.int64, shape=[256])], + )[0].tensor, + input_types=[ + TensorType(DType.uint8, shape=[n]), + ], + ) + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if accelerator_count() == 0 else Accelerator() + + # Set up an inference session for running the graph. + session = InferenceSession(devices=[device], custom_extensions=path) + + # Compile the graph. + model = session.load(graph) + + # Fill an input with random values. + x_values = np.random.randint(0, 256, size=n, dtype=np.uint8) + + # Create a driver tensor from this, and move it to the accelerator. + x = Tensor.from_numpy(x_values).to(device) + + # Perform the calculation on the target device. + model_result = model.execute(x)[0] + + # Copy values back to the CPU to be read. + assert isinstance(model_result, Tensor) + + print("Graph result:") + result = model_result.to_numpy() + print(result) + print() + + print("Expected result:") + expected = np.histogram(x_values, bins=256, range=(0, 256))[0] + print(expected) + + assert all(result == expected), "Result does not match expected" diff --git a/examples/custom_ops/kernels/__init__.mojo b/examples/custom_ops/kernels/__init__.mojo new file mode 100644 index 0000000000..23a1a193df --- /dev/null +++ b/examples/custom_ops/kernels/__init__.mojo @@ -0,0 +1,17 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .add_custom import * +from .mandelbrot import * +from .top_k import * +from .vector_addition import * diff --git a/examples/custom_ops/kernels/add_custom.mojo b/examples/custom_ops/kernels/add_custom.mojo new file mode 100644 index 0000000000..411ea7061e --- /dev/null +++ b/examples/custom_ops/kernels/add_custom.mojo @@ -0,0 +1,82 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import compiler +from max.tensor import ManagedTensorSlice, foreach +from runtime.asyncrt import DeviceContextPtr + +from utils.index import IndexList + + +@compiler.register("add_constant_custom", num_dps_outputs=1) +struct AddConstantCustom[value: Int]: + @staticmethod + fn execute[ + # e.g. "CUDA" or "CPU" + target: StringLiteral, + ]( + # as num_dps_outputs=1, the first argument is the "output" + out: ManagedTensorSlice, + # starting here are the list of inputs + x: ManagedTensorSlice[type = out.type, rank = out.rank], + # the context is needed for some GPU calls + ctx: DeviceContextPtr, + ): + @parameter + @always_inline + fn add_constant[ + width: Int + ](idx: IndexList[x.rank]) -> SIMD[x.type, width]: + return x.load[width](idx) + value + + foreach[add_constant, target=target](out, ctx) + + # You only need to implement this if you do not manually annotate + # output shapes in the graph. + @staticmethod + fn shape( + x: ManagedTensorSlice, + ) raises -> IndexList[x.rank]: + raise "NotImplemented" + + +@compiler.register("add_one_custom", num_dps_outputs=1) +struct AddOneCustom: + @staticmethod + fn execute[ + # The kind of device this will be run on: "cpu" or "gpu" + target: StringLiteral, + ]( + # as num_dps_outputs=1, the first argument is the "output" + out: ManagedTensorSlice, + # starting here are the list of inputs + x: ManagedTensorSlice[type = out.type, rank = out.rank], + # the context is needed for some GPU calls + ctx: DeviceContextPtr, + ): + @parameter + @always_inline + fn elementwise_add_one[ + width: Int + ](idx: IndexList[x.rank]) -> SIMD[x.type, width]: + return x.load[width](idx) + 1 + + foreach[elementwise_add_one, target=target](out, ctx) + + # You only need to implement this if you do not manually annotate + # output shapes in the graph. + @staticmethod + fn shape( + x: ManagedTensorSlice, + ) raises -> IndexList[x.rank]: + raise "NotImplemented" diff --git a/examples/custom_ops/kernels/fused_attention.mojo b/examples/custom_ops/kernels/fused_attention.mojo new file mode 100644 index 0000000000..28a53120b4 --- /dev/null +++ b/examples/custom_ops/kernels/fused_attention.mojo @@ -0,0 +1,360 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +""" +The code below computes the attention score for a tile of size BN x BD. +It follows the exact arithmetic as described in the FlashAttention-2 paper +(https://arxiv.org/pdf/2307.08691). The variable names in this program +reflect the variable names in the algorithms from the paper. + +Here, the following tensors are Q for the query, +K for the key, V for the value, and O for the output. + + Q K V + +----D----+ +----D----+ +--+--BD--+---+ + | | |.........| | |......| | + | | |.........| | |......| | + +---------+ |.........| | |......| | + |.........| |.........| | |......| | + BN........| N.........| N |......| | + |.........| |.........| | |......| | + +---------+ |.........| | |......| | + | | |.........| | |......| | + | | |.........| | |......| | + | | |.........| | |......| | + +---------+ +---------+ +--+------+---+ + +The main trick is in the softmax computation. +As the paper says, S and P are intermediate values. + +Let S = Q * K^T ∈ R^{N, D} + P = Softmax(S) ∈ R^{N, D} +The attention score is O = P * V ∈ R^{N, D}. + +One way to think about this is to consider what happens if we +split the dimensions N in K, and Q into two tiles: K_1 and K_2, V_1 and V_2. +Then we can incrementally compute the output as follows: + S_1 = Q * K_1, S_2 = Q * K_2 + O_i = O_{i-1} * renormalization_factor + softmax(S_i) * V_i + +This allows for the incremental computation of softmax(S_i) * V_i, +leading to the final output. +""" + + +from algorithm import parallelize_over_rows +from compiler import register +from utils.index import IndexList +from layout import Layout, LayoutTensor, RuntimeLayout, RuntimeTuple +from layout.tensor_core import TensorCore +from layout.math import exp, sum, max +from gpu.host import DeviceContext +from gpu.id import block_idx, thread_idx +from gpu.sync import barrier +from gpu.memory import AddressSpace +from utils import Index + +from tensor import ManagedTensorSlice + + +@register("fused_attention_custom", num_dps_outputs=1) +struct FusedAttention: + """Registers the `fused_attention_custom` op, allowing python to use it from the `max` + package. + """ + + @staticmethod + fn execute[ + dtype: DType, + rank: Int, + //, # Forces the previous two params to be inferred from the args + N: Int, # Input length + D: Int, # Head dimension + BN: Int, # Dimension of blocks to split Q into + BD: Int, # Dimension of blocks to split K, V into + target: StringLiteral, # "cpu" or "gpu" + ]( + output: ManagedTensorSlice[type=dtype, rank=rank], + key: ManagedTensorSlice[type=dtype, rank=rank], + query: ManagedTensorSlice[type=dtype, rank=rank], + value: ManagedTensorSlice[type=dtype, rank=rank], + ctx: DeviceContextPtr, + ) raises: + constrained[rank == 2, "rank must be 2"]() + + # Key tensor + K = key.to_layout_tensor() + # Query tensor + Q = query.to_layout_tensor() + # Value tensor + V = value.to_layout_tensor() + # Attention output tensor + O = output.to_layout_tensor() + + @parameter + if target == "cpu": + print("Running on CPU") + fused_attention_cpu[BN, BD](K, Q, V, O) + else: + dev_ctx = ctx.get_device_context() + print("Running on GPU") + fused_attention_gpu[BN, BD](dev_ctx, K, Q, V, O) + + +@always_inline +fn matmul_b_transpose( + lhs: LayoutTensor, + rhs: LayoutTensor, + out res: LayoutTensor[ + lhs.dtype, Layout.row_major(lhs.shape[0](), rhs.shape[0]()) + ], +): + res = __type_of(res).stack_allocation() + + @parameter + for m in range(lhs.shape[0]()): + + @parameter + for n in range(rhs.shape[0]()): + res[m, n] = 0.0 + + @parameter + for k in range(lhs.shape[1]()): + res[m, n] += rebind[res.element_type]( + lhs[m, k].cast[res.dtype]() + ) * rebind[res.element_type](rhs[n, k].cast[res.dtype]()) + + +""" +The bulk of the code below implements what the papers calls +an "online softmax", which is local to each block. +The algorithm is described as: + +$$$ +m_1 = rowmax(S_1) +l_1 = rowsum(e^(S_1-m_1)) +P_1 = diag(l_1)^-1 * e^(S_1-m_1) +O_1 = P_1*V_1 = diag(l_1)^-1 * e^(S_1-m_1) * V_1 +m_2 = max(m_1, rowmax(S_2)) = m +l_2 = e^(m_1-m_2) * l_1 _ rowsum(e^(S_2-m_2)) + = rowsum(e^(S_1-m)) + rowsum(e^(S_2-m)) = ls +P_2 = diag(l_2)^-1 * e^(S_2-m_2) +O_2 = diag(l_1/l_2)^-1 * O_1 + (P_2 * V_2) + = diag(l_2)^-1 * e^(S_2-m) * V +$$$ +""" + + +@always_inline +fn fused_attention_cpu[ + BN: Int, BD: Int +](Q: LayoutTensor, K: LayoutTensor, V: LayoutTensor, mut O: LayoutTensor): + alias N = K.shape[0]() + alias D = K.shape[1]() + + @parameter + for tile_n in range(N // BN): + Q_tile = Q.tile[BN, D](tile_n, 0) + + @parameter + for tile_d in range(D // BD): + m_1 = ( + LayoutTensor[Q_tile.dtype, Layout(BN, 1)] + .stack_allocation() + .fill(Scalar[Q_tile.dtype].MIN) + ) + + l_1 = ( + LayoutTensor[Q_tile.dtype, Layout(BN, 1)] + .stack_allocation() + .fill(0) + ) + + O_i = ( + LayoutTensor[Q_tile.dtype, Layout.row_major(BN, BD)] + .stack_allocation() + .fill(0) + ) + + @parameter + for tile_n_idx in range(N // BN): + K_tile = K.tile[BN, D](tile_n_idx, 0) + V_tile = V.tile[BN, BD](tile_n_idx, tile_d) + + S = matmul_b_transpose(Q_tile, K_tile) + m_2 = max(m_1, rebind[__type_of(m_1)](max[axis=1](S))) + l_2 = exp(m_1 - m_2) * l_1 + sum[axis=1](exp(S - m_2)) + + P = exp(S - m_2) / l_2 + O_i = O_i * (l_1 / l_2) * exp(m_1 - m_2) + matmul["cpu"]( + P, V_tile + ) + m_1 = m_2 + l_1 = rebind[__type_of(l_1)](l_2) + + O.tile[BN, BD](tile_n, tile_d).copy_from(O_i) + + +@always_inline +fn matmul[ + target: StringLiteral, + transpose_b: Bool = False, +]( + lhs: LayoutTensor, + rhs: LayoutTensor, + out res: LayoutTensor[ + lhs.dtype, + Layout.row_major(lhs.shape[0](), rhs.shape[0]()), + address_space = lhs.address_space, + element_layout = lhs.element_layout, + layout_bitwidth = lhs.layout_bitwidth, + ], +): + res = __type_of(res).stack_allocation() + + @parameter + if target == "cpu": + + @parameter + for m in range(lhs.shape[0]()): + + @parameter + for n in range(rhs.shape[1]()): + res[m, n] = 0.0 + + @parameter + for k in range(lhs.shape[1]()): + res[m, n] += rebind[res.element_type]( + lhs[m, k].cast[res.dtype]() + ) * rebind[res.element_type](rhs[k, n].cast[res.dtype]()) + else: + alias M = res.shape[0]() + alias N = res.shape[1]() + alias K = lhs.shape[1]() + + out_sram = LayoutTensor[ + res.dtype, + Layout.row_major(M, N), + address_space = AddressSpace.SHARED, + ].stack_allocation() + + alias BK = 8 + + constrained[K % 8 == 0, "K needs to be a multiple of 8"]() + + mma_b_t = TensorCore[ + lhs.dtype, res.dtype, Index(M, N, BK), transpose_b + ]() + + c_reg = mma_b_t.c_reg_tile_type.stack_allocation().fill(0) + + @parameter + for k_i in range(K // BK): + a_reg = mma_b_t.load_a(lhs.tile[M, BK](0, k_i)) + + b_reg = mma_b_t.load_b(rhs.tile[BK, N](k_i, 0)) + + @parameter + if transpose_b: + b_reg = rebind[__type_of(b_reg)]( + mma_b_t.load_b(rhs.tile[N, BK](0, k_i)) + ) + + d_reg = mma_b_t.mma_op(a_reg, b_reg, c_reg) + c_reg.copy_from(d_reg) + mma_b_t.store_d(out_sram, c_reg) + + barrier() + res.copy_from(out_sram) + + +fn fused_attention_kenel[ + q_dtype: DType, + q_layout: Layout, + k_dtype: DType, + k_layout: Layout, + v_dtype: DType, + v_layout: Layout, + o_dtype: DType, + o_layout: Layout, + BN: Int, + BD: Int, +]( + Q: LayoutTensor[q_dtype, q_layout], + K: LayoutTensor[k_dtype, k_layout], + V: LayoutTensor[v_dtype, v_layout], + O: LayoutTensor[o_dtype, o_layout], +): + alias N = Q.shape[0]() + alias D = Q.shape[1]() + + Q_tile = Q.tile[BN, D](block_idx.y, 0) + + m_1 = ( + LayoutTensor[q_dtype, Layout(BN, 1)] + .stack_allocation() + .fill(Scalar[q_dtype].MIN) + ) + l_1 = LayoutTensor[q_dtype, Layout(BN, 1)].stack_allocation().fill(0) + O_i = ( + LayoutTensor[q_dtype, Layout.row_major(BN, BD)] + .stack_allocation() + .fill(0) + ) + + alias BN_1 = 8 + + @parameter + for tile_n_idx in range(N // BN_1): + K_tile = K.tile[BN_1, D](tile_n_idx, 0) + V_tile = V.tile[BN_1, BD](tile_n_idx, block_idx.x) + S = matmul["gpu", transpose_b=True](Q_tile, K_tile) + m_2 = max(m_1, rebind[__type_of(m_1)](max[axis=1](S))) + l_2 = exp(m_1 - m_2) * l_1 + sum[axis=1](exp(S - m_2)) + P = exp(S - m_2) / l_2 + O_i = O_i * (l_1 / l_2) * exp(m_1 - m_2) + matmul["gpu"](P, V_tile) + m_1 = m_2 + l_1 = rebind[__type_of(l_1)](l_2) + O.tile[BN, BD](block_idx.y, block_idx.x).copy_from(O_i) + + +def fused_attention_gpu[ + BN: Int, + BD: Int, +]( + ctx: DeviceContext, + Q: LayoutTensor, + K: LayoutTensor, + V: LayoutTensor, + mut O: LayoutTensor, +): + alias kernel_func = fused_attention_kenel[ + Q.dtype, + Q.layout, + K.dtype, + K.layout, + V.dtype, + V.layout, + O.dtype, + O.layout, + BN, + BD, + ] + ctx.enqueue_function[kernel_func]( + Q, + K, + V, + O, + grid_dim=(Q.shape[1]() // BD, Q.shape[0]() // BN), + block_dim=(32), + ) diff --git a/examples/custom_ops/kernels/histogram.mojo b/examples/custom_ops/kernels/histogram.mojo new file mode 100644 index 0000000000..01e2237ed1 --- /dev/null +++ b/examples/custom_ops/kernels/histogram.mojo @@ -0,0 +1,83 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from math import ceildiv + +from gpu import block_dim, block_idx, thread_idx, global_idx +from gpu.host import DeviceContext +from runtime.asyncrt import DeviceContextPtr +from tensor import ManagedTensorSlice +from algorithm import vectorize, sync_parallelize +from algorithm.functional import _get_num_workers +from memory import memset +from sys import simdwidthof, sizeof +from os import Atomic + +from utils.index import IndexList +from gpu.host.info import Info, is_cpu, is_gpu + +from memory import UnsafePointer + +alias bin_width = Int(UInt8.MAX) + + +fn _histogram_cpu(out: ManagedTensorSlice, input: ManagedTensorSlice): + for i in range(input.dim_size(0)): + out[Int(input[i])] += 1 + + +fn _histogram_gpu( + output: ManagedTensorSlice, + input: ManagedTensorSlice, + ctx_ptr: DeviceContextPtr, +) raises: + alias block_dim = 1024 + + fn kernel( + output: UnsafePointer[Int64], input: UnsafePointer[UInt8], n: Int + ): + var tid = global_idx.x + + if tid >= n: + return + + _ = Atomic._fetch_add(output + Int(input[tid]), 1) + + var n = input.dim_size(0) + + var grid_dim = ceildiv(n, block_dim) + + var ctx = ctx_ptr.get_device_context() + + ctx.enqueue_function[kernel]( + output.unsafe_ptr(), + input.unsafe_ptr(), + n, + block_dim=block_dim, + grid_dim=grid_dim, + ) + + +@compiler.register("histogram", num_dps_outputs=1) +struct Histogram: + @staticmethod + fn execute[ + target: StringLiteral + ]( + out: ManagedTensorSlice[type = DType.int64, rank=1], + input: ManagedTensorSlice[type = DType.uint8, rank=1], + ctx: DeviceContextPtr, + ) raises: + _histogram_cpu(out, input) if is_cpu[target]() else _histogram_gpu( + out, input, ctx + ) diff --git a/examples/custom_ops/kernels/mandelbrot.mojo b/examples/custom_ops/kernels/mandelbrot.mojo new file mode 100644 index 0000000000..bf190e21c8 --- /dev/null +++ b/examples/custom_ops/kernels/mandelbrot.mojo @@ -0,0 +1,92 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from math import iota + +import compiler +from complex import ComplexSIMD +from max.tensor import ManagedTensorSlice, foreach +from runtime.asyncrt import DeviceContextPtr + +from utils.index import IndexList + + +@always_inline +fn mandelbrot_inner_simd[ + float_type: DType, int_type: DType, simd_width: Int +]( + c: ComplexSIMD[float_type, simd_width], max_iterations: SIMD[int_type, 1] +) -> SIMD[int_type, simd_width]: + """A vectorized implementation of the inner Mandelbrot computation.""" + var z = ComplexSIMD[float_type, simd_width](0, 0) + var iters = SIMD[int_type, simd_width](0) + + var in_set_mask: SIMD[DType.bool, simd_width] = True + for _ in range(max_iterations): + if not any(in_set_mask): + break + in_set_mask = z.squared_norm() <= 4 + iters = in_set_mask.select(iters + 1, iters) + z = z.squared_add(c) + + return iters + + +alias float_dtype = DType.float32 + + +@compiler.register("mandelbrot", num_dps_outputs=1) +struct Mandelbrot: + @staticmethod + fn execute[ + # The kind of device this will be run on: "cpu" or "gpu" + target: StringLiteral, + ]( + # as num_dps_outputs=1, the first argument is the "output" + out: ManagedTensorSlice, + # starting here are the list of inputs + min_x: Float32, + min_y: Float32, + scale_x: Float32, + scale_y: Float32, + max_iterations: Int32, + # the context is needed for some GPU calls + ctx: DeviceContextPtr, + ): + @parameter + @always_inline + fn elementwise_mandelbrot[ + width: Int + ](idx: IndexList[out.rank]) -> SIMD[out.type, width]: + var row = idx[0] + var col = idx[1] + var cx = min_x.cast[float_dtype]() + ( + col + iota[float_dtype, width]() + ) * scale_x.cast[float_dtype]() + var cy = min_y.cast[float_dtype]() + row * SIMD[float_dtype, width]( + scale_y.cast[float_dtype]() + ) + var c = ComplexSIMD[float_dtype, width](cx, cy) + return mandelbrot_inner_simd[cx.type, out.type, width]( + c, max_iterations.cast[out.type]() + ) + + foreach[elementwise_mandelbrot, target=target](out, ctx) + + # You only need to implement this if you do not manually annotate + # output shapes in the graph. + @staticmethod + fn shape( + x: ManagedTensorSlice, + ) raises -> IndexList[x.rank]: + raise "NotImplemented" diff --git a/examples/custom_ops/kernels/matrix_multiplication.mojo b/examples/custom_ops/kernels/matrix_multiplication.mojo new file mode 100644 index 0000000000..afc810b25a --- /dev/null +++ b/examples/custom_ops/kernels/matrix_multiplication.mojo @@ -0,0 +1,1007 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from gpu import WARP_SIZE, block_dim, block_idx, thread_idx +from gpu.host import DeviceBuffer, DeviceContext +from gpu.memory import async_copy_wait_all +from layout.layout_tensor import ( + Layout, + LayoutTensor, + copy_dram_to_sram, + copy_dram_to_sram_async, +) +from layout.math import outer_product_acc +from layout.tensor_builder import LayoutTensorBuild as tb +from layout.tensor_core import TensorCore +from math import ceildiv +from memory import UnsafePointer +from runtime.asyncrt import DeviceContextPtr +from sys.info import simdwidthof +from tensor import ManagedTensorSlice, foreach +from utils.index import Index + +# ===-----------------------------------------------------------------------===# +# Naive matrix multiplication (CPU) +# ===-----------------------------------------------------------------------===# + + +fn naive_matrix_multiplication_cpu( + out: ManagedTensorSlice, + a: ManagedTensorSlice[type = out.type, rank = out.rank], + b: ManagedTensorSlice[type = out.type, rank = out.rank], +): + """A naive matrix multiplication used as a fallback on CPU hardware.""" + var M = a.shape()[0] + var N = b.shape()[1] + var K = b.shape()[0] + + for row in range(M): + for col in range(N): + for k in range(K): + out[row, col] = out[row, col] + a[row, k] * b[k, col] + + +# ===-----------------------------------------------------------------------===# +# Naive matrix multiplication (GPU) +# ===-----------------------------------------------------------------------===# + + +fn naive_matrix_multiplication[ + dtype: DType, + a_layout: Layout, + b_layout: Layout, + c_layout: Layout, + BM: Int, + BN: Int, +]( + a: LayoutTensor[dtype, a_layout], + b: LayoutTensor[dtype, b_layout], + c: LayoutTensor[dtype, c_layout], +): + """ + Tiled GEMM kernel that performs matrix multiplication C = A * B. + + Parameters: + dtype: The data type of the input and output tensors. + a_layout: The layout of the input tensor A. + b_layout: The layout of the input tensor B. + c_layout: The layout of the output tensor C. + BM: The block size in the M dimension. + BN: The block size in the N dimension. + + Args: + a: The input tensor A. + b: The input tensor B. + c: The output tensor C. + + This kernel uses a simple nested loop structure to compute the matrix + multiplication. Each thread computes a single element of the output matrix + C by accumulating the dot product of the corresponding row of A and column + of B. + + The kernel assumes that the input matrices A and B are compatible for + matrix multiplication, i.e., the number of columns in A equals the number + of rows in B. + """ + # Calculate the column and row indices for each thread. + var col = thread_idx.y + var row = thread_idx.x + var bidx = block_idx.x + var bidy = block_idx.y + + # Get the tile of the output matrix C that this thread is + # responsible for computing. + var dst = c.tile[BM, BN](bidy, bidx) + + # Initialize a register to accumulate the result for this thread. + var dst_reg: c.element_type = 0 + + # Iterate over the K dimension to compute the dot product. + for k in range(b.dim(0)): + # Get the corresponding tiles from matrices A and B. + var a_tile = a.tile[BM, 1](bidy, k) + var b_tile = b.tile[1, BN](k, bidx) + + # Multiply the elements and accumulate the result. + dst_reg += a_tile[row, 0] * b_tile[0, col] + + # Write the final accumulated result to the output matrix. + dst[row, col] += dst_reg + + +# ===-----------------------------------------------------------------------===# +# Matrix multiplication with tiling +# ===-----------------------------------------------------------------------===# + + +fn coalescing_matrix_multiplication[ + dtype: DType, + a_layout: Layout, + b_layout: Layout, + c_layout: Layout, + BM: Int, + BN: Int, +]( + a: LayoutTensor[dtype, a_layout], + b: LayoutTensor[dtype, b_layout], + c: LayoutTensor[dtype, c_layout], +): + """ + GEMM kernel that performs matrix multiplication C = A * B with + memory coalescing optimizations. + + Parameters: + dtype: The data type of the input and output tensors. + a_layout: The layout of the input tensor A. + b_layout: The layout of the input tensor B. + c_layout: The layout of the output tensor C. + BM: The block size in the M dimension. + BN: The block size in the N dimension. + + Args: + a: The input tensor A. + b: The input tensor B. + c: The output tensor C. + + This kernel optimizes memory access patterns by ensuring that + threads within a warp access contiguous memory locations. It + tiles the input matrices A and B and computes the matrix + multiplication using register tiling. + + Each thread computes a single element of the output matrix C by + accumulating the partial results in a register. The final result + is then stored back to the output matrix. + """ + + var col = thread_idx.x + var row = thread_idx.y + var bidx = block_idx.x + var bidy = block_idx.y + + # Get the tile of the output matrix C + var dst = c.tile[BM, BN](bidy, bidx) + + # Initialize the register to accumulate the result + var dst_reg: c.element_type = 0 + + # Iterate over the K dimension + for k in range(b.dim(0)): + # Get the tiles of input matrices A and B + var a_tile = a.tile[BM, 1](bidy, k) + var b_tile = b.tile[1, BN](k, bidx) + + # Compute the partial result and accumulate it in the register + dst_reg += a_tile[row, 0] * b_tile[0, col] + + # Store the final result back to the output matrix + dst[row, col] += dst_reg + + +# ===-----------------------------------------------------------------------===# +# Matrix multiplication with shared memory tiling +# ===-----------------------------------------------------------------------===# + + +fn tiled_matrix_multiplication[ + dtype: DType, + a_layout: Layout, + b_layout: Layout, + c_layout: Layout, + BM: Int, + BN: Int, + BK: Int, + NUM_THREADS: Int, +]( + a: LayoutTensor[dtype, a_layout], + b: LayoutTensor[dtype, b_layout], + c: LayoutTensor[dtype, c_layout], +): + """ + Tiled GEMM kernel that performs matrix multiplication C = A * B using + shared memory to improve performance. + + Parameters: + dtype: The data type of the input and output tensors. + a_layout: The layout of the input tensor A. + b_layout: The layout of the input tensor B. + c_layout: The layout of the output tensor C. + BM: The block size in the M dimension. + BN: The block size in the N dimension. + BK: The block size in the K dimension. + NUM_THREADS: The total number of threads per block. + + Args: + a: The input tensor A. + b: The input tensor B. + c: The output tensor C. + + This kernel uses a tiling strategy to compute the matrix multiplication. + Each thread block computes a BM x BN tile of the output matrix C. The + input matrices A and B are loaded into shared memory in tiles of size + BM x BK and BK x BN, respectively. + + The kernel assumes that the input matrices A and B are compatible for + matrix multiplication, i.e., the number of columns in A equals the + number of rows in B. + """ + # Calculate the column and row indices for each thread + var col = thread_idx.x % BN + var row = thread_idx.x // BN + + # Get the tile of the output matrix C that this thread block is responsible for + var dst = c.tile[BM, BN](block_idx.y, block_idx.x) + + # Allocate shared memory for tiles of input matrices A and B + var a_smem = tb[dtype]().row_major[BM, BK]().shared().alloc() + var b_smem = tb[dtype]().row_major[BK, BN]().shared().alloc() + + # Initialize the register to accumulate the result + var dst_reg: c.element_type = 0 + + # Iterate over tiles of input matrices A and B + for block in range(b.dim(0) // BK): + # Define the layout for loading tiles of A and B into shared memory + alias load_a_layout = Layout.row_major(NUM_THREADS // BK, BK) + alias load_b_layout = Layout.row_major(BK, NUM_THREADS // BK) + + # Get the tiles of A and B for the current iteration + var a_tile = a.tile[BM, BK](block_idx.y, block) + var b_tile = b.tile[BK, BN](block, block_idx.x) + + # Asynchronously copy tiles of A and B from global memory to shared memory + copy_dram_to_sram_async[thread_layout=load_a_layout](a_smem, a_tile) + copy_dram_to_sram_async[thread_layout=load_b_layout](b_smem, b_tile) + + # Wait for all asynchronous copies to complete + async_copy_wait_all() + + # Synchronize threads to ensure shared memory is populated + barrier() + + # Perform matrix multiplication on the tiles in shared memory + @parameter + for k in range(BK): + dst_reg += a_smem[row, k] * b_smem[k, col] + + # Synchronize threads before loading the next tiles + barrier() + + # Write the result to the output matrix + dst[row, col] += dst_reg + + +# ===-----------------------------------------------------------------------===# +# Matrix multiplication with shared memory tiling and register tiling +# ===-----------------------------------------------------------------------===# + + +fn tiled_register_matrix_multiplication[ + dtype: DType, + a_layout: Layout, + b_layout: Layout, + c_layout: Layout, + BM: Int, + BN: Int, + BK: Int, + TM: Int, + NUM_THREADS: Int, +]( + a: LayoutTensor[dtype, a_layout], + b: LayoutTensor[dtype, b_layout], + c: LayoutTensor[dtype, c_layout], +): + """ + Tiled GEMM kernel that performs matrix multiplication C = A * B using + shared memory. + + Parameters: + dtype: The data type of the input and output tensors. + a_layout: The layout of the input tensor A. + b_layout: The layout of the input tensor B. + c_layout: The layout of the output tensor C. + BM: The block size in the M dimension. + BN: The block size in the N dimension. + BK: The block size in the K dimension. + TM: The tile size in the M dimension. + NUM_THREADS: The number of threads per block. + + Args: + a: The input tensor A. + b: The input tensor B. + c: The output tensor C. + + This kernel uses a tiled approach to compute the matrix multiplication. It + loads tiles of matrices A and B into shared memory, and then each thread + computes a partial result using the tiles in shared memory. The partial + results are accumulated in registers and finally stored back to the output + matrix C. + + The kernel assumes that the input matrices A and B are compatible for + matrix multiplication, i.e., the number of columns in A equals the number + of rows in B. + """ + # Calculate the column and row indices for each thread. + var col = thread_idx.x % BN + var row = thread_idx.x // BN + var bidx = block_idx.x + var bidy = block_idx.y + + # Get the tile of the output matrix C that this thread is + # responsible for computing. + var dst = c.tile[BM, BN](bidy, bidx).tile[TM, 1](row, col) + + # Allocate shared memory for tiles of A and B. + var a_smem = tb[dtype]().row_major[BM, BK]().shared().alloc() + var b_smem = tb[dtype]().row_major[BK, BN]().shared().alloc() + + # Allocate a register tile to store the partial results. + var dst_reg = tb[dtype]().layout[TM]().local().alloc() + dst_reg.copy_from(dst) + + # Iterate over the tiles of A and B in the K dimension. + for block in range(b.dim(0) // BK): + # Define the layout for loading tiles of A and B into shared + # memory. + alias load_a_layout = Layout.row_major(NUM_THREADS // BK, BK) + alias load_b_layout = Layout.row_major(BK, NUM_THREADS // BK) + + # Get the tiles of A and B for the current block. + var a_tile = a.tile[BM, BK](block_idx.y, block) + var b_tile = b.tile[BK, BN](block, block_idx.x) + + # Load the tiles of A and B into shared memory asynchronously. + copy_dram_to_sram_async[thread_layout=load_a_layout](a_smem, a_tile) + copy_dram_to_sram_async[thread_layout=load_b_layout](b_smem, b_tile) + + # Wait for all asynchronous copies to complete. + async_copy_wait_all() + barrier() + + # Iterate over the elements in the K dimension within the tiles. + @parameter + for k in range(BK): + # Get the corresponding tiles from shared memory. + var a_tile = a_smem.tile[TM, 1](row, k) + var b_tile = b_smem.tile[1, BN](k, 0) + var b_val = b_tile[0, col] + + # Multiply the elements and accumulate the partial results. + @parameter + for t in range(TM): + dst_reg[t] += a_tile[t, 0] * b_val + + # Synchronize all threads before loading the next tiles. + barrier() + + # Write the final accumulated results to the output matrix. + dst.copy_from(dst_reg) + + +# ===-----------------------------------------------------------------------===# +# Matrix multiplication with block tiling +# ===-----------------------------------------------------------------------===# + + +fn block_tiled_matrix_multiplication[ + dtype: DType, + a_layout: Layout, + b_layout: Layout, + c_layout: Layout, + BM: Int, + BN: Int, + BK: Int, + TM: Int, + TN: Int, + NUM_THREADS: Int, +]( + a: LayoutTensor[dtype, a_layout], + b: LayoutTensor[dtype, b_layout], + c: LayoutTensor[dtype, c_layout], +): + """ + Tiled GEMM kernel that performs matrix multiplication C = A * B. + + Parameters: + dtype: The data type of the input and output tensors. + a_layout: The layout of the input tensor A. + b_layout: The layout of the input tensor B. + c_layout: The layout of the output tensor C. + BM: The block size in the M dimension. + BN: The block size in the N dimension. + BK: The block size in the K dimension. + TM: The tile size in the M dimension. + TN: The tile size in the N dimension. + NUM_THREADS: The total number of threads per block. + + Args: + a: The input tensor A. + b: The input tensor B. + c: The output tensor C. + + This kernel uses a 2D block tiling strategy to compute the matrix + multiplication. Each thread block computes a BM x BN tile of the output + matrix C. Within each thread block, threads are further divided into + TM x TN tiles to enable thread-level parallelism. + + The kernel loads tiles of A and B into shared memory to reduce global + memory accesses. It then performs the matrix multiplication using + register-level tiling and accumulates the results in registers. + + The kernel assumes that the input matrices A and B are compatible for + matrix multiplication, i.e., the number of columns in A equals the number + of rows in B. + """ + var partition_col = thread_idx.x % (BN // TN) + var partition_row = thread_idx.x // (BN // TN) + var bidx = block_idx.x + var bidy = block_idx.y + + var dst = c.tile[BM, BN](bidy, bidx).tile[TM, TN]( + partition_row, partition_col + ) + + var a_smem = tb[dtype]().row_major[BM, BK]().shared().alloc() + var b_smem = tb[dtype]().row_major[BK, BN]().shared().alloc() + + var dst_reg = tb[dtype]().row_major[TM, TN]().local().alloc() + dst_reg.copy_from(dst) + var a_reg = tb[dtype]().layout[TM]().local().alloc() + var b_reg = tb[dtype]().layout[TN]().local().alloc() + + var ntiles = b.dim(0) // BK + + for block in range(ntiles): + alias load_a_layout = Layout.row_major(NUM_THREADS // BK, BK) + alias load_b_layout = Layout.row_major(BK, NUM_THREADS // BK) + var a_tile = a.tile[BM, BK](block_idx.y, block) + var b_tile = b.tile[BK, BN](block, block_idx.x) + copy_dram_to_sram_async[thread_layout=load_a_layout](a_smem, a_tile) + copy_dram_to_sram_async[thread_layout=load_b_layout](b_smem, b_tile) + + async_copy_wait_all() + barrier() + + @parameter + for k in range(BK): + var a_tile = a_smem.tile[TM, 1](partition_row, k) + var b_tile = b_smem.tile[1, TN](k, partition_col) + a_reg.copy_from(a_tile) + b_reg.copy_from(b_tile) + outer_product_acc(dst_reg, a_reg, b_reg) + barrier() + + dst.copy_from(dst_reg) + + +# ===-----------------------------------------------------------------------===# +# Matrix multiplication with vectorized memory access +# ===-----------------------------------------------------------------------===# + + +fn block_tiled_vectorized_matrix_multiplication[ + dtype: DType, + a_layout: Layout, + b_layout: Layout, + c_layout: Layout, + BM: Int, + BN: Int, + BK: Int, + TM: Int, + TN: Int, + NUM_THREADS: Int, +]( + a: LayoutTensor[dtype, a_layout], + b: LayoutTensor[dtype, b_layout], + c: LayoutTensor[dtype, c_layout], +): + """ + Tiled GEMM kernel that performs matrix multiplication C = A * B with + vectorized memory access. + + Parameters: + dtype: The data type of the input and output tensors. + a_layout: The layout of the input tensor A. + b_layout: The layout of the input tensor B. + c_layout: The layout of the output tensor C. + BM: The block size in the M dimension. + BN: The block size in the N dimension. + BK: The block size in the K dimension. + TM: The tile size in the M dimension. + TN: The tile size in the N dimension. + NUM_THREADS: The total number of threads per block. + + Args: + a: The input tensor A. + b: The input tensor B. + c: The output tensor C. + + This kernel uses a 2D block tiling strategy to compute the matrix + multiplication. Each thread block computes a BM x BN tile of the output + matrix C. Within each thread block, threads are further divided into TM x + TN tiles to enable thread-level parallelism. + + The kernel loads tiles of A and B into shared memory using vectorized + memory access to improve memory bandwidth utilization. It then performs the + matrix multiplication using register-level tiling and accumulates the + results in registers. + + The kernel assumes that the input matrices A and B are compatible for + matrix multiplication, i.e., the number of columns in A equals the number + of rows in B. + """ + + alias simd_width = simdwidthof[dtype]() + var partition_col = thread_idx.x % (BN // TN) + var partition_row = thread_idx.x // (BN // TN) + var bidx = block_idx.x + var bidy = block_idx.y + + # Get the tile of the output matrix C that this thread is responsible + # for computing. + var dst = c.tile[BM, BN](bidy, bidx).tile[TM, TN]( + partition_row, partition_col + ) + var dst_vec = dst.vectorize[1, simd_width]() + + # Allocate shared memory for tiles of A and B. + # Use column-major layout for A to get the transpose. + var a_smem = tb[dtype]().col_major[BM, BK]().shared().alloc() + var b_smem = tb[dtype]().row_major[BK, BN]().shared().alloc() + + # Allocate register tiles to store the partial results and operands. + var dst_reg = tb[dtype]().row_major[TM, TN]().local().alloc() + var dst_reg_vec = dst_reg.vectorize[1, simd_width]() + dst_reg_vec.copy_from(dst_vec) + + var a_reg = tb[dtype]().layout[TM]().local().alloc() + var b_reg = tb[dtype]().layout[TN]().local().alloc() + + var ntiles = b.dim(0) // BK + + # Iterate over the tiles of A and B in the K dimension. + for block in range(ntiles): + alias load_a_layout = Layout.row_major(NUM_THREADS // BK, BK) + alias load_b_layout = Layout.row_major(BK, NUM_THREADS // BK) + var a_tile = a.tile[BM, BK](block_idx.y, block) + var b_tile = b.tile[BK, BN](block, block_idx.x) + + # Load the tiles of A and B into shared memory using vectorized + # memory access. + copy_dram_to_sram_async[thread_layout=load_a_layout]( + a_smem.vectorize[simd_width, 1](), a_tile.vectorize[simd_width, 1]() + ) + copy_dram_to_sram_async[thread_layout=load_b_layout]( + b_smem.vectorize[1, simd_width](), b_tile.vectorize[1, simd_width]() + ) + + async_copy_wait_all() + barrier() + + # Iterate over the elements in the K dimension within the tiles. + @parameter + for k in range(BK): + # Load the corresponding tiles from shared memory into registers. + var a_tile = a_smem.tile[TM, 1](partition_row, k) + var b_tile = b_smem.tile[1, TN](k, partition_col) + a_reg.copy_from(a_tile) + b_reg.copy_from(b_tile) + + # Perform outer product and accumulate the partial results. + outer_product_acc(dst_reg, a_reg, b_reg) + + barrier() + + # Write the final accumulated results to the output matrix. + dst_vec.copy_from(dst_reg_vec) + + +# ===-----------------------------------------------------------------------===# +# Matrix multiplication using Tensor Cores +# ===-----------------------------------------------------------------------===# + + +fn tensor_core_matrix_multiplication[ + dtype: DType, + layout_a: Layout, + layout_b: Layout, + layout_c: Layout, + BM: Int, + BN: Int, + BK: Int, + WM: Int, + WN: Int, + MMA_M: Int, + MMA_N: Int, + MMA_K: Int, +]( + A: LayoutTensor[dtype, layout_a], + B: LayoutTensor[dtype, layout_b], + C: LayoutTensor[dtype, layout_c], +): + """ + Tiled GEMM kernel that performs matrix multiplication C = A * B using + tensor cores. + + Parameters: + dtype: The data type of the input and output tensors. + layout_a: The layout of the input tensor A. + layout_b: The layout of the input tensor B. + layout_c: The layout of the output tensor C. + BM: The block size in the M dimension. + BN: The block size in the N dimension. + BK: The block size in the K dimension. + WM: The warp tile size in the M dimension. + WN: The warp tile size in the N dimension. + MMA_M: Tensor core instruction shape in M dimension. + MMA_N: Tensor core instruction shape in N dimension. + MMA_K: Tensor core instruction shape in K dimension. + + Args: + A: The input tensor A. + B: The input tensor B. + C: The output tensor C. + + This kernel uses a tiled approach with tensor cores to compute the matrix + multiplication. It loads tiles of matrices A and B into shared memory, and + then each warp computes a partial result using tensor cores. The partial + results are accumulated in registers and finally stored back to the output + matrix C. + + The kernel assumes that the input matrices A and B are compatible for + matrix multiplication, i.e., the number of columns in A equals the number + of rows in B. + """ + alias M = C.shape[0]() # Number of rows in matrix C + alias N = C.shape[1]() # Number of columns in matrix C + alias K = A.shape[1]() # Number of columns in matrix A + + var warp_id = thread_idx.x // WARP_SIZE # Warp ID within the block + + # Calculate warp tile coordinates within the block + warp_y = warp_id // (BN // WN) + warp_x = warp_id % (BN // WN) + + # Get the warp tile of the output matrix C + C_warp_tile = C.tile[BM, BN](block_idx.y, block_idx.x).tile[WM, WN]( + warp_y, warp_x + ) + + # Ensure warp tile dimensions are multiples of instruction shape + constrained[ + WM % MMA_M == 0 and WN % MMA_N == 0 and K % MMA_K == 0, + "Warp tile should be an integer multiple of instruction shape", + ]() + + # Create tensor core operation object + mma_op = TensorCore[A.dtype, C.dtype, Index(MMA_M, MMA_N, MMA_K)]() + + # Allocate shared memory for tiles of A and B + A_sram_tile = tb[A.dtype]().row_major[BM, BK]().shared().alloc() + B_sram_tile = tb[B.dtype]().row_major[BK, BN]().shared().alloc() + + # Allocate register tile for accumulating partial results + c_reg = ( + tb[C.dtype]() + .row_major[WM // MMA_M, (WN * 4) // MMA_N]() + .local() + .alloc() + .fill(0) + ) + + # Iterate over tiles of A and B in the K dimension + for k_i in range(K // BK): + barrier() # Synchronize before loading new tiles + + # Get the tiles of A and B for the current iteration + A_dram_tile = A.tile[BM, BK](block_idx.y, k_i) + B_dram_tile = B.tile[BK, BN](k_i, block_idx.x) + + # Load tiles of A and B into shared memory asynchronously + copy_dram_to_sram_async[thread_layout = Layout.row_major(4, 8)]( + A_sram_tile.vectorize[1, 4](), A_dram_tile.vectorize[1, 4]() + ) + copy_dram_to_sram_async[thread_layout = Layout.row_major(4, 8)]( + B_sram_tile.vectorize[1, 4](), B_dram_tile.vectorize[1, 4]() + ) + + async_copy_wait_all() # Wait for async copies to complete + barrier() # Synchronize after loading tiles + + # Get the warp tiles of A and B from shared memory + A_warp_tile = A_sram_tile.tile[WM, BK](warp_y, 0) + B_warp_tile = B_sram_tile.tile[BK, WN](0, warp_x) + + # Iterate over the elements in the K dimension within the tiles + @parameter + for mma_k in range(BK // MMA_K): + + @parameter + for mma_m in range(WM // MMA_M): + + @parameter + for mma_n in range(WN // MMA_N): + # Get the register tile for the current MMA operation + c_reg_m_n = c_reg.tile[1, 4](mma_m, mma_n) + + # Get the MMA tiles of A and B + A_mma_tile = A_warp_tile.tile[MMA_M, MMA_K](mma_m, mma_k) + B_mma_tile = B_warp_tile.tile[MMA_K, MMA_N](mma_k, mma_n) + + # Load fragments of A and B into registers + a_reg = mma_op.load_a(A_mma_tile) + b_reg = mma_op.load_b(B_mma_tile) + + # Perform MMA operation and accumulate the result + var d_reg_m_n = mma_op.mma_op( + a_reg, + b_reg, + c_reg_m_n, + ) + + # Store the accumulated result back to the register tile + c_reg_m_n.copy_from(d_reg_m_n) + + # Write the final accumulated results to the output matrix + @parameter + for mma_m in range(WM // MMA_M): + + @parameter + for mma_n in range(WN // MMA_N): + var C_mma_tile = C_warp_tile.tile[MMA_M, MMA_N](mma_m, mma_n) + var c_reg_m_n = c_reg.tile[1, 4](mma_m, mma_n) + mma_op.store_d(C_mma_tile, c_reg_m_n) + + +# ===-----------------------------------------------------------------------===# +# The matrix multiplication graph operation +# ===-----------------------------------------------------------------------===# + + +@compiler.register("matrix_multiplication", num_dps_outputs=1) +struct MatrixMultiplication[algorithm: StringLiteral]: + """ + The central custom operation that dispatches to multiple different + matrix multiplication implementations, depending on target hardware and + selected algorithm. + """ + + @staticmethod + fn execute[ + # The kind of device this will be run on: "cpu" or "gpu" + target: StringLiteral, + ]( + # as num_dps_outputs=1, the first argument is the "output" + out: ManagedTensorSlice[rank=2], + # starting here are the list of inputs + a: ManagedTensorSlice[type = out.type, rank = out.rank], + b: ManagedTensorSlice[type = out.type, rank = out.rank], + # the context is needed for some GPU calls + ctx: DeviceContextPtr, + ) raises: + # At graph compilation time, we will know what device we are compiling + # this operation for, so we can specialize it for the target hardware. + @parameter + if target == "gpu": + a_layout = a.to_layout_tensor() + b_layout = b.to_layout_tensor() + out_layout = out.to_layout_tensor() + + M = a_layout.shape[0]() + N = b_layout.shape[1]() + + gpu_ctx = ctx.get_device_context() + + # Zero out the memory in the outbound tensor. + gpu_ctx.memset( + DeviceBuffer[out.type]( + gpu_ctx, + rebind[UnsafePointer[Scalar[out.type]]](out_layout.ptr), + M * N, + owning=False, + ), + 0, + ) + + # We support several compile-time variants for the matrix + # multiplication calculation: + # - "naive": A naive matrix multiplication using LayoutTensors. + # - "coalescing": Matrix multiplication with memory coalescing + # optimizations. + # - "tiled": Matrix multiplication using a tiling strategy. + # - "tiled_register": Matrix multiplication using shared memory + # and register tiling . + # - "block_tiled": Matrix multiplication using a 2D block tiling + # strategy. + # - "block_tiled_vectorized": Matrix multiplication using a + # further-optimized 2D block tiling strategy. + # - "tensor_core": Matrix multiplication using Tensor Cores. + # In each case, the specific matrix multiplication function is + # compiled and enqueued to run on the GPU. + @parameter + if algorithm == "naive": + alias BM = 32 + alias BN = 32 + gpu_ctx.enqueue_function[ + naive_matrix_multiplication[ + out.type, + a_layout.layout, + b_layout.layout, + out_layout.layout, + BM, + BN, + ] + ]( + a_layout, + b_layout, + out_layout, + grid_dim=(ceildiv(N, BN), ceildiv(M, BM)), + block_dim=(BN, BM), + ) + elif algorithm == "coalescing": + alias BM = 32 + alias BN = 32 + gpu_ctx.enqueue_function[ + coalescing_matrix_multiplication[ + out.type, + a_layout.layout, + b_layout.layout, + out_layout.layout, + BM, + BN, + ] + ]( + a_layout, + b_layout, + out_layout, + grid_dim=(ceildiv(N, BN), ceildiv(M, BM)), + block_dim=(BN, BM), + ) + elif algorithm == "tiled": + alias BM = 32 + alias BN = 32 + alias BK = 32 + alias NUM_THREADS = BM * BN + gpu_ctx.enqueue_function[ + tiled_matrix_multiplication[ + out.type, + a_layout.layout, + b_layout.layout, + out_layout.layout, + BM, + BN, + BK, + NUM_THREADS, + ] + ]( + a_layout, + b_layout, + out_layout, + grid_dim=(ceildiv(N, BN), ceildiv(M, BM)), + block_dim=(BM * BN), + ) + elif algorithm == "tiled_register": + alias BM = 64 + alias BN = 64 + alias BK = 8 + alias TM = 8 + alias NUM_THREADS = (BM * BN) // TM + gpu_ctx.enqueue_function[ + tiled_register_matrix_multiplication[ + out.type, + a_layout.layout, + b_layout.layout, + out_layout.layout, + BM, + BN, + BK, + TM, + NUM_THREADS, + ] + ]( + a_layout, + b_layout, + out_layout, + grid_dim=(ceildiv(N, BN), ceildiv(M, BM)), + block_dim=(NUM_THREADS), + ) + elif algorithm == "block_tiled": + alias BM = 128 + alias BN = 128 + alias BK = 8 + alias TM = 8 + alias TN = 8 + alias NUM_THREADS = (BM * BN) // (TM * TN) + gpu_ctx.enqueue_function[ + block_tiled_matrix_multiplication[ + out.type, + a_layout.layout, + b_layout.layout, + out_layout.layout, + BM, + BN, + BK, + TM, + TN, + NUM_THREADS, + ] + ]( + a_layout, + b_layout, + out_layout, + grid_dim=(ceildiv(N, BN), ceildiv(M, BM)), + block_dim=(NUM_THREADS), + ) + elif algorithm == "block_tiled_vectorized": + alias BM = 128 + alias BN = 128 + alias BK = 8 + alias TM = 8 + alias TN = 8 + alias NUM_THREADS = (BM * BN) // (TM * TN) + gpu_ctx.enqueue_function[ + block_tiled_matrix_multiplication[ + out.type, + a_layout.layout, + b_layout.layout, + out_layout.layout, + BM, + BN, + BK, + TM, + TN, + NUM_THREADS, + ] + ]( + a_layout, + b_layout, + out_layout, + grid_dim=(ceildiv(N, BN), ceildiv(M, BM)), + block_dim=(NUM_THREADS), + ) + elif algorithm == "tensor_core": + alias BM = 64 + alias BN = 64 + alias BK = 32 + alias WM = 32 + alias WN = 32 + alias MMA_M = 16 + alias MMA_N = 8 + alias MMA_K = 8 + alias NUM_WARPS = (BM // WM) * (BN // WN) + gpu_ctx.enqueue_function[ + tensor_core_matrix_multiplication[ + out.type, + a_layout.layout, + b_layout.layout, + out_layout.layout, + BM, + BN, + BK, + WM, + WN, + MMA_M, + MMA_N, + MMA_K, + ] + ]( + a_layout, + b_layout, + out_layout, + grid_dim=(ceildiv(N, BN), ceildiv(M, BM)), + block_dim=(NUM_WARPS * WARP_SIZE), + ) + else: + raise Error("No known matmul algorithm:", algorithm) + + else: + naive_matrix_multiplication_cpu(out, a, b) diff --git a/examples/custom_ops/kernels/top_k.mojo b/examples/custom_ops/kernels/top_k.mojo new file mode 100644 index 0000000000..7a03e23d18 --- /dev/null +++ b/examples/custom_ops/kernels/top_k.mojo @@ -0,0 +1,229 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from math import iota +from sys import alignof, sizeof, num_physical_cores + +from algorithm import parallelize_over_rows +from bit import log2_floor +from compiler import register +from gpu import WARP_SIZE, barrier +import gpu.warp as warp +from gpu.memory import AddressSpace, external_memory +from max.tensor import ManagedTensorSlice +from memory import Span +from benchmark import Bench, Bencher, BenchId, BenchMetric, ThroughputMeasure + +from utils.index import IndexList +from utils.numerics import min_or_neg_inf + + +@value +@register_passable("trivial") +struct TopKElement[T: DType]: + """Stores the value with it's index.""" + + var idx: Int32 + var val: Scalar[T] + + fn __gt__(self, rhs: Self) -> Bool: + return self.val > rhs.val + + +@register("top_k_custom", num_dps_outputs=2) +struct TopK: + """Registers the `top_k_custom` op, allowing python to use it from the `max` + package. This is a simplified version without bottom_k and sorting options, + or fused sampling. The purpose is to demonstrate concisely how you can + implement your own custom ops in Mojo that can be called from Python. MAX + has the "mo.top_k" op which is feature complete. + """ + + @staticmethod + fn execute[ + type: DType, + rank: Int, + //, # Forces the previous two params to be inferred from the args + K: Int, + target: StringLiteral, + ]( + out_vals: ManagedTensorSlice[type=type, rank=rank], + out_idxs: ManagedTensorSlice[type = DType.int32, rank=rank], + in_vals: ManagedTensorSlice[type=type, rank=rank], + ctx: DeviceContextPtr, + ) raises: + constrained[rank == 2, "rank must be 2"]() + var shape = in_vals.shape() + var batch_size = shape[0] + + var dev_ctx = ctx.get_device_context() + print("Executing on device:", dev_ctx.name()) + + @parameter + fn top_k_gpu[ + K: Int, + ]( + out_vals: __type_of(out_vals), + out_idxs: __type_of(out_idxs), + in_vals: __type_of(in_vals), + ): + var bid = block_idx.x + var tid = thread_idx.x + + # Get a pointer to shared memory for the indices and values + var top_k_sram = external_memory[ + TopKElement[type], + address_space = AddressSpace.SHARED, + alignment = alignof[TopKElement[type]](), + ]() + + # Threads put their corresponding index and value into shared memory + top_k_sram[tid] = TopKElement(tid, in_vals[bid, tid]) + # Finish packing the values across threads in this block + barrier() + + @parameter + for i in range(K): + var reduced = top_k_sram[tid] + alias limit = log2_floor(WARP_SIZE) + + # TODO(KERN-1544): `gpu.shuffle.warp_max` support index/value + @parameter + for j in reversed(range(limit)): + alias offset = 1 << j + # Parallel reduction using warp shuffle. Each thread gets a + # value from a thread 'offset' positions higher, keeping the + # larger value. + var shuffled = TopKElement( + warp.shuffle_down(reduced.idx, offset), + warp.shuffle_down(reduced.val, offset), + ) + reduced = max(reduced, shuffled) + + # Wait for all threads to finish reducing their values + barrier() + + # Thread 0 now has the reduced max value for this index + if tid == 0: + # Store the reduced top_k index and value in global memory + out_vals[bid, i] = reduced.val + out_idxs[bid, i] = reduced.idx + + # Remove found maximum from consideration in the next iter + var index = reduced.idx % block_dim.x + top_k_sram[index].val = min_or_neg_inf[type]() + + @parameter + fn top_k_cpu(start_idx: Int, end_idx: Int): + for row_idx in range(start_idx, end_idx): + var offset = (row_idx * K) + iota(out_idxs.unsafe_ptr() + offset, K) + + @parameter + fn val_greater_than(lhs: Int32, rhs: Int32) -> Bool: + return ( + in_vals[row_idx, Int(lhs)] > in_vals[row_idx, Int(rhs)] + ) + + sort[val_greater_than](Span(out_idxs.unsafe_ptr() + offset, K)) + + for i in range(K): + var sorted_idx = Int(out_idxs[row_idx, i]) + out_vals[row_idx, i] = in_vals[row_idx, sorted_idx] + + if batch_size <= 10: + + @parameter + if target == "gpu": + # This is a simplified version that only works for K being under + # the warp size. The MAX "mo.top_k" op supports any K and does + # another reduction after each warp has reduced its values. + if K >= WARP_SIZE: + raise Error( + "[top_k_custom] K=", + K, + " but must be less than the WARP_SIZE=", + WARP_SIZE, + ) + + if K < WARP_SIZE: + dev_ctx.enqueue_function[top_k_gpu[K]]( + out_vals, + out_idxs, + in_vals, + grid_dim=batch_size, # One block per batch + block_dim=K, # One thread per K + shared_mem_bytes=K * sizeof[TopKElement[type]](), + ) + else: + # Set grain size to 1 to put each batch in a separate task + parallelize_over_rows[top_k_cpu](shape, 1, grain_size=1) + + # Everything below is for benchmarking when running a stress test + else: + var bench = Bench() + + @parameter + @always_inline + fn bench_gpu(mut b: Bencher, shape: IndexList[rank]) raises: + @parameter + @always_inline + fn kernel_launch(dev_ctx: DeviceContext) raises: + dev_ctx.enqueue_function[top_k_gpu[K]]( + out_vals, + out_idxs, + in_vals, + grid_dim=batch_size, # One block per batch + block_dim=K, # One thread per K + shared_mem_bytes=K * sizeof[TopKElement[type]](), + ) + + b.iter_custom[kernel_launch](ctx.get_device_context()) + + @parameter + @always_inline + fn bench_cpu(mut b: Bencher) raises: + var grain = 1 + # Split job up evenly across physical cores on large batch + if batch_size > 1000: + grain = batch_size // num_physical_cores() + + @parameter + fn run_bench(): + parallelize_over_rows[top_k_cpu](shape, 1, grain_size=grain) + + b.iter[run_bench]() + + var els = ThroughputMeasure( + BenchMetric.elements, shape.flattened_length() + ) + var flops = ThroughputMeasure( + BenchMetric.flops, shape.flattened_length() * log2_floor(K) + ) + + # Only benchmark GPU if it's available + @parameter + if target == "gpu": + bench.bench_with_input[IndexList[rank], bench_gpu]( + BenchId("top_k_custom", "gpu"), shape, els, flops + ) + + # TODO: Always benchmark CPU to compare with GPU + else: + bench.bench_function[bench_cpu]( + BenchId("top_k_custom", "cpu"), els, flops + ) + + bench.config.verbose_metric_names = False + bench.config.verbose_timing = True + print(bench) diff --git a/examples/custom_ops/kernels/vector_addition.mojo b/examples/custom_ops/kernels/vector_addition.mojo new file mode 100644 index 0000000000..9b7f3e7d6b --- /dev/null +++ b/examples/custom_ops/kernels/vector_addition.mojo @@ -0,0 +1,101 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from math import ceildiv + +from gpu import block_dim, block_idx, thread_idx +from gpu.host import DeviceContext +from runtime.asyncrt import DeviceContextPtr +from tensor import ManagedTensorSlice, foreach + +from utils.index import IndexList + + +fn _vector_addition_cpu( + out: ManagedTensorSlice, + lhs: ManagedTensorSlice[type = out.type, rank = out.rank], + rhs: ManagedTensorSlice[type = out.type, rank = out.rank], + ctx: DeviceContextPtr, +): + # Warning: This is an extremely inefficient implementation! It's merely an + # instructional example of how a dedicated CPU-only path can be specified + # for basic vector addition. + var vector_length = out.dim_size(0) + for i in range(vector_length): + var idx = IndexList[out.rank](i) + var result = lhs.load[1](idx) + rhs.load[1](idx) + out.store[1](idx, result) + + +fn _vector_addition_gpu( + out: ManagedTensorSlice, + lhs: ManagedTensorSlice[type = out.type, rank = out.rank], + rhs: ManagedTensorSlice[type = out.type, rank = out.rank], + ctx: DeviceContextPtr, +) raises: + # Note: The following has not been tuned for any GPU hardware, and is an + # instructional example for how a simple GPU function can be constructed + # and dispatched. + alias BLOCK_SIZE = 16 + var gpu_ctx = ctx.get_device_context() + var vector_length = out.dim_size(0) + + # The function that will be launched and distributed across GPU threads. + @parameter + fn vector_addition_gpu_kernel(length: Int): + var tid = block_dim.x * block_idx.x + thread_idx.x + if tid < length: + var idx = IndexList[out.rank](tid) + var result = lhs.load[1](idx) + rhs.load[1](idx) + out.store[1](idx, result) + + # The vector is divided up into blocks, making sure there's an extra + # full block for any remainder. + var num_blocks = ceildiv(vector_length, BLOCK_SIZE) + + # The GPU function is compiled and enqueued to run on the GPU across the + # 1-D vector, split into blocks of `BLOCK_SIZE` width. + gpu_ctx.enqueue_function[vector_addition_gpu_kernel]( + vector_length, grid_dim=num_blocks, block_dim=BLOCK_SIZE + ) + + +@compiler.register("vector_addition", num_dps_outputs=1) +struct VectorAddition: + @staticmethod + fn execute[ + # The kind of device this will be run on: "cpu" or "gpu" + target: StringLiteral, + ]( + # as num_dps_outputs=1, the first argument is the "output" + out: ManagedTensorSlice[rank=1], + # starting here are the list of inputs + lhs: ManagedTensorSlice[type = out.type, rank = out.rank], + rhs: ManagedTensorSlice[type = out.type, rank = out.rank], + # the context is needed for some GPU calls + ctx: DeviceContextPtr, + ) raises: + # For a simple elementwise operation like this, the `foreach` function + # does much more rigorous hardware-specific tuning. We recommend using + # that abstraction, with this example serving purely as an illustration + # of how lower-level functions can be used to program GPUs via Mojo. + + # At graph compilation time, we will know what device we are compiling + # this operation for, so we can specialize it for the target hardware. + @parameter + if target == "cpu": + _vector_addition_cpu(out, lhs, rhs, ctx) + elif target == "gpu": + _vector_addition_gpu(out, lhs, rhs, ctx) + else: + raise Error("No known target:", target) diff --git a/examples/custom_ops/mandelbrot.py b/examples/custom_ops/mandelbrot.py new file mode 100644 index 0000000000..b1d4855171 --- /dev/null +++ b/examples/custom_ops/mandelbrot.py @@ -0,0 +1,99 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +from max.driver import CPU, Accelerator, Tensor, accelerator_count +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import Graph, TensorType, ops + + +def create_mandelbrot_graph( + width: int, + height: int, + min_x: float, + min_y: float, + scale_x: float, + scale_y: float, + max_iterations: int, +) -> Graph: + """Configure a graph to run a Mandelbrot kernel.""" + output_dtype = DType.int32 + with Graph( + "mandelbrot", + ) as graph: + # The custom Mojo operation is referenced by its string name, and we + # need to provide inputs as a list as well as expected output types. + result = ops.custom( + name="mandelbrot", + values=[ + ops.constant(min_x, dtype=DType.float32), + ops.constant(min_y, dtype=DType.float32), + ops.constant(scale_x, dtype=DType.float32), + ops.constant(scale_y, dtype=DType.float32), + ops.constant(max_iterations, dtype=DType.int32), + ], + out_types=[TensorType(dtype=output_dtype, shape=[height, width])], + )[0].tensor + + # Return the result of the custom operation as the output of the graph. + graph.output(result) + return graph + + +if __name__ == "__main__": + # This is necessary only in specific build environments. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + path = Path(__file__).parent / "kernels.mojopkg" + + # Establish Mandelbrot set ranges. + WIDTH = 15 + HEIGHT = 15 + MAX_ITERATIONS = 100 + MIN_X = -1.5 + MAX_X = 0.7 + MIN_Y = -1.12 + MAX_Y = 1.12 + + # Configure our simple graph. + scale_x = (MAX_X - MIN_X) / WIDTH + scale_y = (MAX_Y - MIN_Y) / HEIGHT + graph = create_mandelbrot_graph( + WIDTH, HEIGHT, MIN_X, MIN_Y, scale_x, scale_y, MAX_ITERATIONS + ) + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if accelerator_count() == 0 else Accelerator() + + # Set up an inference session that runs the graph on a GPU, if available. + session = InferenceSession( + devices=[device], + custom_extensions=path, + ) + # Compile the graph. + model = session.load(graph) + + # Perform the calculation on the target device. + result = model.execute()[0] + + # Copy values back to the CPU to be read. + assert isinstance(result, Tensor) + result = result.to(CPU()) + + print("Iterations to escape:") + print(result.to_numpy()) + print() diff --git a/examples/custom_ops/matrix_multiplication.py b/examples/custom_ops/matrix_multiplication.py new file mode 100644 index 0000000000..70335e7b4d --- /dev/null +++ b/examples/custom_ops/matrix_multiplication.py @@ -0,0 +1,169 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +import numpy as np +from max.driver import CPU, Accelerator, Device, Tensor, accelerator_count +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import Graph, TensorType, ops +from numpy.typing import NDArray + + +def matrix_multiplication( + a: NDArray[np.float32], + b: NDArray[np.float32], + algorithm: str, + session: InferenceSession, + device: Device, +) -> Tensor: + dtype = DType.float32 + + # Create driver tensors from the input arrays, and move them to the + # accelerator. + a_tensor = Tensor.from_numpy(a).to(device) + b_tensor = Tensor.from_numpy(b).to(device) + + # Configure our simple one-operation graph. + with Graph( + "matrix_multiplication_graph", + input_types=[ + TensorType(dtype, shape=a_tensor.shape), + TensorType(dtype, shape=b_tensor.shape), + ], + ) as graph: + # Take in the two inputs to the graph. + a_value, b_value = graph.inputs + # The matrix multiplication custom operation takes in two matrices and + # produces a result, with the specific algorithm that is used chosen + # via compile-time parameterization. + output = ops.custom( + name="matrix_multiplication", + values=[a_value, b_value], + out_types=[ + TensorType( + dtype=a_value.tensor.dtype, + shape=[a_value.tensor.shape[0], b_value.tensor.shape[1]], + ) + ], + parameters={"algorithm": algorithm}, + )[0].tensor + graph.output(output) + + # Compile the graph. + print("Compiling...") + model = session.load(graph) + + # Perform the calculation on the target device. + print("Executing...") + result = model.execute(a_tensor, b_tensor)[0] + + # Copy values back to the CPU to be read. + assert isinstance(result, Tensor) + return result.to(CPU()) + + +if __name__ == "__main__": + # This is necessary only in specific build environments. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + path = Path(__file__).parent / "kernels.mojopkg" + + M = 256 + K = 256 + N = 256 + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if accelerator_count() == 0 else Accelerator() + + # Set up an inference session for running the graph. + session = InferenceSession( + devices=[device], + custom_extensions=path, + ) + + # Fill the input matrices with random values. + a = np.random.uniform(size=(M, K)).astype(np.float32) + b = np.random.uniform(size=(K, N)).astype(np.float32) + + # First, perform the matrix multiplication in NumPy. + print("A:") + print(a) + print() + + print("B:") + print(b) + print() + + print("Expected result:") + print(a @ b) + print() + + if accelerator_count() > 0: + # Then, test the various versions of matrix multiplication operations. + naive_result = matrix_multiplication(a, b, "naive", session, device) + print("Naive matrix multiplication:") + print(naive_result.to_numpy()) + print() + + coalescing_result = matrix_multiplication( + a, b, "coalescing", session, device + ) + print("Coalescing matrix multiplication:") + print(coalescing_result.to_numpy()) + print() + + tiled_result = matrix_multiplication(a, b, "tiled", session, device) + print("Tiled matrix multiplication:") + print(tiled_result.to_numpy()) + print() + + tiled_register_result = matrix_multiplication( + a, b, "tiled_register", session, device + ) + print("Shared memory and register tiling matrix multiplication:") + print(tiled_register_result.to_numpy()) + print() + + block_tiled_result = matrix_multiplication( + a, b, "block_tiled", session, device + ) + print("2D block tiled matrix multiplication:") + print(block_tiled_result.to_numpy()) + print() + + block_tiled_vectorized_result = matrix_multiplication( + a, b, "block_tiled_vectorized", session, device + ) + print("2D block tiled matrix multiplication (vectorized):") + print(block_tiled_vectorized_result.to_numpy()) + print() + + tensor_core_result = matrix_multiplication( + a, b, "tensor_core", session, device + ) + print("Matrix multiplication using Tensor Cores:") + print(tensor_core_result.to_numpy()) + print() + else: + print( + "No MAX-compatible accelerator detected, only running a naive matrix multiplication:" + ) + + naive_result = matrix_multiplication(a, b, "naive", session, device) + print("Naive matrix multiplication:") + print(naive_result.to_numpy()) + print() diff --git a/examples/custom_ops/mojoproject.toml b/examples/custom_ops/mojoproject.toml new file mode 100644 index 0000000000..d110891084 --- /dev/null +++ b/examples/custom_ops/mojoproject.toml @@ -0,0 +1,22 @@ +[project] +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +description = "An example of extending a graph with a custom Mojo operation" +name = "Custom Operations" +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] +version = "0.1.0" + +[tasks] +package = "mojo package kernels/ -o kernels.mojopkg" +addition = { cmd = "python addition.py", depends-on = ["package"] } +mandelbrot = { cmd = "python mandelbrot.py", depends-on = ["package"] } +vector_addition = { cmd = "python vector_addition.py", depends-on = ["package"] } +top_k = { cmd = "python top_k.py", depends-on = ["package"] } +fused_attention = { cmd = "python fused_attention.py", depends-on = ["package"] } +matrix_multiplication = { cmd = "python matrix_multiplication.py", depends-on = ["package"] } +histogram = { cmd = "python histogram.py", depends-on = ["package"] } +test = { depends-on = ["addition", "mandelbrot", "vector_addition", "top_k", "fused_attention", "matrix_multiplication"] } + +[dependencies] +python = ">=3.9,<3.13" +max = ">=24.6.0.dev2024090821" diff --git a/examples/custom_ops/parametric_addition.py b/examples/custom_ops/parametric_addition.py new file mode 100644 index 0000000000..e4f0d1d5a7 --- /dev/null +++ b/examples/custom_ops/parametric_addition.py @@ -0,0 +1,82 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +import numpy as np +from max.driver import CPU, Accelerator, Tensor, accelerator_count +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import Graph, TensorType, ops + +if __name__ == "__main__": + # This is necessary only in specific build environments. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + path = Path(__file__).parent / "kernels.mojopkg" + + rows = 5 + columns = 10 + dtype = DType.float32 + + # Configure our simple one-operation graph. + graph = Graph( + "addition", + # The custom Mojo operation is referenced by its string name, and we + # need to provide inputs as a list as well as expected output types. + # Since the custom operation is parametric, we need to provide the + # parameters as a dictionary. + forward=lambda x: ops.custom( + name="add_constant_custom", + values=[x], + out_types=[TensorType(dtype=x.dtype, shape=x.tensor.shape)], + parameters={"value": 5}, + )[0].tensor, + input_types=[ + TensorType(dtype, shape=[rows, columns]), + ], + ) + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if accelerator_count() == 0 else Accelerator() + + # Set up an inference session for running the graph. + session = InferenceSession( + devices=[device], + custom_extensions=path, + ) + + # Compile the graph. + model = session.load(graph) + + # Fill an input matrix with random values. + x_values = np.random.uniform(size=(rows, columns)).astype(np.float32) + + # Create a driver tensor from this, and move it to the accelerator. + x = Tensor.from_numpy(x_values).to(device) + + # Perform the calculation on the target device. + result = model.execute(x)[0] + + # Copy values back to the CPU to be read. + assert isinstance(result, Tensor) + result = result.to(CPU()) + + print("Graph result:") + print(result.to_numpy()) + print() + + print("Expected result:") + print(x_values + 5) diff --git a/examples/custom_ops/top_k.py b/examples/custom_ops/top_k.py new file mode 100644 index 0000000000..887fd444c0 --- /dev/null +++ b/examples/custom_ops/top_k.py @@ -0,0 +1,214 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +import argparse +import os +from collections import defaultdict +from pathlib import Path +from typing import DefaultDict + +import numpy as np +from max.driver import CPU, Accelerator, Tensor, accelerator_count +from max.dtype import DType +from max.engine.api import InferenceSession +from max.graph import Graph, TensorType, ops +from numpy.typing import NDArray + +INPUT_TEXT = """ +The quick rabbit runs past the brown fox +The quick rabbit jumps over the brown dog +The quick dog chases past the lazy fox +The quick dog runs through the tall trees +The quick brown fox jumps over the lazy dog +The brown dog sleeps under the shady tree +The brown rabbit hops under the tall tree +The brown fox runs through the forest trees +The brown fox watches the sleeping rabbit +The lazy fox watches over the sleeping dog +The lazy dog watches the quick rabbit +The shady tree shelters the brown rabbit +The shady fox sleeps under the old tree +The sleeping fox rests beside the shady tree +The lazy rabbit rests beside the brown fox +""" + + +class NextWordFrequency: + def __init__(self, text): + # nested `DefaultDict` to create the keys when first indexed + # Structure looks like: {"word": {"next_word": count}} + self.word_frequencies: DefaultDict[str, DefaultDict[str, int]] = ( + defaultdict(lambda: defaultdict(int)) + ) + + # Track the largest amount of next words to pad the tensor + self.max_next_words = 0 + + # Build word frequencies + words = text.lower().split() + for i in range(len(words) - 1): + current_word = words[i] + next_word = words[i + 1] + self.word_frequencies[current_word][next_word] += 1 + self.max_next_words = max( + self.max_next_words, len(self.word_frequencies[current_word]) + ) + + def next_word_probabilities(self, words) -> NDArray[np.float32]: + if not words: + return np.empty(0, dtype=np.float32) + + # List to store the probability distributions for each word + prob_distributions = [] + + for word in words: + if word not in self.word_frequencies: + raise ValueError( + f"Error: cannot predict word after '{word}', not found in input text" + ) + + for word in words: + frequencies = self.word_frequencies[word] + freq_list = np.array(list(frequencies.values()), dtype=np.float32) + + # Avoid division by zero + total = freq_list.sum() + if total > 0: + freq_list /= total + + # Pad to largest length of next words + padded_dist = np.pad( + freq_list, + (0, self.max_next_words - len(freq_list)), + mode="constant", + constant_values=0, + ) + prob_distributions.append(padded_dist) + + return np.stack(prob_distributions, axis=0) + + def __getitem__(self, idx): + return self.word_frequencies[idx] + + +# Example usage +def main(): + parser = argparse.ArgumentParser( + description="Top-K sampling with custom ops" + ) + parser.add_argument( + "--stress-test", + type=int, + default=0, + help=( + "Number of times to repeat input for stress testing. " + "On NVIDIA A100, 250_000_000 takes less than 0.5ms and uses over 75%% of its memory." + ), + ) + parser.add_argument( + "--cpu", + action="store_true", + help="Run on CPU even if there is a GPU available.", + ) + args = parser.parse_args() + + # This is necessary only for Modular internal CI. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + # Get the path to our compiled custom ops + path = Path(__file__).parent / "kernels.mojopkg" + + # Initialize the next word frequency for each unique word + frequencies = NextWordFrequency(INPUT_TEXT) + word_predictions = ["the", "quick", "brown"] + + # Get probabilities of next word for each word in the `word_predictions` list + probabilities = frequencies.next_word_probabilities(word_predictions) + + # If stress testing, repeat the input the specified number of times + if args.stress_test > 0: + probabilities = np.repeat(probabilities, args.stress_test, axis=0) + + batch_size = len(probabilities) + K = frequencies.max_next_words + + # Configure our simple one-operation graph. + with Graph( + "top_k_sampler", + # The dtype and shape of the probabilities being passed in + input_types=[TensorType(DType.float32, shape=[batch_size, K])], + ) as graph: + # Take the probabilities as a single input to the graph. + probs, *_ = graph.inputs + + results = ops.custom( + # This is the custom op name defined in `kernels/top_k.mojo`. + name="top_k_custom", + # Passes `K` as a compile-time Mojo `Int`. + parameters={"K": K}, + # Passes the probabilities as a single input to the graph. + values=[probs], + out_types=[ + # The output values dtype and shape + TensorType(probs.tensor.dtype, probs.tensor.shape), + # The output indices dtype and shape + TensorType(DType.int32, probs.tensor.shape), + ], + ) + graph.output(*results) + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if args.cpu or accelerator_count() == 0 else Accelerator() + + # Set up an inference session for running the graph. + session = InferenceSession(devices=[device], custom_extensions=path) + + # Compile the graph. + model = session.load(graph) + + # Create a driver tensor from the next word probabilities + input_tensor = Tensor.from_numpy(probabilities).to(device) + + print(f"Sampling top k: {K} for batch size: {batch_size}") + + values, indices = model.execute(input_tensor) + + if args.stress_test > 0: + return + + # Copy values and indices back to the CPU to be read. + assert isinstance(values, Tensor) + values = values.to(CPU()) + np_values = values.to_numpy() + + assert isinstance(indices, Tensor) + indices = indices.to(CPU()) + np_indices = indices.to_numpy() + + for i in range(batch_size): + print(f"\nPredicted word after `{word_predictions[i]}`") + print("-------------------------------") + print("| word | confidence |") + print("-------------------------------") + keys = list(frequencies.word_frequencies[word_predictions[i]].keys()) + + for j in range(len(np_indices[i])): + # If it's a padded index/value, break out of the loop + if j > len(keys) - 1: + break + print(f"| {keys[np_indices[i][j]]:<13}| {np_values[i][j]:<13.8}|") + print("-------------------------------") + + +if __name__ == "__main__": + main() diff --git a/examples/custom_ops/vector_addition.py b/examples/custom_ops/vector_addition.py new file mode 100644 index 0000000000..9ed124f6a8 --- /dev/null +++ b/examples/custom_ops/vector_addition.py @@ -0,0 +1,92 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +import numpy as np +from max.driver import CPU, Accelerator, Tensor, accelerator_count +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import Graph, TensorType, ops + +if __name__ == "__main__": + # This is necessary only in specific build environments. + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + path = Path(__file__).parent / "kernels.mojopkg" + + vector_width = 10 + dtype = DType.float32 + + # Configure our simple one-operation graph. + with Graph( + "vector_addition", + input_types=[ + TensorType(dtype, shape=[vector_width]), + TensorType(dtype, shape=[vector_width]), + ], + ) as graph: + # Take in the two inputs to the graph. + lhs, rhs = graph.inputs + output = ops.custom( + name="vector_addition", + values=[lhs, rhs], + out_types=[ + TensorType(dtype=lhs.tensor.dtype, shape=lhs.tensor.shape) + ], + )[0].tensor + graph.output(output) + + # Place the graph on a GPU, if available. Fall back to CPU if not. + device = CPU() if accelerator_count() == 0 else Accelerator() + + # Set up an inference session for running the graph. + session = InferenceSession( + devices=[device], + custom_extensions=path, + ) + + # Compile the graph. + model = session.load(graph) + + # Fill input matrices with random values. + lhs_values = np.random.uniform(size=(vector_width)).astype(np.float32) + rhs_values = np.random.uniform(size=(vector_width)).astype(np.float32) + + # Create driver tensors from this, and move them to the accelerator. + lhs_tensor = Tensor.from_numpy(lhs_values).to(device) + rhs_tensor = Tensor.from_numpy(rhs_values).to(device) + + # Perform the calculation on the target device. + result = model.execute(lhs_tensor, rhs_tensor)[0] + + # Copy values back to the CPU to be read. + assert isinstance(result, Tensor) + result = result.to(CPU()) + + print("Left-hand-side values:") + print(lhs_values) + print() + + print("Right-hand-side values:") + print(rhs_values) + print() + + print("Graph result:") + print(result.to_numpy()) + print() + + print("Expected result:") + print(lhs_values + rhs_values) diff --git a/examples/gpu_functions/README.md b/examples/gpu_functions/README.md new file mode 100755 index 0000000000..3e09a9c092 --- /dev/null +++ b/examples/gpu_functions/README.md @@ -0,0 +1,50 @@ +# Compiling and running Mojo functions on a GPU + +> [!NOTE] +> This is a preview of an interface for programming GPUs using Mojo, +> and may be subject to change before the next stable release. + +Mojo functions can be compiled and dispatched on a GPU, and these examples +show a few different ways of doing so. They also demonstrate how to allocate +and move tensors between CPU and GPU via the MAX Driver API. These examples are +complementary with +[those that show how to program custom graph operations](../custom_ops/) to +run on the CPU or GPU in Mojo. + +> [!NOTE] +> The Mojo interfaces to the MAX Driver API are under development, are +> not fully documented, and may change before the next stable release. + +A [MAX-compatible GPU](https://docs.modular.com/max/faq/#gpu-requirements) is +necessary to run these examples. + +The four examples of GPU functions defined in Mojo consist of: + +- **vector_addition.mojo**: A common "hello world" example for GPU programming, + this adds two vectors together in the same way as seen in Chapter 2 of + ["Programming Massively Parallel Processors"](https://www.sciencedirect.com/book/9780323912310/programming-massively-parallel-processors). +- **grayscale.mojo**: The parallelized conversion of an RGB image to grayscale, + as seen in Chapter 3 of "Programming Massively Parallel Processors". +- **naive_matrix_multiplication.mojo**: An implementation of naive matrix + multiplication, again inspired by Chapter 3 of "Programming Massively + Parallel Processors". +- **mandelbrot.mojo**: A parallel calculation of the number of iterations to + escape in the Mandelbrot set. An example of the same computation performed as + a custom graph operation can be found [here](../custom_ops/). + +A single Magic command runs each of the examples: + +```sh +magic run vector_addition +magic run grayscale +magic run naive_matrix_multiplication +magic run mandelbrot +``` + +For larger computations, we recommend staging them as part of a +[MAX Graph](https://docs.modular.com/max/tutorials/get-started-with-max-graph-in-python). +The graph compiler within MAX performs intelligent operator fusion, +orchestrates efficient runtime execution, and more. The same Mojo code running +on a GPU in one of these examples can be easily translated to +[a custom operation](https://docs.modular.com/max/tutorials/build-custom-ops) +and placed inside a node in a MAX Graph. diff --git a/examples/gpu_functions/grayscale.mojo b/examples/gpu_functions/grayscale.mojo new file mode 100755 index 0000000000..f0bcc52566 --- /dev/null +++ b/examples/gpu_functions/grayscale.mojo @@ -0,0 +1,124 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +from gpu.host import Dim +from gpu.id import block_dim, block_idx, thread_idx +from math import ceildiv +from max.driver import ( + Device, + DynamicTensor, + Tensor, + accelerator_device, + cpu_device, +) +from max.driver.accelerator import compile + +alias channel_dtype = DType.uint8 +alias internal_float_dtype = DType.float32 +alias tensor_rank = 3 +alias TensorType = DynamicTensor[type=channel_dtype, rank=tensor_rank].Type + + +def print_image[h: Int, w: Int](t: Tensor[channel_dtype, 3]): + """A helper function to print out the grayscale channel intensities.""" + out = t.unsafe_slice() + for row in range(h): + for col in range(w): + var v = out[row, col, 0] + if v < 100: + print(" ", end="") + if v < 10: + print(" ", end="") + print(v, " ", end="") + print("") + + +fn color_to_grayscale_conversion( + width: Int, + height: Int, + image: TensorType, + out: TensorType, +): + """Converting each RGB pixel to grayscale, parallelized across the output tensor on the GPU. + """ + row = block_dim.y * block_idx.y + thread_idx.y + col = block_dim.x * block_idx.x + thread_idx.x + + if col < width and row < height: + red = image[row, col, 0].cast[internal_float_dtype]() + green = image[row, col, 1].cast[internal_float_dtype]() + blue = image[row, col, 2].cast[internal_float_dtype]() + gray = 0.21 * red + 0.71 * green + 0.07 * blue + + out[row, col, 0] = gray.cast[channel_dtype]() + + +def main(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + alias IMAGE_WIDTH = 5 + alias IMAGE_HEIGHT = 10 + alias NUM_CHANNELS = 3 + + # Allocate the input image tensor on the host. + rgb_tensor = Tensor[channel_dtype, tensor_rank]( + (IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS), host_device + ) + + # Fill the image with initial colors. + for row in range(IMAGE_HEIGHT): + for col in range(IMAGE_WIDTH): + rgb_tensor[row, col, 0] = row + col + rgb_tensor[row, col, 1] = row + col + 20 + rgb_tensor[row, col, 2] = row + col + 40 + + # Move the image tensor to the accelerator. + rgb_tensor = rgb_tensor.move_to(gpu_device) + + # Allocate a tensor on the accelerator to host the grayscale image. + gray_tensor = Tensor[channel_dtype, tensor_rank]( + (IMAGE_HEIGHT, IMAGE_WIDTH, 1), gpu_device + ) + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[color_to_grayscale_conversion](gpu_device) + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + num_col_blocks = ceildiv(IMAGE_WIDTH, BLOCK_SIZE) + num_row_blocks = ceildiv(IMAGE_HEIGHT, BLOCK_SIZE) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + IMAGE_WIDTH, + IMAGE_HEIGHT, + rgb_tensor.unsafe_slice(), + gray_tensor.unsafe_slice(), + grid_dim=Dim(num_col_blocks, num_row_blocks), + block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + gray_tensor = gray_tensor.move_to(host_device) + + print("Resulting grayscale image:") + print_image[IMAGE_HEIGHT, IMAGE_WIDTH](gray_tensor) diff --git a/examples/gpu_functions/mandelbrot.mojo b/examples/gpu_functions/mandelbrot.mojo new file mode 100755 index 0000000000..4356e624b1 --- /dev/null +++ b/examples/gpu_functions/mandelbrot.mojo @@ -0,0 +1,129 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from collections.string import StringSlice +from complex import ComplexSIMD +from gpu.host import Dim +from gpu.id import thread_idx, block_dim, block_idx +from math import ceildiv +from max.driver import DynamicTensor, Tensor, accelerator_device, cpu_device +from max.driver.accelerator import compile + +alias float_dtype = DType.float32 +alias int_dtype = DType.int32 +alias TensorType = DynamicTensor[type=int_dtype, rank=2].Type + + +def draw_mandelbrot[h: Int, w: Int](t: Tensor[int_dtype, 2], max: Int): + """A helper function to visualize the Mandelbrot set in ASCII art.""" + alias sr = StringSlice("....,c8M@jawrpogOQEPGJ") + out = t.unsafe_slice() + for row in range(h): + for col in range(w): + var v = out[row, col] + if v < max: + var idx = Int(v % len(sr)) + var p = sr[idx] + print(p, end="") + else: + print(" ", end="") + print("") + + +fn mandelbrot( + min_x: Scalar[float_dtype], + min_y: Scalar[float_dtype], + scale_x: Scalar[float_dtype], + scale_y: Scalar[float_dtype], + max_iterations: Scalar[int_dtype], + out: TensorType, +): + """The per-element calculation of iterations to escape in the Mandelbrot set. + """ + # Obtain the position in the grid from the X, Y thread locations. + var row = block_dim.y * block_idx.y + thread_idx.y + var col = block_dim.x * block_idx.x + thread_idx.x + + # Calculate the complex C corresponding to that grid location. + var cx = min_x + col * scale_x + var cy = min_y + row * scale_y + var c = ComplexSIMD[float_dtype, 1](cx, cy) + + # Perform the Mandelbrot iteration loop calculation. + var z = ComplexSIMD[float_dtype, 1](0, 0) + var iters = Scalar[int_dtype](0) + + var in_set_mask: Scalar[DType.bool] = True + for _ in range(max_iterations): + if not any(in_set_mask): + break + in_set_mask = z.squared_norm() <= 4 + iters = in_set_mask.select(iters + 1, iters) + z = z.squared_add(c) + + # Write out the resulting iterations to escape. + out[row, col] = iters + + +def main(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[mandelbrot](gpu_device) + + # Set the resolution of the Mandelbrot set grid that will be calculated. + alias GRID_WIDTH = 60 + alias GRID_HEIGHT = 25 + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + num_col_blocks = ceildiv(GRID_WIDTH, BLOCK_SIZE) + num_row_blocks = ceildiv(GRID_HEIGHT, BLOCK_SIZE) + + # Set the parameters for the area of the Mandelbrot set we'll be examining. + alias MIN_X: Scalar[float_dtype] = -2.0 + alias MAX_X: Scalar[float_dtype] = 0.7 + alias MIN_Y: Scalar[float_dtype] = -1.12 + alias MAX_Y: Scalar[float_dtype] = 1.12 + alias SCALE_X = (MAX_X - MIN_X) / GRID_WIDTH + alias SCALE_Y = (MAX_Y - MIN_Y) / GRID_HEIGHT + alias MAX_ITERATIONS = 100 + + # Allocate a tensor on the target device to hold the resulting set. + out_tensor = Tensor[int_dtype, 2]((GRID_HEIGHT, GRID_WIDTH), gpu_device) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + MIN_X, + MIN_Y, + SCALE_X, + SCALE_Y, + MAX_ITERATIONS, + out_tensor.unsafe_slice(), + grid_dim=Dim(num_col_blocks, num_row_blocks), + block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + out_tensor = out_tensor.move_to(host_device) + + # Draw the final Mandelbrot set. + draw_mandelbrot[GRID_HEIGHT, GRID_WIDTH](out_tensor, max=MAX_ITERATIONS) diff --git a/examples/gpu_functions/mojoproject.toml b/examples/gpu_functions/mojoproject.toml new file mode 100755 index 0000000000..242792e7fb --- /dev/null +++ b/examples/gpu_functions/mojoproject.toml @@ -0,0 +1,17 @@ +[project] +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +description = "Examples of using Mojo to write GPU functions" +name = "Mojo GPU Functions" +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] +version = "0.1.0" + +[tasks] +vector_addition = "mojo run vector_addition.mojo" +grayscale = "mojo run grayscale.mojo" +naive_matrix_multiplication = "mojo run naive_matrix_multiplication.mojo" +mandelbrot = "mojo run mandelbrot.mojo" +test = { depends-on = ["vector_addition", "grayscale", "naive_matrix_multiplication", "mandelbrot"] } + +[dependencies] +max = ">=24.6.0.dev2024090821" diff --git a/examples/gpu_functions/naive_matrix_multiplication.mojo b/examples/gpu_functions/naive_matrix_multiplication.mojo new file mode 100755 index 0000000000..515f87e2b5 --- /dev/null +++ b/examples/gpu_functions/naive_matrix_multiplication.mojo @@ -0,0 +1,110 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +from gpu.host import Dim +from gpu.id import block_dim, block_idx, thread_idx +from math import ceildiv +from max.driver import ( + Device, + DynamicTensor, + Tensor, + accelerator_device, + cpu_device, +) +from max.driver.accelerator import compile + +alias float_dtype = DType.float32 +alias tensor_rank = 2 +alias TensorType = DynamicTensor[type=float_dtype, rank=tensor_rank].Type + + +fn naive_matrix_multiplication( + i: Int, + j: Int, + k: Int, + m: TensorType, + n: TensorType, + p: TensorType, +): + """Naive matrix multiplication of M_ij x N_jk = P_ik.""" + row = block_dim.y * block_idx.y + thread_idx.y + col = block_dim.x * block_idx.x + thread_idx.x + + if row < i and col < k: + for j_index in range(j): + p[row, col] = p[row, col] + m[row, j_index] * n[j_index, col] + + +def main(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + alias I = 5 + alias J = 4 + alias K = 6 + + # Allocate the two input matrices on the host. + m_tensor = Tensor[float_dtype, tensor_rank]((I, J), host_device) + n_tensor = Tensor[float_dtype, tensor_rank]((J, K), host_device) + + # Fill them with initial values. + for m_row in range(I): + for m_col in range(J): + m_tensor[m_row, m_col] = m_row - m_col + + for n_row in range(J): + for n_col in range(K): + n_tensor[n_row, n_col] = n_row + n_col + + print("M matrix:", m_tensor) + print("N matrix:", n_tensor) + + # Move the input matrices to the accelerator. + m_tensor = m_tensor.move_to(gpu_device) + n_tensor = n_tensor.move_to(gpu_device) + + # Allocate a tensor on the accelerator to host the calculation results. + p_tensor = Tensor[float_dtype, tensor_rank]((I, K), gpu_device) + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[naive_matrix_multiplication](gpu_device) + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + num_col_blocks = ceildiv(I, BLOCK_SIZE) + num_row_blocks = ceildiv(J, BLOCK_SIZE) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + I, + J, + K, + m_tensor.unsafe_slice(), + n_tensor.unsafe_slice(), + p_tensor.unsafe_slice(), + grid_dim=Dim(num_col_blocks, num_row_blocks), + block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + p_tensor = p_tensor.move_to(host_device) + + print("Resulting matrix:", p_tensor) diff --git a/examples/gpu_functions/vector_addition.mojo b/examples/gpu_functions/vector_addition.mojo new file mode 100755 index 0000000000..dd4aaa4361 --- /dev/null +++ b/examples/gpu_functions/vector_addition.mojo @@ -0,0 +1,93 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from gpu.host import Dim +from gpu.id import block_dim, block_idx, thread_idx +from math import ceildiv +from max.driver import ( + Device, + DynamicTensor, + Tensor, + accelerator_device, + cpu_device, +) +from max.driver.accelerator import compile + +alias float_dtype = DType.float32 +alias tensor_rank = 1 +alias TensorType = DynamicTensor[type=float_dtype, rank=tensor_rank].Type + + +fn vector_addition( + length: Int, + lhs: TensorType, + rhs: TensorType, + out: TensorType, +): + """The calculation to perform across the vector on the GPU.""" + tid = block_dim.x * block_idx.x + thread_idx.x + if tid < length: + var result = lhs[tid] + rhs[tid] + out[tid] = result + + +def main(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + alias VECTOR_WIDTH = 10 + + # Allocate the two input tensors on the host. + lhs_tensor = Tensor[float_dtype, 1]((VECTOR_WIDTH), host_device) + rhs_tensor = Tensor[float_dtype, 1]((VECTOR_WIDTH), host_device) + + # Fill them with initial values. + for i in range(VECTOR_WIDTH): + lhs_tensor[i] = 1.25 + rhs_tensor[i] = 2.5 + + # Move the input tensors to the accelerator. + lhs_tensor = lhs_tensor.move_to(gpu_device) + rhs_tensor = rhs_tensor.move_to(gpu_device) + + # Allocate a tensor on the accelerator to host the calculation results. + out_tensor = Tensor[float_dtype, tensor_rank]((VECTOR_WIDTH), gpu_device) + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[vector_addition](gpu_device) + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + var num_blocks = ceildiv(VECTOR_WIDTH, BLOCK_SIZE) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + VECTOR_WIDTH, + lhs_tensor.unsafe_slice(), + rhs_tensor.unsafe_slice(), + out_tensor.unsafe_slice(), + grid_dim=Dim(num_blocks), + block_dim=Dim(BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + out_tensor = out_tensor.move_to(host_device) + + print("Resulting vector:", out_tensor) diff --git a/examples/graph-api/README.md b/examples/graph-api/README.md new file mode 100644 index 0000000000..faf99255c3 --- /dev/null +++ b/examples/graph-api/README.md @@ -0,0 +1,36 @@ +# MAX Graph API examples + +These examples demonstrate the flexibility of the +[MAX Graph API](https://docs.modular.com/max/graph/), a +[Mojo](https://docs.modular.com/mojo/) interface to the advanced graph compiler +within MAX. + +## Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run any of the +following commands: + +```sh +magic run basic +``` + +## Conda instructions + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda activate max-repo + +mojo basics/basic.🔥 + +conda deactivate +``` + +## [Graph API introduction](basics/) + +A basic Mojo Graph API example that provides an introduction to how to +stage and run a computational graph on MAX, following the +[getting started guide](https://docs.modular.com/max/tutorials/get-started-with-max-graph). diff --git a/examples/graph-api/basics/README.md b/examples/graph-api/basics/README.md new file mode 100644 index 0000000000..c37b59b418 --- /dev/null +++ b/examples/graph-api/basics/README.md @@ -0,0 +1,26 @@ +# Basic example for MAX Graph API 🔥 + +This is a very simple example of how to build a model with the MAX Graph API +and execute it with MAX Engine. + +For a walkthrough of this code, see the quickstart guide to [build a graph with +MAX Graph](https://docs.modular.com/max/tutorials/get-started-with-max-graph-in-python/). + +## Usage + +First, [install Magic](https://docs.modular.com/magic/). + +Then run `basic.🔥` from a command line: + +```shell +magic run mojo basic.🔥 +``` + +You should see the following output: + +```shell +Input: input0 +Output: output0 +Tensor([[1.0], +[1.0]], dtype=float32, shape=2x1) +``` diff --git "a/examples/graph-api/basics/basic.\360\237\224\245" "b/examples/graph-api/basics/basic.\360\237\224\245" new file mode 100644 index 0000000000..070a159653 --- /dev/null +++ "b/examples/graph-api/basics/basic.\360\237\224\245" @@ -0,0 +1,52 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.engine import InferenceSession +from max.graph import Graph, TensorType, ops +from max.tensor import Tensor, TensorShape + + +# This example highlights the very basic API structure around building a MAX +# Graph model and executing it through the MAX engine APIS. +# Simply run this mojo file to create, load, and execute this simple model. +def main(): + graph = Graph(TensorType(DType.float32, 2, 6)) + + # Create a constant for usage in the matmul op below: + matmul_constant_value = Tensor[DType.float32](TensorShape(6, 1), 0.15) + matmul_constant = graph.constant(matmul_constant_value) + + # Start adding a sequence of operator calls to build the graph. + # We can use the subscript notation to get the graph's first input tensor: + matmul = graph[0] @ matmul_constant + relu = ops.relu(matmul) + softmax = ops.softmax(relu) + graph.output(softmax) + + # Load the graph: + session = InferenceSession() + model = session.load(graph) + + # Print the input/output names: + in_names = model.get_model_input_names() + for name in in_names: + print("Input:", name[]) + out_names = model.get_model_output_names() + for name in out_names: + print("Output:", name[]) + + # Execute the model: + input = Tensor[DType.float32](TensorShape(2, 6), 0.5) + results = model.execute("input0", input^) + output = results.get[DType.float32]("output0") + print(output) diff --git a/examples/graph-api/environment.yml b/examples/graph-api/environment.yml new file mode 100644 index 0000000000..1395c6958d --- /dev/null +++ b/examples/graph-api/environment.yml @@ -0,0 +1,9 @@ +name: max-repo +channels: + - pytorch + - https://conda.modular.com/max/ + - conda-forge + - defaults +dependencies: + - python>=3.11,<3.12 + - max>=24.4.0dev6 diff --git a/examples/graph-api/pixi.toml b/examples/graph-api/pixi.toml new file mode 100644 index 0000000000..0070a3f9de --- /dev/null +++ b/examples/graph-api/pixi.toml @@ -0,0 +1,15 @@ +[project] +name = "Mojo Graph API Examples" +version = "1.0.0" +description = "MAX Examples" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[tasks] +basic = "mojo basics/basic.🔥" +test = { depends-on = ["basic"] } + +[dependencies] +python = ">=3.9,<3.13" +max = "*" diff --git a/examples/graph-api/requirements.txt b/examples/graph-api/requirements.txt new file mode 100644 index 0000000000..53f6a450c2 --- /dev/null +++ b/examples/graph-api/requirements.txt @@ -0,0 +1 @@ +huggingface_hub >=0.24.6,<0.25 diff --git a/examples/gui/.gitattributes b/examples/gui/.gitattributes new file mode 100644 index 0000000000..07fe41c52c --- /dev/null +++ b/examples/gui/.gitattributes @@ -0,0 +1,2 @@ +# GitHub syntax highlighting +pixi.lock linguist-language=YAML linguist-generated=true diff --git a/examples/gui/.gitignore b/examples/gui/.gitignore new file mode 100644 index 0000000000..a6184bd794 --- /dev/null +++ b/examples/gui/.gitignore @@ -0,0 +1 @@ +app.log diff --git a/examples/gui/.streamlit/config.toml b/examples/gui/.streamlit/config.toml new file mode 100644 index 0000000000..c6c5316861 --- /dev/null +++ b/examples/gui/.streamlit/config.toml @@ -0,0 +1,9 @@ +[client] +showSidebarNavigation = false + +[server] +runOnSave = true + +[theme] +base = "dark" +primaryColor = "#8799ff" diff --git a/examples/gui/README.md b/examples/gui/README.md new file mode 100644 index 0000000000..ddfb444417 --- /dev/null +++ b/examples/gui/README.md @@ -0,0 +1,13 @@ +# MAX Examples GUI + +Example project showing how to create a GUI that calls MAX using the Python +[streamlit](https://streamlit.io/) library. + +## Quick Start + +Install Magic and run the GUI: + +```bash +curl -fsSL https://developer.modular.com/magic | bash +magic run gui +``` diff --git a/examples/gui/home.py b/examples/gui/home.py new file mode 100755 index 0000000000..0b12b27b2d --- /dev/null +++ b/examples/gui/home.py @@ -0,0 +1,32 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import streamlit as st +from shared import menu + +st.set_page_config("MAX", "⚡️") +menu() + +"""# MAX ⚡️ Examples + +Welcome to MAX! Select an example to get started: +""" + +if st.button("🦙 Llama3.1"): + st.switch_page("pages/llama3_1.py") +elif st.button("👓 BERT"): + st.switch_page("pages/bert.py") +elif st.button("🎨 Stable Diffusion 1.5"): + st.switch_page("pages/stable-diffusion.py") +elif st.button("🔍 YOLO"): + st.switch_page("pages/yolo.py") diff --git a/examples/gui/mojoproject.toml b/examples/gui/mojoproject.toml new file mode 100644 index 0000000000..272a71e3db --- /dev/null +++ b/examples/gui/mojoproject.toml @@ -0,0 +1,39 @@ +[project] +name = "gui" +version = "1.0.0" +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +max = "*" +numpy = "1.25.2" +pillow = "<=10.4.0" +protobuf = "<=4.25.3" +tokenizers = "<0.21" +transformers = "<4.47" + +[pypi-dependencies] +pipelines = { path = "../../src/max", editable = true} +accelerate = "<=0.34.2" +diffusers = "<=0.30.3" +gguf = "<=0.10.0" +onnxruntime = "<=1.18.1" +onnxslim = "<=0.1.34" +opencv-python = "<=4.10.0.84" +sentencepiece = "<=0.2.0" +streamlit = "<=1.38.0" +torch = "<=2.5.1" +ultralytics = "<=8.2.103" + +# RAG specific packages +chromadb = "<=0.4.17" +docx2txt = "<=0.8" +EbookLib = "<=0.18" +fastembed = "<=0.3.6" +ftfy = "<=6.2.3" +html2text = "<=2024.2.26" +llama-index = "<=0.11.14" +nbconvert = "<=7.16.4" + +[tasks] +gui = "streamlit run home.py" diff --git a/examples/gui/pages/bert.py b/examples/gui/pages/bert.py new file mode 100644 index 0000000000..b5088e18da --- /dev/null +++ b/examples/gui/pages/bert.py @@ -0,0 +1,151 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +import os +import time + +import pandas as pd +import streamlit as st +import torch +from max import engine +from max.dtype import DType +from shared import menu, modular_cache_dir +from transformers import BertForMaskedLM, BertTokenizer + +st.set_page_config("Bert", page_icon="👓") +menu() + +""" +# 👓 Bert + +A basic implementation of Bert using MAX. Type a text string, using `[MASK]` to indicate where you want the model to predict a word. +""" + +HF_MODEL_NAME = "bert-base-uncased" + + +# If batch, seq_len, or mlm options change, recompile the torchscript. +@st.cache_data +def compile_torchscript(batch: int, seq_len: int): + model = BertForMaskedLM.from_pretrained(HF_MODEL_NAME) + input_dict = { + "input_ids": torch.zeros((batch, seq_len), dtype=torch.int64), + "attention_mask": torch.zeros((batch, seq_len), dtype=torch.int64), + "token_type_ids": torch.zeros((batch, seq_len), dtype=torch.int64), + } + model.eval() + model.config.return_dict = False + with torch.no_grad(): + traced_model = torch.jit.trace( + model, example_kwarg_inputs=dict(input_dict), strict=False + ) + torch.jit.save(traced_model, model_path) + + +@st.cache_resource(show_spinner="Starting MAX Bert Inference Session") +def max_bert_session(model_path: str, batch: int, seq_len: int): + # Wait short time for spinner to start correctly + time.sleep(1) + session = engine.InferenceSession() + inputs = [ + torch.zeros((batch, seq_len), dtype=torch.int64), + torch.zeros((batch, seq_len), dtype=torch.int64), + torch.zeros((batch, seq_len), dtype=torch.int64), + ] + input_spec_list = [ + engine.TorchInputSpec(shape=tensor.size(), dtype=DType.int64) + for tensor in inputs + ] + return session.load(model_path, input_specs=input_spec_list) + + +@st.cache_data() +def get_tokenizer(): + return BertTokenizer.from_pretrained(HF_MODEL_NAME) + + +def softmax(logits): + exp_logits = torch.exp(logits - torch.max(logits)) + return exp_logits / exp_logits.sum(dim=-1, keepdim=True) + + +model_state = st.empty() + +show_predictions = st.sidebar.checkbox("Show top 5 predictions", True) +model_path = st.sidebar.text_input( + "Model Path", + os.path.join(modular_cache_dir(), "bert-mlm.torchscript"), +) +batch = st.sidebar.number_input("Batch Size", 1, 64) +seq_len = st.sidebar.slider("Sequence Length", 128, 1024) +input_text = st.text_input("Text Input", "Don't [MASK] about it") + +compile_torchscript(batch, seq_len) + +if st.button("Predict Word"): + masks = input_text.split("[MASK]") + if len(masks) > 2: + st.error("Cannot have more than a single [MASK] in the input text") + exit(1) + if len(masks) < 2: + st.error("Require at least one [MASK] in the input text") + exit(1) + + model = max_bert_session(model_path, batch, seq_len) + + tokenizer = get_tokenizer() + inputs = tokenizer( + input_text, + return_tensors="pt", + padding="max_length", + truncation=True, + max_length=seq_len, + ) + + masked_index = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero( + as_tuple=True + )[1] + + outputs = model.execute_legacy(**inputs)["result0"] + + logits = torch.from_numpy(outputs[0, masked_index, :]) + + predicted_token_id = logits.argmax(dim=-1) + predicted_tokens = tokenizer.decode( + [predicted_token_id], + skip_special_tokens=True, + clean_up_tokenization_spaces=True, + ) + + st.text_area( + "Filled Mask", input_text.replace("[MASK]", predicted_tokens, 40) + ) + + if show_predictions: + # Get top N predictions for the [MASK] token + top_n = 5 + mask_logits = logits.squeeze(0) # Remove batch dimension + top_n_probs, top_n_indices = torch.topk(softmax(mask_logits), top_n) + + top_n_tokens = tokenizer.convert_ids_to_tokens(top_n_indices.tolist()) + top_n_probs_percent = [prob * 100 for prob in top_n_probs.tolist()] + + # Create a dictionary for the bar chart data + data = {"Token": top_n_tokens, "Probability (%)": top_n_probs_percent} + + # Create a DataFrame for the table + top_n_df = pd.DataFrame(data) + + st.write("Top N predictions for [MASK]:") + st.bar_chart(top_n_df.set_index("Token")) # Display bar chart! diff --git a/examples/gui/pages/llama3_1.py b/examples/gui/pages/llama3_1.py new file mode 100644 index 0000000000..b467bc7792 --- /dev/null +++ b/examples/gui/pages/llama3_1.py @@ -0,0 +1,206 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +import asyncio +import os +import time +from pathlib import Path + +import streamlit as st +import torch +from max.driver import CPU, Accelerator +from max.pipelines import ( + PIPELINE_REGISTRY, + PipelineConfig, + SupportedEncoding, +) +from max.pipelines.architectures.llama3.config import get_llama_huggingface_file +from shared import ( + RAG_PROMPT, + RAG_SYSTEM_PROMPT, + hf_streamlit_download, + load_embed_docs, + menu, + stream_output, +) + +st.set_page_config(page_title="Llama3.1", page_icon="🦙") + +""" +# Llama3.1 🦙 + +Compile and cache Llama3.1 built with MAX graphs so you can continuously +chat with it. + +Tick `Activate RAG` on the sidebar to augment your prompts with +text from documents in the `examples/gui/ragdata` folder in format: `.txt` +`.pdf` `.csv` `.docx` `.epub` `.ipynb` `.md` `.html`. +""" + +menu() + + +@st.cache_resource(show_spinner=False) +def start_llama3( + weight_path: str, + quantization: SupportedEncoding, + max_length: int, + max_new_tokens: int, + use_gpu: bool, +) -> Llama3: + config = PipelineConfig( + architecture="LlamaForCausalLM", + device=Accelerator() if use_gpu else CPU(), + weight_path=[Path(weight_path)], + quantization_encoding=quantization, + max_length=max_length, + max_new_tokens=max_new_tokens, + model_path="modularai/Llama-3.1-8B-Instruct-GGUF", + ) + _, pipeline = PIPELINE_REGISTRY.retrieve_factory(config) + return pipeline + + +def messages_to_llama3_prompt(messages: list[dict[str, str]]) -> str: + prompt_string = "<|begin_of_text|>" + for message in messages: + prompt_string += ( + f"<|start_header_id|>{message['role']}<|end_header_id|>\n\n" + ) + prompt_string += f"{message['content']}<|eot_id|>\n" + prompt_string += "<|start_header_id|>assistant<|end_header_id|>" + return prompt_string + + +if torch.cuda.is_available(): + use_gpu = st.sidebar.checkbox( + f"Use GPU: {torch.cuda.get_device_name(0)}", value=True + ) +else: + use_gpu = st.sidebar.checkbox( + "Use GPU (Accelerator not available)", value=False, disabled=True + ) + +if use_gpu: + encoding = st.sidebar.selectbox("Encoding", [SupportedEncoding.bfloat16]) +else: + encoding = st.sidebar.selectbox( + "Encoding", + [ + SupportedEncoding.q4_k, + SupportedEncoding.q4_0, + SupportedEncoding.q6_k, + ], + ) + +max_length = st.sidebar.number_input( + "Max input and output tokens", 0, 128_000, 12_000 +) +max_new_tokens = st.sidebar.number_input("Max output tokens", 0, 24_000, 6000) + +hf_file = get_llama_huggingface_file("3.1", encoding) +weights = hf_streamlit_download(hf_file.repo_id, hf_file.filename) + +button_state = st.empty() +model_state = st.empty() +if button_state.button("Start Llama3", key=0): + model_state.info("Starting Llama3...", icon="️⚙️") + st.session_state["model"] = start_llama3( + weights, + encoding, + max_length, + max_new_tokens, + use_gpu, + ) + model_state.success("Llama3 is ready!", icon="✅") + +rag = st.sidebar.checkbox("Activate RAG", value=False) + +if rag: + system_prompt = st.sidebar.text_area( + "System Prompt", + value=RAG_SYSTEM_PROMPT, + ) + n_result = st.sidebar.slider( + "Number of Top Embedding Search Results", 1, 7, 5 + ) + rag_directory = st.sidebar.text_input( + "RAG Directory", + value=Path(__file__).parent.parent / "ragdata", + ) + filenames = [ + f + for f in os.listdir(rag_directory) + if os.path.isfile(os.path.join(rag_directory, f)) + ] + # Re-cache reading the documents again if there's a change + collection, embedding_model = load_embed_docs(filenames) + st.success("RAG data is indexed", icon="✅") +else: + system_prompt = st.sidebar.text_area( + "System Prompt", + value="You are a helpful coding assistant named MAX Llama3.", + ) + +# Initialize chat history +if "messages" not in st.session_state: + st.session_state.messages = [] + +# Display chat messages from history on app rerun +for message in st.session_state.messages: + with st.chat_message(message["role"], avatar=message["avatar"]): + st.markdown(message["content"]) + + +disable_chat = True if "model" not in st.session_state else False + +if prompt := st.chat_input("Send a message to llama3", disabled=disable_chat): + messages = [{"role": "system", "content": system_prompt}] + messages += [ + {"role": m["role"], "content": m["content"]} + for m in st.session_state.messages + ] + if rag: + query_embedding = list(embedding_model.embed(prompt))[0].tolist() + ret = collection.query(query_embedding, n_results=n_result) + data = [] + if ret["documents"] is not None and ret["metadatas"] is not None: + for i, (doc, metadata) in enumerate( + zip(ret["documents"], ret["metadatas"]) + ): + data.append(("\n\n".join(doc), metadata[0]["file_name"])) + messages.append( + { + "role": "user", + "content": RAG_PROMPT.format(query=prompt, data=data), + } + ) + else: + messages.append({"role": "user", "content": prompt}) + + with st.chat_message("user", avatar="💬"): + st.markdown(prompt) + + prompt_string = messages_to_llama3_prompt(messages) + + # Sleep short time so prior messages refresh and don't go dark + time.sleep(0.1) + + with st.chat_message("assistant", avatar="🦙"): + response = asyncio.run( + stream_output(st.session_state["model"], prompt_string) + ) + + st.session_state.messages += [ + {"role": "user", "avatar": "💬", "content": prompt}, + {"role": "assistant", "avatar": "🦙", "content": response}, + ] diff --git a/examples/gui/pages/stable-diffusion.py b/examples/gui/pages/stable-diffusion.py new file mode 100644 index 0000000000..efb787cf5a --- /dev/null +++ b/examples/gui/pages/stable-diffusion.py @@ -0,0 +1,136 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import time +from pathlib import Path + +import numpy as np +import streamlit as st +from diffusers.schedulers.scheduling_pndm import PNDMScheduler +from max.engine import InferenceSession +from PIL import Image +from shared import hf_streamlit_download, menu +from transformers.models.clip.tokenization_clip import CLIPTokenizer + +st.set_page_config("Stable Diffusion 1.5", page_icon="🎨") + +menu() + +"""# 🎨 Stable Diffusion 1.5""" + + +@st.cache_data +def load_tokenizer(path): + return CLIPTokenizer.from_pretrained(path) + + +num_steps = st.sidebar.number_input("Number of steps", 1, 100, 15) +seed = st.sidebar.number_input("Seed", 0, 255) +guidance_scale_factor = st.sidebar.number_input( + "Guidance Scale Factor", 0.0, 10.0, 7.5 +) +latent_scale_factor = st.sidebar.number_input( + "Latent Scale Factor", 0.0, 1.0, 0.18215 +) +output_height = st.sidebar.number_input("Output Height", 0, 2048, 512) +output_width = st.sidebar.number_input("Output Width", 0, 2048, 512) +latent_width = output_width // 8 +latent_height = output_height // 8 +latent_channels = 4 + +model_dir = Path(hf_streamlit_download("modularai/stable-diffusion-1.5-onnx")) + +text_encoder_path = model_dir / "text_encoder" / "model.onnx" +img_decoder_path = model_dir / "vae_decoder" / "model.onnx" +img_diffuser_path = model_dir / "unet" / "model.onnx" +scheduler_path = model_dir / "scheduler" / "scheduler_config.json" +tokenizer_path = model_dir / "tokenizer" + +prompt = st.text_input("Prompt", "A puppy playing the drums") +negative_prompt = st.text_input("Negative Prompt", "No overlapping geometry") + +if seed > 0: + np.random.seed(seed) + +if st.button("Generate Image"): + with st.spinner("Compiling models, faster after first run..."): + # Need a small delay so the spinner starts correctly + time.sleep(1) + session = InferenceSession() + txt_encoder = session.load(text_encoder_path) + img_decoder = session.load(img_decoder_path) + img_diffuser = session.load(img_diffuser_path) + + with st.spinner("Processing Input"): + tokenizer = load_tokenizer(tokenizer_path) + prompt_p = tokenizer( + prompt, padding="max_length", max_length=tokenizer.model_max_length + ) + prompt_n = tokenizer( + negative_prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + ) + input_ids = np.stack((prompt_p.input_ids, prompt_n.input_ids)).astype( + np.int32 + ) + encoder_hidden_states = txt_encoder.execute_legacy(input_ids=input_ids)[ + "last_hidden_state" + ] + + with st.spinner("Initializing Latent"): + scheduler = PNDMScheduler.from_pretrained(scheduler_path) + + # Note: For onnx, shapes are given in NCHW format. + latent = np.random.normal( + size=(1, latent_channels, latent_height, latent_width) + ) + latent = latent * scheduler.init_noise_sigma + latent = latent.astype(np.float32) + + # Loop through diffusion model. + scheduler.set_timesteps(num_steps) + progress_bar = st.progress(0.0, "Step 1/25") + for i, t in enumerate(scheduler.timesteps): + progress_bar.progress(i / num_steps, f"Step {i}/{num_steps}") + if i == num_steps: + progress_bar.progress(1.0, "Complete!") + + # Duplicate input and scale based on scheduler. + sample = np.vstack((latent, latent)) + sample = scheduler.scale_model_input(sample, timestep=t) + + # Execute the diffusion model with bs=2. Both batches have same primary input and + # timestep, but the encoder_hidden_states (primary prompt vs negative) differs. + noise_pred = img_diffuser.execute_legacy( + sample=sample, + encoder_hidden_states=encoder_hidden_states, + timestep=np.array([t], dtype=np.int64), + )["out_sample"] + + # Merge conditioned & unconditioned outputs. + noise_pred_text, noise_pred_uncond = np.split(noise_pred, 2) + noise_pred = noise_pred_uncond + guidance_scale_factor * ( + noise_pred_text - noise_pred_uncond + ) + + # Merge latent with previous iteration. + latent = scheduler.step(noise_pred, t, latent).prev_sample + + # Decode finalized latent. + with st.spinner("Decoding Image"): + latent = latent * (1 / latent_scale_factor) + decoded = img_decoder.execute_legacy(latent_sample=latent)["sample"] + image = np.clip(decoded / 2 + 0.5, 0, 1).squeeze() + image = (image.transpose(1, 2, 0) * 255).astype(np.uint8) + st.image(Image.fromarray(image, "RGB")) diff --git a/examples/gui/pages/yolo.py b/examples/gui/pages/yolo.py new file mode 100644 index 0000000000..11e2a2dde4 --- /dev/null +++ b/examples/gui/pages/yolo.py @@ -0,0 +1,178 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +import time +from pathlib import Path + +import cv2 +import numpy as np +import streamlit as st +import torch +from max import engine +from shared import menu, modular_cache_dir +from ultralytics import YOLO +from ultralytics.engine.results import Results +from ultralytics.models.yolo.segment.predict import ops + +st.set_page_config("YOLO", page_icon="🔍") +menu() + +""" +# 🔍 YOLO Segmentation + +Segment objects using your webcam. This downloads and converts YOLOv8n to +ONNX, then compiles it with MAX for faster inference! +""" + +# First do a quick check to see if a webcam is available +capture = cv2.VideoCapture(cv2.CAP_ANY) +if capture.isOpened(): + capture.release() +else: + st.error("This example is only available on a local machine with a webcam") + exit() + + +@st.cache_data(show_spinner="Downloading YOLO and exporting to ONNX") +def download_and_export_yolo(model_path, height, width): + model = YOLO(model_path) + model.export(format="onnx", imgsz=(height, width), simplify=True) + return model.names + + +@st.cache_resource(show_spinner="Starting MAX Inference Session") +def max_yolo_session(onnx_path): + # Have to sleep for a short time for the spinner to start correctly + time.sleep(1) + session = engine.InferenceSession() + return session.load(onnx_path) + + +def resize_and_pad(image, shape): + # Grab shape sizes. + (h, w, _) = image.shape + (target_h, target_w) = shape + + # Resize to fully fit within `shape`. + min_ratio = min(target_h / h, target_w / w) + unpadded_h = int(round(min_ratio * h)) + unpadded_w = int(round(min_ratio * w)) + image = cv2.resize( + image, (unpadded_w, unpadded_h), interpolation=cv2.INTER_LINEAR + ) + + # Pad to be the same size as `shape`. + delta_h = (target_h - unpadded_h) / 2 + delta_w = (target_w - unpadded_w) / 2 + top, bottom = int(round(delta_h - 0.1)), int(round(delta_h + 0.1)) + left, right = int(round(delta_w - 0.1)), int(round(delta_w + 0.1)) + return cv2.copyMakeBorder( + image, + top, + bottom, + left, + right, + cv2.BORDER_CONSTANT, + value=(114, 114, 114), + ) + + +def postprocess(out0, out1, input, frame, class_names): + out0 = torch.from_numpy(out0) + out1 = torch.from_numpy(out1) + + pred = ops.non_max_suppression( + out0, + conf_thres=0.25, + iou_thres=0.70, + agnostic=False, + max_det=10, + nc=len(class_names), + classes=None, + )[0] + + if not len(pred): + result = Results( + orig_img=frame, + path="", + names=class_names, + boxes=pred[:, :6], + ) + else: + masks = ops.process_mask( + out1[0], + pred[:, 6:], + pred[:, :4], + input.shape[2:], + upsample=True, + ) # HWC + pred[:, :4] = ops.scale_boxes(input.shape[2:], pred[:, :4], frame.shape) + result = Results( + orig_img=frame, + path="", + names=class_names, + boxes=pred[:, :6], + masks=masks, + ) + return result + + +model_path = st.sidebar.text_input( + "Model Path", + os.path.join(modular_cache_dir(), "yolov8n-seg.pt"), +) +onnx_path = Path(os.path.dirname(model_path)) / "yolov8n-seg.onnx" +width = st.sidebar.number_input("Image Width", 64, 2048, 640) +height = st.sidebar.number_input("Image Height", 64, 2048, 480) + +class_names = download_and_export_yolo(model_path, height, width) +yolo = max_yolo_session(onnx_path) + +previous_elapsed_ms = [] + +frame_window = st.image([]) +camera = cv2.VideoCapture(0) + +button_placeholder = st.empty() +if button_placeholder.button("Start Webcam"): + button_placeholder.empty() + while True: + _, img = camera.read() + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = resize_and_pad(img, (height, width)) + # Preprocess inputs. + input = ( + img[np.newaxis, :, :, ::-1].transpose(0, 3, 1, 2).astype(np.float32) + / 255 + ).copy() + start = time.time() + outputs = list(yolo.execute_legacy(images=input).values()) + elapsed_ms = (time.time() - start) * 1000 + result = postprocess(outputs[0], outputs[1], input, img, class_names) + img = result.plot() + # Calculated average fps and update window title. + # global previous_elapsed_ms + previous_elapsed_ms.append(elapsed_ms) + previous_elapsed_ms = previous_elapsed_ms[-100:] + fps = 1000.0 / np.average(previous_elapsed_ms) + img = cv2.putText( + img, + f"FPS: {int(fps)}", + org=(50, 50), + fontFace=cv2.FONT_HERSHEY_COMPLEX, + fontScale=1, + color=(255, 0, 0), + thickness=2, + ) + frame_window.image(img) diff --git a/examples/gui/ragdata/mojo_functions.txt b/examples/gui/ragdata/mojo_functions.txt new file mode 100644 index 0000000000..f053dcb669 --- /dev/null +++ b/examples/gui/ragdata/mojo_functions.txt @@ -0,0 +1,28 @@ +# Mojo Functions + +Mojo functions can be declared with either fn or def. + +The fn declaration enforces type-checking and memory-safe behaviors (Rust style), while def allows no type declarations and dynamic behaviors (Python style). + +For example, this def function doesn't require declaration of argument types or the return type: + +```mojo +def greet(name): + return "Hello, " + name + "!" +``` + +While the same thing as an fn function requires that you specify the argument type and the return type like this: + +```mojo +fn greet2(name: String) -> String: + return "Hello, " + name + "!" +``` + +Both functions have the same result, but the fn function provides compile-time checks to ensure the function receives and returns the correct types. Whereas, the def function might fail at runtime if it receives the wrong type. + +Currently, Mojo doesn't support top-level code in a .mojo (or .🔥) file, so every program must include a function named main() as the entry point. You can declare it with either def or fn: + +```mojo +def main(): + print("Hello, world!") +``` diff --git a/examples/gui/shared.py b/examples/gui/shared.py new file mode 100644 index 0000000000..0f3bca2c3e --- /dev/null +++ b/examples/gui/shared.py @@ -0,0 +1,282 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +import threading +import time +from functools import wraps +from pathlib import Path +from typing import List + +import chromadb +import streamlit as st +from chromadb.config import Settings +from fastembed import TextEmbedding +from gguf import Union +from huggingface_hub import hf_hub_download, snapshot_download +from llama_index.core import SimpleDirectoryReader +from max.pipelines import TokenGenerator +from streamlit.runtime.scriptrunner import ( + add_script_run_ctx, + get_script_run_ctx, +) +from tqdm.auto import tqdm + +RAG_SYSTEM_PROMPT = """You are a helpful document search assistant. +Your task is to find an answer to user's query about their given documentation. +DO NOT HALLUCINATE.""" + +RAG_PROMPT = """Answer the users query: {query} using the provided context: {data}. +If you don't have an answer say 'I don't know!' +Make sure to include the filename of any document you use to answer the query. + +## GO""" + + +def menu(): + st.sidebar.page_link("home.py", label="️Home", icon="⚡️") + st.sidebar.page_link("pages/llama3_1.py", label="Llama3.1", icon="🦙") + st.sidebar.page_link("pages/bert.py", label="Bert", icon="👓") + st.sidebar.page_link("pages/yolo.py", label="YOLO", icon="🔍") + st.sidebar.page_link( + "pages/stable-diffusion.py", label="Stable Diffusion 1.5", icon="🎨" + ) + + +def modular_cache_dir() -> str: + cache_folder = os.getenv("XDG_CACHE_PATH", str(Path.home() / ".cache")) + modular_dir = os.path.join(cache_folder, "modular") + os.makedirs(modular_dir, exist_ok=True) + return modular_dir + + +def format_time(seconds): + """Return a pretty format based on how much time is left.""" + hours, remainder = divmod(seconds, 3600) + minutes, seconds = divmod(remainder, 60) + if hours > 0: + return f"{int(hours)}h {int(minutes)}m {int(seconds)}s" + elif minutes > 0: + return f"{int(minutes)}m {int(seconds)}s" + else: + return f"{int(seconds)}s" + + +tqdm_patched = False + + +def hf_streamlit_download(repo_id: str, filename: str = "") -> str: + """Patch tqdm to update st.progress bars, uses `hf_hub_download` if + a `repo_id` and `filename` is provided, otherwise uses `snapshot_download`. + """ + global tqdm_patched + if not tqdm_patched: + tqdm_patched = True + patch_tqdm() + + if filename: + return hf_hub_download(repo_id, filename) + + return snapshot_download(repo_id) + + +# TODO: raise patch to `huggingface_hub` to see if they'd be interested in this +# as a class that inherits from tqdm, which can be installed with +# huggingface_hub[streamlit] +def patch_tqdm(): + """Monkey patches tqdm to update st.progress bars. `hf_hub_download` and + `snapshot_download` use tqdm, so this is a way to hook into the progress + of each file download and display it on the GUI. We need to pass the + script context so other threads can communicate with the main thread, + when interacting with streamlit elements.""" + progress_bars = {} + ctx = get_script_run_ctx() + + def patch_update(original): + @wraps(original) + def wrapper(self, n=1): + # Return early if tqdm instance is describing how many files + # will be downloaded. + if self.desc.startswith("Fetching"): + return + + if self.n is not None and self.total is not None: + # Convert everything to MB + downloaded = self.n / 1024 / 1024 + total = self.total / 1024 / 1024 + speed = ( + self.format_dict["rate"] / 1024 / 1024 + if self.format_dict["rate"] + else 0.0 + ) + if speed != 0: + time_left = format_time((total - downloaded) / speed) + else: + time_left = "N/A" + # Only create/update progress bar if the download is in progress to skip small files + if time_left != "N/A": + add_script_run_ctx(threading.currentThread(), ctx) + if self.pos not in progress_bars: + progress_bars[self.pos] = st.empty() + progress_bar = progress_bars[self.pos] + status = ( + f"{self.desc}: {int(downloaded)}/{int(total)} MB Speed:" + f" {speed:.2f} MB/s Remaining: {time_left}" + ) + progress_bar.progress(downloaded / total, status) + return original(self, n) + + return wrapper + + def patch_del(original): + @wraps(original) + def wrapper(self, *args, **kwargs): + if self.pos in progress_bars: + progress_bars[self.pos].empty() + del progress_bars[self.pos] + + return original(self, *args, **kwargs) + + return wrapper + + tqdm.update = patch_update(tqdm.update) + tqdm.__del__ = patch_del(tqdm.__del__) + + +@st.cache_resource(show_spinner=False) +def load_embed_docs(docs_filenames: List[str]): + """Loads documents from `./ragdir` and embeds them to chromadb + using a text embedding model. + """ + with st.spinner("Loading RAG data..."): + docs = SimpleDirectoryReader("./ragdata").load_data() + client = chromadb.Client(Settings(anonymized_telemetry=False)) + collection = client.get_or_create_collection( + "max-rag-example", metadata={"hnsw:space": "cosine"} + ) + embedding_model = TextEmbedding() + + for i, doc in enumerate(docs): + embedding = list(embedding_model.embed(doc.text))[0].tolist() + collection.upsert( + documents=doc.text, + embeddings=embedding, + ids=[str(i)], + metadatas=[doc.metadata], + ) + + return collection, embedding_model + + +# Generate metrics for streamlit +class TextGenerationMetrics: + """Metrics capturing and reporting for a text generation pipeline.""" + + prompt_size: int + output_size: int + startup_time: Union[float, str] + time_to_first_token: Union[float, str] + prompt_eval_throughput: Union[float, str] + eval_throughput: Union[float, str] + + _start_time: float + _signposts: dict[str, float] + + def __init__(self): + self.signposts = {} + self.prompt_size = 0 + self.output_size = 0 + self.start_time = time.time() + + def signpost(self, name: str): + """Measure the current time and tag it with a name for later reporting.""" + self.signposts[name] = time.time() + + def new_token(self): + """Report that a new token has been generated.""" + self.output_size += 1 + + def calculate_results(self): + end_generation = time.time() + begin_generation = self.signposts.get("begin_generation") + if begin_generation: + self.startup_time = ( + self.signposts["begin_generation"] - self.start_time + ) * 1000.0 + else: + self.startup_time = "n/a" + + first_token = self.signposts.get("first_token") + if first_token and begin_generation: + self.time_to_first_token = ( + self.signposts["first_token"] + - self.signposts["begin_generation"] + ) * 1000.0 + else: + self.time_to_first_token = "n/a" + + st.sidebar.metric( + "Input/Output Tokens", + value=f"{self.prompt_size}/{self.output_size}", + ) + st.sidebar.metric( + "Time to first token", value=f"{self.time_to_first_token:.2f} ms" + ) + + if first_token and begin_generation: + generation_time = end_generation - self.signposts["first_token"] + assert isinstance(self.time_to_first_token, float) + self.prompt_eval_throughput = self.prompt_size / ( + self.time_to_first_token / 1000.0 + ) + self.eval_throughput = (self.output_size - 1) / generation_time + st.sidebar.metric( + "Prompt eval throughput (context-encoding):", + value=f"{self.prompt_eval_throughput:.2f} tokens/s", + ) + st.sidebar.metric( + "Eval throughput (token-generation):", + value=f"{self.eval_throughput:.2f} tokens/s", + ) + + +async def stream_output(model: TokenGenerator, prompt: str) -> str: + metrics = TextGenerationMetrics() + context = model.new_context(prompt) + prompt_size = context.current_length + + response_display = st.empty() + response_str = "" + + if metrics: + metrics.prompt_size = prompt_size + metrics.signpost("begin_generation") + + is_first_token = True + request_id = str(id(prompt)) + while True: + response = model.next_token({request_id: context}, num_steps=1)[0] + if request_id not in response: + break + response_str += response[request_id] + response_display.markdown(response_str) + if metrics: + if is_first_token: + is_first_token = False + metrics.signpost("first_token") + metrics.new_token() + if metrics: + metrics.signpost("end_generation") + + metrics.calculate_results() + return response_str diff --git a/examples/inference/README.md b/examples/inference/README.md new file mode 100644 index 0000000000..7903926872 --- /dev/null +++ b/examples/inference/README.md @@ -0,0 +1,18 @@ +# PyTorch and ONNX inference in MAX + +MAX can accelerate the inference of existing PyTorch or ONNX models. These +examples show several common PyTorch or ONNX models running in MAX through the +Mojo, Python, and C APIs: + +## PyTorch (via TorchScript) + +- BERT, [with the Mojo API](./bert-mojo-torchscript/), +[with the Python API](./bert-python-torchscript/), +and [with the C API](./bert-c-torchscript/) +- [ResNet50 with the Python API](./resnet50-python-torchscript/) + +## ONNX + +- Stable Diffusion, [with the Mojo API](./stable-diffusion-mojo-onnx/) and +[with the Python API](./stable-diffusion-python-onnx/) +- [YOLOv8 with the Python API](./yolo-python-onnx/) diff --git a/examples/inference/bert-c-torchscript/.gitignore b/examples/inference/bert-c-torchscript/.gitignore new file mode 100644 index 0000000000..2f2295c1a6 --- /dev/null +++ b/examples/inference/bert-c-torchscript/.gitignore @@ -0,0 +1,7 @@ +# Binaries generated +inputs +outputs.bin + +# Don't check in / version control the downloaded model +*.pt +*.torchscript diff --git a/examples/inference/bert-c-torchscript/CMakeLists.txt b/examples/inference/bert-c-torchscript/CMakeLists.txt new file mode 100644 index 0000000000..650f7040be --- /dev/null +++ b/examples/inference/bert-c-torchscript/CMakeLists.txt @@ -0,0 +1,22 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +cmake_minimum_required(VERSION 3.24) + +project(max-examples LANGUAGES C) + +list(APPEND CMAKE_MODULE_PATH "$ENV{MAX_PKG_DIR}/lib/cmake") +include(AddMaxEngine) + +add_executable(bert main.c) +target_link_libraries(bert PUBLIC max-engine) diff --git a/examples/inference/bert-c-torchscript/README.md b/examples/inference/bert-c-torchscript/README.md new file mode 100644 index 0000000000..f7d6f9d490 --- /dev/null +++ b/examples/inference/bert-c-torchscript/README.md @@ -0,0 +1,69 @@ +# TorchScript BERT inference with C + +This directory includes scripts used to run simple BERT inference via the MAX +Engine C API to predict the sentiment of the given text. + +## Quickstart + +For this example, you need a cmake installed on your system and a C compiler. + +### Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run the +following command: + +```sh +magic run bash run.sh +``` + +### Conda instructions + +Create a Conda environment, activate that environment, and install the +requirements: + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda run -n max-repo --live-stream bash run.sh +``` + +## Scripts included + +- `pre-process.py`: Prepares an example input and saves the pre-processed input +to a local directory, for use use in the `main.c` program. Example: + + ```sh + python3 pre-process.py --text "Paris is the [MASK] of France." + ``` + +- `post-process.py`: Loads the generated output, post-processes it, and outputs +the prediction. Example: + + ```sh + python3 post-process.py + ``` + +## Building the example + +This example uses CMake. To build the executable, please use the following +commands: + +```sh +export MAX_PKG_DIR=`modular config max.path` +cmake -B build -S . +cmake --build build +``` + +The executable is called `bert` and will be present in the build directory. + +## Usage + +```sh +python3 ../common/bert-torchscript/download-model.py +python3 pre-process.py --text "Your text" +./build/bert ../../models/bert.torchscript +python3 post-process.py +``` diff --git a/examples/inference/bert-c-torchscript/environment.yml b/examples/inference/bert-c-torchscript/environment.yml new file mode 100644 index 0000000000..13c6313bfc --- /dev/null +++ b/examples/inference/bert-c-torchscript/environment.yml @@ -0,0 +1,8 @@ +name: max-repo +channels: + - https://conda.modular.com/max-nightly/ + - conda-forge +dependencies: + - python>=3.9,<3.13 + - max + - pip>=24.0,<25 diff --git a/examples/inference/bert-c-torchscript/main.c b/examples/inference/bert-c-torchscript/main.c new file mode 100644 index 0000000000..5c277174bc --- /dev/null +++ b/examples/inference/bert-c-torchscript/main.c @@ -0,0 +1,222 @@ +/******************************************************************************* + * Copyright (c) 2025, Modular Inc. All rights reserved. + * + * Licensed under the Apache License v2.0 with LLVM Exceptions: + * https://llvm.org/LICENSE.txt + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + *******************************************************************************/ + +#include "max/c/common.h" +#include "max/c/context.h" +#include "max/c/model.h" +#include "max/c/pytorch/config.h" +#include "max/c/tensor.h" +#include "max/c/value.h" + +#include +#include +#include + +void logHelper(const char *level, const char *message, const char delimiter) { + printf("%s: %s%c", level, message, delimiter); +} +void logDebug(const char *message) { logHelper("DEBUG", message, ' '); } +void logInfo(const char *message) { logHelper("INFO", message, '\n'); } +void logError(const char *message) { logHelper("ERROR", message, '\n'); } + +#define CHECK(x) \ + if (M_isError(x)) { \ + logError(M_getError(x)); \ + return EXIT_FAILURE; \ + } + +// Read file at the given path. On failure abort. +char *readFileOrExit(const char *filepath) { + FILE *file; + file = fopen(filepath, "rb"); + if (!file) { + printf("failed to open %s. Aborting.\n", filepath); + abort(); + } + fseek(file, 0, SEEK_END); + long fileSize = ftell(file); + rewind(file); + + char *buffer = (char *)malloc(fileSize * sizeof(char)); + size_t count = fread(buffer, fileSize, 1, file); + if (count != 1) { + printf("failed to read %s. Aborting.\n", filepath); + abort(); + } + fclose(file); + return buffer; +} + +int main(int argc, char **argv) { + if (argc != 2) { + printf("Usage: bert "); + return EXIT_FAILURE; + } + + M_Status *status = M_newStatus(); + + M_RuntimeConfig *runtimeConfig = M_newRuntimeConfig(); + M_RuntimeContext *context = M_newRuntimeContext(runtimeConfig, status); + CHECK(status); + + logInfo("Compiling Model"); + M_CompileConfig *compileConfig = M_newCompileConfig(); + const char *modelPath = argv[1]; + M_setModelPath(compileConfig, /*path=*/modelPath); + + logInfo("Setting InputSpecs for compilation"); + int64_t *inputIdsShape = + (int64_t *)readFileOrExit("inputs/input_ids_shape.bin"); + M_TorchInputSpec *inputIdsInputSpec = + M_newTorchInputSpec(inputIdsShape, /*dimNames=*/NULL, /*rankSize=*/2, + /*type=*/M_INT32, /*device=*/"", status); + CHECK(status); + + int64_t *attentionMaskShape = + (int64_t *)readFileOrExit("inputs/attention_mask_shape.bin"); + M_TorchInputSpec *attentionMaskInputSpec = + M_newTorchInputSpec(attentionMaskShape, /*dimNames=*/NULL, /*rankSize=*/2, + /*type=*/M_INT32, /*device=*/"", status); + CHECK(status); + + int64_t *tokenTypeIdsShape = + (int64_t *)readFileOrExit("inputs/token_type_ids_shape.bin"); + M_TorchInputSpec *tokenTypeIdsInputSpec = + M_newTorchInputSpec(tokenTypeIdsShape, /*dimNames=*/NULL, /*rankSize=*/2, + /*type=*/M_INT32, /*device=*/"", status); + CHECK(status); + + M_TorchInputSpec *inputSpecs[3] = {inputIdsInputSpec, attentionMaskInputSpec, + tokenTypeIdsInputSpec}; + M_setTorchInputSpecs(compileConfig, inputSpecs, 3); + + M_AsyncCompiledModel *compiledModel = + M_compileModel(context, &compileConfig, status); + CHECK(status); + + logInfo("Initializing Model"); + M_AsyncModel *model = + M_initModel(context, compiledModel, /*weightsRegistry=*/NULL, status); + CHECK(status); + + logInfo("Waiting for model compilation to finish"); + M_waitForModel(model, status); + CHECK(status); + + logInfo("Inspecting model metadata"); + size_t numInputs = M_getNumModelInputs(compiledModel, status); + CHECK(status); + printf("Num inputs: %ld\n", numInputs); + + M_TensorNameArray *tensorNames = M_getInputNames(compiledModel, status); + CHECK(status); + logDebug("Model input names:"); + for (size_t i = 0; i < numInputs; i++) { + const char *tensorName = M_getTensorNameAt(tensorNames, i); + printf("%s ", tensorName); + } + printf("\n"); + + logInfo("Preparing inputs..."); + M_AsyncTensorMap *inputToModel = M_newAsyncTensorMap(context); + + M_TensorSpec *inputIdsSpec = + M_newTensorSpec(inputIdsShape, /*rankSize=*/2, /*dtype=*/M_INT32, + /*tensorName=*/"input_ids"); + int32_t *inputIdsTensor = (int32_t *)readFileOrExit("inputs/input_ids.bin"); + M_borrowTensorInto(inputToModel, inputIdsTensor, inputIdsSpec, status); + CHECK(status); + + M_TensorSpec *attentionMaskSpec = + M_newTensorSpec(attentionMaskShape, /*rankSize=*/2, /*dtype=*/M_INT32, + /*tensorName=*/"attention_mask"); + int32_t *attentionMaskTensor = + (int32_t *)readFileOrExit("inputs/attention_mask.bin"); + M_borrowTensorInto(inputToModel, attentionMaskTensor, attentionMaskSpec, + status); + CHECK(status); + + M_TensorSpec *tokenTypeIdsSpec = + M_newTensorSpec(tokenTypeIdsShape, /*rankSize=*/2, /*dtype=*/M_INT32, + /*tensorName=*/"token_type_ids"); + int32_t *tokenTypeIdsTensor = + (int32_t *)readFileOrExit("inputs/token_type_ids.bin"); + M_borrowTensorInto(inputToModel, tokenTypeIdsTensor, tokenTypeIdsSpec, + status); + CHECK(status); + + logInfo("Running Inference..."); + M_AsyncTensorMap *outputs = + M_executeModelSync(context, model, inputToModel, status); + CHECK(status); + + M_AsyncValue *resultValue = + M_getValueByNameFrom(outputs, /*tensorName=*/"result0", status); + CHECK(status); + + logInfo("Extracting output values"); + // Convert the value we found to a tensor and save it to disk. + M_AsyncTensor *result = M_getTensorFromValue(resultValue); + size_t numElements = M_getTensorNumElements(result); + printf("Tensor size: %ld\n", numElements); + M_Dtype dtype = M_getTensorType(result); + const char *outputFilePath = "outputs.bin"; + FILE *file = fopen(outputFilePath, "wb"); + if (!file) { + printf("failed to open %s. Aborting.\n", outputFilePath); + return EXIT_FAILURE; + } + fwrite(M_getTensorData(result), M_sizeOf(dtype), numElements, file); + fclose(file); + + // free memory buffers + free(tokenTypeIdsTensor); + free(attentionMaskTensor); + free(inputIdsTensor); + + free(tokenTypeIdsShape); + free(attentionMaskShape); + free(inputIdsShape); + + // free resources + M_freeTensor(result); + + M_freeValue(resultValue); + + M_freeAsyncTensorMap(outputs); + + M_freeTensorSpec(tokenTypeIdsSpec); + M_freeTensorSpec(attentionMaskSpec); + M_freeTensorSpec(inputIdsSpec); + + M_freeAsyncTensorMap(inputToModel); + + M_freeTensorNameArray(tensorNames); + + M_freeModel(model); + + M_freeCompileConfig(compileConfig); + M_freeCompiledModel(compiledModel); + + M_freeTorchInputSpec(tokenTypeIdsInputSpec); + M_freeTorchInputSpec(attentionMaskInputSpec); + M_freeTorchInputSpec(inputIdsInputSpec); + + M_freeCompileConfig(compileConfig); + M_freeRuntimeContext(context); + + M_freeStatus(status); + + logInfo("Inference successfully completed"); + return EXIT_SUCCESS; +} diff --git a/examples/inference/bert-c-torchscript/pixi.toml b/examples/inference/bert-c-torchscript/pixi.toml new file mode 100644 index 0000000000..eae282bdfe --- /dev/null +++ b/examples/inference/bert-c-torchscript/pixi.toml @@ -0,0 +1,25 @@ +[project] +name = "BERT C Torchscript" +version = "1.0.0" +description = "MAX Examples" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/", "pytorch"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +python = ">=3.9,<3.13" +max = "*" +transformers = ">=4.44.0" +numpy = "<2.0" +cmake = ">=3.24" + +# For performance, prefer pytorch over anything else. +# The linux ARM package doesn't exist there, so prefer PyPi instead. +[target.linux-64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.osx-arm64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.linux-aarch64.pypi-dependencies] +torch = "==2.5.1" +[pypi-options] +extra-index-urls = ["https://download.pytorch.org/whl/cpu"] diff --git a/examples/inference/bert-c-torchscript/post-process.py b/examples/inference/bert-c-torchscript/post-process.py new file mode 100644 index 0000000000..b5f683dd70 --- /dev/null +++ b/examples/inference/bert-c-torchscript/post-process.py @@ -0,0 +1,29 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import numpy as np +import torch + + +def post_process(): + logits = torch.from_numpy(np.fromfile("outputs.bin", dtype=np.float32)) + predictions = torch.argmax(logits, dim=-1) + + predicted_label = predictions.item() + sentiment_labels = {0: "Negative", 1: "Positive"} + print(f"Predicted sentiment: {sentiment_labels[predicted_label]}") + + +if __name__ == "__main__": + torch.set_default_device("cpu") + post_process() diff --git a/examples/inference/bert-c-torchscript/pre-process.py b/examples/inference/bert-c-torchscript/pre-process.py new file mode 100644 index 0000000000..bc1d55b322 --- /dev/null +++ b/examples/inference/bert-c-torchscript/pre-process.py @@ -0,0 +1,63 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +#!/usr/bin/env python3 + +from argparse import ArgumentParser +from pathlib import Path + +import numpy as np +from transformers import BertTokenizer + +HF_MODEL_NAME = "bert-base-uncased" + + +def main(): + parser = ArgumentParser(description="Preprocessing for BERT inputs") + parser.add_argument( + "--text", + type=str, + metavar="", + required=True, + help="Statement to classify.", + ) + + args = parser.parse_args() + + print("Generating input tensors...") + print(f'Input sentence: "{args.text}".') + + tokenizer = BertTokenizer.from_pretrained(HF_MODEL_NAME) + encoded_inputs = tokenizer(args.text, return_tensors="pt") + + print("Saving inputs to disk...") + input_dir = Path("inputs") + input_dir.mkdir(exist_ok=True) + + created_files = [] + for name, value in encoded_inputs.items(): + value = value.numpy().astype(np.int32) + filename = input_dir / name + filename = filename.with_suffix(".bin") + filename.unlink(missing_ok=True) + value.tofile(filename) + + shape = np.array(value.shape).astype(np.int64) + shape_file = input_dir / f"{name}_shape.bin" + shape.tofile(shape_file) + created_files += [str(filename), str(shape_file)] + print("Inputs saved.") + + +if __name__ == "__main__": + main() diff --git a/examples/inference/bert-c-torchscript/run.sh b/examples/inference/bert-c-torchscript/run.sh new file mode 100755 index 0000000000..046b914abb --- /dev/null +++ b/examples/inference/bert-c-torchscript/run.sh @@ -0,0 +1,50 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +set -e + +CURRENT_DIR=$(dirname "$0") +# Make sure we're running from inside the directory containing this file. +cd "$CURRENT_DIR" + +# remove the build artifacts +rm -rf build + +if [[ -n "$CONDA_PREFIX" ]]; then + # Required for the CMake build + MAX_PKG_DIR="${MAX_PKG_DIR:-$CONDA_PREFIX}" +else + echo "This script should be run with Conda. Try \`magic run run.sh\`." && false +fi + +export MAX_PKG_DIR + +MODEL_PATH="$CURRENT_DIR/../../models/bert.torchscript" + +# Example input for the model +INPUT_EXAMPLE="My dog is cute." + +# Download model from HuggingFace +python3 "$CURRENT_DIR/../common/bert-torchscript/download-model.py" -o "$MODEL_PATH" +python3 "$CURRENT_DIR/pre-process.py" --text "$INPUT_EXAMPLE" + +# Build the example +cmake -B build -S "$CURRENT_DIR" +cmake --build build + +# Run example +./build/bert "$MODEL_PATH" + +# Post process +python3 "$CURRENT_DIR/post-process.py" diff --git a/examples/inference/bert-mojo-torchscript/README.md b/examples/inference/bert-mojo-torchscript/README.md new file mode 100644 index 0000000000..436c1fdcf2 --- /dev/null +++ b/examples/inference/bert-mojo-torchscript/README.md @@ -0,0 +1,30 @@ +# TorchScript BERT inference with Mojo + +This directory includes scripts used to run simple BERT inference via the [MAX +Engine Mojo API](https://docs.modular.com/max/api/mojo/engine/) to +predict the masked words in a sentence. + +## Quickstart + +### Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run the +following command: + +```sh +magic run bash run.sh +``` + +### Conda instructions + +Create a Conda environment, activate that environment, and install the +requirements: + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda run -n max-repo --live-stream bash run.sh +``` diff --git a/examples/inference/bert-mojo-torchscript/environment.yml b/examples/inference/bert-mojo-torchscript/environment.yml new file mode 100644 index 0000000000..b600eef8fc --- /dev/null +++ b/examples/inference/bert-mojo-torchscript/environment.yml @@ -0,0 +1,9 @@ +name: max-repo +channels: + - https://conda.modular.com/max-nightly/ + - conda-forge + - defaults +dependencies: + - python>=3.9,<3.13 + - max + - pip>=24.0,<25 diff --git a/examples/inference/bert-mojo-torchscript/pixi.toml b/examples/inference/bert-mojo-torchscript/pixi.toml new file mode 100644 index 0000000000..a5c8b581f9 --- /dev/null +++ b/examples/inference/bert-mojo-torchscript/pixi.toml @@ -0,0 +1,24 @@ +[project] +name = "BERT Mojo Torchscript" +version = "1.0.0" +description = "MAX Examples" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/", "pytorch"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +python = ">=3.9,<3.13" +max = "*" +transformers = ">=4.44.0" +numpy = "<2.0" + +# For performance, prefer pytorch over anything else. +# The linux ARM package doesn't exist there, so prefer PyPi instead. +[target.linux-64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.osx-arm64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.linux-aarch64.pypi-dependencies] +torch = "==2.5.1" +[pypi-options] +extra-index-urls = ["https://download.pytorch.org/whl/cpu"] diff --git a/examples/inference/bert-mojo-torchscript/run.sh b/examples/inference/bert-mojo-torchscript/run.sh new file mode 100755 index 0000000000..0aa4710039 --- /dev/null +++ b/examples/inference/bert-mojo-torchscript/run.sh @@ -0,0 +1,28 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +set -e + +# Example input for the model +INPUT_EXAMPLE="Paris is the [MASK] of France." + +MODEL_PATH="../../models/bert-mlm.torchscript" + +# Make sure we're running from inside the directory containing this file. +cd "$(dirname "$0")" + +# Download model from HuggingFace +python3 ../common/bert-torchscript/download-model.py -o "$MODEL_PATH" --mlm + +mojo simple_inference.🔥 "$INPUT_EXAMPLE" diff --git "a/examples/inference/bert-mojo-torchscript/simple_inference.\360\237\224\245" "b/examples/inference/bert-mojo-torchscript/simple_inference.\360\237\224\245" new file mode 100755 index 0000000000..9e9e486c18 --- /dev/null +++ "b/examples/inference/bert-mojo-torchscript/simple_inference.\360\237\224\245" @@ -0,0 +1,127 @@ +#!/usr/bin/env mojo +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.engine import InputSpec, InferenceSession, Model +from pathlib import Path +from python import Python, PythonObject +from max.tensor import Tensor, TensorSpec +import sys + + +def execute(model: Model, text: String, transformers: PythonObject) -> String: + # The model was compiled with a maximum seqlen, so read that out from the model output metadata + output_spec = model.get_model_output_metadata()[0] + max_seqlen = output_spec[1].value() + + tokenizer = transformers.AutoTokenizer.from_pretrained("bert-base-uncased") + + inputs = tokenizer( + text=text, + add_special_tokens=True, + padding="max_length", + truncation=True, + max_length=max_seqlen, + return_tensors="np", + ) + + input_ids = inputs["input_ids"] + token_type_ids = inputs["token_type_ids"] + attention_mask = inputs["attention_mask"] + + outputs = model.execute( + "input_ids", + input_ids, + "token_type_ids", + token_type_ids, + "attention_mask", + attention_mask, + ) + + logits = outputs.get[DType.float32]("result0") + + mask_idx = -1 + for i in range(len(input_ids[0])): + if input_ids[0][i] == tokenizer.mask_token_id: + mask_idx = i + + predicted_token_id = argmax(logits)[mask_idx] + return String( + tokenizer.decode( + predicted_token_id, + skip_special_tokens=True, + clean_up_tokenization_spaces=True, + ) + ) + + +def argmax(t: Tensor) -> List[Int]: + var res = List[Int](capacity=t.dim(1)) + for i in range(t.dim(1)): + var max_val = Scalar[t.type].MIN + var max_idx = 0 + for j in range(t.dim(2)): + if t[0, i, j] > max_val: + max_val = t[0, i, j] + max_idx = j + res.append(max_idx) + return res + + +def load_model(session: InferenceSession) -> Model: + batch = 1 + seqlen = 128 + + input_ids_spec = TensorSpec(DType.int64, batch, seqlen) + token_type_ids_spec = TensorSpec(DType.int64, batch, seqlen) + attention_mask_spec = TensorSpec(DType.int64, batch, seqlen) + input_specs = List[InputSpec]() + + input_specs.append(input_ids_spec) + input_specs.append(attention_mask_spec) + input_specs.append(token_type_ids_spec) + + model = session.load( + Path("../../models/bert-mlm.torchscript"), input_specs=input_specs + ) + + return model + + +def read_input() -> String: + USAGE = ( + 'Usage: ./run.mojo \n\t e.g., ./run.mojo "Paris is the [MASK] of' + ' France"' + ) + + argv = sys.argv() + if len(argv) != 2: + raise Error("\nPlease enter a prompt." + "\n" + USAGE) + + return String(sys.argv()[1]) + + +def main(): + # Import HF Transformers dependency (for the tokenizer) + transformers = Python.import_module("transformers") + + # Read user prompt, create an InferenceSession, and load the model + text = read_input() + session = InferenceSession() + model = load_model(session) + + # Run inference + decoded_result = execute(model, text, transformers) + + print("input text: ", text) + print("filled mask: ", text.replace("[MASK]", decoded_result)) diff --git a/examples/inference/bert-python-torchscript/.gitignore b/examples/inference/bert-python-torchscript/.gitignore new file mode 100644 index 0000000000..2c785ab505 --- /dev/null +++ b/examples/inference/bert-python-torchscript/.gitignore @@ -0,0 +1 @@ +model-repository/ diff --git a/examples/inference/bert-python-torchscript/README.md b/examples/inference/bert-python-torchscript/README.md new file mode 100644 index 0000000000..325e2a64d7 --- /dev/null +++ b/examples/inference/bert-python-torchscript/README.md @@ -0,0 +1,47 @@ +# TorchScript BERT inference with Python + +This directory includes scripts used to run simple BERT inference via the MAX +Engine Python API to predict the masked words in a sentence. + +## Quickstart + +### Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run the +following command: + +```sh +# Run the MAX Engine example +magic run bash run.sh +# Run the MAX Serving example +magic run bash deploy.sh +``` + +### Conda instructions + +Create a Conda environment, activate that environment, and install the +requirements: + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda run -n max-repo --live-stream bash run.sh +# Run the MAX Serving example +conda run -n max-repo --live-stream bash deploy.sh +``` + +## Scripts included + +- `simple-inference.py`: Predicts the masked words in the input text using the +MAX Engine Python API. The script prepares an example input, executes the +model, and generates the filled mask. + + You can use the `--text` CLI flag to specify an input sentence. + For example: + + ```sh + python3 simple-inference.py --text "Paris is the [MASK] of France." + ``` diff --git a/examples/inference/bert-python-torchscript/environment.yml b/examples/inference/bert-python-torchscript/environment.yml new file mode 100644 index 0000000000..13c6313bfc --- /dev/null +++ b/examples/inference/bert-python-torchscript/environment.yml @@ -0,0 +1,8 @@ +name: max-repo +channels: + - https://conda.modular.com/max-nightly/ + - conda-forge +dependencies: + - python>=3.9,<3.13 + - max + - pip>=24.0,<25 diff --git a/examples/inference/bert-python-torchscript/pixi.toml b/examples/inference/bert-python-torchscript/pixi.toml new file mode 100644 index 0000000000..49912bf471 --- /dev/null +++ b/examples/inference/bert-python-torchscript/pixi.toml @@ -0,0 +1,24 @@ +[project] +name = "BERT Python Torchscript" +version = "1.0.0" +description = "MAX Examples" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/", "pytorch"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +python = ">=3.9,<3.13" +max = "*" +numpy = "<2.0" +transformers = ">=4.44.0" + +# For performance, prefer pytorch over anything else. +# The linux ARM package doesn't exist there, so prefer PyPi instead. +[target.linux-64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.osx-arm64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.linux-aarch64.pypi-dependencies] +torch = "==2.5.1" +[pypi-options] +extra-index-urls = ["https://download.pytorch.org/whl/cpu"] diff --git a/examples/inference/bert-python-torchscript/run.sh b/examples/inference/bert-python-torchscript/run.sh new file mode 100755 index 0000000000..6e175dddc0 --- /dev/null +++ b/examples/inference/bert-python-torchscript/run.sh @@ -0,0 +1,28 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +set -ex + +# Example input for the model +INPUT_EXAMPLE="Paris is the [MASK] of France." + +MODEL_PATH="../../models/bert-mlm.torchscript" + +# Make sure we're running from inside the directory containing this file. +cd "$(dirname "$0")" + +# Download model from HuggingFace +python3 ../common/bert-torchscript/download-model.py -o "$MODEL_PATH" --mlm + +python3 simple-inference.py --text "$INPUT_EXAMPLE" --model-path "$MODEL_PATH" diff --git a/examples/inference/bert-python-torchscript/simple-inference.py b/examples/inference/bert-python-torchscript/simple-inference.py new file mode 100644 index 0000000000..6f69632558 --- /dev/null +++ b/examples/inference/bert-python-torchscript/simple-inference.py @@ -0,0 +1,105 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# suppress extraneous logging +import os +import platform +import signal +from argparse import ArgumentParser + +import torch +from max import engine +from max.dtype import DType +from transformers import BertTokenizer + +os.environ["TRANSFORMERS_VERBOSITY"] = "critical" +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +BATCH = 1 +SEQLEN = 128 +DEFAULT_MODEL_PATH = "../../models/bert-mlm.torchscript" +DESCRIPTION = "BERT model" +HF_MODEL_NAME = "bert-base-uncased" + + +def execute(model_path, text, input_dict): + session = engine.InferenceSession() + input_spec_list = [ + engine.TorchInputSpec(shape=tensor.size(), dtype=DType.int64) + for tensor in input_dict.values() + ] + model = session.load(model_path, input_specs=input_spec_list) + tokenizer = BertTokenizer.from_pretrained(HF_MODEL_NAME) + print("Processing input...") + inputs = tokenizer( + text, + return_tensors="pt", + padding="max_length", + truncation=True, + max_length=SEQLEN, + ) + print("Input processed.\n") + masked_index = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero( + as_tuple=True + )[1] + outputs = model.execute_legacy(**inputs)["result0"] + logits = torch.from_numpy(outputs[0, masked_index, :]) + predicted_token_id = logits.argmax(dim=-1) + predicted_tokens = tokenizer.decode( + [predicted_token_id], + skip_special_tokens=True, + clean_up_tokenization_spaces=True, + ) + return predicted_tokens + + +def main(): + # Parse args + parser = ArgumentParser(description=DESCRIPTION) + parser.add_argument( + "--text", + type=str, + metavar="", + required=True, + help="Masked language model.", + ) + parser.add_argument( + "--model-path", + type=str, + default=DEFAULT_MODEL_PATH, + help="Directory for the downloaded model.", + ) + args = parser.parse_args() + + # Improves model compilation speed dramatically on intel CPUs + if "Intel" in platform.processor(): + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["MKL_NUM_THREADS"] = "1" + + signal.signal(signal.SIGINT, signal.SIG_DFL) + + torch.set_default_device("cpu") + input_dict = { + "input_ids": torch.zeros((BATCH, SEQLEN), dtype=torch.int64), + "attention_mask": torch.zeros((BATCH, SEQLEN), dtype=torch.int64), + "token_type_ids": torch.zeros((BATCH, SEQLEN), dtype=torch.int64), + } + + outputs = execute(args.model_path, args.text, input_dict) + # Get the predictions for the masked token + print(f"input text: {args.text}") + print(f"filled mask: {args.text.replace('[MASK]', outputs)}") + + +if __name__ == "__main__": + main() diff --git a/examples/inference/common/bert-torchscript/download-model.py b/examples/inference/common/bert-torchscript/download-model.py new file mode 100644 index 0000000000..d72f9f7350 --- /dev/null +++ b/examples/inference/common/bert-torchscript/download-model.py @@ -0,0 +1,91 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os + +# suppress extraneous logging +os.environ["TRANSFORMERS_VERBOSITY"] = "critical" +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +from argparse import ArgumentParser +from pathlib import Path + +import torch +from transformers import ( + AutoModelForSequenceClassification, + BertForMaskedLM, + logging, +) + +HF_MODEL_NAME = "bert-base-uncased" +DEFAULT_MODEL_PATH = "../../models/bert-mlm.torchscript" + + +def main(): + parser = ArgumentParser(description="Download model for inference.") + parser.add_argument( + "--mlm", + action="store_true", + help="Whether to use the Bert's Masked Language Model variant", + ) + parser.add_argument( + "--output-path", + "-o", + type=str, + help="Location to save the model", + default=DEFAULT_MODEL_PATH, + ) + + args = parser.parse_args() + + torch.set_default_device("cpu") + + model_path = Path(args.output_path) + + print("Downloading model...") + logging.set_verbosity_error() # Disable warning suggesting to train the model + if args.mlm: + model = BertForMaskedLM.from_pretrained(HF_MODEL_NAME) + else: + model = AutoModelForSequenceClassification.from_pretrained( + HF_MODEL_NAME + ) + + model.eval() + # We set return_dict to False to return Tensors directly + model.config.return_dict = False + + print("Saving model in TorchScript format...") + model_path = Path(args.output_path) + if model_path.exists(): + print(f"'{args.output_path}' already exists.\n") + else: + print("Converting the model to TorchScript format...") + batch = 1 + seqlen = 128 + inputs = { + "input_ids": torch.zeros((batch, seqlen), dtype=torch.int64), + "attention_mask": torch.zeros((batch, seqlen), dtype=torch.int64), + "token_type_ids": torch.zeros((batch, seqlen), dtype=torch.int64), + } + with torch.no_grad(): + traced_model = torch.jit.trace( + model, example_kwarg_inputs=dict(inputs), strict=False + ) + + torch.jit.save(traced_model, model_path) + print("Model saved.") + + +if __name__ == "__main__": + main() diff --git a/examples/inference/resnet50-python-torchscript/.gitignore b/examples/inference/resnet50-python-torchscript/.gitignore new file mode 100644 index 0000000000..2c785ab505 --- /dev/null +++ b/examples/inference/resnet50-python-torchscript/.gitignore @@ -0,0 +1 @@ +model-repository/ diff --git a/examples/inference/resnet50-python-torchscript/README.md b/examples/inference/resnet50-python-torchscript/README.md new file mode 100644 index 0000000000..c72dcc3561 --- /dev/null +++ b/examples/inference/resnet50-python-torchscript/README.md @@ -0,0 +1,50 @@ +# TorchScript ResNet-50 inference with Python + +This directory includes scripts used to run simple ResNet-50 inference via the +MAX Engine Python API to classify an input image. In this case, we use an image +of a leatherback turtle as an example. + +## Quickstart + +### Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run the +following command: + +```sh +magic run bash run.sh +``` + +### Conda instructions + +Create a Conda environment, activate that environment, and install the +requirements: + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda run -n max-repo --live-stream bash run.sh +``` + +## Scripts included + +- `download-model.py`: Downloads the model from HuggingFace, converts it to +TorchScript, and saves it to an output directory of your choosing, or defaults +to `../../models/resnet50.torchscript`. + + For more information about the model, please refer to the + [model card](https://huggingface.co/microsoft/resnet-50). + +- `simple-inference.py`: Classifies example input image using MAX Engine. +The script prepares an example input, executes the model, and generates the +resultant classification output. + + You can use the `--input` CLI flag to specify an input example. + For example: + + ```sh + python3 simple-inference.py --input= + ``` diff --git a/examples/inference/resnet50-python-torchscript/download-model.py b/examples/inference/resnet50-python-torchscript/download-model.py new file mode 100644 index 0000000000..b93669138a --- /dev/null +++ b/examples/inference/resnet50-python-torchscript/download-model.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from argparse import ArgumentParser + +# suppress extraneous logging +os.environ["TRANSFORMERS_VERBOSITY"] = "critical" + +from pathlib import Path + +import torch +from transformers import ResNetForImageClassification + +DEFAULT_MODEL_PATH = "../../models/resnet50.torchscript" +DESCRIPTION = "Download a ResNet-50 model." +HF_MODEL_NAME = "microsoft/resnet-50" + + +def main(): + # Parse args + parser = ArgumentParser(description=DESCRIPTION) + parser.add_argument( + "-o", + "--output-path", + type=str, + default=DEFAULT_MODEL_PATH, + help="Location to save the model.", + ) + args = parser.parse_args() + + torch.set_default_device("cpu") + + print("Downloading model...") + model = ResNetForImageClassification.from_pretrained(HF_MODEL_NAME) + model.eval() + # We set return_dict to False to return Tensors directly + model.config.return_dict = False + + print("Saving model in TorchScript format...") + model_path = Path(args.output_path) + if model_path.exists(): + print(f"'{args.output_path}' already exists.\n") + else: + print("Converting the model to TorchScript format...") + input_batch = torch.zeros((1, 3, 224, 224), dtype=torch.float32) + with torch.no_grad(): + traced_model = torch.jit.trace(model, input_batch) + + torch.jit.save(traced_model, model_path) + print(f"Model saved to {args.output_path}.\n") + + +if __name__ == "__main__": + main() diff --git a/examples/inference/resnet50-python-torchscript/environment.yml 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zuTu+QD!~*9=#Ok@6nuYftF!Gy(n!wpbXy~GY)Hjj``UEo$yJ^=3s)a6FcL0dfh@yk zKx2qlzTERjLMZ8uuo=lei^ISqK5PE4zD)K$jVp8zk4b^ zI&ST_syfR2?7;-70G3~Gq~v>7`val>0Fl+(v+>m8p#GRrpP>!R30th z56=1@QZKl)j@Kzo8%ZvsfICHAACxCwz0d6<`RdDnhC3DeiL6GFx33V}sAK;CL4Tf& z)2Rd$R6zt$540_$Kt0>}{{UXAktEVcE}BMpYYqClT$ZGdT1o!YTJ-@>NmG0q+Cv5h zz&&@`sRdgUYhp8(lbypXiDF>Vu_t7Z-{m_atHuVjFO_2PM#ZtQ79~O7y}El7jG}TC ZrZnsNzia#bCFT3SopqXR3i~H#|JhfAzlHz+ literal 0 HcmV?d00001 diff --git a/examples/inference/resnet50-python-torchscript/pixi.toml b/examples/inference/resnet50-python-torchscript/pixi.toml new file mode 100644 index 0000000000..8d3f09c09c --- /dev/null +++ b/examples/inference/resnet50-python-torchscript/pixi.toml @@ -0,0 +1,26 @@ +[project] +name = "Resnet50 Python Torchscript" +version = "1.0.0" +description = "MAX Examples" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/", "pytorch"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +python = ">=3.9,<3.13" +max = "*" +networkx = "==3.1" +transformers = ">=4.44.0" +pillow = ">=10.3.0" +numpy = "<2.0" + +# For performance, prefer pytorch over anything else. +# The linux ARM package doesn't exist there, so prefer PyPi instead. +[target.linux-64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.osx-arm64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.linux-aarch64.pypi-dependencies] +torch = "==2.5.1" +[pypi-options] +extra-index-urls = ["https://download.pytorch.org/whl/cpu"] diff --git a/examples/inference/resnet50-python-torchscript/run.sh b/examples/inference/resnet50-python-torchscript/run.sh new file mode 100755 index 0000000000..c31d2eeb60 --- /dev/null +++ b/examples/inference/resnet50-python-torchscript/run.sh @@ -0,0 +1,28 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +set -ex + +# Example input for the model +INPUT_EXAMPLE="input/leatherback_turtle.jpg" + +MODEL_PATH="../../models/resnet50.torchscript" + +# Make sure we're running from inside the directory containing this file. +cd "$(dirname "$0")" + +# Download model from HuggingFace +python3 download-model.py -o "$MODEL_PATH" + +python3 simple-inference.py --input "$INPUT_EXAMPLE" --model-path "$MODEL_PATH" diff --git a/examples/inference/resnet50-python-torchscript/simple-inference.py b/examples/inference/resnet50-python-torchscript/simple-inference.py new file mode 100644 index 0000000000..572ad10bf5 --- /dev/null +++ b/examples/inference/resnet50-python-torchscript/simple-inference.py @@ -0,0 +1,98 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# suppress extraneous logging +import os +import platform +import signal +from argparse import ArgumentParser + +import numpy as np +from max import engine +from max.dtype import DType +from PIL import Image +from transformers import AutoImageProcessor, AutoModelForImageClassification + +os.environ["TRANSFORMERS_VERBOSITY"] = "critical" +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +DEFAULT_MODEL_PATH = "../../models/resnet50.torchscript" +DESCRIPTION = "Classify an input image." +HF_MODEL_NAME = "microsoft/resnet-50" + + +def execute(model_path, inputs): + session = engine.InferenceSession() + input_spec_list = [ + engine.TorchInputSpec(shape=(1, 3, 224, 224), dtype=DType.float32) + ] + + print("Loading and compiling model...") + model = session.load(model_path, input_specs=input_spec_list) + print("Model compiled.\n") + + print("Executing model...") + outputs = model.execute_legacy(pixel_values=inputs["pixel_values"]) + print("Model executed.\n") + return outputs + + +def main(): + # Parse args + parser = ArgumentParser(description=DESCRIPTION) + parser.add_argument( + "--input", + type=str, + metavar="", + required=True, + help="Path to input image.", + ) + parser.add_argument( + "--model-path", + type=str, + default=DEFAULT_MODEL_PATH, + help="Location of the downloaded model.", + ) + args = parser.parse_args() + + # Improves model compilation speed dramatically on intel CPUs + if "Intel" in platform.processor(): + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["MKL_NUM_THREADS"] = "1" + + signal.signal(signal.SIGINT, signal.SIG_DFL) + + # Preprocess input image + print("Processing input...") + image = Image.open(args.input) + processor = AutoImageProcessor.from_pretrained(HF_MODEL_NAME) + inputs = processor(images=image, return_tensors="np") + print("Input processed.\n") + + # Classify input image + outputs = execute(args.model_path, inputs) + + # Extract class predictions from output + print("Extracting class from outputs...") + predicted_label = np.argmax(outputs["result0"], axis=-1)[0] + model = AutoModelForImageClassification.from_pretrained(HF_MODEL_NAME) + predicted_class = model.config.id2label[predicted_label] + + print( + "\nThe input image is likely one of the following classes:" + f" \n{predicted_class}" + ) + + +if __name__ == "__main__": + main() diff --git a/examples/inference/stable-diffusion-mojo-onnx/.gitignore b/examples/inference/stable-diffusion-mojo-onnx/.gitignore new file mode 100644 index 0000000000..593cb73472 --- /dev/null +++ b/examples/inference/stable-diffusion-mojo-onnx/.gitignore @@ -0,0 +1 @@ +output.png diff --git a/examples/inference/stable-diffusion-mojo-onnx/README.md b/examples/inference/stable-diffusion-mojo-onnx/README.md new file mode 100644 index 0000000000..e8eee6302f --- /dev/null +++ b/examples/inference/stable-diffusion-mojo-onnx/README.md @@ -0,0 +1,51 @@ +# Stable Diffusion inference with Mojo + +This directory illustrates how to run Stable Diffusion through MAX Engine. +Specifically, this example extracts StableDiffusion-1.5 from Hugging Face and executes +it via the MAX Engine Mojo API. + +## Quickstart + +### Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run the +following command: + +```sh +magic run bash run.sh +``` + +### Conda instructions + +Create a Conda environment, activate that environment, and install the +requirements: + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda run -n max-repo --live-stream bash run.sh +``` + +## Custom Images + +Getting started with your own creative prompts is as simple as: + +```sh +./text_to_image.🔥 --prompt "my image description" -o my-image.png +``` + +But of course, there are some additional settings that can be tweaked for more +fine-grained control over image output. See `./text_to_image.🔥 --help` for +details. + +## Files + +- `download-model.py`: Downloads [runwayml/stable-diffusion-v1-5 +](https://huggingface.co/runwayml/stable-diffusion-v1-5) +and exports it as ONNX. + +- `text_to_image.🔥`: Example program that runs full stable-diffusion pipeline +through MAX Engine in order to generate images from the given prompt. diff --git a/examples/inference/stable-diffusion-mojo-onnx/environment.yml b/examples/inference/stable-diffusion-mojo-onnx/environment.yml new file mode 100644 index 0000000000..13c6313bfc --- /dev/null +++ b/examples/inference/stable-diffusion-mojo-onnx/environment.yml @@ -0,0 +1,8 @@ +name: max-repo +channels: + - https://conda.modular.com/max-nightly/ + - conda-forge +dependencies: + - python>=3.9,<3.13 + - max + - pip>=24.0,<25 diff --git a/examples/inference/stable-diffusion-mojo-onnx/pixi.toml b/examples/inference/stable-diffusion-mojo-onnx/pixi.toml new file mode 100644 index 0000000000..9af82f125a --- /dev/null +++ b/examples/inference/stable-diffusion-mojo-onnx/pixi.toml @@ -0,0 +1,26 @@ +[project] +name = "Stable Diffusion Mojo Onnx" +version = "1.0.0" +description = "MAX Examples" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/", "pytorch"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +python = ">=3.9,<3.13" +max = "*" +transformers = ">=4.44.0" +huggingface_hub = ">=0.24.6,<0.25" +pillow = "==10.3.0" +numpy = "<2.0" + +# For performance, prefer pytorch over anything else. +# The linux ARM package doesn't exist there, so prefer PyPi instead. +[target.linux-64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.osx-arm64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +[target.linux-aarch64.pypi-dependencies] +torch = "==2.5.1" +[pypi-options] +extra-index-urls = ["https://download.pytorch.org/whl/cpu"] diff --git "a/examples/inference/stable-diffusion-mojo-onnx/python_utils.\360\237\224\245" "b/examples/inference/stable-diffusion-mojo-onnx/python_utils.\360\237\224\245" new file mode 100644 index 0000000000..9303072f1a --- /dev/null +++ "b/examples/inference/stable-diffusion-mojo-onnx/python_utils.\360\237\224\245" @@ -0,0 +1,91 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from python import Python, PythonObject +from memory import memcpy, UnsafePointer +from collections import List + +from max.tensor import Tensor, TensorShape + + +@always_inline +fn numpy_data_pointer[ + type: DType +](numpy_array: PythonObject) raises -> UnsafePointer[Scalar[type]]: + return numpy_array.__array_interface__["data"][0].unsafe_get_as_pointer[ + type + ]() + + +@always_inline +fn memcpy_to_numpy[ + type: DType +](array: PythonObject, tensor: Tensor[type]) raises: + var dst = numpy_data_pointer[type](array) + var src = tensor._ptr + var length = tensor.num_elements() + memcpy(dst.address, src.address, length) + + +@always_inline +fn memcpy_from_numpy[ + type: DType +](array: PythonObject, tensor: Tensor[type]) raises: + var src = numpy_data_pointer[type](array) + var dst = tensor._ptr + var length = tensor.num_elements() + memcpy(dst.address, src.address, length) + + +@always_inline +fn shape_to_python_list(shape: TensorShape) raises -> PythonObject: + var python_list = Python.evaluate("list()") + for i in range(shape.rank()): + _ = python_list.append(shape[i]) + return python_list^ + + +@always_inline +fn get_np_dtype[type: DType](np: PythonObject) raises -> PythonObject: + @parameter + if type is DType.float32: + return np.float32 + elif type is DType.int32: + return np.int32 + elif type is DType.int64: + return np.int64 + elif type is DType.uint8: + return np.uint8 + + raise "Unknown datatype" + + +@always_inline +fn tensor_to_numpy[ + type: DType +](tensor: Tensor[type], np: PythonObject) raises -> PythonObject: + var shape = shape_to_python_list(tensor.shape()) + var tensor_as_numpy = np.zeros(shape, get_np_dtype[type](np)) + memcpy_to_numpy(tensor_as_numpy, tensor) + return tensor_as_numpy^ + + +@always_inline +fn numpy_to_tensor[type: DType](array: PythonObject) raises -> Tensor[type]: + var shape = List[Int]() + var array_shape = array.shape + for dim in array_shape: + shape.append(dim) + var out = Tensor[type](shape) + memcpy_from_numpy(array, out) + return out^ diff --git a/examples/inference/stable-diffusion-mojo-onnx/run.sh b/examples/inference/stable-diffusion-mojo-onnx/run.sh new file mode 100755 index 0000000000..9a89ecfb28 --- /dev/null +++ b/examples/inference/stable-diffusion-mojo-onnx/run.sh @@ -0,0 +1,26 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +# If anything goes wrong, stop running the script. +set -e + +NPROMPT="bad anatomy, looking away, looking sideways, crooked stick" +NPROMPT="$NPROMPT, stick not going through jaw, orange tongue" +PPROMPT="Cute puppy chewing on a stick" + +# Make sure we're running from inside the directory containing this file. +cd "$(dirname "$0")" + +# Execute model +mojo text_to_image.🔥 --seed 7 --num-steps 20 --prompt "$PPROMPT" --negative-prompt "$NPROMPT" diff --git "a/examples/inference/stable-diffusion-mojo-onnx/scheduler.\360\237\224\245" "b/examples/inference/stable-diffusion-mojo-onnx/scheduler.\360\237\224\245" new file mode 100644 index 0000000000..36678ef9d1 --- /dev/null +++ "b/examples/inference/stable-diffusion-mojo-onnx/scheduler.\360\237\224\245" @@ -0,0 +1,155 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# +# This code is directly ported from the hugging face diffusers library. +# +# Copyright 2024 Zhejiang University Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""A simple helper implementation of a pndm schedular for stable diffusion """ + +from max.tensor import Tensor + +alias FloatTensor = Tensor[DType.float32] + + +struct Scheduler: + alias training_steps = 1000 + alias init_noise_sigma = 1.0 + alias beta_start = 0.00085 + alias beta_end = 0.012 + + var counter: Int + var step_ratio: Int + var timesteps: List[Int] + var alphas_cumprod: List[Float32] + var previous_outputs: List[FloatTensor] + + def __init__(out self, inference_steps: Int): + self.counter = 0 + self.previous_outputs = List[FloatTensor]() + self.alphas_cumprod = List[Float32]() + + # Generate timesteps. + self.step_ratio = Self.training_steps // inference_steps + self.timesteps = List[Int]() + for i in range(1, Self.training_steps, self.step_ratio): + self.timesteps.append(i) + + self.timesteps.reverse() + + # Duplicate the second timestep because this scheduler skips the prk steps. + if len(self.timesteps) >= 2: + self.timesteps.insert(1, self.timesteps[1]) + + # Generate alpha cumulative product. + var start = Self.beta_start**0.5 + var end = Self.beta_end**0.5 + var step = (end - start) / Self.training_steps + var cumprod = 1.0 + for i in range(Self.training_steps): + var beta = start + (end - start) * (i / Self.training_steps) + var alpha = 1.0 - (beta**2) + cumprod *= alpha + self.alphas_cumprod.append(cumprod.cast[DType.float32]()) + + def step( + mut self, + model_output: FloatTensor, + timestep: Int, + sample: FloatTensor, + ) -> FloatTensor: + var previous_timestep = timestep - self.step_ratio + + if self.counter == 1: + # Special case: the second timestep is repeated. + previous_timestep = timestep + timestep = timestep + self.step_ratio + else: + # TODO: clear out old tensors. + # For some reason, doing this leads to a crash. + # self.previous_outputs = self.previous_outputs[-3:] + self.previous_outputs.append(FloatTensor(model_output)) + + averaged_output = FloatTensor(model_output) + if len(self.previous_outputs) == 1 and self.counter == 0: + # First input, there is no data to average. + pass + elif len(self.previous_outputs) == 1 and self.counter == 1: + averaged_output = ( + averaged_output + self.previous_outputs[-1] + ) / 2.0 + elif len(self.previous_outputs) == 2: + averaged_output = ( + 3 * self.previous_outputs[-1] - self.previous_outputs[-2] + ) / 2.0 + elif len(self.previous_outputs) == 3: + averaged_output = ( + 23 * self.previous_outputs[-1] + - 16 * self.previous_outputs[-2] + + 5 * self.previous_outputs[-3] + ) / 12.0 + else: + averaged_output = (1.0 / 24.0) * ( + 55 * self.previous_outputs[-1] + - 59 * self.previous_outputs[-2] + + 37 * self.previous_outputs[-3] + - 9 * self.previous_outputs[-4] + ) + + previous_sample = self._get_previous_sample( + sample, timestep, previous_timestep, averaged_output + ) + self.counter += 1 + + return previous_sample + + def _get_previous_sample( + mut self, + sample: FloatTensor, + timestep: Int, + previous_timestep: Int, + model_output: FloatTensor, + ) -> FloatTensor: + alpha_prod_t = self.alphas_cumprod[timestep] + alpha_prod_t_previous = ( + self.alphas_cumprod[previous_timestep] if previous_timestep + >= 0 else self.alphas_cumprod[0] + ) + beta_prod_t = 1 - alpha_prod_t + beta_prod_t_previous = 1 - alpha_prod_t_previous + + sample_coeff = (alpha_prod_t_previous / alpha_prod_t) ** (0.5) + + model_output_denom_coeff = alpha_prod_t * beta_prod_t_previous ** ( + 0.5 + ) + (alpha_prod_t * beta_prod_t * alpha_prod_t_previous) ** (0.5) + + previous_sample = ( + sample_coeff * sample + - (alpha_prod_t_previous - alpha_prod_t) + * model_output + / model_output_denom_coeff + ) + return previous_sample diff --git "a/examples/inference/stable-diffusion-mojo-onnx/text_to_image.\360\237\224\245" "b/examples/inference/stable-diffusion-mojo-onnx/text_to_image.\360\237\224\245" new file mode 100755 index 0000000000..8f78f144dc --- /dev/null +++ "b/examples/inference/stable-diffusion-mojo-onnx/text_to_image.\360\237\224\245" @@ -0,0 +1,223 @@ +#!/usr/bin/env mojo +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import sys +import random +import os +from pathlib import Path + +from collections import List +from memory import memcpy +from python import Python +from random import seed + +from max.engine import InferenceSession +from max.tensor import Tensor, TensorShape, TensorSpec + +# Local Mojo imports +import python_utils +from scheduler import Scheduler + + +var GUIDANCE_SCALE_FACTOR = Float32(7.5) +var LATENT_SCALE_FACTOR = Float32(0.18215) +var OUTPUT_HEIGHT = 512 +var OUTPUT_WIDTH = 512 +var LATENT_WIDTH = OUTPUT_WIDTH // 8 +var LATENT_HEIGHT = OUTPUT_HEIGHT // 8 +var LATENT_CHANNELS = 4 + + +def vstack[dtype: DType](a: Tensor[dtype], b: Tensor[dtype]) -> Tensor[dtype]: + """Concatenate tensors a & b along the outermost dimension.""" + # Generate return shape. + out_shape = List[Int]() + out_shape.append(a.shape()[0] + b.shape()[0]) + for i in range(1, a.shape().rank()): + out_shape.append(a.shape()[i]) + + # Allocate return tensor. + out = Tensor[dtype](TensorShape(out_shape)) + + # Fill data. + memcpy(out.unsafe_ptr().address, a.unsafe_ptr().address, a.num_elements()) + memcpy( + (out.unsafe_ptr() + a.num_elements()).address, + b.unsafe_ptr().address, + b.num_elements(), + ) + return out + + +def split[dtype: DType](x: Tensor[dtype], i: Int) -> Tensor[dtype]: + """Return the ith slice of the outermost dim; i.e., x[idx, :, :, ..., :].""" + # Generate return shape + shape = List[Int]() + shape.append(1) + for i in range(1, x.shape().rank()): + shape.append(x.shape()[i]) + # Allocate return tensor + ret = Tensor[dtype](TensorShape(shape)) + # Fill data + memcpy( + ret.unsafe_ptr().address, + (x.unsafe_ptr() + (x.num_elements() // 2) * i).address, + x.num_elements() // 2, + ) + return ret + + +def main(): + # Parse args. + USAGE = ( + "Usage: ./text_to_image.🔥 --prompt [--negative-prompt ]" + " [--num-steps ] [--seed ] [-o ]" + ) + + argv = sys.argv() + if len(argv) % 2 == 0: + print(USAGE) + raise Error("All options require an argument") + + # Set default values + seed() + prompt = String("") + negative_prompt = String("") + num_steps = 25 + hf = Python.import_module("huggingface_hub") + model_dir = Path( + String(hf.snapshot_download("modularai/stable-diffusion-1.5-onnx")) + ) + output = String("output.png") + + for i in range(1, len(argv), 2): + if argv[i] == "--prompt": + prompt = String(argv[i + 1]) + elif argv[i] == "--negative-prompt": + negative_prompt = String(argv[i + 1]) + elif argv[i] == "--num-steps": + num_steps = atol(argv[i + 1]) + elif argv[i] == "--seed": + seed(atol(argv[i + 1])) + elif argv[i] == "-o" or argv[i] == "--output": + output = String(argv[i + 1]) + else: + print(USAGE) + raise Error(String("Unknown option: ") + argv[i]) + + # Only required arg is --prompt + if prompt == "": + print(USAGE) + raise Error("--prompt option is required") + + # Import python modules. + np = Python.import_module("numpy") + Image = Python.import_module("PIL.Image") + transformers = Python.import_module("transformers") + + # Compile & load models - this may take a few minutes. + print("Loading and compiling models...") + session = InferenceSession() + txt_encoder = session.load(model_dir / "text_encoder" / "model.onnx") + img_decoder = session.load(model_dir / "vae_decoder" / "model.onnx") + img_diffuser = session.load(model_dir / "unet" / "model.onnx") + print("Models compiled.\n") + + # Tokenize inputs and run through text encoder. + print("Processing input...") + tokenizer = transformers.CLIPTokenizer.from_pretrained( + os.path.join(model_dir.path, "tokenizer") + ) + max_length = Int(tokenizer.model_max_length) + prompt_p = tokenizer(prompt, padding="max_length", max_length=max_length) + prompt_n = tokenizer( + negative_prompt, padding="max_length", max_length=max_length + ) + + prompt_p_tensor = python_utils.numpy_to_tensor[DType.int32]( + np.array(prompt_p["input_ids"], dtype=np.int32) + ) + prompt_n_tensor = python_utils.numpy_to_tensor[DType.int32]( + np.array(prompt_n["input_ids"], dtype=np.int32) + ) + + input_ids = vstack[DType.int32]( + prompt_p_tensor.reshape(TensorShape(1, max_length)), + prompt_n_tensor.reshape(TensorShape(1, max_length)), + ) + + encoder_output = txt_encoder.execute("input_ids", input_ids) + encoder_hidden_states = encoder_output.get[DType.float32]( + "last_hidden_state" + ) + print("Input processed.\n") + + # Initialize latent. + print("Initializing latent...") + + schedule = Scheduler(num_steps) + + # Note: For onnx, shapes are given in NCHW format. + latent = ( + Tensor[DType.float32].randn( + TensorShape(1, LATENT_CHANNELS, LATENT_HEIGHT, LATENT_WIDTH) + ) + * Scheduler.init_noise_sigma + ) + + # Loop through diffusion model. + for i in range(len(schedule.timesteps)): + print("\rGenerating image:", i, "/", num_steps, end="") + + # Duplicate latent to create full sample. + sample = vstack(latent, latent) + + # Execute the diffusion model with bs=2. Both batches have same primary input and + # timestep, but the encoder_hidden_states (primary prompt vs negative) differs. + timestep_tensor = Tensor[DType.int64](TensorSpec(DType.int64, 1)) + timestep_tensor[0] = Int(schedule.timesteps[i]) + + diffuser_output = img_diffuser.execute( + "sample", + sample, + "encoder_hidden_states", + encoder_hidden_states, + "timestep", + timestep_tensor, + ) + + noise_pred = diffuser_output.get[DType.float32]("out_sample") + + # Merge conditioned & unconditioned outputs. + noise_pred_text = split[DType.float32](noise_pred, 0) + noise_pred_uncond = split[DType.float32](noise_pred, 1) + noise_pred = noise_pred_uncond + GUIDANCE_SCALE_FACTOR * ( + noise_pred_text - noise_pred_uncond + ) + + # Merge latent with previous iteration. + latent = schedule.step(noise_pred, schedule.timesteps[i], latent) + + # Decode finalized latent. + print("\n\nDecoding image...") + latent = latent * (1 / LATENT_SCALE_FACTOR) + + decoder_output = img_decoder.execute("latent_sample", latent) + decoded = decoder_output.get[DType.float32]("sample") + + image = ((decoded / 2.0 + 0.5).clip(0, 1) * 255.0).astype[DType.uint8]() + np_pixels = python_utils.tensor_to_numpy[DType.uint8](image, np) + np_pixels = np_pixels.squeeze().transpose(1, 2, 0) + Image.fromarray(np_pixels, "RGB").save(output) + print("Image saved to " + output + ".") diff --git a/examples/inference/stable-diffusion-python-onnx/.gitignore b/examples/inference/stable-diffusion-python-onnx/.gitignore new file mode 100644 index 0000000000..593cb73472 --- /dev/null +++ b/examples/inference/stable-diffusion-python-onnx/.gitignore @@ -0,0 +1 @@ +output.png diff --git a/examples/inference/stable-diffusion-python-onnx/README.md b/examples/inference/stable-diffusion-python-onnx/README.md new file mode 100644 index 0000000000..a96732bcde --- /dev/null +++ b/examples/inference/stable-diffusion-python-onnx/README.md @@ -0,0 +1,51 @@ +# Stable Diffusion inference with Python + +This directory illustrates how to run Stable Diffusion through MAX Engine. +Specifically, this example extracts StableDiffusion-1.5 from Hugging Face and executes +it via the MAX Engine Python API. + +## Quickstart + +### Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run the +following command: + +```sh +magic run bash run.sh +``` + +### Conda instructions + +Create a Conda environment, activate that environment, and install the +requirements: + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda run -n max-repo --live-stream bash run.sh +``` + +## Custom Images + +Getting started with your own creative prompts is as simple as: + +```sh +./text_to_image.py --prompt "my image description" -o my-image.png +``` + +But of course, there are some additional settings that can be tweaked for more +fine-grained control over image output. See `./text_to_image.py --help` for +details. + +## Files + +- `download-model.py`: Downloads [runwayml/stable-diffusion-v1-5 +](https://huggingface.co/runwayml/stable-diffusion-v1-5) +and exports it as ONNX. + +- `text_to_image.py`: Example program that runs full stable-diffusion pipeline +through MAX Engine in order to generate images from the given prompt. diff --git a/examples/inference/stable-diffusion-python-onnx/environment.yml b/examples/inference/stable-diffusion-python-onnx/environment.yml new file mode 100644 index 0000000000..13c6313bfc --- /dev/null +++ b/examples/inference/stable-diffusion-python-onnx/environment.yml @@ -0,0 +1,8 @@ +name: max-repo +channels: + - https://conda.modular.com/max-nightly/ + - conda-forge +dependencies: + - python>=3.9,<3.13 + - max + - pip>=24.0,<25 diff --git a/examples/inference/stable-diffusion-python-onnx/pixi.toml b/examples/inference/stable-diffusion-python-onnx/pixi.toml new file mode 100644 index 0000000000..b6f5be2a07 --- /dev/null +++ b/examples/inference/stable-diffusion-python-onnx/pixi.toml @@ -0,0 +1,29 @@ +[project] +name = "Stable Diffusion Python Onnx" +version = "1.0.0" +description = "MAX Examples" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/", "pytorch"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +python = ">=3.9,<3.13" +max = "*" +transformers = ">=4.44.0" +huggingface_hub = ">=0.24.6,<0.25" +pillow = "==10.3.0" +numpy = "<2.0" + +# For performance, prefer pytorch over anything else. +# The linux ARM package doesn't exist there, so prefer PyPi instead. +[target.linux-64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +diffusers = "==0.27.2" +[target.osx-arm64.dependencies] +pytorch = {version = "==2.5.1", channel = "pytorch"} +diffusers = "==0.27.2" +[target.linux-aarch64.pypi-dependencies] +torch = "==2.5.1" +diffusers = "==0.27.2" +[pypi-options] +extra-index-urls = ["https://download.pytorch.org/whl/cpu"] diff --git a/examples/inference/stable-diffusion-python-onnx/run.sh b/examples/inference/stable-diffusion-python-onnx/run.sh new file mode 100755 index 0000000000..87a0e0566f --- /dev/null +++ b/examples/inference/stable-diffusion-python-onnx/run.sh @@ -0,0 +1,27 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +# If anything goes wrong, stop running the script. +set -e + +MODEL_DIR="../../models/stable-diffusion-onnx" +NPROMPT="bad anatomy, looking away, looking sideways, crooked stick" +NPROMPT="$NPROMPT, stick not going through jaw, orange tongue" +PPROMPT="Cute puppy chewing on a stick" + +# Make sure we're running from inside the directory containing this file. +cd "$(dirname "$0")" + +# Execute model +python3 text_to_image.py --seed 7 --num-steps 20 --prompt "$PPROMPT" --negative-prompt "$NPROMPT" diff --git a/examples/inference/stable-diffusion-python-onnx/text_to_image.py b/examples/inference/stable-diffusion-python-onnx/text_to_image.py new file mode 100755 index 0000000000..b3cfbfb5ef --- /dev/null +++ b/examples/inference/stable-diffusion-python-onnx/text_to_image.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python3 +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import signal +import sys +from argparse import ArgumentParser +from pathlib import Path + +import numpy as np +from diffusers import PNDMScheduler +from huggingface_hub import snapshot_download +from max.engine import InferenceSession +from PIL import Image +from transformers import CLIPTokenizer + +DESCRIPTION = "Generate an image based on the given prompt." +GUIDANCE_SCALE_FACTOR = 7.5 +LATENT_SCALE_FACTOR = 0.18215 +OUTPUT_HEIGHT = 512 +OUTPUT_WIDTH = 512 +LATENT_WIDTH = OUTPUT_WIDTH // 8 +LATENT_HEIGHT = OUTPUT_HEIGHT // 8 +LATENT_CHANNELS = 4 + + +def run_stable_diffusion( + args, txt_encoder, img_decoder, img_diffuser, tokenizer, scheduler +): + # Tokenize inputs and run through text encoder. + print("Processing input...") + prompt_p = tokenizer( + args.prompt, padding="max_length", max_length=tokenizer.model_max_length + ) + prompt_n = tokenizer( + args.negative_prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + ) + + input_ids = np.stack((prompt_p.input_ids, prompt_n.input_ids)).astype( + np.int32 + ) + encoder_hidden_states = txt_encoder.execute_legacy(input_ids=input_ids)[ + "last_hidden_state" + ] + print("Input processed.\n") + + # Initialize latent and scheduler. + print("Initializing latent...") + + # Note: For onnx, shapes are given in NCHW format. + latent = np.random.normal( + size=(1, LATENT_CHANNELS, LATENT_HEIGHT, LATENT_WIDTH) + ) + latent = latent * scheduler.init_noise_sigma + latent = latent.astype(np.float32) + + # Loop through diffusion model. + scheduler.set_timesteps(args.num_steps) + for i, t in enumerate(scheduler.timesteps): + print(f"\rGenerating image: {i}/{args.num_steps}", end="") + + # Duplicate input and scale based on scheduler. + sample = np.vstack((latent, latent)) + sample = scheduler.scale_model_input(sample, timestep=t) + + # Execute the diffusion model with bs=2. Both batches have same primary input and + # timestep, but the encoder_hidden_states (primary prompt vs negative) differs. + noise_pred = img_diffuser.execute_legacy( + sample=sample, + encoder_hidden_states=encoder_hidden_states, + timestep=np.array([t], dtype=np.int64), + )["out_sample"] + + # Merge conditioned & unconditioned outputs. + noise_pred_text, noise_pred_uncond = np.split(noise_pred, 2) + noise_pred = noise_pred_uncond + GUIDANCE_SCALE_FACTOR * ( + noise_pred_text - noise_pred_uncond + ) + + # Merge latent with previous iteration. + latent = scheduler.step(noise_pred, t, latent).prev_sample + + # Decode finalized latent. + print("\n\nDecoding image...") + latent = latent * (1 / LATENT_SCALE_FACTOR) + decoded = img_decoder.execute_legacy(latent_sample=latent)["sample"] + image = np.clip(decoded / 2 + 0.5, 0, 1).squeeze() + image = (image.transpose(1, 2, 0) * 255).astype(np.uint8) + Image.fromarray(image, "RGB").save(args.output) + print(f"Image saved to {args.output}.") + return + + +def parse(args): + # Parse args. + parser = ArgumentParser(description=DESCRIPTION) + parser.add_argument( + "--prompt", + type=str, + metavar="", + required=True, + help="Description of desired image.", + ) + parser.add_argument( + "--negative-prompt", + type=str, + metavar="", + default="", + help="Objects or styles to avoid in generated image.", + ) + parser.add_argument( + "--num-steps", + type=int, + metavar="", + default=25, + help="# of diffusion steps; trades-off speed vs quality", + ) + parser.add_argument( + "--seed", + type=int, + metavar="", + default=None, + help="Seed for psuedo-random number generation.", + ) + parser.add_argument( + "--output", + "-o", + type=str, + metavar="", + default="output.png", + help="Output filename.", + ) + parsed_args = parser.parse_args(args) + + signal.signal(signal.SIGINT, signal.SIG_DFL) + + # Set seed if requested. + if parsed_args.seed: + np.random.seed(parsed_args.seed) + + return parsed_args + + +def main(): + args = parse(sys.argv[1:]) + + # Compile & load models - this may take a few minutes. + session = InferenceSession() + model_dir = Path(snapshot_download("modularai/stable-diffusion-1.5-onnx")) + print("Loading and compiling models...") + txt_encoder = session.load(model_dir / "text_encoder" / "model.onnx") + img_decoder = session.load(model_dir / "vae_decoder" / "model.onnx") + img_diffuser = session.load(model_dir / "unet" / "model.onnx") + print("Models compiled.\n") + + # Instantiate tokenizer and scheduler. + tokenizer = CLIPTokenizer.from_pretrained(model_dir / "tokenizer") + scheduler = PNDMScheduler.from_pretrained(model_dir / "scheduler") + + run_stable_diffusion( + args, txt_encoder, img_decoder, img_diffuser, tokenizer, scheduler + ) + + +if __name__ == "__main__": + main() diff --git a/examples/inference/yolo-python-onnx/.gitignore b/examples/inference/yolo-python-onnx/.gitignore new file mode 100644 index 0000000000..b24b699c42 --- /dev/null +++ b/examples/inference/yolo-python-onnx/.gitignore @@ -0,0 +1 @@ +output.mp4 diff --git a/examples/inference/yolo-python-onnx/README.md b/examples/inference/yolo-python-onnx/README.md new file mode 100644 index 0000000000..2b8a14d180 --- /dev/null +++ b/examples/inference/yolo-python-onnx/README.md @@ -0,0 +1,81 @@ +# YOLOv8 Segmentation with Python + +This directory illustrates how to run YOLOv8 Segmentation through the MAX Engine. +Specifically, this example uses Ultralytics YOLO with opencv to segment images +from the webcam. + +## Quickstart + +This example will change depending on if you have a webcam or not. +If you have a webcam, the example will capture the webcam +and display a window running live segmentation. + +If you do not have a webcam, the example will segment +[a downloaded video file](https://drive.google.com/file/d/1H9abV76VohmT-J2RmDrbDhF-FCHt1Sbh/view?usp=sharing) +and generate `output.mp4`. + +### Magic instructions + +If you have [`magic`](https://docs.modular.com/magic), you can run the +following command: + +```sh +magic run bash run.sh +``` + +### Conda instructions + +Create a Conda environment, activate that environment, and install the +requirements: + +```sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Update the environment with the environment.yml file +conda env update -n max-repo -f environment.yml --prune +# Run the example +conda run -n max-repo --live-stream bash run.sh +``` + +## Note: GUI Dependencies + +The webcam version of this example depends on `opencv-python` +and its ability to render GUIs. +The dependencies for this are not always installed on linux. + +Downloading these dependencies is distro dependent. +On Ubuntu, it should be: + +```bash +apt install -y libgl1 +``` + +## Performance Comparison + +To get an idea of the performance difference between the ONNX Runtime and MAX Engine, +you can run the webcam script with/without the `--onnx` flag. + +The model execution time is printed as part of the window title. +Note, it can take a bit for the execution time to stabilize. + +MAX Engine: + +```sh +./segment.py webcam +``` + +ONNX Runtime: + +```sh +./segment.py webcam --onnx +``` + +## Files + +- `download-model.py`: Downloads YOLOv8n-seg from +[ultralytics](https://github.com/ultralytics/ultralytics) +and exports it as ONNX. + +- `segment.py`: Example program that runs the full YOLO segmentation pipeline +through the MAX Engine on images from the webcam or video files. +Use `--help` to see the various commands. diff --git a/examples/inference/yolo-python-onnx/constants.py b/examples/inference/yolo-python-onnx/constants.py new file mode 100644 index 0000000000..476fd8cd41 --- /dev/null +++ b/examples/inference/yolo-python-onnx/constants.py @@ -0,0 +1,95 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +CLASS_NAMES = [ + "person", + "bicycle", + "car", + "motorcycle", + "airplane", + "bus", + "train", + "truck", + "boat", + "traffic light", + "fire hydrant", + "stop sign", + "parking meter", + "bench", + "bird", + "cat", + "dog", + "horse", + "sheep", + "cow", + "elephant", + "bear", + "zebra", + "giraffe", + "backpack", + "umbrella", + "handbag", + "tie", + "suitcase", + "frisbee", + "skis", + "snowboard", + "sports ball", + "kite", + "baseball bat", + "baseball glove", + "skateboard", + "surfboard", + "tennis racket", + "bottle", + "wine glass", + "cup", + "fork", + "knife", + "spoon", + "bowl", + "banana", + "apple", + "sandwich", + "orange", + "broccoli", + "carrot", + "hot dog", + "pizza", + "donut", + "cake", + "chair", + "couch", + "potted plant", + "bed", + "dining table", + "toilet", + "tv", + "laptop", + "mouse", + "remote", + "keyboard", + "cell phone", + "microwave", + "oven", + "toaster", + "sink", + "refrigerator", + "book", + "clock", + "vase", + "scissors", + "teddy bear", + "hair drier", + "toothbrush", +] diff --git a/examples/inference/yolo-python-onnx/download-model.py b/examples/inference/yolo-python-onnx/download-model.py new file mode 100644 index 0000000000..daf067c1d9 --- /dev/null +++ b/examples/inference/yolo-python-onnx/download-model.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +import signal +from argparse import ArgumentParser + +from ultralytics import YOLO + +DEFAULT_MODEL_DIR = "../../models/yolo" +DESCRIPTION = "Download a Yolo segmentation model." + + +def main(): + # Parse args + parser = ArgumentParser(description=DESCRIPTION) + parser.add_argument( + "-o", + "--output-dir", + type=str, + default=DEFAULT_MODEL_DIR, + help="Output directory for the downloaded model.", + ) + args = parser.parse_args() + + signal.signal(signal.SIGINT, signal.SIG_DFL) + + # Download and export model from huggingface. + if os.path.exists(args.output_dir): + print(f"Skipping {args.output_dir} (already exists)") + return + + print("Downloading Model and Exporting...\n") + model = YOLO(f"{args.output_dir}/yolov8n-seg.pt") + model.export(format="onnx", imgsz=(480, 640), simplify=True) + print(f"Model exported to {args.output_dir}/.\n") + + +if __name__ == "__main__": + main() diff --git a/examples/inference/yolo-python-onnx/environment.yml b/examples/inference/yolo-python-onnx/environment.yml new file mode 100644 index 0000000000..13c6313bfc --- /dev/null +++ b/examples/inference/yolo-python-onnx/environment.yml @@ -0,0 +1,8 @@ +name: max-repo +channels: + - https://conda.modular.com/max-nightly/ + - conda-forge +dependencies: + - python>=3.9,<3.13 + - max + - pip>=24.0,<25 diff --git a/examples/inference/yolo-python-onnx/input.mp4 b/examples/inference/yolo-python-onnx/input.mp4 new file mode 100644 index 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"linux-aarch64", "linux-64"] + +[dependencies] +python =">=3.9,<3.13" +max = "*" +pip = ">=24.0,<25" diff --git a/examples/inference/yolo-python-onnx/requirements.txt b/examples/inference/yolo-python-onnx/requirements.txt new file mode 100644 index 0000000000..f1a8034a23 --- /dev/null +++ b/examples/inference/yolo-python-onnx/requirements.txt @@ -0,0 +1,8 @@ +--extra-index-url https://download.pytorch.org/whl/cpu + +networkx==3.1 +torch==2.5.1 +onnxruntime==1.17.3 +opencv-python==4.9.0.80 +ultralytics==8.2.5 +numpy<2.0 diff --git a/examples/inference/yolo-python-onnx/run.sh b/examples/inference/yolo-python-onnx/run.sh new file mode 100755 index 0000000000..42b9126556 --- /dev/null +++ b/examples/inference/yolo-python-onnx/run.sh @@ -0,0 +1,32 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +# If anything goes wrong, stop running the script. +set -e + +MODEL_DIR="../../models/yolo" + +# Make sure we're running from inside the directory containing this file. +cd "$(dirname "$0")" + +# If CONDA_PREFIX is set, install requirements +if [[ -n "$CONDA_PREFIX" ]]; then + python3 -m pip install -r requirements.txt +fi + +# Download model +python3 download-model.py -o "$MODEL_DIR" + +# Execute model +python3 segment.py diff --git a/examples/inference/yolo-python-onnx/segment.py b/examples/inference/yolo-python-onnx/segment.py new file mode 100755 index 0000000000..b64cedd533 --- /dev/null +++ b/examples/inference/yolo-python-onnx/segment.py @@ -0,0 +1,382 @@ +#!/usr/bin/env python3 +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +import os +import platform +import shutil +import signal +import subprocess +import time +from argparse import ArgumentParser + +import cv2 +import numpy as np +import onnxruntime +import torch +from constants import CLASS_NAMES +from max.engine import InferenceSession +from ultralytics.engine.results import Results +from ultralytics.models.yolo.segment.predict import ops + +DESCRIPTION = "Segment images from a webcam or video file using YOLO." +DEFAULT_MODEL_DIR = "../../models/yolo" +DEFAULT_INPUT_FILE = "input.mp4" +DEFAULT_OUTPUT_FILE = "output.mp4" +WINDOW_NAME = "YOLOv8 Segmentation" + + +def resize_and_pad(image, shape): + # Grab shape sizes. + (h, w, _) = image.shape + (target_h, target_w) = shape + + # Resize to fully fit within `shape`. + min_ratio = min(target_h / h, target_w / w) + unpadded_h = int(round(min_ratio * h)) + unpadded_w = int(round(min_ratio * w)) + image = cv2.resize( + image, (unpadded_w, unpadded_h), interpolation=cv2.INTER_LINEAR + ) + + # Pad to be the same size as `shape`. + delta_h = (target_h - unpadded_h) / 2 + delta_w = (target_w - unpadded_w) / 2 + top, bottom = int(round(delta_h - 0.1)), int(round(delta_h + 0.1)) + left, right = int(round(delta_w - 0.1)), int(round(delta_w + 0.1)) + return cv2.copyMakeBorder( + image, + top, + bottom, + left, + right, + cv2.BORDER_CONSTANT, + value=(114, 114, 114), + ) + + +def postprocess(out0, out1, input, frame): + out0 = torch.from_numpy(out0) + out1 = torch.from_numpy(out1) + + pred = ops.non_max_suppression( + out0, + conf_thres=0.25, + iou_thres=0.70, + agnostic=False, + max_det=10, + nc=len(CLASS_NAMES), + classes=None, + )[0] + + if not len(pred): + result = Results( + orig_img=frame, + path="", + names=CLASS_NAMES, + boxes=pred[:, :6], + ) + else: + masks = ops.process_mask( + out1[0], + pred[:, 6:], + pred[:, :4], + input.shape[2:], + upsample=True, + ) # HWC + pred[:, :4] = ops.scale_boxes(input.shape[2:], pred[:, :4], frame.shape) + result = Results( + orig_img=frame, + path="", + names=CLASS_NAMES, + boxes=pred[:, :6], + masks=masks, + ) + return result + + +def camera_functional(): + cap = cv2.VideoCapture(0) + if cap is None: + return False + if not cap.isOpened(): + cap.release() + return False + + can_read_frame, _ = cap.read() + cap.release() + return can_read_frame + + +def process_webcam(args): + # Compile & load models - this may take a few minutes. + print("Loading and compiling model...") + if args.onnx: + engine_name = "ONNX Runtime" + yolo = onnxruntime.InferenceSession( + f"{args.model_dir}/yolov8n-seg.onnx" + ) + else: + engine_name = "MAX Engine" + session = InferenceSession() + yolo = session.load(f"{args.model_dir}/yolov8n-seg.onnx") + print("Model compiled.\n") + + # Setup camera. + print("Grabbing camera input...") + print("Press escape or q to quit") + cap = cv2.VideoCapture(0) + + if cap is None or not cap.isOpened(): + print("Failed to open stream from webcam.") + print( + "If you don't have a webcam, try running with the `video`" + " subcommand instead." + ) + if cap is not None: + cap.release() + exit(1) + + previous_elapsed_ms = [] + + # Loop camera frames running yolo. + while cap.isOpened(): + # Load frame from camera. + success, frame = cap.read() + + if not success: + print("Can't load frame. Camera stream ended?") + break + + # Resize to 640x480 + frame = resize_and_pad(frame, (480, 640)) + + # Preprocess inputs. + input = ( + frame[np.newaxis, :, :, ::-1] + .transpose(0, 3, 1, 2) + .astype(np.float32) + / 255 + ).copy() + + # Run prediction. + start = time.time() + if args.onnx: + outputs = yolo.run(None, {"images": input}) + else: + outputs = list(yolo.execute(images=input).values()) + elapsed_ms = (time.time() - start) * 1000 + + # Postprocess outputs. + result = postprocess(outputs[0], outputs[1], input, frame) + + # Annotate and display frame. + annotated_frame = result.plot() + try: + cv2.imshow(WINDOW_NAME, annotated_frame) + except: + print("Failed to open window to display the annotated frame.") + print( + "If you don't have a gui, try running with the `video`" + " subcommand instead." + ) + break + + # Calculated average fps and update window title. + previous_elapsed_ms.append(elapsed_ms) + previous_elapsed_ms = previous_elapsed_ms[-100:] + average_elapsed_ms = np.average(previous_elapsed_ms) + cv2.setWindowTitle( + WINDOW_NAME, + ( + f"{engine_name}: YOLOv8 Segmentation (Average Model Time:" + f" {average_elapsed_ms:0.1f}ms)" + ), + ) + + # Exit on escape or q. + ESC = 27 + key = cv2.waitKey(1) & 0xFF + if key in [ord("q"), ESC]: + break + + # Clean up resources. + cap.release() + cv2.destroyAllWindows() + + +def process_video(args): + if not os.path.exists(args.input): + print(f"Input file not found: {args.input}") + print("You can use your own video file with the --input flag") + exit(1) + + # Compile & load models - this may take a few minutes. + print("Loading and compiling model...") + session = InferenceSession() + yolo = session.load(f"{args.model_dir}/yolov8n-seg.onnx") + print("Model compiled.\n") + + # Setup video capture. + print("Processing input video...") + cap = cv2.VideoCapture(args.input) + + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + fps = cap.get(cv2.CAP_PROP_FPS) + out = cv2.VideoWriter(args.output, fourcc, fps, (640, 480)) + + # Loop camera frames running yolo. + while cap.isOpened(): + # Load frame from camera. + success, frame = cap.read() + + if not success: + # Out of frames. Everything is done. + break + + # Resize to 640x480 + frame = resize_and_pad(frame, (480, 640)) + + # Preprocess inputs. + input = ( + frame[np.newaxis, :, :, ::-1] + .transpose(0, 3, 1, 2) + .astype(np.float32) + / 255 + ).copy() + + # Run prediction. + outputs = list(yolo.execute(images=input).values()) + + # Postprocess outputs. + result = postprocess(outputs[0], outputs[1], input, frame) + + # Annotate and output frame. + annotated_frame = result.plot() + out.write(annotated_frame) + + # Clean up resources. + cap.release() + out.release() + + # Full path so users can find video easily + output_name = f"{os.getcwd()}/{args.output}" + + # Workaround for linux python-opencv/ffmpeg not being able to encode h264 + if shutil.which("ffmpeg"): + print("Changing video encoding for wider video player support.") + result = subprocess.run( + [ + "ffmpeg", + "-y", + "-loglevel", + "error", + "-i", + output_name, + "-vcodec", + "libx264", + "encoded.mp4", + ] + ) + + # If succesfully encoded write over original file + if result.returncode == 0: + subprocess.run(["mv", "encoded.mp4", output_name]) + else: + print("\nFailed to encode video, but video may still be playable.") + + # Open the video if running in vscode and `code` is on path + if "TERM_PROGRAM" in os.environ and os.environ["TERM_PROGRAM"] == "vscode": + if shutil.which("code"): + result = subprocess.run(["code", output_name]) + + print("Video saved to:", output_name) + + +def main(): + # Parse args. + parser = ArgumentParser(description=DESCRIPTION) + subparsers = parser.add_subparsers(title="Commands", dest="command") + webcam_parser = subparsers.add_parser( + "webcam", help="Segment images from the webcam and display them" + ) + webcam_parser.add_argument( + "--model-dir", + type=str, + default=DEFAULT_MODEL_DIR, + help="Directory for the downloaded model", + ) + webcam_parser.add_argument( + "--onnx", + "--onnx-runtime", + action="store_true", + default=False, + help=( + "Run video segmentation with the ONNX Runtime to compare" + " performance" + ), + ) + video_parser = subparsers.add_parser("video", help="Segment a video file") + video_parser.add_argument( + "--model-dir", + type=str, + default=DEFAULT_MODEL_DIR, + help="Directory for the downloaded model", + ) + video_parser.add_argument( + "--input", + "-i", + type=str, + default=DEFAULT_INPUT_FILE, + help="Input video file to segment", + ) + video_parser.add_argument( + "--output", + "-o", + type=str, + default=DEFAULT_OUTPUT_FILE, + help="Output video file to write to", + ) + args = parser.parse_args() + + # Improves model compilation speed dramatically on intel CPUs + if "Intel" in platform.processor(): + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["MKL_NUM_THREADS"] = "1" + + signal.signal(signal.SIGINT, signal.SIG_DFL) + + if args.command == "webcam": + process_webcam(args) + elif args.command == "video": + process_video(args) + else: + # No command was given. First attempt to run webcam with default value. + # If there is no webcam, run video segmentation with default values. + args.model_dir = DEFAULT_MODEL_DIR + args.input = DEFAULT_INPUT_FILE + args.output = DEFAULT_OUTPUT_FILE + args.onnx = False + + # Check if a webcam is available. + if camera_functional(): + print("Webcam is available.") + print("Running live segmentation.\n") + process_webcam(args) + else: + print("Webcam was not available.") + print("Falling back to video file segmentation.\n") + process_video(args) + + +if __name__ == "__main__": + main() diff --git a/examples/lit.cfg.py b/examples/lit.cfg.py deleted file mode 100644 index 1c3516870d..0000000000 --- a/examples/lit.cfg.py +++ /dev/null @@ -1,71 +0,0 @@ -# ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. -# -# Licensed under the Apache License v2.0 with LLVM Exceptions: -# https://llvm.org/LICENSE.txt -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ===----------------------------------------------------------------------=== # - -import os -from pathlib import Path - -import lit.formats -import lit.llvm - -config.test_format = lit.formats.ShTest(True) - -# name: The name of this test suite. -config.name = "Mojo Public Examples" - -# suffixes: A list of file extensions to treat as test files. -# TODO: Enable notebooks -config.suffixes = [".mojo", ".🔥"] - -config.excludes = [ - # No RUN: directive, just bare examples - "hello_interop.mojo", - "matmul.mojo", -] + [path.name for path in os.scandir("examples") if path.is_dir()] - -# Have the examples run in the build directory. -# The `run-examples.sh` script creates the build directory. -build_root = Path.cwd().parent / "build" - -# Execute the examples inside this part of the build -# directory to avoid polluting the source tree. -config.test_exec_root = build_root / "examples" - -# test_source_root: The root path where tests are located. -config.test_source_root = Path(__file__).parent.resolve() - -# Substitute %mojo for just `mojo` itself. -config.substitutions.insert(0, ("%mojo", "mojo")) - -pre_built_packages_path = os.environ.get( - "MODULAR_MOJO_NIGHTLY_IMPORT_PATH", - Path(os.environ["MODULAR_HOME"]) - / "pkg" - / "packages.modular.com_nightly_mojo" - / "lib" - / "mojo", -) - -os.environ[ - "MODULAR_MOJO_NIGHTLY_IMPORT_PATH" -] = f"{build_root},{pre_built_packages_path}" - -# Pass through several environment variables -# to the underlying subprocesses that run the tests. -lit.llvm.initialize(lit_config, config) -lit.llvm.llvm_config.with_system_environment( - [ - "MODULAR_HOME", - "MODULAR_MOJO_NIGHTLY_IMPORT_PATH", - "PATH", - ] -) diff --git a/examples/models/README.md b/examples/models/README.md new file mode 100644 index 0000000000..8784898ce8 --- /dev/null +++ b/examples/models/README.md @@ -0,0 +1,4 @@ +# Downloaded Example Models + +This is where we store downloaded models used across different examples in this +repository. diff --git a/examples/.gitignore b/examples/mojo/.gitignore similarity index 100% rename from examples/.gitignore rename to examples/mojo/.gitignore diff --git a/examples/mojo/README.md b/examples/mojo/README.md new file mode 100644 index 0000000000..6c651cd92d --- /dev/null +++ b/examples/mojo/README.md @@ -0,0 +1,48 @@ +# Mojo code examples + +A collection of sample programs written in the +[Mojo](https://docs.modular.com/mojo/manual/) programming language. + +## Getting Started + +The easiest way to get started with Mojo is to install the Magic package and +virtual environment manager. By following the instructions in [Get started with +Magic](https://docs.modular.com/magic/), you can quickly install Magic and use +it to create a virtual environment for Mojo programming. Alternatively, you can +follow the instructions for how to [Add MAX/Mojo to a conda +project](https://docs.modular.com/magic/conda). + +After you have set up a Mojo programming environment, you can then use `git` to +clone this repository of Mojo samples using the command below: + +```bash +git clone https://github.com/modular/mojo.git +``` + +## Running + +If you're using [`magic`](https://docs.modular.com/magic), navigate into +the `examples` directory and use `magic run` to invoke `mojo` and run the basic +example programs. For example: + +```bash +magic run mojo matmul.mojo +``` + +You'll also find several complete Mojo projects as subdirectories of the +`examples` directory. + +## License + +The Mojo examples in this repository are licensed under the Apache License v2.0 +with LLVM Exceptions (see the LLVM [License](https://llvm.org/LICENSE.txt)). + +## Contributing + +As a contributor, your efforts and expertise are invaluable in driving the +evolution of the Mojo programming language. The [Mojo contributor +guide](../CONTRIBUTING.md) provides all the information necessary to make +meaningful contributions—from understanding the submission process to +adhering to best practices: + +- [Mojo contributor guide](../CONTRIBUTING.md) diff --git a/examples/check_mod.py b/examples/mojo/check_mod.py similarity index 100% rename from examples/check_mod.py rename to examples/mojo/check_mod.py diff --git a/examples/deviceinfo.mojo b/examples/mojo/deviceinfo.mojo similarity index 100% rename from examples/deviceinfo.mojo rename to examples/mojo/deviceinfo.mojo diff --git "a/examples/hello.\360\237\224\245" "b/examples/mojo/hello.\360\237\224\245" similarity index 100% rename from "examples/hello.\360\237\224\245" rename to "examples/mojo/hello.\360\237\224\245" diff --git a/examples/hello_interop.mojo b/examples/mojo/hello_interop.mojo similarity index 100% rename from examples/hello_interop.mojo rename to examples/mojo/hello_interop.mojo diff --git a/examples/life/README.md 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b/examples/mojo/testing/.gitattributes @@ -0,0 +1,2 @@ +# SCM syntax highlighting +pixi.lock linguist-language=YAML linguist-generated=true diff --git a/examples/testing/.gitignore b/examples/mojo/testing/.gitignore similarity index 100% rename from examples/testing/.gitignore rename to examples/mojo/testing/.gitignore diff --git a/examples/testing/README.md b/examples/mojo/testing/README.md similarity index 100% rename from examples/testing/README.md rename to examples/mojo/testing/README.md diff --git a/examples/testing/mojoproject.toml b/examples/mojo/testing/mojoproject.toml similarity index 100% rename from examples/testing/mojoproject.toml rename to examples/mojo/testing/mojoproject.toml diff --git a/examples/testing/src/example.mojo b/examples/mojo/testing/src/example.mojo similarity index 100% rename from examples/testing/src/example.mojo rename to examples/mojo/testing/src/example.mojo diff --git a/examples/testing/src/my_math/__init__.mojo 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index 0000000000..eb804ba88a --- /dev/null +++ b/examples/notebooks/.gitignore @@ -0,0 +1,3 @@ +roberta/ +roberta.torchscript +onnx diff --git a/examples/notebooks/README.md b/examples/notebooks/README.md new file mode 100644 index 0000000000..b2c895bb95 --- /dev/null +++ b/examples/notebooks/README.md @@ -0,0 +1,68 @@ +# MAX Jupyter notebooks + +This page explains how to get started with MAX example notebooks, and this +repo directory contains notebooks that demonstrate some of MAX features. + +If you're not familiar with Jupyter notebooks, they're files that allow you to +create documents with live code, equations, visualizations, and explanatory +text. They're basically documents with executable code blocks, making them +great for sharing code experiments and programming tutorials. + +## Get started in VS Code + +Visual Studio Code is a great environment for programming with Jupyter notebooks. +Especially if you're developing with MAX on a remote system, using VS +Code is ideal because it allows you to edit and interact with notebooks on the +remote machine where you've installed MAX. + +All you need is MAX (via Magic) and the Jupyter VS Code extension: + +1. [Install Magic](https://docs.modular.com/magic). + +2. Install [Visual Studio Code](https://code.visualstudio.com/) and the + [Jupyter extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter). + +3. Open any `.ipynb` file in this repo and start running the code. + +## Get started with JupyterLab + +You can also run MAX notebooks in a local instance of JupyterLab. The following +is just a quick setup guide for Linux users, but it might not work with your +system (these instructions don't support remote access to the JupyterLab). For +more details about using JupyterLab, see the complete [JupyterLab installation +guide](https://jupyterlab.readthedocs.io/en/latest/getting_started/installation.html). + +### 1. Launch JupyterLab + +You can use either Magic or conda. + +#### Using Magic + +If you have [`magic`](https://docs.modular.com/magic) you can run the following +command to launch JupyterLab from this directory: + +```sh +magic run jupyter lab +``` + +After a moment, it will open a browser window with JupterLab running. + +#### Using conda + +Create a Conda environment, activate that enviroment, and install JupyterLab. + +``` sh +# Create a Conda environment if you don't have one +conda create -n max-repo +# Activate the environment +conda env update -n max-repo -f environment.yml --prune +# run JupyterLab +conda run -n max-repo jupyter lab +``` + +After a moment, it will open a browser window with JupterLab running. + +### 2. Run the .ipynb notebooks + +The left nav bar should show all the notebooks in this directory. +Open any `.ipynb` file and start running the code. diff --git a/examples/notebooks/gemma2b-python-onnx.ipynb b/examples/notebooks/gemma2b-python-onnx.ipynb new file mode 100644 index 0000000000..16423dcad6 --- /dev/null +++ b/examples/notebooks/gemma2b-python-onnx.ipynb @@ -0,0 +1,425 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Gemma-2B with MAX Engine 🏎️ on CPU\n", + "\n", + "**In this notebook we will walk through an example of using [Gemma-2B](https://huggingface.co/google/gemma-2b) model with MAX Engine 🏎️ on CPU and float32. Check out the [roadmap](https://docs.modular.com/max/roadmap) for quantization and the GPU support.**\n", + "\n", + "The Gemma-2B Large Language Model (LLM) is a pretrained generative text model with 2 billion parameters. Generative text models generate the next token iteratively given a sequence of past tokens representing the input prompt plus already generated response tokens.\n", + "\n", + "Thus the underlying transformer model is invoked in each iteration of this loop until we reach the stopping condition (either the maximum number of generated tokens or a token designated as the end).\n", + "\n", + "\n", + "### Requirements\n", + "\n", + "1. Please make sure you have installed `max` as described in our [getting started](https://docs.modular.com/max/get-started/) guide\n", + "2. Valid [huggingface access token](https://huggingface.co/docs/hub/security-tokens)\n", + "3. Gemma-2b is a gated model. Make sure you have accepted the [terms of use](https://huggingface.co/google/gemma-2b) to be able to continue\n", + "4. **The model size is 13G**. Please make sure you have enough disk space to download the model and for the converted ONNX counterpart as we will use them later in this tutorial.\n", + "5. The runtime memory requirement is around **24G** at minimum (skipping the optional part 👇) and **50G** to run all the cells\n", + "\n", + "With all that, we install the required packages as follows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 -m pip install -q torch --index-url https://download.pytorch.org/whl/cpu\n", + "!python3 -m pip install -q transformers onnx huggingface_hub" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Huggingface hub login" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "from huggingface_hub import login\n", + "\n", + "access_token = getpass.getpass('Enter your Hugging Face Access Token')\n", + "login(token=access_token)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vanilla transformers\n", + "\n", + "Let's first see how the model generates a response using the vanilla `transformers`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "import torch\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "\n", + "PROMPT = \"Why did the chicken cross the road?\"\n", + "hf_path = \"google/gemma-2b\"\n", + "hfmodel = AutoModelForCausalLM.from_pretrained(hf_path)\n", + "hftokenizer = AutoTokenizer.from_pretrained(hf_path)\n", + "hftokenizer.pad_token = hftokenizer.eos_token\n", + "\n", + "# Tokenize the text prompt\n", + "input_ids = hftokenizer(PROMPT, return_tensors=\"pt\", max_length=128, truncation=True)\n", + "\n", + "# Run generation\n", + "out_ids = hfmodel.generate(**input_ids, max_new_tokens=15, do_sample=False)\n", + "\n", + "# De-tokenize the generated response\n", + "response = hftokenizer.batch_decode(out_ids, skip_special_tokens=True)[0][len(PROMPT):]\n", + "print(\"Response:\", response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next token generation\n", + "\n", + "Now that we see that the model works, let's try to decompose its `hfmodel.generate` method because we will encounter it later. We should be able to get the same output as before, but by only using `forward` method of the model.\n", + "\n", + "The code below is a simplified version of the actual loop you can find in the transformer's source code. It starts by initializing the current sequense to the given prompt and then generates 10 subsequent tokens - these tokens constitute the response of the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from time import time\n", + "from transformers.generation.logits_process import LogitsProcessorList\n", + "\n", + "logits_processor = LogitsProcessorList()\n", + "\n", + "time_start = time()\n", + "current_seq = input_ids.input_ids\n", + "N_TOKENS = 10\n", + "for idx in range(N_TOKENS):\n", + " # Run model's `forward` on the current sequence.\n", + " # 'logits' output would let us determine the next token for this sequence\n", + " outputs = hfmodel(current_seq, return_dict=True).logits\n", + "\n", + " # Get the newly generated next token\n", + " next_token_logits = outputs[:, -1, :]\n", + " next_tokens_scores = logits_processor(current_seq, next_token_logits)\n", + " next_tokens = torch.argmax(next_tokens_scores, dim=-1)\n", + "\n", + " print(hftokenizer.decode(next_tokens), end=' ', flush=True)\n", + "\n", + " # Append the new token to our sequence\n", + " current_seq = torch.cat([current_seq, next_tokens[:, None]], dim=-1)\n", + "\n", + "time_finish = time()\n", + "print(f\"Prompt: {PROMPT}\")\n", + "print(\"Response:\", hftokenizer.batch_decode(current_seq, skip_special_tokens=True)[0][len(PROMPT):])\n", + "print(f\"Tokens per second: {N_TOKENS / (time_finish - time_start):.2f}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert to ONNX with optimum\n", + "\n", + "Great! We were able to see the same response now with using only `forward` method of our model. We're now ready to use MAX Engine 🏎️ inference.\n", + "To do that, we start by getting an ONNX version of the model. The easiest way to do it is to use HuggingFace `optimum` tool which you can install as follows.\n", + "At the moment of writing this notebook, optimum gemma support is unreleased and but is included in the upstream [here](https://github.com/huggingface/optimum/pull/1714)\n", + "which we can get as follows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 -m pip install -q git+https://github.com/huggingface/optimum.git@e0cbf7dea2531603ecd0f36dfce0657baa4310ec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then the conversion to ONNX. This part can take a while. Also please make sure you've enough disk space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!optimum-cli export onnx --model \"google/gemma-2b\" \"./onnx/gemma-2b-onnx\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional: Examine the ONNX model\n", + "\n", + "**Caveat: 📢 if you want to run this 👇 part you will need around 50G of memory to be able to continue finishing the notebook. Otherwise, check out the included results below.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import onnx\n", + "\n", + "onnxmodel = onnx.load(\"./onnx/gemma-2b-onnx/model.onnx\")\n", + "\n", + "def print_dims(tensor):\n", + " dims = []\n", + " for dim in tensor.type.tensor_type.shape.dim:\n", + " if dim.HasField(\"dim_value\"):\n", + " dims.append(String(dim.dim_value))\n", + " elif dim.HasField(\"dim_param\"):\n", + " dims.append(String(dim.dim_param))\n", + " print(onnx.TensorProto.DataType.Name(tensor.type.tensor_type.elem_type), end=\" \")\n", + " print(\"[\", \", \".join(dims), \"]\")\n", + "\n", + "print(\"=== Inputs ===\")\n", + "for input_tensor in onnxmodel.graph.input:\n", + " print(input_tensor.name, end=\": \")\n", + " print_dims(input_tensor)\n", + "\n", + "print(\"\\n=== Outputs ===\")\n", + "for output_tensor in onnxmodel.graph.output:\n", + " print(output_tensor.name, end=\": \")\n", + " print_dims(output_tensor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "=== Inputs ===\n", + "input_ids: INT64 [ batch_size, sequence_length ]\n", + "attention_mask: INT64 [ batch_size, past_sequence_length + 1 ]\n", + "position_ids: INT64 [ batch_size, sequence_length ]\n", + "past_key_values.0.key: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.0.value: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.1.key: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.1.value: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.2.key: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.2.value: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.3.key: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.3.value: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.4.key: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.4.value: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.5.key: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "past_key_values.5.value: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "\n", + "...\n", + "\n", + "=== Outputs ===\n", + "logits: FLOAT [ batch_size, sequence_length, 256000 ]\n", + "present.0.key: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.0.value: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.1.key: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.1.value: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.2.key: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.2.value: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.3.key: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.3.value: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.4.key: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.4.value: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.5.key: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "present.5.value: FLOAT [ batch_size, 1, past_sequence_length + 1, 256 ]\n", + "\n", + "...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It might be quite surprising to see so many inputs and outputs in the model!\n", + "\n", + "\n", + "In short, we have the following inputs:\n", + "* input_ids\n", + "* position_ids\n", + "* attention_mask\n", + "* past_key_values\n", + "\n", + "And the outputs will be:\n", + "* logits\n", + "* present_key_value\n", + "\n", + "**Note** that since ONNX doesn't support dictionaries as a input/output type, the `key_value` is expanded into 18 pairs of individual tensors (18 is the number of attention layers). The number of KV heads is 1 and the size of each key/value vector is 256.\n", + "\n", + "```\n", + "past_key_values.0.key: FLOAT [ batch_size, 1, past_sequence_length, 256 ]\n", + "```\n", + "\n", + "\n", + "\n", + "In order to use this model we will need to slightly modify our glue code to correctly weave all these values from each iteration to the next.\n", + "Specifically, we will need to pass the `key_values` from previous iteration to the current (for the first iteration they are initializes as empty tensors).\n", + "We will also need to correctly fill in `position_ids` and `attention_mask` tensors and update them on each iteration. We will not get into all the details of how exactly all these tensors affect the model behavior and should be used - this is an extremely interesting topic, but it lays beyond the scope of this walkthrough." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MAX Engine 🏎️\n", + "\n", + "With that we're finally ready to use the MAX Engine 🏎️ for inference.\n", + "\n", + "The code modifications follows our previous approach and is quite minimal. All we need to do is to load the ONNX model (whch can take a while) into an `InferenceSession` object and instead of using the `hfmodel` we will need to use `maxmodel.execute`, and pack the input values into a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "from max import engine\n", + "# Create an InferenceSession and load the ONNX model\n", + "session = engine.InferenceSession()\n", + "maxmodel = session.load(\"./onnx/gemma-2b-onnx/model.onnx\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also quickly inspect the input and output metadata that match the ONNX version above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for tensor in maxmodel.input_metadata:\n", + " print(f'name: {tensor.name}, shape: {tensor.shape}, dtype: {tensor.dtype}')\n", + "\n", + "for tensor in maxmodel.output_metadata:\n", + " print(f'name: {tensor.name}, shape: {tensor.shape}, dtype: {tensor.dtype}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the how to get the response from our `maxmodel`. The token per second can be up to **2X** faster comparing to the PyTorch version. Note that this tutorial doesn't provide an accurate benchmark. For more, please check out our [performance dashboard](https://performance.modular.com/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = {}\n", + "N_BATCH = 1\n", + "N_LAYERS = 18\n", + "N_HEADS = 1\n", + "KV_LEN = 256\n", + "# Initialize the additional layer to 0 for the first iteration:\n", + "for i in range(N_LAYERS):\n", + " inputs[f\"past_key_values.{i}.key\"] = torch.zeros([N_BATCH, N_HEADS, 0, KV_LEN], dtype=torch.float).numpy()\n", + " inputs[f\"past_key_values.{i}.value\"] = torch.zeros([N_BATCH, N_HEADS, 0, KV_LEN], dtype=torch.float).numpy()\n", + "\n", + "current_seq = input_ids.input_ids\n", + "\n", + "time_start = time()\n", + "for idx in range(N_TOKENS):\n", + " # Prepare inputs dictionary\n", + " inputs[\"input_ids\"] = current_seq.numpy()\n", + " inputs[\"position_ids\"] = torch.arange(inputs[\"input_ids\"].shape[1], dtype=torch.long).unsqueeze(0).numpy()\n", + " inputs[\"attention_mask\"] = torch.ones([1, inputs[\"past_key_values.0.key\"].shape[2] + inputs[\"input_ids\"].shape[1]], dtype=torch.int64).numpy()\n", + "\n", + " # Run the model with MAX engine\n", + " max_outputs = maxmodel.execute(**inputs)\n", + " outputs = torch.from_numpy(max_outputs[\"logits\"])\n", + "\n", + " # Get the newly generated next token\n", + " next_token_logits = outputs[:, -1, :]\n", + " next_tokens_scores = logits_processor(current_seq, next_token_logits)\n", + " next_tokens = torch.argmax(next_tokens_scores, dim=-1)\n", + "\n", + " print(hftokenizer.decode(next_tokens), end=' ', flush=True)\n", + "\n", + " # Append the new token to our sequence\n", + " current_seq = torch.cat([current_seq, next_tokens[:, None]], dim=-1)\n", + "\n", + " # Update the KV cache for the next iteration\n", + " for i in range(N_LAYERS):\n", + " inputs[f\"past_key_values.{i}.key\"] = max_outputs[f\"present.{i}.key\"]\n", + " inputs[f\"past_key_values.{i}.value\"] = max_outputs[f\"present.{i}.value\"]\n", + "\n", + "time_finish = time()\n", + "\n", + "print(f\"Prompt: {PROMPT}\")\n", + "print(\"Response:\", hftokenizer.batch_decode(current_seq.numpy(), skip_special_tokens=True)[0][len(PROMPT):])\n", + "print(f\"Tokens per second: {idx/(time_finish-time_start):.2f}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That is it! 🎉\n", + "\n", + "Serving an LLM has historically been not an easy task, but hopefully this example lifts the curtain on how this can be done. MAX Engine 🏎️ doesn't (yet) make this process easier, however, if you've already gone this path with ONNX or TorchScript, switching to MAX should be trivial and bring easy performance wins." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/mistral7b-python-onnx.ipynb b/examples/notebooks/mistral7b-python-onnx.ipynb new file mode 100644 index 0000000000..400a4dae53 --- /dev/null +++ b/examples/notebooks/mistral7b-python-onnx.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Mistral-7B with MAX Engine 🏎️ on CPU\n", + "\n", + "**In this notebook we will walk through an example of using [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) model with MAX Engine 🏎️ on CPU and float32. Check out the [roadmap](https://docs.modular.com/max/roadmap) for quantization and the GPU support.**\n", + "\n", + "The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Generative text models generate the next token iteratively given a sequence of past tokens representing the input prompt plus already generated response tokens.\n", + "\n", + "Thus the underlying transformer model is invoked in each iteration of this loop until we reach the stopping condition (either the maximum number of generated tokens or a token designated as the end).\n", + "\n", + "**Caveats:**\n", + "\n", + "* **The model size is 28G**. Please make sure you have enough disk space to download the model and for the converted ONNX counterpart as we will use them later in this tutorial.\n", + "* **The runtime memory requirement is around 65G** to be able finish the notebook and run all the cells.\n", + "\n", + "First, make sure you have installed `max` as described in the [getting started](https://docs.modular.com/max/get-started/) as well as `PyTorch` and `transformers` packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 -m pip install -q torch --index-url https://download.pytorch.org/whl/cpu\n", + "!python3 -m pip install -q transformers onnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vanilla transformers\n", + "\n", + "Let's first see how the model generates a response using the vanilla `transformers`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "import torch\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "\n", + "PROMPT = \"Why did the chicken cross the road?\"\n", + "\n", + "hf_path = \"mistralai/Mistral-7B-v0.1\"\n", + "hfmodel = AutoModelForCausalLM.from_pretrained(hf_path)\n", + "hftokenizer = AutoTokenizer.from_pretrained(hf_path)\n", + "hftokenizer.pad_token = hftokenizer.eos_token\n", + "\n", + "# Tokenize the text prompt\n", + "input_ids = hftokenizer(PROMPT, return_tensors=\"pt\", max_length=128, truncation=True).input_ids\n", + "\n", + "# Run generation\n", + "out_ids = hfmodel.generate(input_ids=input_ids, max_new_tokens=15, do_sample=False)\n", + "\n", + "# De-tokenize the generated response\n", + "response = hftokenizer.batch_decode(out_ids.numpy(), skip_special_tokens=True)[0][len(PROMPT):]\n", + "print(\"Response:\", response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next token generation\n", + "\n", + "Now that we see that the model works, let's try to decompose its `hfmodel.generate` method because we will encounter it later. We should be able to get the same output as before, but by only using `forward` method of the model.\n", + "\n", + "The code below is a simplified version of the actual loop you can find in the transformer's source code. It starts by initializing the current sequense to the given prompt and then generates 10 subsequent tokens - these tokens constitute the response of the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from time import time\n", + "from transformers.generation.logits_process import LogitsProcessorList\n", + "\n", + "logits_processor = LogitsProcessorList()\n", + "\n", + "time_start = time()\n", + "current_seq = input_ids\n", + "N_TOKENS = 10\n", + "for idx in range(N_TOKENS):\n", + " # Run model's `forward` on the current sequence.\n", + " # 'logits' output would let us determine the next token for this sequence\n", + " outputs = hfmodel(current_seq, return_dict=True).logits\n", + "\n", + " # Get the newly generated next token\n", + " next_token_logits = outputs[:, -1, :]\n", + " next_tokens_scores = logits_processor(current_seq, next_token_logits)\n", + " next_tokens = torch.argmax(next_tokens_scores, dim=-1)\n", + "\n", + " print(hftokenizer.decode(next_tokens), end=' ', flush=True)\n", + "\n", + " # Append the new token to our sequence\n", + " current_seq = torch.cat([current_seq, next_tokens[:, None]], dim=-1)\n", + "\n", + "time_finish = time()\n", + "print(f\"Prompt: {PROMPT}\")\n", + "print(\"Response:\", hftokenizer.batch_decode(current_seq.numpy(), skip_special_tokens=True)[0][len(PROMPT):])\n", + "print(f\"Tokens per second: {N_TOKENS / (time_finish - time_start):.2f}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert to ONNX with optimum\n", + "\n", + "Great! We were able to see the same response now with using only `forward` method of our model. We're now ready to use MAX Engine 🏎️ inference.\n", + "To do that, we start by getting an ONNX version of the model. The easiest way to do it is to use HuggingFace `optimum` tool which you can install as follows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 -m pip install -q optimum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then the conversion to ONNX. This part can take a while. Also please make sure you've enough disk space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!optimum-cli export onnx --model \"mistralai/Mistral-7B-v0.1\" \"./onnx/mistral-7b-onnx\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional: Examine the ONNX model\n", + "\n", + "**Caveat: 📢 if you want to run this 👇 part you will need around 95G of memory to be able to continue finishing the notebook. Otherwise, check out the included results below.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import onnx\n", + "\n", + "# It will take a while to load in ONNX as well as occupying 28G more memory!\n", + "onnxmodel = onnx.load(\"./onnx/mistral-7b-onnx/model.onnx\")\n", + "\n", + "def print_dims(tensor):\n", + " dims = []\n", + " for dim in tensor.type.tensor_type.shape.dim:\n", + " if dim.HasField(\"dim_value\"):\n", + " dims.append(String(dim.dim_value))\n", + " elif dim.HasField(\"dim_param\"):\n", + " dims.append(String(dim.dim_param))\n", + " print(onnx.TensorProto.DataType.Name(tensor.type.tensor_type.elem_type), end=\" \")\n", + " print(\"[\", \", \".join(dims), \"]\")\n", + "\n", + "print(\"=== Inputs ===\")\n", + "for input_tensor in onnxmodel.graph.input:\n", + " print(input_tensor.name, end=\": \")\n", + " print_dims(input_tensor)\n", + "\n", + "print(\"\\n=== Outputs ===\")\n", + "for output_tensor in onnxmodel.graph.output:\n", + " print(output_tensor.name, end=\": \")\n", + " print_dims(output_tensor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "=== Inputs ===\n", + "input_ids: INT64 [ batch_size, sequence_length ]\n", + "attention_mask: INT64 [ batch_size, past_sequence_length + 1 ]\n", + "position_ids: INT64 [ batch_size, sequence_length ]\n", + "past_key_values.0.key: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.0.value: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.1.key: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.1.value: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.2.key: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.2.value: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.3.key: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.3.value: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.4.key: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.4.value: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.5.key: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "past_key_values.5.value: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "\n", + "...\n", + "\n", + "=== Outputs ===\n", + "logits: FLOAT [ batch_size, sequence_length, 32000 ]\n", + "present.0.key: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.0.value: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.1.key: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.1.value: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.2.key: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.2.value: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.3.key: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.3.value: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.4.key: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.4.value: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.5.key: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "present.5.value: FLOAT [ batch_size, 8, past_sequence_length + 1, 128 ]\n", + "\n", + "...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It might be quite surprising to see so many inputs and outputs in the model!\n", + "\n", + "To decode what they all mean and how they should be used we will need to look into the [documentation of the MistralModel](https://huggingface.co/docs/transformers/v4.38.2/en/model_doc/mistral#transformers.MistralModel).\n", + "\n", + "In short, we have the following inputs:\n", + "* input_ids\n", + "* position_ids\n", + "* attention_mask\n", + "* past_key_values\n", + "\n", + "And the outputs will be:\n", + "* logits\n", + "* present_key_value\n", + "\n", + "The `past_key_values.n.key` and `past_key_values.n.value` represents cached states from previous tokens in the sequence to avoid redundant recalculations, thus speeding up the generation process.\n", + "\n", + "**Note** that since ONNX doesn't support dictionaries as a input/output type, the `key_value` is expanded into 32 pairs of individual tensors (32 is the number of attention layers). The number of KV heads is 8 which are part of its [Grouped Query Attention (GQA)](https://arxiv.org/abs/2305.13245) mechanism. The GQA mechanism allows the model to achieve faster inference times by optimizing the attention mechanism.\n", + "\n", + "```\n", + "past_key_values.0.key: FLOAT [ batch_size, 8, past_sequence_length, 128 ]\n", + "```\n", + "\n", + "Moreover, the size of each key/value vector is 128.\n", + "\n", + "In order to use this model we will need to slightly modify our glue code to correctly weave all these values from each iteration to the next.\n", + "Specifically, we will need to pass the `key_values` from previous iteration to the current (for the first iteration they are initializes as empty tensors).\n", + "We will also need to correctly fill in `position_ids` and `attention_mask` tensors and update them on each iteration. We will not get into all the details of how exactly all these tensors affect the model behavior and should be used - this is an extremely interesting topic, but it is beyond the scope of this walkthrough." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MAX Engine 🏎️\n", + "\n", + "With that we're finally ready to use the MAX Engine 🏎️ for inference.\n", + "\n", + "The code modifications follows our previous approach and is quite minimal. All we need to do is to load the ONNX model (whch can take a while) into an `InferenceSession` object and instead of using the `hfmodel` we will need to use `maxmodel.execute`, and pack the input values into a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "from max import engine\n", + "# Create an InferenceSession and load the ONNX model\n", + "session = engine.InferenceSession()\n", + "maxmodel = session.load(\"./onnx/mistral-7b-onnx/model.onnx\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also quickly inspect the input and output metadata that match the ONNX version above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for tensor in maxmodel.input_metadata:\n", + " print(f'name: {tensor.name}, shape: {tensor.shape}, dtype: {tensor.dtype}')\n", + "\n", + "for tensor in maxmodel.output_metadata:\n", + " print(f'name: {tensor.name}, shape: {tensor.shape}, dtype: {tensor.dtype}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the how to get the response from our `maxmodel`. The token per second can be up to **2X** faster comparing to the PyTorch version. Note that this tutorial doesn't provide an accurate benchmark. For more, please check out our [performance dashboard](https://performance.modular.com/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = {}\n", + "N_BATCH = 1\n", + "N_LAYERS = 32\n", + "N_HEADS = 8\n", + "KV_LEN = 128\n", + "# Initialize the additional layer to 0 for the first iteration:\n", + "for i in range(N_LAYERS):\n", + " inputs[f\"past_key_values.{i}.key\"] = torch.zeros([N_BATCH, N_HEADS, 0, KV_LEN], dtype=torch.float).numpy()\n", + " inputs[f\"past_key_values.{i}.value\"] = torch.zeros([N_BATCH, N_HEADS, 0, KV_LEN], dtype=torch.float).numpy()\n", + "\n", + "current_seq = input_ids\n", + "\n", + "time_start = time()\n", + "for idx in range(N_TOKENS):\n", + " # Prepare inputs dictionary\n", + " inputs[\"input_ids\"] = current_seq.numpy()\n", + " inputs[\"position_ids\"] = torch.arange(inputs[\"input_ids\"].shape[1], dtype=torch.long).unsqueeze(0).numpy()\n", + " inputs[\"attention_mask\"] = torch.ones([1, inputs[\"past_key_values.0.key\"].shape[2] + inputs[\"input_ids\"].shape[1]], dtype=torch.int64).numpy()\n", + "\n", + " # Run the model with MAX engine\n", + " max_outputs = maxmodel.execute(**inputs)\n", + " outputs = torch.from_numpy(max_outputs[\"logits\"])\n", + "\n", + " # Get the newly generated next token\n", + " next_token_logits = outputs[:, -1, :]\n", + " next_tokens_scores = logits_processor(current_seq, next_token_logits)\n", + " next_tokens = torch.argmax(next_tokens_scores, dim=-1)\n", + "\n", + " print(hftokenizer.decode(next_tokens), end=' ', flush=True)\n", + "\n", + " # Append the new token to our sequence\n", + " current_seq = torch.cat([current_seq, next_tokens[:, None]], dim=-1)\n", + "\n", + " # Update the KV cache for the next iteration\n", + " for i in range(N_LAYERS):\n", + " inputs[f\"past_key_values.{i}.key\"] = max_outputs[f\"present.{i}.key\"]\n", + " inputs[f\"past_key_values.{i}.value\"] = max_outputs[f\"present.{i}.value\"]\n", + "\n", + "time_finish = time()\n", + "\n", + "print(f\"Prompt: {PROMPT}\")\n", + "print(\"Response:\", hftokenizer.batch_decode(current_seq.numpy(), skip_special_tokens=True)[0][len(PROMPT):])\n", + "print(f\"Tokens per second: {idx/(time_finish-time_start):.2f}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That is it! 🎉\n", + "\n", + "Serving an LLM has historically been not an easy task, but hopefully this example lifts the curtain on how this can be done. MAX Engine 🏎️ doesn't (yet) make this process easier, however, if you've already gone this path with ONNX or TorchScript, switching to MAX should be trivial and bring easy performance wins." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/pixi.toml b/examples/notebooks/pixi.toml new file mode 100644 index 0000000000..a6ad3f7694 --- /dev/null +++ b/examples/notebooks/pixi.toml @@ -0,0 +1,13 @@ +[project] +name = "MAX notebooks" +version = "1.0.0" +description = "Environment for running JupyterLab" +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +python = ">=3.9,<3.13" +max = "*" +pip = ">=24.0,<25" +jupyterlab = ">=4.2.5,<5" diff --git a/examples/notebooks/roberta-python-pytorch.ipynb b/examples/notebooks/roberta-python-pytorch.ipynb new file mode 100644 index 0000000000..683efa156e --- /dev/null +++ b/examples/notebooks/roberta-python-pytorch.ipynb @@ -0,0 +1,389 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Copyright 2025 Modular, Inc: Licensed under the Apache License v2.0 with LLVM Exceptions.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MAX Engine and PyTorch model example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The [Python API for MAX Engine](https://docs.modular.com/engine/reference/python/engine) enables you\n", + "to upgrade your runtime performance for PyTorch, TensorFlow, and ONNX models,\n", + "on a wide range of hardware, with just three lines of code (not counting the\n", + "import):\n", + "\n", + "```python\n", + "from max import engine\n", + "\n", + "# Load your model:\n", + "session = engine.InferenceSession()\n", + "model = session.load(model_path)\n", + "\n", + "# Prepare the inputs, then run an inference:\n", + "outputs = model.execute(**inputs)\n", + "\n", + "# Process the output here.\n", + "```\n", + "\n", + "That's all you need! Everything else is the usual code to prepare your\n", + "inputs and process the outputs.\n", + "\n", + "But, it's always nice to see a fully working example. So the\n", + "rest of this page shows how to run an inference using a version of\n", + "[RoBERTa from Cardiff\n", + "NLP](https://huggingface.co/cardiffnlp/twitter-roberta-base-emotion-multilabel-latest),\n", + "which is a language model trained on tweets to perform sentiment analysis.\n", + "\n", + "This example uses is a PyTorch model (which must be converted to TorchScript\n", + "format), and it's just as easy to load a model from ONNX or TensorFlow (in\n", + "SavedModel format)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install the MAX Engine Python package" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Naturally, you first need to install the `max` Python package.\n", + "This package is not hosted in a package repository (PyPI), and can only be\n", + "installed with the `modular` CLI tool.\n", + "\n", + "For instructions, see\n", + "[Get started with MAX Engine](https://docs.modular.com/max/get-started)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install the MAX Engine Python package\n", + "!python3 -m pip install -q --find-links \"$(modular config max.path)/wheels\" max-engine\n", + "# Install other packages\n", + "!python3 -m pip install -q transformers\n", + "!python3 -m pip install -q torch --index-url https://download.pytorch.org/whl/cpu" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Python modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To start coding, we need some libraries that help us get the model and process\n", + "the input/output data.\n", + "\n", + "NOTE: Make sure you have these packages installed:\n", + "\n", + "```sh\n", + "python3 -m pip install torch transformers\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# suppress extraneous logging\n", + "import os\n", + "os.environ[\"TRANSFORMERS_VERBOSITY\"] = \"critical\"\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import torch\n", + "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", + "\n", + "from max import engine\n", + "from max.dtype import DType" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we download the [RoBERTa model](https://huggingface.co/cardiffnlp/twitter-roberta-base-emotion-multilabel-latest)\n", + "from HuggingFace and save it in the PyTorch TorchScript format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HF_MODEL_NAME = \"cardiffnlp/twitter-roberta-base-emotion-multilabel-latest\"\n", + "hf_model = AutoModelForSequenceClassification.from_pretrained(HF_MODEL_NAME)\n", + "hf_model.config.return_dict = False\n", + "\n", + "# Converting model to TorchScript\n", + "model_path = Path(\"roberta.torchscript\")\n", + "batch = 1\n", + "seqlen = 128\n", + "inputs = {\n", + " \"input_ids\": torch.zeros((batch, seqlen), dtype=torch.int64),\n", + " \"attention_mask\": torch.zeros((batch, seqlen), dtype=torch.int64),\n", + "}\n", + "with torch.no_grad():\n", + " traced_model = torch.jit.trace(\n", + " hf_model, example_kwarg_inputs=inputs, strict=False\n", + " )\n", + "\n", + "torch.jit.save(traced_model, model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load and compile the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we load and compile the model in MAX Engine using an\n", + "[`InferenceSession`](https://docs.modular.com/engine/reference/python/engine.html#max.engine.InferenceSession).\n", + "\n", + "When loading a TorchScript model, you need to specify the shape and data type\n", + "for all input tensors. This is required because TorchScript models do not\n", + "include input spec annotations (unlike TensorFlow and ONNX models), which MAX\n", + "Engine needs to compile the model.\n", + "\n", + "To define the input specs, you need to create a list of\n", + "[`TorchInputSpec`](https://docs.modular.com/engine/reference/python/engine#max.engine.TorchInputSpec)\n", + "objects (one item for each input), and pass the list to\n", + "[`InferenceSession.load`](https://docs.modular.com/engine/reference/python/engine.html#max.engine.InferenceSession.load).\n", + "For example, here's how to declare the input specs for the RoBERTa TorchScript\n", + "model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We use the same `inputs` that we used above to trace the model\n", + "input_spec_list = [\n", + " engine.TorchInputSpec(shape=tensor.size(), dtype=DType.int64)\n", + " for tensor in inputs.values()\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can load the model: (If you're loading a TensorFlow SavedModel or ONNX\n", + "model, then you don't need the `input_specs` argument.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session = engine.InferenceSession()\n", + "model = session.load(model_path, input_specs=input_spec_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's two lines down, just one to go.\n", + "\n", + "NOTE: The first time you load a model, it might take a few minutes to compile it,\n", + "but this up-front cost will pay dividends in latency savings provided by\n", + "our next-generation graph compiler." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This part is your usual pre-processing. \n", + "For the RoBERTa model, we need to process the text input into a sequence of tokens, so we'll do that with [`transformers.AutoTokenizer`](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer).\n", + "\n", + "First, let's take a look at the model's inputs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for tensor in model.input_metadata:\n", + " print(f'name: {tensor.name}, shape: {tensor.shape}, dtype: {tensor.dtype}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us the model needs 2 inputs. (If your model shows a dimension size\n", + "is `None`, that means it's dynamic.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "INPUT=\"There are many exciting developments in the field of AI Infrastructure!\"\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_NAME)\n", + "inputs = tokenizer(INPUT, return_tensors=\"pt\", padding='max_length', truncation=True, max_length=seqlen)\n", + "print(inputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run an inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now for that third line of code, we pass the inputs to\n", + "[`execute()`](https://docs.modular.com/engine/reference/python/engine#max.engine.Model.execute). This\n", + "function requires all inputs as keyword arguments, so we'll\n", + "unpack the `inputs` dictionary as we pass it through:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "outputs = model.execute(**inputs)\n", + "print(outputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it!\n", + "\n", + "The output from [`execute()`](https://docs.modular.com/engine/reference/python/engine.html#max.engine.Model.execute) is a dictionary of output tensors, each in an `ndarray`. Let's now figure out what they say." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Process the outputs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we'll use some help from the [transformers library](https://huggingface.co/docs/transformers/main/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification) to convert the output ids to labels:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract class prediction from output\n", + "predicted_class_id = outputs[\"result0\"].argmax(axis=-1)[0]\n", + "classification = hf_model.config.id2label[predicted_class_id]\n", + "\n", + "print(f\"The sentiment is: {classification}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ta-da! 🎉\n", + "\n", + "If you're running this notebook yourself, beware that this notebook does not\n", + "illustrate MAX Engine's runtime performance. For actual benchmark results,\n", + "check out our [performance dashboard](https://performance.modular.com).\n", + "\n", + "For more details about the inferencing API, see the [Python API reference](https://docs.modular.com/max/api/python/engine)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/offline-inference/README.md b/examples/offline-inference/README.md new file mode 100644 index 0000000000..5362bdbbb8 --- /dev/null +++ b/examples/offline-inference/README.md @@ -0,0 +1,10 @@ +# Using MAX pipelines to run LLMs offline + +Examples of using the MAX stack to directly query LLMs from python +without spinning up a webserver. + +A single Magic command runs the example: + +```sh +magic run basic +``` diff --git a/examples/offline-inference/basic.py b/examples/offline-inference/basic.py new file mode 100644 index 0000000000..028cbf6820 --- /dev/null +++ b/examples/offline-inference/basic.py @@ -0,0 +1,52 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import os + +from max.entrypoints import LLM +from max.pipelines import PipelineConfig +from max.pipelines.architectures import register_all_models +from max.serve.config import Settings + + +def main(): + register_all_models() + + model_path = "modularai/Llama-3.1-8B-Instruct-GGUF" + print(f"Loading model: {model_path}") + pipeline_config = PipelineConfig(model_path) + settings = Settings() + llm = LLM(settings, pipeline_config) + + prompts = [ + "In the beginning, there was", + "I believe the meaning of life is", + "The fastest way to learn python is", + ] + + print("Generating responses...") + responses = llm.generate(prompts, max_new_tokens=50) + + for i, (prompt, response) in enumerate(zip(prompts, responses)): + print(f"========== Response {i} ==========") + print(prompt + response) + print() + + +if __name__ == "__main__": + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + main() diff --git a/examples/offline-inference/pixi.toml b/examples/offline-inference/pixi.toml new file mode 100644 index 0000000000..3115b4596d --- /dev/null +++ b/examples/offline-inference/pixi.toml @@ -0,0 +1,14 @@ +[project] +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +description = "Examples exercising the max stack offline and in python" +name = "Python Offline Inference Examples" +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] +version = "0.1.0" + +[tasks] +basic = "python basic.py" + +[dependencies] +python = ">=3.9,<3.13" +max-pipelines = "*" diff --git a/magic.lock b/magic.lock deleted file mode 100644 index ebf11b183f..0000000000 --- a/magic.lock +++ /dev/null @@ -1,2197 +0,0 @@ -version: 6 -environments: - 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license_family: MOZILLA - size: 281565 - timestamp: 1731585108039 -- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda - sha256: 567c04f124525c97a096b65769834b7acb047db24b15a56888a322bf3966c3e1 - md5: 0c3cc595284c5e8f0f9900a9b228a332 - depends: - - python >=3.9 - license: MIT - license_family: MIT - size: 21809 - timestamp: 1732827613585 diff --git a/.github/CODEOWNERS b/mojo/.github/CODEOWNERS similarity index 100% rename from .github/CODEOWNERS rename to mojo/.github/CODEOWNERS diff --git a/mojo/.github/ISSUE_TEMPLATE/doc_issue.yaml b/mojo/.github/ISSUE_TEMPLATE/doc_issue.yaml new file mode 100644 index 0000000000..daf162cefc --- /dev/null +++ b/mojo/.github/ISSUE_TEMPLATE/doc_issue.yaml @@ -0,0 +1,50 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: Documentation issue +description: Report a problem with the Mojo docs +title: "[Docs]" +labels: + - documentation +body: + - type: markdown + attributes: + value: | + Thank you for helping us improve the Mojo docs! + + Please add a title above and fill in the following fields so we can understand the problem. + + - type: input + attributes: + label: Where is the problem? + description: Provide a link to the problematic page (with a heading anchor). + validations: + required: true + + - type: textarea + attributes: + label: What can we do better? + description: Describe the documentation problem and how you suggest we fix it. + validations: + required: true + + - type: textarea + attributes: + label: Anything else? + validations: + required: false + + - type: markdown + attributes: + value: | + Thank you! diff --git a/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml b/mojo/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml similarity index 100% rename from .github/ISSUE_TEMPLATE/mojo_bug_report.yaml rename to mojo/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml diff --git a/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml b/mojo/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml similarity index 100% rename from .github/ISSUE_TEMPLATE/mojo_feature_request.yaml rename to mojo/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/mojo/.github/PULL_REQUEST_TEMPLATE.md similarity index 100% rename from .github/PULL_REQUEST_TEMPLATE.md rename to mojo/.github/PULL_REQUEST_TEMPLATE.md diff --git a/.github/workflows/check_pr_target.yml b/mojo/.github/workflows/check_pr_target.yml similarity index 100% rename from .github/workflows/check_pr_target.yml rename to mojo/.github/workflows/check_pr_target.yml diff --git a/.github/workflows/check_pr_title.yml b/mojo/.github/workflows/check_pr_title.yml similarity index 100% rename from .github/workflows/check_pr_title.yml rename to mojo/.github/workflows/check_pr_title.yml diff --git a/.github/workflows/standard_library_tests_and_examples.yml b/mojo/.github/workflows/standard_library_tests_and_examples.yml similarity index 100% rename from .github/workflows/standard_library_tests_and_examples.yml rename to mojo/.github/workflows/standard_library_tests_and_examples.yml diff --git a/.github/workflows/test_pre_commit.yml b/mojo/.github/workflows/test_pre_commit.yml similarity index 100% rename from .github/workflows/test_pre_commit.yml rename to mojo/.github/workflows/test_pre_commit.yml diff --git a/mojo/CODE_OF_CONDUCT.md b/mojo/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000..f4f5ad5a25 --- /dev/null +++ b/mojo/CODE_OF_CONDUCT.md @@ -0,0 +1,71 @@ +# Code of conduct + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, +body size, disability, ethnicity, gender identity and expression, level of +experience, nationality, personal appearance, race, religion, or sexual +identity and orientation. + +## Our standards + +All community forums and spaces are meant for professional interactions that +are friendly, inclusive, helpful, and collaborative. Examples of behavior that +contributes to creating a positive environment include: + +- Using welcoming and inclusive language. +- Being respectful of differing viewpoints and experiences. +- Gracefully accepting constructive criticism. +- Focusing on what is best for the community. +- Showing empathy towards other community members. + +Any behavior that could reasonably be considered inappropriate in a +professional setting is unacceptable. Examples of unacceptable behavior by +participants include: + +- The use of sexualized language or imagery and unwelcome sexual attention or + advances. +- Trolling, insulting/derogatory comments, and personal or political attacks. +- Public or private harassment. +- Publishing others' private information, such as a physical or electronic + address, without explicit permission. +- Conduct which could reasonably be considered inappropriate for the forum in + which it occurs. + +## Our responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies to all project content and public spaces on the +Mojo GitHub repo, the rest of Modular’s GitHub organization, and all other +official Mojo community spaces and communication mediums, whether offline or +online. + +## Enforcement + +Instances of abusive, harassment, or otherwise unacceptable behavior should be +reported to the project team at . All complaints will +be reviewed and investigated and will result in a response that is deemed +necessary and appropriate to the circumstances. The project team is obligated +to maintain confidentiality with regard to the reporter of an incident. Further +details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the Contributor Covenant, version 1.4, +available at , and includes some +aspects of the Geek Feminism Code of Conduct and the Drupal Code of Conduct. diff --git a/CONTRIBUTING.md b/mojo/CONTRIBUTING.md similarity index 100% rename from CONTRIBUTING.md rename to mojo/CONTRIBUTING.md diff --git a/mojo/LICENSE b/mojo/LICENSE new file mode 100644 index 0000000000..c1af4075d5 --- /dev/null +++ b/mojo/LICENSE @@ -0,0 +1,235 @@ +============================================================================================== +The Mojo repository is licensed under the Apache License v2.0 with LLVM Exceptions: +============================================================================================== + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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All such code will be identified clearly using at least one of two +mechanisms: + +1) It will be in a separate directory tree with its own `LICENSE.txt` or + `LICENSE` file at the top containing the specific license and restrictions + which apply to that software, or +2) It will contain specific license and restriction terms at the top of every + file. diff --git a/mojo/README.md b/mojo/README.md new file mode 100644 index 0000000000..2a4878c789 --- /dev/null +++ b/mojo/README.md @@ -0,0 +1,113 @@ +
    + + + [Website][Mojo] | [Getting Started] | [API Documentation] | [Contributing] | [Changelog] +
    + +[Mojo]: https://www.modular.com/mojo/ +[Getting Started]: https://docs.modular.com/mojo/manual/get-started/ +[API Documentation]: https://docs.modular.com/mojo/lib +[Contributing]: ./CONTRIBUTING.md +[Changelog]: ./docs/changelog.md + +# Welcome to Mojo 🔥 + +Mojo is a new programming language that bridges the gap between research +and production by combining Python syntax and ecosystem with systems +programming and metaprogramming features. Mojo is still young, but it is +designed to write blazing-fast code for CPUs, GPUs, and more as part of +the [MAX Platform](https://www.modular.com/max). + +This repo includes source code for: + +- Mojo examples +- Mojo documentation hosted at [modular.com](https://docs.modular.com/mojo/) +- The [Mojo standard library](https://docs.modular.com/mojo/lib) + +This repo has two primary branches: + +- The [`stable`](https://github.com/modular/mojo/tree/stable) branch, which +is in sync with the last stable released version of Mojo. Use the examples +here if you’re using a [release build of Mojo](#latest-released). + +- The [`main`](https://github.com/modular/mojo/tree/main) branch, which +is in sync with the Mojo nightly build and subject to breakage. Use this branch +for [contributions](./CONTRIBUTING.md), or if you're using the latest +[nightly build of Mojo](#latest-nightly). + +To learn more about Mojo, see the +[Mojo Manual](https://docs.modular.com/mojo/manual/). + +## Installing Mojo + +### Latest Released + +To install the last released build of Mojo, follow the guide to +[Get started with Mojo](https://docs.modular.com/mojo/manual/get-started). + +### Latest Nightly + +The nightly Mojo builds are subject to breakage and provide an inside +view of how the development of Mojo is progressing. Use at your own risk +and be patient! + +To get nightly builds, see the same instructions to [Get started with +Mojo](https://docs.modular.com/mojo/manual/get-started), but when you create +your project, instead use the following `magic init` command to set the +conda package channel to `max-nightly`: + +```bash +magic init hello-world-nightly --format mojoproject \ + -c conda-forge -c https://conda.modular.com/max-nightly +``` + +Or, if you're [using conda](https://docs.modular.com/magic/conda), add the +`https://conda.modular.com/max-nightly/` channel to your `environment.yaml` +file. For example: + +```yaml +[project] +name = "Mojo nightly example" +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] + +[dependencies] +max = "*" +``` + +When you clone this repo, you'll be on the `main` branch by default, +which includes code matching the latest nightly build: + +```bash +git clone https://github.com/modular/mojo.git +``` + +If you want to instead see the source from the most recent stable +release, then you can switch to the `stable` branch. + +## Contributing + +When you want to report issues or request features, [please create a GitHub +issue here](https://github.com/modular/mojo/issues). +See [here](./CONTRIBUTING.md) for guidelines on filing good bugs. + +We welcome contributions to this repo on the +[`main`](https://github.com/modular/mojo/tree/main) +branch. If you’d like to contribute to Mojo, please first read our [Contributor +Guide](https://github.com/modular/mojo/blob/main/CONTRIBUTING.md). + +For more general questions or to chat with other Mojo developers, check out our +[Discord](https://discord.gg/modular). + +## License + +This repository and its contributions are licensed under the Apache License v2.0 +with LLVM Exceptions (see the LLVM [License](https://llvm.org/LICENSE.txt)). +MAX and Mojo usage and distribution are licensed under the +[MAX & Mojo Community License](https://www.modular.com/legal/max-mojo-license). + +## Thanks to our contributors + +     + + diff --git a/docs/README.md b/mojo/docs/README.md similarity index 100% rename from docs/README.md rename to mojo/docs/README.md diff --git a/docs/changelog-released.md b/mojo/docs/changelog-released.md similarity index 100% rename from docs/changelog-released.md rename to mojo/docs/changelog-released.md diff --git a/docs/changelog.md b/mojo/docs/changelog.md similarity 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similarity index 100% rename from stdlib/test/utils/test_unroll.mojo rename to mojo/stdlib/test/utils/test_unroll.mojo diff --git a/stdlib/test/utils/test_variant.mojo b/mojo/stdlib/test/utils/test_variant.mojo similarity index 100% rename from stdlib/test/utils/test_variant.mojo rename to mojo/stdlib/test/utils/test_variant.mojo diff --git a/stdlib/test/utils/test_write.mojo b/mojo/stdlib/test/utils/test_write.mojo similarity index 100% rename from stdlib/test/utils/test_write.mojo rename to mojo/stdlib/test/utils/test_write.mojo diff --git a/stdlib/test/utils/test_write_to_stdout.mojo b/mojo/stdlib/test/utils/test_write_to_stdout.mojo similarity index 100% rename from stdlib/test/utils/test_write_to_stdout.mojo rename to mojo/stdlib/test/utils/test_write_to_stdout.mojo diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000..46c3124e43 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,14 @@ +[tool.black] +include = '\.pyi?$' +line-length = 80 +preview = true +fast = true + +[tool.pyright] +typeCheckingMode = "off" +reportMissingImports = false + +[project] +requires-python = ">=3.8" +name = "max-repo" + diff --git a/src/max/Dockerfile.cpu b/src/max/Dockerfile.cpu new file mode 100644 index 0000000000..08395e8a21 --- /dev/null +++ b/src/max/Dockerfile.cpu @@ -0,0 +1,46 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# This is an example of how to run max-pipelines serve in a docker container. + +# Create a build image to download depedencies +FROM ghcr.io/modular/magic:latest AS production + +RUN apt-get update && apt-get install -y --no-install-recommends build-essential git + +COPY . /app +WORKDIR /app + +RUN magic install + +EXPOSE 8000 + +# TODO: Add HEALTHCHECK instruction +# HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 CMD curl --fail http://localhost:8000/health || exit 1 + +LABEL maintainer "Modular Inc " + +ENV PYTHONUNBUFFERED=1 +# suppress transformers logging +ENV TRANSFORMERS_VERBOSITY="critical" +ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1 +ENV MODULAR_STRUCTURED_LOGGING=1 +ENV PROMETHEUS_MULTIPROC_DIR=/tmp + +# OTEL configuration +ENV OTEL_SERVICE_NAME="max-serve" +# OTEL's default histogram bucktes have poor resolution. Prefer exponential histograms. +ENV OTEL_EXPORTER_OTLP_METRICS_DEFAULT_HISTOGRAM_AGGREGATION="base2_exponential_bucket_histogram" + +ENTRYPOINT ["magic", "run", "serve"] +CMD ["--model-path=modularai/Llama-3.1-8B-Instruct-GGUF"] diff --git a/src/max/Dockerfile.cuda-simple b/src/max/Dockerfile.cuda-simple new file mode 100644 index 0000000000..eb288f8e29 --- /dev/null +++ b/src/max/Dockerfile.cuda-simple @@ -0,0 +1,50 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# This is a simplified Dockerfile that just runs the max-pipelines serve command +# targeting a CUDA-enabled image. This build includes magic and build tools +# in the production image. + +ARG CUDA_VERSION=12.6.0 + +# Create a build image to download depedencies +FROM ghcr.io/modular/magic:noble-cuda-${CUDA_VERSION} AS production + +RUN apt-get update && apt-get install -y --no-install-recommends build-essential git + +COPY . /app +WORKDIR /app + +RUN magic install + +EXPOSE 8000 + +# TODO: Add HEALTHCHECK instruction +# HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 CMD curl --fail http://localhost:8000/health || exit 1 + +LABEL maintainer "Modular Inc " + +ENV PYTHONUNBUFFERED=1 +# suppress transformers logging +ENV TRANSFORMERS_VERBOSITY="critical" +ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1 +ENV MODULAR_STRUCTURED_LOGGING=1 +ENV PROMETHEUS_MULTIPROC_DIR=/tmp + +# OTEL configuration +ENV OTEL_SERVICE_NAME="max-serve" +# OTEL's default histogram bucktes have poor resolution. Prefer exponential histograms. +ENV OTEL_EXPORTER_OTLP_METRICS_DEFAULT_HISTOGRAM_AGGREGATION="base2_exponential_bucket_histogram" + +ENTRYPOINT ["magic", "run", "serve"] +CMD ["--model-path=modularai/Llama-3.1-8B-Instruct-GGUF"] diff --git a/src/max/Dockerfile.cuda-small b/src/max/Dockerfile.cuda-small new file mode 100644 index 0000000000..ac53dbdbf5 --- /dev/null +++ b/src/max/Dockerfile.cuda-small @@ -0,0 +1,63 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# This build avoids having magic and build dependencies in the production image +# targeting a CUDA-enabled image. It optimizes for size over customizability by +# not including build tools in the production image. + +ARG CUDA_VERSION=12.6.0 +ARG BASE_IMAGE=nvidia/cuda:{CUDA_VERSION}-base-ubuntu24.04 + +# Create a build image to download depedencies +FROM ghcr.io/modular/magic:noble AS build + +RUN apt-get update && apt-get install -y --no-install-recommends build-essential git + +COPY . /app +WORKDIR /app + +RUN magic install + +# Create the shell-hook bash script to activate the environment +RUN magic shell-hook > /shell-hook.sh +RUN echo 'python -m max.entrypoints.pipelines serve "$@"' >> /shell-hook.sh + +# Create the production image +FROM ${BASE_IMAGE} AS production + +COPY --from=build /app/.magic/envs/default /app/.magic/envs/default +COPY --from=build /shell-hook.sh /shellhook.sh +COPY . /app + +WORKDIR /app +EXPOSE 8000 + +# TODO: Add HEALTHCHECK instruction +# HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 CMD curl --fail http://localhost:8000/health || exit 1 + +LABEL maintainer "Modular Inc " + +ENV PYTHONUNBUFFERED=1 +# suppress transformers logging +ENV TRANSFORMERS_VERBOSITY="critical" +ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1 +ENV MODULAR_STRUCTURED_LOGGING=1 +ENV PROMETHEUS_MULTIPROC_DIR=/tmp + +# OTEL configuration +ENV OTEL_SERVICE_NAME="max-serve" +# OTEL's default histogram bucktes have poor resolution. Prefer exponential histograms. +ENV OTEL_EXPORTER_OTLP_METRICS_DEFAULT_HISTOGRAM_AGGREGATION="base2_exponential_bucket_histogram" + +ENTRYPOINT ["/bin/bash", "/shell-hook.sh"] +CMD ["--model-path=modularai/Llama-3.1-8B-Instruct-GGUF"] diff --git a/src/max/README.md b/src/max/README.md new file mode 100644 index 0000000000..5a27ab75da --- /dev/null +++ b/src/max/README.md @@ -0,0 +1,41 @@ +# MAX Pipelines + +These are end-to-end pipelines that demonstrate the power of +[MAX](https://docs.modular.com/max/) for accelerating common AI workloads, and +more. Each of the supported pipelines can be served via an OpenAI-compatible +endpoint. + +MAX can also serve most PyTorch-based large language models that are +present on Hugging Face, although not at the same performance as native MAX +Graph versions. + +## Usage + +The easiest way to try out any of the pipelines is with our Magic command-line +tool. + +1. Install Magic on macOS and Linux with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Install max-pipelines command to run the pipelines. + + ```shell + magic global install max-pipelines + ``` + +3. Serve a model. + + ```shell + max-pipelines serve --model-path deepseek-ai/DeepSeek-R1-Distill-Llama-8B + ``` + +See [https://builds.modular.com/](https://builds.modular.com/) to discover many +of the models supported by MAX. diff --git a/src/max/entrypoints/cli/__init__.py b/src/max/entrypoints/cli/__init__.py new file mode 100644 index 0000000000..cb55ab269b --- /dev/null +++ b/src/max/entrypoints/cli/__init__.py @@ -0,0 +1,51 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Pipeline cli utilities.""" + +from .config import ( + config_to_flag, + get_default, + get_field_type, + is_flag, + is_multiple, + is_optional, + pipeline_config_options, + validate_field_type, +) +from .device_options import DevicesOptionType +from .encode import pipeline_encode +from .generate import generate_text_for_pipeline, stream_text_to_console +from .list import list_pipelines_to_console, list_pipelines_to_json +from .metrics import TextGenerationMetrics +from .serve import serve_pipeline + +__all__ = [ + "DevicesOptionType", + "TextGenerationMetrics", + "config_to_flag", + "pipeline_config_options", + "serve_pipeline", + "generate_text_for_pipeline", + "stream_text_to_console", + "list_pipelines_to_console", + "list_pipelines_to_json", + "pipeline_encode", + "get_default", + "get_field_type", + "is_flag", + "is_multiple", + "is_optional", + "validate_field_type", +] diff --git a/src/max/entrypoints/cli/config.py b/src/max/entrypoints/cli/config.py new file mode 100644 index 0000000000..c303a0d4a5 --- /dev/null +++ b/src/max/entrypoints/cli/config.py @@ -0,0 +1,202 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Utilities for working with Config objects in Click.""" + +from __future__ import annotations + +import functools +import inspect +import pathlib +from dataclasses import MISSING, Field, fields +from enum import Enum +from pathlib import Path +from typing import Any, Union, get_args, get_origin, get_type_hints + +import click +from max.driver import DeviceSpec +from max.pipelines import PipelineConfig + +from .device_options import DevicesOptionType + +VALID_CONFIG_TYPES = [str, bool, Enum, Path, DeviceSpec, int, float] + + +def get_interior_type(type_hint: Union[type, str, Any]) -> type[Any]: + interior_args = set(get_args(type_hint)) - set([type(None)]) + if len(interior_args) > 1: + msg = ( + "Parsing does not currently supported Union type, with more than" + " one non-None type: {type_hint}" + ) + raise ValueError(msg) + + return get_args(type_hint)[0] + + +def is_optional(type_hint: Union[type, str, Any]) -> bool: + return get_origin(type_hint) is Union and type(None) in get_args(type_hint) + + +def is_flag(field_type: Any) -> bool: + return field_type is bool + + +def validate_field_type(field_type: Any) -> bool: + if is_optional(field_type): + test_type = get_args(field_type)[0] + elif get_origin(field_type) is list: + test_type = get_interior_type(field_type) + else: + test_type = field_type + + for valid_type in VALID_CONFIG_TYPES: + if valid_type == test_type: + return True + + if get_origin(valid_type) is None and inspect.isclass(test_type): + if issubclass(test_type, valid_type): + return True + msg = f"type '{test_type}' not supported in config." + raise ValueError(msg) + + +def get_field_type(field_type: Any): + validate_field_type(field_type) + + # Get underlying core field type, is Optional or list. + if is_optional(field_type): + field_type = get_interior_type(field_type) + elif get_origin(field_type) is list: + field_type = get_interior_type(field_type) + + # Update the field_type to be format specific. + if field_type == Path: + field_type = click.Path(path_type=pathlib.Path) + elif inspect.isclass(field_type): + if issubclass(field_type, Enum): + field_type = click.Choice(list(field_type)) + + return field_type + + +def get_default(dataclass_field: Field) -> Any: + if dataclass_field.default_factory != MISSING: + default = dataclass_field.default_factory() + elif dataclass_field.default != MISSING: + default = dataclass_field.default + else: + default = None + + return default + + +def is_multiple(field_type: Any) -> bool: + return get_origin(field_type) is list + + +def create_click_option( + help_for_fields: dict[str, str], + dataclass_field: Field, + field_type: Any, +) -> click.option: # type: ignore + # Get name. + normalized_name = dataclass_field.name.lower().replace("_", "-") + + # Get Help text. + help_text = help_for_fields.get(dataclass_field.name, None) + + # Get help field. + return click.option( + f"--{normalized_name}", + show_default=True, + help=help_text, + is_flag=is_flag(field_type), + default=get_default(dataclass_field), + multiple=is_multiple(field_type), + type=get_field_type(field_type), + ) + + +def config_to_flag(cls): + options = [] + if hasattr(cls, "help"): + help_text = cls.help() + else: + help_text = {} + field_types = get_type_hints(cls) + for _field in fields(cls): + # Skip private config fields. + # We also skip device_specs as it should not be used directly via the CLI entrypoint. + if _field.name.startswith("_") or _field.name == "device_specs": + continue + + new_option = create_click_option( + help_text, _field, field_types[_field.name] + ) + options.append(new_option) + + def apply_flags(func): + for option in reversed(options): + func = option(func) # type: ignore + return func + + return apply_flags + + +def pipeline_config_options(func): + @config_to_flag(PipelineConfig) + @click.option( + "--devices", + is_flag=False, + type=DevicesOptionType(), + show_default=False, + default="", + flag_value="0", + help=( + "Whether to run the model on CPU (--devices=cpu), GPU (--devices=gpu)" + " or a list of GPUs (--devices=gpu:0,1) etc. An ID value can be" + " provided optionally to indicate the device ID to target. If not" + " provided, the model will run on the first available GPU (--devices=gpu)," + " or CPU if no GPUs are available (--devices=cpu)." + ), + ) + # Kept for backwards compatibility. + @click.option( + "--use-gpu", + is_flag=False, + type=DevicesOptionType(), + show_default=False, + default="", + flag_value="0", + help=( + "Whether to run the model on the available GPU. An ID value can be" + " provided optionally to indicate the device ID to target." + ), + ) + @functools.wraps(func) + def wrapper(*args, **kwargs): + # Remove the options from kwargs and replace with unified device_specs. + devices: str | list[int] = kwargs.pop("devices") + legacy_use_gpu = kwargs.pop("use_gpu", None) + + # Apply legacy flag if no modern devices specified. + if not devices and legacy_use_gpu: + devices = legacy_use_gpu + + kwargs["device_specs"] = DevicesOptionType.device_specs(devices) + + return func(*args, **kwargs) + + return wrapper diff --git a/src/max/entrypoints/cli/device_options.py b/src/max/entrypoints/cli/device_options.py new file mode 100644 index 0000000000..ce14d9a8fe --- /dev/null +++ b/src/max/entrypoints/cli/device_options.py @@ -0,0 +1,90 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Custom Click Options used in pipelines""" + +from __future__ import annotations + +from typing import Any + +import click +from max.driver import DeviceSpec, accelerator_count + + +class DevicesOptionType(click.ParamType): + name = "devices" + + @staticmethod + def device_specs(devices: str | list[int]) -> list[DeviceSpec]: + """Converts parsed devices input into validated :obj:`DeviceSpec` objects. + + Args: + devices: The value provided by the --devices option. + Valid arguments: + - "cpu" → use the CPU, + - "gpu" → default to GPU 0, or, + - a list of ints (GPU IDs). + + Raises: + ValueError: If a requested GPU ID is invalid. + + Returns: + A list of DeviceSpec objects. + """ + num_available_gpus = accelerator_count() + if devices == "cpu" or num_available_gpus == 0: + return [DeviceSpec.cpu()] + + requested_ids: list[int] = [] + if devices == "gpu": + requested_ids = [0] + elif isinstance(devices, list): + requested_ids = devices + + if not requested_ids: + # Return device 0 when no specific IDs are requested. + return [DeviceSpec.accelerator(id=0)] + + # Validate requested GPU IDs. + for gpu_id in requested_ids: + if gpu_id >= num_available_gpus: + msg = ( + f"GPU {gpu_id} requested but only {num_available_gpus} " + "available. Use valid device IDs or '--devices=cpu'." + ) + raise ValueError(msg) + + return [DeviceSpec.accelerator(id=id) for id in requested_ids] + + def convert( + self, + value: Any, + param: click.Parameter | None = None, + ctx: click.Context | None = None, + ) -> str | list[int]: + if not value: + return [] + + if value.lower() in {"cpu", "gpu"}: + return value.lower() + + try: + # Support both "gpu:0,1" and old "0,1" formats. + return [int(part) for part in value.replace("gpu:", "").split(",")] + except ValueError: + self.fail( + f"'{value}' is not a valid device list. Use format 'cpu', 'gpu', or 'gpu:0,1'.", + param, + ctx, + ) diff --git a/src/max/entrypoints/cli/encode.py b/src/max/entrypoints/cli/encode.py new file mode 100644 index 0000000000..42f662f106 --- /dev/null +++ b/src/max/entrypoints/cli/encode.py @@ -0,0 +1,97 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Utilities for encoding text in the cli.""" + +import asyncio +import logging +import uuid +from typing import Optional + +from max.pipelines import ( + PIPELINE_REGISTRY, + EmbeddingsGenerator, + EmbeddingsResponse, + PipelineConfig, +) +from max.pipelines.interfaces import ( + PipelineTask, + PipelineTokenizer, + TokenGeneratorRequest, +) + +from .metrics import EmbeddingsMetrics + +logger = logging.getLogger(__name__) + +MODEL_NAME = "model" + + +async def _run_pipeline_encode( + pipeline: EmbeddingsGenerator, + tokenizer: PipelineTokenizer, + prompt: str, + metrics: Optional[EmbeddingsMetrics] = None, +) -> EmbeddingsResponse: + req_id = str(uuid.uuid4()) + context = await tokenizer.new_context( + TokenGeneratorRequest( + id=req_id, + index=0, + prompt=prompt, + model_name=MODEL_NAME, + ) + ) + pipeline_request = {req_id: context} + + if metrics: + metrics.prompt_size = context.current_length + metrics.signpost("begin_encoding") + + response = pipeline.encode(pipeline_request) + + if metrics: + metrics.signpost("end_encoding") + return response[req_id] + + +def pipeline_encode( + pipeline_config: PipelineConfig, + prompt: str, + num_warmups: int = 0, +) -> None: + # Run timed run & print results. + with EmbeddingsMetrics(print_report=True) as metrics: + tokenizer, pipeline = PIPELINE_REGISTRY.retrieve( + pipeline_config, task=PipelineTask.EMBEDDINGS_GENERATION + ) + assert isinstance(pipeline, EmbeddingsGenerator) + + if num_warmups > 0: + logger.info("Running warmup") + for _ in range(num_warmups): + asyncio.run( + _run_pipeline_encode( + pipeline, tokenizer, prompt, metrics=None + ) + ) + + # Run and print results. + logger.info("Running model...") + print("Encoding:", prompt) + + pipeline_output = asyncio.run( + _run_pipeline_encode(pipeline, tokenizer, prompt, metrics=metrics) + ) + print("Embeddings:", pipeline_output.embeddings) diff --git a/src/max/entrypoints/cli/entrypoint.py b/src/max/entrypoints/cli/entrypoint.py new file mode 100644 index 0000000000..972028a959 --- /dev/null +++ b/src/max/entrypoints/cli/entrypoint.py @@ -0,0 +1,34 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Entrypoint for generalized click cli.""" + +import click + + +class ModelGroup(click.Group): + def get_command(self, ctx, cmd_name): + rv = click.Group.get_command(self, ctx, cmd_name) + if rv is not None: + return rv + supported = ", ".join(self.list_commands(ctx)) + ctx.fail( + f"Command not supported: {cmd_name}\nSupported commands:" + f" {supported}" + ) + + +@click.command(cls=ModelGroup) +def run_cli(): + pass diff --git a/src/max/entrypoints/cli/generate.py b/src/max/entrypoints/cli/generate.py new file mode 100644 index 0000000000..97718673a8 --- /dev/null +++ b/src/max/entrypoints/cli/generate.py @@ -0,0 +1,156 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Utilities for generating text in the cli.""" + +from __future__ import annotations + +import asyncio +import logging +import uuid +from collections.abc import Iterable +from typing import Optional + +import requests +from max.pipelines import ( + PIPELINE_REGISTRY, + PipelineConfig, +) +from max.pipelines.interfaces import ( + PipelineTokenizer, + TokenGenerator, + TokenGeneratorRequest, +) + +from .metrics import TextGenerationMetrics + +logger = logging.getLogger(__name__) + +MODEL_NAME = "model" + + +async def stream_text_to_console( + pipeline: TokenGenerator, + tokenizer: PipelineTokenizer, + prompt: str, + images: Optional[list[bytes]], + num_steps: int, + metrics: Optional[TextGenerationMetrics] = None, + print_tokens: bool = True, +) -> None: + req_id = str(uuid.uuid4()) + context = await tokenizer.new_context( + TokenGeneratorRequest( + id=req_id, + index=0, + prompt=prompt, + images=images, + model_name=MODEL_NAME, + ) + ) + pipeline_request = {req_id: context} + if print_tokens: + print(prompt, end="", flush=True) + + prompt_size = context.current_length + if metrics: + metrics.prompt_size = prompt_size + metrics.signpost("begin_generation") + + try: + first_token = True + generate_again = True + while generate_again: + responses = pipeline.next_token( + pipeline_request, + num_steps=num_steps, + ) + + for response in responses: + if req_id not in response: + # next_token is expected to omit the return if + # it encounters eos. + generate_again = False + break + + encoded_text = response[req_id].next_token + response_text = await tokenizer.decode(context, encoded_text) + if metrics: + if first_token: + first_token = False + metrics.signpost("first_token") + metrics.new_token() + if print_tokens: + print(response_text, end="", flush=True) + + # Yield to the event loop. If at no other point (e.g. + # tokenizer.decode which we await earlier does not yield to the + # event loop), it will be at this point that we'll receive a + # CancelledError if our future was canceled (e.g., we received a + # SIGINT). + await asyncio.sleep(0) + + finally: + if metrics: + metrics.signpost("end_generation") + + pipeline.release(context) + + if print_tokens: + print() + + +def generate_text_for_pipeline( + pipeline_config: PipelineConfig, + prompt: str, + image_urls: Iterable[str] = (), + num_warmups: int = 0, +) -> None: + # Run timed run & print results. + with TextGenerationMetrics(print_report=True) as metrics: + tokenizer, pipeline = PIPELINE_REGISTRY.retrieve(pipeline_config) + assert isinstance(pipeline, TokenGenerator) + if image_urls: + logger.info("Downloading images") + images = [requests.get(url).content for url in image_urls] + else: + images = None + + if num_warmups > 0: + logger.info("Running warmup") + for _ in range(num_warmups): + asyncio.run( + stream_text_to_console( + pipeline, + tokenizer, + prompt, + images, + num_steps=pipeline_config.max_num_steps, + metrics=None, + print_tokens=False, + ) + ) + + # Run and print results. + logger.info("Beginning text generation") + asyncio.run( + stream_text_to_console( + pipeline, + tokenizer, + prompt, + images, + num_steps=pipeline_config.max_num_steps, + metrics=metrics, + print_tokens=True, + ) + ) diff --git a/src/max/entrypoints/cli/list.py b/src/max/entrypoints/cli/list.py new file mode 100644 index 0000000000..e844a95fc0 --- /dev/null +++ b/src/max/entrypoints/cli/list.py @@ -0,0 +1,64 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# ===----------------------------------------------------------------------=== # +# +# This file is Modular Inc proprietary. +# +# ===----------------------------------------------------------------------=== # + +"""Utilities for exploring supported pipelines.""" + +import json + +from max.pipelines import PIPELINE_REGISTRY + + +def list_pipelines_to_console(): + print() + # Print human readable format + for arch in PIPELINE_REGISTRY.architectures.values(): + print() + print(f" Architecture: {arch.name}") + print() + print(" Example Huggingface Repo Ids: ") + for repo_id in arch.example_repo_ids: + print(f" {repo_id}") + + print() + for ( + encoding_name, + kv_cache_strategies, + ) in arch.supported_encodings.items(): + print( + f" Encoding Supported: {encoding_name}, with Cache Strategies: {kv_cache_strategies}" + ) + + print() + + +def list_pipelines_to_json(): + """Print the list of pipelines architecture options in JSON format.""" + architectures = {} + for arch in PIPELINE_REGISTRY.architectures.values(): + architectures[arch.name] = { + "example_repo_ids": list(arch.example_repo_ids), + "supported_encodings": [ + { + "encoding": encoding, + "supported_kv_cache_strategies": list(strategies), + } + for encoding, strategies in arch.supported_encodings.items() + ], + } + print(json.dumps({"architectures": architectures}, indent=2)) diff --git a/src/max/entrypoints/cli/metrics.py b/src/max/entrypoints/cli/metrics.py new file mode 100644 index 0000000000..b1b1cb1678 --- /dev/null +++ b/src/max/entrypoints/cli/metrics.py @@ -0,0 +1,225 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Metric-gathering utilities for the pipelines.""" + +import time +from typing import Any, Union + +import psutil + + +class TextGenerationMetrics: + """Metrics capturing and reporting for a text generation pipeline.""" + + prompt_size: int + output_size: int + startup_time: Union[float, str] + time_to_first_token: Union[float, str] + prompt_eval_throughput: Union[float, str] + eval_throughput: Union[float, str] + + _start_time: float + _signposts: dict[str, float] + _mem_usage_marker: dict[str, float] + _should_print_report: bool + _process: psutil.Process + _print_raw: bool + + def __init__(self, print_report: bool = False, print_raw: bool = False): + self._signposts = {} + self._mem_usage_marker = {} + self.batch_size = 1 + self.prompt_size = 0 + self.output_size = 0 + self._should_print_report = print_report + self._start_time = time.time() + self._process = psutil.Process() + self._print_raw = print_raw + + def __enter__(self): + return self + + def __exit__(self, *exc): + self._calculate_results() + if self._should_print_report: + self._print_report(self._print_raw) + + def signpost(self, name: str): + """Measure the current time and memory usage, tagging it with a name for later reporting.""" + self._signposts[name] = time.time() + self._mem_usage_marker[name] = (self._process.memory_info().rss) / ( + 1024 * 1024 * 1024 + ) + + def new_token(self): + """Report that a new token has been generated.""" + self.new_tokens(1) + + def new_tokens(self, num_tokens: int): + """Report that a num_tokens tokens have been generated.""" + self.output_size += num_tokens + + def _calculate_results(self): + begin_generation = self._signposts.get("begin_generation") + if begin_generation: + self.startup_time = ( + self._signposts["begin_generation"] - self._start_time + ) * 1000.0 + else: + self.startup_time = "n/a" + + # Calculate TTFT & context-encoding throughput + first_token = self._signposts.get("first_token") + if first_token and begin_generation: + self.time_to_first_token = ( + self._signposts["first_token"] + - self._signposts["begin_generation"] + ) * 1000.0 + self.prompt_eval_throughput = ( + self.prompt_size + * self.batch_size + / (self.time_to_first_token / 1000.0) + ) + else: + self.time_to_first_token = "n/a" + self.prompt_eval_throughput = "n/a" + + # Calculate TPOT & token-gen throughput + end_generation = self._signposts.get("end_generation") + if end_generation and first_token and self.output_size > 1: + generation_time = ( + self._signposts["end_generation"] + - self._signposts["first_token"] + ) + self.eval_throughput = ( + (self.output_size - 1) * self.batch_size / generation_time + ) + self.time_per_output_token: Any = ( + generation_time * 1000.0 / (self.output_size - 1) + ) + else: + self.eval_throughput = "n/a" + self.time_per_output_token = "n/a" + + if end_generation and begin_generation: + total_batch_time = ( + self._signposts["end_generation"] + - self._signposts["begin_generation"] + ) + self.requests_per_second: Any = self.batch_size / total_batch_time + self.total_exe_time: Any = total_batch_time * 1000 + else: + self.total_exe_time = "n/a" + self.requests_per_second = "n/a" + + def _print_report(self, print_raw=False): + print() + print("Prompt size:", self.prompt_size) + print("Output size:", self.output_size) + print("Startup time:", self.startup_time, "ms") + print("Time to first token:", self.time_to_first_token, "ms") + print( + "Prompt eval throughput (context-encoding):", + self.prompt_eval_throughput, + "tokens per second", + ) + print("Time per Output Token:", self.time_per_output_token, "ms") + print( + "Eval throughput (token-generation):", + self.eval_throughput, + "tokens per second", + ) + print("Total Latency:", self.total_exe_time, "ms") + print("Total Throughput:", self.requests_per_second, "req/s") + if print_raw: + print("=============raw stats=================") + for k, v in self._signposts.items(): + print( + f"Started {k} at {v} with memory" + f" {self._mem_usage_marker[k]} GB" + ) + + +class EmbeddingsMetrics: + prompt_size: int + startup_time: Union[float, str] + + _start_time: float + _signposts: dict[str, float] + _mem_usage_marker: dict[str, float] + _should_print_report: bool + _process: psutil.Process + _print_raw: bool + + def __init__(self, print_report: bool = False, print_raw: bool = False): + self._signposts = {} + self._mem_usage_marker = {} + self.batch_size = 1 + self.prompt_size = 0 + self._should_print_report = print_report + self._start_time = time.time() + self._process = psutil.Process() + self._print_raw = print_raw + + def __enter__(self): + return self + + def __exit__(self, *exc): + self._calculate_results() + if self._should_print_report: + self._print_report(self._print_raw) + + def signpost(self, name: str): + """Measure the current time and memory usage, tagging it with a name for later reporting.""" + self._signposts[name] = time.time() + self._mem_usage_marker[name] = (self._process.memory_info().rss) / ( + 1024 * 1024 * 1024 + ) + + def _calculate_results(self): + begin_generation = self._signposts.get("begin_encoding") + if begin_generation: + self.startup_time = ( + self._signposts["begin_encoding"] - self._start_time + ) * 1000.0 + else: + self.startup_time = "n/a" + + # Calculate TPOT & token-gen throughput + end_generation = self._signposts.get("end_generation") + if end_generation and begin_generation: + total_batch_time = ( + self._signposts["end_generation"] + - self._signposts["begin_generation"] + ) + self.requests_per_second: Any = self.batch_size / total_batch_time + self.total_exe_time: Any = total_batch_time * 1000 + else: + self.total_exe_time = "n/a" + self.requests_per_second = "n/a" + + def _print_report(self, print_raw=False): + print() + print("Prompt size:", self.prompt_size) + print("Startup time:", self.startup_time, "ms") + print("Total Latency:", self.total_exe_time, "ms") + print("Total Throughput:", self.requests_per_second, "req/s") + if print_raw: + print("=============raw stats=================") + for k, v in self._signposts.items(): + print( + f"Started {k} at {v} with memory" + f" {self._mem_usage_marker[k]} GB" + ) diff --git a/src/max/entrypoints/cli/serve.py b/src/max/entrypoints/cli/serve.py new file mode 100644 index 0000000000..148ddbbaf7 --- /dev/null +++ b/src/max/entrypoints/cli/serve.py @@ -0,0 +1,115 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +"""Utilities for serving cli.""" + +import functools +import logging +from typing import Optional, Union + +import uvloop +from max.pipelines import PIPELINE_REGISTRY, PipelineConfig, PipelineTask +from max.pipelines.kv_cache import KVCacheStrategy +from max.serve.api_server import ( + ServingTokenGeneratorSettings, + fastapi_app, + fastapi_config, +) +from max.serve.config import Settings +from max.serve.pipelines.llm import batch_config_from_pipeline_config +from max.serve.pipelines.performance_fake import ( + PerformanceFakingPipelineTokenizer, + get_performance_fake, +) +from transformers import AutoTokenizer +from uvicorn import Server + +logger = logging.getLogger(__name__) + + +def serve_pipeline( + pipeline_config: PipelineConfig, + performance_fake: str = "none", + profile: bool = False, + batch_timeout: float = 0.0, + model_name: Union[str, None] = None, + failure_percentage: Optional[int] = None, +): + # Initialize settings + settings = Settings(MAX_SERVE_USE_HEARTBEAT=False) + + # TODO: make validate_pipeline_config more generic or cleanly handle the + # case where this is a generalized model unsupported by MAX + if pipeline_config.architecture in PIPELINE_REGISTRY.architectures: + # Retrieve tokenizer and pipeline. + pipeline_config = PIPELINE_REGISTRY.validate_pipeline_config( + pipeline_config + ) + + # TODO: This is a workaround to support embeddings generation until the + # changes to tie pipelines to tasks is complete. This will be removed. + pipeline_task = PipelineTask.TEXT_GENERATION + if pipeline_config.model_path == "sentence-transformers/all-mpnet-base-v2": + pipeline_task = PipelineTask.EMBEDDINGS_GENERATION + + if performance_fake == "none": + logger.info(f"Starting server using {pipeline_config.model_path}") + # Load tokenizer and pipeline from PIPELINE_REGISTRY. + tokenizer, pipeline_factory = PIPELINE_REGISTRY.retrieve_factory( + pipeline_config, + task=pipeline_task, + ) + else: + logger.info( + f"Starting server using performance fake {performance_fake}." + ) + tokenizer = PerformanceFakingPipelineTokenizer( + AutoTokenizer.from_pretrained(pipeline_config.model_path) + ) + pipeline_factory = functools.partial( + get_performance_fake, + performance_fake, # type: ignore + failure_percentage, + ) + + pipeline_config.cache_strategy = KVCacheStrategy.CONTINUOUS + + # Load batch config. + batch_config = batch_config_from_pipeline_config( + pipeline_config=pipeline_config, + pipeline_task=pipeline_task, + batch_timeout=batch_timeout, + ) + + # If explicit model name is not provided, set to model_path. + if model_name is None: + model_name = pipeline_config.model_path + assert model_name is not None + + pipeline_settings = ServingTokenGeneratorSettings( + model_name=model_name, + model_factory=pipeline_factory, + pipeline_config=batch_config, + tokenizer=tokenizer, + ) + + # Intialize and serve webserver. + app = fastapi_app( + settings, + pipeline_settings, + ) + config = fastapi_config(app=app, server_settings=settings) + + server = Server(config) + uvloop.run(server.serve()) diff --git a/src/max/entrypoints/llm.py b/src/max/entrypoints/llm.py new file mode 100644 index 0000000000..5f745b72c1 --- /dev/null +++ b/src/max/entrypoints/llm.py @@ -0,0 +1,185 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""A high level interface for interacting with LLMs built from MAX pipelines""" + +from __future__ import annotations + +import asyncio +import queue +import threading +from queue import Queue +from threading import Thread +from typing import Optional, Sequence + +import tqdm +from max.pipelines.config import PipelineConfig +from max.pipelines.registry import PIPELINE_REGISTRY +from max.serve.config import Settings +from max.serve.pipelines.llm import ( + TokenGeneratorPipeline, + TokenGeneratorRequest, + batch_config_from_pipeline_config, +) +from max.serve.pipelines.model_worker import start_model_worker +from max.serve.pipelines.telemetry_worker import start_telemetry_consumer +from max.serve.scheduler.process_control import ProcessControl + +RequestQueue = Queue[tuple[Sequence[str], Optional[int], bool]] +ResponseQueue = Queue[list[str]] + + +# For now, the LLM class only supports the direct token generation use case. +# Long term, there are multiple other potential use cases to support. +# This class loosely mirrors vllm.LLM for offline inference: https://docs.vllm.ai/en/stable/dev/offline_inference/llm.html +class LLM: + """A high level interface for interacting with LLMs.""" + + _pc: ProcessControl + _async_runner: Thread + _request_queue: RequestQueue + _response_queue: ResponseQueue + + def __init__(self, settings: Settings, pipeline_config: PipelineConfig): + self._pc = ProcessControl(threading, "LLM") + self._request_queue = Queue() + self._response_queue = Queue() + self._async_runner = Thread( + target=_run_async_worker, + args=( + self._pc, + pipeline_config, + self._request_queue, + self._response_queue, + settings, + ), + ) + self._async_runner.start() + # TODO: set a timeout on wait + self._pc.started_event.wait() + + def __del__(self): + self._pc.set_canceled() + self._async_runner.join() + + def generate( + self, + prompts: str | Sequence[str], + max_new_tokens: int | None = 100, + use_tqdm: bool = True, + ) -> list[str]: + if isinstance(prompts, str): + # Handle the degenerate case where the users just passes in a single string + return self.generate((prompts,), max_new_tokens, use_tqdm) + + self._request_queue.put((prompts, max_new_tokens, use_tqdm)) + return self._response_queue.get() + + +def _run_async_worker( + pc: ProcessControl, + pipeline_config: PipelineConfig, + request_queue: RequestQueue, + response_queue: ResponseQueue, + settings: Settings, +): + asyncio.run( + _async_worker( + pc, + pipeline_config, + request_queue, + response_queue, + settings, + ) + ) + + +async def _async_worker( + pc: ProcessControl, + pipeline_config: PipelineConfig, + request_queue: RequestQueue, + response_queue: ResponseQueue, + settings: Settings, +): + tokenizer, model_factory = PIPELINE_REGISTRY.retrieve_factory( + pipeline_config + ) + batch_config = batch_config_from_pipeline_config(pipeline_config) + model_name = pipeline_config.model_path + + async with start_telemetry_consumer(settings) as metric_client: + async with start_model_worker( + model_factory=model_factory, + batch_config=batch_config, + settings=settings, + metric_client=metric_client, + ) as engine_queue: + # Start the model worker process. + # Create dynamic and continuous batching workers and associated queues + # to feed the model worker process. + async with TokenGeneratorPipeline( + model_name=model_name, + tokenizer=tokenizer, + engine_queue=engine_queue, + ) as pipeline: + pc.set_started() + while True: + pc.beat() + if pc.is_canceled(): + break + + try: + (prompts, max_new_tokens, use_tqdm) = request_queue.get( + timeout=0.3 + ) + + if use_tqdm: + pbar = tqdm.tqdm(total=len(prompts)) + + # Lambda to do a full text generation for a request. + async def all_tokens( + i: int, + prompt: str, + ) -> tuple[int, str]: + request = TokenGeneratorRequest( + id=str(i), + index=0, + model_name=model_name, + prompt=prompt, + max_new_tokens=max_new_tokens, + ) + + # Generate this request until complete + tokens = await pipeline.all_tokens(request) + if use_tqdm: + pbar.update(1) + return (i, "".join(t.decoded_token for t in tokens)) + + all_tokens_tasks = [ + all_tokens(i, prompt) + for i, prompt in enumerate(prompts) + ] + responses = [""] * len(prompts) + for i, response in await asyncio.gather( + *all_tokens_tasks + ): + responses[i] = response + + if use_tqdm: + pbar.close() + + response_queue.put(responses) + + except queue.Empty: + continue + pc.set_completed() diff --git a/src/max/entrypoints/pipelines.py b/src/max/entrypoints/pipelines.py new file mode 100644 index 0000000000..bdd102aa9f --- /dev/null +++ b/src/max/entrypoints/pipelines.py @@ -0,0 +1,274 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +import functools +import logging +import os +import signal + +import click +from max.entrypoints.cli import ( + generate_text_for_pipeline, + list_pipelines_to_console, + list_pipelines_to_json, + pipeline_config_options, + pipeline_encode, + serve_pipeline, +) +from max.pipelines import PIPELINE_REGISTRY, PipelineConfig +from max.pipelines.architectures import register_all_models +from max.serve.config import Settings +from max.serve.telemetry.common import configure_logging, configure_metrics + +logger = logging.getLogger(__name__) + + +class ModelGroup(click.Group): + def get_command(self, ctx, cmd_name): + rv = click.Group.get_command(self, ctx, cmd_name) + if rv is not None: + return rv + supported = ", ".join(self.list_commands(ctx)) + ctx.fail( + f"Command not supported: {cmd_name}\nSupported commands:" + f" {supported}" + ) + + +@click.command(cls=ModelGroup) +def main(): + settings = Settings() + configure_logging(settings) + configure_metrics(settings) + register_all_models() + + +def common_server_options(func): + @click.option( + "--profile-serve", + is_flag=True, + show_default=True, + default=False, + help=( + "Whether to enable pyinstrument profiling on the serving endpoint." + ), + ) + @click.option( + "--performance-fake", + type=click.Choice(["none", "no-op", "speed-of-light", "vllm"]), + default="none", + help="Fake the engine performance (for benchmarking)", + ) + @click.option( + "--batch-timeout", + type=float, + default=0.0, + help="Custom timeout for any particular batch.", + ) + @click.option( + "--model-name", + type=str, + help="Deprecated, please use `model_path` instead. Optional model alias for serving the model.", + ) + @click.option( + "--sim-failure", + type=int, + default=0, + help="Simulate fake-perf with failure percentage", + ) + @functools.wraps(func) + def wrapper(*args, **kwargs): + return func(*args, **kwargs) + + return wrapper + + +@main.command(name="serve") +@pipeline_config_options +@common_server_options +def cli_serve( + profile_serve, + performance_fake, + batch_timeout, + model_name, + sim_failure, + **config_kwargs, +): + """Start a model serving endpoint for inference. + + This command launches a server that can handle inference requests for the + specified model. The server supports various performance optimization + options and monitoring capabilities. + """ + # Initialize config, and serve. + pipeline_config = PipelineConfig(**config_kwargs) + failure_percentage = None + if sim_failure > 0: + failure_percentage = sim_failure + serve_pipeline( + pipeline_config=pipeline_config, + profile=profile_serve, + performance_fake=performance_fake, + batch_timeout=batch_timeout, + model_name=model_name, + failure_percentage=failure_percentage, + ) + + +@main.command(name="generate") +@pipeline_config_options +@click.option( + "--prompt", + type=str, + default="I believe the meaning of life is", + help="The text prompt to use for further generation.", +) +@click.option( + "--image_url", + type=str, + multiple=True, + default=[], + help=( + "Images to include along with prompt, specified as URLs." + " The images are ignored if the model does not support" + " image inputs." + ), +) +@click.option( + "--num-warmups", + type=int, + default=0, + show_default=True, + help="# of warmup iterations to run before the final timed run.", +) +def cli_pipeline(prompt, image_url, num_warmups, **config_kwargs): + """Generate text using the specified model. + + This command runs text generation using the loaded model, optionally + accepting image inputs for multimodal models. + """ + # Replit model_paths are kinda broken due to transformers + # version mismatch. We manually update trust_remote_code to True + # because the modularai version does not have the custom Python code needed + # Without this, we get: + # ValueError: `attn_type` has to be either `multihead_attention` or + # `multiquery_attention`. Received: grouped_query_attention + # Another reason why we override this flag here is because at PipelineConfig + # instantiation below, we'll call AutoConfig.from_pretrained, which will + # trigger the error above if not set to True. + if "replit" in config_kwargs["model_path"]: + config_kwargs["trust_remote_code"] = True + + if config_kwargs["max_new_tokens"] == -1: + # Limit generate default max_new_tokens to 100. + config_kwargs["max_new_tokens"] = 100 + + # Load tokenizer & pipeline. + pipeline_config = PipelineConfig(**config_kwargs) + generate_text_for_pipeline( + pipeline_config, + prompt=prompt, + image_urls=image_url, + num_warmups=num_warmups, + ) + + +@main.command(name="encode") +@pipeline_config_options +@click.option( + "--prompt", + type=str, + default="I believe the meaning of life is", + help="The text prompt to use for further generation.", +) +@click.option( + "--num-warmups", + type=int, + default=0, + show_default=True, + help="# of warmup iterations to run before the final timed run.", +) +def encode(prompt, num_warmups, **config_kwargs): + """Encode text input into model embeddings. + + This command processes the input text through the model's encoder, producing + embeddings that can be used for various downstream tasks. + """ + # Load tokenizer & pipeline. + pipeline_config = PipelineConfig(**config_kwargs) + pipeline_encode( + pipeline_config, + prompt=prompt, + num_warmups=num_warmups, + ) + + +@main.command(name="warm-cache") +@pipeline_config_options +def cli_warm_cache(**config_kwargs) -> None: + """Load and compile the model to prepare caches. + + This command is particularly useful in combination with + --save-to-serialized-model-path. Providing that option to this command + will result in a compiled model being stored to that path. Subsequent + invocations of other commands can then use --serialized-model-path to + reuse the previously-compiled model. + + Even without --save-to-serialized-model-path, this command will as a side + effect warm the Hugging Face cache and in some cases, MAX compilation + caches. + """ + pipeline_config = PipelineConfig(**config_kwargs) + _ = PIPELINE_REGISTRY.retrieve(pipeline_config) + + +@main.command(name="list") +@click.option( + "--json", + is_flag=True, + show_default=True, + default=False, + help="Print the list of pipelines options in JSON format.", +) +def cli_list(json): + """List available pipeline configurations and models. + + This command displays information about all registered pipelines and their + configurations. Output can be formatted as human-readable text or JSON. + """ + if json: + list_pipelines_to_json() + else: + list_pipelines_to_console() + + +if __name__ == "__main__": + if directory := os.getenv("BUILD_WORKSPACE_DIRECTORY"): + os.chdir(directory) + + # Workaround for https://github.com/buildbuddy-io/buildbuddy/issues/8326 + if ( + signal.getsignal(signal.SIGINT) == signal.SIG_IGN + and signal.getsignal(signal.SIGTERM) == signal.SIG_IGN + and signal.getsignal(signal.SIGQUIT) == signal.SIG_IGN + ): + # For SIGINT, Python remaps SIG_DFL to default_int_handler on startup. + # We do the same here to retain the same behavior we would get if we + # started normally. (SIG_DFL terminates the process immediately; + # default_int_handler raises KeyboardInterrupt.) + signal.signal(signal.SIGINT, signal.default_int_handler) + signal.signal(signal.SIGTERM, signal.SIG_DFL) + signal.signal(signal.SIGQUIT, signal.SIG_DFL) + + main() diff --git a/src/max/pipelines/README.md b/src/max/pipelines/README.md new file mode 100644 index 0000000000..160952f64b --- /dev/null +++ b/src/max/pipelines/README.md @@ -0,0 +1,119 @@ +# MAX Pipelines + +These are end-to-end pipelines that demonstrate the power of +[MAX](https://docs.modular.com/max/) for accelerating common AI workloads, and +more. Each of the supported pipelines can be served via an OpenAI-compatible +endpoint. + +MAX can also serve most PyTorch-based large language models that are +present on Hugging Face, although not at the same performance as native MAX +Graph versions. + +## Pipelines + +Highly optimized MAX Graph implementations exist for several core model +architectures. These include: + +- [Llama 3.1](llama3): A text completion pipeline using the Llama 3.1 model, +implemented using the MAX Graph API. This pipeline contains everything +needed to run a self-hosted large language model in the `LlamaForCausalLM` +family with state-of-the-art serving throughput. +- [Mistral](architectures/mistral): Support for the `MistralForCausalLM` family +of text completion models, by default using the Mistral NeMo 12B model. This pipeline +has been tuned for performance using the MAX Graph API. +- [Replit Code](architectures/replit): Code generation via the +Replit Code V1.5 3B model, implemented using the MAX Graph API. + +Instructions for how to run each pipeline can be found in their respective +subdirectories, along with all configuration parameters. A shared driver is +used to execute the pipelines. + +## Usage + +The easiest way to try out any of the pipelines is with our Magic command-line +tool. + +1. Install Magic on macOS and Ubuntu with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Clone the MAX examples repository: + + If you don't already have a local clone of this repository, create one via: + + ```shell + git clone https://github.com/modular/max.git + ``` + + The following instructions assume that you're present within this + directory, and you can change to it after cloning: + + ```shell + cd max/src/max/ + ``` + +3. Now run one of the text completion demos with any of following commands: + + ```shell + magic run generate --model-path=modularai/llama-3.1 --prompt "Why is the sky blue?" + magic run generate --model-path=modularai/replit-code-1.5 --prompt "def fibonacci(n):" + magic run generate --model-path=mistralai/Mistral-Nemo-Instruct-2407 --prompt "Why is the sky blue?" + ``` + +4. Host a chat completion endpoint via MAX Serve. + + MAX Serve provides functionality to host performant OpenAI compatible + endpoints using the FastAPI framework. + + Finetuned weights hosted on Hugging Face can be used with one + of these optimized pipeline architectures when serving via the `serve` + command: + + ```shell + magic run serve --model-path=modularai/llama-3.1 + ``` + + A request can then be submitted via a cURL command. + + ```shell + curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "modularai/llama-3.1", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who won the world series in 2020?"} + ] + }' + ``` + +## Verified Hugging Face model architectures + +If you provide a repository ID for a Hugging Face large language model +that does not currently have an optimized MAX Graph implementation, MAX +falls back to serving a PyTorch eager version of the model. + +The following table lists the model architectures tested to work with MAX. + +| **Architecture** | **Example Model** | +| --- | --- | +| AquilaForCausalLM | BAAI/Aquila-7B | +| ChatGLMModel | THUDM/chatglm3-6b | +| GPT2LMHeadModel | openai-community/gpt2 | +| GPTJForCausalLM | EleutherAI/gpt-j-6b | +| LlamaForCausalLM | meta-llama/Llama-3.2-3B-Instruct | +| LlamaForCausalLM | Skywork/Skywork-o1-Open-Llama-3.1-8B | +| LlamaForCausalLM | deepseek-ai/deepseek-coder-1.3b-instruct | +| PhiForCausalLM | microsoft/phi-2 | +| Phi3ForCausalLM | microsoft/Phi-3-mini-4k-instruct | +| GraniteForCausalLM | ibm-granite/granite-3.1-8b-instruct | +| Qwen2ForCausalLM | Qwen/Qwen2.5-1.5B-Instruct | +| OlmoForCausalLM | allenai/OLMo-1B-0724-hf | diff --git a/src/max/pipelines/__init__.py b/src/max/pipelines/__init__.py new file mode 100644 index 0000000000..3020fb1d21 --- /dev/null +++ b/src/max/pipelines/__init__.py @@ -0,0 +1,98 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Types to interface with ML pipelines such as text/token generation.""" + +from typing import Callable as _Callable +from typing import Union as _Union + +from .config import ( + PipelineConfig, + PipelineEngine, + RopeType, + SamplingParams, + SupportedEncoding, + WeightsFormat, +) +from .context import InputContext, TextAndVisionContext, TextContext +from .embeddings_pipeline import EmbeddingsPipeline +from .hf_utils import HuggingFaceFile +from .interfaces import ( + EmbeddingsGenerator, + PipelineTask, + PipelineTokenizer, + TokenGenerator, + TokenGeneratorContext, + TokenGeneratorRequest, + TokenGeneratorRequestFunction, + TokenGeneratorRequestMessage, + TokenGeneratorRequestTool, + TokenGeneratorResponseFormat, +) +from .pipeline import ( + ModelInputs, + ModelOutputs, + PipelineModel, + TextGenerationPipeline, + upper_bounded_default, +) +from .registry import PIPELINE_REGISTRY, SupportedArchitecture +from .response import EmbeddingsResponse, LogProbabilities, TextResponse +from .tokenizer import ( + IdentityPipelineTokenizer, + PreTrainedPipelineTokenizer, + TextAndVisionTokenizer, + TextTokenizer, +) + +PipelinesFactory = _Callable[[], _Union[TokenGenerator, EmbeddingsGenerator]] + + +__all__ = [ + "HuggingFaceFile", + "PipelineConfig", + "PipelineEngine", + "PipelineTask", + "PIPELINE_REGISTRY", + "SamplingParams", + "SupportedArchitecture", + "SupportedEncoding", + "TokenGenerator", + "TokenGeneratorContext", + "TokenGeneratorRequest", + "TokenGeneratorRequestMessage", + "TokenGeneratorResponseFormat", + "TokenGeneratorRequestTool", + "TokenGeneratorResponseFormat", + "TokenGeneratorRequestFunction", + "IdentityPipelineTokenizer", + "InputContext", + "TextContext", + "TextAndVisionContext", + "PipelineTokenizer", + "PreTrainedPipelineTokenizer", + "TextTokenizer", + "TextAndVisionTokenizer", + "TextGenerationPipeline", + "WeightsFormat", + "RopeType", + "PipelineModel", + "ModelInputs", + "ModelOutputs", + "TextResponse", + "LogProbabilities", + "EmbeddingsGenerator", + "EmbeddingsPipeline", + "EmbeddingsResponse", + "upper_bounded_default", +] diff --git a/src/max/pipelines/architectures/__init__.py b/src/max/pipelines/architectures/__init__.py new file mode 100644 index 0000000000..cfb90c5bd3 --- /dev/null +++ b/src/max/pipelines/architectures/__init__.py @@ -0,0 +1,49 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import PIPELINE_REGISTRY + + +def register_all_models(): + """Imports model architectures, thus registering the architecture in the shared PIPELINE_REGISTRY.""" + from .exaone import exaone_arch + from .granite import granite_arch + from .llama3 import llama_arch + from .llama_vision import llama_vision_arch + from .mistral import mistral_arch + from .mpnet import mpnet_arch + from .olmo import olmo_arch + from .phi3 import phi3_arch + from .pixtral import pixtral_arch + from .qwen2 import qwen2_arch + from .replit import replit_arch + + architectures = [ + exaone_arch, + llama_arch, + llama_vision_arch, + mistral_arch, + mpnet_arch, + olmo_arch, + phi3_arch, + pixtral_arch, + qwen2_arch, + replit_arch, + granite_arch, + ] + + for arch in architectures: + PIPELINE_REGISTRY.register(arch) + + +__all__ = ["register_all_models"] diff --git a/src/max/pipelines/architectures/deepseekV2/layers/__init__.py b/src/max/pipelines/architectures/deepseekV2/layers/__init__.py new file mode 100644 index 0000000000..fcf4807305 --- /dev/null +++ b/src/max/pipelines/architectures/deepseekV2/layers/__init__.py @@ -0,0 +1,14 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""DeepseekV2 layer modules.""" diff --git a/src/max/pipelines/architectures/deepseekV2/layers/moe_gate.py b/src/max/pipelines/architectures/deepseekV2/layers/moe_gate.py new file mode 100644 index 0000000000..fd814e5663 --- /dev/null +++ b/src/max/pipelines/architectures/deepseekV2/layers/moe_gate.py @@ -0,0 +1,82 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Mixture of Experts Gate Layer.""" + +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import TensorValue, ops +from max.pipelines.nn import Linear +from max.pipelines.nn.layer import LayerV2 + + +@dataclass +class MaxMoEGate(LayerV2): + """Mixture of Experts Gate Layer. + + Args: + gate_score: Linear layer that projects from hidden_size to intermediate_size. + num_experts_per_tok: Number of experts to route each token to. + n_routed_experts: Total number of experts in the model. + routed_scaling_factor: Scaling factor for routing weights. + aux_loss_alpha: Weight for auxiliary loss. + n_group: Number of groups for expert routing. + topk_group: Number of top experts per group. + gating_dim: Hidden dimension size for gating. + + Shape: + Input: (batch_size, seq_length, hidden_size) + Output: tuple of: + - topk_idx: (batch_size * seq_length, num_experts_per_tok) + - topk_weight: (batch_size * seq_length, num_experts_per_tok) + """ + + def __post_init__(self): + super().__init__() + + gate_score: Linear + num_experts_per_tok: int = 6 + n_routed_experts: int = 64 + routed_scaling_factor: float = 1.0 + aux_loss_alpha: float = 0.001 + n_group: int = 1 + topk_group: int = 1 + gating_dim: int = 2048 # equal to config.hidden_size + + def __call__( + self, hidden_states: TensorValue + ) -> tuple[TensorValue, TensorValue]: + """Compute expert routing weights and indices for input hidden states. + + Args: + hidden_states: Input tensor of shape (batch_size, seq_length, hidden_size) + + Returns: + tuple containing: + - topk_idx: Indices of top-k selected experts of shape (batch_size * seq_length, num_experts_per_tok) + - topk_weight: Routing weights for selected experts of shape (batch_size * seq_length, num_experts_per_tok) + """ + # compute gating score + bsz, seq_len, h = hidden_states.shape + hidden_states = hidden_states.reshape([bsz * seq_len, h]) + + logits = self.gate_score(hidden_states.cast(DType.float32)) + scores = ops.softmax(logits[-1].cast(DType.float32)) + + # select top k experts + topk_weight, topk_idx = ops.top_k( + scores, self.num_experts_per_tok, -1, False + ) + + return topk_idx, topk_weight diff --git a/src/max/pipelines/architectures/exaone/__init__.py b/src/max/pipelines/architectures/exaone/__init__.py new file mode 100644 index 0000000000..5a5f6a2981 --- /dev/null +++ b/src/max/pipelines/architectures/exaone/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import exaone_arch + +__all__ = ["exaone_arch"] diff --git a/src/max/pipelines/architectures/exaone/arch.py b/src/max/pipelines/architectures/exaone/arch.py new file mode 100644 index 0000000000..ce2bfbb8e4 --- /dev/null +++ b/src/max/pipelines/architectures/exaone/arch.py @@ -0,0 +1,59 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + RopeType, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from ..llama3 import weight_adapters +from ..llama3.model import Llama3Model +from .weight_adapters import convert_exaone_safetensor_state_dict + +exaone_arch = SupportedArchitecture( + name="ExaoneForCausalLM", + default_encoding=SupportedEncoding.float32, + task=PipelineTask.TEXT_GENERATION, + supported_encodings={ + SupportedEncoding.q4_k: [KVCacheStrategy.NAIVE], + SupportedEncoding.q6_k: [KVCacheStrategy.NAIVE], + SupportedEncoding.float32: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + SupportedEncoding.bfloat16: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + }, + example_repo_ids=[ + "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct", + "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct", + "LGAI-EXAONE/EXAONE-3.5-32B-Instruct", + ], + pipeline_model=Llama3Model, + tokenizer=TextTokenizer, + rope_type=RopeType.neox, + default_weights_format=WeightsFormat.gguf, + weight_adapters={ + WeightsFormat.safetensors: convert_exaone_safetensor_state_dict, + WeightsFormat.gguf: weight_adapters.convert_gguf_state_dict, + }, +) diff --git a/src/max/pipelines/architectures/exaone/weight_adapters.py b/src/max/pipelines/architectures/exaone/weight_adapters.py new file mode 100644 index 0000000000..39fcd27b0b --- /dev/null +++ b/src/max/pipelines/architectures/exaone/weight_adapters.py @@ -0,0 +1,56 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from max.graph.weights import WeightData, Weights +from transformers import LlamaConfig + +from ..llama3.weight_adapters import _compute_safetensor_rope_scaling + +# Maps Exaone Safetensor to MAX weight names. +EXAONE_SAFETENSOR_MAPPING = { + "transformer.wte": "embed_tokens", + "transformer.h": "layers", + "mlp.c_fc_1": "mlp.up_proj", + "mlp.c_proj": "mlp.down_proj", + "mlp.c_fc_0": "mlp.gate_proj", + "ln_2": "post_attention_layernorm", + "ln_1": "input_layernorm", + "attn.attention.q_proj": "self_attn.q_proj", + "attn.attention.v_proj": "self_attn.v_proj", + "attn.attention.k_proj": "self_attn.k_proj", + "attn.attention.out_proj": "self_attn.o_proj", + "transformer.ln_f": "norm", +} + + +def convert_exaone_safetensor_state_dict( + state_dict: dict[str, Weights], + huggingface_config: LlamaConfig, + **unused_kwargs, +) -> dict[str, WeightData]: + new_state_dict: dict[str, WeightData] = {} + # Map the weight names. + for safetensor_name, value in state_dict.items(): + max_name = safetensor_name + for before, after in EXAONE_SAFETENSOR_MAPPING.items(): + max_name = max_name.replace(before, after) + new_state_dict[max_name] = value.data() + # Add rope scaling to the state dict. + rope_scaling = _compute_safetensor_rope_scaling(huggingface_config) + if rope_scaling is not None: + new_state_dict["rope_freqs.weight"] = WeightData.from_numpy( + rope_scaling, "rope_freqs.weight" + ) + return new_state_dict diff --git a/src/max/pipelines/architectures/granite/__init__.py b/src/max/pipelines/architectures/granite/__init__.py new file mode 100644 index 0000000000..95f180a1e2 --- /dev/null +++ b/src/max/pipelines/architectures/granite/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import granite_arch + +__all__ = ["granite_arch"] diff --git a/src/max/pipelines/architectures/granite/arch.py b/src/max/pipelines/architectures/granite/arch.py new file mode 100644 index 0000000000..e8d4fb19de --- /dev/null +++ b/src/max/pipelines/architectures/granite/arch.py @@ -0,0 +1,55 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + RopeType, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from ..llama3 import weight_adapters +from .model import GraniteModel + +granite_arch = SupportedArchitecture( + name="GraniteForCausalLM", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=[ + "ibm-granite/granite-3.1-8b-instruct", + "ibm-granite/granite-3.1-8b-base", + ], + default_weights_format=WeightsFormat.gguf, + default_encoding=SupportedEncoding.float32, + supported_encodings={ + SupportedEncoding.float32: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + SupportedEncoding.bfloat16: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + }, + pipeline_model=GraniteModel, + tokenizer=TextTokenizer, + rope_type=RopeType.normal, + weight_adapters={ + WeightsFormat.safetensors: weight_adapters.convert_safetensor_state_dict, + WeightsFormat.gguf: weight_adapters.convert_gguf_state_dict, + }, +) diff --git a/src/max/pipelines/architectures/granite/model.py b/src/max/pipelines/architectures/granite/model.py new file mode 100644 index 0000000000..a3dc42da60 --- /dev/null +++ b/src/max/pipelines/architectures/granite/model.py @@ -0,0 +1,38 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from max.engine import InferenceSession +from max.graph import ops +from max.pipelines import PipelineConfig + +from ..llama3.model import Llama3Model + + +class GraniteModel(Llama3Model): + """Granite pipeline model implementation.""" + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) + + logits_scaling = getattr( + self.pipeline_config.huggingface_config, "logits_scaling", 1.0 + ) + + if logits_scaling != 1.0: + self.logits_processor = lambda logits: logits / ops.constant( + logits_scaling, logits.dtype + ) diff --git a/src/max/pipelines/architectures/llama3/README.md b/src/max/pipelines/architectures/llama3/README.md new file mode 100644 index 0000000000..dfff0ded91 --- /dev/null +++ b/src/max/pipelines/architectures/llama3/README.md @@ -0,0 +1,134 @@ +# Llama 3.1 + +**Language:** Python + +**API**: MAX Graph + +This pipeline provides optimized support for the `LlamaForCausalLM` family +of large language models, as exemplified by the Llama 3.1 text completion +model. The model itself has been constructed in Python +using the [MAX Graph API](https://docs.modular.com/max/graph/). + +The MAX Graph API provides an accessible interface to the construction of +flexible accelerated compute graphs, which are then optimized by the MAX +Engine's advanced graph compiler. This pipeline showcases how a large language +model can be fully defined using Python and MAX Graphs and then compiled for +optimal inference performance via the MAX Engine. + +## Model + +[Llama 3.1](https://llama.meta.com/llama3/) is an open source large language +model released by Meta. The structure of this implementation was inspired by +Andrej Karpathy's [llama2.c](https://github.com/karpathy/llama2.c) and its [Mojo +port by Aydyn Tairov](https://github.com/tairov/llama2.mojo). + +The text completion demo is compatible with the the official Llama 3 +[text completion demo](https://github.com/meta-llama/llama3/blob/14aab0428d3ec3a9596f1dea06d9c564f9c0e35f/example_text_completion.py). + +The default settings for this pipeline use the 8B set of pretrained weights in +`q4_k` quantized encodings. + +## Usage + +The easiest way to try out this pipeline is with our Magic command-line tool. + +1. Install Magic on macOS and Ubuntu with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Clone the MAX examples repository: + + If you don't already have a local clone of this repository, create one via: + + ```shell + git clone https://github.com/modular/max.git + ``` + + The following instructions assume that you're present within this pipeline's + directory, and you can change to it after cloning: + + ```shell + cd max/src/max/pipelines + ``` + +3. Now run the Llama 3.1 text completion demo with the following command: + + ```shell + magic run generate --model-path="modularai/Llama-3.1-8B-Instruct-GGUF" --prompt "I believe the meaning of life is" + ``` + +4. Host a chat completion endpoint via MAX Serve. + + MAX Serve provides functionality to host performant OpenAI compatible + endpoints using the FastAPI framework. + + You can configure the pipeline to be hosted by using the `--serve` argument. + For example: + + ```shell + magic run serve --model-path="modularai/Llama-3.1-8B-Instruct-GGUF" + ``` + + A request can be submitted via a cURL command. + + ```shell + curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "modularai/llama-3.1", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who won the world series in 2020?"} + ] + }' + ``` + + Additionally, finetuned weights hosted on Hugging Face for any compatible + `LlamaForCausalLM` model can be used with this optimized architecture + via the `serve` command: + + ```shell + magic run serve --model-path=meta-llama/Llama-3.2-1B + ``` + +## Options + +The following command-line options are available to customize operation of the +pipeline: + +- `--max-length`: Controls the maximum length of the text sequence (includes the + input tokens). Defaults to huggingface model config's max sequence length. +- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 + value is provided, the model will continue to generate tokens for the entire + context length. (Default value: -1) +- `--prompt`: The text prompt to use for further generation. +- `--quantization-encoding`: The encoding to use for a datatype that can be + quantized to a low bits per weight format. The options for quantized formats + will download and cache default weights, but `float32` requires the use of + `--weight-path` to specify locally downloaded full-precision weights for use + in the model. + Valid values: `q4_0`, `q4_k`, `q6_k`, `bfloat16`, `float32`. + (Default value: `float32`). +- `--save-to-serialized-model-path`: If specified, writes the serialized model + to this path. +- `--serialized-model-path`: If specified, tries to load a serialized model + from this path. +- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. +- `--version`: Selects which version in the Llama 3 family to use. + Valid values: `3`, `3.1`. + (Default value: `3.1`) +- `--weight-path`: Overrides the default URL, and allows for an + already-downloaded pretrained weight file to be used with the model. +- `--max-batch-size`: Specifies the maximum batch size to be used. + Default is 1. +- `--devices`: Specifies the device(s) to use for the model. This can optionally + be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". + Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/llama3/__init__.py b/src/max/pipelines/architectures/llama3/__init__.py new file mode 100644 index 0000000000..a70f69fa33 --- /dev/null +++ b/src/max/pipelines/architectures/llama3/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import llama_arch + +__all__ = ["llama_arch"] diff --git a/src/max/pipelines/architectures/llama3/arch.py b/src/max/pipelines/architectures/llama3/arch.py new file mode 100644 index 0000000000..2bdd4dd5c2 --- /dev/null +++ b/src/max/pipelines/architectures/llama3/arch.py @@ -0,0 +1,66 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + RopeType, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from . import weight_adapters +from .model import Llama3Model + +llama_arch = SupportedArchitecture( + name="LlamaForCausalLM", + example_repo_ids=[ + "meta-llama/Llama-3.1-8B-Instruct", + "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", + "meta-llama/Llama-Guard-3-8B", + "meta-llama/Llama-3.2-1B-Instruct", + "meta-llama/Llama-3.2-3B-Instruct", + "deepseek-ai/deepseek-coder-6.7b-instruct", + "modularai/llama-3.1", + ], + default_encoding=SupportedEncoding.q4_k, + supported_encodings={ + SupportedEncoding.gptq: [ + KVCacheStrategy.PAGED, + ], + SupportedEncoding.q4_k: [KVCacheStrategy.NAIVE], + SupportedEncoding.q4_0: [KVCacheStrategy.NAIVE], + SupportedEncoding.q6_k: [KVCacheStrategy.NAIVE], + SupportedEncoding.float32: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + SupportedEncoding.bfloat16: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + }, + pipeline_model=Llama3Model, + tokenizer=TextTokenizer, + rope_type=RopeType.normal, + default_weights_format=WeightsFormat.safetensors, + weight_adapters={ + WeightsFormat.safetensors: weight_adapters.convert_safetensor_state_dict, + WeightsFormat.gguf: weight_adapters.convert_gguf_state_dict, + }, + task=PipelineTask.TEXT_GENERATION, +) diff --git a/src/max/pipelines/architectures/llama3/config.py b/src/max/pipelines/architectures/llama3/config.py new file mode 100644 index 0000000000..50f0849b0c --- /dev/null +++ b/src/max/pipelines/architectures/llama3/config.py @@ -0,0 +1,54 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""All configurable parameters for Llama3.""" + +from __future__ import annotations + +from max.pipelines import HuggingFaceFile, SupportedEncoding + + +def get_llama_huggingface_file( + version: str, encoding: SupportedEncoding, revision: str | None = None +) -> HuggingFaceFile: + if version == "3": + filenames = { + SupportedEncoding.bfloat16: "llama-3-8b-instruct-bf16.gguf", + SupportedEncoding.float32: "llama-3-8b-f32.gguf", + SupportedEncoding.q4_k: "llama-3-8b-instruct-q4_k_m.gguf", + SupportedEncoding.q4_0: "llama-3-8b-instruct-q4_0.gguf", + SupportedEncoding.q6_k: "llama-3-8b-instruct-q6_k.gguf", + } + filename = filenames.get(encoding) + if filename is None: + raise ValueError( + f"encoding does not have default hf file: {encoding}" + ) + return HuggingFaceFile("modularai/llama-3", filename, revision) + + elif version == "3.1": + filenames = { + SupportedEncoding.bfloat16: "llama-3.1-8b-instruct-bf16.gguf", + SupportedEncoding.float32: "llama-3.1-8b-instruct-f32.gguf", + SupportedEncoding.q4_k: "llama-3.1-8b-instruct-q4_k_m.gguf", + SupportedEncoding.q4_0: "llama-3.1-8b-instruct-q4_0.gguf", + SupportedEncoding.q6_k: "llama-3.1-8b-instruct-q6_k.gguf", + } + filename = filenames.get(encoding) + if filename is None: + raise ValueError( + f"encoding does not have default hf file: {encoding}" + ) + return HuggingFaceFile("modularai/llama-3.1", filename, revision) + + else: + raise ValueError(f"version {version} not supported for llama") diff --git a/src/max/pipelines/architectures/llama3/gguf.py b/src/max/pipelines/architectures/llama3/gguf.py new file mode 100644 index 0000000000..97255112b2 --- /dev/null +++ b/src/max/pipelines/architectures/llama3/gguf.py @@ -0,0 +1,468 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Build a Llama3 model via Graph API from GGUF weights.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import List, Literal, Optional + +import numpy as np +from max.dtype import DType +from max.graph import ( + DeviceRef, + Graph, + TensorValue, + TensorValueLike, + ops, +) +from max.graph.quantization import QuantizationEncoding +from max.graph.weights import Weights +from max.pipelines import PipelineConfig, RopeType, WeightsFormat +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + KVCacheParams, + KVCacheStrategy, +) +from max.pipelines.nn import ( + MLP, + AttentionWithRope, + DistributedAttentionWithRope, + DistributedMLP, + DistributedRMSNorm, + DistributedTransformer, + DistributedTransformerBlock, + Embedding, + Linear, + LinearV2, + OptimizedRotaryEmbedding, + RMSNorm, + VocabParallelEmbedding, +) +from max.pipelines.nn.layer import Layer + + +def distribute_value( + v: TensorValue, devices: List[DeviceRef] +) -> List[TensorValue]: + return [v.to(device) for device in devices] + + +def shard_col_value( + x: TensorValueLike, devices: List[DeviceRef] +) -> List[TensorValue]: + n_devices = len(devices) + v = TensorValue(x) + col_size = int(v.shape[1]) // n_devices + return [ + v[:, i * col_size : (i + 1) * col_size].to(device) + for i, device in enumerate(devices) + ] + + +def shard_row_value( + x: TensorValueLike, devices: List[DeviceRef] +) -> List[TensorValue]: + n_devices = len(devices) + v = TensorValue(x) + row_size = int(v.shape[0]) // n_devices + return [ + v[i * row_size : (i + 1) * row_size, :].to(device) + for i, device in enumerate(devices) + ] + + +@dataclass +class Phi3MLP(Layer): + """ + A multi-layer perceptron composed of two linear layers. Where the + gate_up_proj is a stacked linear layer which contains the up_proj and + gate_proj. This is used by the Phi3 models. + """ + + gate_up_proj: Linear + down_proj: Linear + + def __call__(self, x: TensorValue) -> TensorValue: + up_states = self.gate_up_proj(x) + + gate = up_states[:, : up_states.shape.static_dims[0] // 2] + up_states = up_states[:, up_states.shape.static_dims[0] // 2 :] + + return self.down_proj(ops.silu(gate) * up_states) + + +def rms_norm(dims: int, eps: float, weights: Weights) -> RMSNorm: + return RMSNorm(weights.weight.allocate(DType.float32, [dims]), eps) + + +@dataclass +class LayerNorm(Layer): + """Layer normalization block.""" + + gamma: TensorValue + beta: TensorValue + eps: float = 1e-5 + + def __call__(self, input: TensorValue): + return ops.cast( + ops.layer_norm( + ops.cast(input, DType.float32), + gamma=ops.cast(self.gamma, DType.float32), + beta=ops.cast(self.beta, DType.float32), + epsilon=self.eps, + ), + input.dtype, + ) + + +def layer_norm(dims: int, eps: float = 1e-5) -> LayerNorm: + gamma = ops.constant(np.ones(dims), DType.float32) + beta = ops.constant(np.zeros(dims), DType.float32) + return LayerNorm(gamma, beta, eps=eps) + + +def norm( + norm_method: Literal["rms_norm"] | Literal["layer_norm"], + pipeline_config: PipelineConfig, + weights: Weights, + weight_name: str, +) -> RMSNorm | LayerNorm: + if norm_method == "rms_norm": + if pipeline_config.huggingface_config.model_type == "exaone": + rms_norm_eps = pipeline_config.huggingface_config.layer_norm_epsilon + else: + rms_norm_eps = pipeline_config.huggingface_config.rms_norm_eps + + return rms_norm( + pipeline_config.huggingface_config.hidden_size, + rms_norm_eps, + weights[weight_name], + ) + else: + return layer_norm(pipeline_config.huggingface_config.hidden_size) + + +def distributed_norm( + norm_method: Literal["rms_norm"] | Literal["layer_norm"], + pipeline_config: PipelineConfig, + weights: Weights, + weight_name: str, + devices: List[DeviceRef], +) -> DistributedRMSNorm: + assert norm_method == "rms_norm" + + weights_ = TensorValue( + weights[weight_name].weight.allocate( + DType.float32, + [pipeline_config.huggingface_config.hidden_size], + ) + ) + weights_devs = distribute_value(weights_, devices) + + rms_norms = [ + RMSNorm(weights_dev, pipeline_config.huggingface_config.rms_norm_eps) + for weights_dev in weights_devs + ] + + return DistributedRMSNorm(rms_norms, devices) + + +def embedding( + pipeline_config: PipelineConfig, + vocab_size: int, + hidden_dim: int, + weights: Weights, +) -> Embedding: + if pipeline_config.quantization_encoding == "gptq": + return Embedding( + weights.weight.allocate( + DType.bfloat16, + [vocab_size, hidden_dim], + None, + ) + ) + + else: + return Embedding( + weights.weight.allocate( + pipeline_config.dtype, + [vocab_size, hidden_dim], + pipeline_config.graph_quantization_encoding, + ) + ) + + +def distributed_feed_forward( + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + hidden_dim: int, + feed_forward_length: int, + weights: Weights, + devices: List[DeviceRef], +) -> DistributedMLP: + w_ffn_down_full = weights.ffn_down.weight.allocate( + dtype, [hidden_dim, feed_forward_length], quantization_encoding + ) + ffn_down_sharded = shard_col_value(w_ffn_down_full, devices) + w_ffn_gate_full = weights.ffn_gate.weight.allocate( + dtype, [feed_forward_length, hidden_dim], quantization_encoding + ) + ffn_gate_sharded = shard_row_value(w_ffn_gate_full, devices) + w_ffn_up_full = weights.ffn_up.weight.allocate( + dtype, [feed_forward_length, hidden_dim], quantization_encoding + ) + ffn_up_sharded = shard_row_value(w_ffn_up_full, devices) + + mlps = [ + MLP( + Linear(ffn_gate_sharded[rank]), + Linear(ffn_down_sharded[rank]), + Linear(ffn_up_sharded[rank]), + ) + for rank in range(len(devices)) + ] + + return DistributedMLP(mlps, len(devices)) + + +def distributed_attention_opaque( + kv_params: KVCacheParams, + pipeline_config: PipelineConfig, + rope: OptimizedRotaryEmbedding, + weights: Weights, + layer_idx: TensorValue, + devices: List[DeviceRef], +) -> DistributedAttentionWithRope: + wq_full = ops.transpose( + weights.attn_q.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.hidden_size, + pipeline_config.huggingface_config.hidden_size, + ], + pipeline_config.graph_quantization_encoding, + ), + 0, + 1, + ) + kv_weight_dim = ( + pipeline_config.huggingface_config.hidden_size + // pipeline_config.huggingface_config.num_attention_heads + ) * pipeline_config.huggingface_config.num_key_value_heads + wk_full = ops.transpose( + weights.attn_k.weight.allocate( + pipeline_config.dtype, + [kv_weight_dim, pipeline_config.huggingface_config.hidden_size], + pipeline_config.graph_quantization_encoding, + ), + 0, + 1, + ) + wv_full = ops.transpose( + weights.attn_v.weight.allocate( + pipeline_config.dtype, + [kv_weight_dim, pipeline_config.huggingface_config.hidden_size], + pipeline_config.graph_quantization_encoding, + ), + 0, + 1, + ) + + wo_full = weights.attn_output.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.hidden_size, + pipeline_config.huggingface_config.hidden_size, + ], + pipeline_config.graph_quantization_encoding, + ) + wq_shards = shard_col_value(wq_full, devices) + wk_shards = shard_col_value(wk_full, devices) + wv_shards = shard_col_value(wv_full, devices) + + # Didn't transpose here since linear will transpose so shard on col instead + # of row + wo_shards = shard_col_value(wo_full, devices) + attns = [ + AttentionWithRope( + n_heads=pipeline_config.huggingface_config.num_attention_heads + // len(devices), + kv_params=kv_params, + wqkv=ops.concat( + (wq_shards[rank], wk_shards[rank], wv_shards[rank]), axis=1 + ).transpose(0, 1), + wo=Linear(wo_shards[rank]), + rope=rope, + layer_idx=layer_idx, + scale=math.sqrt(1.0 / kv_params.head_dim), + ) + for rank in range(len(devices)) + ] + + return DistributedAttentionWithRope(attns, devices) + + +def _kv_collection_constructor( + kv_params: KVCacheParams, +) -> FetchContinuousBatchingKVCacheCollection | FetchPagedKVCacheCollection: + """Gets the fetch KV collection based on the KV cache strategy. + + Returns: + Callable that stages an op to fetch a KV cache collection. + + Raises: + ValueError: If the cache strategy is unsupported. + """ + if kv_params.cache_strategy == KVCacheStrategy.CONTINUOUS: + return FetchContinuousBatchingKVCacheCollection(kv_params) + elif kv_params.cache_strategy == KVCacheStrategy.PAGED: + return FetchPagedKVCacheCollection(kv_params) + + msg = f"Unsupported caching strategy {kv_params.cache_strategy}" + raise ValueError(msg) + + +def distributed_transformer_opaque( + graph: Graph, + pipeline_config: PipelineConfig, + weights: Weights, + max_seq_len: int, + kv_params: KVCacheParams, + norm_method: Literal["rms_norm"] | Literal["layer_norm"], +) -> DistributedTransformer: + devices = [ + DeviceRef(spec.device_type, spec.id) + for spec in pipeline_config.device_specs + ] + with graph: + if weights.rope_freqs.weight.exists(): + rope_scaling = weights.rope_freqs.weight.raw_tensor() + else: + rope_scaling = None + + interleaved_rope_weights = ( + pipeline_config.weights_format == WeightsFormat.gguf + and pipeline_config.rope_type == RopeType.normal + ) + partial_rotary_factor = getattr( + pipeline_config.huggingface_config, "partial_rotary_factor", 1 + ) + rope = OptimizedRotaryEmbedding( + dim=pipeline_config.huggingface_config.hidden_size, + n_heads=partial_rotary_factor + * pipeline_config.huggingface_config.num_attention_heads, + theta=pipeline_config.huggingface_config.rope_theta, + max_seq_len=max_seq_len, + rope_scaling=rope_scaling, + interleaved=interleaved_rope_weights, + ) + + layers = [ + DistributedTransformerBlock( + attention=distributed_attention_opaque( + kv_params, + pipeline_config, + rope, + weights.blk[i], + layer_idx=ops.constant(i, DType.uint32), + devices=devices, + ), + mlp=distributed_feed_forward( + pipeline_config.dtype, + pipeline_config.graph_quantization_encoding, + pipeline_config.huggingface_config.hidden_size, + pipeline_config.huggingface_config.intermediate_size, + weights.blk[i], + devices=devices, + ), + attention_norm=distributed_norm( + norm_method, + pipeline_config, + weights.blk[i], + "attn_norm", + devices=devices, + ), + mlp_norm=distributed_norm( + norm_method, + pipeline_config, + weights.blk[i], + "ffn_norm", + devices=devices, + ), + devices=devices, + ) + for i in range(pipeline_config.huggingface_config.num_hidden_layers) + ] + + # TODO(max.nn.Model): We still rely on the `Weights` mechanism for + # constructing the Python-side weights registry. + # So we have to "allocate" a spot in the weights registry for + # output/embedding weights here. + embedding_weight = weights.token_embd.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.vocab_size, + pipeline_config.huggingface_config.hidden_size, + ], + ) + weights.output.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.vocab_size, + pipeline_config.huggingface_config.hidden_size, + ], + ) + + embedding_layer = VocabParallelEmbedding( + vocab_size=pipeline_config.huggingface_config.vocab_size, + hidden_dim=pipeline_config.huggingface_config.hidden_size, + dtype=pipeline_config.dtype, + devices=devices, + # Use the embedding weight's name, which mismatches between + # Safetensors and GGUF Llama 3. + name=embedding_weight.name, + ) + + output = LinearV2( + in_dim=pipeline_config.huggingface_config.hidden_size, + out_dim=pipeline_config.huggingface_config.vocab_size, + dtype=pipeline_config.dtype, + # Only compute output embedding on device 0 for now. + # TODO(MODELS-378): More optimal would be to: + # - Shard embedding table across devices. + # - Compute output on all devices for multistep. + device=devices[0], + # Smaller model variants lack dedicated weights for a final linear + # layer, and share the embedding layer. + name=( + weights.output.name + if weights.output.weight.exists() + else embedding_layer.weight.name + ), + ) + + return DistributedTransformer( + dim=pipeline_config.huggingface_config.hidden_size, + n_heads=pipeline_config.huggingface_config.num_attention_heads, + layers=layers, + norm=norm(norm_method, pipeline_config, weights, "output_norm"), # type:ignore + output=output, + embedding=embedding_layer, + kv_params=kv_params, + kv_collection_constructor=_kv_collection_constructor(kv_params), + devices=devices, + all_logits=pipeline_config.enable_echo, + ) diff --git a/src/max/pipelines/architectures/llama3/llama3.py b/src/max/pipelines/architectures/llama3/llama3.py new file mode 100644 index 0000000000..a35e90e60a --- /dev/null +++ b/src/max/pipelines/architectures/llama3/llama3.py @@ -0,0 +1,198 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Build a Llama3 model that uses continuous or paged kv-caching""" + +from __future__ import annotations + +import functools +from typing import Callable, Literal, Optional + +import numpy as np +from max.dtype import DType +from max.graph import DeviceRef, TensorValue +from max.graph.quantization import QuantizationConfig, QuantizationEncoding +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + KVCacheParams, + KVCacheStrategy, +) +from max.pipelines.nn import ( + AttentionWithRopeV2, + EmbeddingV2, + GPTQAttentionWithRope, + GPTQLinearV2, + LayerV2, + LinearV2, + OptimizedRotaryEmbedding, + RMSNormV2, + Transformer, + TransformerBlock, +) + +from .naive_llama3 import ConstantLayerNorm, Llama3MLP, StackedMLP + + +class Llama3(Transformer): + def __init__( + self, + *, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: int, + num_hidden_layers: int, + rope_theta: float, + max_seq_len: int, + intermediate_size: int, + interleaved_rope_weights: bool, + rope_scaling: Optional[np.ndarray], + vocab_size: int, + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + quantization_config: Optional[QuantizationConfig], + kv_params: KVCacheParams, + all_logits: bool, + norm_method: Literal["rms_norm"] | Literal["layer_norm"], + rms_norm_eps: Optional[float], + tie_word_embeddings: bool, + stacked_mlp: bool, + stacked_qkv: bool, + logits_postprocessor: Callable[[TensorValue], TensorValue] | None, + attention_multiplier: float, + embedding_multiplier: float, + residual_multiplier: float, + devices: list[DeviceRef], + clip_qkv: Optional[float], + ): + rope = OptimizedRotaryEmbedding( + dim=hidden_size, + n_heads=num_attention_heads, + theta=rope_theta, + max_seq_len=max_seq_len, + rope_scaling=rope_scaling, + interleaved=interleaved_rope_weights, + ) + create_norm: Callable[..., LayerV2] + if norm_method == "rms_norm": + if rms_norm_eps is None: + raise ValueError( + "rms_norm_eps cannot be None for model that uses RMSNorm." + ) + create_norm = functools.partial( + RMSNormV2, hidden_size, rms_norm_eps + ) + else: + create_norm = functools.partial(ConstantLayerNorm, hidden_size) + + linear_cls: Callable[..., LinearV2] + if quantization_config: + linear_cls = functools.partial( + GPTQLinearV2, quantization_config=quantization_config + ) + else: + linear_cls = LinearV2 + mlp_cls = StackedMLP if stacked_mlp else Llama3MLP + attention_cls: Callable[..., AttentionWithRopeV2] + if quantization_config: + attention_cls = functools.partial( + GPTQAttentionWithRope, + quantization_config=quantization_config, + scale=attention_multiplier, + ) + else: + attention_cls = functools.partial( + AttentionWithRopeV2, + stacked_qkv=stacked_qkv, + scale=attention_multiplier, + clip_qkv=clip_qkv, + ) + + layers = [ + TransformerBlock( + attention=attention_cls( + num_attention_heads=num_attention_heads, + num_key_value_heads=num_key_value_heads, + hidden_size=hidden_size, + kv_params=kv_params, + layer_idx=i, + dtype=dtype, + rope=rope, + linear_cls=linear_cls, + device=devices[0], + ), + mlp=mlp_cls( + dtype, + quantization_encoding, + hidden_size, + intermediate_size, + linear_cls, + # devices=devices, # TODO(kathywu): setting devices causes issues + ), + attention_norm=create_norm(), + mlp_norm=create_norm(), + residual_multiplier=residual_multiplier, + ) + for i in range(num_hidden_layers) + ] + + # Create Embedding and output layers. + embedding_output_dtype = dtype + embedding_output_quantization = quantization_encoding + if quantization_encoding == QuantizationEncoding.GPTQ: + embedding_output_dtype = DType.bfloat16 + embedding_output_quantization = None + + embedding_layer = EmbeddingV2( + vocab_size, + hidden_size, + embedding_output_dtype, + None, # TODO(kathywu): setting devices causes issues + quantization_encoding=embedding_output_quantization, + ) + output = LinearV2( + hidden_size, + vocab_size, + embedding_output_dtype, + None, # TODO(kathywu): setting devices causes issues + quantization_encoding=embedding_output_quantization, + ) + + if tie_word_embeddings: + output.set_shared_weight("weight", embedding_layer.weight) + + kv_collection_cls: ( + type[FetchContinuousBatchingKVCacheCollection] + | type[FetchPagedKVCacheCollection] + ) + if kv_params.cache_strategy == KVCacheStrategy.CONTINUOUS: + kv_collection_cls = FetchContinuousBatchingKVCacheCollection + elif kv_params.cache_strategy == KVCacheStrategy.PAGED: + kv_collection_cls = FetchPagedKVCacheCollection + else: + raise ValueError( + "Unsupported caching strategy " + str(kv_params.cache_strategy) + ) + + super().__init__( + dim=hidden_size, + n_heads=num_attention_heads, + layers=layers, + norm=create_norm(), + output=output, + embedding=embedding_layer, + kv_params=kv_params, + kv_collection_constructor=kv_collection_cls(kv_params), + all_logits=all_logits, + embedding_multiplier=embedding_multiplier, + logits_postprocessor=logits_postprocessor, + ) diff --git a/src/max/pipelines/architectures/llama3/model.py b/src/max/pipelines/architectures/llama3/model.py new file mode 100644 index 0000000000..eeff3afa16 --- /dev/null +++ b/src/max/pipelines/architectures/llama3/model.py @@ -0,0 +1,817 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import logging +import math +import time +from typing import Any, Callable, List, Literal, Sequence, cast + +import numpy as np +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession, Model +from max.graph import DeviceRef, Graph, TensorType, TensorValue +from max.graph.weights import Weights +from max.pipelines import ( + LogProbabilities, + ModelInputs, + ModelOutputs, + PipelineConfig, + PipelineModel, + RopeType, + SupportedEncoding, + TextContext, + WeightsFormat, + upper_bounded_default, +) +from max.pipelines.dataprocessing import batch_padded_tokens_and_mask +from max.pipelines.kv_cache import ( + KVCacheInputs, + KVCacheManager, + KVCacheParams, + estimate_kv_cache_size, + load_kv_manager, +) +from max.pipelines.nn import Signals +from max.pipelines.nn.compute_log_probabilities import compute_log_probabilities + +from .gguf import distributed_transformer_opaque +from .llama3 import Llama3 +from .naive_llama3 import NaiveLlama3 +from .weight_adapters import LlamaSafetensorWeights + +logger = logging.getLogger("max.pipelines") + + +class Llama3Inputs(ModelInputs): + """A class representing inputs for the Llama3 model. + + This class encapsulates the input tensors required for the Llama3 model + execution. + """ + + tokens: np.ndarray | Tensor + """Tensor containing the input token IDs.""" + + input_row_offsets_or_attn_mask: np.ndarray | Tensor + """Tensor containing the offsets for each row in the ragged input sequence, + or the attention mask for the padded input sequence.""" + + signal_buffers: list[Tensor] + """Device buffers used for synchronization in communication collectives.""" + + def __init__( + self, + tokens: np.ndarray | Tensor, + input_row_offsets_or_attn_mask: np.ndarray | Tensor, + signal_buffers: list[Tensor], + ) -> None: + """ + Args: + tokens: Input token IDs. + input_row_offsets_or_attn_mask: Input row offsets (ragged tensors) + or attention mask (padded tensors). + signal_buffers: Device buffers used for synchronization in + communication collectives. + """ + self.tokens = tokens + self.input_row_offsets_or_attn_mask = input_row_offsets_or_attn_mask + self.signal_buffers = signal_buffers + + @property + def input_row_offsets(self) -> np.ndarray | Tensor: + """Gets the row offsets of the ragged input sequence.""" + # TODO(bduke): this should implement a ragged tensor interface. + return self.input_row_offsets_or_attn_mask + + +class LlamaModelBase(PipelineModel[TextContext]): + """Base Llama pipeline model implementation.""" + + model: Model + """Compiled and initialized model ready for inference.""" + + signal_buffers: list[Tensor] + """Device buffers used for synchronization in communication collectives.""" + + norm_method: Literal["rms_norm"] | Literal["layer_norm"] + """Normalization layer.""" + + logits_postprocessor: Callable[[TensorValue], TensorValue] | None = None + """Postprocessor for the logits.""" + + state_dict: dict[str, Any] + """Weights to load into the model.""" + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + """ + Args: + pipeline_config: The configuration for this pipeline. + session: The container for the runtime for this model. + """ + super().__init__(pipeline_config, session) + self.model = self.load_model(session) + + # Initialize state needed for communication collectives. + self.signal_buffers = ( + [ + Tensor.zeros( + shape=(Signals.NUM_BYTES,), + dtype=DType.uint8, + device=dev, + ) + for dev in pipeline_config.devices + ] + if len(pipeline_config.devices) > 1 + # Skip creating buffers for single-device, where communication + # collectives shouldn't be called. + else [] + ) + + @classmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + return KVCacheParams( + dtype=pipeline_config.cache_dtype, + n_kv_heads=pipeline_config.huggingface_config.num_key_value_heads, + head_dim=( + pipeline_config.huggingface_config.hidden_size + // pipeline_config.huggingface_config.num_attention_heads + ), + page_size=pipeline_config.kv_cache_page_size, + cache_strategy=pipeline_config.cache_strategy, + enable_prefix_caching=pipeline_config.enable_prefix_caching, + n_devices=len(pipeline_config.devices), + ) + + @classmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + return pipeline_config.huggingface_config.num_hidden_layers + + def execute( + self, + model_inputs: ModelInputs, + kv_cache_inputs: KVCacheInputs | None = None, + ) -> ModelOutputs: + model_inputs = cast(Llama3Inputs, model_inputs) + if kv_cache_inputs is None: + kv_cache_inputs = () # type: ignore + model_outputs = self.model.execute( + model_inputs.tokens, + model_inputs.input_row_offsets_or_attn_mask, + *model_inputs.signal_buffers, + *kv_cache_inputs, # type: ignore + copy_inputs_to_device=( + not self.pipeline_config.cache_strategy.uses_opaque() + ), + ) + + if self.pipeline_config.enable_echo: + return ModelOutputs( + next_token_logits=cast(Tensor, model_outputs[0]), + logits=cast(Tensor, model_outputs[1]), + ) + else: + return ModelOutputs( + next_token_logits=cast(Tensor, model_outputs[0]) + ) + + def _prepare_ragged_initial_token_inputs( + self, context_batch: Sequence[TextContext] + ) -> Llama3Inputs: + # Get input_row_offsets: start and end position of each batch in the + # combined total_seq_len dimension. + input_row_offsets = np.cumsum( + [0] + [ctx.active_length for ctx in context_batch], + dtype=np.uint32, + ) + + # Create a ragged token vector of length: sum(len(t) for t in tokens). + tokens = np.concatenate([ctx.next_tokens for ctx in context_batch]) + + return Llama3Inputs( + tokens=Tensor.from_numpy(tokens).to( + self.pipeline_config.devices[0] + ), + input_row_offsets_or_attn_mask=Tensor.from_numpy( + input_row_offsets + ).to(self.pipeline_config.devices[0]), + signal_buffers=self.signal_buffers, + ) + + def _prepare_padded_initial_token_inputs( + self, context_batch: Sequence[TextContext] + ) -> Llama3Inputs: + # Get tokens and seq_ids + tokens = [ctx.next_tokens for ctx in context_batch] + + # Pad tokens and compute attention mask for the batch. + max_seq_len = self.kv_manager.max_sequence_length + start_pos = [max_seq_len] * len(context_batch) + next_tokens_batch, _, attn_mask = batch_padded_tokens_and_mask( + start_pos=start_pos, + tokens=tokens, + pad_to_multiple_of=self.pipeline_config.pad_to_multiple_of, + ) + + return Llama3Inputs( + tokens=next_tokens_batch, + input_row_offsets_or_attn_mask=attn_mask, + signal_buffers=self.signal_buffers, + ) + + def prepare_initial_token_inputs( + self, context_batch: Sequence[TextContext] + ) -> Llama3Inputs: + """Prepare the inputs for the first pass in multistep execution.""" + if self.pipeline_config.cache_strategy.uses_opaque(): + return self._prepare_ragged_initial_token_inputs(context_batch) + else: + return self._prepare_padded_initial_token_inputs(context_batch) + + def _prepare_ragged_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: Llama3Inputs, + ) -> Llama3Inputs: + row_offsets_size = ( + prev_model_inputs.input_row_offsets_or_attn_mask.shape[0] + ) + next_row_offsets = self._input_row_offsets_prealloc[:row_offsets_size] + + return Llama3Inputs( + tokens=next_tokens, + input_row_offsets_or_attn_mask=next_row_offsets, + signal_buffers=self.signal_buffers, + ) + + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: ModelInputs, + ) -> Llama3Inputs: + """Prepare the inputs for the next token in multistep execution. + This should avoid any device synchronization or copy operations. + """ + prev_model_inputs = cast(Llama3Inputs, prev_model_inputs) + if self.pipeline_config.cache_strategy.uses_opaque(): + return self._prepare_ragged_next_token_inputs( + next_tokens, prev_model_inputs + ) + else: + # TODO(MODELS-407): Consider deleting the padded path entirely. + msg = "multistep unsupported for padded token batches" + raise ValueError(msg) + + @classmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + try: + return upper_bounded_default( + upper_bound=pipeline_config.huggingface_config.max_position_embeddings, + default=pipeline_config.max_length, + ) + except ValueError as e: + msg = ( + "Unable to infer max_length for Llama3, the provided " + f"max_length ({pipeline_config.max_length}) exceeds the " + f"model's max_position_embeddings " + f"({pipeline_config.huggingface_config.max_position_embeddings})." + ) + raise ValueError(msg) from e + + def load_kv_manager( + self, + session: InferenceSession, + available_cache_memory: int, + ) -> KVCacheManager: + return load_kv_manager( + params=self.get_kv_params(self.pipeline_config), + max_batch_size=self.pipeline_config.max_batch_size, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + num_layers=self.pipeline_config.huggingface_config.num_hidden_layers, + devices=self.pipeline_config.devices, + available_cache_memory=available_cache_memory, + page_size=self.pipeline_config.kv_cache_page_size, + session=session, + ) + + @classmethod + def estimate_kv_cache_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + devices: List[Device], + ) -> int: + """Estimates the size of the kv cache in bytes.""" + return estimate_kv_cache_size( + params=cls.get_kv_params(pipeline_config), + max_batch_size=pipeline_config.max_batch_size, + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=pipeline_config.huggingface_config.num_hidden_layers, + available_cache_memory=available_cache_memory, + devices=devices, + ) + + def load_model( + self, + session: InferenceSession, + ) -> Model: + # Pre-allocate a buffer for input_row_offsets in multistep execution. + # We do this to avoid materializing and copying a buffer with each multistep step + assert self.pipeline_config.max_batch_size, ( + "Expected max_batch_size to be set" + ) + self._input_row_offsets_prealloc = Tensor.from_numpy( + np.arange(self.pipeline_config.max_batch_size + 1, dtype=np.uint32) + ).to(self.pipeline_config.devices[0]) + + # Read in weights. + self._weights = self.pipeline_config.load_weights() + + if serialized_path := self.pipeline_config.serialized_model_path: + # Hydrate all weights to be referenced by the serialized path. + weights_registry = {} + for name, weight in self._weights.items(): + weights_registry[name] = weight.raw_tensor() + + logger.info("Loading serialized model from %s", serialized_path) + + return session.load( + serialized_path, weights_registry=weights_registry + ) + + else: + logger.info("Building and compiling model...") + before = time.perf_counter() + graph = self._build_graph(self._weights) + model = session.load(graph, weights_registry=self.state_dict) + after = time.perf_counter() + logger.info( + f"Building and compiling model took {after - before:.6f} seconds" + ) + if ( + export_path + := self.pipeline_config.save_to_serialized_model_path + ): + logger.info("Exporting serialized model to %s", export_path) + model._export_mef(export_path) + return model + + def _unflatten_kv_inputs( + self, kv_inputs_flat: Sequence[TensorValue] + ) -> List[tuple[TensorValue, ...]]: + kv_params = self.get_kv_params(self.pipeline_config) + n_devices = kv_params.n_devices + fetch_types = self.kv_manager.input_symbols()[0] + len_of_kv_tuple_per_dev = len(list(fetch_types)) + kv_caches_per_dev = [ + tuple( + kv_inputs_flat[ + i * len_of_kv_tuple_per_dev : (i + 1) + * len_of_kv_tuple_per_dev + ] + ) + for i in range(n_devices) + ] + return kv_caches_per_dev + + @property + def _attention_multiplier(self) -> float: + """The attention multiplier is a scalar that scales the attention scores. + It is used to control the variance of the attention scores. + + This function is used to get the attention multiplier from the + huggingface config. If the attention multiplier is not set, it will be + calculated as the square root of 1.0 divided by the head dimension. + """ + return getattr( + self.pipeline_config.huggingface_config, + "attention_multiplier", + math.sqrt(1.0 / self.get_kv_params(self.pipeline_config).head_dim), + ) + + def _build_opaque_graph(self, weights: Weights) -> Graph: + device0 = self.pipeline_config.devices[0] + device_ref = DeviceRef(device0.label, device0.id) + tokens_type = TensorType( + DType.int64, shape=["total_seq_len"], device=device_ref + ) + # NOTE: input_row_offsets_len should be batch_size + 1. + input_row_offsets_type = TensorType( + DType.uint32, shape=["input_row_offsets_len"], device=device_ref + ) + + huggingface_config = self.pipeline_config.huggingface_config + + if len(self.pipeline_config.devices) > 1: + kv_cache_args = self.kv_manager.input_symbols() + flattened_kv_types = [ + kv_type for sublist in kv_cache_args for kv_type in sublist + ] + + # Create metadata for signal buffers. + signals = Signals( + devices=( + DeviceRef(d.label, d.id) + for d in self.pipeline_config.devices + ) + ) + + # Distributed Llama still uses GGUF weights, so make sure that + # safetensor weights are converted to GGUF. + if self.pipeline_config.weights_format == WeightsFormat.safetensors: + weights = LlamaSafetensorWeights.from_safetensor_weights( + weights, huggingface_config + ) + with Graph( + getattr(huggingface_config, "model_type", "llama3"), + input_types=[ + tokens_type, + input_row_offsets_type, + *signals.input_types(), + *flattened_kv_types, + ], + ) as graph: + distributed_model = distributed_transformer_opaque( + graph=graph, + pipeline_config=self.pipeline_config, + weights=weights, + max_seq_len=self.calculate_max_seq_len( + self.pipeline_config + ), + kv_params=self.get_kv_params(self.pipeline_config), + norm_method=self.norm_method, + ) + self.state_dict = weights.allocated_weights + tokens, input_row_offsets, *variadic_args = graph.inputs + + # Multi-GPU passes a signal buffer per device: unmarshal those. + signal_buffers = [ + v.buffer + for v in variadic_args[: len(self.pipeline_config.devices)] + ] + + # Unmarshal the remaining arguments, which are for KV cache. + kv_cache = [ + v.tensor + for v in variadic_args[len(self.pipeline_config.devices) :] + ] + + kv_caches_per_dev = self._unflatten_kv_inputs(kv_cache) + + outputs = distributed_model( + tokens.tensor, + signal_buffers, + kv_caches_per_dev, + input_row_offsets=input_row_offsets, + ) + graph.output(*outputs) + return graph + else: + adapter = self.pipeline_config._weight_adapters.get( + self.pipeline_config.weights_format + ) + if adapter: + state_dict = adapter( + dict(weights.items()), + huggingface_config=huggingface_config, + pipeline_config=self.pipeline_config, + ) + else: + state_dict = { + key: value.data() for key, value in weights.items() + } + + if ( + rope_freqs := state_dict.pop("rope_freqs.weight", None) + ) is not None: + rope_scaling = rope_freqs.data + else: + rope_scaling = None + + interleaved_rope_weights = ( + self.pipeline_config.weights_format == WeightsFormat.gguf + and self.pipeline_config.rope_type == RopeType.normal + ) + rms_norm_eps = None + if self.norm_method == "rms_norm": + if huggingface_config.model_type == "exaone": + rms_norm_eps = huggingface_config.layer_norm_epsilon + else: + rms_norm_eps = huggingface_config.rms_norm_eps + + device_refs = [ + DeviceRef(spec.device_type, spec.id) + for spec in self.pipeline_config.device_specs + ] + + # When tie_word_embeddings=True, the embedding weights are shared with + # the output weights. + tie_word_embeddings = ( + getattr(huggingface_config, "tie_word_embeddings", False) + or "lm_head.weight" not in state_dict + ) + embedding_multiplier = getattr( + huggingface_config, "embedding_multiplier", 1.0 + ) + residual_multiplier = getattr( + huggingface_config, "residual_multiplier", 1.0 + ) + nn_model = Llama3( + hidden_size=huggingface_config.hidden_size, + num_attention_heads=huggingface_config.num_attention_heads, + num_key_value_heads=huggingface_config.num_key_value_heads, + num_hidden_layers=huggingface_config.num_hidden_layers, + rope_theta=huggingface_config.rope_theta, + rms_norm_eps=rms_norm_eps, + intermediate_size=huggingface_config.intermediate_size, + interleaved_rope_weights=interleaved_rope_weights, + rope_scaling=rope_scaling, + vocab_size=huggingface_config.vocab_size, + dtype=self.pipeline_config.dtype, + quantization_encoding=self.pipeline_config.graph_quantization_encoding, + quantization_config=self.pipeline_config._quant_config, + all_logits=self.pipeline_config.enable_echo, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + kv_params=self.get_kv_params(self.pipeline_config), + norm_method=self.norm_method, + tie_word_embeddings=tie_word_embeddings, + stacked_mlp="layers.0.mlp.gate_up_proj.weight" in state_dict, + stacked_qkv="layers.0.self_attn.qkv_proj.weight" in state_dict, + logits_postprocessor=self.logits_postprocessor, + attention_multiplier=self._attention_multiplier, + embedding_multiplier=embedding_multiplier, + residual_multiplier=residual_multiplier, + devices=device_refs, + clip_qkv=getattr( + self.pipeline_config.huggingface_config, "clip_qkv", None + ), + ) + nn_model.load_state_dict(state_dict) + self.state_dict = nn_model.state_dict() + with Graph( + "llama3", + input_types=[ + tokens_type, + input_row_offsets_type, + *self.kv_manager.input_symbols()[0], + ], + ) as graph: + tokens, input_row_offsets, *kv_cache_inputs = graph.inputs + outputs = nn_model( + tokens.tensor, + [inp.tensor for inp in kv_cache_inputs], + input_row_offsets=input_row_offsets, + ) + graph.output(*outputs) + return graph + + def _build_graph(self, weights: Weights) -> Graph: + if self.pipeline_config.cache_strategy.uses_opaque(): + return self._build_opaque_graph(weights) + + tokens_type = TensorType(DType.int64, shape=["batch_size", "seq_len"]) + attn_mask_type = TensorType( + DType.float32, shape=["batch_size", "seq_len", "post_seq_len"] + ) + + if len(self.pipeline_config.devices) > 1: + raise ValueError( + "Naive mode does not support distributed execution" + ) + + kv_inputs = self.kv_manager.input_symbols()[0] + + interleaved_rope_weights = ( + self.pipeline_config.weights_format == WeightsFormat.gguf + and self.pipeline_config.rope_type == RopeType.normal + ) + adapter = self.pipeline_config._weight_adapters.get( + self.pipeline_config.weights_format + ) + huggingface_config = self.pipeline_config.huggingface_config + if adapter: + state_dict = adapter( + dict(weights.items()), + huggingface_config=huggingface_config, + pipeline_config=self.pipeline_config, + ) + else: + state_dict = {key: value.data() for key, value in weights.items()} + if ( + rope_freqs := state_dict.pop("rope_freqs.weight", None) + ) is not None: + rope_scaling = rope_freqs.data + else: + rope_scaling = None + + rms_norm_eps = None + if self.norm_method == "rms_norm": + if huggingface_config.model_type == "exaone": + rms_norm_eps = huggingface_config.layer_norm_epsilon + else: + rms_norm_eps = huggingface_config.rms_norm_eps + + device_refs = [ + DeviceRef(spec.device_type, spec.id) + for spec in self.pipeline_config.device_specs + ] + + # When tie_word_embeddings=True, the embedding weights are shared with + # the output weights. + tie_word_embeddings = ( + getattr(huggingface_config, "tie_word_embeddings", False) + or "lm_head.weight" not in state_dict + ) + embedding_multiplier = getattr( + huggingface_config, "embedding_multiplier", 1.0 + ) + residual_multiplier = getattr( + huggingface_config, "residual_multiplier", 1.0 + ) + nn_model = NaiveLlama3( + hidden_size=huggingface_config.hidden_size, + num_attention_heads=huggingface_config.num_attention_heads, + num_key_value_heads=huggingface_config.num_key_value_heads, + num_hidden_layers=huggingface_config.num_hidden_layers, + rope_theta=huggingface_config.rope_theta, + rms_norm_eps=rms_norm_eps, + intermediate_size=huggingface_config.intermediate_size, + interleaved_rope_weights=interleaved_rope_weights, + rope_scaling=rope_scaling, + vocab_size=huggingface_config.vocab_size, + dtype=self.pipeline_config.dtype, + quantization_encoding=self.pipeline_config.graph_quantization_encoding, + quantization_config=self.pipeline_config._quant_config, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + kv_params=self.get_kv_params(self.pipeline_config), + norm_method=self.norm_method, + tie_word_embeddings=tie_word_embeddings, + stacked_mlp="layers.0.mlp.gate_up_proj.weight" in state_dict, + stacked_qkv="layers.0.self_attn.qkv_proj.weight" in state_dict, + logits_postprocessor=self.logits_postprocessor, + attention_multiplier=self._attention_multiplier, + embedding_multiplier=embedding_multiplier, + residual_multiplier=residual_multiplier, + devices=device_refs, + clip_qkv=getattr( + self.pipeline_config.huggingface_config, "clip_qkv", None + ), + ) + + # Load weights. We allow the weight types to be overriden due to + # multiple quantization enodings in GGUF checkpoints. + nn_model.load_state_dict( + state_dict, override_quantization_encoding=True + ) + self.state_dict = nn_model.state_dict() + + with Graph( + getattr(huggingface_config, "model_type", "llama3"), + input_types=[ + tokens_type, + attn_mask_type, + *kv_inputs, + ], + ) as graph: + tokens, attention_mask, k_cache, v_cache, start_pos, _ = ( + graph.inputs + ) + mask_dtype = ( + self.pipeline_config.dtype + if self.pipeline_config.quantization_encoding + in [ + SupportedEncoding.float32, + SupportedEncoding.bfloat16, + ] + else ( + DType.float32 + if self.pipeline_config.devices[0].label == "cpu" + else DType.bfloat16 + ) + ) + logits = nn_model( + tokens.tensor, + attention_mask.tensor.cast(mask_dtype), + k_cache.buffer, + v_cache.buffer, + start_pos.tensor, + )[0] + + if self.pipeline_config.enable_echo: + graph.output(logits[:, -1], logits) + else: + graph.output(logits[:, -1]) + + return graph + + def compute_log_probabilities( + self, + model_inputs: ModelInputs, + model_outputs: ModelOutputs, + next_tokens: Tensor, + batch_top_n: list[int], + batch_echo: list[bool], + ) -> list[LogProbabilities | None] | None: + if any(echo for echo in batch_echo): + if model_outputs.logits is None: + logger.warning( + "Could not get logprobs with echo because the full logits" + f" were not returned by {self.pipeline_config.model_path}" + " model. Please ensure that this model is started with " + "`--enable-echo`." + ) + assert not self.pipeline_config.enable_echo, ( + "Echo was enabled but logits were not returned." + ) + return None + logits = model_outputs.logits.to_numpy() + + llama3_inputs = cast(Llama3Inputs, model_inputs) + next_token_logits = cast( + Tensor, model_outputs.next_token_logits + ).to_numpy() + + sampled_tokens = next_tokens.to_numpy() + if self.pipeline_config.cache_strategy.uses_opaque(): + # Handle the ragged inputs + tokens = cast(Tensor, llama3_inputs.tokens).to_numpy() + input_row_offsets = cast( + Tensor, llama3_inputs.input_row_offsets + ).to_numpy() + + def _get_logits_and_samples( + batch_index: int, echo: bool + ) -> tuple[np.ndarray, np.ndarray]: + if echo: + start_offset = input_row_offsets[batch_index] + end_offset = input_row_offsets[batch_index + 1] + batch_logits = logits[start_offset:end_offset] + samples = np.concatenate( + ( + tokens[start_offset + 1 : end_offset], + sampled_tokens[batch_index : batch_index + 1], + ) + ) + else: + batch_logits = next_token_logits[ + batch_index : batch_index + 1 + ] + samples = sampled_tokens[batch_index : batch_index + 1] + return batch_logits, samples + + else: + # Handle batched inputs. Llama pads them to the right so the seq + # lengths can be computed by finding the first 0 token. + tokens = cast(np.ndarray, llama3_inputs.tokens) + seq_lens = np.sum(tokens > 0, axis=1) + + def _get_logits_and_samples( + batch_index: int, echo: bool + ) -> tuple[np.ndarray, np.ndarray]: + if echo: + seq_len = seq_lens[batch_index] + padded_tokens = tokens[batch_index] + + batch_logits = logits[batch_index, :seq_len, :] + samples = np.concatenate( + ( + padded_tokens[1:seq_len], + sampled_tokens[batch_index : batch_index + 1], + ) + ) + else: + batch_logits = next_token_logits[ + batch_index : batch_index + 1, : + ] + samples = sampled_tokens[batch_index : batch_index + 1] + return batch_logits, samples + + return compute_log_probabilities( + _get_logits_and_samples, batch_top_n, batch_echo + ) + + +class Llama3Model(LlamaModelBase): + """Llama 3 pipeline model implementation.""" + + norm_method: Literal["rms_norm"] | Literal["layer_norm"] = "rms_norm" + """Normalization layer.""" + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) diff --git a/src/max/pipelines/architectures/llama3/naive_llama3.py b/src/max/pipelines/architectures/llama3/naive_llama3.py new file mode 100644 index 0000000000..c79badcbde --- /dev/null +++ b/src/max/pipelines/architectures/llama3/naive_llama3.py @@ -0,0 +1,317 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Builds a Llama3 model that uses naive KV-caching.""" + +from __future__ import annotations + +import functools +from collections.abc import Sequence +from typing import Callable, Literal, Optional, Union + +import numpy as np +from max.dtype import DType +from max.graph import DeviceRef, TensorValue, ops +from max.graph.quantization import QuantizationConfig, QuantizationEncoding +from max.pipelines.kv_cache import KVCacheParams +from max.pipelines.nn import ( + MLPV2, + EmbeddingV2, + GPTQLinearV2, + LayerV2, + LinearV2, + NaiveAttentionWithRope, + NaiveTransformer, + NaiveTransformerBlock, + OptimizedRotaryEmbedding, + RMSNormV2, + RotaryEmbedding, +) + + +class ConstantLayerNorm(LayerV2): + """Layer normalization block with constant gamma and beta values.""" + + gamma: np.ndarray + beta: np.ndarray + eps: float = 1e-5 + + def __init__(self, dims, eps: float = 1e-5): + super().__init__() + self.gamma = np.ones(dims) + self.beta = np.zeros(dims) + self.eps = eps + + def __call__(self, input: TensorValue): + gamma = ops.constant(self.gamma, DType.float32) + beta = ops.constant(self.beta, DType.float32) + return ops.cast( + ops.layer_norm( + ops.cast(input, DType.float32), + gamma=gamma, + beta=beta, + epsilon=self.eps, + ), + input.dtype, + ) + + +class NaiveLlama3(NaiveTransformer): + def __init__( + self, + *, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: int, + num_hidden_layers: int, + rope_theta: float, + max_seq_len: int, + rms_norm_eps: Optional[float], + intermediate_size: int, + interleaved_rope_weights: bool, + rope_scaling: Optional[np.ndarray], + vocab_size: int, + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + quantization_config: Optional[QuantizationConfig], + kv_params: KVCacheParams, + norm_method: Literal["rms_norm"] | Literal["layer_norm"], + tie_word_embeddings: bool, + stacked_mlp: bool, + stacked_qkv: bool, + logits_postprocessor: Callable[[TensorValue], TensorValue] | None, + attention_multiplier: float, + embedding_multiplier: float, + residual_multiplier: float, + devices: list[DeviceRef], + clip_qkv: float | None, + ): + if stacked_qkv: + raise ValueError( + "Stacked QKV is not supported with naive caching strategy." + ) + rope = RotaryEmbedding( + dim=hidden_size, + n_heads=num_attention_heads, + theta=rope_theta, + max_seq_len=max_seq_len, + rope_scaling=rope_scaling, + interleaved=interleaved_rope_weights, + ) + + create_norm: Callable[..., LayerV2] + if norm_method == "rms_norm": + if rms_norm_eps is None: + raise ValueError( + "rms_norm_eps cannot be None for model that uses RMSNorm." + ) + create_norm = functools.partial( + RMSNormV2, hidden_size, rms_norm_eps + ) + else: + create_norm = functools.partial(ConstantLayerNorm, hidden_size) + + linear_cls: Callable[..., LinearV2] + if quantization_config: + linear_cls = functools.partial( + GPTQLinearV2, quantization_config=quantization_config + ) + else: + linear_cls = LinearV2 + + mlp_cls = StackedMLP if stacked_mlp else Llama3MLP + layers = [ + NaiveTransformerBlock( + attention=NaiveLLama3Attention( + kv_params, + hidden_size, + num_attention_heads, + num_key_value_heads, + rope, + dtype, + quantization_encoding, + linear_cls, + scale=attention_multiplier, + device=devices[0], + clip_qkv=clip_qkv, + ), + mlp=mlp_cls( + dtype, + quantization_encoding, + hidden_size, + intermediate_size, + linear_cls, + devices=devices, + ), + attention_norm=create_norm(), + mlp_norm=create_norm(), + residual_multiplier=residual_multiplier, + ) + for i in range(num_hidden_layers) + ] + + embedding_layer = EmbeddingV2( + vocab_size, + hidden_size, + dtype, + devices[0], + quantization_encoding=quantization_encoding, + ) + + output = LinearV2( + hidden_size, + vocab_size, + dtype, + devices[0], + quantization_encoding=quantization_encoding, + ) + if tie_word_embeddings: + output.set_shared_weight("weight", embedding_layer.weight) + + super().__init__( + dim=hidden_size, + n_heads=num_attention_heads, + layers=layers, + norm=create_norm(), + output=output, + theta=rope_theta, + embedding=embedding_layer, + embedding_multiplier=embedding_multiplier, + logits_postprocessor=logits_postprocessor, + ) + + +class NaiveLLama3Attention(NaiveAttentionWithRope): + def __init__( + self, + kv_params: KVCacheParams, + hidden_size: int, + num_attention_heads: int, + num_key_value_heads: int, + rope: Union[OptimizedRotaryEmbedding, RotaryEmbedding], + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + linear_cls: Callable[..., LinearV2], + scale: float | None, + device: DeviceRef, + clip_qkv: float | None, + ): + kv_weight_dim = ( + hidden_size // num_attention_heads + ) * num_key_value_heads + + super().__init__( + n_heads=num_attention_heads, + kv_params=kv_params, + dim=hidden_size, + wk=linear_cls( + in_dim=hidden_size, + out_dim=kv_weight_dim, + dtype=dtype, + device=device, + quantization_encoding=quantization_encoding, + ), + wv=linear_cls( + in_dim=hidden_size, + out_dim=kv_weight_dim, + dtype=dtype, + device=device, + quantization_encoding=quantization_encoding, + ), + wq=linear_cls( + in_dim=hidden_size, + out_dim=hidden_size, + dtype=dtype, + device=device, + quantization_encoding=quantization_encoding, + ), + wo=linear_cls( + in_dim=hidden_size, + out_dim=hidden_size, + dtype=dtype, + device=device, + quantization_encoding=quantization_encoding, + ), + rope=rope, + scale=scale, + clip_qkv=clip_qkv, + ) + + +class Llama3MLP(MLPV2): + def __init__( + self, + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + hidden_dim: int, + feed_forward_length: int, + linear_cls: Callable[..., LinearV2], + devices: Sequence[DeviceRef] = (), + ): + super().__init__( + gate_proj=linear_cls( + in_dim=hidden_dim, + out_dim=feed_forward_length, + dtype=dtype, + device=devices[0] if devices else None, + quantization_encoding=quantization_encoding, + ), + down_proj=linear_cls( + in_dim=feed_forward_length, + out_dim=hidden_dim, + dtype=dtype, + device=devices[0] if devices else None, + quantization_encoding=quantization_encoding, + ), + up_proj=linear_cls( + in_dim=hidden_dim, + out_dim=feed_forward_length, + dtype=dtype, + device=devices[0] if devices else None, + quantization_encoding=quantization_encoding, + ), + ) + + +class StackedMLP(LayerV2): + def __init__( + self, + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + hidden_dim: int, + feed_forward_length: int, + linear_cls: Callable[..., LinearV2], + devices: Sequence[DeviceRef] = (), + ): + super().__init__() + self.gate_up_proj = linear_cls( + in_dim=hidden_dim, + out_dim=feed_forward_length * 2, + dtype=dtype, + device=devices[0] if devices else None, + quantization_encoding=quantization_encoding, + ) + self.down_proj = linear_cls( + in_dim=feed_forward_length, + out_dim=hidden_dim, + dtype=dtype, + device=devices[0] if devices else None, + quantization_encoding=quantization_encoding, + ) + + def __call__(self, x: TensorValue) -> TensorValue: + up_states = self.gate_up_proj(x) + + gate = up_states[:, : up_states.shape.static_dims[0] // 2] + up_states = up_states[:, up_states.shape.static_dims[0] // 2 :] + + return self.down_proj(ops.silu(gate) * up_states) diff --git a/src/max/pipelines/architectures/llama3/weight_adapters.py b/src/max/pipelines/architectures/llama3/weight_adapters.py new file mode 100644 index 0000000000..fb3831322a --- /dev/null +++ b/src/max/pipelines/architectures/llama3/weight_adapters.py @@ -0,0 +1,289 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import math +from functools import cached_property +from os import PathLike +from typing import Sequence + +import numpy as np +import torch +from max.dtype import DType +from max.graph.weights import SafetensorWeights, WeightData, Weights +from max.graph.weights._torch_dtype_map import ( + modular_to_torch_type, + torch_to_modular_type, +) +from max.pipelines import PipelineConfig +from transformers import LlamaConfig + + +def _compute_safetensor_rope_scaling( + huggingface_config: LlamaConfig, +) -> np.ndarray | None: + # Unlike the `transformers` library's Llama model, MAX Llama expects the + # rope scaling value to be in the state dict (this is similar to GGUF). + if rope_scaling := getattr(huggingface_config, "rope_scaling", None): + if rope_scaling.get("rope_type", "").lower() == "llama3": + return _compute_rope_scaling( + rope_scaling, huggingface_config + ).numpy() + return None + + +# Maps from Safetensor to MAX weight names. +LLAMA_SAFETENSOR_MAPPING = { + "model.": "", # Removes the "model" prefix. + "g_idx": "perm_idx", # Specific to Llama GPT-Q weights. +} + + +def convert_safetensor_state_dict( + state_dict: dict[str, Weights], + huggingface_config: LlamaConfig, + pipeline_config: PipelineConfig, + **unused_kwargs, +) -> dict[str, WeightData]: + new_state_dict: dict[str, WeightData] = {} + # Map the weight names. + for safetensor_name, value in state_dict.items(): + max_name = safetensor_name + for before, after in LLAMA_SAFETENSOR_MAPPING.items(): + max_name = max_name.replace(before, after) + new_state_dict[max_name] = value.data() + # Add rope scaling to the state dict. + rope_scaling = _compute_safetensor_rope_scaling(huggingface_config) + if rope_scaling is not None: + new_state_dict["rope_freqs.weight"] = WeightData.from_numpy( + rope_scaling, "rope_freqs.weight" + ) + if pipeline_config._quant_config: + # hack: argsort the perm_idx array + for key, weight_data in new_state_dict.items(): + if key.endswith("perm_idx"): + new_state_dict[key] = WeightData.from_numpy( + np.argsort(weight_data.data).astype(np.int32), key + ) + return new_state_dict + + +# Maps from GGUF to MAX weight names. +LLAMA_GGUF_MAPPING = { + "token_embd": "embed_tokens", + "blk": "layers", + "ffn_up": "mlp.up_proj", + "ffn_down": "mlp.down_proj", + "ffn_gate": "mlp.gate_proj", + "ffn_norm": "post_attention_layernorm", + "attn_norm": "input_layernorm", + "attn_q": "self_attn.q_proj", + "attn_v": "self_attn.v_proj", + "attn_k": "self_attn.k_proj", + "attn_output": "self_attn.o_proj", + "output.weight": "lm_head.weight", + "output_norm": "norm", +} + + +def convert_gguf_state_dict( + state_dict: dict[str, Weights], **unused_kwargs +) -> dict[str, WeightData]: + new_state_dict: dict[str, WeightData] = {} + # Map the weight names. + for gguf_name, value in state_dict.items(): + max_name = gguf_name + for before, after in LLAMA_GGUF_MAPPING.items(): + max_name = max_name.replace(before, after) + new_state_dict[max_name] = value.data() + return new_state_dict + + +def _compute_rope_scaling( + rope_scaling, huggingface_config: LlamaConfig +) -> torch.Tensor: + # From llama.cpp's HF to GGUF conversion script: + # https://github.com/ggerganov/llama.cpp/blob/40c6d79fb52f995f47507fedfeaae2ac05d9b35c/convert_hf_to_gguf.py#L1627-L1654 + base = huggingface_config.rope_theta + dim = huggingface_config.head_dim + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = rope_scaling.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / ( + high_freq_factor - low_freq_factor + ) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + return torch.tensor(rope_factors, dtype=torch.float32) + + +# Required for Multi-GPU LLama3 until it is migrated to new Layers API + +# Map from GGUF tensor names to Safetensor names. +# https://github.com/huggingface/transformers/blob/7bbc62474391aff64f63fcc064c975752d1fa4de/src/transformers/integrations/ggml.py#L36 +LLAMA_GGUF_TENSOR_MAPPING = { + "token_embd": "model.embed_tokens", + "blk": "model.layers", + "ffn_up": "mlp.up_proj", + "ffn_down": "mlp.down_proj", + "ffn_gate": "mlp.gate_proj", + "ffn_norm": "post_attention_layernorm", + "attn_norm": "input_layernorm", + "attn_q": "self_attn.q_proj", + "attn_v": "self_attn.v_proj", + "attn_k": "self_attn.k_proj", + "attn_output": "self_attn.o_proj", + "output.weight": "lm_head.weight", + "output_norm": "model.norm", +} + + +class LlamaSafetensorWeights(SafetensorWeights): + """Loads Safetensor weights with GGUF names. + + Does the following when loading weights: + (1) converts Safetensor weight names to and from GGUF names. For example, + the GGUF weight "blk.{i}.attn_q.weight" is instead saved as + "model.layers.{i}.self_attn.q_proj.weight" in Safetensor. + (2) Computes the rope_freqs.weight using the HuggingFace config + (3) Transposes the q_proj and k_proj weights. + + """ + + def __init__( + self, + filepaths: Sequence[PathLike], + huggingface_config: LlamaConfig, + has_rope_scaling: bool, + rope_freqs_tensor: torch.Tensor | None, + **kwargs, + ): + super().__init__(filepaths, **kwargs) + self._gguf_name_map = LLAMA_GGUF_TENSOR_MAPPING + self._huggingface_config = huggingface_config + self._has_rope_scaling = has_rope_scaling + self._rope_freqs_tensor = rope_freqs_tensor + + @classmethod + def from_safetensor_weights( + cls, weights: Weights, huggingface_config: LlamaConfig + ): + assert isinstance(weights, SafetensorWeights) + has_rope_scaling = False + rope_freqs_tensor = None + if rope_scaling := getattr(huggingface_config, "rope_scaling", None): + if rope_scaling.get("rope_type", "").lower() == "llama3": + has_rope_scaling = True + rope_freqs_tensor = _compute_rope_scaling( + rope_scaling, huggingface_config + ) + return cls( + weights._filepaths, + huggingface_config=huggingface_config, + has_rope_scaling=has_rope_scaling, + rope_freqs_tensor=rope_freqs_tensor, + tensors=weights._tensors, + tensors_to_file_idx=weights._tensors_to_file_idx, + prefix="", + allocated=weights._allocated, + _st_weight_map=weights._st_weight_map, + ) + + def items(self): + """Iterate through all allocable weights that start with the prefix.""" + # `self._tensor` contains Safetensor names, but the Llama pipeline + # expects GGUF names so this function should return GGUF names. + for safetensor_name in self._tensors: + if safetensor_name.startswith(self.name): + gguf_name = safetensor_name + + # The _gguf_name_map maps gguf -> safetensor names. + # We want the reverse transformation from safetensor -> gguf. + for after, before in self._gguf_name_map.items(): + gguf_name = gguf_name.replace(before, after) + yield ( + gguf_name, + LlamaSafetensorWeights( + self._filepaths, + huggingface_config=self._huggingface_config, + has_rope_scaling=self._has_rope_scaling, + rope_freqs_tensor=self._rope_freqs_tensor, + tensors=self._tensors, + tensors_to_file_idx=self._tensors_to_file_idx, + prefix=gguf_name, + allocated=self._allocated, + _st_weight_map=self._st_weight_map, + ), + ) + + @cached_property + def name(self) -> str: + """The current weight name or prefix.""" + # Convert the prefix, which follows the GGUF naming pattern, to + # Safetensor weight name. + name = self._prefix + for before, after in self._gguf_name_map.items(): + name = name.replace(before, after) + return name + + def __getattr__(self, attr) -> LlamaSafetensorWeights: + if self._prefix: + full_path = f"{self._prefix}.{attr}" + else: + full_path = str(attr) + return LlamaSafetensorWeights( + self._filepaths, + huggingface_config=self._huggingface_config, + has_rope_scaling=self._has_rope_scaling, + rope_freqs_tensor=self._rope_freqs_tensor, + tensors=self._tensors, + tensors_to_file_idx=self._tensors_to_file_idx, + prefix=full_path, + allocated=self._allocated, + _st_weight_map=self._st_weight_map, + ) + + def exists(self) -> bool: + return self.name in self._tensors_to_file_idx or ( + self._has_rope_scaling and self.name == "rope_freqs.weight" + ) + + def _load_tensor(self, dtype: DType | None = None): + if self._has_rope_scaling and self.name == "rope_freqs.weight": + tensor = self._rope_freqs_tensor + assert isinstance(tensor, torch.Tensor) + if ( + dtype is not None + and torch_to_modular_type(tensor.dtype) != dtype + ): + tensor = tensor.to(modular_to_torch_type(dtype)) + return tensor + tensor = super()._load_tensor(dtype) + + return tensor diff --git a/src/max/pipelines/architectures/llama_vision/README.md b/src/max/pipelines/architectures/llama_vision/README.md new file mode 100644 index 0000000000..d0656223e0 --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/README.md @@ -0,0 +1,138 @@ +# Llama 3.2 Vision + +**Language:** Python + +**API**: MAX Graph + +This pipeline provides optimized support for the +`MllamaForConditionalGeneration` family of multimodal models, as exemplified +by the Llama 3.2 Vision multimodal text generation model. The model itself has +been constructed in Python using the +[MAX Graph API](https://docs.modular.com/max/graph/). + +The MAX Graph API provides an accessible interface to the construction of +flexible accelerated compute graphs, which are then optimized by the MAX +Engine's advanced graph compiler. This pipeline showcases how a multimodal +model can be fully defined using Python and MAX Graphs and then compiled for +optimal inference performance via the MAX Engine. + +> [!NOTE] +> This pipeline is under active development, and while many layers have been +> implemented, the entire pipeline is not fully functional at present. + +## Model + +[Llama 3.2 Vision](https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/) +is an open source multimodal model released by Meta. It handles both text and +image input, and allows for text generation based on those multimodal inputs. +This implementation is based on +[the version located on Hugging Face](https://huggingface.co/blog/llama32), and +follows its convention of only attending to a single image at a time. + +Note that the Llama 3.2 1B and 3B text-only models use the `LlamaForCausalLM` +architecture, which is covered in [our Llama 3.x pipeline](../../llama3/). + +The default settings for this pipeline use the 11B set of pretrained weights in +the `bfloat16` encoding. + +## Usage + +The easiest way to try out this pipeline is with our Magic command-line tool. + +1. Install Magic on macOS and Ubuntu with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Clone the MAX examples repository: + + If you don't already have a local clone of this repository, create one via: + + ```shell + git clone https://github.com/modular/max.git + ``` + + The following instructions assume that you're present within this pipeline's + directory, and you can change to it after cloning: + + ```shell + cd max/src/max/pipelines + ``` + +3. Host a multimodal chat completion endpoint via MAX Serve. + + MAX Serve provides functionality to host performant OpenAI compatible + endpoints using the FastAPI framework. + + You can configure the pipeline to be hosted by using the `serve` command. + Weights hosted on Hugging Face for any compatible + `MllamaForConditionalGeneration` model can be used with this optimized + architecture. For example: + + ```shell + magic run serve --model-path meta-llama/Llama-3.2-11B-Vision-Instruct + ``` + + A request can be submitted via a cURL command. + + ```shell + curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "meta-llama/Llama-3.2-11B-Vision-Instruct", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": [ + {"type": "text", "text": "What is in this image?"}, + {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"}} + ]} + ] + }' + ``` + +## Options + +The following command-line options are available to customize operation of the +pipeline: + +- `--model-path`: Specify the repository ID of a Hugging Face model + repository to use. This is used to load tokenizers, architectures and model + weights. +- `--force-download`: Specify whether to force a download of configuration + files and weights even if they already exist in the local cache. Set this + if you want to ensure you have the correct version of the model. +- `--max-batch-size`: Specifies the maximum batch size to be used. + Default is 1. +- `--max-ce-batch-size`: Set the maximum cache size reserved for a single + context encoding batch. The effective limit will be the lesser of this value + and `max-cache-batch-size`. + Default is 32. +- `--max-length`: Controls the maximum length of the text sequence (includes the + input tokens). Defaults to huggingface model config's max sequence length. +- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 + value is provided, the model will continue to generate tokens for the entire + context length. (Default value: -1) +- `--quantization-encoding`: The encoding to use for a datatype that can be + quantized to a low bits per weight format. + Valid values: `q4_0`, `q4_k`, `q6_k`, `bfloat16`, `float32`. + (Default value: `bfloat16`). +- `--save-to-serialized-model-path`: If specified, writes the serialized model + to this path. +- `--serialized-model-path`: If specified, tries to load a serialized model + from this path. +- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. +- `--trust-remote-code`: Indicate whether to allow custom modeling files from + Hugging Face repositories. Set this to true with caution, as it may + introduce security risks. +- `--weight-path`: Overrides the default URL, and allows for an + already-downloaded pretrained weight file to be used with the model. +- `--devices`: Specifies the device(s) to use for the model. This can optionally + be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". + Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/llama_vision/__init__.py b/src/max/pipelines/architectures/llama_vision/__init__.py new file mode 100644 index 0000000000..10bbcab277 --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import llama_vision_arch + +__all__ = ["llama_vision_arch"] diff --git a/src/max/pipelines/architectures/llama_vision/arch.py b/src/max/pipelines/architectures/llama_vision/arch.py new file mode 100644 index 0000000000..a1ac78137d --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/arch.py @@ -0,0 +1,36 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + SupportedArchitecture, + SupportedEncoding, + TextAndVisionTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from .llama_vision import LlamaVision + +llama_vision_arch = SupportedArchitecture( + name="MllamaForConditionalGeneration", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=["meta-llama/Llama-3.2-11B-Vision-Instruct"], + default_encoding=SupportedEncoding.bfloat16, + supported_encodings={ + SupportedEncoding.bfloat16: [KVCacheStrategy.CONTINUOUS] + }, + pipeline_model=LlamaVision, + tokenizer=TextAndVisionTokenizer, + default_weights_format=WeightsFormat.safetensors, +) diff --git a/src/max/pipelines/architectures/llama_vision/attention.py b/src/max/pipelines/architectures/llama_vision/attention.py new file mode 100644 index 0000000000..7129da3b7f --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/attention.py @@ -0,0 +1,107 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""An attention layer used in Llama 3.2 vision pipeline.""" + +import math +from dataclasses import dataclass + +from max.graph import TensorValue, ops +from max.pipelines.nn import Linear +from max.pipelines.nn.layer import Layer + + +@dataclass +class Attention(Layer): + n_heads: int + head_dim: int + + wq: Linear + wk: Linear + wv: Linear + wo: Linear + + def attention( + self, + xq: TensorValue, + xk: TensorValue, + xv: TensorValue, + attn_mask: TensorValue, + ) -> TensorValue: + # Broadcast the attention mask across heads. + # Do so in the graph so that the broadcast can be fused into downstream + # ops. + batch, _, seq_len, post_seq_len = attn_mask.shape + attn_mask = attn_mask.broadcast_to( + ( + batch, + self.n_heads, + seq_len, + post_seq_len, + ) + ) + + scale = math.sqrt(1.0 / self.head_dim) + scores = xq @ ops.transpose(xk, -2, -1) + # Note, the graph compiler currently requires the order of operands + # to be `scores * scale` in order to pattern match the fused attention + # operator. + scores = ops.softmax(scores * scale + attn_mask) + + return scores @ xv + + def __call__( + self, x: TensorValue, attention_mask: TensorValue + ) -> TensorValue: + """Computes attention on x, reusing the KV cache. + + Args: + x: Activations with shape (batch, seq_len, dim). + Returns the result of multi-headed self attention on the input. + """ + batch, seq_len = x.shape[0], x.shape[1] + # matmul weights + xq = self.wq(x) + xk = self.wk(x) + xv = self.wv(x) + + xq = ops.reshape(xq, [batch, seq_len, self.n_heads, self.head_dim]) + + xk = ops.reshape( + xk, + [ + batch, + seq_len, + self.n_heads, + self.head_dim, + ], + ) + xv = ops.reshape( + xv, + [ + batch, + seq_len, + self.n_heads, + self.head_dim, + ], + ) + + xq = xq.transpose(1, 2) + xk = xk.transpose(1, 2) + xv = xv.transpose(1, 2) + + # (1, 16, 4128, 80) -> (1, 4128, 16, 80) + attn_out = self.attention(xq, xk, xv, attention_mask).transpose(1, 2) + output = attn_out.reshape( + [batch, seq_len, self.n_heads * self.head_dim] + ) # (1, 4128, 16 * 80) + return self.wo(output) diff --git a/src/max/pipelines/architectures/llama_vision/cross_attention_decoder.py b/src/max/pipelines/architectures/llama_vision/cross_attention_decoder.py new file mode 100644 index 0000000000..d4635f2a3c --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/cross_attention_decoder.py @@ -0,0 +1,173 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Llama 3.2 Transformer Vision Language Model cross attention decoder.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import TensorValue, Weight, ops +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + KVCacheParams, +) +from max.pipelines.nn import MLP, RMSNorm +from max.pipelines.nn.kernels import ( + MHAMaskVariant, + cross_attention_ragged, + matmul_kv_cache_ragged, + rms_norm_key_cache, +) +from max.pipelines.nn.layer import Layer +from max.pipelines.nn.linear import Linear + + +@dataclass +class CrossSdpaAttention(Layer): + """Cross attention layer using SDPA (Scaled Dot Product Attention).""" + + n_heads: int + """The number of attention heads.""" + + kv_params: KVCacheParams + """KV Cache Params, including the number of kv heads, the head dim, and data type.""" + + layer_idx: int + """Index into the cross attention layers' KV cache.""" + + q_proj: Linear + """A linear layer for the query projection.""" + + wk: Weight + """The k weight vector. Combines with wv to form a Linear.""" + + wv: Weight + """The v weight vector. Combines with wk to form a Linear.""" + + o_proj: Linear + """A linear layer for the output projection.""" + + q_norm: RMSNorm + """Layer normalization.""" + + k_norm: RMSNorm + """Layer normalization.""" + + def __call__( + self, + hidden_states: TensorValue, + hidden_input_row_offsets: TensorValue, + hidden_max_seq_len: TensorValue, + cross_attention_states: TensorValue, + cross_input_row_offsets: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection, + ) -> TensorValue: + """Computes attention on hidden (query) and cross (key and value). + + Returns: + Attended hidden activation. + """ + wkv = ops.concat((self.wk, self.wv), axis=0) + + query_states = self.q_proj(hidden_states) + query_states = query_states.reshape( + [ + -1, + self.n_heads, + self.kv_params.head_dim, + ] + ) + query_states = self.q_norm(query_states) + + matmul_kv_cache_ragged( + kv_params=self.kv_params, + # Here, hidden_states correspond to cross_attention_states. + hidden_states=cross_attention_states, + layer_idx=self.layer_idx, + input_row_offsets=cross_input_row_offsets, + weight=wkv, + kv_collection=kv_collection, + ) + rms_norm_key_cache( + self.kv_params, + kv_collection, + gamma=ops.cast(self.k_norm.weight, hidden_states.dtype), + epsilon=self.k_norm.eps, + layer_idx=self.layer_idx, + # Use the total sequence length of the cross attention states. + total_seq_len=cross_attention_states.shape[0], + input_row_offsets=cross_input_row_offsets, + ) + + # Calculate Flash Attention. + attn_out = cross_attention_ragged( + self.kv_params, + input=query_states, + kv_collection=kv_collection, + layer_idx=ops.constant(self.layer_idx, DType.uint32), + input_row_offsets=hidden_input_row_offsets, + # Use the null mask to attend to all vision tokens. + mask_variant=MHAMaskVariant.NULL_MASK, + kv_input_row_offsets=cross_input_row_offsets, + q_max_seq_len=hidden_max_seq_len, + scale=math.sqrt(1.0 / self.kv_params.head_dim), + ) + + # Reshape back to (hidden total seq len, hidden size). + attn_out = ops.reshape(attn_out, shape=[hidden_states.shape[0], -1]) + + return self.o_proj(attn_out) + + +@dataclass +class CrossAttentionDecoderLayer(Layer): + """Cross-attention transformer block with tanh-gated attention and feedforward.""" + + cross_attn: CrossSdpaAttention + input_layernorm: RMSNorm + cross_attn_attn_gate: Weight + mlp: MLP + post_attention_layernorm: RMSNorm + cross_attn_mlp_gate: Weight + + def __call__( + self, + hidden_states: TensorValue, + hidden_input_row_offsets: TensorValue, + hidden_max_seq_len: TensorValue, + cross_attention_states: TensorValue, + cross_input_row_offsets: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection, + ) -> TensorValue: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + + hidden_states = self.cross_attn( + hidden_states, + hidden_input_row_offsets, + hidden_max_seq_len, + cross_attention_states, + cross_input_row_offsets, + kv_collection, + ) + hidden_states = ( + residual + ops.tanh(self.cross_attn_attn_gate) * hidden_states + ) + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + return residual + ops.tanh(self.cross_attn_mlp_gate) * hidden_states diff --git a/src/max/pipelines/architectures/llama_vision/encoder.py b/src/max/pipelines/architectures/llama_vision/encoder.py new file mode 100644 index 0000000000..604057c531 --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/encoder.py @@ -0,0 +1,105 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Llama 3.2 Transformer Vision Encoder.""" + +from __future__ import annotations + +from dataclasses import dataclass + +from max.graph import TensorValue, TensorValueLike, ops +from max.pipelines.nn import LayerNorm +from max.pipelines.nn.layer import Layer + +from .attention import Attention +from .mlp import MLP + + +@dataclass +class VisionEncoderLayer(Layer): + """ + This class implements a layer within Llama 3.2 vision transformer encoder. + """ + + mlp: MLP + input_layernorm: LayerNorm + post_attention_layernorm: LayerNorm + self_attn: Attention + is_gated: bool = False + gate_attn: TensorValueLike | None = None + gate_ffn: TensorValueLike | None = None + + def __call__( + self, hidden_state: TensorValue, attention_mask: TensorValue + ) -> TensorValue: + # Self Attention. + residual = hidden_state + hidden_state = self.input_layernorm(hidden_state) + + hidden_state = self.self_attn( + x=hidden_state, + attention_mask=attention_mask, + ) + + if self.is_gated: + hidden_state = ops.tanh(self.gate_attn) * hidden_state + hidden_state = residual + hidden_state + + # Feed forward. + residual = hidden_state + hidden_state = self.post_attention_layernorm(hidden_state) + hidden_state = self.mlp(hidden_state) + + # Gating criteria. + if self.is_gated: + hidden_state = ops.tanh(self.gate_ffn) * hidden_state + return residual + hidden_state + + +@dataclass +class VisionEncoder(Layer): + """ + Transformer encoder consisting of # of hidden self attention + layers. Each layer is a [`VisionEncoderLayer`]. + """ + + layers: list[VisionEncoderLayer] + + def __call__( + self, + hidden_states: TensorValue, + attention_mask: TensorValue, + output_hidden_states: bool, + ) -> tuple[TensorValue, tuple[TensorValue] | None]: + r""" + Args: + hidden_states (Tensor of shape `(batch_size, sequence_length, hidden_size)`): + attention_mask (Tensor of shape `(batch_size, sequence_length)`): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + """ + encoder_states: tuple | None = () if output_hidden_states else None + for encoder_layer in self.layers: + if encoder_states is not None: + encoder_states = encoder_states + (hidden_states,) + hidden_states = encoder_layer(hidden_states, attention_mask) + + if encoder_states is not None: + encoder_states = encoder_states + (hidden_states,) + + return hidden_states, encoder_states diff --git a/src/max/pipelines/architectures/llama_vision/language_model.py b/src/max/pipelines/architectures/llama_vision/language_model.py new file mode 100644 index 0000000000..0d0e282185 --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/language_model.py @@ -0,0 +1,468 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Llama 3.2 Transformer Vision Language Model.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import TensorValue, ops +from max.graph.weights import Weights +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + KVCacheParams, +) +from max.pipelines.nn import ( + MLP, + AttentionWithRopeQKV, + Embedding, + Linear, + OptimizedRotaryEmbedding, + RMSNorm, + TransformerBlock, +) +from max.pipelines.nn.layer import Layer + +from .cross_attention_decoder import ( + CrossAttentionDecoderLayer, + CrossSdpaAttention, +) + + +@dataclass +class TextModel(Layer): + """ + The Llama text model which consists of transformer with self and cross attention layers. + """ + + dtype: DType + kv_params: KVCacheParams + embed_tokens: Embedding + layers: list[CrossAttentionDecoderLayer | TransformerBlock] + norm: RMSNorm + cross_attention_layers: list[int] + rotary_emb: OptimizedRotaryEmbedding + + def __call__( + self, + text_kv_cache_inputs: tuple[TensorValue, ...], + vision_kv_cache_inputs: tuple[TensorValue, ...], + input_ids: TensorValue, + hidden_input_row_offsets: TensorValue, + hidden_max_seq_len: TensorValue, + cross_attention_states: TensorValue, + cross_input_row_offsets: TensorValue, + ) -> TensorValue: + inputs_embeds = self.embed_tokens(input_ids) + + hidden_states = ops.cast(inputs_embeds, self.dtype) + + before_attention_blocks_shape = hidden_states.shape + + # Assume that text and vision KV caches have the same KV params for now. + # So they can share the KV collection constructor object. + kv_collection_constructor = FetchContinuousBatchingKVCacheCollection( + self.kv_params + ) + + # Construct text and vision KV collections with their distinct inputs. + text_kv_collection = kv_collection_constructor(*text_kv_cache_inputs) + vision_kv_collection = kv_collection_constructor( + *vision_kv_cache_inputs + ) + + for decoder_layer in self.layers: + # For text-only path we should skip cross attention layers. + # We expect cross_attention_states to be zeroes if it's a text-only path. + # The underlying implementation should be a no-op when a zeroed out cross + # attention states is passed in. + + if isinstance(decoder_layer, CrossAttentionDecoderLayer): + hidden_states = decoder_layer( + hidden_states, + hidden_input_row_offsets, + hidden_max_seq_len, + cross_attention_states, + cross_input_row_offsets, + vision_kv_collection, + ) + else: + hidden_states = decoder_layer( + hidden_states, + text_kv_collection, + input_row_offsets=hidden_input_row_offsets, + ) + + assert hidden_states.shape == before_attention_blocks_shape + + return self.norm(hidden_states) + + +@dataclass +class CausalLanguageModel(Layer): + """The Llama Vision Text Model with a language modeling head on top.""" + + dtype: DType + kv_params: KVCacheParams + model: TextModel + lm_head: Linear + + def __call__( + self, + text_kv_cache_inputs: tuple[TensorValue, ...], + vision_kv_cache_inputs: tuple[TensorValue, ...], + input_ids: TensorValue, + hidden_input_row_offsets: TensorValue, + hidden_max_seq_len: TensorValue, + cross_attention_states: TensorValue, + cross_input_row_offsets: TensorValue, + ) -> TensorValue: + last_hidden_state = self.model( + text_kv_cache_inputs, + vision_kv_cache_inputs, + input_ids, + hidden_input_row_offsets, + hidden_max_seq_len, + cross_attention_states, + cross_input_row_offsets, + ) + + # For ragged tensors gather the last tokens from packed dim 0. + last_token_indices = hidden_input_row_offsets[1:] - 1 + last_token_logits = ops.gather( + last_hidden_state, last_token_indices, axis=0 + ) + return ops.cast(self.lm_head(last_token_logits), self.dtype) # logits + + +def cross_attention_decoder_layer( + dtype: DType, + num_attention_heads: int, + hidden_size: int, + num_key_value_heads: int, + rms_norm_eps: float, + kv_params: KVCacheParams, + intermediate_size: int, + weights: Weights, + layer_idx: int, +) -> CrossAttentionDecoderLayer: + head_dim = hidden_size // num_attention_heads + sdpa_attn = CrossSdpaAttention( + n_heads=num_attention_heads, + kv_params=kv_params, + layer_idx=layer_idx, + q_proj=Linear( + weights.cross_attn.q_proj.weight.allocate( + dtype, + [ + num_attention_heads * head_dim, + hidden_size, + ], + ), + bias=None, + ), + wk=weights.cross_attn.k_proj.weight.allocate( + dtype, + [ + num_key_value_heads * head_dim, + hidden_size, + ], + ), + wv=weights.cross_attn.v_proj.weight.allocate( + dtype, + [ + num_key_value_heads * head_dim, + hidden_size, + ], + ), + o_proj=Linear( + weight=weights.cross_attn.o_proj.weight.allocate( + dtype, + [ + hidden_size, + num_attention_heads * head_dim, + ], + ), + bias=None, + ), + q_norm=RMSNorm( + weight=weights.cross_attn.q_norm.weight.allocate( + dtype, + [head_dim], + ), + eps=rms_norm_eps, + ), + k_norm=RMSNorm( + weight=weights.cross_attn.k_norm.weight.allocate( + dtype, + [head_dim], + ), + eps=rms_norm_eps, + ), + ) + return CrossAttentionDecoderLayer( + cross_attn=sdpa_attn, + input_layernorm=RMSNorm( + weight=weights.input_layernorm.weight.allocate( + dtype, + [hidden_size], + ), + eps=rms_norm_eps, + ), + cross_attn_attn_gate=weights.cross_attn_attn_gate.allocate( + dtype, + [1], + ), + mlp=MLP( + gate_proj=Linear( + weight=weights.mlp.gate_proj.weight.allocate( + dtype, + [ + intermediate_size, + hidden_size, + ], + ), + bias=None, + ), + down_proj=Linear( + weight=weights.mlp.down_proj.weight.allocate( + dtype, + [ + hidden_size, + intermediate_size, + ], + ), + bias=None, + ), + up_proj=Linear( + weight=weights.mlp.up_proj.weight.allocate( + dtype, + [ + intermediate_size, + hidden_size, + ], + ), + bias=None, + ), + ), + post_attention_layernorm=RMSNorm( + weight=weights.post_attention_layernorm.weight.allocate( + dtype, + [hidden_size], + ), + eps=rms_norm_eps, + ), + cross_attn_mlp_gate=weights.cross_attn_mlp_gate.allocate( + dtype, + [1], + ), + ) + + +def self_attention_decoder_layer( + dtype: DType, + num_attention_heads: int, + hidden_size: int, + num_key_value_heads: int, + intermediate_size: int, + rms_norm_eps: float, + kv_params: KVCacheParams, + weights: Weights, + layer_idx: int, + rotary_embedding: OptimizedRotaryEmbedding, +) -> TransformerBlock: + head_dim = hidden_size // num_attention_heads + + wq = weights.self_attn.q_proj.weight.allocate( + dtype, shape=[num_attention_heads * head_dim, hidden_size] + ) + wk = weights.self_attn.k_proj.weight.allocate( + dtype, shape=[num_key_value_heads * head_dim, hidden_size] + ) + wv = weights.self_attn.v_proj.weight.allocate( + dtype, shape=[num_key_value_heads * head_dim, hidden_size] + ) + o_proj = Linear( + weight=weights.self_attn.o_proj.weight.allocate( + dtype, + shape=[hidden_size, num_attention_heads * head_dim], + ) + ) + + attention = AttentionWithRopeQKV( + n_heads=num_attention_heads, + kv_params=kv_params, + layer_idx=layer_idx, + wq=wq, + wk=wk, + wv=wv, + wo=o_proj, + rope=rotary_embedding, + scale=math.sqrt(1.0 / head_dim), + ) + + return TransformerBlock( + attention=attention, + mlp=MLP( + gate_proj=Linear( + weight=weights.mlp.gate_proj.weight.allocate( + dtype, + [intermediate_size, hidden_size], + ), + bias=None, + ), + down_proj=Linear( + weight=weights.mlp.down_proj.weight.allocate( + dtype, + [hidden_size, intermediate_size], + ), + bias=None, + ), + up_proj=Linear( + weight=weights.mlp.up_proj.weight.allocate( + dtype, + [intermediate_size, hidden_size], + ), + bias=None, + ), + ), + attention_norm=RMSNorm( + weight=weights.input_layernorm.weight.allocate( + dtype, + [hidden_size], + ), + eps=rms_norm_eps, + ), + mlp_norm=RMSNorm( + weight=weights.post_attention_layernorm.weight.allocate( + dtype, [hidden_size] + ), + eps=rms_norm_eps, + ), + ) + + +def instantiate_language_model( + dtype: DType, + hidden_size: int, + n_heads: int, + rope_theta: int, + max_seq_len: int, + num_hidden_layers: int, + cross_attention_layers: list[int], + vocab_size: int, + rms_norm_eps: float, + num_key_value_heads: int, + intermediate_size: int, + kv_params: KVCacheParams, + weights: Weights, +) -> CausalLanguageModel: + layers: list[CrossAttentionDecoderLayer | TransformerBlock] = [] + + # We don't really have a rotary embedding layer within the graph as it's largely + # folded into the custom kernel, but leaving this here for now. + rotary_embedding = OptimizedRotaryEmbedding( + dim=hidden_size, + n_heads=n_heads, + theta=rope_theta, + max_seq_len=max_seq_len, + # TODO: Figure out how we want to pass this + # rope_scaling=params.rope_scaling, + interleaved=False, + ) + + # Track the cross attention KV cache layer index to compute the self + # attention KV layer index. + cross_kv_layer_idx = -1 + for layer_idx in range( + num_hidden_layers, + ): + curr_layer_weight = weights.language_model.model.layers[layer_idx] + + if layer_idx in cross_attention_layers: + cross_kv_layer_idx = cross_attention_layers.index(layer_idx) + layers.append( + cross_attention_decoder_layer( + dtype=dtype, + num_attention_heads=n_heads, + hidden_size=hidden_size, + num_key_value_heads=num_key_value_heads, + rms_norm_eps=rms_norm_eps, + kv_params=kv_params, + intermediate_size=intermediate_size, + weights=curr_layer_weight, + layer_idx=cross_kv_layer_idx, + ) + ) + else: + layers.append( + self_attention_decoder_layer( + dtype=dtype, + num_attention_heads=n_heads, + hidden_size=hidden_size, + num_key_value_heads=num_key_value_heads, + intermediate_size=intermediate_size, + rms_norm_eps=rms_norm_eps, + kv_params=kv_params, + weights=curr_layer_weight, + # Self KV layer index is the total layer index minus the + # number of cross KV layers so far. + layer_idx=layer_idx - cross_kv_layer_idx + 1, + rotary_embedding=rotary_embedding, + ) + ) + + text_model = TextModel( + dtype=dtype, + kv_params=kv_params, + embed_tokens=Embedding( + weights.language_model.model.embed_tokens.weight.allocate( + dtype, + [ + # Upstream in the Huggingface llama reference, 8 is added to the vocab size. + vocab_size + 8, + hidden_size, + ], + ), + ), + norm=RMSNorm( + weight=weights.language_model.model.norm.weight.allocate( + dtype, + [hidden_size], + ), + eps=rms_norm_eps, + ), + layers=layers, + cross_attention_layers=cross_attention_layers, + # TODO: Verify if these values passed are even correct. + rotary_emb=rotary_embedding, + ) + + return CausalLanguageModel( + dtype=dtype, + kv_params=kv_params, + model=text_model, + lm_head=Linear( + weights.language_model.lm_head.weight.allocate( + dtype, + [ + vocab_size, + hidden_size, + ], + ), + bias=None, + ), + ) diff --git a/src/max/pipelines/architectures/llama_vision/llama_vision.py b/src/max/pipelines/architectures/llama_vision/llama_vision.py new file mode 100644 index 0000000000..3dc23904eb --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/llama_vision.py @@ -0,0 +1,1184 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import logging +import time +from collections.abc import Sequence +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass +from typing import Any, Iterable, Optional, cast, final + +import numpy as np +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession, Model +from max.graph import Dim, Graph, Shape, TensorType, TensorValue, ops +from max.graph.weights import Weights +from max.pipelines import ( + ModelInputs, + ModelOutputs, + PipelineConfig, + PipelineModel, + TextAndVisionContext, + upper_bounded_default, +) +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheManager, + KVCacheInputs, + KVCacheInputSymbols, + KVCacheManager, + KVCacheParams, + KVCacheStrategy, + PaddedKVCacheInputs, + RaggedKVCacheInputs, + estimate_kv_cache_size, + infer_optimal_batch_size, + load_kv_manager, +) +from max.pipelines.kv_cache._utils import build_max_lengths_tensor +from max.pipelines.nn import Linear +from max.pipelines.nn.layer import Layer + +from .language_model import CausalLanguageModel, instantiate_language_model +from .vision_model import instantiate_vision_model + +logger = logging.getLogger("max.pipelines") + + +@dataclass +class MultimodalKVCacheInputSymbols(KVCacheInputSymbols): + text_kv_input_symbols: KVCacheInputSymbols + vision_kv_input_symbols: KVCacheInputSymbols + + +@dataclass +class MultimodalKVCacheInputs(KVCacheInputs): + text_kv_cache_inputs: KVCacheInputs + vision_kv_cache_inputs: KVCacheInputs + + +class MultimodalKVCacheManager(KVCacheManager): + """A lightweight wrapper around text and vision KV managers. + + Note on runtime and graph build time return types: + - Currently the multi modal KV manager doesn't support multiple devices. + So all lists that should be of length num_devices will have length 1. + - Individual modality KV cache managers return a 4-tuple of KV cache inputs. + Since this is a pair of KV cache managers, it returns an 8-tuple, + where the first 4 elements are the text KV cache inputs and the remaining + 4 elements are the vision KV cache inputs. + - This 8-tuple applies for both input symbols and return KV cache inputs. + - TODO(bduke): We should fix both multi-device and multi-modality using an + extensible KVCacheInput type. + """ + + text_kv_manager: KVCacheManager + """KV cache manager for text inputs.""" + + vision_kv_manager: ContinuousBatchingKVCacheManager + """KV cache manager for image inputs.""" + + def __init__( + self, + params: KVCacheParams, + max_batch_size: Optional[int], + text_max_seq_len: int, + vision_max_seq_len: int, + text_num_layers: int, + vision_num_layers: int, + devices: list[Device], + session: InferenceSession, + available_cache_memory: int, + page_size: int, + ) -> None: + assert max_batch_size, "Expected max_batch_size to be set" + self.text_kv_manager = load_kv_manager( + params=params, + max_batch_size=max_batch_size, + max_seq_len=text_max_seq_len, + num_layers=text_num_layers, + devices=devices, + available_cache_memory=available_cache_memory, + page_size=page_size, + session=session, + ) + + # Always use continuous batching KV cache for the vision KV projections, + # since the number of vision tokens is fixed per batch until we support + # multi-image, at least. + self.vision_kv_manager = ContinuousBatchingKVCacheManager( + params=params, + max_batch_size=max_batch_size, + max_seq_len=vision_max_seq_len, + num_layers=vision_num_layers, + devices=devices, + session=session, + ) + + # Call superclass after initializing modality KV managers since the + # superclass ctor calls methods that use the modality KV managers. + super().__init__( + params, + max_batch_size, + text_max_seq_len, + text_num_layers, + devices, + session, + is_ragged=True, + ) + + @classmethod + @final + def estimated_memory_size( + cls, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: list[Device], + **kwargs: Any, + ) -> int: + """Returns the estimated total memory usage of the kv cache.""" + assert "num_vision_layers" in kwargs, "num_vision_layers must be set" + num_vision_layers = kwargs["num_vision_layers"] + assert "max_vision_seq_len" in kwargs, "max_vision_seq_len must be set" + max_vision_seq_len = kwargs["max_vision_seq_len"] + + vision_kv_cache_size = ( + ContinuousBatchingKVCacheManager.estimated_memory_size( + params, + max_batch_size, + max_vision_seq_len, + num_vision_layers, + available_cache_memory, + devices, + ) + ) + + remaining_memory = available_cache_memory - vision_kv_cache_size + if remaining_memory > 0: + text_kv_cache_size = estimate_kv_cache_size( + params, + max_batch_size, + max_seq_len, + num_layers, + remaining_memory, + devices, + ) + return vision_kv_cache_size + text_kv_cache_size + + @classmethod + def infer_optimal_batch_size( + cls, + params: KVCacheParams, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: list[Device], + **kwargs: Any, + ) -> int: + """Returns the estimated optimal batch size for the kv cache.""" + assert "num_vision_layers" in kwargs, "num_vision_layers must be set" + num_vision_layers = kwargs["num_vision_layers"] + assert "max_vision_seq_len" in kwargs, "max_vision_seq_len must be set" + max_vision_seq_len = kwargs["max_vision_seq_len"] + + # figure out the relative sizes of caches based on KV Cach settings + text_size_per_token = num_layers * max_seq_len + vision_size_per_token = num_vision_layers * max_vision_seq_len + text_to_vision_ratio = text_size_per_token / ( + text_size_per_token + vision_size_per_token + ) + + # divvy up our allocation based on this ratio + text_cache_size = available_cache_memory * text_to_vision_ratio + vision_cache_size = available_cache_memory - text_cache_size + + # infer the optimal batch size for each modality based on its cache size + text_batch_size = infer_optimal_batch_size( + params, + max_seq_len, + num_layers, + text_cache_size, + devices, + ) + vision_batch_size = ( + ContinuousBatchingKVCacheManager.infer_optimal_batch_size( + params, + max_vision_seq_len, + num_vision_layers, + vision_cache_size, + devices, + ) + ) + + return min(text_batch_size, vision_batch_size) + + @final + def _fetch( + self, seq_ids_and_prompts: dict[int, np.ndarray], num_steps: int = 1 + ) -> list[KVCacheInputs]: + """Returns KV cache inputs for both modalities' KV managers.""" + # Here we call into the text KV manager's fetch method to update + # its fetch metadata. + text_fetch_results = self.text_kv_manager.fetch( + seq_ids_and_prompts, num_steps + )[0] + + # For the vision KV manager, fetch metadata isn't applicable since + # autoregressive generation is text only. + active_batch_size = len(seq_ids_and_prompts) + + # Lookup table and seq_ids are redundant identical tensors. + lookup_table_tensor = Tensor.from_numpy( + np.array(list(seq_ids_and_prompts.keys()), np.uint32) + ) + cache_lengths_np = np.zeros(active_batch_size, np.uint32) + + max_seq_length = 0 + max_cache_length = 0 + + device = self.vision_kv_manager.devices[0] + for i, seq_id in enumerate(seq_ids_and_prompts): + # Assumption: all seq_ids with + # `vision_kv_manager.cache_lengths[seq_id] == 0` + # are context encoding steps and have the max image sequence length. + # TODO(bduke): pass the vision sequence lengths in from next_token. + + # Omit validity checks on seq ids, which are done in the text fetch. + cache_len = self.vision_kv_manager.cache_lengths[seq_id] + if cache_len == 0: + max_seq_length = self.vision_kv_manager.max_seq_len + + cache_lengths_np[i] = cache_len + + # Update the maximum lengths seen so far. + max_cache_length = max(max_cache_length, cache_len) + + # Build a tensor of maximum lengths. Each step slices the first row to + # advance to the values for the next row. + max_lengths_host = build_max_lengths_tensor( + num_steps, max_seq_length, max_cache_length + ) + + vision_fetch_results = RaggedKVCacheInputs( + # Block 0 for the first device (since MultimodalKVCacheManager + # assumes only 1 device). + blocks=self.vision_kv_manager.blocks[0], + cache_lengths=Tensor.from_numpy(cache_lengths_np).to(device), + lookup_table=lookup_table_tensor.to(device), + max_lengths=max_lengths_host, + ) + + multimodal_kv_inputs = [ + MultimodalKVCacheInputs(text_fetch_results, vision_fetch_results) + ] + return cast(list[KVCacheInputs], multimodal_kv_inputs) + + @final + def input_symbols( + self, + ) -> Sequence[MultimodalKVCacheInputSymbols]: + """Returns concatenated input symbols for text and vision KV managers. + + This has to rename input symbols that aren't necessarily the same: + `num_layers` and `max_seq_len` differ in general between text and + vision modalities. + """ + + def _input_symbols( + manager: KVCacheManager, num_layers_key: str, max_seq_len_key: str + ) -> KVCacheInputSymbols: + input_symbols = manager.input_symbols()[0] + # Get first element from input_symbols sequence + first_input_symbols = input_symbols[0] + assert isinstance(first_input_symbols, TensorType) + first_input_symbols.shape = Shape( + [ + "num_blocks", + 2, + num_layers_key, + max_seq_len_key, + "num_kv_heads", + "head_dim", + ] + ) + return input_symbols + + return [ + MultimodalKVCacheInputSymbols( + text_kv_input_symbols=_input_symbols( + self.text_kv_manager, "text_num_layers", "text_max_seq_len" + ), + vision_kv_input_symbols=_input_symbols( + self.vision_kv_manager, + "vision_num_layers", + "vision_max_seq_len", + ), + ) + ] + + def step(self, seq_ids_and_new_tokens: dict[int, np.ndarray]) -> None: + """Steps both text and vision modalities' KV managers.""" + # Step the text KV manager as usual for autoregressive text generation. + self.text_kv_manager.step(seq_ids_and_new_tokens) + + # Keep the base class's state in sync with the text KV manager's. + super().step(seq_ids_and_new_tokens) + + # Increment cache lengths for the vision KV manager iff this is a + # context encoding (CE) step with an image input. + # It's a CE step if the existing cache_lengths[seq_id] is 0. + for seq_id in seq_ids_and_new_tokens: + self.vision_kv_manager.cache_lengths[seq_id] += ( + self.vision_kv_manager.max_seq_len + if self.vision_kv_manager.cache_lengths[seq_id] == 0 + else 0 + ) + + def external_claim(self, seq_ids: list[int]) -> None: + """Reserves the same sequence ids for both modalities' KV caches.""" + self.text_kv_manager.external_claim(seq_ids) + self.vision_kv_manager.external_claim(seq_ids) + + # Keep the base class's state in sync with the text KV manager's. + super().external_claim(seq_ids) + + def release(self, seq_id: int) -> None: + """Marks the sequence complete for both modalities' KV caches.""" + self.text_kv_manager.release(seq_id) + self.vision_kv_manager.release(seq_id) + super().release(seq_id) + + def contains(self, seq_id: int) -> bool: + """Returns whether `seq_id` is in the KV cache.""" + text_kv_contains = self.text_kv_manager.contains(seq_id) + + # Assume that the modalities' KV caches have consistent sequence ids. + assert text_kv_contains == self.vision_kv_manager.contains(seq_id) + + return text_kv_contains + + def num_kv_inputs(self) -> int: + """Returns the sum of the KV input lengths for both modalities.""" + return ( + self.text_kv_manager.num_kv_inputs() + + self.vision_kv_manager.num_kv_inputs() + ) + + def increment_cache_lengths( + self, + kv_cache_inputs: list[RaggedKVCacheInputs] | list[PaddedKVCacheInputs], + prev_model_inputs: Iterable[Any], + ) -> list[RaggedKVCacheInputs] | list[PaddedKVCacheInputs]: + """Updates the cache lengths for multistep execution. + + This increments the text and vision KV cache lengths separately using + their respective KV cache inputs. + """ + # Cast the input to MultimodalKVCacheInputs to access its components + multimodal_inputs = cast(list[MultimodalKVCacheInputs], kv_cache_inputs) + text_kv_inputs = multimodal_inputs[0].text_kv_cache_inputs + vision_kv_inputs = multimodal_inputs[0].vision_kv_cache_inputs + + multimodal_kv_inputs = [ + MultimodalKVCacheInputs( + text_kv_cache_inputs=self.text_kv_manager.increment_cache_lengths( + [text_kv_inputs], # type: ignore + prev_model_inputs, + )[0], + vision_kv_cache_inputs=self.vision_kv_manager.increment_cache_lengths( + [vision_kv_inputs], # type: ignore + prev_model_inputs, + )[0], + ) + ] + return cast(list[RaggedKVCacheInputs], multimodal_kv_inputs) + + +# TODO(bduke): use `@dataclass(slots=True)` when we drop 3.9 support. +class LlamaVisionInputs(ModelInputs): + """Holds language model inputs and (optionally) vision model inputs.""" + + # Language model inputs. + input_id_values: Tensor + input_row_offsets: Tensor + input_id_max_seq_len: Tensor + pixel_row_offsets: Tensor + + # Vision model inputs. + _pixel_values: Tensor | None = None + _aspect_ratio_ids: Tensor | None = None + _aspect_ratio_mask: Tensor | None = None + + def __init__( + self, + input_id_values: Tensor, + input_row_offsets: Tensor, + input_id_max_seq_len: Tensor, + pixel_row_offsets: Tensor, + pixel_values: Tensor | None = None, + aspect_ratio_ids: Tensor | None = None, + aspect_ratio_mask: Tensor | None = None, + ): + self.input_id_values = input_id_values + self.input_row_offsets = input_row_offsets + self.input_id_max_seq_len = input_id_max_seq_len + self.pixel_row_offsets = pixel_row_offsets + self._pixel_values = pixel_values + self._aspect_ratio_ids = aspect_ratio_ids + self._aspect_ratio_mask = aspect_ratio_mask + + def __post_init__(self) -> None: + """Validate consistency between vision fields. + + If pixel_values is set, then aspect_ratio_ids, aspect_ratio_mask, + and pixel_row_offsets must also be set, and vice versa. + """ + if self.has_vision_inputs: + if not all( + x is not None + for x in ( + self._aspect_ratio_ids, + self._aspect_ratio_mask, + self.pixel_row_offsets, + ) + ): + msg = "provide all or none of Llama Vision vision model inputs" + raise ValueError(msg) + else: + for field_name in ("_aspect_ratio_ids", "_aspect_ratio_mask"): + if getattr(self, field_name) is not None: + msg = f"{field_name} must be None if _pixel_values is None" + raise ValueError(msg) + + @property + def has_vision_inputs(self) -> bool: + """Returns true iff this includes vision model inputs.""" + return self._pixel_values is not None + + @property + def pixel_values(self) -> Tensor: + assert self._pixel_values is not None + return self._pixel_values + + @property + def aspect_ratio_ids(self) -> Tensor: + assert self._aspect_ratio_ids is not None + return self._aspect_ratio_ids + + @property + def aspect_ratio_mask(self) -> Tensor: + assert self._aspect_ratio_mask is not None + return self._aspect_ratio_mask + + def update_for_next_token( + self, next_tokens: Tensor, next_row_offsets: Tensor + ) -> LlamaVisionInputs: + """Updates next_tokens and row_offsets after an initial step.""" + return LlamaVisionInputs( + input_id_values=next_tokens, + input_row_offsets=next_row_offsets, + input_id_max_seq_len=self.input_id_max_seq_len, + pixel_row_offsets=self.pixel_row_offsets, + # Set vision model inputs to None after the first `next_token`. + pixel_values=None, + aspect_ratio_ids=None, + aspect_ratio_mask=None, + ) + + +class LlamaVisionModel(Layer): + """The Llama 3.2 vision model.""" + + def __init__( + self, pipeline_config: PipelineConfig, weights: Weights + ) -> None: + # Set convenience attributes for the text and vision configs. + self.vision_config = pipeline_config.huggingface_config.vision_config + self.text_config = pipeline_config.huggingface_config.text_config + + self.vision_model = instantiate_vision_model( + dtype=pipeline_config.dtype, + image_size=self.vision_config.image_size, + patch_size=self.vision_config.patch_size, + supported_aspect_ratios=self.vision_config.supported_aspect_ratios, + hidden_size=self.vision_config.hidden_size, + max_num_tiles=self.vision_config.max_num_tiles, + num_channels=self.vision_config.num_channels, + norm_eps=self.vision_config.norm_eps, + attention_heads=self.vision_config.attention_heads, + num_hidden_layers=self.vision_config.num_hidden_layers, + intermediate_size=self.vision_config.intermediate_size, + num_global_layers=self.vision_config.num_global_layers, + intermediate_layers_indices=self.vision_config.intermediate_layers_indices, + weights=weights, + ) + + self.multi_modal_projector = Linear( + weights.multi_modal_projector.weight.allocate( + pipeline_config.dtype, + [ + self.text_config.hidden_size, + self.vision_config.vision_output_dim, + ], + ), + weights.multi_modal_projector.bias.allocate( + pipeline_config.dtype, + [self.text_config.hidden_size], + ), + ) + + def __call__( + self, + pixel_values: TensorValue, + aspect_ratio_ids: TensorValue, + aspect_ratio_mask: TensorValue, + ) -> TensorValue: + if aspect_ratio_ids is None: + msg = "`aspect_ratio_ids` must be provided if `pixel_values` is provided" + raise ValueError(msg) + + # Get vision tokens from vision model. + vision_outputs = self.vision_model( + pixel_values=pixel_values, + aspect_ratio_ids=aspect_ratio_ids, + aspect_ratio_mask=aspect_ratio_mask, + ) + cross_attention_states = vision_outputs[0] + + num_patches = cross_attention_states.shape[-2] + + return self.multi_modal_projector(cross_attention_states).reshape( + [ + Dim("batch_size") + * Dim("num_concurrent_media") + * self.vision_config.max_num_tiles + * num_patches, + self.text_config.hidden_size, + ] + ) + + +class LlamaVisionLanguageModel(Layer): + """The Llama 3.2 vision language model.""" + + language_model: CausalLanguageModel + """Language model composed of self and cross attention layers.""" + + num_text_kv_cache_inputs: int + """Number of KV cache inputs for self attention layers.""" + + def __init__( + self, + pipeline_config: PipelineConfig, + weights: Weights, + kv_params: KVCacheParams, + max_seq_len: int, + num_text_kv_cache_inputs: int, + ) -> None: + text_config = pipeline_config.huggingface_config.text_config + + self.language_model = instantiate_language_model( + dtype=pipeline_config.dtype, + hidden_size=text_config.hidden_size, + n_heads=text_config.num_attention_heads, + rope_theta=text_config.rope_theta, + max_seq_len=max_seq_len, + num_hidden_layers=text_config.num_hidden_layers, + cross_attention_layers=text_config.cross_attention_layers, + vocab_size=text_config.vocab_size, + rms_norm_eps=text_config.rms_norm_eps, + num_key_value_heads=text_config.num_key_value_heads, + intermediate_size=text_config.intermediate_size, + kv_params=kv_params, + weights=weights, + ) + self.num_text_kv_cache_inputs = num_text_kv_cache_inputs + + def __call__( + self, + cross_attention_states: TensorValue, + input_ids: TensorValue, + hidden_input_row_offsets: TensorValue, + hidden_max_seq_len: TensorValue, + cross_input_row_offsets: TensorValue, + *kv_cache_inputs: TensorValue, + ) -> TensorValue: + logits = self.language_model( + text_kv_cache_inputs=kv_cache_inputs[ + : self.num_text_kv_cache_inputs + ], + vision_kv_cache_inputs=kv_cache_inputs[ + self.num_text_kv_cache_inputs : + ], + input_ids=input_ids, + hidden_input_row_offsets=hidden_input_row_offsets, + hidden_max_seq_len=hidden_max_seq_len, + cross_attention_states=cross_attention_states, + cross_input_row_offsets=cross_input_row_offsets, + ) + + # Always return float32 logits, no matter the activation type + return ops.cast(logits, DType.float32) + + +class LlamaVision(PipelineModel[TextAndVisionContext]): + """The entire (multimodal) Llama3.2 vision model. + + A note on multi-step and vision inputs: + + - `has_images` in `prepare_initial_token_inputs` is determined by whether or + not `pixel_values` is set on each TextAndVisionContext in the batch. + So on the context encoding call, the caller sets pixel_values, making + `has_images` True. + - `prepare_initial_token_inputs` unsets `ctx.pixel_values` (sets it to an + empty list). + So the next prepare_initial_token_inputs will have has_images == False + (the next multi-step train will skip the vision encoder). + - That covers the num_steps = 1 case. + - For multistep, the prepare_next_token_inputs function will unset + LlamaVisionInputs.pixel_values (and aspect ratio ids/mask). + So for multistep, step > 1, subsequent steps won't run the vision encoder. + - Note the 2 different mechanisms: `has_images` is determined by + `TextAndVisionContext.pixel_values` in `prepare_initial_token_inputs`, + but it is determined by `LlamaVisionInputs.pixel_values` in + `PipelineModel.execute` (which is called multiple times in a multi-step + train, so `prepare_next_token_inputs` needs to unset + `LlamaVisionInputs.pixel_values`). + """ + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + # Set convenience attributes for the text and vision configs. + self.vision_config = pipeline_config.huggingface_config.vision_config + self.text_config = pipeline_config.huggingface_config.text_config + + # These need to be set at graph instantiation time. + self.vision_graph_input_size = -1 + self.language_graph_input_size = -1 + + super().__init__(pipeline_config, session) + self.vision_model, self.language_model = self.load_model(session) + # Note that in a multimodal model, the language model is the last model in the + # pipeline. Unfortunately, self.model is still being used (and exposed) + # in the token generation code, so we still need to set it here. + self.model = self.language_model + + def _llama3_vision_vision_graph(self) -> Graph: + # Inserted a manual CHW -> HWC transpose here. + pixel_values_type = TensorType( + # This has to be of type float32 as we construct tensors from a numpy + # array (which has no notion of some dtypes like bfloat16). Explicit + # casting will happen inside the graph. + DType.float32, + shape=[ + "batch_size", + "num_concurrent_media", + self.vision_config.max_num_tiles, + self.vision_config.image_size, # height + self.vision_config.image_size, # width + self.vision_config.num_channels, + ], + ) + aspect_ratio_ids_type = TensorType( + DType.int64, + shape=["batch_size", "num_concurrent_media"], + ) + aspect_ratio_mask_type = TensorType( + DType.int64, + shape=[ + "batch_size", + "num_concurrent_media", + self.vision_config.max_num_tiles, + ], + ) + + input_types = [ + pixel_values_type, + aspect_ratio_ids_type, + aspect_ratio_mask_type, + ] + self.vision_graph_input_size = len(input_types) + return Graph( + "llama3-vision-vision-model-graph", + forward=LlamaVisionModel( + pipeline_config=self.pipeline_config, weights=self.weights + ), + input_types=input_types, + ) + + @classmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + try: + return upper_bounded_default( + upper_bound=pipeline_config.huggingface_config.text_config.max_position_embeddings, + default=pipeline_config.max_length, + ) + except ValueError as e: + msg = ( + "Unable to infer max_length for Llama Vision, the provided " + f"max_length ({pipeline_config.max_length}) exceeds the " + f"model's max_position_embeddings " + f"({pipeline_config.huggingface_config.text_config.max_position_embeddings})." + ) + raise ValueError(msg) from e + + def _llama3_vision_language_graph(self) -> Graph: + # Pre-allocate a buffer for input_row_offsets in multistep execution. + # We do this to avoid materializing and copying a buffer with each multistep step + assert self.pipeline_config.max_batch_size, ( + "Expected max_batch_size to be set" + ) + self._input_row_offsets_prealloc = Tensor.from_numpy( + np.arange(self.pipeline_config.max_batch_size + 1, dtype=np.uint32) + ).to(self.pipeline_config.devices[0]) + + input_ids_type = TensorType(DType.int64, shape=["total_seq_len"]) + # image_size = self.vision_config.image_size + # patch_size = self.vision_config.patch_size + cross_attention_states_type = TensorType( + self.pipeline_config.dtype, + shape=[ + # TODO(bduke): fix algebraic dim creation outside of graph + # contexts. + # Dim("batch_size") + # * "num_concurrent_media" + # * self.vision_config.max_num_tiles + # * ((image_size // patch_size) ** 2 + 1), + "num_vision_embeddings", + self.text_config.hidden_size, + ], + ) + input_ids_max_seq_len_type = TensorType(DType.uint32, [1]) + input_row_offsets_type = TensorType( + DType.uint32, shape=["input_row_offsets_len"] + ) + cross_row_offsets_type = input_row_offsets_type + + # Unpack multimodal KV inputs. + assert isinstance(self.kv_manager, MultimodalKVCacheManager) + input_symbols = self.kv_manager.input_symbols()[0] + text_kv_input_symbols = input_symbols.text_kv_input_symbols + vision_kv_input_symbols = input_symbols.vision_kv_input_symbols + + input_types = [ + cross_attention_states_type, + input_ids_type, + input_row_offsets_type, + input_ids_max_seq_len_type, + cross_row_offsets_type, + *text_kv_input_symbols, + *vision_kv_input_symbols, + ] + self.language_graph_input_size = len(input_types) + + return Graph( + "llama3-vision-language-model-graph", + forward=LlamaVisionLanguageModel( + pipeline_config=self.pipeline_config, + weights=self.weights, + kv_params=self.get_kv_params(self.pipeline_config), + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + num_text_kv_cache_inputs=len(list(text_kv_input_symbols)), + ), + input_types=input_types, + ) + + @property + def vision_max_seq_len(self) -> int: + """Returns the maximum number of vision tokens.""" + return self._calculate_vision_max_seq_len(self.pipeline_config) + + @classmethod + def _calculate_vision_max_seq_len(cls, config: PipelineConfig) -> int: + """Returns the maximum number of vision tokens.""" + # Marshal out hyperparameters. + height = config.huggingface_config.vision_config.image_size + width = config.huggingface_config.vision_config.image_size + max_num_tiles = config.huggingface_config.vision_config.max_num_tiles + patch_size = config.huggingface_config.vision_config.patch_size + # TODO(bduke): account for the actual instead of max number of tiles. + # num_tiles * (image_dim**2 // patch_dim**2 + 1 (cls token)) + return max_num_tiles * ((height * width) // patch_size**2 + 1) + + @classmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + return ( + pipeline_config.huggingface_config.vision_config.num_hidden_layers + ) + + def _prepare_vision_inputs( + self, + context_batch: Sequence[TextAndVisionContext], + ) -> tuple[Tensor, Tensor, Tensor]: + """Batches up pixel_values, aspect_ratio_ids, and aspect_ratio_masks.""" + images = [] + aspect_ratio_ids_list = [] + aspect_ratio_mask_list = [] + for context in context_batch: + # Get first image in first batch and permute the order to (HWC). + image = np.transpose(context.pixel_values, (0, 1, 3, 4, 2)) + + # Add batch_size, num_concurrent_media, and max_num_tiles dimensions + # [1, num_concurrent_media, max_num_tiles, H, W, C] + image = np.expand_dims(image, axis=(0)) + images.append(image) + + if "aspect_ratio_ids" not in context.extra_model_args: + msg = "aspect_ratio_ids is required for image / vision model input" + raise ValueError(msg) + + if "aspect_ratio_mask" not in context.extra_model_args: + msg = "aspect_ratio_mask is required for image / vision model input" + raise ValueError(msg) + + aspect_ratio_ids_list.append( + context.extra_model_args["aspect_ratio_ids"] + ) + aspect_ratio_mask_list.append( + context.extra_model_args["aspect_ratio_mask"] + ) + + # Convert the list into a single NumPy array with shape + # (batch_size, 1, max_num_tiles, H, W, C). + final_images = np.concatenate(images, axis=0) + + pixel_values = Tensor.from_numpy(final_images).to( + self.pipeline_config.devices[0] + ) + + final_aspect_ratio_ids = np.concatenate(aspect_ratio_ids_list, axis=0) + + aspect_ratio_ids = Tensor.from_numpy(final_aspect_ratio_ids).to( + self.pipeline_config.devices[0] + ) + + final_aspect_ratio_mask = np.concatenate(aspect_ratio_mask_list, axis=0) + + aspect_ratio_mask = Tensor.from_numpy(final_aspect_ratio_mask).to( + self.pipeline_config.devices[0] + ) + + return pixel_values, aspect_ratio_ids, aspect_ratio_mask + + def prepare_initial_token_inputs( + self, + context_batch: Sequence[TextAndVisionContext], + ) -> LlamaVisionInputs: + """Creates tensors of token and image inputs, if applicable.""" + if self.pipeline_config.cache_strategy != KVCacheStrategy.CONTINUOUS: + msg = "Llama Vision only supports continuous batching" + raise ValueError(msg) + + def has_image(pixel_values: np.ndarray | list[np.ndarray]) -> bool: + return pixel_values is not None and len(pixel_values) > 0 + + has_images = any(has_image(ctx.pixel_values) for ctx in context_batch) + if has_images and not all( + has_image(ctx.pixel_values) for ctx in context_batch + ): + msg = ( + "expected context batch to all have images, or no images at all" + ) + raise RuntimeError(msg) + + def initial_prompt_missing_image(ctx: TextAndVisionContext) -> bool: + return ctx.is_initial_prompt and not has_image(ctx.pixel_values) + + if any(initial_prompt_missing_image(ctx) for ctx in context_batch): + msg = "The Llama Vision model currently requires a prompt with an image. Consider using the regular text-only models for non-image prompts" + raise RuntimeError(msg) + + # Prepare vision inputs if applicable. + pixel_values = None + aspect_ratio_ids = None + aspect_ratio_mask = None + if has_images: + pixel_values, aspect_ratio_ids, aspect_ratio_mask = ( + self._prepare_vision_inputs(context_batch) + ) + + # Input row offset type: ["input_row_offsets_len"], UInt32 + input_id_row_offsets = Tensor.from_numpy( + np.cumsum( + [0] + [ctx.active_length for ctx in context_batch], + dtype=np.uint32, + ) + ).to(self.pipeline_config.devices[0]) + + pixel_row_offsets = Tensor.from_numpy( + np.cumsum( + [0] + + [ + # Use an input row offset of 0 to mean no image. + self.vision_max_seq_len + if has_image(ctx.pixel_values) + else 0 + for ctx in context_batch + ], + dtype=np.uint32, + ) + ).to(self.pipeline_config.devices[0]) + + # Input Ids: ["total_seq_len"], Int64 + # Create a ragged token vector of length: sum(len(t) for t in tokens). + tokens = np.concatenate([ctx.next_tokens for ctx in context_batch]) + input_id_values = Tensor.from_numpy(tokens).to( + self.pipeline_config.devices[0] + ) + # This lives on host / in the CPU kernel, but is later casted to a scalar on + # device kernel side. No need for explicit .to(pipeline_config.device) call here. + input_id_max_seq_len = Tensor.from_numpy( + np.array( + [max(ctx.active_length for ctx in context_batch)], + dtype=np.uint32, + ) + ) + + # Unset the context's pixel values so that subsequent next_token + # calls reusing the same context won't run the vision encoder. + for ctx in context_batch: + ctx.pixel_values = [] + + return LlamaVisionInputs( + input_id_values=input_id_values, + input_row_offsets=input_id_row_offsets, + input_id_max_seq_len=input_id_max_seq_len, + pixel_row_offsets=pixel_row_offsets, + pixel_values=pixel_values, + aspect_ratio_ids=aspect_ratio_ids, + aspect_ratio_mask=aspect_ratio_mask, + ) + + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_inputs: ModelInputs, + ) -> LlamaVisionInputs: + """Produce the updated LlamaVisionInputs for the next token. + + This sets existing vision inputs to none and replaces text tokens and + row offsets. + """ + prev_inputs = cast(LlamaVisionInputs, prev_inputs) + next_row_offsets = self._input_row_offsets_prealloc[ + : prev_inputs.input_row_offsets.shape[0] + ] + + return prev_inputs.update_for_next_token(next_tokens, next_row_offsets) + + def execute( + self, + model_inputs: ModelInputs, + # TODO(zheng): This should be folded as KVCacheInputs into ModelInputs. + kv_cache_inputs: Optional[KVCacheInputs] = None, + ) -> ModelOutputs: + assert kv_cache_inputs is not None + # batch_size * num_concurrent_media * max_num_tiles * num_patches + # are set to 0 here to imitate a dummy tensor (used in text-only mode). + cross_attention_states = Tensor.zeros( + shape=[0, self.text_config.hidden_size], + dtype=self.pipeline_config.dtype, + ).to(self.pipeline_config.devices[0]) + + model_inputs = cast(LlamaVisionInputs, model_inputs) + if model_inputs.has_vision_inputs: + # Compute the cross attention states if this is a CE step. + exec_result = self.vision_model.execute( + model_inputs.pixel_values, + model_inputs.aspect_ratio_ids, + model_inputs.aspect_ratio_mask, + copy_inputs_to_device=False, + )[0] + assert isinstance(exec_result, Tensor) + cross_attention_states = exec_result + + all_kv_cache_inputs: list[Tensor] = [] + if isinstance(kv_cache_inputs, MultimodalKVCacheInputs): + all_kv_cache_inputs.extend(kv_cache_inputs.text_kv_cache_inputs) + all_kv_cache_inputs.extend(kv_cache_inputs.vision_kv_cache_inputs) + else: + all_kv_cache_inputs = list(kv_cache_inputs) + + model_outputs = self.language_model.execute( + cross_attention_states, + model_inputs.input_id_values, + model_inputs.input_row_offsets, + model_inputs.input_id_max_seq_len, + model_inputs.pixel_row_offsets, + *all_kv_cache_inputs, + copy_inputs_to_device=False, + ) + assert not self.pipeline_config.enable_echo + assert isinstance(model_outputs[0], Tensor) + return ModelOutputs(next_token_logits=model_outputs[0]) + + @classmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + return KVCacheParams( + dtype=pipeline_config.cache_dtype, + n_kv_heads=pipeline_config.huggingface_config.text_config.num_key_value_heads, + head_dim=( + pipeline_config.huggingface_config.text_config.hidden_size + // pipeline_config.huggingface_config.text_config.num_attention_heads + ), + page_size=pipeline_config.kv_cache_page_size, + cache_strategy=pipeline_config.cache_strategy, + enable_prefix_caching=pipeline_config.enable_prefix_caching, + ) + + def load_kv_manager( + self, + session: InferenceSession, + available_cache_memory: int, + ) -> KVCacheManager: + """Loads KV cache management objects for Llama vision. + + Args: + session: Inference session to compile and init the KV cache. + available_cache_memory: Amount of memory available to the KV cache, + in bytes. + + Returns: + A pair of KV managers: one for self the other for cross attention. + """ + num_cross_attn_layers = len(self.text_config.cross_attention_layers) + return MultimodalKVCacheManager( + params=self.get_kv_params(self.pipeline_config), + max_batch_size=self.pipeline_config.max_batch_size, + text_max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + vision_max_seq_len=self.vision_max_seq_len, + text_num_layers=self.text_config.num_hidden_layers + - num_cross_attn_layers, + vision_num_layers=num_cross_attn_layers, + devices=self.pipeline_config.devices, + session=session, + available_cache_memory=available_cache_memory, + page_size=self.pipeline_config.kv_cache_page_size, + ) + + @classmethod + def estimate_kv_cache_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + devices: list[Device], + ) -> int: + """Estimates the size of the kv cache in bytes.""" + assert pipeline_config.max_batch_size is not None + + num_cross_attn_layers = len( + pipeline_config.huggingface_config.text_config.cross_attention_layers + ) + return MultimodalKVCacheManager.estimated_memory_size( + params=cls.get_kv_params(pipeline_config), + max_batch_size=pipeline_config.max_batch_size, + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=pipeline_config.huggingface_config.text_config.num_hidden_layers + - num_cross_attn_layers, + available_cache_memory=available_cache_memory, + devices=devices, + max_vision_seq_len=cls._calculate_vision_max_seq_len( + pipeline_config + ), + num_vision_layers=num_cross_attn_layers, + ) + + @classmethod + def infer_optimal_batch_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + ) -> int: + if ( + len(pipeline_config.devices) == 1 + and pipeline_config.devices[0].is_host + ): + return 1 + + num_cross_attn_layers = len( + pipeline_config.huggingface_config.text_config.cross_attention_layers + ) + optimal_batch_size = MultimodalKVCacheManager.infer_optimal_batch_size( + params=cls.get_kv_params(pipeline_config), + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=pipeline_config.huggingface_config.text_config.num_hidden_layers + - num_cross_attn_layers, + # TODO(GEX-1843): we underestimate the memory usage of the + # vision model due to multiple activations in flight while executing the + # vision encoder and first few layers of the text encoder in parallel. + # This is a hacky workaround to account for this, in the long term we + # should more accurately measure a model's memory consumption via + # an interface in the graph compiler. + available_cache_memory=int(available_cache_memory * 0.8), + devices=pipeline_config.devices, + max_vision_seq_len=cls._calculate_vision_max_seq_len( + pipeline_config + ), + num_vision_layers=num_cross_attn_layers, + ) + return max( + cls._MIN_DEFAULT_BATCH_SIZE, + min(optimal_batch_size, cls._MAX_DEFAULT_BATCH_SIZE), + ) + + def load_model( + self, + session: InferenceSession, + ) -> tuple[Model, Model]: + """ + Load the Llama vision multimodal model. Since this is a multimodal model, + we have vision and language models (graph) loaded. + """ + self.weights = self.pipeline_config.load_weights() + + def build_vision_model(): + logger.info("Building and compiling vision model...") + before = time.perf_counter() + vision_model_graph = self._llama3_vision_vision_graph() + vision_model = session.load( + vision_model_graph, + weights_registry=self.weights.allocated_weights, + ) + after = time.perf_counter() + logger.info( + f"Compiling vision model took {after - before:.6f} seconds" + ) + return vision_model + + def build_language_model(): + logger.info("Building and compiling language model...") + before = time.perf_counter() + language_model_graph = self._llama3_vision_language_graph() + language_model = session.load( + language_model_graph, + weights_registry=self.weights.allocated_weights, + ) + after = time.perf_counter() + logger.info( + f"Building and compiling language model took {after - before:.6f} seconds" + ) + return language_model + + with ThreadPoolExecutor(max_workers=2) as executor: + vision_model_future = executor.submit(build_vision_model) + language_model_future = executor.submit(build_language_model) + vision_model = vision_model_future.result() + language_model = language_model_future.result() + + return (vision_model, language_model) diff --git a/src/max/pipelines/architectures/llama_vision/mlp.py b/src/max/pipelines/architectures/llama_vision/mlp.py new file mode 100644 index 0000000000..01909f6e42 --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/mlp.py @@ -0,0 +1,36 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Multi-layer Perceptron for Llama 3.2 vision transformer.""" + +from dataclasses import dataclass + +from max.graph import TensorValue, ops +from max.pipelines.nn import Linear +from max.pipelines.nn.layer import Layer + + +@dataclass +class MLP(Layer): + """ + Simple multi-layer perceptron composed of two linear layers. + Uses GELU activation function. + """ + + fc1: Linear + fc2: Linear + + def __call__(self, hidden_states: TensorValue) -> TensorValue: + hidden_states = self.fc1(hidden_states) + hidden_states = ops.gelu(hidden_states) + return self.fc2(hidden_states) diff --git a/src/max/pipelines/architectures/llama_vision/positional_embedding.py b/src/max/pipelines/architectures/llama_vision/positional_embedding.py new file mode 100644 index 0000000000..b581fa76fc --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/positional_embedding.py @@ -0,0 +1,131 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Llama 3.2 Transformer Vision Model Positional Embeddings.""" + +from __future__ import annotations + +from dataclasses import dataclass + +from max.graph import TensorValue, Weight, ops +from max.pipelines.nn import Embedding +from max.pipelines.nn.layer import Layer + + +@dataclass +class PrecomputedAspectRatioEmbedding(Layer): + """ + Llama 3.2 precomputed aspect ratio embedding. + + Args: + params: Hyperparameters for this PrecomputedAspectRatioEmbedding layer. + gate: The gating parameter to control the contribution of the aspect + ratio embedding. + embedding: The aspect ratio embedding. + """ + + max_num_tiles: int + hidden_size: int + gate: Weight + embedding: Embedding + is_gated: bool = False + + def __call__( + self, hidden_state: TensorValue, aspect_ratio_ids: TensorValue + ) -> TensorValue: + batch_size, num_tiles, _, hidden_size = hidden_state.shape + embeddings = self.embedding(aspect_ratio_ids) + embeddings = embeddings.reshape((-1, num_tiles, 1, self.hidden_size)) + + if self.is_gated: + embeddings = embeddings * ops.tanh(self.gate) + + # We're broadcasting in the add operation below, so we call reshape() + # on embeddings first. + embeddings = embeddings.reshape( + ( + batch_size, + num_tiles, + embeddings.shape[2], + hidden_size, + ) + ) + return hidden_state + embeddings + + +@dataclass +class PrecomputedPositionEmbedding(Layer): + """ + Llama 3.2 precomputed position embedding. + + Args: + gate: The gating parameter to control the contribution of the position + embedding or another component of the model. + embedding: The precomputed position embedding. + tile_embedding: The embedding associated with tiles or patches in a + vision model. + """ + + image_size: int + patch_size: int + hidden_size: int + max_num_tiles: int + gate: Weight + embedding: Weight + tile_embedding: Embedding + + def __call__( + self, hidden_state: TensorValue, aspect_ratio_ids: TensorValue + ) -> TensorValue: + # position embeddings + gated_position_embedding = (1 - ops.tanh(self.gate)) * self.embedding + + num_patches = (self.image_size // self.patch_size) ** 2 + 1 + gated_position_embedding = gated_position_embedding.reshape( + ( + 1, + 1, + num_patches, + self.hidden_size, + ) + ) + # We're broadcasting gated_position_embedding in the add operation below, + # so we call reshape() on hidden_state first. + batch_size, num_tiles, _, _ = hidden_state.shape + hidden_state = hidden_state.reshape( + ( + batch_size, + num_tiles, + num_patches, + self.hidden_size, + ) + ) + hidden_state = hidden_state + gated_position_embedding + + # precomputed tile position embeddings + tile_position_embedding = self.tile_embedding(aspect_ratio_ids) + tile_position_embedding = tile_position_embedding.reshape( + ( + batch_size, + self.max_num_tiles, + num_patches, + self.hidden_size, + ) + ) + gated_tile_position_embedding = ( + ops.tanh(self.gate) * tile_position_embedding + ) + # This explicit reshape is called only to match num_tiles dim in + # tile_position_embedding. + hidden_state = hidden_state.reshape(tile_position_embedding.shape) + return hidden_state + gated_tile_position_embedding diff --git a/src/max/pipelines/architectures/llama_vision/vision_model.py b/src/max/pipelines/architectures/llama_vision/vision_model.py new file mode 100644 index 0000000000..695f2666cb --- /dev/null +++ b/src/max/pipelines/architectures/llama_vision/vision_model.py @@ -0,0 +1,741 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Llama 3.2 Transformer Vision Model.""" + +from __future__ import annotations + +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import Dim, StaticDim, TensorValue, TensorValueLike, ops +from max.graph.weights import Weights +from max.pipelines.nn import Conv2D, Embedding, LayerNorm, Linear +from max.pipelines.nn.layer import Layer + +from .attention import Attention +from .encoder import VisionEncoder, VisionEncoderLayer +from .mlp import MLP +from .positional_embedding import ( + PrecomputedAspectRatioEmbedding, + PrecomputedPositionEmbedding, +) + + +# TODO(MAXCORE-170): We should clean this up. This is just a RSCF layout permutation so it +# conforms with our conv op API. +@dataclass +class VisionConv2D(Conv2D): + def __call__(self, x: TensorValue) -> TensorValue: + # Permute first before calling the parent forward pass. + self.filter = ops.permute(self.filter, [2, 3, 1, 0]) + return super().__call__(x=x) + + +@dataclass +class VisionModel(Layer): + """ + Llama 3.2 vision model responsible for encoding images. It consists of two + vision encoders. + + This model is designed to process input images through a combination of convolutional + layers and transformer-based encoders. It utilizes gated and precomputed positional + embeddings to handle spatial information effectively, and supports multi-aspect ratio inputs. + + Args: + params : Hyperparameters that define the architecture and training behavior of the vision model. + gated_positional_embedding: Precomputed positional embeddings that are gated for enhanced spatial encoding. + pre_tile_positional_embedding: Precomputed aspect ratio positional embeddings applied before tiling the input patches. + post_tile_positional_embedding: Precomputed aspect ratio positional embeddings applied after tiling the input patches. + patch_embedding: Convolutional layer that extracts features from input image patches. + class_embedding: Embedding that is concatenated to the sequence for classification tasks. + layernorm_pre: Layer normalization applied before feeding inputs into the transformer encoders. + layernorm_post: Layer normalization applied after processing through the transformer layers. + transformer: Transformer responsible for capturing local spatial relationships in the image. + global_transformer: Transformer focused on global context and capturing long-range dependencies within the image. + """ + + gated_positional_embedding: PrecomputedPositionEmbedding + pre_tile_positional_embedding: PrecomputedAspectRatioEmbedding + post_tile_positional_embedding: PrecomputedAspectRatioEmbedding + patch_embedding: VisionConv2D + class_embedding: TensorValueLike + layernorm_pre: LayerNorm + layernorm_post: LayerNorm + transformer: VisionEncoder + global_transformer: VisionEncoder + dtype: DType + intermediate_layers_indices: list[int] + num_patches: int + + def apply_class_embedding(self, hidden_state: TensorValue) -> TensorValue: + """ + Adds a learnable class token embedding to the sequence of patch embeddings for a vision transformer. + + This function is responsible for prepending a class token to the sequence of image patch embeddings. + The class token is a learnable parameter that captures global information from the image through + the self-attention mechanism. After processing through the transformer layers, the class token + serves as a summary representation of the entire image, typically used for classification tasks. + + Args: + embedding_sequence (TensorValue): A tensor representing the sequence of embedded image patches. + Shape: (batch_size, num_patches, embedding_dim) + + Returns: + TensorValue: A tensor with the class token prepended to the sequence of patch embeddings. + Shape: (batch_size, num_patches + 1, embedding_dim) + The first token in the sequence is the class token, followed by the image patch embeddings. + + Example: + >>> class_token = model.apply_class_embedding(patch_embeddings) + >>> # class_token now holds the class embedding prepended to the patch embeddings + """ + batch_size, _, hidden_size = hidden_state.shape + # This was a reshape in torch reference implementation but we need to + # broadcast this into the right shapes. + class_embedding = TensorValue(self.class_embedding) + + class_embedding = class_embedding.broadcast_to( + (batch_size, 1, hidden_size) + ) + return ops.concat((class_embedding, hidden_state), axis=1) + + def _prepare_aspect_ratio_attention_mask( + self, + aspect_ratio_mask: TensorValue, + num_patches: int, + target_length: int, + dtype: DType, + ) -> TensorValue: + # Expand aspect ratio mask to target_length + batch_size, max_num_tiles = aspect_ratio_mask.shape + attention_mask = aspect_ratio_mask.reshape( + (batch_size, max_num_tiles, 1, 1) + ).cast(dtype) # (1, 4, 1, 1) + # attention_shape (1, 4, 1, 1) -> (1, 4, 1032, 1) + attention_mask = ops.tile(attention_mask, (1, 1, target_length, 1)) + + # Mask padding patches + pad_patches = target_length - num_patches + + # The snippet below is a workaround for + # attention_mask[:, :, 0 - pad_patches :] = 0 + valid_mask = attention_mask[:, :, :-pad_patches, :] + zero_pad = ops.constant(0, dtype).broadcast_to( + (batch_size, max_num_tiles, pad_patches, attention_mask.shape[-1]) + ) + attention_mask = ops.concat((valid_mask, zero_pad), axis=2) + + # Invert the mask (0 -> 1, 1 -> 0) + attention_mask = 1 - attention_mask + + # Reshape to 2D and create 4D attention mask + # (batch_size, 1, max_num_tiles * target_length, max_num_tiles * target_length) + attention_mask = attention_mask.reshape( + (batch_size, max_num_tiles * target_length, 1) + ) + + # TODO: Hardcoded for now. Reference implementation uses torch.finfo(torch.bfloat16).min + bfloat_dtype_min_val = -3.3895313892515355e38 + # Perform outer product by broadcasting elementwise multiplication. + attention_mask = ( + attention_mask + * attention_mask.reshape( + (batch_size, 1, max_num_tiles * target_length) + ) + ) * bfloat_dtype_min_val + + # before unsqueeze: attention_mask shape: (1, 4128, 4128) + return ops.unsqueeze(attention_mask, axis=1) + + def _manual_constant_pad_4d( + self, + dtype: DType, + input_tensor, + pad: tuple[int, int, int, int], + value=0, + ) -> TensorValue: + """ + Manually pads a 4D tensor (batch of images) with constant values. + + Args: + input_tensor (TensorValue): The input 4D tensor (batch_size, channels, height, width). + pad (tuple): Tuple of the form (left, right, top, bottom) specifying padding sizes. + value (float): The value to pad with. + + Returns: + TensorValue: Padded tensor. + """ + left, right, top, bottom = pad + batch_size, channels, height, width = input_tensor.shape + + # Compute new height and width after padding + new_height = height + top + bottom + new_width = width + left + right + + padded_tensor = ops.constant(value, dtype).broadcast_to( + (batch_size, channels, new_height, new_width) + ) + + # Insert the original tensor into the center of the padded tensor + # The code snippet below is a workaround for: + # padded_tensor[ + # :, :, top : top + height, left : left + width + # ] = input_tensor + + # Slice regions along height (dim=2) + # Unchanged region above + top_region = padded_tensor[:, :, :top, :] + # Unchanged region below + bottom_region = padded_tensor[:, :, top + height.dim :, :] + + # Slice regions along width (dim=3) + # Unchanged region to the left + left_region = padded_tensor[:, :, top : top + height.dim, :left] + width_tuple: tuple[TensorValue, ...] = (left_region, input_tensor) + if left > 0: + # Unchanged region to the right + right_region = padded_tensor[ + :, :, top : top + height.dim, left + width.dim : + ] + width_tuple += (right_region,) + + # Concatenate along width (axis=3) + middle_region = ops.concat(width_tuple, axis=3) + + # Concatenate along height (axis=2) + updated_padded_tensor = ops.concat( + (top_region, middle_region, bottom_region), axis=2 + ) + + return updated_padded_tensor + + def __call__( + self, + pixel_values: TensorValue, + aspect_ratio_ids: TensorValue, + aspect_ratio_mask: TensorValue, + ) -> tuple[TensorValue, TensorValue | None, TensorValue | None]: + ( + batch_size, + num_concurrent_media, + num_tiles, + height, + width, + num_channels, + ) = pixel_values.shape + + # Pixel values isn't always guaranteed to be the same dtype as configured + # at the pipeline config level. We insert an explicit cast here to account + # for it. + pixel_values = ops.cast(pixel_values, self.dtype) + + pixel_values = pixel_values.reshape( + ( + batch_size * num_concurrent_media * num_tiles, + height, + width, + num_channels, + ) + ) + + aspect_ratio_ids = aspect_ratio_ids.reshape( + (batch_size * num_concurrent_media, -1) + ) + + # Patch embedding + patch_embeds = self.patch_embedding(pixel_values) + + # Permute it back to original dim of (4, 1280, 32, 32) + patch_embeds = patch_embeds.permute([0, 3, 1, 2]) + + hidden_state = patch_embeds.flatten(2).transpose(1, 2) + + # Tile embeddings + _, num_patches, dim = hidden_state.shape + + hidden_state = hidden_state.reshape( + (batch_size * num_concurrent_media, num_tiles, -1, dim) + ) + + hidden_state = self.pre_tile_positional_embedding( + hidden_state, aspect_ratio_ids + ) + + # Add cls token + hidden_state = hidden_state.reshape( + (batch_size * num_concurrent_media * num_tiles, num_patches, dim) + ) + hidden_state = self.apply_class_embedding(hidden_state) + num_patches += 1 + + # Position embeddings + hidden_state = hidden_state.reshape( + (batch_size * num_concurrent_media, num_tiles, num_patches, dim) + ) + hidden_state = self.gated_positional_embedding( + hidden_state, aspect_ratio_ids + ) + + hidden_state = self.layernorm_pre(hidden_state) + + # Compute the number of tokens to pad + curr_num_patches = StaticDim(hidden_state.shape[-2]).dim + num_padding_patches = (8 - (curr_num_patches % 8)) % 8 + # Compute padding tuple for pad function + padding = ( + 0, + 0, + 0, + num_padding_patches, + ) # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2) + # Pad the tensor + hidden_state = self._manual_constant_pad_4d( + dtype=self.dtype, + input_tensor=hidden_state, + pad=padding, + value=0, + ) + + slice_index = -num_padding_patches if num_padding_patches > 0 else None + + # Prepare attention mask + attention_mask = aspect_ratio_mask.reshape( + (batch_size * num_concurrent_media, -1) + ) # (1, 4) + + attention_mask = self._prepare_aspect_ratio_attention_mask( + aspect_ratio_mask=attention_mask, + num_patches=self.num_patches, + target_length=StaticDim(hidden_state.shape[2]).dim, + dtype=self.dtype, + ) + + # Apply encoder + hidden_state = hidden_state.reshape( + (batch_size * num_concurrent_media, -1, dim) + ) + + # hidden_state: 1, 4128, 1280 + # attention_mask: 1, 1, 4128, 4128 + + hidden_state, all_intermediate_hidden_states = self.transformer( + hidden_state, + attention_mask=attention_mask, + output_hidden_states=True, + ) + assert all_intermediate_hidden_states is not None, ( + "expect intermediate hidden states output" + ) + + hidden_state = self.layernorm_post(hidden_state) + + # Apply global encoder + hidden_state = hidden_state.reshape( + ( + batch_size * num_concurrent_media, # 1 + num_tiles, # 4 + num_patches + num_padding_patches, # 1025 + 7 = 1032 + dim, + ) + ) + hidden_state = self.post_tile_positional_embedding( + hidden_state, aspect_ratio_ids + ) + hidden_state = hidden_state.reshape( + ( + batch_size * num_concurrent_media, + num_tiles * (num_patches + num_padding_patches), + dim, + ) + ) + + global_output = self.global_transformer( + hidden_state, + attention_mask=attention_mask, + output_hidden_states=False, + ) + hidden_state = global_output[0] + + # Remove padding from hidden state. + hidden_state = hidden_state.reshape( + ( + batch_size * num_concurrent_media, # 1 + num_tiles, # 4 + num_patches + num_padding_patches, # 1025 + 7 = 1032 + dim, + ) + ) + hidden_state = hidden_state[:, :, :slice_index] + hidden_state = hidden_state.reshape( + (batch_size, num_concurrent_media, num_tiles, num_patches, dim) + ) + + # Collect intermediate layer outputs from encoder output. + intermediate_hidden_states = ops.stack( + all_intermediate_hidden_states, axis=-1 + ) + + # These two operations are similar to: + # `intermediate_hidden_states + # = intermediate_hidden_states[..., self.intermediate_layers_indices]` + # We don't currently support slicing based on a provided list of indices + # yet. + selected_hidden_states_list = [ + intermediate_hidden_states[:, :, :, idx] + for idx in self.intermediate_layers_indices + ] + intermediate_hidden_states = ops.stack( + selected_hidden_states_list, axis=-1 + ) + + # Remove padding from intermediate hidden states. + # ('batch_size' * 'num_concurrent_media', 4128, 1280, 5) + intermediate_hidden_states = intermediate_hidden_states.reshape( + ( + Dim(batch_size) * num_concurrent_media, # 1 + num_tiles, # 4 + num_patches + num_padding_patches, # 1025 + 7 = 1032 + dim * len(self.intermediate_layers_indices), + ) + ) + + # (1, 4, 1032, 6400) -> (1, 4, 1025, 6400) + intermediate_hidden_states = intermediate_hidden_states[ + :, :, :slice_index + ] + + intermediate_hidden_states = intermediate_hidden_states.reshape( + ( + batch_size, + num_concurrent_media, + num_tiles, + num_patches, + dim * len(self.intermediate_layers_indices), + ) + ) + + # Concatenate final hidden state and intermediate hidden states. + hidden_state = ops.concat( + (hidden_state, intermediate_hidden_states), axis=-1 + ) + + # output_attentions: False, output_hidden_states: False in reference + # implementation, so these are just returned as `None`s. + return ( + hidden_state, # "last_hidden_state" + None, # "hidden_states" + None, # "attentions" + ) + + +def instantiate_vision_model( + dtype: DType, + image_size: int, + patch_size: int, + supported_aspect_ratios: list[list[int]], + hidden_size: int, + max_num_tiles: int, + num_channels: int, + norm_eps: float, + attention_heads: int, + num_hidden_layers: int, + intermediate_size: int, + num_global_layers: int, + intermediate_layers_indices: list[int], + weights: Weights, +) -> VisionModel: + # Shared variables. + num_patches = (image_size // patch_size) ** 2 + 1 + max_aspect_ratio_id = (len(supported_aspect_ratios)) + 1 + + gated_positional_embedding = PrecomputedPositionEmbedding( + image_size=image_size, + patch_size=patch_size, + hidden_size=hidden_size, + max_num_tiles=max_num_tiles, + gate=weights.vision_model.gated_positional_embedding.gate.allocate( + dtype, [1] + ), + embedding=weights.vision_model.gated_positional_embedding.embedding.allocate( + dtype, + [ + num_patches, + hidden_size, + ], + ), + tile_embedding=Embedding( + weights.vision_model.gated_positional_embedding.tile_embedding.weight.allocate( + dtype, + [ + max_aspect_ratio_id, + max_num_tiles * num_patches * hidden_size, + ], + ), + ), + ) + + pre_tile_positional_embedding = PrecomputedAspectRatioEmbedding( + max_num_tiles=max_num_tiles, + hidden_size=hidden_size, + gate=weights.vision_model.pre_tile_positional_embedding.gate.allocate( + dtype, [1] + ), + embedding=Embedding( + weights.vision_model.pre_tile_positional_embedding.embedding.weight.allocate( + dtype, + [max_aspect_ratio_id, max_num_tiles * hidden_size], + ), + ), + is_gated=True, + ) + + post_tile_positional_embedding = PrecomputedAspectRatioEmbedding( + max_num_tiles=max_num_tiles, + hidden_size=hidden_size, + gate=weights.vision_model.post_tile_positional_embedding.gate.allocate( + dtype, [1] + ), + embedding=Embedding( + weights.vision_model.post_tile_positional_embedding.embedding.weight.allocate( + dtype, + [ + max_aspect_ratio_id, + max_num_tiles * hidden_size, + ], + ), + ), + is_gated=True, + ) + + # patch_embedding filter has a shape of (1280, 3, 14, 14). + patch_embedding = VisionConv2D( + filter=weights.vision_model.patch_embedding.weight.allocate( + dtype, + [hidden_size, num_channels, patch_size, patch_size], + ), + stride=patch_size, + padding=(0, 0, 0, 0), + bias=None, + ) + + class_embedding = weights.vision_model.class_embedding.allocate( + dtype, + [hidden_size], + ) + + layernorm_pre = LayerNorm( + weights.vision_model.layernorm_pre.weight.allocate( + dtype, [hidden_size] + ), + bias=weights.vision_model.layernorm_pre.bias.allocate( + dtype, [hidden_size] + ), + eps=norm_eps, + ) + + layernorm_post = LayerNorm( + weights.vision_model.layernorm_post.weight.allocate( + dtype, [hidden_size] + ), + bias=weights.vision_model.layernorm_post.bias.allocate( + dtype, [hidden_size] + ), + eps=norm_eps, + ) + + transformer_encoder_layers: list[VisionEncoderLayer] = [] + + head_dim = hidden_size // attention_heads + + for index in range(num_hidden_layers): + curr_layer_weight = weights.vision_model.transformer.layers[index] + transformer_encoder_layers.append( + VisionEncoderLayer( + mlp=MLP( + Linear( + curr_layer_weight.mlp.fc1.weight.allocate( + dtype, + [intermediate_size, hidden_size], + ), + bias=curr_layer_weight.mlp.fc1.bias.allocate( + dtype, [intermediate_size] + ), + ), + Linear( + curr_layer_weight.mlp.fc2.weight.allocate( + dtype, + [hidden_size, intermediate_size], + ), + bias=curr_layer_weight.mlp.fc2.bias.allocate( + dtype, [hidden_size] + ), + ), + ), + input_layernorm=LayerNorm( + curr_layer_weight.input_layernorm.weight.allocate( + dtype, [hidden_size] + ), + bias=curr_layer_weight.input_layernorm.bias.allocate( + dtype, [hidden_size] + ), + eps=norm_eps, + ), + post_attention_layernorm=LayerNorm( + curr_layer_weight.post_attention_layernorm.weight.allocate( + dtype, [hidden_size] + ), + bias=curr_layer_weight.post_attention_layernorm.bias.allocate( + dtype, [hidden_size] + ), + eps=norm_eps, + ), + self_attn=Attention( + n_heads=attention_heads, + head_dim=head_dim, + wk=Linear( + curr_layer_weight.self_attn.k_proj.weight.allocate( + dtype, + [attention_heads * head_dim, hidden_size], + ), + bias=None, + ), + wv=Linear( + curr_layer_weight.self_attn.v_proj.weight.allocate( + dtype, + [attention_heads * head_dim, hidden_size], + ), + bias=None, + ), + wq=Linear( + curr_layer_weight.self_attn.q_proj.weight.allocate( + dtype, + [attention_heads * head_dim, hidden_size], + ), + bias=None, + ), + wo=Linear( + curr_layer_weight.self_attn.o_proj.weight.allocate( + dtype, + [hidden_size, attention_heads * head_dim], + ), + bias=None, + ), + ), + is_gated=False, + gate_attn=None, + gate_ffn=None, + ) + ) + transformer = VisionEncoder(transformer_encoder_layers) + + global_transformer_layers: list[VisionEncoderLayer] = [] + + for index in range(num_global_layers): + curr_layer_weight = weights.vision_model.global_transformer.layers[ + index + ] + + global_transformer_layers.append( + VisionEncoderLayer( + mlp=MLP( + Linear( + curr_layer_weight.mlp.fc1.weight.allocate( + dtype, + [intermediate_size, hidden_size], + ), + bias=curr_layer_weight.mlp.fc1.bias.allocate( + dtype, [intermediate_size] + ), + ), + Linear( + curr_layer_weight.mlp.fc2.weight.allocate( + dtype, + [hidden_size, intermediate_size], + ), + bias=curr_layer_weight.mlp.fc2.bias.allocate( + dtype, [hidden_size] + ), + ), + ), + input_layernorm=LayerNorm( + curr_layer_weight.input_layernorm.weight.allocate( + dtype, [hidden_size] + ), + bias=curr_layer_weight.input_layernorm.bias.allocate( + dtype, [hidden_size] + ), + eps=norm_eps, + ), + post_attention_layernorm=LayerNorm( + curr_layer_weight.post_attention_layernorm.weight.allocate( + dtype, [hidden_size] + ), + curr_layer_weight.post_attention_layernorm.bias.allocate( + dtype, [hidden_size] + ), + eps=norm_eps, + ), + self_attn=Attention( + n_heads=attention_heads, + head_dim=head_dim, + wk=Linear( + curr_layer_weight.self_attn.k_proj.weight.allocate( + dtype, + [hidden_size, attention_heads * head_dim], + ), + bias=None, + ), + wv=Linear( + curr_layer_weight.self_attn.v_proj.weight.allocate( + dtype, + [hidden_size, attention_heads * head_dim], + ), + bias=None, + ), + wq=Linear( + curr_layer_weight.self_attn.q_proj.weight.allocate( + dtype, + [hidden_size, attention_heads * head_dim], + ), + bias=None, + ), + wo=Linear( + curr_layer_weight.self_attn.o_proj.weight.allocate( + dtype, + [attention_heads * head_dim, hidden_size], + ), + bias=None, + ), + ), + is_gated=True, + gate_attn=curr_layer_weight.gate_attn.allocate(dtype, [1]), + gate_ffn=curr_layer_weight.gate_ffn.allocate(dtype, [1]), + ) + ) + global_transformer = VisionEncoder(global_transformer_layers) + + return VisionModel( + gated_positional_embedding=gated_positional_embedding, + pre_tile_positional_embedding=pre_tile_positional_embedding, + post_tile_positional_embedding=post_tile_positional_embedding, + patch_embedding=patch_embedding, + class_embedding=class_embedding, + layernorm_pre=layernorm_pre, + layernorm_post=layernorm_post, + transformer=transformer, + global_transformer=global_transformer, + dtype=dtype, + intermediate_layers_indices=intermediate_layers_indices, + num_patches=(image_size // patch_size) ** 2 + 1, + ) diff --git a/src/max/pipelines/architectures/mistral/README.md b/src/max/pipelines/architectures/mistral/README.md new file mode 100644 index 0000000000..4603761d44 --- /dev/null +++ b/src/max/pipelines/architectures/mistral/README.md @@ -0,0 +1,129 @@ +# Mistral NeMo + +**Language:** Python + +**API**: MAX Graph + +This pipeline provides optimized support for the `MistralForCausalLM` family +of large language models, as exemplified by the Mistral NeMo 12B text +completion model. The model itself has been constructed in Python +using the [MAX Graph API](https://docs.modular.com/max/graph/). + +The MAX Graph API provides an accessible interface to the construction of +flexible accelerated compute graphs, which are then optimized by the MAX +Engine's advanced graph compiler. This pipeline showcases how a large language +model can be fully defined using Python and MAX Graphs and then compiled for +optimal inference performance via the MAX Engine. + +## Model + +[Mistral NeMo](https://mistral.ai/news/mistral-nemo/) is an open source large +language model released by [Mistral AI](https://mistral.ai) in partnership +with NVIDIA. The weight checkpoints have been released under an Apache 2.0 +license. + +The default settings for this pipeline use the NeMo 12B set of pretrained +weights in the `bfloat16` encoding. + +## Usage + +The easiest way to try out this pipeline is with our Magic command-line tool. + +1. Install Magic on macOS and Ubuntu with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Clone the MAX examples repository: + + If you don't already have a local clone of this repository, create one via: + + ```shell + git clone https://github.com/modular/max.git + ``` + + The following instructions assume that you're present within this pipeline's + directory, and you can change to it after cloning: + + ```shell + cd max/src/max/ + ``` + +3. Now run the Mistral text completion demo with the following command: + + ```shell + magic run mistral --prompt "I believe the meaning of life is" + ``` + +4. Host a chat completion endpoint via MAX Serve. + + MAX Serve provides functionality to host performant OpenAI compatible + endpoints using the FastAPI framework. + + You can configure the pipeline to be hosted by using the `--serve` argument. + For example: + + ```shell + magic run mistral --serve + ``` + + A request can be submitted via a cURL command. + + ```shell + curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "mistralai/Mistral-Nemo-Instruct-2407", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who won the world series in 2020?"} + ] + }' + ``` + + Additionally, finetuned weights hosted on Hugging Face for any compatible + `MistralForCausalLM` model can be used with this optimized architecture + via the `serve` command: + + ```shell + magic run serve --model-path=mistralai/Ministral-8B-Instruct-2410 + ``` + +## Options + +The following command-line options are available to customize operation of the +pipeline: + +- `--max-length`: Controls the maximum length of the text sequence + (includes the input tokens). + (Default value: 512) +- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 + value is provided, the model will continue to generate tokens for the entire + context length. (Default value: -1) +- `--prompt`: The text prompt to use for further generation. +- `--quantization-encoding`: The encoding to use for a datatype that can be + quantized to a low bits per weight format. The options for quantized formats + will download and cache default weights, but `float32` requires the use of + `--weight-path` to specify locally downloaded full-precision weights for use + in the model. + Valid values: `bfloat16`, `float32`. + (Default value: `float32`). +- `--save-to-serialized-model-path`: If specified, writes the serialized model + to this path. +- `--serialized-model-path`: If specified, tries to load a serialized model + from this path. +- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. +- `--weight-path`: Overrides the default URL, and allows for an + already-downloaded pretrained weight file to be used with the model. +- `--max-batch-size`: Specifies the maximum batch size to be used. + Default is 1. +- `--devices`: Specifies the device(s) to use for the model. This can optionally + be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". + Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/mistral/__init__.py b/src/max/pipelines/architectures/mistral/__init__.py new file mode 100644 index 0000000000..ca84b22cc4 --- /dev/null +++ b/src/max/pipelines/architectures/mistral/__init__.py @@ -0,0 +1,19 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import PIPELINE_REGISTRY + +from .arch import mistral_arch +from .model import MistralModel + +__all__ = ["MistralModel", "mistral_arch"] diff --git a/src/max/pipelines/architectures/mistral/arch.py b/src/max/pipelines/architectures/mistral/arch.py new file mode 100644 index 0000000000..fecc610b70 --- /dev/null +++ b/src/max/pipelines/architectures/mistral/arch.py @@ -0,0 +1,36 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from .model import MistralModel + +mistral_arch = SupportedArchitecture( + name="MistralForCausalLM", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=["mistralai/Mistral-Nemo-Instruct-2407"], + default_encoding=SupportedEncoding.bfloat16, + supported_encodings={ + SupportedEncoding.bfloat16: [KVCacheStrategy.CONTINUOUS] + }, + pipeline_model=MistralModel, + tokenizer=TextTokenizer, + default_weights_format=WeightsFormat.safetensors, +) diff --git a/src/max/pipelines/architectures/mistral/graph.py b/src/max/pipelines/architectures/mistral/graph.py new file mode 100644 index 0000000000..57056f8fcd --- /dev/null +++ b/src/max/pipelines/architectures/mistral/graph.py @@ -0,0 +1,253 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Build a Mistral model via Graph API from Safetensor weights.""" + +import math + +from max.dtype import DType +from max.graph import Graph, TensorType, ops +from max.graph.weights import SafetensorWeights +from max.pipelines import PipelineConfig +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + KVCacheManager, + KVCacheParams, +) +from max.pipelines.nn import ( + MLP, + AttentionWithRope, + Embedding, + Linear, + OptimizedRotaryEmbedding, + RMSNorm, + Transformer, + TransformerBlock, +) + + +def feed_forward( + dtype: DType, + hidden_dim: int, + feed_forward_length: int, + weights: SafetensorWeights, +): + return MLP( + linear( + dtype, + feed_forward_length, + hidden_dim, + weights.gate_proj, + ), + linear( + dtype, + hidden_dim, + feed_forward_length, + weights.down_proj, + ), + linear( + dtype, + feed_forward_length, + hidden_dim, + weights.up_proj, + ), + ) + + +def linear( + dtype: DType, + in_features: int, + out_features: int, + weights: SafetensorWeights, +) -> Linear: + return Linear( + weights.weight.allocate(dtype, [in_features, out_features], None) + ) + + +def rms_norm(dims: int, eps: float, weights: SafetensorWeights) -> RMSNorm: + return RMSNorm(weights.weight.allocate(DType.bfloat16, [dims]), eps) + + +def embedding( + params: PipelineConfig, + vocab_size: int, + hidden_dim: int, + weights: SafetensorWeights, +): + return Embedding( + weights.weight.allocate( + params.dtype, + [vocab_size, hidden_dim], + ) + ) + + +def _attention_opaque( + kv_params: KVCacheParams, + params: PipelineConfig, + rope: OptimizedRotaryEmbedding, + weights: SafetensorWeights, + layer_idx: int, +): + kv_weight_dim = ( + params.huggingface_config.head_dim + * params.huggingface_config.num_key_value_heads + ) + + wq = weights.self_attn.q_proj.weight.allocate( + params.dtype, + [ + params.huggingface_config.num_attention_heads + * params.huggingface_config.head_dim, + params.huggingface_config.hidden_size, + ], + ) + wk = weights.self_attn.k_proj.weight.allocate( + params.dtype, + [kv_weight_dim, params.huggingface_config.hidden_size], + ) + wv = weights.self_attn.v_proj.weight.allocate( + params.dtype, + [kv_weight_dim, params.huggingface_config.hidden_size], + ) + wqkv = ops.concat((wq, wk, wv)) + + return AttentionWithRope( + n_heads=params.huggingface_config.num_attention_heads, + kv_params=kv_params, + wqkv=wqkv, + wo=linear( + params.dtype, + params.huggingface_config.hidden_size, + params.huggingface_config.num_attention_heads + * params.huggingface_config.head_dim, + weights.self_attn.o_proj, + ), + rope=rope, + layer_idx=layer_idx, # type: ignore + scale=math.sqrt(1 / kv_params.head_dim), + ) + + +def _transformer( + graph: Graph, + params: PipelineConfig, + weights: SafetensorWeights, + max_seq_len: int, + kv_params: KVCacheParams, +): + with graph: + rope = OptimizedRotaryEmbedding( + dim=params.huggingface_config.num_attention_heads + * params.huggingface_config.head_dim, + n_heads=params.huggingface_config.num_attention_heads, + theta=params.huggingface_config.rope_theta, + max_seq_len=max_seq_len, + rope_scaling=None, + interleaved=False, + ) + + layers = [ + TransformerBlock( + attention=_attention_opaque( + kv_params, + params, + rope, + weights.model.layers[i], + layer_idx=ops.constant(i, DType.uint32), # type: ignore + ), + mlp=feed_forward( + params.dtype, + params.huggingface_config.hidden_size, + params.huggingface_config.intermediate_size, + weights.model.layers[i].mlp, + ), + attention_norm=rms_norm( + params.huggingface_config.hidden_size, + params.huggingface_config.rms_norm_eps, + weights.model.layers[i].input_layernorm, + ), + mlp_norm=rms_norm( + params.huggingface_config.hidden_size, + params.huggingface_config.rms_norm_eps, + weights.model.layers[i].post_attention_layernorm, + ), + ) + for i in range(params.huggingface_config.num_hidden_layers) + ] + + embedding_layer = embedding( + params, + params.huggingface_config.vocab_size, + params.huggingface_config.hidden_size, + weights.model.embed_tokens, + ) + + output = linear( + params.dtype, + params.huggingface_config.vocab_size, + params.huggingface_config.hidden_size, + weights.lm_head, + ) + + kv_collection_cls = FetchContinuousBatchingKVCacheCollection + + return Transformer( + dim=params.huggingface_config.hidden_size, + n_heads=params.huggingface_config.num_attention_heads, + layers=layers, + norm=rms_norm( + params.huggingface_config.hidden_size, + params.huggingface_config.rms_norm_eps, + weights.model.norm, + ), + output=output, + embedding=embedding_layer, + kv_params=kv_params, + kv_collection_constructor=kv_collection_cls(kv_params), + ) + + +def _build_graph( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, + max_seq_len: int, + kv_params: KVCacheParams, + kv_manager: KVCacheManager, +) -> Graph: + tokens_type = TensorType(DType.int64, shape=["total_seq_len"]) + input_row_offsets_type = TensorType( + DType.uint32, shape=["input_row_offsets_len"] + ) + + kv_cache_args = kv_manager.input_symbols()[0] + + with Graph( + "mistral", + input_types=[ + tokens_type, + input_row_offsets_type, + *kv_cache_args, + ], + ) as graph: + model = _transformer( + graph=graph, + params=pipeline_config, + weights=weights, + max_seq_len=max_seq_len, + kv_params=kv_params, + ) + tokens, input_row_offsets, *kv_cache = graph.inputs + outputs = model(tokens, kv_cache, input_row_offsets=input_row_offsets) + graph.output(*outputs) + return graph diff --git a/src/max/pipelines/architectures/mistral/model.py b/src/max/pipelines/architectures/mistral/model.py new file mode 100644 index 0000000000..69ba410ffa --- /dev/null +++ b/src/max/pipelines/architectures/mistral/model.py @@ -0,0 +1,254 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import logging +import time +from typing import Sequence, cast + +import numpy as np +from max.driver import Device, Tensor +from max.engine import InferenceSession, Model +from max.graph.weights import SafetensorWeights +from max.pipelines import ( + ModelInputs, + ModelOutputs, + PipelineConfig, + PipelineModel, + TextContext, + upper_bounded_default, +) +from max.pipelines.kv_cache import ( + KVCacheInputs, + KVCacheManager, + KVCacheParams, + estimate_kv_cache_size, + load_kv_manager, +) + +from .graph import _build_graph + +logger = logging.getLogger("max.pipelines") + + +class MistralInputs(ModelInputs): + """A class representing inputs for the Mistral model. + + This class encapsulates the input tensors required for the Mistral model execution: + - input_tokens: A tensor containing the input token IDs + - input_row_offsets: A tensor containing the offsets for each row in the ragged input sequence + """ + + input_tokens: Tensor + input_row_offsets: Tensor + + def __init__( + self, + input_tokens: Tensor, + input_row_offsets: Tensor, + ) -> None: + self.input_tokens = input_tokens + self.input_row_offsets = input_row_offsets + + +class MistralModel(PipelineModel[TextContext]): + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) + self.model = self.load_model(session) + + def execute( + self, + model_inputs: ModelInputs, + kv_cache_inputs: KVCacheInputs | None = None, + ) -> ModelOutputs: + """Runs the graph.""" + model_inputs = cast(MistralInputs, model_inputs) + assert kv_cache_inputs is not None, ( + "Mistral has KV cache inputs, but none were provided" + ) + model_outputs = self.model.execute( + model_inputs.input_tokens, + model_inputs.input_row_offsets, + *kv_cache_inputs, + copy_inputs_to_device=False, + ) + assert isinstance(model_outputs[0], Tensor) + return ModelOutputs(next_token_logits=model_outputs[0]) + + def prepare_initial_token_inputs( + self, + context_batch: Sequence[TextContext], + ) -> MistralInputs: + # Get tokens and seq ids + tokens = [ctx.next_tokens for ctx in context_batch] + + # Get input_row_offsets: start and end position of each batch in the + # combined total_seq_len dimension. + input_row_offsets = Tensor.from_numpy( + np.cumsum( + [0] + [ctx.active_length for ctx in context_batch], + dtype=np.uint32, + ) + ).to(self.pipeline_config.devices[0]) + + # Create a ragged token vector of length: sum(len(t) for t in tokens). + next_tokens_batch = np.concatenate(tokens) + next_tokens_batch = Tensor.from_numpy(next_tokens_batch).to( + self.pipeline_config.devices[0] + ) + + return MistralInputs( + input_tokens=next_tokens_batch, + input_row_offsets=input_row_offsets, + ) + + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: ModelInputs, + ) -> MistralInputs: + prev_model_inputs = cast(MistralInputs, prev_model_inputs) + row_offsets_size = prev_model_inputs.input_row_offsets.shape[0] + next_row_offsets = self._input_row_offsets_prealloc[:row_offsets_size] + return MistralInputs( + input_tokens=next_tokens, + input_row_offsets=next_row_offsets, + ) + + @classmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + return KVCacheParams( + page_size=pipeline_config.kv_cache_page_size, + dtype=pipeline_config.cache_dtype, + n_kv_heads=pipeline_config.huggingface_config.num_key_value_heads, + head_dim=pipeline_config.huggingface_config.head_dim, + cache_strategy=pipeline_config.cache_strategy, + enable_prefix_caching=pipeline_config.enable_prefix_caching, + ) + + @classmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + return pipeline_config.huggingface_config.num_hidden_layers + + @classmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + try: + return upper_bounded_default( + upper_bound=pipeline_config.huggingface_config.max_position_embeddings, + default=pipeline_config.max_length, + ) + except ValueError as e: + msg = ( + "Unable to infer max_length for Mistral, the provided " + f"max_length ({pipeline_config.max_length}) exceeds the " + f"model's max_position_embeddings " + f"({pipeline_config.huggingface_config.max_position_embeddings})." + ) + raise ValueError(msg) from e + + def load_kv_manager( + self, + session: InferenceSession, + available_cache_memory: int, + ) -> KVCacheManager: + assert self.pipeline_config.devices, ( + "devices must be provided to load kv manager." + ) + return load_kv_manager( + params=self.get_kv_params(self.pipeline_config), + max_batch_size=self.pipeline_config.max_batch_size, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + num_layers=self.pipeline_config.huggingface_config.num_hidden_layers, + devices=self.pipeline_config.devices, + available_cache_memory=available_cache_memory, + page_size=self.pipeline_config.kv_cache_page_size, + session=session, + ) + + @classmethod + def estimate_kv_cache_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + devices: list[Device], + ) -> int: + """Estimates the size of the kv cache in bytes.""" + assert devices, "devices must be provided to estimate kv cache size." + return estimate_kv_cache_size( + params=cls.get_kv_params(pipeline_config), + max_batch_size=pipeline_config.max_batch_size, + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=pipeline_config.huggingface_config.num_hidden_layers, + available_cache_memory=available_cache_memory, + devices=devices, + ) + + def load_model(self, session: InferenceSession) -> Model: + if self.pipeline_config.enable_echo: + msg = "Mistral model does not currently implement enable echo." + raise ValueError(msg) + + # Pre-allocate a buffer for input_row_offsets in multistep execution. + # We do this to avoid materializing and copying a buffer with each multistep step + assert self.pipeline_config.max_batch_size, ( + "Expected max_batch_size to be set" + ) + self._input_row_offsets_prealloc = Tensor.from_numpy( + np.arange(self.pipeline_config.max_batch_size + 1, dtype=np.uint32) + ).to(self.pipeline_config.devices[0]) + + self._weights = self.pipeline_config.load_weights() + + if not isinstance(self._weights, SafetensorWeights): + msg = "only safetensors weights are currently supported in Mistral models." + raise ValueError(msg) + + if serialized_path := self.pipeline_config.serialized_model_path: + # Hydrate all weights to be referenced by the serialized graph. + weights_registry = {} + for name, weight in self._weights.items(): + weights_registry[name] = weight.raw_tensor() + logger.info( + "Loading serialized model from ", serialized_path, "..." + ) + return session.load( + serialized_path, + weights_registry=weights_registry, + ) + else: + logger.info("Building and compiling model...") + before = time.perf_counter() + graph = _build_graph( + pipeline_config=self.pipeline_config, + weights=self._weights, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + kv_params=self.get_kv_params(self.pipeline_config), + kv_manager=self.kv_manager, + ) + model = session.load( + graph, weights_registry=self._weights.allocated_weights + ) + after = time.perf_counter() + logger.info( + f"Building and compiling model took {after - before:.6f} seconds" + ) + if ( + export_path + := self.pipeline_config.save_to_serialized_model_path + ): + logger.info("Exporting serialized model to %s", export_path) + model._export_mef(export_path) + return model diff --git a/src/max/pipelines/architectures/mpnet/__init__.py b/src/max/pipelines/architectures/mpnet/__init__.py new file mode 100644 index 0000000000..b1252f1b47 --- /dev/null +++ b/src/max/pipelines/architectures/mpnet/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import mpnet_arch + +__all__ = ["mpnet_arch"] diff --git a/src/max/pipelines/architectures/mpnet/arch.py b/src/max/pipelines/architectures/mpnet/arch.py new file mode 100644 index 0000000000..9c124954a6 --- /dev/null +++ b/src/max/pipelines/architectures/mpnet/arch.py @@ -0,0 +1,38 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) + +from .model import MPNetPipelineModel + +mpnet_arch = SupportedArchitecture( + name="MPNetForMaskedLM", + task=PipelineTask.EMBEDDINGS_GENERATION, + example_repo_ids=[ + "sentence-transformers/all-mpnet-base-v2", + ], + default_encoding=SupportedEncoding.bfloat16, + supported_encodings={ + SupportedEncoding.float32: [], + SupportedEncoding.bfloat16: [], + }, + pipeline_model=MPNetPipelineModel, + tokenizer=TextTokenizer, + default_weights_format=WeightsFormat.safetensors, +) diff --git a/src/max/pipelines/architectures/mpnet/graph.py b/src/max/pipelines/architectures/mpnet/graph.py new file mode 100644 index 0000000000..89173e9064 --- /dev/null +++ b/src/max/pipelines/architectures/mpnet/graph.py @@ -0,0 +1,493 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +from __future__ import annotations + +import math + +import numpy as np +from max.dtype import DType +from max.graph import Graph, TensorType, TensorValue, ops +from max.graph.quantization import QuantizationEncoding +from max.graph.weights import Weights +from max.pipelines import PipelineConfig +from max.pipelines.nn import Embedding, LayerNorm, Linear, Sequential +from max.pipelines.nn.layer import Layer + + +def _quantization_encoding( + pipeline_config: PipelineConfig, +) -> QuantizationEncoding | None: + if supported_encoding := pipeline_config.quantization_encoding: + return supported_encoding.quantization_encoding + return None + + +class MPNetEmbeddings(Layer): + """An embeddings layer that combines the tokens embeddings and positions + embeddings.""" + + def __init__(self, pipeline_config: PipelineConfig, weights: Weights): + config = self.config = pipeline_config.huggingface_config + self.word_embeddings = Embedding( + weights.word_embeddings.weight.allocate( + pipeline_config.dtype, + [ + config.vocab_size, + config.hidden_size, + ], + _quantization_encoding(pipeline_config), + ) + ) + self.position_embeddings = Embedding( + weights.position_embeddings.weight.allocate( + pipeline_config.dtype, + [ + config.max_position_embeddings, + config.hidden_size, + ], + ) + ) + self.layer_norm = LayerNorm( + weight=weights.LayerNorm.weight.allocate( + pipeline_config.dtype, + [config.hidden_size], + ), + bias=weights.LayerNorm.bias.allocate( + pipeline_config.dtype, [config.hidden_size] + ), + eps=config.layer_norm_eps, + ) + self.position_ids = weights.position_ids.allocate( + DType.int64, + [ + 1, + config.max_position_embeddings, + ], + ) + + def __call__( + self, + input_ids: TensorValue, + ) -> TensorValue: + position_ids = _create_position_ids_from_input_ids( + input_ids, self.config.pad_token_id + ) + inputs_embeds = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) + embeddings = inputs_embeds + position_embeddings + return self.layer_norm(embeddings) + + +def _create_position_ids_from_input_ids( + input_ids: TensorValue, padding_idx: int +) -> TensorValue: + mask = (input_ids != padding_idx).cast(DType.int64) + incremental_indices = ops.cumsum(mask, axis=1) * mask + return incremental_indices + padding_idx + + +class MPNetSelfAttention(Layer): + """Self-attention layer with position compensation.""" + + def __init__(self, pipeline_config: PipelineConfig, weights: Weights): + config = pipeline_config.huggingface_config + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int( + config.hidden_size / config.num_attention_heads + ) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.q = Linear( + weights.q.weight.allocate( + pipeline_config.dtype, + [self.all_head_size, config.hidden_size], + _quantization_encoding(pipeline_config), + ), + bias=weights.q.bias.allocate( + pipeline_config.dtype, + [self.all_head_size], + _quantization_encoding(pipeline_config), + ), + ) + self.k = Linear( + weights.k.weight.allocate( + pipeline_config.dtype, + [self.all_head_size, config.hidden_size], + _quantization_encoding(pipeline_config), + ), + bias=weights.k.bias.allocate( + pipeline_config.dtype, + [self.all_head_size], + _quantization_encoding(pipeline_config), + ), + ) + self.v = Linear( + weights.v.weight.allocate( + pipeline_config.dtype, + [self.all_head_size, config.hidden_size], + _quantization_encoding(pipeline_config), + ), + bias=weights.v.bias.allocate( + pipeline_config.dtype, + [self.all_head_size], + _quantization_encoding(pipeline_config), + ), + ) + self.o = Linear( + weights.o.weight.allocate( + pipeline_config.dtype, + [config.hidden_size, config.hidden_size], + _quantization_encoding(pipeline_config), + ), + bias=weights.o.bias.allocate( + pipeline_config.dtype, + [config.hidden_size], + _quantization_encoding(pipeline_config), + ), + ) + + def transpose_for_scores(self, x: TensorValue) -> TensorValue: + new_x_shape = x.shape[:-1] + [ + self.num_attention_heads, + self.attention_head_size, + ] + x = ops.reshape(x, new_x_shape) + return ops.permute(x, [0, 2, 1, 3]) + + def __call__( + self, + hidden_states, + attention_mask: TensorValue, + position_bias: TensorValue, + ) -> TensorValue: + q = self.q(hidden_states) + k = self.k(hidden_states) + v = self.v(hidden_states) + + q = self.transpose_for_scores(q) + k = self.transpose_for_scores(k) + v = self.transpose_for_scores(v) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = q @ k.transpose(-1, -2) + attention_scores = attention_scores / math.sqrt( + self.attention_head_size + ) + + # Apply relative position embedding (precomputed in MPNetEncoder). + attention_scores += position_bias + + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = ops.softmax(attention_scores) + + c = attention_probs @ v + + c = ops.permute(c, [0, 2, 1, 3]) + new_c_shape = c.shape[:-2] + [self.all_head_size] + c = ops.reshape(c, new_c_shape) + + return self.o(c) + + +class MPNetAttention(Layer): + """Container for the attention and attention output layer norm layers.""" + + def __init__(self, pipeline_config: PipelineConfig, weights: Weights): + config = pipeline_config.huggingface_config + self.attn = MPNetSelfAttention(pipeline_config, weights.attn) + self.layer_norm = LayerNorm( + weight=weights.LayerNorm.weight.allocate( + DType.float32, [config.hidden_size] + ), + bias=weights.LayerNorm.bias.allocate( + DType.float32, [config.hidden_size] + ), + eps=config.layer_norm_eps, + ) + + def __call__( + self, + hidden_states: TensorValue, + attention_mask: TensorValue, + position_bias: TensorValue, + ) -> TensorValue: + attn_output = self.attn( + hidden_states, + attention_mask, + position_bias, + ) + return self.layer_norm(attn_output + hidden_states) + + +_ACTIVATIONS = { + "gelu": ops.gelu, + "relu": ops.relu, + "silu": ops.silu, + "sigmoid": ops.sigmoid, + "tanh": ops.tanh, +} + + +class MPNetIntermediate(Layer): + """Fully connected layer with an activation function.""" + + def __init__(self, pipeline_config: PipelineConfig, weights: Weights): + config = pipeline_config.huggingface_config + self.dense = Linear( + weights.dense.weight.allocate( + pipeline_config.dtype, + [config.intermediate_size, config.hidden_size], + _quantization_encoding(pipeline_config), + ), + bias=weights.dense.bias.allocate( + pipeline_config.dtype, + [config.intermediate_size], + _quantization_encoding(pipeline_config), + ), + ) + self.intermediate_act_fn = _ACTIVATIONS[config.hidden_act] + + def __call__(self, hidden_states: TensorValue) -> TensorValue: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class MPNetOutput(Layer): + """Layer that combines the outputs of the intermediate and attention layers.""" + + def __init__(self, pipeline_config: PipelineConfig, weights: Weights): + config = pipeline_config.huggingface_config + self.dense = Linear( + weights.dense.weight.allocate( + pipeline_config.dtype, + [config.hidden_size, config.intermediate_size], + _quantization_encoding(pipeline_config), + ), + bias=weights.dense.bias.allocate( + pipeline_config.dtype, + [config.hidden_size], + _quantization_encoding(pipeline_config), + ), + ) + self.layer_norm = LayerNorm( + weight=weights.LayerNorm.weight.allocate( + DType.float32, [config.hidden_size] + ), + bias=weights.LayerNorm.bias.allocate( + DType.float32, [config.hidden_size] + ), + eps=config.layer_norm_eps, + ) + + def __call__( + self, hidden_states: TensorValue, input_tensor: TensorValue + ) -> TensorValue: + hidden_states = self.dense(hidden_states) + hidden_states = self.layer_norm(hidden_states + input_tensor) + return hidden_states + + +class MPNetLayer(Layer): + """An Encoder layer block.""" + + def __init__(self, pipeline_config: PipelineConfig, weights: Weights): + self.attention = MPNetAttention(pipeline_config, weights.attention) + self.intermediate = MPNetIntermediate( + pipeline_config, weights.intermediate + ) + self.output = MPNetOutput(pipeline_config, weights.output) + + def __call__( + self, + hidden_states: TensorValue, + attention_mask: TensorValue, + position_bias: TensorValue, + ) -> TensorValue: + attention_output = self.attention( + hidden_states, + attention_mask, + position_bias=position_bias, + ) + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class MPNetEncoder(Layer): + """Encoder that contains stacks of MPNetLayers.""" + + def __init__(self, pipeline_config: PipelineConfig, weights: Weights): + config = self.config = pipeline_config.huggingface_config + self.n_heads = config.num_attention_heads + num_hidden_layers = config.num_hidden_layers + self.layer = Sequential( + [ + MPNetLayer(pipeline_config, weights.layer[n]) + for n in range(num_hidden_layers) + ] + ) + self.relative_attention_bias = Embedding( + weights.relative_attention_bias.weight.allocate( + pipeline_config.dtype, + [ + config.relative_attention_num_buckets, + config.num_attention_heads, + ], + ) + ) + self.num_attention_heads = config.num_attention_heads + + def __call__( + self, + hidden_states: TensorValue, + attention_mask: TensorValue, + ) -> TensorValue: + position_bias = self.compute_position_bias(hidden_states) + for layer in self.layer.layers: + hidden_states = layer( + hidden_states, + attention_mask, + position_bias, + ) + return hidden_states + + def compute_position_bias(self, hidden_states: TensorValue) -> TensorValue: + shape = hidden_states.shape + bsz, qlen, klen = shape[0], shape[1], shape[1] + start = ops.constant(0, DType.int64) + step = ops.constant(1, DType.int64) + context_position = ops.range(start, qlen, step, qlen).cast(DType.int64)[ + :, None + ] + memory_position = ops.range(start, klen, step, klen).cast(DType.int64)[ + None, : + ] + relative_position = memory_position - context_position + rp_bucket = self.relative_position_bucket( + relative_position, + num_buckets=self.config.relative_attention_num_buckets, + ) + values = self.relative_attention_bias(rp_bucket) + values = ops.unsqueeze(ops.permute(values, [2, 0, 1]), 0) + values = ops.broadcast_to( + values, + [bsz, self.num_attention_heads, qlen, klen], + ) + return values + + @staticmethod + def relative_position_bucket( + relative_position: TensorValue, num_buckets=32, max_distance=128 + ) -> TensorValue: + n = -relative_position + + num_buckets //= 2 + ret = (n < 0).cast(DType.int64) * num_buckets + n = ops.abs(n) + + max_exact = num_buckets // 2 + is_small = n < max_exact + + val_if_large = max_exact + ops.cast( + ops.log(ops.cast(n, DType.float32) / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact), + DType.int64, + ) + + # Roundabout implementation of full_like(val_if_large, num_buckets - 1). + max_bucket = ops.broadcast_to( + ops.constant(num_buckets - 1, DType.int64), val_if_large.shape + ) + + val_if_large = ops.min(val_if_large, max_bucket) + ret += ops.select(is_small, n, val_if_large) + return ret + + +class MPNetModel(Layer): + """The MPNet encoder model. + + Based on the MPNetModel transformers implementation.""" + + def __init__( + self, + pipeline_config: PipelineConfig, + weights: Weights, + ): + self.embeddings = MPNetEmbeddings(pipeline_config, weights.embeddings) + self.encoder = MPNetEncoder(pipeline_config, weights.encoder) + self.pool_outputs = pipeline_config.pool_embeddings + + def __call__( + self, + input_ids: TensorValue, + attention_mask: TensorValue, + ) -> TensorValue: + embedding_output = self.embeddings( + input_ids=input_ids, + ) + extended_attention_mask = ops.reshape( + attention_mask, ("batch_size", 1, 1, "seq_len") + ) + extended_attention_mask = (1 - extended_attention_mask) * ops.constant( + np.finfo(np.float32).min, DType.float32 + ) + encoded_results = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + ) + if self.pool_outputs: + # Pool the embeddings. + # TODO(KERN-1550): Since GPU can only apply reductions along the + # inner-most dimension, transpose the mask so the seq_len is last. + encoded_results = encoded_results.transpose(1, 2) + input_mask_expanded = ops.broadcast_to( + ops.unsqueeze(attention_mask, 1), + ("batch_size", encoded_results.shape[1], "seq_len"), + ) + input_lengths = ops.max( + ops.sum(input_mask_expanded), ops.constant(1e-9, DType.float32) + ) + pooled_output = ( + ops.sum(encoded_results * input_mask_expanded) / input_lengths + ) + return ops.squeeze(pooled_output, 2) + else: + return encoded_results + + +def build_graph( + pipeline_config: PipelineConfig, + weights: Weights, +) -> Graph: + # Graph input types. + input_ids_type = TensorType(DType.int64, shape=["batch_size", "seq_len"]) + attention_mask_type = TensorType( + DType.float32, shape=["batch_size", "seq_len"] + ) + + mpnet = MPNetModel(pipeline_config, weights) + + # Initialize Graph. + return Graph( + "mpnet", + mpnet, + input_types=[ + input_ids_type, + attention_mask_type, + ], + ) diff --git a/src/max/pipelines/architectures/mpnet/model.py b/src/max/pipelines/architectures/mpnet/model.py new file mode 100644 index 0000000000..72180fe367 --- /dev/null +++ b/src/max/pipelines/architectures/mpnet/model.py @@ -0,0 +1,200 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Defines the MPNet pipeline model. + +Implementation is based on MPNetModel from the transformers library. +""" + +from __future__ import annotations + +import logging +import time +from collections.abc import Sequence +from typing import cast + +import numpy as np +from max.driver import Tensor +from max.engine import InferenceSession, Model +from max.pipelines import ( + ModelInputs, + ModelOutputs, + PipelineConfig, + PipelineModel, + TextContext, + upper_bounded_default, +) +from max.pipelines.dataprocessing import collate_batch +from max.pipelines.kv_cache import KVCacheInputs, KVCacheParams + +from .graph import build_graph + +logger = logging.getLogger("max.pipelines") + +PAD_VALUE = 1 + + +class MPNetInputs(ModelInputs): + """A class representing inputs for the MPNet model. + + This class encapsulates the input tensors required for the MPNet model execution: + - next_tokens_batch: A tensor containing the input token IDs + - attention_mask: A tensor containing the extended attention mask + """ + + next_tokens_batch: Tensor + attention_mask: Tensor + + def __init__( + self, + next_tokens_batch: Tensor, + attention_mask: Tensor, + ) -> None: + self.next_tokens_batch = next_tokens_batch + self.attention_mask = attention_mask + + +class MPNetPipelineModel(PipelineModel[TextContext]): + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) + self.model = self.load_model(session) + + @classmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + return KVCacheParams( + dtype=pipeline_config.cache_dtype, + n_kv_heads=pipeline_config.huggingface_config.num_attention_heads, + head_dim=( + pipeline_config.huggingface_config.hidden_size + // pipeline_config.huggingface_config.num_attention_heads + ), + cache_strategy=pipeline_config.cache_strategy, + enable_prefix_caching=pipeline_config.enable_prefix_caching, + ) + + @classmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + return pipeline_config.huggingface_config.num_hidden_layers + + @classmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + try: + return upper_bounded_default( + upper_bound=pipeline_config.huggingface_config.max_position_embeddings, + default=pipeline_config.max_length, + ) + except ValueError as e: + msg = ( + "Unable to infer max_length for MPNet, the provided " + f"max_length ({pipeline_config.max_length}) exceeds the " + f"model's max_position_embeddings " + f"({pipeline_config.huggingface_config.max_position_embeddings})." + ) + raise ValueError(msg) from e + + def execute( + self, + model_inputs: ModelInputs, + kv_cache_inputs: KVCacheInputs | None = None, + ) -> ModelOutputs: + model_inputs = cast(MPNetInputs, model_inputs) + assert kv_cache_inputs is None, "MPNet does not have KV cache inputs" + model_outputs = self.model.execute( + model_inputs.next_tokens_batch, + model_inputs.attention_mask, + copy_inputs_to_device=False, + ) + assert isinstance(model_outputs[0], Tensor) + return ModelOutputs(logits=model_outputs[0]) + + def prepare_initial_token_inputs( + self, + context_batch: Sequence[TextContext], + ) -> MPNetInputs: + # Get tokens and seq_ids. + tokens = [ctx.next_tokens for ctx in context_batch] + + # Pad tokens for the batch. + pad_value = getattr( + self.pipeline_config.huggingface_config, "pad_token_id", 1 + ) + next_tokens_batch, _ = collate_batch( + tokens, + pad_value=pad_value, + batch_size=len(tokens), + pad_to_multiple_of=self.pipeline_config.pad_to_multiple_of, + ) + + # Compute attention mask. + attention_mask = (next_tokens_batch != pad_value).astype(np.float32) + + return MPNetInputs( + next_tokens_batch=Tensor.from_numpy(next_tokens_batch).to( + self.pipeline_config.devices[0] + ), + attention_mask=Tensor.from_numpy(attention_mask).to( + self.pipeline_config.devices[0] + ), + ) + + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: ModelInputs, + ) -> MPNetInputs: + raise NotImplementedError( + "MPNet does not support preparing next tokens inputs." + ) + + def load_model( + self, + session: InferenceSession, + ) -> Model: + # Read in weights. + weights = self.pipeline_config.load_weights() + self._weights = weights + + if serialized_path := self.pipeline_config.serialized_model_path: + # Hydrate all weights to be referenced by the serialized path. + weights_registry = {} + for name, weight in self._weights.items(): + weights_registry[name] = weight.raw_tensor() + + logger.info("Loading serialized model from ", serialized_path) + + return session.load( + serialized_path, weights_registry=weights_registry + ) + + else: + logger.info("Building and compiling model...") + before = time.perf_counter() + graph = build_graph( + self.pipeline_config, + self._weights, + ) + model = session.load( + graph, weights_registry=self._weights.allocated_weights + ) + after = time.perf_counter() + logger.info( + f"Building and compiling model took {after - before:.6f} seconds" + ) + if ( + export_path + := self.pipeline_config.save_to_serialized_model_path + ): + logger.info("Exporting serialized model to %s", export_path) + model._export_mef(export_path) + return model diff --git a/src/max/pipelines/architectures/olmo/__init__.py b/src/max/pipelines/architectures/olmo/__init__.py new file mode 100644 index 0000000000..41fbdf80ac --- /dev/null +++ b/src/max/pipelines/architectures/olmo/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import olmo_arch + +__all__ = ["olmo_arch"] diff --git a/src/max/pipelines/architectures/olmo/arch.py b/src/max/pipelines/architectures/olmo/arch.py new file mode 100644 index 0000000000..b6047631c7 --- /dev/null +++ b/src/max/pipelines/architectures/olmo/arch.py @@ -0,0 +1,52 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + RopeType, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from ..llama3 import weight_adapters +from .model import OlmoModel + +olmo_arch = SupportedArchitecture( + name="OlmoForCausalLM", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=["allenai/OLMo-1B-hf", "allenai/OLMo-1B-0724-hf"], + default_weights_format=WeightsFormat.gguf, + default_encoding=SupportedEncoding.float32, + supported_encodings={ + SupportedEncoding.float32: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + SupportedEncoding.bfloat16: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + }, + pipeline_model=OlmoModel, + tokenizer=TextTokenizer, + rope_type=RopeType.normal, + weight_adapters={ + WeightsFormat.safetensors: weight_adapters.convert_safetensor_state_dict, + WeightsFormat.gguf: weight_adapters.convert_gguf_state_dict, + }, +) diff --git a/src/max/pipelines/architectures/olmo/model.py b/src/max/pipelines/architectures/olmo/model.py new file mode 100644 index 0000000000..5abe981ecf --- /dev/null +++ b/src/max/pipelines/architectures/olmo/model.py @@ -0,0 +1,32 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from typing import Literal + +from max.engine import InferenceSession +from max.pipelines import PipelineConfig + +from ..llama3.model import LlamaModelBase + + +class OlmoModel(LlamaModelBase): + """Olmo pipeline model implementation.""" + + norm_method: Literal["rms_norm"] | Literal["layer_norm"] = "layer_norm" + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) diff --git a/src/max/pipelines/architectures/phi3/__init__.py b/src/max/pipelines/architectures/phi3/__init__.py new file mode 100644 index 0000000000..577e2e1a5b --- /dev/null +++ b/src/max/pipelines/architectures/phi3/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import phi3_arch + +__all__ = ["phi3_arch"] diff --git a/src/max/pipelines/architectures/phi3/arch.py b/src/max/pipelines/architectures/phi3/arch.py new file mode 100644 index 0000000000..ad4bf4a656 --- /dev/null +++ b/src/max/pipelines/architectures/phi3/arch.py @@ -0,0 +1,52 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + RopeType, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from ..llama3 import weight_adapters +from .model import Phi3Model + +phi3_arch = SupportedArchitecture( + name="Phi3ForCausalLM", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=["microsoft/phi-4", "microsoft/Phi-3.5-mini-instruct"], + default_weights_format=WeightsFormat.gguf, + default_encoding=SupportedEncoding.bfloat16, + supported_encodings={ + SupportedEncoding.float32: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + # KVCacheStrategy.NAIVE, # TODO(kathywu): Support naive caching for phi models + ], + SupportedEncoding.bfloat16: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + # KVCacheStrategy.NAIVE, # TODO(kathywu): Support naive caching for phi models + ], + }, + pipeline_model=Phi3Model, + tokenizer=TextTokenizer, + rope_type=RopeType.normal, + weight_adapters={ + WeightsFormat.safetensors: weight_adapters.convert_safetensor_state_dict, + WeightsFormat.gguf: weight_adapters.convert_gguf_state_dict, + }, +) diff --git a/src/max/pipelines/architectures/phi3/model.py b/src/max/pipelines/architectures/phi3/model.py new file mode 100644 index 0000000000..494e8a53f9 --- /dev/null +++ b/src/max/pipelines/architectures/phi3/model.py @@ -0,0 +1,33 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from typing import Literal + +from max.engine import InferenceSession +from max.pipelines import PipelineConfig + +from ..llama3.model import LlamaModelBase + + +class Phi3Model(LlamaModelBase): + """Phi 3 pipeline model implementation.""" + + norm_method: Literal["rms_norm"] | Literal["layer_norm"] = "rms_norm" + """Normalization layer.""" + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) diff --git a/src/max/pipelines/architectures/pixtral/README.md b/src/max/pipelines/architectures/pixtral/README.md new file mode 100644 index 0000000000..4911c4ac85 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/README.md @@ -0,0 +1,134 @@ +# Pixtral + +**Language:** Python + +**API**: MAX Graph + +This pipeline provides optimized support for the +`LlavaForConditionalGeneration` family of multimodal models, as exemplified +by the Pixtral multimodal text generation model. The model itself has +been constructed in Python using the +[MAX Graph API](https://docs.modular.com/max/graph/). + +The MAX Graph API provides an accessible interface to the construction of +flexible accelerated compute graphs, which are then optimized by the MAX +Engine's advanced graph compiler. This pipeline showcases how a multimodal +model can be fully defined using Python and MAX Graphs and then compiled for +optimal inference performance via the MAX Engine. + +> [!NOTE] +> This pipeline is under active development, and while many layers have been +> implemented, the entire pipeline is not fully functional at present. + +## Model + +[Pixtral](https://mistral.ai/news/pixtral-12b/) +is an open source multimodal model released by Mistral AI. It handles both text +and image input, and allows for text generation based on those multimodal +inputs. The underlying architecture provides support for the broader +`LlavaForConditionalGeneration` family of models. + +The default settings for this pipeline use the 12B set of pretrained weights in +the `bfloat16` encoding. + +## Usage + +The easiest way to try out this pipeline is with our Magic command-line tool. + +1. Install Magic on macOS and Ubuntu with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Clone the MAX examples repository: + + If you don't already have a local clone of this repository, create one via: + + ```shell + git clone https://github.com/modular/max.git + ``` + + The following instructions assume that you're present within this pipeline's + directory, and you can change to it after cloning: + + ```shell + cd max/src/max/pipelines + ``` + +3. Host a multimodal chat completion endpoint via MAX Serve. + + MAX Serve provides functionality to host performant OpenAI compatible + endpoints using the FastAPI framework. + + You can configure the pipeline to be hosted by using the `serve` command. + Weights hosted on Hugging Face for any compatible + `LlavaForConditionalGeneration` model can be used with this optimized + architecture. For example: + + ```shell + magic run serve --model-path mistral-community/pixtral-12b + ``` + + A request can be submitted via a cURL command. + + ```shell + curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "mistral-community/pixtral-12b", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": [ + {"type": "text", "text": "What is in this image?"}, + {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"}} + ]} + ] + }' + ``` + +## Options + +The following command-line options are available to customize operation of the +pipeline: + +- `--model-path`: Specify the repository ID of a Hugging Face model + repository to use. This is used to load tokenizers, architectures and model + weights. +- `--force-download`: Specify whether to force a download of configuration + files and weights even if they already exist in the local cache. Set this + if you want to ensure you have the correct version of the model. +- `--max-batch-size`: Specifies the maximum batch size to be used. + Default is 1. +- `--max-ce-batch-size`: Set the maximum cache size reserved for a single + context encoding batch. The effective limit will be the lesser of this value + and `max-cache-batch-size`. + Default is 32. +- `--max-length`: Controls the maximum length of the text sequence (includes the + input tokens). Defaults to huggingface model config's max sequence length. +- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 + value is provided, the model will continue to generate tokens for the entire + context length. (Default value: -1) +- `--quantization-encoding`: The encoding to use for a datatype that can be + quantized to a low bits per weight format. + Valid values: `q4_0`, `q4_k`, `q6_k`, `bfloat16`, `float32`. + (Default value: `bfloat16`). +- `--save-to-serialized-model-path`: If specified, writes the serialized model + to this path. +- `--serialized-model-path`: If specified, tries to load a serialized model + from this path. +- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. +- `--trust-remote-code`: Indicate whether to allow custom modeling files from + Hugging Face repositories. Set this to true with caution, as it may + introduce security risks. +- `--weight-path`: Overrides the default URL, and allows for an + already-downloaded pretrained weight file to be used with the model. +- `--devices`: Specifies the device(s) to use for the model. This can optionally + be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". + Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/pixtral/__init__.py b/src/max/pipelines/architectures/pixtral/__init__.py new file mode 100644 index 0000000000..06f1d43e25 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import pixtral_arch + +__all__ = ["pixtral_arch"] diff --git a/src/max/pipelines/architectures/pixtral/arch.py b/src/max/pipelines/architectures/pixtral/arch.py new file mode 100644 index 0000000000..580d90e126 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/arch.py @@ -0,0 +1,39 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + SupportedArchitecture, + SupportedEncoding, + TextAndVisionTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from .pixtral import PixtralModel + +pixtral_arch = SupportedArchitecture( + name="LlavaForConditionalGeneration", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=["mistral-community/pixtral-12b"], + default_encoding=SupportedEncoding.bfloat16, + supported_encodings={ + SupportedEncoding.bfloat16: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + ], + }, + pipeline_model=PixtralModel, + tokenizer=TextAndVisionTokenizer, + default_weights_format=WeightsFormat.safetensors, +) diff --git a/src/max/pipelines/architectures/pixtral/llava/__init__.py b/src/max/pipelines/architectures/pixtral/llava/__init__.py new file mode 100644 index 0000000000..aae087fcca --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/llava/__init__.py @@ -0,0 +1,14 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Llava Code layer modules.""" diff --git a/src/max/pipelines/architectures/pixtral/llava/llava.py b/src/max/pipelines/architectures/pixtral/llava/llava.py new file mode 100644 index 0000000000..e6c5fe0dd5 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/llava/llava.py @@ -0,0 +1,178 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from dataclasses import dataclass + +from max.graph import TensorValue, TensorValueLike, ops +from max.pipelines.architectures.pixtral.vision_encoder.vision_encoder import ( + VisionEncoder, +) +from max.pipelines.nn.layer import Layer + +from .llava_decoder import Transformer +from .llava_projector import LlavaMultiModalConnector + + +@dataclass +class LlavaConditionalGeneration(Layer): + """The LLAVA model which consists of a vision encoder and a language model. + + image_token_index: a specific token index used to denote images in input_ids. + """ + + vision_encoder: VisionEncoder + multi_modal_projector: LlavaMultiModalConnector + language_model: Transformer + vocab_size: int + image_token_index: int = 10 + vision_feature_layer: int = -1 + vision_feature_select_strategy: str = "full" + image_seq_length: int = 1 + + # TODO: change pixel_values type to List[TensorValue] to support multiple images. + def __call__( + self, + input_ids: TensorValueLike, + pixel_values: TensorValueLike, + attention_mask: TensorValueLike, + kv_cache_inputs: tuple[ + TensorValue, TensorValue, TensorValue, TensorValue + ], + **kwargs, + ) -> tuple[TensorValue, ...]: + """ + Args: + input_ids (ragged `TensorValue` of shape `(batch_size * sequence_length for each batch)`): + Indices of input sequence tokens in the vocabulary. Can be obtained from language model tokenizer. + input_ids[i] is a sequence of token ids (indices) in sequence i. Expanding inputs for + image tokens in LLaVa should be done in processing. Each image is represented in the + input_ids sequence by a sequence of patches that have index(id) = self.image_token_index. + The maximum number of image tokens in one sequence (prompt) = + (input_ids == self.image_token_index).sum(1).max()) + Padding will be ignored by default should you provide it. + pixel_values (list`TensorValue` of length batch_size + The tensors corresponding to the input image are of shape `(image_height, image_width, num_channels)): + Pixel values can be obtained using ImageProcessor. + """ + # inputs_embeds shape (total_sequence_length=text_and_image_tokens_length for all seqs, + # language_model_hidden_dim) + inputs_embeds = self.language_model.embedding(input_ids) + + # Replace image place-holders in inputs_embeds with image embeddings. + # Obtain image embeddings from the vision encoder and project it to text embeddings space. + # Output shape = (num_images, num_patches_in_image, language_model_hidden_dim) + # TODO(AIPIPE-320): If input pixel_values is a list of images, don't wrap it in a list. + image_embeds = self.multi_modal_projector( + self.vision_encoder( + imgs=[pixel_values], attention_mask=attention_mask + ) + ) + image_embeds = ops.cast(image_embeds, inputs_embeds.dtype) + special_image_mask = ops.broadcast_to( + ops.unsqueeze((input_ids == self.image_token_index), -1), + inputs_embeds.shape, + ) + inputs_embeds = ops.masked_scatter( + inputs_embeds, special_image_mask, image_embeds + ) + + logits = self.language_model(inputs_embeds, kv_cache_inputs, **kwargs) + + return logits + + +@dataclass +class LlavaVisionEncoder(Layer): + """The LLAVA model which consists of a vision encoder and a language model. + + image_token_index: a specific token index used to denote images + """ + + vision_encoder: VisionEncoder + multi_modal_projector: LlavaMultiModalConnector + + # TODO: change pixel_values type to List[TensorValue] to support multiple images. + def __call__( + self, + pixel_values: TensorValueLike, + attention_mask: TensorValueLike, + **kwargs, + ) -> TensorValue: + """ + Args: + pixel_values (list`TensorValue` of length batch_size + The tensors corresponding to the input image are of shape `(image_height, image_width, num_channels)): + Pixel values can be obtained using ImageProcessor. + """ + # Replace image place-holders in inputs_embeds with image embeddings. + # Obtain image embeddings from the vision encoder and project it to text embeddings space. + # Output shape = (num_images, num_patches_in_image, language_model_hidden_dim) + # TODO(AIPIPE-320): If input pixel_values is a list of images, don't wrap it in a list. + return self.multi_modal_projector( + self.vision_encoder( + imgs=[pixel_values], attention_mask=attention_mask + ) + ) + + +@dataclass +class LlavaConditionalGenerationTextOnly(Layer): + """The LLAVA model which consists of a vision encoder and a language model. + + Because flow control is not added to the graph API yet. We have to create 2 llava models. + + image_token_index: a specific token index used to denote images + """ + + language_model: Transformer + vocab_size: int + image_token_index: int = 10 + + def __call__( + self, + input_ids: TensorValueLike, + image_embeds: TensorValue, + kv_cache_inputs: tuple[ + TensorValue, TensorValue, TensorValue, TensorValue + ], + **kwargs, + ) -> tuple[TensorValue, ...]: + """ + Args: + input_ids (ragged `TensorValue` of shape `(batch_size * sequence_length for each batch)`): + Indices of input sequence tokens in the vocabulary. Can be obtained from language model tokenizer. + input_ids[i] is a sequence of token ids (indices) in sequence i. Expanding inputs for + image tokens in LLaVa should be done in processing. Each image is represented in the + input_ids sequence by a sequence of patches that have index(id) = self.image_token_index. + The maximum number of image tokens in one sequence (prompt) = + (input_ids == self.image_token_index).sum(1).max()) + Padding will be ignored by default should you provide it. + inputs_embeds: + Embeddings of pixel_values generated by the vision encoder and projected to the + embedding space of the language model. + """ + # inputs_embeds shape (total_sequence_length=text_and_image_tokens_length for all seqs, + # language_model_hidden_dim) + inputs_embeds = self.language_model.embedding(input_ids) + + image_embeds = ops.cast(image_embeds, inputs_embeds.dtype) + special_image_mask = ops.broadcast_to( + ops.unsqueeze((input_ids == self.image_token_index), -1), + inputs_embeds.shape, + ) + inputs_embeds = ops.masked_scatter( + inputs_embeds, special_image_mask, image_embeds + ) + logits = self.language_model(inputs_embeds, kv_cache_inputs, **kwargs) + + return logits diff --git a/src/max/pipelines/architectures/pixtral/llava/llava_decoder.py b/src/max/pipelines/architectures/pixtral/llava/llava_decoder.py new file mode 100644 index 0000000000..3e4c1c4c8e --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/llava/llava_decoder.py @@ -0,0 +1,128 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from dataclasses import dataclass +from typing import Union + +from max.dtype import DType +from max.graph import TensorValue, TensorValueLike, ops +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + KVCacheParams, +) +from max.pipelines.nn import ( + Embedding, + LayerNorm, + Linear, + RMSNorm, + TransformerBlock, +) +from max.pipelines.nn.layer import Layer + + +@dataclass +class Transformer(Layer): + """Transformer model consisting for TransformerBlock layers. + + The differences between this transformer and the transformer in nn: + + - It takes as input the token embeddings rather than the token ids. + - It skips the embedding generation (first step in nn.Transformer). + + TODO(AIPIPE-273): Once we have mo.if, we can update nn.Transformer + to only generate embeddings if token ids are passed. That would + eliminate the need for this class. + """ + + dim: int + n_heads: int + layers: list[TransformerBlock] + norm: Union[RMSNorm, LayerNorm] + output: Linear + embedding: Embedding + kv_params: KVCacheParams + kv_collection_constructor: Union[ + FetchContinuousBatchingKVCacheCollection, FetchPagedKVCacheCollection + ] + all_logits: bool = False + + def __call__( + self, + embeds: TensorValue, + kv_cache_inputs: tuple[ + TensorValue, TensorValue, TensorValue, TensorValue + ], + **kwargs, + ) -> tuple[TensorValue, ...]: + """Transformer model consisting of TransformerBlock layers. + + Args: + embeds: embeddings of the sequence of text tokens and possibly images. + shape = [batch_size, n_patches, hidden_dim] + kv_cache_inputs: A tuple of 4 tensor values. In the case of paged attention, + (blocks, cache_lengths, lookup_table, is_cache_empty). In the case of + continuous attention, (blocks, cache_lengths, lookup_table, max_lengths). + """ + h = embeds + + # Construct a kv cache for use downstream. + kv_collection = self.kv_collection_constructor(*kv_cache_inputs) + + for _, layer in enumerate(self.layers): + h = layer(h, kv_collection, **kwargs) + + if self.all_logits: + # When echo is enabled, the logits of the input tokens are + # returned. + logits = ops.cast(self.output(self.norm(h)), DType.float32) + if "input_row_offsets" in kwargs: + # For ragged tensors gather the last tokens from packed dim 0. + input_row_offsets: TensorValueLike = kwargs["input_row_offsets"] + last_token_indices = input_row_offsets[1:] - 1 # type: ignore + last_token_logits = ops.gather( + logits, last_token_indices, axis=0 + ) + else: + # For padded tensors, use `gather_nd`. + # Unsqueeze since `gather_nd` expects a static last dim. + valid_lengths: TensorValueLike = kwargs["valid_lengths"] + last_token_logits = ops.gather_nd( + logits, + indices=ops.unsqueeze(valid_lengths - 1, -1), # type: ignore + batch_dims=1, + ) + return (last_token_logits, logits) + else: + # Otherwise, only return the logits for the last non-pad token + # (right-padded). + if "input_row_offsets" in kwargs: + # For ragged tensors gather the last tokens from packed dim 0. + input_row_offsets = kwargs["input_row_offsets"] + last_token_indices = input_row_offsets[1:] - 1 # type: ignore + # Should be: last_token = h[last_token_indices] + last_token = ops.gather(h, last_token_indices, axis=0) + else: + # For padded tensors, use `gather_nd`. + # Unsqueeze since `gather_nd` expects a static last dim. + valid_lengths = kwargs["valid_lengths"] + last_token = ops.gather_nd( + h, + indices=ops.unsqueeze(valid_lengths - 1, -1), # type: ignore + batch_dims=1, + ) + + # Always return float32 logits, no matter the activation type + return ( + ops.cast(self.output(self.norm(last_token)), DType.float32), + ) diff --git a/src/max/pipelines/architectures/pixtral/llava/llava_projector.py b/src/max/pipelines/architectures/pixtral/llava/llava_projector.py new file mode 100644 index 0000000000..c75c0adabd --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/llava/llava_projector.py @@ -0,0 +1,34 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +from dataclasses import dataclass + +from max.graph import TensorValue, ops +from max.pipelines.nn.layer import Layer +from max.pipelines.nn.linear import Linear + + +@dataclass +class LlavaMultiModalConnector(Layer): + """ + Simple multi-layer cross-modal connector to connect image features into the + text token embedding space. + Uses Gelu activation function. + """ + + linear_1: Linear + linear_2: Linear + + def __call__(self, x: TensorValue) -> TensorValue: + return self.linear_2(ops.gelu(self.linear_1(x))) diff --git a/src/max/pipelines/architectures/pixtral/model/graph.py b/src/max/pipelines/architectures/pixtral/model/graph.py new file mode 100644 index 0000000000..cebc6bab2a --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/model/graph.py @@ -0,0 +1,311 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.dtype import DType +from max.graph import Graph, TensorType +from max.graph.weights import SafetensorWeights +from max.pipelines import PipelineConfig +from max.pipelines.kv_cache import KVCacheManager, KVCacheParams +from max.pipelines.nn import Linear + +from ..llava.llava import ( + LlavaConditionalGeneration, + LlavaConditionalGenerationTextOnly, + LlavaVisionEncoder, +) +from ..llava.llava_projector import LlavaMultiModalConnector +from ..vision_encoder.graph import _vision_encoder +from .mistral_graph import _transformer + + +def _linear( + dtype: DType, + in_features: int, + out_features: int, + weights: SafetensorWeights, +) -> Linear: + """Unlike the vision encoder's version, this linear layer has a bias. + This linear layer is used by the LlavaMultiModalConnector + """ + return Linear( + weights.weight.allocate(dtype, [in_features, out_features], None), + bias=weights.bias.allocate(dtype, [in_features], None), + ) + + +def _multi_modal_projector( + dtype: DType, + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +) -> LlavaMultiModalConnector: + """Connects the vision encoder to the text decoder. + This MLP projects the patch embeddings to the text-encoder's embeddings space. + Input shape: + Output shape: + """ + return LlavaMultiModalConnector( + _linear( + dtype, + pipeline_config.huggingface_config.text_config.hidden_size, + pipeline_config.huggingface_config.vision_config.hidden_size, + weights.linear_1, + ), + _linear( + dtype, + pipeline_config.huggingface_config.text_config.hidden_size, + pipeline_config.huggingface_config.text_config.hidden_size, + weights.linear_2, + ), + ) + + +def _llava_vision_encoder_and_projector( + graph: Graph, + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +) -> LlavaVisionEncoder: + # TODO(AIPIPE-273): Once we have mo.if, use this version of Llava rather than creating 2 graphs + vision_encoder = _vision_encoder(graph, pipeline_config, weights) + multi_modal_projector = _multi_modal_projector( + pipeline_config.dtype, pipeline_config, weights.multi_modal_projector + ) + return LlavaVisionEncoder( + vision_encoder=vision_encoder, + multi_modal_projector=multi_modal_projector, + ) + + +def _llava_decoder( + graph: Graph, + pipeline_config: PipelineConfig, + weights: SafetensorWeights, + max_seq_len: int, + kv_params: KVCacheParams, +) -> LlavaConditionalGenerationTextOnly: + # Weights of pixtral decoder have the same names and shapes as weights of mistral. + language_model = _transformer( + graph=graph, + params=pipeline_config, + weights=weights, + max_seq_len=max_seq_len, + kv_params=kv_params, + ) + + return LlavaConditionalGenerationTextOnly( + language_model=language_model, + vocab_size=pipeline_config.huggingface_config.text_config.vocab_size, + image_token_index=pipeline_config.huggingface_config.image_token_index, + ) + + +def _llava( + graph: Graph, + pipeline_config: PipelineConfig, + weights: SafetensorWeights, + max_seq_len: int, + kv_params: KVCacheParams, +) -> LlavaConditionalGeneration: + # TODO: Once we have mo.if, use this version of Llava rather than creating 2 graphs + vision_encoder = _vision_encoder(graph, pipeline_config, weights) + multi_modal_projector = _multi_modal_projector( + dtype=pipeline_config.dtype, + pipeline_config=pipeline_config, + weights=weights.multi_modal_projector, + ) + # Weights of pixtral have the same names and shapes as weights of mistral. + language_model = _transformer( + graph=graph, + params=pipeline_config, + weights=weights, + max_seq_len=max_seq_len, + kv_params=kv_params, + ) + + return LlavaConditionalGeneration( + vision_encoder=vision_encoder, + multi_modal_projector=multi_modal_projector, + language_model=language_model, + vocab_size=pipeline_config.huggingface_config.text_config.vocab_size, + image_token_index=pipeline_config.huggingface_config.image_token_index, + vision_feature_layer=pipeline_config.huggingface_config.vision_feature_layer, + vision_feature_select_strategy=pipeline_config.huggingface_config.vision_feature_select_strategy, + image_seq_length=pipeline_config.huggingface_config.image_seq_length, + ) + + +def _build_graph( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, + max_seq_len: int, + kv_params: KVCacheParams, + kv_manager: KVCacheManager, +) -> Graph: + # TODO: Make this work for multiple devices. Now getting the types for device [0] + kv_cache_types = kv_manager.input_symbols()[0] + + input_ids_type = TensorType( + DType.int64, + shape=["total_seq_len"], + ) + # TODO: should be changed to add "batch_size", "n_images" dims when working with multiple images + pixel_values_type = TensorType( + DType.float32, + shape=["image_height", "image_width", "num_channels"], + ) + + attention_mask_type = TensorType( + DType.float32, + shape=["batch_size", 1, "num_patches", "num_patches"], + ) + + # Type of start and end position of each batch in the combined total_seq_len dimension. + input_row_offsets_type = TensorType( + DType.uint32, shape=["input_row_offsets_len"] + ) + + # Initialize Graph. + with Graph( + "pixtral", + input_types=[ + input_ids_type, + pixel_values_type, + attention_mask_type, + input_row_offsets_type, + *kv_cache_types, + ], + ) as graph: + model = _llava( + graph=graph, + pipeline_config=pipeline_config, + weights=weights, + max_seq_len=max_seq_len, + kv_params=kv_params, + ) + ( + input_ids, + pixel_values, + attention_mask, + input_row_offsets, + *kv_cache_inputs, + ) = graph.inputs + outputs = model( + input_ids=input_ids.tensor, + pixel_values=pixel_values.tensor, + attention_mask=attention_mask.tensor, + kv_cache_inputs=kv_cache_inputs, # type: ignore + input_row_offsets=input_row_offsets, + ) + graph.output(*outputs) + return graph + + +def _build_vision_graph( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +) -> Graph: + # Graph input types. + pixel_values_type = TensorType( + DType.float32, + shape=["image_height", "image_width", "num_channels"], + ) + + attention_mask_type = TensorType( + DType.float32, + shape=["batch_size", 1, "num_patches", "num_patches"], + ) + + # Initialize Graph. + with Graph( + "pixtral_vision_encoder", + input_types=[ + pixel_values_type, + attention_mask_type, + ], + ) as graph: + model = _llava_vision_encoder_and_projector( + graph, pipeline_config, weights + ) + ( + pixel_values, + attention_mask, + ) = graph.inputs + outputs = model( + pixel_values=pixel_values.tensor, + attention_mask=attention_mask.tensor, + ) + graph.output(outputs) + return graph + + +def _build_text_graph( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, + max_seq_len: int, + kv_params: KVCacheParams, + kv_manager: KVCacheManager, +) -> Graph: + # TODO: Make this work for multiple devices. Now getting the types for device [0] + kv_cache_types = kv_manager.input_symbols()[0] + + input_ids_type = TensorType( + DType.int64, + shape=["total_seq_len"], + ) + + # Type of start and end position of each batch in the combined total_seq_len dimension. + input_row_offsets_type = TensorType( + DType.uint32, shape=["input_row_offsets_len"] + ) + + # num_images, num_patches_in_image, language_model_hidden_dim + image_embeddings_type = TensorType( + pipeline_config.dtype, + shape=[ + # TODO(bduke): fix algebraic dim creation outside of graph contexts. + "num_images", + "num_patches_in_image", + pipeline_config.huggingface_config.text_config.hidden_size, + ], + ) + + # Initialize Graph. + with Graph( + "pixtral", + input_types=[ + input_ids_type, + image_embeddings_type, + input_row_offsets_type, + *kv_cache_types, + ], + ) as graph: + model = _llava_decoder( + graph=graph, + pipeline_config=pipeline_config, + weights=weights, + max_seq_len=max_seq_len, + kv_params=kv_params, + ) + ( + input_ids, + image_embeds, + input_row_offsets, + *kv_cache_inputs, + ) = graph.inputs + outputs = model( + input_ids=input_ids.tensor, + image_embeds=image_embeds.tensor, + kv_cache_inputs=kv_cache_inputs, # type: ignore + input_row_offsets=input_row_offsets, + ) + graph.output(*outputs) + return graph diff --git a/src/max/pipelines/architectures/pixtral/model/mistral_graph.py b/src/max/pipelines/architectures/pixtral/model/mistral_graph.py new file mode 100644 index 0000000000..855e94614e --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/model/mistral_graph.py @@ -0,0 +1,236 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Build a Mistral model via Graph API from Safetensor weights.""" + +import math +from typing import Union + +from max.dtype import DType +from max.graph import Graph, ops +from max.graph.weights import SafetensorWeights +from max.pipelines import PipelineConfig +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + KVCacheParams, + KVCacheStrategy, +) +from max.pipelines.nn import ( + MLP, + AttentionWithRope, + Embedding, + Linear, + OptimizedRotaryEmbedding, + RMSNorm, + TransformerBlock, +) + +from ..llava.llava_decoder import Transformer + + +def feed_forward( + dtype: DType, + hidden_dim: int, + feed_forward_length: int, + weights: SafetensorWeights, +): + return MLP( + linear( + dtype, + feed_forward_length, + hidden_dim, + weights.mlp.gate_proj, + ), + linear( + dtype, + hidden_dim, + feed_forward_length, + weights.mlp.down_proj, + ), + linear( + dtype, + feed_forward_length, + hidden_dim, + weights.mlp.up_proj, + ), + ) + + +def linear( + dtype: DType, + in_features: int, + out_features: int, + weights: SafetensorWeights, +) -> Linear: + return Linear( + weights.weight.allocate(dtype, [in_features, out_features], None) + ) + + +def rms_norm(dims: int, eps: float, weights: SafetensorWeights) -> RMSNorm: + return RMSNorm(weights.weight.allocate(DType.bfloat16, [dims]), eps) + + +def embedding( + params: PipelineConfig, + vocab_size: int, + hidden_dim: int, + weights: SafetensorWeights, +): + return Embedding( + weights.weight.allocate( + params.dtype, + [vocab_size, hidden_dim], + ) + ) + + +def _attention_opaque( + kv_params: KVCacheParams, + params: PipelineConfig, + rope: OptimizedRotaryEmbedding, + weights: SafetensorWeights, + layer_idx: int, +): + kv_weight_dim = ( + params.huggingface_config.text_config.head_dim + * params.huggingface_config.text_config.num_key_value_heads + ) + + wq = weights.self_attn.q_proj.weight.allocate( + params.dtype, + [ + params.huggingface_config.text_config.num_attention_heads + * params.huggingface_config.text_config.head_dim, + params.huggingface_config.text_config.hidden_size, + ], + ) + wk = weights.self_attn.k_proj.weight.allocate( + params.dtype, + [kv_weight_dim, params.huggingface_config.text_config.hidden_size], + ) + wv = weights.self_attn.v_proj.weight.allocate( + params.dtype, + [kv_weight_dim, params.huggingface_config.text_config.hidden_size], + ) + wqkv = ops.concat((wq, wk, wv)) + + return AttentionWithRope( + n_heads=params.huggingface_config.text_config.num_attention_heads, + kv_params=kv_params, + wqkv=wqkv, + wo=linear( + params.dtype, + params.huggingface_config.text_config.hidden_size, + params.huggingface_config.text_config.num_attention_heads + * params.huggingface_config.text_config.head_dim, + weights.self_attn.o_proj, + ), + rope=rope, + layer_idx=ops.constant(layer_idx, DType.uint32), + scale=math.sqrt(1 / kv_params.head_dim), + ) + + +def _transformer( + graph: Graph, + params: PipelineConfig, + weights: SafetensorWeights, + max_seq_len: int, + kv_params: KVCacheParams, +): + with graph: + rope = OptimizedRotaryEmbedding( + dim=params.huggingface_config.text_config.num_attention_heads + * params.huggingface_config.text_config.head_dim, + n_heads=params.huggingface_config.text_config.num_attention_heads, + theta=params.huggingface_config.text_config.rope_theta, + max_seq_len=max_seq_len, + rope_scaling=None, + interleaved=False, + ) + + layers = [ + TransformerBlock( + attention=_attention_opaque( + kv_params, + params, + rope, + weights.language_model.model.layers[i], + layer_idx=i, + ), + mlp=feed_forward( + params.dtype, + params.huggingface_config.text_config.hidden_size, + params.huggingface_config.text_config.intermediate_size, + weights.language_model.model.layers[i], + ), + attention_norm=rms_norm( + params.huggingface_config.text_config.hidden_size, + params.huggingface_config.text_config.rms_norm_eps, + weights.language_model.model.layers[i].input_layernorm, + ), + mlp_norm=rms_norm( + params.huggingface_config.text_config.hidden_size, + params.huggingface_config.text_config.rms_norm_eps, + weights.language_model.model.layers[ + i + ].post_attention_layernorm, + ), + ) + for i in range( + params.huggingface_config.text_config.num_hidden_layers + ) + ] + + embedding_layer = embedding( + params, + params.huggingface_config.text_config.vocab_size, + params.huggingface_config.text_config.hidden_size, + weights.language_model.model.embed_tokens, + ) + + output = linear( + params.dtype, + params.huggingface_config.text_config.vocab_size, + params.huggingface_config.text_config.hidden_size, + weights.language_model.lm_head, + ) + + kv_collection_cls: Union[ + type[FetchContinuousBatchingKVCacheCollection], + type[FetchPagedKVCacheCollection], + ] + if kv_params.cache_strategy == KVCacheStrategy.CONTINUOUS: + kv_collection_cls = FetchContinuousBatchingKVCacheCollection + elif kv_params.cache_strategy == KVCacheStrategy.PAGED: + kv_collection_cls = FetchPagedKVCacheCollection + else: + raise ValueError( + f"Unsupported caching strategy {kv_params.cache_strategy}" + ) + + return Transformer( + dim=params.huggingface_config.text_config.hidden_size, + n_heads=params.huggingface_config.text_config.num_attention_heads, + layers=layers, + norm=rms_norm( + params.huggingface_config.text_config.hidden_size, + params.huggingface_config.text_config.rms_norm_eps, + weights.language_model.model.norm, + ), + output=output, + embedding=embedding_layer, + kv_params=kv_params, + kv_collection_constructor=kv_collection_cls(kv_params), + ) diff --git a/src/max/pipelines/architectures/pixtral/pixtral.py b/src/max/pipelines/architectures/pixtral/pixtral.py new file mode 100644 index 0000000000..31f4e13682 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/pixtral.py @@ -0,0 +1,362 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import logging +import time +from collections.abc import Sequence +from concurrent.futures import ThreadPoolExecutor +from typing import cast + +import numpy as np +from max.driver import Device, Tensor +from max.engine import InferenceSession, Model +from max.graph.weights import SafetensorWeights +from max.pipelines import ( + ModelInputs, + ModelOutputs, + PipelineConfig, + PipelineModel, + TextAndVisionContext, + upper_bounded_default, +) +from max.pipelines.kv_cache import ( + KVCacheInputs, + KVCacheManager, + KVCacheParams, + estimate_kv_cache_size, + load_kv_manager, +) + +from .model.graph import _build_text_graph, _build_vision_graph +from .vision_encoder.attention_utils import causal_attention_mask_2d_from_imgs + +logger = logging.getLogger("max.pipelines") + + +class PixtralInputs(ModelInputs): + """Holds inputs for the Pixtral model.""" + + input_ids: Tensor + input_row_offsets: Tensor + + # Image inputs + _pixel_values: Tensor | None = None + _attention_mask: Tensor | None = None + + def __init__( + self, + input_ids: Tensor, + input_row_offsets: Tensor, + pixel_values: Tensor | None = None, + attention_mask: Tensor | None = None, + ): + self.input_ids = input_ids + self.input_row_offsets = input_row_offsets + self._pixel_values = pixel_values + self._attention_mask = attention_mask + + @property + def has_vision_inputs(self) -> bool: + """Returns true iff this includes vision model inputs.""" + return self._pixel_values is not None + + @property + def pixel_values(self) -> Tensor: + assert self._pixel_values is not None + return self._pixel_values + + @property + def attention_mask(self) -> Tensor: + assert self._attention_mask is not None + return self._attention_mask + + +class PixtralModel(PipelineModel[TextAndVisionContext]): + """The overall interface to the Pixtral model.""" + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) + self.vision_model, self.language_model = self.load_model(session) + # Note that in a multimodal model, the language model is the last model in the + # pipeline. Unfortunately, self.model is still being used (and exposed) + # in the token generation code, so we still need to set it here. + self.model = self.language_model + + def execute( + self, + model_inputs: ModelInputs, + # TODO(zheng): This should be folded as KVCacheInputs into ModelInputs. + kv_cache_inputs: KVCacheInputs | None = None, + ) -> ModelOutputs: + model_inputs = cast(PixtralInputs, model_inputs) + if model_inputs.has_vision_inputs: + image_embeds = self.vision_model.execute( + model_inputs.pixel_values, + model_inputs.attention_mask, + copy_inputs_to_device=False, + )[0] + else: + # batch_size * num_concurrent_media * num_patches are set to 0 here to imitate a dummy tensor (used in text-only mode). + image_embeds = Tensor.zeros( + shape=[ + 0, + 0, + self.pipeline_config.huggingface_config.text_config.hidden_size, + ], + dtype=self.pipeline_config.dtype, + ).to(self.pipeline_config.devices[0]) + assert kv_cache_inputs is not None, ( + "Pixtral has KV cache inputs, but none were provided" + ) + model_outputs = self.language_model.execute( + model_inputs.input_ids, + image_embeds, + model_inputs.input_row_offsets, + *kv_cache_inputs, + copy_inputs_to_device=False, + ) + assert not self.pipeline_config.enable_echo + assert isinstance(model_outputs[0], Tensor) + return ModelOutputs(next_token_logits=model_outputs[0]) + + def prepare_initial_token_inputs( + self, + context_batch: Sequence[TextAndVisionContext], + ) -> PixtralInputs: + # Input row offset type: ["input_row_offsets_len"], UInt32 + input_row_offsets = Tensor.from_numpy( + np.cumsum( + [0] + [ctx.active_length for ctx in context_batch], + dtype=np.uint32, + ) + ).to(self.pipeline_config.devices[0]) + + # Input Ids: ["total_seq_len"], Int64 + # Create a ragged token vector of length: sum(len(t) for t in tokens). + tokens = np.ascontiguousarray( + np.concatenate([ctx.next_tokens for ctx in context_batch]) + ) + input_ids = Tensor.from_numpy(tokens).to( + self.pipeline_config.devices[0] + ) + + # TODO: change this to work with all contexts in the batch. + if context_batch[ + 0 + ].pixel_values: # check if the request has pixel_values + # Get first image in first batch and permute the order to (HWC). + # Pixtral processor returns CHW images. + image = np.ascontiguousarray( + np.transpose(context_batch[0].pixel_values[0], (1, 2, 0)) + ) + pixel_values = Tensor.from_numpy(image).to( + self.pipeline_config.devices[0] + ) + # TODO(KERN-782): This should be -inf but softmax saturates with NaNs. + fill_val = -10000.0 + attention_mask = causal_attention_mask_2d_from_imgs( + [image], + self.pipeline_config.huggingface_config.vision_config.patch_size, + 1, + fill_val, + ) + attention_mask = Tensor.from_numpy(attention_mask).to( + self.pipeline_config.devices[0] + ) + return PixtralInputs( + input_ids=input_ids, + input_row_offsets=input_row_offsets, + pixel_values=pixel_values, + attention_mask=attention_mask, + ) + + return PixtralInputs( + input_ids=input_ids, + input_row_offsets=input_row_offsets, + ) + + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: ModelInputs, + ) -> PixtralInputs: + prev_model_inputs = cast(PixtralInputs, prev_model_inputs) + # input_ids, old_row_offsets, Optional: [pixel_values, attention_mask] + old_row_offsets = prev_model_inputs.input_row_offsets + + row_offsets_size = old_row_offsets.shape[0] + next_row_offsets = self._input_row_offsets_prealloc[:row_offsets_size] + # In multi-step execution, don't re-pass the pixel_values and attention_mask. + return PixtralInputs( + input_ids=next_tokens, + input_row_offsets=next_row_offsets, + ) + + @classmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + return pipeline_config.huggingface_config.text_config.num_hidden_layers + + @classmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + return KVCacheParams( + page_size=pipeline_config.kv_cache_page_size, + dtype=pipeline_config.cache_dtype, + n_kv_heads=pipeline_config.huggingface_config.text_config.num_key_value_heads, + head_dim=pipeline_config.huggingface_config.text_config.head_dim, + cache_strategy=pipeline_config.cache_strategy, + enable_prefix_caching=pipeline_config.enable_prefix_caching, + ) + + @classmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + try: + return upper_bounded_default( + upper_bound=pipeline_config.huggingface_config.text_config.max_position_embeddings, + default=pipeline_config.max_length, + ) + except ValueError as e: + msg = ( + "Unable to infer max_length for Pixtral, the provided " + f"max_length ({pipeline_config.max_length}) exceeds the " + f"model's max_position_embeddings " + f"({pipeline_config.huggingface_config.text_config.max_position_embeddings})." + ) + raise ValueError(msg) from e + + def load_kv_manager( + self, + session: InferenceSession, + available_cache_memory: int, + ) -> KVCacheManager: + return load_kv_manager( + params=self.get_kv_params(self.pipeline_config), + max_batch_size=self.pipeline_config.max_batch_size, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + num_layers=self.get_num_layers(self.pipeline_config), + devices=self.pipeline_config.devices, + available_cache_memory=available_cache_memory, + page_size=self.pipeline_config.kv_cache_page_size, + session=session, + ) + + @classmethod + def estimate_kv_cache_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + devices: list[Device], + ) -> int: + """Estimates the size of the kv cache in bytes.""" + return estimate_kv_cache_size( + params=cls.get_kv_params(pipeline_config), + max_batch_size=pipeline_config.max_batch_size, + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=cls.get_num_layers(pipeline_config), + available_cache_memory=available_cache_memory, + devices=devices, + ) + + def load_model(self, session: InferenceSession) -> tuple[Model, Model]: + if self.pipeline_config.enable_echo: + msg = "Pixtral model does not currently implement enable echo." + raise ValueError(msg) + + # Pre-allocate a buffer for input_row_offsets in multistep execution. + # We do this to avoid materializing and copying a buffer with each multistep step + assert self.pipeline_config.max_batch_size, ( + "Expected max_batch_size to be set" + ) + self._input_row_offsets_prealloc = Tensor.from_numpy( + np.arange(self.pipeline_config.max_batch_size + 1, dtype=np.uint32) + ).to(self.pipeline_config.devices[0]) + + weights = self.pipeline_config.load_weights() + + if not isinstance(weights, SafetensorWeights): + msg = ( + "only safetensors weights are currently supported in Pixtral" + " models." + ) + raise ValueError(msg) + + self._weights = weights + + if serialized_path := self.pipeline_config.serialized_model_path: + # Hydrate all weights to be referenced by the serialized path. + weights_registry = {} + for name, weight in self._weights.items(): + weights_registry[name] = weight.raw_tensor() + + def serialized_load(serialized_path): + logger.info("Loading serialized model from %s", serialized_path) + model = session.load( + f"{serialized_path}", weights_registry=weights_registry + ) + return model + + vision_model = serialized_load(f"{serialized_path}.vision") + text_model = serialized_load(f"{serialized_path}.text") + + else: + + def build_and_compile_model(build, label, export_path=None): + logger.info(f"Building and compiling {label} model...") + graph = build() + before = time.perf_counter() + model = session.load( + graph, + weights_registry=self._weights.allocated_weights, + ) + after = time.perf_counter() + logger.info( + f"Building and compiling {label} model took {after - before:.6f} seconds" + ) + if export_path: + mef_path = f"{export_path}.{label}" + logger.info( + f"Exporting serialized {label} model to {mef_path}" + ) + model._export_mef(mef_path) + return model + + export_path = self.pipeline_config.save_to_serialized_model_path + with ThreadPoolExecutor(max_workers=2) as executor: + build = lambda: _build_vision_graph( + pipeline_config=self.pipeline_config, + weights=self._weights, + ) + vision_model_future = executor.submit( + build_and_compile_model, build, "vision", export_path + ) + + build = lambda: _build_text_graph( + pipeline_config=self.pipeline_config, + weights=self._weights, + max_seq_len=self.calculate_max_seq_len( + self.pipeline_config + ), + kv_params=self.get_kv_params(self.pipeline_config), + kv_manager=self.kv_manager, + ) + text_model_future = executor.submit( + build_and_compile_model, build, "text", export_path + ) + + vision_model = vision_model_future.result() + text_model = text_model_future.result() + + return vision_model, text_model diff --git a/src/max/pipelines/architectures/pixtral/vision_encoder/__init__.py b/src/max/pipelines/architectures/pixtral/vision_encoder/__init__.py new file mode 100644 index 0000000000..1ce61c35f1 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/vision_encoder/__init__.py @@ -0,0 +1,14 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Pixtral Code vision encoder layer modules.""" diff --git a/src/max/pipelines/architectures/pixtral/vision_encoder/attention.py b/src/max/pipelines/architectures/pixtral/vision_encoder/attention.py new file mode 100644 index 0000000000..530f58e26a --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/vision_encoder/attention.py @@ -0,0 +1,139 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +import math +from dataclasses import dataclass + +from max.graph import TensorValue, TensorValueLike, ops +from max.pipelines.nn.layer import Layer +from max.pipelines.nn.linear import Linear + +from .attention_utils import rotate_half + + +@dataclass +class Attention(Layer): + n_heads: int + dim: int + head_dim: int # hidden_size // self.n_heads + + dropout: float + + wq: Linear + wk: Linear + wv: Linear + wo: Linear + + def apply_rotary_embedding( + self, + xq: TensorValue, + xk: TensorValue, + cos: TensorValue, + sin: TensorValue, + unsqueeze_dim=0, + ) -> tuple[TensorValue, TensorValue]: + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + xq (`TensorValueLike`): The query tensor. + xk (`TensorValueLike`): The key tensor. + cos (`TensorValueLike`): The cosine part of the rotary embedding. + sin (`TensorValueLike`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos and + sin so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos and sin have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos and sin broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(TensorValueLike)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = ops.unsqueeze(cos, unsqueeze_dim) + sin = ops.unsqueeze(sin, unsqueeze_dim) + xq = xq.transpose(1, 2) + xk = xk.transpose(1, 2) + + q_embed = (xq * cos) + (rotate_half(xq) * sin) + k_embed = (xk * cos) + (rotate_half(xk) * sin) + return q_embed, k_embed + + def attention( + self, + xq: TensorValue, + xk: TensorValue, + xv: TensorValue, + attn_mask: TensorValueLike, + ) -> TensorValue: + xv = xv.transpose(1, 2) + + scale = math.sqrt(1.0 / self.head_dim) + # xk shape = batch_size=1, n_heads=16, head_dim=64, image_seq_len=160 + scores = xq @ ops.transpose(xk, 2, 3) + # Note, the graph compiler currently requires the order of operands + # to be `scores * scale` in order to pattern match the fused attention + # operator. + # attn_mask and pixel_values are model inputs. scores is self-attention + # scores between all patches in pixel_values. + # attn_mask shape = ("n_images", 1, "num_patches_in_image", "num_patches_in_image") + # scores shape = ("n_images", "n_heads", "num_patches_in_image", "num_patches_in_image") + attn_mask = ops.rebind( + attn_mask, (scores.shape[0], 1, scores.shape[2], scores.shape[3]) + ) + scores = ops.softmax(scores * scale + attn_mask) + + return scores @ xv + + def __call__( + self, + x: TensorValue, + attention_mask: TensorValueLike, + position_embeddings: tuple[TensorValue, TensorValue], + ) -> TensorValue: + """Computes attention on x. + + Args: + x: Activations with shape (batch, seq_len, dim). + attention_mask: a mask to ensure different blocks of patches (images) + can only attend to patches within their respective block (image). + position_embeddings: + + Returns the result of multi-headed self attention on the input. + """ + + batch_size, n_patches = x.shape[0], x.shape[1] + # matmul weights + xq = self.wq(x) + xk = self.wk(x) + xv = self.wv(x) + + xq = ops.reshape( + xq, [batch_size, n_patches, self.n_heads, self.head_dim] + ) + xk = ops.reshape( + xk, [batch_size, n_patches, self.n_heads, self.head_dim] + ) + xv = ops.reshape( + xv, [batch_size, n_patches, self.n_heads, self.head_dim] + ) + + cos, sin = position_embeddings + xq, xk = self.apply_rotary_embedding(xq, xk, cos, sin, unsqueeze_dim=0) + + output = ( + self.attention(xq, xk, xv, attention_mask) + .transpose(1, 2) + .reshape([batch_size, n_patches, -1]) + ) + return self.wo(output) diff --git a/src/max/pipelines/architectures/pixtral/vision_encoder/attention_utils.py b/src/max/pipelines/architectures/pixtral/vision_encoder/attention_utils.py new file mode 100644 index 0000000000..735e37041b --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/vision_encoder/attention_utils.py @@ -0,0 +1,125 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from typing import List + +import numpy as np +from max.graph import TensorValue, ops + + +def causal_attention_mask_2d_from_imgs( + imgs: List[np.ndarray], + patch_size: int, + batch_size: int, + fill_val: float = -10000.0, +): + """Generates a 2D mask to ensure different blocks of patches (images) can only attend + to patches within their respective block (image). + + Args: + + num_patches_list: A list of images (blocks). Each image is of shape + (height, width, num_channels). + + patch_size: size of one dim of each patch in the image. + + batch_size: num of images. + + + Returns an ndarray of shape (batch_size, 1, seq_len, seq_len) representing the + attention mask for the blocks of patches attended to by the transformer. + """ + # generate list of (num_patches_in_height * num_patches_in_width) for each image + num_patches_list = [ + img.shape[0] // patch_size * img.shape[1] // patch_size for img in imgs + ] + + # seq_length is number of patches in all images + seq_len = sum(num_patches_list) + mask_shape = (seq_len, seq_len) + + # TODO(KERN-782): This fill_val should be -inf but softmax saturates with NaNs. + fill_matrix = np.full(mask_shape, fill_val, dtype=np.float32) + + # block_end_idx and block_start_idx are calculated using cumulative sums of + # patch_embeds_list. These indicate the starting and ending indices of each + # block of embeddings. + block_end_idx = np.cumsum(num_patches_list) + block_start_idx = np.cumsum(np.concatenate(([0], num_patches_list[:-1]))) + + # TODO(KERN-782): This should be -inf but softmax saturates with NaNs. + for start, end in zip(block_start_idx, block_end_idx): + fill_matrix[int(start) : int(end), int(start) : int(end)] = 0 + + # Expand the mask dimensions to match the expected input shape + fill_matrix = np.expand_dims(fill_matrix, axis=(0, 1)) # Add two new axes + fill_matrix = np.broadcast_to( + fill_matrix, (batch_size, 1, seq_len, seq_len) + ) + return fill_matrix + + +def causal_attention_mask_2d( + num_patches_list: list[int], patch_embeds: TensorValue +): + """Generates a 2D mask to ensure different blocks of patches (images) can only attend + to patches within their respective block (image). + + Args: + + num_patches_list: A list of integers, where each entry represents the number of patches + in a block (e.g., (num_patches_in_height × num_patches_in_width) patches per image block). + It is list representing the sizes of different blocks in terms of patches. + + patch_embeds:A tensor of shape [batch_size, num_patches, hidden_size] representing the + embeddings of patches in a batch of images. + + Returns an ndarray of shape (batch_size, 1, seq_len, seq_len) representing the + attention mask for the blocks of patches attended to by the transformer. + """ + # The total number of patches for all image in the batch. + # TODO: change this to take a dim not an int. + seq_len = int(patch_embeds.shape[1]) + mask_shape = (seq_len, seq_len) + + # TODO(KERN-782): This should be -inf but softmax saturates with NaNs. + fill_val = -10000.0 + fill_matrix = np.full(mask_shape, fill_val, dtype=np.float32) + + # block_end_idx and block_start_idx are calculated using cumulative sums of + # patch_embeds_list. These indicate the starting and ending indices of each + # image (block of embeddings). + block_end_idx = np.cumsum(num_patches_list) + block_start_idx = np.cumsum(np.concatenate(([0], num_patches_list[:-1]))) + + # For each block, set the diagonal region corresponding to that block to 0. + # This allows patches within the same block to attend to each other. + for start, end in zip(block_start_idx, block_end_idx): + fill_matrix[int(start) : int(end), int(start) : int(end)] = 0 + + # Expand the mask dimensions to match the expected transformer input shape. + fill_matrix = np.expand_dims(fill_matrix, axis=(0, 1)) # Add two new axes + fill_matrix = np.broadcast_to( + fill_matrix, + (int(patch_embeds.shape[0]), 1, seq_len, seq_len), + ) + return fill_matrix + + +def rotate_half(x: TensorValue): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return ops.concat((-x2, x1), axis=-1) diff --git a/src/max/pipelines/architectures/pixtral/vision_encoder/graph.py b/src/max/pipelines/architectures/pixtral/vision_encoder/graph.py new file mode 100644 index 0000000000..b6ee70ec28 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/vision_encoder/graph.py @@ -0,0 +1,218 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +from max.dtype import DType +from max.graph import Graph, ops +from max.graph.weights import SafetensorWeights +from max.pipelines import PipelineConfig +from max.pipelines.nn import Conv2D, Linear, RMSNorm + +from .attention import Attention +from .rotary_embedding_2d import RotaryEmbedding2D +from .transformer import MLP, Transformer, TransformerBlock +from .vision_encoder import VisionEncoder + + +def _patch_conv2d( + dtype: DType, + in_channels: int, + patch_size: int, + out_channels: int, + weights: SafetensorWeights, +) -> Conv2D: + """Creates a 2D convolution layer with the following assumptions: + - kernel size = (patch_size, patch_size) + - stride = (patch_size, patch_size) + - padding = (0, 0, 0, 0) + + This convolution splits the image into patches and then learns an embedding + of each patch. The embedding dim is out_channels. + """ + # Loaded torch weights shape = torch.Size([1024, 3, 16, 16]). + # Conv2D expects (height, width, in_channels, out_channels) = [16, 16, 3, 1024]. + weights = ops.permute( # type: ignore + weights.weight.allocate( + dtype, [out_channels, in_channels, patch_size, patch_size], None + ), + [2, 3, 1, 0], + ) + return Conv2D( + weights, # type: ignore + stride=(patch_size, patch_size), + ) + + +def _linear( + dtype: DType, + in_features: int, + out_features: int, + weights: SafetensorWeights, +) -> Linear: + return Linear( + weights.weight.allocate(dtype, [in_features, out_features], None) + ) + + +def _feed_forward( + dtype: DType, + hidden_dim: int, + feed_forward_length: int, + weights: SafetensorWeights, +): + return MLP( + _linear( # gate_proj + dtype, + feed_forward_length, + hidden_dim, + weights.feed_forward.gate_proj, + ), + _linear( # down_proj + dtype, + hidden_dim, + feed_forward_length, + weights.feed_forward.down_proj, + ), + _linear( # up_proj + dtype, + feed_forward_length, + hidden_dim, + weights.feed_forward.up_proj, + ), + ) + + +def _rms_norm(dims: int, eps: float, weights: SafetensorWeights) -> RMSNorm: + return RMSNorm(weights.weight.allocate(DType.bfloat16, [dims]), eps) + + +def _encoder_attention( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +) -> Attention: + # TODO: Do we need to transpose weights? Not obvious from shapes. Both dims are the same. + hidden_dim = pipeline_config.huggingface_config.vision_config.hidden_size + wq = _linear( + pipeline_config.dtype, + hidden_dim, + hidden_dim, + weights.attention.q_proj, + ) + wk = _linear( + pipeline_config.dtype, + hidden_dim, + hidden_dim, + weights.attention.k_proj, + ) + wv = _linear( + pipeline_config.dtype, + hidden_dim, + hidden_dim, + weights.attention.v_proj, + ) + wo = _linear( + pipeline_config.dtype, + hidden_dim, + hidden_dim, + weights.attention.o_proj, + ) + + return Attention( + n_heads=pipeline_config.huggingface_config.vision_config.num_attention_heads, + dim=hidden_dim, + head_dim=pipeline_config.huggingface_config.vision_config.head_dim, + dropout=pipeline_config.huggingface_config.vision_config.attention_dropout, + wq=wq, + wk=wk, + wv=wv, + wo=wo, + ) + + +def _transformer( + graph: Graph, + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +): + with graph: + layers = [ + TransformerBlock( + attention=_encoder_attention( + pipeline_config, weights.layers[i] + ), + mlp=_feed_forward( + pipeline_config.dtype, + pipeline_config.huggingface_config.vision_config.hidden_size, + pipeline_config.huggingface_config.vision_config.intermediate_size, + weights.layers[i], + ), + attention_norm=_rms_norm( + pipeline_config.huggingface_config.vision_config.hidden_size, + 1e-5, + weights.layers[i].attention_norm, + ), + mlp_norm=_rms_norm( + pipeline_config.huggingface_config.vision_config.hidden_size, + 1e-5, + weights.layers[i].ffn_norm, + ), + ) + for i in range( + pipeline_config.huggingface_config.vision_config.num_hidden_layers + ) + ] + + return Transformer( + n_heads=pipeline_config.huggingface_config.vision_config.num_attention_heads, + layers=layers, + dtype=pipeline_config.dtype, + ) + + +def _vision_encoder( + graph: Graph, + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +) -> VisionEncoder: + patch_conv = _patch_conv2d( + pipeline_config.dtype, + pipeline_config.huggingface_config.vision_config.num_channels, + pipeline_config.huggingface_config.vision_config.patch_size, + pipeline_config.huggingface_config.vision_config.hidden_size, + weights.vision_tower.patch_conv, + ) + ln_pre = _rms_norm( + pipeline_config.huggingface_config.vision_config.hidden_size, + 1e-5, + weights.vision_tower.ln_pre, + ) + patch_rope = RotaryEmbedding2D( + dim=pipeline_config.huggingface_config.vision_config.hidden_size, + n_heads=pipeline_config.huggingface_config.vision_config.num_attention_heads, + theta=pipeline_config.huggingface_config.vision_config.rope_theta, + max_patches_per_side=pipeline_config.huggingface_config.vision_config.image_size + // pipeline_config.huggingface_config.vision_config.patch_size, + ) + encoder_transformer = _transformer( + graph, pipeline_config, weights.vision_tower.transformer + ) + + return VisionEncoder( + patch_conv=patch_conv, + layer_norm=ln_pre, + patch_positional_embedding=patch_rope, + transformer=encoder_transformer, + dtype=pipeline_config.dtype, + patch_size=pipeline_config.huggingface_config.vision_config.patch_size, + max_image_size=pipeline_config.huggingface_config.vision_config.image_size, + ) diff --git a/src/max/pipelines/architectures/pixtral/vision_encoder/rotary_embedding_2d.py b/src/max/pipelines/architectures/pixtral/vision_encoder/rotary_embedding_2d.py new file mode 100644 index 0000000000..65b38bfb50 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/vision_encoder/rotary_embedding_2d.py @@ -0,0 +1,200 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +from dataclasses import dataclass +from functools import cached_property +from typing import List, Optional + +import numpy as np +from max.dtype import DType +from max.graph import Dim, DimLike, TensorValue, TensorValueLike, ops +from max.pipelines.nn.layer import Layer + + +def meshgrid(height: DimLike, width: DimLike, indexing="ij") -> TensorValue: + """Returns row indices and col indices of each point on the grid.""" + height = Dim(height) + width = Dim(width) + row_indices = ops.range( + ops.constant(0, DType.int64), + TensorValue.from_dim(height), + ops.constant(1, DType.int64), + out_dim=height, + ) + col_indices = ops.range( + ops.constant(0, DType.int64), + TensorValue.from_dim(width), + ops.constant(1, DType.int64), + out_dim=width, + ) + + # repeat row indices for each row [[0, ..., 0], ..., [width=n_cols-1, ..., width-1]] + h_grid = ops.tile( + ops.unsqueeze(row_indices, 1), [1, width] + ) # Shape: (height, width) + # repeat col indices for each col [[0, 1, ..., height-1=n_rows-1], ...] + v_grid = ops.tile( + ops.unsqueeze(col_indices, 0), [height, 1] + ) # Shape: (height, width) + return h_grid, v_grid # type: ignore + + +def patch_position_ids( + patch_embeds: List[TensorValue], max_width: int +) -> TensorValue: + """ + Takes a list of patches, calculates the positional indices for each patch by + flattening the array, and returns these indices in the positions tensor. + max_width is the maximum numbers of patches you can have in an image on one + side ie.e max_image_width_or_height // patch_size. + """ + positions = [] + for patch in patch_embeds: + height, width = patch.shape[ + 1:3 + ] # img_height/patch_size, img_width/patch_size + # TODO(MSDK-1194): replace with ops.meshgrid() + mesh = meshgrid(height, width, indexing="ij") + # TODO(MSDK-1193): replace ? by ops.chunk() or ops.split_tensor() + # Combine row and col indices into 1 tensor of paired coordinates. Shape = (height, width, 2) + # Then split into 2 tensors: 1st and 2nd coordinate of points in the mesh. + mesh_coords = ops.stack(mesh, axis=-1).reshape((-1, 2)) # type: ignore + h_grid, v_grid = mesh_coords[:, 0], mesh_coords[:, 1] + # Calculates a unique ID for each coordinate pair. + # TODO: Understand if using max_width here leads to memory inefficiency + ids = h_grid * max_width + v_grid + positions.append(ids[:]) + return ops.concat(positions) + + +@dataclass +class RotaryEmbedding2D(Layer): + """ + RotaryEmbedding layer to calculate and apply the frequency tensor for complex exponentials. + """ + + dim: DimLike + n_heads: int + theta: float + """Hyperparameter used to control the frequency scaling of the sinusoidal components of the embeddings.""" + max_patches_per_side: int + """The maximum number of patches per side for model's input (images).""" + rope_scaling: Optional[np.ndarray] = None + """Scaling factor for the positional frequencies.""" + + def freqs_cis_base(self) -> TensorValue: + """ + Computes the frequency tensor for complex exponentials (cis) + for a given seq_len = num_patches in a block (image). + Tensor is scaled with theta parameter. + Required to apply Rotary Position Embedding (RoPE) to tensor. + See 'Roformer: Enhanced Transformer with Rotary Embedding' + (arxiv.org/pdf/2104.09864). + + Returns: + The frequency tensor for complex exponentials with shape + (max_seq_len, dim//(n_heads), 2) + ??(batch_size, height * width, dim) with dim the embed dim. + """ + head_dim = ( + self.dim // self.n_heads # type: ignore + ) # hidden_size // num_attention_heads + # Note: using float64 to avoid an overflow on the exponential, then converting back to float32. + # 1D tensor of length head_dim // 2 = 32 + iota = ops.range( + ops.constant(0, DType.float64), + ops.constant(head_dim, DType.float64), # type: ignore + ops.constant(2, DType.float64), + out_dim=head_dim // 2, + ) + if self.rope_scaling is not None: + iota = iota * self.rope_scaling + # 1D tensor of length head_dim // 2 = 32 + freqs = ops.cast(1.0 / (self.theta ** (iota / head_dim)), DType.float32) + + # Indices of patches in each side (height and width) of image. + # 1D tensor of length max_patches_per_side = 64 + h = ops.range( + ops.constant(0, DType.float32), + ops.constant(self.max_patches_per_side, DType.float32), + ops.constant(1, DType.float32), + out_dim=self.max_patches_per_side, + ) + # 1D tensor of length max_patches_per_side = 64 + w = ops.range( + ops.constant(0, DType.float32), + ops.constant(self.max_patches_per_side, DType.float32), + ops.constant(1, DType.float32), + out_dim=self.max_patches_per_side, + ) + # create matrices of freqs = outer product of height and width indices with their respective frequency. + # 2D tensors mapping patch positions to rotary embeddings. shape =(max_patches_per_side = 64, head_dim//4 =16) + freqs_h = ops.outer(h, freqs[::2]) + freqs_w = ops.outer(w, freqs[1::2]) + + # Combines the frequency matrices of horizontal and vertical patch indices into a single tensor of patches + # 2D tensor of shape (max_patches_per_side*max_patches_per_side = 4096, head_dim // 2 = 32) + _inv_freq = ops.concat( + [ + ops.tile( + ops.unsqueeze(freqs_h, 1), + (1, self.max_patches_per_side, 1), + ), + ops.tile( + ops.unsqueeze(freqs_w, 0), + (self.max_patches_per_side, 1, 1), + ), + ], + axis=-1, + ).reshape((-1, head_dim // 2)) + + # In Hugging Face Code, double copies to have rotary embeddings that match head_dim + # 2D tensor of shape (max_patches_per_side*max_patches_per_side =4096, head_dim=64) + _inv_freq = ops.concat((_inv_freq, _inv_freq), axis=-1) + + # 2D tensor of shape (max_patches_per_side*max_patches_per_side =4096, head_dim*2=128) + # self._freqs_cis = ops.stack( + # [ops.cos(_inv_freq), ops.sin(_inv_freq)], axis=-1 + # ) + return TensorValue(_inv_freq) + + @cached_property + def freqs_cis(self) -> TensorValue: + self._freqs_cis = self.freqs_cis_base() + return self._freqs_cis + + def __call__( + self, x: TensorValueLike, position_ids: TensorValue + ) -> tuple[TensorValue, TensorValue]: + """Applies rotary positional embeddings (RoPE) to `x`. + + Args: + x: Activation tensor with shape (batch, seq_len, n_kv_heads, head_dim). + position_ids: starting position of input tensor + + Returns: + Input activation tensor with rotary positional embeddings applied and + the same shape as `x`. + """ + v = TensorValue(x) + + # TODO: Figure out if this is the correct axis. + freqs_cis_sliced = ops.gather(self.freqs_cis, position_ids, 0) + # TODO(MSDK-1188): Ideally this cast would happen inside of the cached + # self.freqs_cis property instead of here, but complex.dtype is not + # known at that point. + cos = ops.cast(ops.cos(freqs_cis_sliced), v.dtype) + sin = ops.cast(ops.sin(freqs_cis_sliced), v.dtype) + + return cos, sin diff --git a/src/max/pipelines/architectures/pixtral/vision_encoder/transformer.py b/src/max/pipelines/architectures/pixtral/vision_encoder/transformer.py new file mode 100644 index 0000000000..e2089ead22 --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/vision_encoder/transformer.py @@ -0,0 +1,113 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +from max.dtype import DType +from max.graph import TensorValue, TensorValueLike, ops +from max.pipelines.nn.layer import Layer + +if TYPE_CHECKING: + from max.pipelines.nn.linear import Linear + from max.pipelines.nn.norm import RMSNorm + + from .attention import Attention + + +@dataclass +class MLP(Layer): + """ + Simple multi-layer perceptron composed of three linear layers. + Uses Gelu activation function. + """ + + gate_proj: Linear + down_proj: Linear + up_proj: Linear + + def __call__(self, x: TensorValueLike) -> TensorValue: + return self.down_proj((ops.silu(self.gate_proj(x)) * self.up_proj(x))) # type: ignore + + +@dataclass +class TransformerBlock(Layer): + """Stack of Attention, FeedForward, and RMSNorm layers.""" + + attention: Attention + mlp: MLP + attention_norm: RMSNorm + mlp_norm: RMSNorm + residual_multiplier: float = 1.0 + + def __call__( + self, + x: TensorValue, + attention_mask: TensorValueLike, + position_embeddings: tuple[TensorValue, TensorValue], + ) -> TensorValue: + residual_multiplier = ops.constant(self.residual_multiplier, x.dtype) + attn_out = self.attention( + self.attention_norm(x), + attention_mask, + position_embeddings, + ) + + if self.residual_multiplier != 1.0: + attn_out = attn_out * residual_multiplier + + h = x + attn_out + mlp = self.mlp(self.mlp_norm(h)) + if self.residual_multiplier != 1.0: + mlp = mlp * residual_multiplier + + return h + mlp + + +@dataclass +class Transformer(Layer): + """Transformer model consisting of TransformerBlock layers. + The input is embeddings created using convolution followed by normalization. + + The differences between this transformer and other decoder model transformers: + 1. Input to the transformer is patch embeddings created by convolutions not tokens. + 2. No linear(norm(output)) at the transformer output. + 3. It uses the 2d rotary embeddings defined for images which is different + from the rotary embeddings defined in other classes as rope: RotaryEmbedding + """ + + n_heads: int + layers: list[TransformerBlock] + dtype: DType + + def __call__( + self, + patch_embeds: TensorValue, + attention_mask: TensorValueLike, + position_embeddings: tuple[TensorValue, TensorValue], + **kwargs, + ): + h = patch_embeds + + for _, layer in enumerate(self.layers): + h = layer( + x=h, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + return ops.cast(h, self.dtype) diff --git a/src/max/pipelines/architectures/pixtral/vision_encoder/vision_encoder.py b/src/max/pipelines/architectures/pixtral/vision_encoder/vision_encoder.py new file mode 100644 index 0000000000..1c044dd18c --- /dev/null +++ b/src/max/pipelines/architectures/pixtral/vision_encoder/vision_encoder.py @@ -0,0 +1,93 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from dataclasses import dataclass +from typing import List + +from max.dtype import DType +from max.graph import TensorValueLike, ops +from max.pipelines.nn import Conv2D +from max.pipelines.nn.layer import Layer +from max.pipelines.nn.norm import RMSNorm + +from .rotary_embedding_2d import RotaryEmbedding2D, patch_position_ids +from .transformer import Transformer + + +@dataclass +class VisionEncoder(Layer): + """The bare Pixtral vision encoder outputting raw hidden-states without any + specific head on top. + + It tokenizes the list of images and returns a representation of these images + embeddings of patches. + """ + + patch_conv: Conv2D + layer_norm: RMSNorm + patch_positional_embedding: RotaryEmbedding2D + transformer: Transformer + dtype: DType + patch_size: int = 16 + max_image_size: int = 1024 + + def __call__( + self, imgs: List[TensorValueLike], attention_mask: TensorValueLike + ): + """ + imgs: list of images of shape = (height, width, num_channels) + """ + # Images go through a convolution independently to get patched. + # Returns a list of [batch_size, hidden_size, height/patch_size, width/patch_size] tensors + patch_embeds_list = [ + self.patch_conv(ops.unsqueeze(ops.cast(img, self.dtype), 0)) + for img in imgs + ] + + # Flatten all images to a single tensor of patches of size (n_patches=seq_length, hidden_size). + # 1. Flattens each image's patches to (batch_size, n_patches in image, hidden_size). + # 2. Concat patches vertically on dim 1 to get a sequence of all patches + # TODO(MSDK-1192): replace transpose(1, 2) by permute(0, 2, 1) + # TODO(MSDK-1195): replace p.reshape((p.shape[0], p.shape[1], -1)) by p.flatten(2) + patch_embeds = ops.concat( + [ # p.shape = batch_size, patches_per_height, patches_per_width, hidden_size + p.reshape((p.shape[0], -1, p.shape[3])) + for p in patch_embeds_list + ], + axis=1, + ) + + # Pre-attention layer normalization + patch_embeds = self.layer_norm(patch_embeds) + + # Get unique ids of tokens (patches) based on row and col idx in the image (position). + # These help the model understand the spatial layout of the image. + position_ids = patch_position_ids( + patch_embeds_list, max_width=self.max_image_size // self.patch_size + ) + + # Positional Encodings + # map each position id to its corresponding embedding representing that posiiton + position_embedding = self.patch_positional_embedding( + patch_embeds, position_ids + ) + + encoder_output = self.transformer( + patch_embeds=patch_embeds, + attention_mask=attention_mask, + position_embeddings=position_embedding, + ) + + return encoder_output diff --git a/src/max/pipelines/architectures/qwen2/README.md b/src/max/pipelines/architectures/qwen2/README.md new file mode 100644 index 0000000000..d42abdc47d --- /dev/null +++ b/src/max/pipelines/architectures/qwen2/README.md @@ -0,0 +1,120 @@ +# Qwen 2.5 + +**Language:** Python + +**API**: MAX Graph + +This pipeline provides optimized support for the `Qwen2ForCausalLM` family +of large language models, as exemplified by the Qwen 2.5 7B Instruct text +completion model. The model itself has been constructed in Python +using the [MAX Graph API](https://docs.modular.com/max/graph/). + +The MAX Graph API provides an accessible interface to the construction of +flexible accelerated compute graphs, which are then optimized by the MAX +Engine's advanced graph compiler. This pipeline showcases how a large language +model can be fully defined using Python and MAX Graphs and then compiled for +optimal inference performance via the MAX Engine. + +## Model + +[Qwen 2.5](https://qwenlm.github.io/blog/qwen2.5/) is an open source large +language model released by [Qwen AI](https://qwenlm.github.io/). The weight +checkpoints have been released under an Apache 2.0 license. + +The default settings for this pipeline use the 7B Instruct set of pretrained +weights in the `bfloat16` encoding. + +## Usage + +The easiest way to try out this pipeline is with our Magic command-line tool. + +1. Install Magic on macOS and Ubuntu with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Clone the MAX examples repository: + + If you don't already have a local clone of this repository, create one via: + + ```shell + git clone https://github.com/modular/max.git + ``` + + The following instructions assume that you're present within this pipeline's + directory, and you can change to it after cloning: + + ```shell + cd max/src/max/ + ``` + +3. Now run the Qwen text completion demo with the following command: + + ```shell + magic run qwen --prompt "I believe the meaning of life is" + ``` + +4. Host a chat completion endpoint via MAX Serve. + + MAX Serve provides functionality to host performant OpenAI compatible + endpoints using the FastAPI framework. + + You can configure the pipeline to be hosted by using the `--serve` argument. + For example: + + ```shell + magic run qwen --serve + ``` + + A request can be submitted via a cURL command. + + ```shell + curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "Qwen/Qwen2.5-7B-Instruct", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who won the world series in 2020?"} + ] + }' + ``` + + Additionally, finetuned weights hosted on Hugging Face for any compatible + `Qwen2ForCausalLM` model can be used with this optimized architecture + via the `serve` command: + + ```shell + magic run serve --model-path=Qwen/Qwen2.5-7B-Instruct + ``` + +## Options + +The following command-line options are available to customize operation of the +pipeline: + +- `--max-length`: Controls the maximum length of the text sequence + (includes the input tokens). +- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 + value is provided, the model will continue to generate tokens for the entire + context length. (Default value: -1) +- `--prompt`: The text prompt to use for further generation. +- `--save-to-serialized-model-path`: If specified, writes the serialized model + to this path. +- `--serialized-model-path`: If specified, tries to load a serialized model + from this path. +- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. +- `--weight-path`: Overrides the default URL, and allows for an + already-downloaded pretrained weight file to be used with the model. +- `--max-batch-size`: Specifies the maximum batch size to be used. + Default is 1. +- `--devices`: Specifies the device(s) to use for the model. This can optionally + be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". + Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/qwen2/__init__.py b/src/max/pipelines/architectures/qwen2/__init__.py new file mode 100644 index 0000000000..69d2f5bacf --- /dev/null +++ b/src/max/pipelines/architectures/qwen2/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import qwen2_arch + +__all__ = ["qwen2_arch"] diff --git a/src/max/pipelines/architectures/qwen2/arch.py b/src/max/pipelines/architectures/qwen2/arch.py new file mode 100644 index 0000000000..f181ebfd1b --- /dev/null +++ b/src/max/pipelines/architectures/qwen2/arch.py @@ -0,0 +1,47 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from .model import Qwen2Model + +qwen2_arch = SupportedArchitecture( + name="Qwen2ForCausalLM", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=[ + "Qwen/Qwen2.5-7B-Instruct", + ], + default_weights_format=WeightsFormat.safetensors, + default_encoding=SupportedEncoding.bfloat16, + supported_encodings={ + SupportedEncoding.float32: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + SupportedEncoding.bfloat16: [ + KVCacheStrategy.PAGED, + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.NAIVE, + ], + }, + pipeline_model=Qwen2Model, + tokenizer=TextTokenizer, +) diff --git a/src/max/pipelines/architectures/qwen2/graph.py b/src/max/pipelines/architectures/qwen2/graph.py new file mode 100644 index 0000000000..cb5c51bbdf --- /dev/null +++ b/src/max/pipelines/architectures/qwen2/graph.py @@ -0,0 +1,309 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Build a Qwen2 model via Graph API from GGUF weights.""" + +import math +from typing import List, Union, cast + +from max.dtype import DType +from max.graph import ( + DeviceRef, + Graph, + TensorValue, + TensorValueLike, + Weight, + ops, +) +from max.graph.weights import Weights +from max.pipelines import PipelineConfig, RopeType, WeightsFormat +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + KVCacheParams, + KVCacheStrategy, +) +from max.pipelines.nn import ( + MLP, + AttentionWithRope, + Embedding, + Linear, + OptimizedRotaryEmbedding, + RMSNorm, + Transformer, + TransformerBlock, +) + + +def distribute_value( + v: TensorValue, devices: List[DeviceRef] +) -> List[TensorValue]: + return [v.to(device) for device in devices] + + +def shard_col_value( + x: TensorValueLike, devices: List[DeviceRef] +) -> List[TensorValue]: + n_devices = len(devices) + v = TensorValue(x) + col_size = v.shape[1] // n_devices + return [ + v[:, i * col_size : (i + 1) * col_size].to(device) + for i, device in enumerate(devices) + ] + + +def shard_row_value( + x: TensorValueLike, devices: List[DeviceRef] +) -> List[TensorValue]: + n_devices = len(devices) + v = TensorValue(x) + row_size = v.shape[0] // n_devices + return [ + v[i * row_size : (i + 1) * row_size, :].to(device) + for i, device in enumerate(devices) + ] + + +def feed_forward( + dtype: DType, + hidden_dim: int, + feed_forward_length: int, + weights: Weights, +) -> MLP: + return MLP( + linear( + dtype, + feed_forward_length, + hidden_dim, + weights.mlp.gate_proj, + ), + linear( + dtype, + hidden_dim, + feed_forward_length, + weights.mlp.down_proj, + ), + linear( + dtype, + feed_forward_length, + hidden_dim, + weights.mlp.up_proj, + ), + ) + + +def linear( + dtype: DType, + in_features: int, + out_features: int, + weights: Weights, +) -> Linear: + return Linear( + weights.weight.allocate( + dtype, + [in_features, out_features], + ) + ) + + +def rms_norm(dims: int, eps: float, weights: Weights) -> RMSNorm: + return RMSNorm(weights.weight.allocate(DType.float32, [dims]), eps) + + +def embedding( + pipeline_config: PipelineConfig, + vocab_size: int, + hidden_dim: int, + weights: Weights, +) -> Embedding: + return Embedding( + weights.weight.allocate( + pipeline_config.dtype, + [vocab_size, hidden_dim], + ) + ) + + +def attention( + kv_params: KVCacheParams, + pipeline_config: PipelineConfig, + rope: OptimizedRotaryEmbedding, + weights: Weights, + layer_idx: TensorValue, +) -> AttentionWithRope: + kv_weight_dim = ( + pipeline_config.huggingface_config.hidden_size + // pipeline_config.huggingface_config.num_attention_heads + ) * pipeline_config.huggingface_config.num_key_value_heads + + wq = weights.self_attn.q_proj.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.hidden_size, + pipeline_config.huggingface_config.hidden_size, + ], + ) + wk = weights.self_attn.k_proj.weight.allocate( + pipeline_config.dtype, + [kv_weight_dim, pipeline_config.huggingface_config.hidden_size], + ) + wv = weights.self_attn.v_proj.weight.allocate( + pipeline_config.dtype, + [kv_weight_dim, pipeline_config.huggingface_config.hidden_size], + ) + + wqkv = ops.concat((wq, wk, wv)) + + bias_q = weights.self_attn.q_proj.bias.allocate( + pipeline_config.dtype, + [pipeline_config.huggingface_config.hidden_size], + ) + + bias_k = weights.self_attn.k_proj.bias.allocate( + pipeline_config.dtype, + [kv_weight_dim], + ) + + bias_v = weights.self_attn.v_proj.bias.allocate( + pipeline_config.dtype, + [kv_weight_dim], + ) + + bias_qkv = ops.concat((bias_q, bias_k, bias_v)) + + return AttentionWithRope( + n_heads=pipeline_config.huggingface_config.num_attention_heads, + kv_params=kv_params, + wqkv=wqkv, + wo=linear( + pipeline_config.dtype, + pipeline_config.huggingface_config.hidden_size, + pipeline_config.huggingface_config.hidden_size, + weights.self_attn.o_proj, + ), + rope=rope, + layer_idx=layer_idx, + bias=bias_qkv, + scale=math.sqrt(1.0 / kv_params.head_dim), + ) + + +def transformer( + graph: Graph, + pipeline_config: PipelineConfig, + weights: Weights, + kv_params: KVCacheParams, +) -> Transformer: + with graph: + if weights.rope_freqs.weight.exists(): + rope_scaling = weights.rope_freqs.weight.raw_tensor() + else: + rope_scaling = None + + interleaved_rope_weights = ( + pipeline_config.weights_format == WeightsFormat.gguf + and pipeline_config.rope_type == RopeType.normal + ) + rope = OptimizedRotaryEmbedding( + dim=pipeline_config.huggingface_config.hidden_size, + n_heads=pipeline_config.huggingface_config.num_attention_heads, + theta=pipeline_config.huggingface_config.rope_theta, + max_seq_len=pipeline_config.huggingface_config.max_position_embeddings, + rope_scaling=rope_scaling, + interleaved=interleaved_rope_weights, + ) + + rms_norm_eps = pipeline_config.huggingface_config.rms_norm_eps + layers = [ + TransformerBlock( + attention=attention( + kv_params, + pipeline_config, + rope, + weights.model.layers[i], + layer_idx=ops.constant(i, DType.uint32), + ), + mlp=feed_forward( + pipeline_config.dtype, + pipeline_config.huggingface_config.hidden_size, + pipeline_config.huggingface_config.intermediate_size, + weights.model.layers[i], + ), + attention_norm=rms_norm( + pipeline_config.huggingface_config.hidden_size, + rms_norm_eps, + weights.model.layers[i].input_layernorm, + ), + mlp_norm=rms_norm( + pipeline_config.huggingface_config.hidden_size, + rms_norm_eps, + weights.model.layers[i].post_attention_layernorm, + ), + ) + for i in range(pipeline_config.huggingface_config.num_hidden_layers) + ] + + embedding_layer = embedding( + pipeline_config, + pipeline_config.huggingface_config.vocab_size, + pipeline_config.huggingface_config.hidden_size, + weights.model.embed_tokens, + ) + + # Some model variants lack dedicated weights for a final linear + # layer, and share the embedding layer. + if weights.lm_head.weight.exists(): + output = Linear.create( + pipeline_config.dtype, + pipeline_config.graph_quantization_encoding, + pipeline_config.huggingface_config.vocab_size, + pipeline_config.huggingface_config.hidden_size, + weights.lm_head, + ) + else: + output = Linear.create( + pipeline_config.dtype, + pipeline_config.graph_quantization_encoding, + pipeline_config.huggingface_config.vocab_size, + pipeline_config.huggingface_config.hidden_size, + cast(Weight, embedding_layer.weights), + ) + + kv_collection: Union[ + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + ] + if kv_params.cache_strategy == KVCacheStrategy.CONTINUOUS: + kv_collection = FetchContinuousBatchingKVCacheCollection(kv_params) + elif kv_params.cache_strategy == KVCacheStrategy.PAGED: + kv_collection = FetchPagedKVCacheCollection(kv_params) + else: + raise ValueError( + "Unsupported caching strategy " + str(kv_params.cache_strategy) + ) + + return Transformer( + dim=pipeline_config.huggingface_config.hidden_size, + n_heads=pipeline_config.huggingface_config.num_attention_heads, + layers=layers, + norm=rms_norm( + pipeline_config.huggingface_config.hidden_size, + rms_norm_eps, + weights.model.norm, + ), + output=output, + embedding=embedding_layer, + kv_params=kv_params, + kv_collection_constructor=kv_collection, + all_logits=pipeline_config.enable_echo, + ) diff --git a/src/max/pipelines/architectures/qwen2/model.py b/src/max/pipelines/architectures/qwen2/model.py new file mode 100644 index 0000000000..526234d031 --- /dev/null +++ b/src/max/pipelines/architectures/qwen2/model.py @@ -0,0 +1,498 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import logging +import time +import warnings +from typing import List, Sequence, Union, cast + +import numpy as np +from max.driver import CPU, Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession, Model +from max.graph import DeviceRef, Graph, TensorType, TensorValue +from max.graph.weights import Weights +from max.pipelines import ( + LogProbabilities, + ModelInputs, + ModelOutputs, + PipelineConfig, + PipelineModel, + TextContext, + upper_bounded_default, +) +from max.pipelines.dataprocessing import batch_padded_tokens_and_mask +from max.pipelines.kv_cache import ( + KVCacheInputs, + KVCacheManager, + KVCacheParams, + estimate_kv_cache_size, + load_kv_manager, +) +from max.pipelines.nn.compute_log_probabilities import compute_log_probabilities + +from .graph import transformer + + +class Qwen2Inputs(ModelInputs): + """A class representing inputs for the Qwen2 model. + + This class encapsulates the input tensors required for the Qwen2 model execution: + - tokens: A tensor containing the input token IDs + - input_row_offsets_or_attn_mask: A tensor containing the offsets for each row in the ragged input sequence, + or the attention mask for the padded input sequence + """ + + tokens: Tensor + input_row_offsets_or_attn_mask: Tensor + + def __init__( + self, + tokens: Tensor, + input_row_offsets_or_attn_mask: Tensor, + ) -> None: + self.tokens = tokens + self.input_row_offsets_or_attn_mask = input_row_offsets_or_attn_mask + + @property + def input_row_offsets(self) -> Tensor: + """Gets the row offsets of the ragged input sequence.""" + # TODO(bduke): this should implement a ragged tensor interface. + return self.input_row_offsets_or_attn_mask + + +class Qwen2Model(PipelineModel[TextContext]): + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) + self.model = self.load_model(session) + + def execute( + self, + model_inputs: ModelInputs, + kv_cache_inputs: KVCacheInputs | None = None, + ) -> ModelOutputs: + assert kv_cache_inputs is not None + + model_inputs = cast(Qwen2Inputs, model_inputs) + model_outputs = self.model.execute( + model_inputs.tokens, + model_inputs.input_row_offsets_or_attn_mask, + *kv_cache_inputs, + copy_inputs_to_device=( + not self.pipeline_config.cache_strategy.uses_opaque() + ), + ) + + assert isinstance(model_outputs[0], Tensor) + if len(model_outputs) > 1: + assert isinstance(model_outputs[1], Tensor) + logits = model_outputs[1] + else: + logits = None + + if self.pipeline_config.enable_echo: + return ModelOutputs( + next_token_logits=model_outputs[0], + logits=logits, + ) + else: + return ModelOutputs(next_token_logits=model_outputs[0]) + + def _prepare_ragged_initial_token_inputs( + self, context_batch: Sequence[TextContext] + ) -> Qwen2Inputs: + # Get input_row_offsets: start and end position of each batch in the + # combined total_seq_len dimension. + input_row_offsets = np.cumsum( + [0] + [ctx.active_length for ctx in context_batch], + dtype=np.uint32, + ) + + # Create a ragged token vector of length: sum(len(t) for t in tokens). + tokens = np.concatenate([ctx.next_tokens for ctx in context_batch]) + + return Qwen2Inputs( + tokens=Tensor.from_numpy(tokens).to( + self.pipeline_config.devices[0] + ), + input_row_offsets_or_attn_mask=Tensor.from_numpy( + input_row_offsets + ).to(self.pipeline_config.devices[0]), + ) + + def _prepare_padded_initial_token_inputs( + self, context_batch: Sequence[TextContext] + ) -> Qwen2Inputs: + # Get tokens and seq_ids + tokens = [ctx.next_tokens for ctx in context_batch] + + # Pad tokens and compute attention mask for the batch. + max_seq_len = self.kv_manager.max_sequence_length + start_pos = [max_seq_len] * len(context_batch) + next_tokens_batch, _, attn_mask = batch_padded_tokens_and_mask( + start_pos=start_pos, + tokens=tokens, + pad_to_multiple_of=self.pipeline_config.pad_to_multiple_of, + ) + + return Qwen2Inputs( + tokens=Tensor.from_numpy(next_tokens_batch).to( + self.pipeline_config.devices[0] + ), + input_row_offsets_or_attn_mask=Tensor.from_numpy(attn_mask).to( + self.pipeline_config.devices[0] + ), + ) + + # Ignored type due to challenge with Interface implementation and mypy rules. + def prepare_initial_token_inputs( + self, + context_batch: Sequence[TextContext], + ) -> Qwen2Inputs: + """Prepare the inputs for the first pass in multistep execution.""" + if self.pipeline_config.cache_strategy.uses_opaque(): + return self._prepare_ragged_initial_token_inputs(context_batch) + else: + return self._prepare_padded_initial_token_inputs(context_batch) + + def _prepare_ragged_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: Qwen2Inputs, + ) -> Qwen2Inputs: + row_offsets_size = ( + prev_model_inputs.input_row_offsets_or_attn_mask.shape[0] + ) + next_row_offsets = self._input_row_offsets_prealloc[:row_offsets_size] + + return Qwen2Inputs( + tokens=next_tokens, + input_row_offsets_or_attn_mask=next_row_offsets, + ) + + def _prepare_padded_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: Qwen2Inputs, + ) -> Qwen2Inputs: + batch_size = prev_model_inputs.tokens.shape[0] + start_pos = [ + prev_model_inputs.input_row_offsets_or_attn_mask.shape[-1] + ] * batch_size + + next_tokens_batch, _, attn_mask = batch_padded_tokens_and_mask( + start_pos=start_pos, + tokens=[next_tokens.to_numpy()], + pad_to_multiple_of=self.pipeline_config.pad_to_multiple_of, + ) + return Qwen2Inputs( + tokens=Tensor.from_numpy(next_tokens_batch).to( + self.pipeline_config.devices[0] + ), + input_row_offsets_or_attn_mask=Tensor.from_numpy(attn_mask).to( + self.pipeline_config.devices[0] + ), + ) + + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: ModelInputs, + ) -> Qwen2Inputs: + """Prepare the inputs for the next token in multistep execution. + This should avoid any device synchronization or copy operations. + """ + prev_model_inputs = cast(Qwen2Inputs, prev_model_inputs) + if self.pipeline_config.cache_strategy.uses_opaque(): + return self._prepare_ragged_next_token_inputs( + next_tokens, prev_model_inputs + ) + else: + return self._prepare_padded_next_token_inputs( + next_tokens, prev_model_inputs + ) + + @classmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + return KVCacheParams( + dtype=pipeline_config.cache_dtype, + n_kv_heads=pipeline_config.huggingface_config.num_key_value_heads, + head_dim=pipeline_config.huggingface_config.hidden_size + // pipeline_config.huggingface_config.num_attention_heads, + page_size=pipeline_config.kv_cache_page_size, + cache_strategy=pipeline_config.cache_strategy, + n_devices=len(pipeline_config.devices), + enable_prefix_caching=pipeline_config.enable_prefix_caching, + ) + + @classmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + return pipeline_config.huggingface_config.num_hidden_layers + + @classmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + try: + return upper_bounded_default( + upper_bound=pipeline_config.huggingface_config.max_position_embeddings, + default=pipeline_config.max_length, + ) + except ValueError as e: + msg = ( + "Unable to infer max_length for Qwen2, the provided " + f"max_length ({pipeline_config.max_length}) exceeds the " + f"model's max_position_embeddings " + f"({pipeline_config.huggingface_config.max_position_embeddings})." + ) + raise ValueError(msg) from e + + def load_kv_manager( + self, + session: InferenceSession, + available_cache_memory: int, + ) -> KVCacheManager: + return load_kv_manager( + params=self.get_kv_params(self.pipeline_config), + max_batch_size=self.pipeline_config.max_batch_size, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + num_layers=self.pipeline_config.huggingface_config.num_hidden_layers, + devices=self.pipeline_config.devices, + available_cache_memory=available_cache_memory, + page_size=self.pipeline_config.kv_cache_page_size, + session=session, + ) + + @classmethod + def estimate_kv_cache_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + devices: List[Device], + ) -> int: + """Estimates the size of the kv cache in bytes.""" + return estimate_kv_cache_size( + params=cls.get_kv_params(pipeline_config), + max_batch_size=pipeline_config.max_batch_size, + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=pipeline_config.huggingface_config.num_hidden_layers, + available_cache_memory=available_cache_memory, + devices=devices, + ) + + def load_model( + self, + session: InferenceSession, + ) -> Model: + # Pre-allocate a buffer for input_row_offsets in multistep execution. + # We do this to avoid materializing and copying a buffer with each multistep step + + if self.pipeline_config.max_batch_size is None: + batch_size = 1 + else: + batch_size = self.pipeline_config.max_batch_size + 1 + + self._input_row_offsets_prealloc = Tensor.from_numpy( + np.arange(batch_size, dtype=np.uint32) + ).to(self.pipeline_config.devices[0]) + + # Read in weights. + self._weights = self.pipeline_config.load_weights() + + if serialized_path := self.pipeline_config.serialized_model_path: + # Hydrate all weights to be referenced by the serialized path. + weights_registry = {} + for name, weight in self._weights.items(): + weights_registry[name] = weight.raw_tensor() + + logging.info("Loading serialized model from %s", serialized_path) + + return session.load( + serialized_path, weights_registry=weights_registry + ) + + else: + logging.info("Building model...") + graph = self._build_graph(self._weights) + logging.info("Compiling...") + before = time.perf_counter() + model = session.load( + graph, weights_registry=self._weights.allocated_weights + ) + after = time.perf_counter() + logging.info(f"Compiling model took {after - before:.6f} seconds") + if ( + export_path + := self.pipeline_config.save_to_serialized_model_path + ): + logging.info("Exporting serialized model to %s", export_path) + model._export_mef(export_path) + return model + + def _unflatten_kv_inputs( + self, kv_inputs_flat: Sequence[Tensor] + ) -> List[tuple[Tensor, ...]]: + kv_params = self.get_kv_params(self.pipeline_config) + n_devices = kv_params.n_devices + fetch_types = self.kv_manager.input_symbols() + len_of_kv_tuple_per_dev = len(list(fetch_types[0])) + kv_caches_per_dev = [ + tuple( + kv_inputs_flat[ + i * len_of_kv_tuple_per_dev : (i + 1) + * len_of_kv_tuple_per_dev + ] + ) + for i in range(n_devices) + ] + return kv_caches_per_dev + + def _flatten_kv_inputs( + self, kv_caches_per_dev: List[tuple[Union[Tensor, TensorType], ...]] + ) -> Sequence[Union[Tensor, TensorType]]: + return [item for sublist in kv_caches_per_dev for item in sublist] + + def _build_opaque_graph(self, weights: Weights) -> Graph: + device0 = self.pipeline_config.devices[0] + device_ref = DeviceRef(device0.label, device0.id) + tokens_type = TensorType( + DType.int64, shape=["total_seq_len"], device=device_ref + ) + # NOTE: input_row_offsets_len should be batch_size + 1. + input_row_offsets_type = TensorType( + DType.uint32, shape=["input_row_offsets_len"], device=device_ref + ) + + kv_cache_args = self.kv_manager.input_symbols()[0] + + with Graph( + "Qwen2", + input_types=[ + tokens_type, + input_row_offsets_type, + *kv_cache_args, + ], + ) as graph: + model = transformer( + graph, + self.pipeline_config, + weights, + self.get_kv_params(self.pipeline_config), + ) + tokens, input_row_offsets, *kv_cache = graph.inputs + + assert isinstance(tokens, TensorValue) + assert len(kv_cache) == 4 + assert isinstance(kv_cache[0], TensorValue) + assert isinstance(kv_cache[1], TensorValue) + assert isinstance(kv_cache[2], TensorValue) + assert isinstance(kv_cache[3], TensorValue) + + outputs = model( + tokens, + (kv_cache[0], kv_cache[1], kv_cache[2], kv_cache[3]), + input_row_offsets=input_row_offsets, + ) + graph.output(*outputs) + return graph + + def _build_graph(self, weights: Weights) -> Graph: + g = self._build_opaque_graph(weights) + return g + + def compute_log_probabilities( + self, + model_inputs: ModelInputs, + model_outputs: ModelOutputs, + next_tokens: Tensor, + batch_top_n: list[int], + batch_echo: list[bool], + ) -> list[LogProbabilities | None] | None: + if any(echo for echo in batch_echo): + if model_outputs.logits is None: + warnings.warn( + "Could not get logprobs with echo because the full logits" + f" were not returned by {self.pipeline_config.model_path}" + " model. Please ensure that this model is started with " + "`--enable-echo`." + ) + assert not self.pipeline_config.enable_echo, ( + "Echo was enabled but logits were not returned." + ) + return None + logits = model_outputs.logits.to(CPU()).to_numpy() + + assert model_outputs.next_token_logits is not None + next_token_logits = model_outputs.next_token_logits.to(CPU()).to_numpy() + + sampled_tokens = next_tokens.to(CPU()).to_numpy() + assert isinstance(model_inputs, Qwen2Inputs) + tokens = model_inputs.tokens.to(CPU()).to_numpy() + + if self.pipeline_config.cache_strategy.uses_opaque(): + # Handle the ragged inputs + input_row_offsets = model_inputs.input_row_offsets_or_attn_mask.to( + CPU() + ).to_numpy() + + def _get_logits_and_samples( + batch_index: int, echo: bool + ) -> tuple[np.ndarray, np.ndarray]: + if echo: + start_offset = input_row_offsets[batch_index] + end_offset = input_row_offsets[batch_index + 1] + batch_logits = logits[start_offset:end_offset] + samples = np.concatenate( + ( + tokens[start_offset + 1 : end_offset], + sampled_tokens[batch_index : batch_index + 1], + ) + ) + else: + batch_logits = next_token_logits[ + batch_index : batch_index + 1 + ] + samples = sampled_tokens[batch_index : batch_index + 1] + return batch_logits, samples + + else: + # Handle batched inputs. The model pads them to the right so the seq + # lengths can be computed by finding the first 0 token. + seq_lens = np.sum(tokens > 0, axis=1) + + def _get_logits_and_samples( + batch_index: int, echo: bool + ) -> tuple[np.ndarray, np.ndarray]: + if echo: + seq_len = seq_lens[batch_index] + padded_tokens = tokens[batch_index] + + batch_logits = logits[batch_index, :seq_len, :] + samples = np.concatenate( + ( + padded_tokens[1:seq_len], + sampled_tokens[batch_index : batch_index + 1], + ) + ) + else: + batch_logits = next_token_logits[ + batch_index : batch_index + 1, : + ] + samples = sampled_tokens[batch_index : batch_index + 1] + return batch_logits, samples + + return compute_log_probabilities( + _get_logits_and_samples, batch_top_n, batch_echo + ) diff --git a/src/max/pipelines/architectures/replit/README.md b/src/max/pipelines/architectures/replit/README.md new file mode 100644 index 0000000000..a77f156e3a --- /dev/null +++ b/src/max/pipelines/architectures/replit/README.md @@ -0,0 +1,119 @@ +# Replit Code V1.5 3B + +**Language:** Python + +**API**: MAX Graph + +This pipeline demonstrates code completion from an initial prompt using +Replit's Code V1.5 3B large language model. The model itself has been +constructed in Python using the +[MAX Graph API](https://docs.modular.com/max/graph/). + +The MAX Graph API provides an accessible interface to the construction of +flexible accelerated compute graphs, which are then optimized by the MAX +Engine's advanced graph compiler. This pipeline showcases how a large language +model can be fully defined using Python and MAX Graphs and then compiled for +optimal inference performance via the MAX Engine. + +## Model + +[Replit Code](https://blog.replit.com/replit-code-v1_5) is an open source code +generation model trained on permissively licensed code and released by +[Replit](https://replit.com). The V1.5, 3B variant is the basis for this +implementation, and weights are +[obtained via Hugging Face](https://huggingface.co/replit/replit-code-v1-3b). + +## Usage + +The easiest way to try out this pipeline is with our Magic command-line tool. + +1. Install Magic on macOS and Ubuntu with this command: + + ```shell + curl -ssL https://magic.modular.com | bash + ``` + + Then run the source command that's printed in your terminal. + + To see the available commands, you can run `magic --help`. + [Learn more about Magic here](https://docs.modular.com/magic). + +2. Clone the MAX examples repository: + + If you don't already have a local clone of this repository, create one via: + + ```shell + git clone https://github.com/modular/max.git + ``` + + The following instructions assume that you're present within this pipeline's + directory, and you can change to it after cloning: + + ```shell + cd max/src/max/ + ``` + +3. Now run the Replit code completion demo with the following command: + + ```shell + magic run replit --prompt "def fibonacci(n):" + ``` + +4. Host a code completion endpoint via MAX Serve. + + MAX Serve provides functionality to host performant OpenAI compatible + endpoints using the FastAPI framework. + + You can configure the pipeline to be hosted by using the `--serve` argument. + For example: + + ```shell + magic run replit --serve + ``` + + A request can be submitted via a cURL command. + + ```shell + curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "replit/replit-code-v1_5-3b", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "def fibonacci(n)"} + ] + }' + ``` + +## Options + +The following command-line options are available to customize operation of the +pipeline: + +- `--max-length`: Controls the maximum length of the text sequence + (includes the input tokens). + (Default value: 512) +- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 + value is provided, the model will continue to generate tokens for the entire + context length. (Default value: -1) +- `--prompt`: The text prompt to use for further generation. +- `--quantization-encoding`: The encoding to use for a datatype that can be + quantized to a low bits per weight format. The options for quantized formats + will download and cache default weights, but `float32` requires the use of + `--weight-path` to specify locally downloaded full-precision weights for use + in the model. + Valid values: `bfloat16`, `float32`. + (Default value: `float32`). +- `--save-to-serialized-model-path`: If specified, writes the serialized model + to this path. +- `--serialized-model-path`: If specified, tries to load a serialized model + from this path. +- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. +- `--weight-path`: Overrides the default URL, and allows for an + already-downloaded pretrained weight file to be used with the model. +- `--max-batch-size`: Specifies the maximum batch size to be used. + Default is 1. +- `--devices`: Specifies the device(s) to use for the model. This can optionally + be "cpu" or a comma-separated list of GPU ID(s). Defaults to the first + available GPU. diff --git a/src/max/pipelines/architectures/replit/__init__.py b/src/max/pipelines/architectures/replit/__init__.py new file mode 100644 index 0000000000..c49833f4ec --- /dev/null +++ b/src/max/pipelines/architectures/replit/__init__.py @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .arch import replit_arch + +__all__ = ["replit_arch"] diff --git a/src/max/pipelines/architectures/replit/arch.py b/src/max/pipelines/architectures/replit/arch.py new file mode 100644 index 0000000000..1bd1dae74b --- /dev/null +++ b/src/max/pipelines/architectures/replit/arch.py @@ -0,0 +1,39 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.pipelines import ( + PipelineTask, + SupportedArchitecture, + SupportedEncoding, + TextTokenizer, + WeightsFormat, +) +from max.pipelines.kv_cache import KVCacheStrategy + +from .model import ReplitModel + +replit_arch = SupportedArchitecture( + name="MPTForCausalLM", + task=PipelineTask.TEXT_GENERATION, + example_repo_ids=[ + "modularai/replit-code-1.5", + ], + default_encoding=SupportedEncoding.bfloat16, + supported_encodings={ + SupportedEncoding.float32: [KVCacheStrategy.CONTINUOUS], + SupportedEncoding.bfloat16: [KVCacheStrategy.CONTINUOUS], + }, + pipeline_model=ReplitModel, + tokenizer=TextTokenizer, + default_weights_format=WeightsFormat.gguf, +) diff --git a/src/max/pipelines/architectures/replit/graph.py b/src/max/pipelines/architectures/replit/graph.py new file mode 100644 index 0000000000..91b01a59c2 --- /dev/null +++ b/src/max/pipelines/architectures/replit/graph.py @@ -0,0 +1,204 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import math +from typing import Optional + +from max.dtype import DType +from max.graph import Graph, TensorType, TensorValue, ops +from max.graph.quantization import QuantizationEncoding +from max.graph.weights import GGUFWeights +from max.pipelines import PipelineConfig +from max.pipelines.kv_cache import ( + FetchContinuousBatchingKVCacheCollection, + KVCacheManager, + KVCacheParams, +) +from max.pipelines.nn import ( + AttentionImpl, + AttentionWithoutMask, + Embedding, + LayerNorm, + Linear, + MHAMaskVariant, + Sequential, + Transformer, + TransformerBlock, +) + + +def _feed_forward( + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + input_dim: int, + hidden_dim: int, + weights: GGUFWeights, +): + return Sequential( + layers=[ + Linear( + weights.ffn_up.weight.allocate( + dtype, [hidden_dim, input_dim], quantization_encoding + ) + ), + ops.gelu, + Linear( + weights.ffn_down.weight.allocate( + dtype, [input_dim, hidden_dim], quantization_encoding + ) + ), + ] + ) + + +def _layer_norm(dims: int, eps: float, weights: GGUFWeights) -> LayerNorm: + return LayerNorm( + weight=weights.weight.allocate(DType.float32, [dims]), + eps=eps, + ) + + +def _attention( + pipeline_config: PipelineConfig, + weights: GGUFWeights, + kv_params: KVCacheParams, + layer_index: int, +) -> AttentionImpl: + k_in_dim = kv_params.n_kv_heads * kv_params.head_dim + v_in_dim = kv_params.n_kv_heads * kv_params.head_dim + q_in_dim = pipeline_config.huggingface_config.d_model + wqkv = TensorValue( + weights.attn_qkv.weight.allocate( + pipeline_config.dtype, + [ + k_in_dim + v_in_dim + q_in_dim, + pipeline_config.huggingface_config.d_model, + ], + pipeline_config.quantization_encoding.quantization_encoding, # type: ignore + ) + ) + + return AttentionWithoutMask( + n_heads=pipeline_config.huggingface_config.n_heads, + kv_params=kv_params, + wqkv=wqkv, + wo=Linear( + weights.attn_output.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.d_model, + pipeline_config.huggingface_config.d_model, + ], + pipeline_config.quantization_encoding.quantization_encoding, # type: ignore + ) + ), + layer_idx=ops.constant(layer_index, dtype=DType.uint32), + mask_variant=MHAMaskVariant.CAUSAL_ALIBI_MASK, + scale=math.sqrt(1 / kv_params.head_dim), + ) + + +def _transformer( + graph: Graph, + pipeline_config: PipelineConfig, + weights: GGUFWeights, + kv_params: KVCacheParams, +): + with graph: + # Initialize Attention. + layers = [ + TransformerBlock( + attention=_attention( + pipeline_config, weights.blk[i], kv_params, i + ), + mlp=_feed_forward( + pipeline_config.dtype, + pipeline_config.quantization_encoding.quantization_encoding, # type: ignore + pipeline_config.huggingface_config.d_model, + 12288, + weights.blk[i], + ), + attention_norm=_layer_norm( + pipeline_config.huggingface_config.d_model, + 1e-5, + weights.blk[i].attn_norm, + ), + mlp_norm=_layer_norm( + pipeline_config.huggingface_config.d_model, + 1e-5, + weights.blk[i].ffn_norm, + ), + ) + for i in range(pipeline_config.huggingface_config.n_layers) + ] + + # Initialize Shared Embedding Weights. + shared_embedding_weight = weights.token_embd.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.vocab_size, + pipeline_config.huggingface_config.d_model, + ], + pipeline_config.quantization_encoding.quantization_encoding, # type: ignore + ) + + return Transformer( + dim=pipeline_config.huggingface_config.d_model, + n_heads=pipeline_config.huggingface_config.n_heads, + layers=layers, + norm=_layer_norm( + pipeline_config.huggingface_config.d_model, + 1e-5, + weights.output_norm, + ), + output=Linear(shared_embedding_weight), + embedding=Embedding(shared_embedding_weight), + kv_params=kv_params, + kv_collection_constructor=FetchContinuousBatchingKVCacheCollection( + kv_params + ), + all_logits=pipeline_config.enable_echo, + ) + + +def _build_graph( + pipeline_config: PipelineConfig, + weights: GGUFWeights, + kv_params: KVCacheParams, + kv_manager: KVCacheManager, +) -> Graph: + # Graph input types. + tokens_type = TensorType(DType.int64, shape=["total_seq_len"]) + input_row_offsets_type = TensorType( + DType.uint32, shape=["input_row_offsets_len"] + ) + kv_cache_types = kv_manager.input_symbols()[0] + + # Initialize Graph. + with Graph( + "replit", + input_types=[ + tokens_type, + input_row_offsets_type, + *kv_cache_types, + ], + ) as graph: + model = _transformer(graph, pipeline_config, weights, kv_params) + tokens, input_row_offsets, *kv_cache_inputs = graph.inputs + outputs = model( + tokens=tokens, + input_row_offsets=input_row_offsets, + kv_cache_inputs=kv_cache_inputs, + ) + graph.output(*outputs) + return graph diff --git a/src/max/pipelines/architectures/replit/model.py b/src/max/pipelines/architectures/replit/model.py new file mode 100644 index 0000000000..211d63a840 --- /dev/null +++ b/src/max/pipelines/architectures/replit/model.py @@ -0,0 +1,316 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import logging +import time +import warnings +from collections.abc import Sequence +from typing import cast + +import numpy as np +from max.driver import Device, DeviceSpec, Tensor +from max.engine import InferenceSession, Model +from max.graph.weights import GGUFWeights +from max.pipelines import ( + LogProbabilities, + ModelInputs, + ModelOutputs, + PipelineConfig, + PipelineModel, + TextContext, + upper_bounded_default, +) +from max.pipelines.kv_cache import ( + KVCacheInputs, + KVCacheManager, + KVCacheParams, + estimate_kv_cache_size, + load_kv_manager, +) +from max.pipelines.nn.compute_log_probabilities import compute_log_probabilities + +from .graph import _build_graph + +logger = logging.getLogger("max.pipelines") + + +class ReplitInputs(ModelInputs): + """A class representing inputs for the Replit model. + + This class encapsulates the input tensors required for the Replit model execution: + - tokens: A tensor containing the input token IDs + - input_row_offsets: A tensor containing the offsets for each row in the ragged input sequence + """ + + tokens: Tensor + input_row_offsets: Tensor + + def __init__( + self, + tokens: Tensor, + input_row_offsets: Tensor, + ) -> None: + self.tokens = tokens + self.input_row_offsets = input_row_offsets + + +class ReplitModel(PipelineModel[TextContext]): + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + if pipeline_config.device_specs[0] == DeviceSpec.cpu(): + msg = "Replit currently only supported on gpu." + raise ValueError(msg) + + super().__init__(pipeline_config, session) + self.model = self.load_model(session) + + def execute( + self, + model_inputs: ModelInputs, + kv_cache_inputs: KVCacheInputs | None = None, + ) -> ModelOutputs: + model_inputs = cast(ReplitInputs, model_inputs) + assert kv_cache_inputs is not None, "Replit has KV cache inputs" + model_outputs = self.model.execute( + model_inputs.tokens, + model_inputs.input_row_offsets, + *kv_cache_inputs, + copy_inputs_to_device=False, + ) + if self.pipeline_config.enable_echo: + assert len(model_outputs) == 2 + assert isinstance(model_outputs[0], Tensor) + assert isinstance(model_outputs[1], Tensor) + return ModelOutputs( + next_token_logits=model_outputs[0], logits=model_outputs[1] + ) + else: + assert len(model_outputs) == 1 + assert isinstance(model_outputs[0], Tensor) + return ModelOutputs(next_token_logits=model_outputs[0]) + + def prepare_initial_token_inputs( + self, + context_batch: Sequence[TextContext], + ) -> ReplitInputs: + # Get input_row_offsets: start and end position of each batch in the + # combined total_seq_len dimension. + input_row_offsets = np.cumsum( + [0] + [ctx.active_length for ctx in context_batch], + dtype=np.uint32, + ) + + # Create a ragged token vector of length: sum(len(t) for t in tokens). + tokens = np.concatenate([ctx.next_tokens for ctx in context_batch]) + + return ReplitInputs( + tokens=Tensor.from_numpy(tokens).to( + self.pipeline_config.devices[0] + ), + input_row_offsets=Tensor.from_numpy(input_row_offsets).to( + self.pipeline_config.devices[0] + ), + ) + + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: ModelInputs, + ) -> ReplitInputs: + prev_model_inputs = cast(ReplitInputs, prev_model_inputs) + row_offsets_size = prev_model_inputs.input_row_offsets.shape[0] + next_row_offsets = self._input_row_offsets_prealloc[:row_offsets_size] + return ReplitInputs( + tokens=next_tokens, + input_row_offsets=next_row_offsets, + ) + + @classmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + return pipeline_config.huggingface_config.n_layers + + @classmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + return KVCacheParams( + dtype=pipeline_config.cache_dtype, + n_kv_heads=pipeline_config.huggingface_config.attn_config[ + "kv_n_heads" + ], + head_dim=pipeline_config.huggingface_config.d_model + // pipeline_config.huggingface_config.n_heads, + cache_strategy=pipeline_config.cache_strategy, + page_size=pipeline_config.kv_cache_page_size, + enable_prefix_caching=pipeline_config.enable_prefix_caching, + ) + + @classmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + try: + return upper_bounded_default( + upper_bound=pipeline_config.huggingface_config.max_seq_len, + default=pipeline_config.max_length, + ) + except ValueError as e: + msg = ( + "Unable to infer max_length for Replit, the provided " + f"max_length ({pipeline_config.max_length}) exceeds the " + f"model's max_seq_len " + f"({pipeline_config.huggingface_config.max_seq_len})." + ) + raise ValueError(msg) from e + + def load_kv_manager( + self, + session: InferenceSession, + available_cache_memory: int, + ) -> KVCacheManager: + return load_kv_manager( + params=self.get_kv_params(self.pipeline_config), + max_batch_size=self.pipeline_config.max_batch_size, + max_seq_len=self.calculate_max_seq_len(self.pipeline_config), + num_layers=self.pipeline_config.huggingface_config.n_layers, + devices=self.pipeline_config.devices, + available_cache_memory=available_cache_memory, + page_size=self.pipeline_config.kv_cache_page_size, + session=session, + ) + + @classmethod + def estimate_kv_cache_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + devices: list[Device], + ) -> int: + """Estimates the size of the kv cache in bytes.""" + return estimate_kv_cache_size( + params=cls.get_kv_params(pipeline_config), + max_batch_size=pipeline_config.max_batch_size, + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=pipeline_config.huggingface_config.n_layers, + available_cache_memory=available_cache_memory, + devices=devices, + ) + + def load_model( + self, + session: InferenceSession, + ) -> Model: + # Pre-allocate a buffer for input_row_offsets in multistep execution. + # We do this to avoid materializing and copying a buffer with each multistep step + assert self.pipeline_config.max_batch_size, ( + "Expected max_batch_size to be set" + ) + self._input_row_offsets_prealloc = Tensor.from_numpy( + np.arange(self.pipeline_config.max_batch_size + 1, dtype=np.uint32) + ).to(self.pipeline_config.devices[0]) + + # Read in weights. + weights = self.pipeline_config.load_weights() + if not isinstance(weights, GGUFWeights): + msg = "only gguf weights supported in Replit." + raise ValueError(msg) + + self._weights = weights + + if serialized_path := self.pipeline_config.serialized_model_path: + # Hydrate all weights to be referenced by the serialized path. + weights_registry = {} + for name, weight in self._weights.items(): + weights_registry[name] = weight.raw_tensor() + + logger.info("Loading serialized model from ", serialized_path) + + return session.load( + serialized_path, weights_registry=weights_registry + ) + + else: + logger.info("Building and compiling model...") + before = time.perf_counter() + graph = _build_graph( + self.pipeline_config, + self._weights, + self.get_kv_params(self.pipeline_config), + kv_manager=self.kv_manager, + ) + model = session.load( + graph, weights_registry=self._weights.allocated_weights + ) + after = time.perf_counter() + logger.info( + f"Building and compiling model took {after - before:.6f} seconds" + ) + if ( + export_path + := self.pipeline_config.save_to_serialized_model_path + ): + logger.info("Exporting serialized model to %s", export_path) + model._export_mef(export_path) + return model + + def compute_log_probabilities( + self, + model_inputs: ModelInputs, + model_outputs: ModelOutputs, + next_tokens: Tensor, + batch_top_n: list[int], + batch_echo: list[bool], + ) -> list[LogProbabilities | None] | None: + if any(echo for echo in batch_echo): + if model_outputs.logits is None: + warnings.warn( + "Could not get logprobs with echo because the full logits" + f" were not returned by {self.pipeline_config.model_path}" + " model. Please ensure that this model is started with " + "`--enable-echo`." + ) + assert not self.pipeline_config.enable_echo, ( + "Echo was enabled but logits were not returned." + ) + return None + logits = model_outputs.logits.to_numpy() + assert model_outputs.next_token_logits + next_token_logits = model_outputs.next_token_logits.to_numpy() + + sampled_tokens = next_tokens.to_numpy() + + # Handle the ragged inputs + model_inputs = cast(ReplitInputs, model_inputs) + tokens = model_inputs.tokens.to_numpy() + input_row_offsets = model_inputs.input_row_offsets.to_numpy() + + def _get_logits_and_samples( + batch_index: int, echo: bool + ) -> tuple[np.ndarray, np.ndarray]: + if echo: + start_offset = input_row_offsets[batch_index] + end_offset = input_row_offsets[batch_index + 1] + batch_logits = logits[start_offset:end_offset] + samples = np.concatenate( + ( + tokens[start_offset + 1 : end_offset], + sampled_tokens[batch_index : batch_index + 1], + ) + ) + else: + batch_logits = next_token_logits[batch_index : batch_index + 1] + samples = sampled_tokens[batch_index : batch_index + 1] + return batch_logits, samples + + return compute_log_probabilities( + _get_logits_and_samples, batch_top_n, batch_echo + ) diff --git a/src/max/pipelines/architectures/whisper/encoder.py b/src/max/pipelines/architectures/whisper/encoder.py new file mode 100644 index 0000000000..ce403a6390 --- /dev/null +++ b/src/max/pipelines/architectures/whisper/encoder.py @@ -0,0 +1,187 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +from __future__ import annotations + +import math +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import TensorValue, TensorValueLike, ops +from max.pipelines.nn import ( + Conv1D, + Embedding, + LayerNorm, + Linear, + Sequential, +) +from max.pipelines.nn.layer import Layer + + +@dataclass +class WhisperSdpaAttention(Layer): + n_heads: int + head_dim: int + + wq: Linear + wk: Linear + wv: Linear + wo: Linear + + def scaled_dot_product_attention( + self, + xq: TensorValueLike, + xk: TensorValueLike, + xv: TensorValueLike, + ) -> TensorValue: + xq = TensorValue(xq) + xk = TensorValue(xk) + xv = TensorValue(xv) + xq = xq.transpose(1, 2) + xk = xk.transpose(1, 2) + xv = xv.transpose(1, 2) + + scale = math.sqrt(1.0 / self.head_dim) + scores = xq @ ops.transpose(xk, 2, 3) + # Note, the graph compiler currently requires the order of operands + # to be `scores * scale` in order to pattern match the fused attention + # operator. + return ops.softmax(scores * scale) @ xv + + def __call__( + self, + x: TensorValue, + **kwargs, + ) -> TensorValue: + """Computes attention on x. + + Args: + x: Activations with shape (batch, seq_len, dim). + + Returns the result of WhisperSdpaAttention self attention on the input. + """ + x = TensorValue(x) + batch, seq_len = x.shape[0], x.shape[1] + # matmul weights + xq = self.wq(x) + xk = self.wk(x) + xv = self.wv(x) + + xq = ops.reshape(xq, [batch, seq_len, self.n_heads, self.head_dim]) + + xk = ops.reshape( + xk, + [ + batch, + seq_len, + self.n_heads, + self.head_dim, + ], + ) + xv = ops.reshape( + xv, + [ + batch, + seq_len, + self.n_heads, + self.head_dim, + ], + ) + + output = ( + self.scaled_dot_product_attention(xq, xk, xv) + .transpose(1, 2) + .reshape([batch, seq_len, -1]) + ) + return self.wo(output) + + +@dataclass +class WhisperEncoderLayer(Layer): + """Stack of Attention, FeedForward, and LayerNorm layers.""" + + attention: WhisperSdpaAttention + mlp: Sequential + attention_norm: LayerNorm + mlp_norm: LayerNorm + + def __call__( + self, + x: TensorValue, + **kwargs, + ) -> TensorValue: + attn_out = self.attention( + self.attention_norm(x), + **kwargs, + ) + + h = x + attn_out + h = h + self.mlp(self.mlp_norm(h)) + + return h + + +@dataclass +class WhisperEncoder(Layer): + """A Transformer consisting of a stem, positional embeddings, and self attention layers. + + The differences between this transformer and `nn.Transformer` are: + 1. Whisper passes the input through a stem of: + Two convolution layers with a filter width of 3 and the GELU activation + function where the second convolution layer has a stride of two. + + 2. After that, Sinusoidal position embeddings are then added to the output of the stem. + + After that, the usual Transformer blocks (with pre-activation residual blocks) are applied. + We use naive attention where the linear projections have a bias. + + 3. No final transformer linear layer "output". + """ + + conv1: Conv1D + conv2: Conv1D + embed_positions: Embedding + layers: list[WhisperEncoderLayer] + norm: LayerNorm # TODO: Is LayerNorm here not the same as nn.LayerNorm + + all_logits: bool = False + + def __call__( + self, + input_features: TensorValueLike, + **kwargs, + ) -> tuple[TensorValue, ...]: + """ + Args: + input_features: Tensor of shape (batch_size, feature_size, sequence_length) + expected_seq_length = config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0] + + """ + # Encoder stem: two convolution layers and the GELU activation function. + inputs_embeds = ops.gelu(self.conv1(input_features)) + inputs_embeds = ops.gelu(self.conv2(inputs_embeds)) + + # self.embed_positions.weights layers is of shape = (1500, 1280) + # TODO: Do we need the reshape to (batch_size, sequence_length, feature_size) or is it already in the right shape? + # inputs_embeds = ops.permute(inputs_embeds, [0, 2, 1]) + + # Add sinusoidal position embeddings to the output of the stem + h = inputs_embeds + self.embed_positions.weights + + for _, layer in enumerate(self.layers): + h = layer(h, **kwargs) + + # # A final layer normalization is applied to the encoder output + normalized = self.norm(h) + + # Always return float32 logits, no matter the activation type. + return (ops.cast(normalized, DType.float32),) diff --git a/src/max/pipelines/architectures/whisper/graph.py b/src/max/pipelines/architectures/whisper/graph.py new file mode 100644 index 0000000000..fc3ca1ab5a --- /dev/null +++ b/src/max/pipelines/architectures/whisper/graph.py @@ -0,0 +1,294 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import numpy as np +from max.dtype import DType +from max.graph import Graph, TensorType, ops +from max.graph.weights import SafetensorWeights +from max.pipelines import PipelineConfig +from max.pipelines.nn import ( + Conv1D, + Embedding, + LayerNorm, + Linear, + Sequential, +) + +from .encoder import WhisperEncoder, WhisperEncoderLayer, WhisperSdpaAttention + + +def conv1d( + dtype: DType, + in_channels: int, + kernel_size: int, + stride: int, + padding: int, + out_channels: int, + weights: SafetensorWeights, +) -> Conv1D: + """Creates a 1D convolution layer. + For conv1: ( hugging_face weights: model.encoder.conv1.weight) + in_channels = 128 + out_channels = 1280 + kernel_size = 3 + stride = 1 + padding = 1 + + For conv2: ( hugging_face weights: model.encoder.conv2.weight) + in_channels = 1280 + out_channels = 1280 + kernel_size = 3 + stride = 2 + padding = 1 + """ + # Loaded torch weights shape = (out_channels, in_channels, kernel_size) = [1280, 128, 3]. + # Graph-API Conv1D expects (kernel_size, in_channels, out_channels) = [3, 128, 1280]. + # TODO: Implement Conv1D with bias and use it here. + bias = weights.bias.allocate(dtype, [out_channels]) + return Conv1D( + filter=ops.permute( + weights.weight.allocate( + dtype, [out_channels, in_channels, 1, kernel_size], None + ), + [2, 1, 0], + ), + stride=stride, + padding=padding, + ) + + +def embedding( + dtype: DType, + max_source_positions: int, + hidden_dim: int, + weights: SafetensorWeights, +): + return Embedding( + weights.weight.allocate( + dtype, + [max_source_positions, hidden_dim], + ) + ) + + +def layer_norm(dims: int, eps: float, weights: SafetensorWeights) -> LayerNorm: + # TODO: check the shape of bias + return LayerNorm( + weight=weights.weight.allocate(DType.bfloat16, [dims]), + eps=eps, + bias=weights.bias.allocate(DType.bfloat16, [dims]), + ) + + +def linear( + dtype: DType, + in_features: int, + out_features: int, + weights: SafetensorWeights, +) -> Linear: + # TODO: Check we are passing the correct dim for bias + return Linear( + weights.weight.allocate(dtype, [in_features, out_features], None), + bias=weights.bias.allocate(dtype, [out_features], None), + ) + + +def feed_forward( + dtype: DType, + hidden_dim: int, + feed_forward_length: int, + weights: SafetensorWeights, +): + return Sequential( + layers=[ + linear( + dtype, + feed_forward_length, + hidden_dim, + weights.fc1, + ), + ops.gelu, + linear( + dtype, + hidden_dim, + feed_forward_length, + weights.fc2, + ), + ] + ) + + +def attention( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, + layer_index: int, +): + wq = weights.self_attn.q_proj.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.d_model, + pipeline_config.huggingface_config.d_model, + ], + ) + wk = weights.self_attn.k_proj.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.d_model, + pipeline_config.huggingface_config.d_model, + ], + ) + wv = weights.self_attn.v_proj.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.d_model, + pipeline_config.huggingface_config.d_model, + ], + ) + + bias_q = weights.self_attn.q_proj.bias.allocate( + pipeline_config.dtype, [pipeline_config.huggingface_config.d_model] + ) + bias_v = weights.self_attn.v_proj.bias.allocate( + pipeline_config.dtype, [pipeline_config.huggingface_config.d_model] + ) + bias_k = ops.constant( + np.zeros(pipeline_config.huggingface_config.d_model), + pipeline_config.dtype, + ) + + wo = weights.attn_output.weight.allocate( + pipeline_config.dtype, + [ + pipeline_config.huggingface_config.d_model, + pipeline_config.huggingface_config.d_model, + ], + ) + bias_o = weights.self_attn.out_proj.bias.allocate( + pipeline_config.dtype, [pipeline_config.huggingface_config.d_model] + ) + return WhisperSdpaAttention( + n_heads=pipeline_config.huggingface_config.n_heads, + head_dim=pipeline_config.huggingface_config.d_model + // pipeline_config.huggingface_config.encoder_attention_heads, + wq=Linear(wq, bias=bias_q), + wk=Linear(wk, bias=bias_k), + wv=Linear(wv, bias=bias_v), + wo=Linear( + wo, + bias=bias_o, + ), + ) + + +def encoder( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +) -> WhisperEncoder: + conv1 = conv1d( + dtype=pipeline_config.dtype, + in_channels=pipeline_config.huggingface_config.num_mel_bins, + kernel_size=3, + stride=1, + padding=1, + out_channels=pipeline_config.huggingface_config.d_model, + weights=weights.model.encoder.conv1, + ) + + conv2 = conv1d( + dtype=pipeline_config.dtype, + in_channels=pipeline_config.huggingface_config.d_model, + kernel_size=3, + stride=2, + padding=1, + out_channels=pipeline_config.huggingface_config.d_model, + weights=weights.model.encoder.conv2, + ) + + # TODO: Not sure how to handle this. It learns embeddings to a max size. + embed_positions = embedding( + dtype=pipeline_config.dtype, + max_source_positions=pipeline_config.huggingface_config.max_source_positions, + hidden_dim=pipeline_config.huggingface_config.d_model, + weights=weights.model.encoder.embed_positions, + ) + + # EncoderBlocks + # TODO: Which cache strategy to use? Will both Continuous and paged will work? + layers = [ + WhisperEncoderLayer( + attention=attention( + pipeline_config, + weights.language_model.model.layers[i], + layer_idx=ops.constant(i, DType.uint32), # type: ignore + ), + mlp=feed_forward( + pipeline_config.dtype, + pipeline_config.huggingface_config.d_model, + pipeline_config.huggingface_config.encoder_ffn_dim, + weights.model.encoder.layers[i], + ), + attention_norm=layer_norm( + dims=pipeline_config.huggingface_config.d_model, + eps=1e-5, + weights=weights.model.encoder.layers[i].self_attn_layer_norm, + ), + mlp_norm=layer_norm( + dims=pipeline_config.huggingface_config.d_model, + eps=1e-5, + weights=weights.model.encoder.layers[i].final_layer_norm, + ), + ) + for i in range(pipeline_config.huggingface_config.encoder_layers) + ] + + # Hugging Face model uses default eps for nn.LayerNorm which is = 1e-5 + norm = layer_norm( + dims=pipeline_config.huggingface_config.d_model, + eps=1e-5, + weights=weights.model.encoder.layer_norm, + ) + + return WhisperEncoder( + conv1=conv1, + conv2=conv2, + embed_positions=embed_positions, + layers=layers, + norm=norm, + all_logits=False, + ) + + +def build_graph( + pipeline_config: PipelineConfig, + weights: SafetensorWeights, +) -> Graph: + # Audio input_features. + input_features_type = TensorType( + DType.float32, + shape=["batch_size", "num_mel_bins", "sequence_length"], + ) + + # Initialize Graph. + with Graph( + "whisper_audio_encoder", + input_types=[ + input_features_type, + ], + ) as graph: + model = encoder(pipeline_config, weights) + input_features = graph.inputs[0] + outputs = model( + input_features=input_features.tensor, + ) + graph.output(*outputs) + return graph diff --git a/src/max/pipelines/architectures/whisper/model.py b/src/max/pipelines/architectures/whisper/model.py new file mode 100644 index 0000000000..9e1e49eed4 --- /dev/null +++ b/src/max/pipelines/architectures/whisper/model.py @@ -0,0 +1,81 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import logging +import time + +from max.driver import Tensor +from max.engine import InferenceSession, Model +from max.pipelines import ( + ModelInputs, + PipelineConfig, + PipelineModel, +) + +from .graph import build_graph + +logger = logging.getLogger("max.pipelines") + + +class WhisperInputs(ModelInputs): + """A class representing inputs for the Whisper model. + + input_features: + Float values mel features extracted from the raw speech waveform. + Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* viathe soundfile library (`pip install soundfile`). + To prepare the array into `input_features`, the [`AutoFeatureExtractor`] from the transformers library should be used for extracting the mel features, padding and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`] + Shape = (batch_size, feature_size, sequence_length) + + decoder_input_ids: + Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`WhisperTokenizer`]. + Whisper uses the `decoder_start_token_id` as the starting token for `decoder_input_ids` generation. + Shape = (batch_size, target_sequence_length) + """ + + input_features: Tensor + decoder_input_ids: Tensor + + +class Whisper(PipelineModel): + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + super().__init__(pipeline_config, session) + self.model = self.load_model(session) + + def load_model( + self, + session: InferenceSession, + ) -> Model: + """ + Load the Whisper speech recognition model. + """ + self.weights = self.pipeline_config.load_weights() + + logger.info("Building and compiling Whisper encoder-decoder model...") + before = time.perf_counter() + graph = build_graph( + self.pipeline_config, + self.weights, # type: ignore + ) + model = session.load( + graph, weights_registry=self.weights.allocated_weights + ) + after = time.perf_counter() + logger.info( + f"Compiling Whisper model took {after - before:.6f} seconds" + ) + + return model diff --git a/src/max/pipelines/config.py b/src/max/pipelines/config.py new file mode 100644 index 0000000000..d254ca7c0b --- /dev/null +++ b/src/max/pipelines/config.py @@ -0,0 +1,1093 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Standardized config for Pipeline Inference.""" + +from __future__ import annotations + +import datetime +import glob +import json +import logging +import os +import struct +import time +from dataclasses import dataclass, field +from enum import Enum +from functools import cached_property +from pathlib import Path +from typing import Any, Optional, Union, cast + +import torch +from huggingface_hub import ( + HfFileSystem, + file_exists, + get_hf_file_metadata, + hf_hub_download, + hf_hub_url, + model_info, + repo_exists, +) +from huggingface_hub import constants as hf_hub_constants +from huggingface_hub.hf_api import ModelInfo +from huggingface_hub.utils import ( + EntryNotFoundError, + GatedRepoError, + HfHubHTTPError, + RepositoryNotFoundError, + RevisionNotFoundError, +) +from huggingface_hub.utils import tqdm as hf_tqdm +from max.driver import CPU, Accelerator, Device, DeviceSpec, accelerator_count +from max.dtype import DType +from max.graph.quantization import QuantizationConfig, QuantizationEncoding +from max.graph.weights import ( + GGUFWeights, + SafetensorWeights, + Weights, + WeightsAdapter, +) +from max.pipelines.kv_cache import KVCacheStrategy +from tqdm.contrib.concurrent import thread_map +from transformers import AutoConfig + +logger = logging.getLogger("max.pipelines") + + +class PipelineEngine(str, Enum): + MAX = "max" + HUGGINGFACE = "huggingface" + + +class SupportedEncoding(str, Enum): + """All possible encodings which may be supported by a particular model.""" + + float32 = "float32" + bfloat16 = "bfloat16" + q4_k = "q4_k" + q4_0 = "q4_0" + q6_k = "q6_k" + gptq = "gptq" + + def __repr__(self) -> str: + return self.name + + def __str__(self) -> str: + return self.name + + @classmethod + def parse_from_file_name(cls, name: str): + # TODO(AITLIB-127): Robustify detection of quantization encoding + name = name.lower() + if "f32" in name or "float32" in name: + return SupportedEncoding.float32 + elif "bf16" in name or "bfloat16" in name: + return SupportedEncoding.bfloat16 + elif "q4_k_m" in name: + return SupportedEncoding.q4_k + elif "q4_0" in name: + return SupportedEncoding.q4_0 + elif "q6_k" in name: + return SupportedEncoding.q6_k + elif "gptq" in name: + return SupportedEncoding.gptq + else: + return None + + @property + def quantization_encoding(self) -> Optional[QuantizationEncoding]: + if self not in _SUPPORTED_ENCODING_TO_QUANTIZATION_ENCODING: + raise ValueError( + f"SupportedEncoding({self}) does not have corresponding QuantizationEncoding." + ) + return _SUPPORTED_ENCODING_TO_QUANTIZATION_ENCODING[self] + + @property + def dtype(self) -> DType: + """The underlying model dtype associated with a quantization_encoding.""" + if self not in _SUPPORTED_ENCODING_TO_DTYPE: + raise ValueError( + f"SupportedEncoding({self}) does not have corresponding dtype." + ) + return _SUPPORTED_ENCODING_TO_DTYPE[self] + + @property + def cache_dtype(self) -> DType: + """The dtype that must be used in the kvcache for correctness.""" + if self not in _SUPPORTED_ENCODING_TO_CACHE_DTYPE: + raise ValueError( + f"SupportedEncoding({self}) does not have corresponding cache dtype." + ) + return _SUPPORTED_ENCODING_TO_CACHE_DTYPE[self] + + def supported_on(self, device_spec: DeviceSpec) -> bool: + """Returns whether this quantization encoding is supported on a device.""" + return device_spec.device_type in _SUPPORTED_DEVICES[self] + + +_SUPPORTED_ENCODING_TO_DTYPE = { + SupportedEncoding.float32: DType.float32, + SupportedEncoding.bfloat16: DType.bfloat16, + SupportedEncoding.q4_k: DType.uint8, + SupportedEncoding.q4_0: DType.uint8, + SupportedEncoding.q6_k: DType.uint8, + SupportedEncoding.gptq: DType.uint8, +} + + +_SUPPORTED_ENCODING_TO_CACHE_DTYPE = { + SupportedEncoding.float32: DType.float32, + SupportedEncoding.bfloat16: DType.bfloat16, + SupportedEncoding.q4_k: DType.float32, + SupportedEncoding.q4_0: DType.float32, + SupportedEncoding.q6_k: DType.float32, + SupportedEncoding.gptq: DType.bfloat16, +} + +_SUPPORTED_ENCODING_TO_QUANTIZATION_ENCODING = { + SupportedEncoding.float32: None, + SupportedEncoding.bfloat16: None, + SupportedEncoding.q4_k: QuantizationEncoding.Q4_K, + SupportedEncoding.q4_0: QuantizationEncoding.Q4_0, + SupportedEncoding.q6_k: QuantizationEncoding.Q6_K, + SupportedEncoding.gptq: QuantizationEncoding.GPTQ, +} + + +# Basic validation for supported devices for each type of encoding. +_SUPPORTED_DEVICES: dict[SupportedEncoding, tuple[str, ...]] = { + SupportedEncoding.float32: ("cpu", "gpu"), + SupportedEncoding.bfloat16: ("gpu",), + SupportedEncoding.q4_k: ("cpu",), + SupportedEncoding.q4_0: ("cpu",), + SupportedEncoding.q6_k: ("cpu",), + SupportedEncoding.gptq: ("gpu",), +} + + +class WeightsFormat(str, Enum): + gguf = "gguf" + safetensors = "safetensors" + pytorch = "pytorch" + + +class RepoType(str, Enum): + online = "online" + local = "local" + + +# Reference: https://github.com/ggerganov/llama.cpp/blob/eb5c3dc64bd967f2e23c87d9dec195f45468de60/src/llama.cpp#L20778 +class RopeType(str, Enum): + none = "none" + normal = "normal" + neox = "neox" + + +def _repo_exists_with_retry(repo_id: str) -> bool: + """ + Wrapper around huggingface_hub.repo_exists with retry logic. + Retries after 5, 30 and 60 seconds if we get a transient HTTP error. + + See huggingface_hub.repo_exists for details + """ + max_attempts = 3 + retry_delays_in_seconds = [ + 5, + 30, + 60, + ] + + for attempt, delay_in_seconds in enumerate(retry_delays_in_seconds): + try: + return repo_exists(repo_id) + except ( + RepositoryNotFoundError, + GatedRepoError, + RevisionNotFoundError, + EntryNotFoundError, + ) as e: + # Forward these specific errors to the user + logger.error(f"Hugging Face repository error: {str(e)}") + raise + except HfHubHTTPError as e: + if attempt == max_attempts - 1: + logger.error( + f"Failed to connect to Hugging Face Hub after {max_attempts} attempts: {str(e)}" + ) + raise + + logger.warning( + f"Transient Hugging Face Hub connection error (attempt {attempt + 1}/{max_attempts}): {str(e)}" + ) + logger.warning( + f"Retrying Hugging Face connection in {delay_in_seconds} seconds..." + ) + time.sleep(delay_in_seconds) + + assert False, ( + "This should never be reached due to the raise in the last attempt" + ) + + +@dataclass +class HuggingFaceRepo: + repo_id: str + trust_remote_code: bool = False + repo_type: Optional[RepoType] = None + + def __post_init__(self) -> None: + # Get repo type. + if not self.repo_type: + if os.path.exists(self.repo_id): + self.repo_type = RepoType.local + else: + self.repo_type = RepoType.online + + if self.repo_type == RepoType.online and not _repo_exists_with_retry( + self.repo_id + ): + raise ValueError(f"model_path: {self.repo_id} does not exist") + + def __str__(self) -> str: + return self.repo_id + + def __repr__(self) -> str: + return self.repo_id + + @cached_property + def info(self) -> ModelInfo: + if self.repo_type == RepoType.local: + raise ValueError( + "using model info, on local repos is not supported." + ) + elif self.repo_type == RepoType.online: + return model_info(self.repo_id, files_metadata=False) + else: + raise ValueError(f"Unsupported repo type: {self.repo_type}") + + @cached_property + def weight_files(self) -> dict[WeightsFormat, list[str]]: + safetensor_search_pattern = "*.safetensors" + gguf_search_pattern = "*.gguf" + pytorch_search_pattern = "*.bin" + + weight_files = {} + if self.repo_type == RepoType.local: + safetensor_paths = glob.glob( + os.path.join(self.repo_id, safetensor_search_pattern) + ) + gguf_paths = glob.glob( + os.path.join(self.repo_id, gguf_search_pattern) + ) + pytorch_paths = glob.glob( + os.path.join(self.repo_id, pytorch_search_pattern) + ) + elif self.repo_type == RepoType.online: + fs = HfFileSystem() + safetensor_paths = cast( + list[str], + fs.glob(f"{self.repo_id}/{safetensor_search_pattern}"), + ) + gguf_paths = cast( + list[str], + fs.glob(f"{self.repo_id}/{gguf_search_pattern}"), + ) + pytorch_paths = cast( + list[str], + fs.glob(f"{self.repo_id}/{pytorch_search_pattern}"), + ) + else: + raise ValueError(f"Unsupported repo type: {self.repo_type}") + + if safetensor_paths: + if len(safetensor_paths) == 1: + # If there is only one weight allow any name. + weight_files[WeightsFormat.safetensors] = [ + safetensor_paths[0].replace(f"{self.repo_id}/", "") + ] + else: + # If there is more than one weight, ignore consolidated tensors. + weight_files[WeightsFormat.safetensors] = [ + f.replace(f"{self.repo_id}/", "") + for f in safetensor_paths + if "consolidated" not in f + ] + + if gguf_paths: + weight_files[WeightsFormat.gguf] = [ + f.replace(f"{self.repo_id}/", "") for f in gguf_paths + ] + + if pytorch_paths: + weight_files[WeightsFormat.pytorch] = [ + f.replace(f"{self.repo_id}/", "") for f in pytorch_paths + ] + + return weight_files + + def size_of(self, filename: str) -> Union[int, None]: + if self.repo_type == RepoType.online: + url = hf_hub_url(self.repo_id, filename) + metadata = get_hf_file_metadata(url) + return metadata.size + raise NotImplementedError("not implemented for non-online repos.") + + @cached_property + def supported_encodings(self) -> list[SupportedEncoding]: + # TODO(AITLIB-128): Detection of supported encodings in weights can be cleaned up + supported_encodings = set([]) + + # Parse gguf file names. + for gguf_path in self.weight_files.get(WeightsFormat.gguf, []): + encoding = SupportedEncoding.parse_from_file_name(gguf_path) + if encoding: + supported_encodings.add(encoding) + + # Get Safetensor Metadata. + if WeightsFormat.safetensors in self.weight_files: + if self.repo_type == RepoType.local: + # Safetensor repos are assumed to only have one encoding in them. + with open( + os.path.join( + self.repo_id, + self.weight_files[WeightsFormat.safetensors][0], + ), + "rb", + ) as file: + # Read the first 8 bytes of the file + length_bytes = file.read(8) + # Interpret the bytes as a little-endian unsigned 64-bit integer + length_of_header = struct.unpack(" dict[WeightsFormat, list[Path]]: + files = [] + for gguf_file in self.weight_files.get(WeightsFormat.gguf, []): + file_encoding = SupportedEncoding.parse_from_file_name(gguf_file) + if file_encoding == encoding: + files.append(Path(gguf_file)) + + if files: + return {WeightsFormat.gguf: files} + else: + return {} + + def _get_safetensor_files_for_encoding( + self, encoding: SupportedEncoding + ) -> dict[WeightsFormat, list[Path]]: + if ( + WeightsFormat.safetensors in self.weight_files + and encoding == self.supported_encodings[0] + ): + return { + WeightsFormat.safetensors: [ + Path(f) + for f in self.weight_files[WeightsFormat.safetensors] + ] + } + + return {} + + def _get_pytorch_files_for_encoding( + self, encoding: SupportedEncoding + ) -> dict[WeightsFormat, list[Path]]: + if ( + WeightsFormat.pytorch in self.weight_files + and encoding == self.supported_encodings[0] + ): + return { + WeightsFormat.pytorch: [ + Path(f) for f in self.weight_files[WeightsFormat.pytorch] + ] + } + + return {} + + def files_for_encoding( + self, + encoding: SupportedEncoding, + weights_format: Optional[WeightsFormat] = None, + alternate_encoding: Optional[SupportedEncoding] = None, + ) -> dict[WeightsFormat, list[Path]]: + if weights_format == WeightsFormat.pytorch: + logger.warning( + "cannot infer encoding from .bin files, returning all bin files" + ) + return self._get_pytorch_files_for_encoding(encoding) + + if weights_format is WeightsFormat.gguf: + return self._get_gguf_files_for_encoding(encoding) + elif weights_format == WeightsFormat.safetensors: + return self._get_safetensor_files_for_encoding(encoding) + + gguf_files = self._get_gguf_files_for_encoding(encoding) + + safetensor_files = self._get_safetensor_files_for_encoding(encoding) + gguf_files.update(safetensor_files) + + pytorch_files = self._get_pytorch_files_for_encoding(encoding) + gguf_files.update(pytorch_files) + + if not gguf_files and alternate_encoding: + logger.warning( + "Could not find checkpoint with %s encoding, searching for %s files instead.", + encoding, + alternate_encoding, + ) + return self.files_for_encoding(alternate_encoding, weights_format) + return gguf_files + + def file_exists(self, filename: str) -> bool: + return file_exists(self.repo_id, filename) + + def download(self, filename: str, force_download: bool = False) -> Path: + return Path( + hf_hub_download( + self.repo_id, filename, force_download=force_download + ) + ) + + @property + def formats_available(self) -> list[WeightsFormat]: + return list(self.weight_files.keys()) + + def encoding_for_file(self, file: Union[str, Path]) -> SupportedEncoding: + if str(file).endswith(".safetensors"): + # If this file is safetensors, return the first encoding, as Safetensor repos can only have one. + return self.supported_encodings[0] + elif str(file).endswith(".gguf"): + encoding = SupportedEncoding.parse_from_file_name(str(file)) + if encoding: + return encoding + + raise ValueError( + f"gguf file, but encoding not found in file name: {file}" + ) + elif str(file).endswith(".bin"): + # If this file is pytorch, return the first encoding, as Pytorch repos only likely have one. + return self.supported_encodings[0] + else: + raise ValueError( + f"weight path: {file} not gguf or safetensors, cannot infer encoding from file." + ) + + +@dataclass +class SamplingParams: + top_k: int + enable_structured_output: bool + in_dtype: DType + out_dtype: DType + + +def _scan_available_devices() -> list[DeviceSpec]: + accel_count = accelerator_count() + if accel_count == 0: + return [DeviceSpec.cpu()] + else: + return [DeviceSpec.accelerator(i) for i in range(accel_count)] + + +@dataclass(frozen=False) +class PipelineConfig: + # When adding a new config parameter here, please remember to add a + # description to the `help()` method below + + # NOTE: model_path is made a str of "" by default, to avoid having + # it be Optional to check for None and then littering the codebase with + # asserts just to keep mypy happy. + model_path: str = "" + """repo_id of a Hugging Face model repository to use.""" + + huggingface_repo_id: str = "" + """DEPRECATED: repo_id of a Hugging Face model repository to use. Use `model_path` instead.""" + + huggingface_revision: str = hf_hub_constants.DEFAULT_REVISION + """Branch or Git revision of Hugging Face model repository to use.""" + + engine: Optional[PipelineEngine] = None + """Engine backend to use for serving, 'max' for the max engine, or 'huggingface' as fallback option for improved model coverage.""" + + architecture: Optional[str] = None + """Model architecture to run.""" + + weight_path: list[Path] = field(default_factory=list) + """Optional path or url of the model weights to use.""" + + device_specs: list[DeviceSpec] = field( + default_factory=_scan_available_devices + ) + """Devices to run inference upon. This option is not documented in help() as it shouldn't be used directly via the CLI entrypoint.""" + + quantization_encoding: Optional[SupportedEncoding] = None + """Weight encoding type.""" + + serialized_model_path: Optional[str] = None + """If specified, tries to load a serialized model from this path.""" + + save_to_serialized_model_path: Optional[str] = None + """If specified, tries to save a serialized model to this path.""" + + max_length: Optional[int] = None + """Maximum sequence length of the model.""" + + max_new_tokens: int = -1 + """Maximum number of new tokens to generate during a single inference pass of the model.""" + + max_batch_size: Optional[int] = None + """Maximum batch size to execute with the model. + This is set to one, to minimize memory consumption for the base case, in which a person is + running a local server to test out MAX. For users launching in a server scenario, the expectation + is that this value should be set higher based on server capacity.""" + + max_ce_batch_size: int = 32 + """Maximum cache size to reserve for a single context encoding batch. + The actual limit is the lesser of this and `max_batch_size`.""" + + enable_chunked_prefill: bool = True + """Enable chunked prefill to split context encoding requests into multiple chunks + based on 'target_num_new_tokens'.""" + + enable_in_flight_batching: bool = False + """When enabled, prioritizes token generation by batching it with context + encoding requests. Requires chunked prefill.""" + + cache_strategy: KVCacheStrategy = KVCacheStrategy.MODEL_DEFAULT + """The cache strategy to use. This defaults to `model_default`, which will set the cache + strategy based on the default strategy for the architecture requested. + + You can also force the engine to use a specific caching strategy: `naive` | `continuous` | `paged`. + """ + + max_num_steps: int = -1 + """The number of steps to run for multi-step scheduling. -1 specifies a default value based on + configuration and platform. Ignored for models which are not auto-regressive (e.g. embedding + models).""" + + pad_to_multiple_of: int = 2 + """Pad input tensors to be a multiple of value provided.""" + + kv_cache_page_size: int = 128 + """The number of tokens in a single page in the paged KVCache.""" + + enable_prefix_caching: bool = False + """Whether to enable prefix caching for the paged attention KVCache.""" + + device_memory_utilization: float = 0.9 + """The fraction of available device memory that the process should consume. + + This is used to inform the size of the KVCache workspace: + kv_cache_workspace = (total_free_memory * device_memory_utilization) - model_weights_size + """ + + target_num_new_tokens: Optional[int] = None + """The target number of un-encoded tokens to include in each batch. + If not set, this will be set to a best-guess optimal value based on model, hardware, and available memory.""" + + top_k: int = 1 + """Limits the sampling to the K most probable tokens. This defaults to 1, which enables greedy sampling.""" + + enable_structured_output: bool = False + """Enable structured generation/guided decoding for the server. This allows the user to pass a json + schema in the response_format field, which the LLM will adhere to.""" + + trust_remote_code: bool = False + """Whether or not to allow for custom modelling files on Hugging Face.""" + + force_download: bool = False + """Whether to force download a given file if it's already present in the local cache.""" + + enable_echo: bool = False + """Whether the model should be built with echo capabilities.""" + + rope_type: Optional[RopeType] = None + """Force using a specific rope type: `none` | `normal` | `neox`. Only matters for GGUF weights.""" + + pool_embeddings: bool = True + """Whether to pool embedding outputs.""" + + _huggingface_config: Optional[AutoConfig] = None + """The Hugging Face config associated with the `model-path`.""" + + _devices: list[Device] = field(default_factory=list) + """The underlying initialized devices, created by the specific `device_specs`.""" + + _weight_adapters: dict[WeightsFormat, WeightsAdapter] = field( + default_factory=dict + ) + """Weight adapter for the provided `weight_path`.""" + + _weights_repo_id: Optional[str] = None + """Hugging Face repo id to load weights from only. This should only be set by internal code.""" + + _available_cache_memory: Optional[int] = None + """The amount of available cache memory in bytes. This should only be set by internal code.""" + + _quant_config: Optional[QuantizationConfig] = None + """Optional config for specifying quantization parameters. This should only be set by internal code.""" + + max_cache_batch_size: Optional[int] = None + """DEPRECATED: The maximum cache batch size to use for the model. Use max_batch_size instead.""" + + gpu_profiling: str = os.environ.get("MODULAR_ENABLE_PROFILING", "false") + """Whether to enable GPU profiling of the model.""" + + use_experimental_kernels: str = os.environ.get( + "USE_EXPERIMENTAL_KERNELS", "false" + ) + + def __post_init__(self) -> None: + # Validate if a provided max_length is non-negative. + if self.max_length is not None and self.max_length < 0: + raise ValueError("max_length must be non-negative.") + + if self.huggingface_repo_id != "": + logger.warning( + "--huggingface-repo-id is deprecated, use `--model-path` instead. This setting will stop working in a future release." + ) + self.model_path = self.huggingface_repo_id + + if self.max_cache_batch_size is not None: + logger.warning( + "--max-cache-batch-size is deprecated, use `--max-batch-size` instead. This setting will stop working in a future release." + ) + self.max_batch_size = self.max_cache_batch_size + + # Validate that if weight_paths are passed as strings, they are converted to Path. + if isinstance(self.weight_path, tuple): + self.weight_path = list(self.weight_path) + elif not isinstance(self.weight_path, list): + self.weight_path = [self.weight_path] + weight_paths = [] + # Validate that if weight_paths are passed as strings, they are converted to Path. + for path in self.weight_path: + if isinstance(path, str): + path = Path(path) + elif not isinstance(path, Path): + raise ValueError( + "weight_path provided must either be string or Path:" + f" '{path}'" + ) + elif path.is_file(): + # If we already exist on the OS. Dont parse the path, just continue. + weight_paths.append(path) + continue + + # If the path, looks like it may start with a Hugging Face repo id, + # check if the repo_id is the same as the one provided. + # If it is the same, set the weight_path to just be the file_name post repo_id + # If it is different, set the _weights_repo_id to be that repo_id + # and set the path to be the file_name without the repo_id. + if path_pieces := str(path).split("/"): + if len(path_pieces) >= 3: + repo_id = f"{path_pieces[0]}/{path_pieces[1]}" + file_name = "/".join(path_pieces[2:]) + if self.model_path != "" and repo_id == self.model_path: + path = Path(file_name) + elif file_exists(repo_id, file_name): + self._weights_repo_id = repo_id + path = Path(file_name) + elif self.model_path == "": + raise ValueError( + "Unable to derive model_path from weight_path, " + "please provide a valid Hugging Face repository id." + ) + + weight_paths.append(path) + + self.weight_path = weight_paths + + # If we cannot infer the weight path, we lean on the model_path + # to provide it. + if len(self.weight_path) == 0: + if self.model_path == "": + raise ValueError( + "model_path must be provided and must be a valid Hugging Face repository" + ) + elif (not os.path.exists(self.model_path)) and ( + not _repo_exists_with_retry(self.model_path) + ): + raise ValueError( + f"{self.model_path} is not a valid Hugging Face repository" + ) + elif self.model_path == "" and self._weights_repo_id is not None: + # weight_path is used and we should derive the repo_id from it. + # At this point, we should have a resolved weight path - be it local or remote HF. + # weight_path should not be used directly anymore. + self.model_path = self._weights_repo_id + + # Set sensible defaults. These are platform-specific. + if self.max_num_steps < 0: + if ( + self.enable_structured_output + or self.device_specs[0] == DeviceSpec.cpu() + ): + self.max_num_steps = 1 + else: + self.max_num_steps = 10 + + if self.max_num_steps > 1 and self.enable_structured_output: + raise ValueError( + "max_num_steps > 1 not supported when enable_structured_output = True" + ) + + if self.enable_structured_output: + if self.device_specs[0] == DeviceSpec.cpu(): + raise ValueError( + "enable_structured_output is not currently supported on CPU." + ) + + if self.gpu_profiling not in ( + "false", + "off", + "no", + "0", + "true", + "on", + "yes", + "1", + "detailed", + ): + raise ValueError("gpu_profiling must be a boolean or 'detailed'") + + def __getstate__(self) -> dict[str, Any]: + """Override `__getstate__` to exclude the Hugging Face config.""" + state = self.__dict__.copy() + state.pop("_huggingface_config") + state["_devices"] = [] + return state + + @property + def graph_quantization_encoding(self) -> Optional[QuantizationEncoding]: + """Converts the CLI encoding to a MAX graph quantization encoding. + + Returns: + The graph quantization encoding corresponding to the CLI encoding. + + Raises: + ValueError: If no CLI encoding was specified. + """ + if self.quantization_encoding is None: + raise ValueError( + "can't convert `None` CLI encoding to graph quantization encoding" + ) + + return self.quantization_encoding.quantization_encoding + + def finalize_encoding_config(self): + """Depending on the encoding picked, we get some more parameters from the hf config""" + if self.quantization_encoding == SupportedEncoding.gptq: + hf_config = self.huggingface_config + hf_quant_config = hf_config.quantization_config + self._quant_config = QuantizationConfig( + quant_method=hf_quant_config["quant_method"], + bits=hf_quant_config["bits"], + group_size=hf_quant_config["group_size"], + desc_act=hf_quant_config["desc_act"], + sym=hf_quant_config["sym"], + ) + + def update_architecture(self) -> None: + if self.architecture is None: + # Retrieve architecture from model_path. + # This is done without using the huggingface config, to reduce the + # memory stored in this object, before it reaches the model worker. + hf_config = AutoConfig.from_pretrained( + self.model_path, + trust_remote_code=self.trust_remote_code, + ) + + # If we cannot get an architecture from the model_path, + # we cannot map the model to an internal architecture, and cannot + # be run using the MAX engine. + + architectures = getattr(hf_config, "architectures", None) + if architectures: + if len(architectures) > 1: + logger.warning( + "more than one architecture listed in Hugging Face config," + " using the first one." + ) + self.architecture = architectures[0] + else: + logger.warning( + "architectures not listed in Hugging Face config, trying with general `huggingface` engine" + ) + + self.engine = PipelineEngine.HUGGINGFACE + + @property + def huggingface_config(self) -> AutoConfig: + """Given the model_path, return the Hugging Face Config.""" + if self._huggingface_config is None: + # Lazy initialize the Hugging Face config field. + self._huggingface_config = AutoConfig.from_pretrained( + self.model_path, + trust_remote_code=self.trust_remote_code, + ) + assert self._huggingface_config is not None, ( + "Failed to load Hugging Face config" + ) + + return self._huggingface_config + + @property + def dtype(self) -> DType: + if self.quantization_encoding is None: + raise ValueError( + "quantization_encoding must be provided to infer dtype." + ) + + return self.quantization_encoding.dtype + + @property + def cache_dtype(self) -> DType: + if self.quantization_encoding is None: + raise ValueError( + "quantization_encoding must be provided to infer cache dtype." + ) + + return self.quantization_encoding.cache_dtype + + @property + def devices(self) -> list[Device]: + """Initialize and return a list of devices, given a list of device specs.""" + if self._devices: + return self._devices + num_devices_available = accelerator_count() + for device_spec in self.device_specs: + if device_spec.id >= num_devices_available: + msg = f"Device {device_spec.id} was requested but " + if num_devices_available == 0: + msg += "no devices were found." + else: + msg += f"only found {num_devices_available} devices." + raise ValueError(msg) + self._devices.append( + CPU(device_spec.id) + if device_spec.device_type == "cpu" + else Accelerator(device_spec.id) + ) + return self._devices + + @property + def weights_format(self) -> WeightsFormat: + """Identify which format our weights are expected in.""" + + if not self.weight_path: + raise ValueError( + "no weight_path provided cannot infer weights format." + ) + + # Get all weight paths. + if all( + [weight_path.suffix == ".gguf" for weight_path in self.weight_path] + ): + return WeightsFormat.gguf + elif all( + [ + weight_path.suffix == ".safetensors" + for weight_path in self.weight_path + ] + ): + return WeightsFormat.safetensors + elif all( + [weight_path.suffix == ".bin" for weight_path in self.weight_path] + ): + return WeightsFormat.pytorch + else: + raise ValueError( + f"weights type cannot be inferred from {self.weight_path}" + ) + + def weights_size(self) -> int: + size = 0 + hf_repo = HuggingFaceRepo( + ( + self._weights_repo_id + if self._weights_repo_id + else self.model_path + ), + trust_remote_code=self.trust_remote_code, + ) + for file_path in self.weight_path: + if os.path.exists(file_path): + size += os.path.getsize(file_path) + continue + + next_size = hf_repo.size_of(str(file_path)) + + if next_size is None: + raise ValueError( + f"Failed to get size of weight file {file_path}" + ) + size += next_size + + return size + + def download_weights(self) -> None: + # Try to load locally. + if all([os.path.exists(file_path) for file_path in self.weight_path]): + logger.info("All files exist locally, skipping download.") + return + + start_time = datetime.datetime.now() + weights_repo_id = ( + self._weights_repo_id if self._weights_repo_id else self.model_path + ) + logger.info(f"Starting download of model: {weights_repo_id}") + # max_workers=8 setting copied from default for + # huggingface_hub.snapshot_download. + self.weight_path = list( + thread_map( + lambda filename: Path( + hf_hub_download( + weights_repo_id, + str(filename), + revision=self.huggingface_revision, + force_download=self.force_download, + ) + ), + self.weight_path, + max_workers=8, + tqdm_class=hf_tqdm, + ) + ) + + logger.info( + f"Finished download of model: {weights_repo_id} in {(datetime.datetime.now() - start_time).total_seconds()} seconds." + ) + + def load_weights(self) -> Weights: + self.download_weights() + + if self.weights_format == WeightsFormat.gguf: + if len(self.weight_path) > 1: + raise ValueError("loading multiple gguf files is not supported") + return GGUFWeights(self.weight_path[0]) + + elif self.weights_format == WeightsFormat.safetensors: + return SafetensorWeights(self.weight_path) + + else: + raise ValueError( + f"loading weights format '{self.weights_format}' not supported" + ) + + @staticmethod + def help() -> dict[str, str]: + return { + "model_path": "Specify the repository ID of a Hugging Face model repository to use. This is used to load both Tokenizers, architectures and model weights.", + "huggingface_repo_id": "DEPRECATED: Use `model_path` instead.", + "huggingface_revision": "Branch or Git revision of Hugging Face model repository to use.", + "engine": "Specify the engine backend to use for serving the model. Options include `max` for the MAX engine, or `huggingface` as a fallback option that provides improved model coverage.", + "architecture": "Deprecated - Please set `model-path` instead. Define the model architecture to run. This should match one of the supported architectures for your selected engine.", + "weight_path": "Provide an optional local path or path relative to the root of a Hugging Face repo to the model weights you want to use. This allows you to specify custom weights instead of using defaults. You may pass multiple, ie. `--weight-path=model-00001-of-00002.safetensors --weight-path=model-00002-of-00002.safetensors`", + "quantization_encoding": "Define the weight encoding type for quantization. This can help optimize performance and memory usage during inference. ie. q4_k, bfloat16 etc.", + "serialized_model_path": "If specified, this flag attempts to load a serialized MEF model from the given path. This is useful for reusing previously saved models.", + "save_to_serialized_model_path": "If specified, this flag attempts to save the current model state to a serialized format at the given path for later use.", + "max_length": "Set the maximum sequence length for input data processed by the model. This must be less than the value specified in the Hugging Face configuration file. The default is derived from the Hugging Face configuration value. Larger values may consume more memory.", + "max_new_tokens": "Specify the maximum number of new tokens to generate during a single inference pass of the model. Default is -1, which means the model will generate until the maximum sequence length is hit, or and eos token is generated.", + "max_batch_size": "Define the maximum cache size reserved for a single batch. This value defaults to 1. Increase this value based on server capacity when deploying in production.", + "max_ce_batch_size": "Set the maximum cache size reserved for a single context encoding batch. The effective limit will be the lesser of this value and `max-cache-batch-size`. Default is 32.", + "enable_chunked_prefill": "Enable chunked prefill to split context encoding requests into multiple chunks based on `target-num-new-tokens`", + "enable_in_flight_batching": "When enabled, prioritizes token generation by batching it with context encoding requests. Requires chunked prefill.", + "max_cache_batch_size": "DEPRECATED: Use `max_batch_size` instead.", + "cache_strategy": "Force a specific cache strategy: 'naive' or 'continuous'. If not provided, the optimal caching strategy for the model requested will be selected.", + "rope_type": "Force using a specific rope type, `none` | `normal' | `nexo`. Only matters for GGUF weights.", + "max_num_steps": "Specify the number of steps to run for multi-step scheduling during inference. Default is set to 1.", + "pad_to_multiple_of": "Pad input tensors to be a multiple of value provided. Default is set to 2.", + "kv_cache_page_size": "The number of tokens in a single page in the paged KVCache. Default is set to 512.", + "enable_prefix_caching": "Whether to enable prefix caching for the paged attention KVCache. This defaults to false.", + "enable_structured_output": "Whether to enable constrained decoding in the text generation pipeline. This defaults to false.", + "device_memory_utilization": "The fraction of available device memory that the process should consume. This is used to inform the size of the KVCache workspace: kv_cache_workspace = (total_free_memory * device_memory_utilization) - model_weights_size. Default is set to 0.9.", + "top_k": "Limit sampling to the top K most probable tokens during generation. This can help control randomness and improve output quality. This defaults to 1, which defaults to greedy sampling.", + "trust_remote_code": "Indicate whether to allow custom modelling files from Hugging Face repositories. Set this to true with caution, as it may introduce security risks.", + "force_download": "Specify whether to forcefully download a file even if it already exists in local cache. Set this to true if you want to ensure you have the latest version.", + "enable_echo": "Whether the model should be built with echo capabilities. This defaults to false.", + "gpu_profiling": "Whether to enable GPU profiling of the model. This defaults to false.", + } + + def huggingface_weights_repo(self) -> HuggingFaceRepo: + return HuggingFaceRepo( + ( + self._weights_repo_id + if self._weights_repo_id + else self.model_path + ), + trust_remote_code=self.trust_remote_code, + ) + + @cached_property + def sampling_params(self) -> SamplingParams: + return SamplingParams( + top_k=self.top_k, + enable_structured_output=self.enable_structured_output, + in_dtype=DType.float32, + out_dtype=DType.float32, + ) diff --git a/src/max/pipelines/context.py b/src/max/pipelines/context.py new file mode 100644 index 0000000000..6390eb4403 --- /dev/null +++ b/src/max/pipelines/context.py @@ -0,0 +1,294 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Standardized context object for Pipeline Inference.""" + +from __future__ import annotations + +from typing import Any, Optional, Protocol, Sequence, Union, runtime_checkable + +import numpy as np + +CHUNK_SIZE = 128 + + +@runtime_checkable +class InputContext(Protocol): + """A base class for model contexts, represent model inputs for TokenGenerators.""" + + @property + def cache_seq_id(self) -> int: ... + + @property + def active_idx(self) -> int: ... + + @property + def start_idx(self) -> int: ... + + @property + def end_idx(self) -> int: ... + + @property + def current_length(self) -> int: + """The current length of the sequence, including completed and active tokens.""" + ... + + @property + def max_length(self) -> int | None: + """The maximum length of this sequence.""" + ... + + @property + def log_probabilities(self) -> int: + """When > 0, returns the log probabilities for the top N tokens for each + element token in the sequence.""" + ... + + @property + def log_probabilities_echo(self) -> bool: + """When True, the input tokens are added to the returned logprobs.""" + ... + + @property + def active_length(self) -> int: + """Current sequence length: num tokens input this iteration. + + This will be the prompt size for context encoding, and simply 1 for + token generation. + """ + ... + + @property + def next_tokens(self) -> np.ndarray: + """The next prompt tokens to be input during this iteration. + + This should be a 1D array of tokens of length active_length. + """ + ... + + def update( + self, + new_token: int, + ) -> None: + """Updates the next_tokens and extends existing tokens to include all generated tokens.""" + ... + + def bump_token_indices( + self, + start_idx: Optional[int] = None, + active_idx: Optional[int] = None, + end_idx: Optional[int] = None, + ) -> None: + """Update the start_idx, active_idx and end_idx without manipulating the token array.""" + ... + + @property + def matcher(self) -> Optional["xgr.GrammarMatcher"]: # type: ignore + """An optional xgr Grammar Matcher provided when using structured output.""" + ... + + @property + def json_schema(self) -> str | None: + """A json schema to use during constrained decoding.""" + ... + + def set_matcher(self, matcher: "xgr.GrammarMatcher") -> None: # type: ignore + """Set a grammar matcher for use during constrained decoding.""" + ... + + def reset(self) -> None: + """Resets the context's state by combining all tokens into a new prompt. + This method is used when a request is evicted, meaning that the context + needed to be re-encoded in the following CE iteration.""" + ... + + +class TextContext: + """A base class for model context, specifically for Text model variants.""" + + def __init__( + self, + cache_seq_id: int, + prompt: Union[str, Sequence[int]], + max_length: int | None, + tokens: np.ndarray, + log_probabilities: int = 0, + log_probabilities_echo: bool = False, + json_schema: str | None = None, + ) -> None: + self.cache_seq_id = cache_seq_id + self.prompt = prompt + self.max_length = max_length + + if tokens.ndim != 1: + msg = f"tokens must be one dimensional array: got shape '{tokens.shape}'" + raise ValueError(msg) + + self.size = int(np.ceil(len(tokens) / CHUNK_SIZE) * CHUNK_SIZE) + + # Create a fresh array since the input tokens may be a view or share memory with + # another array in the caller, which prevents us from resizing it directly. + # The extra space is initialized to zero and will be filled with generated tokens. + assert len(tokens) <= self.size + self.tokens = np.zeros(self.size, dtype=tokens.dtype) + self.tokens[: len(tokens)] = tokens + + self._active_idx = len(tokens) + self._start_idx = 0 + self._end_idx = self._active_idx + + self.log_probabilities = log_probabilities + self.log_probabilities_echo = log_probabilities_echo + + self.matcher = None + self.json_schema = json_schema + self.is_initial_prompt = True + + @property + def start_idx(self) -> int: + return self._start_idx + + @property + def active_idx(self) -> int: + return self._active_idx + + @property + def end_idx(self) -> int: + return self._end_idx + + def set_matcher(self, matcher: "xgr.GrammarMatcher") -> None: # type: ignore + self.matcher = matcher + + @property + def current_length(self) -> int: + """The current length of the sequence, including completed and active tokens.""" + return self._end_idx + + @property + def active_length(self) -> int: + """Current sequence length: num tokens input this iteration. + + This will be the prompt size for context encoding, and simply 1 (or more) for + token generation. + """ + return self._active_idx - self._start_idx + + def bump_token_indices( + self, + start_idx: Optional[int] = None, + active_idx: Optional[int] = None, + end_idx: Optional[int] = None, + ) -> None: + """Update the start_idx, active_idx and end_idx without manipulating the token array.""" + new_start_idx = (start_idx if start_idx else 0) + self._start_idx + new_active_idx = (active_idx if active_idx else 0) + self._active_idx + new_end_idx = (end_idx if end_idx else 0) + self._end_idx + + if new_start_idx >= new_active_idx: + msg = f""" + active_idx must always be greater than start_idx, unable to bump token indices + as new start_idx ({new_start_idx}) is greater than new active_idx ({new_active_idx}). + """ + raise ValueError(msg) + + if new_active_idx > new_end_idx: + msg = f""" + end_idx must always be greater than active_idx, unable to bump token indices + as new active_idx ({new_active_idx}) is greater than new end_idx ({new_end_idx}). + """ + raise ValueError(msg) + + self._start_idx = new_start_idx + self._active_idx = new_active_idx + self._end_idx = new_end_idx + + @property + def next_tokens(self) -> np.ndarray: + return self.tokens[self._start_idx : self._active_idx] + + def update( + self, + new_token: int, + ) -> None: + """Updates the next_tokens and extends existing tokens to include all generated tokens.""" + # This is required for chunked prefill. + # The scheduler will update the active_idx via bump_token_indices and pass through the model + # To accomodate for this, if we identify that the active_idx is not at the end of the completed + # token array, we only update the start_idx and active_idx, leaving the token array alone. + if self._active_idx < self._end_idx: + self._start_idx = self._active_idx + self._active_idx = self._end_idx + return + + if self._end_idx >= self.size: + self.size += CHUNK_SIZE + if self.tokens.flags.owndata: + self.tokens.resize(self.size) + else: + self.tokens = np.resize(self.tokens, self.size) + + self.tokens[self._active_idx] = new_token + self._start_idx = self._active_idx + self._active_idx += 1 + self._end_idx += 1 + + # Accept the token, and move the FSM for constrained decoding forward. + if self.matcher: + assert self.matcher.accept_token(new_token) + + self.is_initial_prompt = False + + def reset(self) -> None: + """Resets the context's state by combining all tokens into a new prompt.""" + self._start_idx = 0 + + self.is_initial_prompt = True + + +class TextAndVisionContext(TextContext): + """A base class for model context, specifically for Vision model variants.""" + + def __init__( + self, + cache_seq_id: int, + prompt: Union[str, Sequence[int]], + max_length: int | None, + tokens: np.ndarray, + pixel_values: Union[np.ndarray, list[np.ndarray]], + extra_model_args: dict[str, Any], + log_probabilities: int = 0, + log_probabilities_echo: bool = False, + json_schema: str | None = None, + ) -> None: + super().__init__( + cache_seq_id=cache_seq_id, + prompt=prompt, + max_length=max_length, + tokens=tokens, + log_probabilities=log_probabilities, + log_probabilities_echo=log_probabilities_echo, + json_schema=json_schema, + ) + self.pixel_values = pixel_values + self.extra_model_args = extra_model_args + + def update( + self, + new_token: int, + ) -> None: + """Updates the next_tokens and extends existing tokens to include all generated tokens.""" + super().update(new_token=new_token) + + # Update context not to re-encode the same image in next steps. There are no image tokens + # expected after context encoding. + self.pixel_values = [] diff --git a/src/max/pipelines/dataprocessing/__init__.py b/src/max/pipelines/dataprocessing/__init__.py new file mode 100644 index 0000000000..52a3d7df46 --- /dev/null +++ b/src/max/pipelines/dataprocessing/__init__.py @@ -0,0 +1,30 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .causal_attention_mask import causal_attention_mask +from .causal_attention_mask_with_alibi import causal_attention_mask_with_alibi +from .collate_batch import ( + PaddingDirection, + batch_padded_tokens_and_mask, + collate_batch, +) +from .max_tokens_to_generate import max_tokens_to_generate + +__all__ = [ + "causal_attention_mask", + "causal_attention_mask_with_alibi", + "collate_batch", + "batch_padded_tokens_and_mask", + "PaddingDirection", + "max_tokens_to_generate", +] diff --git a/src/max/pipelines/dataprocessing/causal_attention_mask.py b/src/max/pipelines/dataprocessing/causal_attention_mask.py new file mode 100644 index 0000000000..a882007aaa --- /dev/null +++ b/src/max/pipelines/dataprocessing/causal_attention_mask.py @@ -0,0 +1,67 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import math + +import numpy as np + + +def causal_attention_mask( + original_start_pos: list[int], + original_seq_len: list[int], + pad_to_multiple_of: int = 1, +) -> np.ndarray: + # Each example in the batch has a "start position", which is the length + # of the previously encoded tokens ("context"), and a "sequence length", + # which is the number of additional tokens to be encoded in this pass. + # + # "Causal attention" means that each token can "see" tokens before it, + # as well as itself. + # The attention layer adds the mask to the attention scores and then + # performs a softmax, so for tokens that a given token can "see" the mask + # wants to produce a 0, meaning to pass the attention through as normal, + # and for tokens that can't be "seen" the mask should produce -inf, which + # will result in them being functionally ignored after the softmax operation. + # + # We call the total length "post_seq_len", referring to the total context + # length after this pass concludes. + start_pos: np.ndarray = np.array(original_start_pos, dtype=np.int64) + seq_len: np.ndarray = np.array(original_seq_len, dtype=np.int64) + + # Provided `pad_to_multiple_of` ensure the padded_length is cleanly divisible + # by this multiple. + # If max_len is 1, we are presumably in a token generation phase batch. + # W scenario, padding from 1 -> 2, does not result in a performance gain. + if seq_len.max() == 1: + padded_length = 1 + else: + padded_length = ( + math.ceil(seq_len.max() / pad_to_multiple_of) * pad_to_multiple_of + ) + + # Mask shape: for each token being generated, attend to tokens _before_ it + # in the entire sequence including context. Pad all values to the longest + # sequence length and total length. + post_seq_len = (start_pos + padded_length).max() + mask_shape = (padded_length, post_seq_len) + + # TODO(KERN-782): This should be -inf but softmax saturates with NaNs. + fill_val = -10000.0 + fill_matrix = np.full(mask_shape, fill_val, dtype=np.float32) + + return np.stack( + # Set diagonal to k + 1 so that tokens attend to themselves. + [np.triu(fill_matrix, k=k + 1) for k in start_pos] + ) diff --git a/src/max/pipelines/dataprocessing/causal_attention_mask_with_alibi.py b/src/max/pipelines/dataprocessing/causal_attention_mask_with_alibi.py new file mode 100644 index 0000000000..d3ebbb63bd --- /dev/null +++ b/src/max/pipelines/dataprocessing/causal_attention_mask_with_alibi.py @@ -0,0 +1,68 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import numpy as np + +from .causal_attention_mask import causal_attention_mask + + +def _alibi_bias( + max_seq_len: int, alibi_bias_max: int, n_heads: int +) -> np.ndarray: + # This bias has to be calculated in fp32, as numpy does not have support for bf16. + bias = np.arange(1 - max_seq_len, 1, 1).reshape((1, 1, 1, max_seq_len)) + rounded_n_heads = int( + np.power(np.asfarray(2), np.ceil(np.log2(np.asfarray(n_heads)))) + ) + m = np.arange(1.0, 1.0 + rounded_n_heads) * ( + np.asfarray(alibi_bias_max) / np.asfarray(rounded_n_heads) + ) + + slopes = np.asfarray(1.0) / np.power(2.0, m) + + if rounded_n_heads != n_heads: + slopes = np.concatenate( + [slopes[1:rounded_n_heads:2], slopes[0:rounded_n_heads:2]] + ) + slopes = slopes[0:n_heads] + + slopes = slopes.reshape(1, n_heads, 1, 1) + + alibi_bias = bias * slopes + + return alibi_bias[:, :, :, :max_seq_len] + + +def causal_attention_mask_with_alibi( + original_start_pos: list[int], + original_seq_len: list[int], + alibi_bias_max: int, + n_heads: int, + pad_to_multiple_of: int = 1, +) -> np.ndarray: + # Get original causal mask + causal_mask = causal_attention_mask( + original_start_pos, original_seq_len, pad_to_multiple_of + ) + + max_seq_len = causal_mask.shape[2] + + # Broadcast causal_mask out for n_heads + causal_mask = np.expand_dims(causal_mask, axis=1) + + # Get alibi bias + alibi_bias = _alibi_bias(max_seq_len, alibi_bias_max, n_heads) + + return causal_mask + np.float32(alibi_bias) diff --git a/src/max/pipelines/dataprocessing/collate_batch.py b/src/max/pipelines/dataprocessing/collate_batch.py new file mode 100644 index 0000000000..22d0787785 --- /dev/null +++ b/src/max/pipelines/dataprocessing/collate_batch.py @@ -0,0 +1,120 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Batch padding.""" + +from __future__ import annotations + +import enum +import math + +import numpy as np + +from .causal_attention_mask import causal_attention_mask + + +class PaddingDirection(enum.Enum): + """Padding (from) direction for batch collation.""" + + LEFT = "left" + RIGHT = "right" + + +def collate_batch( + batch: list[np.ndarray], + direction: PaddingDirection = PaddingDirection.RIGHT, + pad_value: int = 0, + batch_size: int | None = None, + pad_to_multiple_of: int = 1, +) -> tuple[np.ndarray, np.ndarray]: + """Generates a single batch tensor from a batch of inputs. + + These input tensors may have different lengths. The `pad_value` will be used + to pad out the inputs to the same length. + + If `batch_size` is present, add additional values to the batch up to that + size. + + Returns: + A tuple of: + A matrix with all rows padded to the max sequence length. + A list with last token indices prior to any padding. + + Raises: + ValueError: if the batch is empty. + NotImplementedError: if the batch contains anything other than vectors. + """ + if not batch: + msg = "Must provide at least one batch item." + raise ValueError(msg) + + if not all(a.ndim == 1 for a in batch): + msg = "Collate only supports rank 1 tensors for now." + raise NotImplementedError(msg) + + max_len = max((len(a) for a in batch), default=0) + + # If max_len is 1, we are presumably in a token generation phase batch. + # W scenario, padding from 1 -> 2, does not result in a performance gain. + if max_len == 1: + pad_to = 1 + else: + pad_to = math.ceil(max_len / pad_to_multiple_of) * pad_to_multiple_of + + def pad(a: np.ndarray) -> np.ndarray: + npad = pad_to - len(a) + if npad == 0: + return a + padding = (npad, 0) if direction == PaddingDirection.LEFT else (0, npad) + return np.pad(a, padding, mode="constant", constant_values=pad_value) + + if batch_size is not None: + pad_batch_item = np.array([pad_value] * pad_to) + batch.extend([pad_batch_item] * (batch_size - len(batch))) + + # Generate unpadded last token index. + unpadded_last_token_index = ( + np.full(len(batch), -1) + if direction == PaddingDirection.LEFT + else np.array([len(a) - 1 for a in batch]) + ) + + return np.stack([pad(a) for a in batch], axis=0), unpadded_last_token_index + + +def batch_padded_tokens_and_mask( + start_pos: list[int], + tokens: list[np.ndarray], + pad_to_multiple_of: int = 1, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Batches input tokens and computes a batched attention mask. + + Args: + start_pos: index into the end of the KV cache for each batch item. + tokens: unpadded input tokens for this batch. + + Returns: + A (batched tokens, unpadded last token indices, batch attention mask) pair. + """ + # Grab attention mask. + attn_mask = causal_attention_mask( + original_start_pos=start_pos, + original_seq_len=[len(t) for t in tokens], + pad_to_multiple_of=pad_to_multiple_of, + ) + + # Create batched input token tensor by padding all input token tensors + # to the maximum sequence length in the batch. + next_tokens_batch, unpadded_last_token_index = collate_batch( + tokens, batch_size=len(tokens), pad_to_multiple_of=pad_to_multiple_of + ) + return next_tokens_batch, unpadded_last_token_index, attn_mask diff --git a/src/max/pipelines/dataprocessing/max_tokens_to_generate.py b/src/max/pipelines/dataprocessing/max_tokens_to_generate.py new file mode 100644 index 0000000000..5163c06753 --- /dev/null +++ b/src/max/pipelines/dataprocessing/max_tokens_to_generate.py @@ -0,0 +1,24 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +def max_tokens_to_generate( + prompt_size: int, + max_length: int, + max_new_tokens: int = -1, +) -> int: + """Returns the max number of new tokens to generate.""" + _difference_between_max_and_prompt = max(max_length - prompt_size, 0) + if max_new_tokens < 0: + return _difference_between_max_and_prompt + return min(max_new_tokens, _difference_between_max_and_prompt) diff --git a/src/max/pipelines/embeddings_pipeline.py b/src/max/pipelines/embeddings_pipeline.py new file mode 100644 index 0000000000..85ed8932ec --- /dev/null +++ b/src/max/pipelines/embeddings_pipeline.py @@ -0,0 +1,86 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Pipeline for running text embeddings.""" + +from __future__ import annotations + +from typing import Any, Type, TypeVar + +from max.engine import InferenceSession +from max.profiler import Tracer, traced + +from .config import PipelineConfig +from .context import InputContext +from .interfaces import EmbeddingsGenerator +from .pipeline import PipelineModel +from .response import EmbeddingsResponse + +T = TypeVar("T", bound=InputContext) + + +class EmbeddingsPipeline(EmbeddingsGenerator[T]): + """Generalized token generator pipeline.""" + + def __init__( + self, + pipeline_config: PipelineConfig, + pipeline_model: Type[PipelineModel], + **unused_kwargs, + ) -> None: + self._pipeline_config = pipeline_config + # Initialize Session. + session = InferenceSession(devices=self._pipeline_config.devices) + + # Load model. + self._pipeline_model = pipeline_model( + pipeline_config=self._pipeline_config, session=session + ) + + @traced + def encode(self, batch: dict[str, T]) -> dict[str, EmbeddingsResponse]: + """Provided a batch, process batch inputs, execute the graph for num_steps in a multi-step scenario, + then decode the tokens holistically and return the list of decoded tokens. + """ + + tracer: Tracer = Tracer() + # Flatten our batch for consistent indexing. + context_batch = list(batch.values()) + + tracer.next("prepare_initial_token_inputs") + # Prepare inputs for the first token in multistep execution. + model_inputs = self._pipeline_model.prepare_initial_token_inputs( + context_batch + ) + + tracer.next("execute") + model_outputs = self._pipeline_model.execute( + model_inputs=model_inputs, kv_cache_inputs=None + ) + assert model_outputs.logits + # Do the copy to host for each token generated. + tracer.next("logits.to(CPU())") + batch_embeddings = model_outputs.logits.to_numpy() + + # Prepare the response. + res: dict[str, Any] = {} + tracer.push("prepare_response") + for batch_index, request_id in enumerate(batch.keys()): + request_embeddings = batch_embeddings[batch_index] + if not self._pipeline_config.pool_embeddings: + # Remove padded tokens from embeddings + request_embeddings = request_embeddings[ + : context_batch[batch_index].active_length, : + ] + res[request_id] = EmbeddingsResponse(request_embeddings) + return res diff --git a/src/max/pipelines/hf_pipeline.py b/src/max/pipelines/hf_pipeline.py new file mode 100644 index 0000000000..0d03506701 --- /dev/null +++ b/src/max/pipelines/hf_pipeline.py @@ -0,0 +1,384 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Generalized Token Generation Pipeline""" + +from __future__ import annotations + +import logging +import warnings +from typing import Any, Optional, cast + +import numpy as np +import torch +from max.driver import Tensor +from transformers import ( + AutoModel, + AutoModelForCausalLM, + AutoTokenizer, + BatchEncoding, +) + +from .config import PipelineConfig +from .context import TextContext +from .interfaces import EmbeddingsGenerator, TokenGenerator +from .kv_cache import ContinuousHFStaticCache +from .response import EmbeddingsResponse, TextResponse + +logger = logging.getLogger("max.pipelines") + +DEFAULT_MAX_SEQ_LEN = 512 + + +class HFTextGenerationPipeline(TokenGenerator[TextContext]): + """HuggingFace text token generator pipeline.""" + + def __init__( + self, + pipeline_config: PipelineConfig, + torch_device_type: str, + ): + self._pipeline_config = pipeline_config + self._torch_device = torch.device(torch_device_type) + + self._model = AutoModelForCausalLM.from_pretrained( + pipeline_config.model_path, + trust_remote_code=pipeline_config.trust_remote_code, + ).to(self._torch_device) + self._dtype = self._model.dtype + + if self._model.config.model_type == "gemma2": + raise RuntimeError( + "Gemma2 architecture is currently not supported." + ) + + self._tokenizer = AutoTokenizer.from_pretrained( + pipeline_config.model_path + ) + + eos_token_id = self._tokenizer.eos_token_id + + # Expand eos tokens if more are provided in pipeline_config + if "eos_token_id" in pipeline_config.huggingface_config: + eos_tokens = pipeline_config.huggingface_config.eos_token_id + if isinstance(eos_tokens, int): + if eos_tokens != eos_token_id: + msg = f"eos_token_id provided in huggingface config ({eos_tokens}), does not match provided eos_token_id ({eos_token_id}), using provided eos_token_id" + logger.warning(msg) + + eos_token_id = set([eos_tokens]) + elif isinstance(eos_tokens, list): + if eos_token_id in eos_tokens: + eos_token_id = set(eos_tokens) + else: + eos_token_id = set([eos_token_id]) + else: + msg = f"eos_token_id in huggingface_config, is neither int or list: {eos_tokens}" + logger.warning(msg) + self._eos_token_id = set([eos_token_id]) + else: + eos_token_id = set([eos_token_id]) + + self._eos_token_id = eos_token_id + if self._tokenizer.pad_token is None: + self._tokenizer.pad_token = self._tokenizer.eos_token + + max_batch_size = pipeline_config.max_batch_size + assert max_batch_size is not None, ( + "max_batch_size must be set before constructing ContinuousHFStaticCache" + ) + self._cache = ContinuousHFStaticCache( + config=self._model.config, + max_batch_size=max_batch_size, + max_seq_len=DEFAULT_MAX_SEQ_LEN + if pipeline_config.max_length is None + else pipeline_config.max_length, + dtype=self._dtype, + device=self._torch_device, + ) + + # TODO(deep): Implement sampling and remove this warning + if self._pipeline_config.top_k > 1: + warnings.warn( + "Only argmax sampling is currently implemented, and the top_k config will be ignored", + ) + + def next_token( + self, batch: dict[str, TextContext], num_steps: int + ) -> list[dict[str, TextResponse]]: + """Provided a batch, process batch inputs, execute the graph for num_steps in a multi-step scenario, + then decode the tokens holistically and return the list of decoded tokens. + """ + + context_batch = list(batch.values()) + new_sequences = False + + # Claim cache slots for new sequences + for ctx in context_batch: + seq_id = ctx.cache_seq_id + if seq_id in self._cache.available_slots: + self._cache.external_claim([seq_id]) + self._cache.tokens[seq_id] = ctx.next_tokens + new_sequences = True + + cache_seq_ids = [ctx.cache_seq_id for ctx in context_batch] + self._cache.set_active_slots(cache_seq_ids) + + # Initial inputs preparation + input_ids, attention_mask, cache_position = ( + self._prepare_initial_token_inputs( + cache_seq_ids, context_batch, new_sequences + ) + ) + + # Pre-allocate tensor for generated tokens + generated_tokens = torch.zeros( + (len(context_batch), num_steps), + dtype=torch.long, + device=self._torch_device, + ) + + # Generate tokens + with torch.no_grad(): + for step in range(num_steps): + self._cache.set_cache_position(cache_position) + + outputs = self._model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=self._cache, + use_cache=True, + ) + + next_token = outputs.logits[:, -1, :].argmax(dim=-1) + generated_tokens[:, step] = next_token + + # if last step, no need to prepare next batch input + if step == num_steps - 1: + break + + # Next inputs preparation + input_ids, attention_mask, cache_position = ( + self._prepare_next_token_inputs( + cache_seq_ids, + next_tokens=next_token.unsqueeze(-1), + ) + ) + + # Move generated tokens to CPU for processing + generated_tokens = generated_tokens.cpu() + + # Prepare the response, pruning away completed requests as we go. + res: list[dict[str, Any]] = [{} for i in range(num_steps)] + for batch_idx, (request_id, context) in enumerate(batch.items()): + for step in range(num_steps): + next_token_id = generated_tokens[batch_idx, step].item() + + # Update context + context.update(next_token_id) + next_tokens = np.array([next_token_id]) + self._cache.tokens[context.cache_seq_id] = np.append( + self._cache.tokens[context.cache_seq_id], next_tokens + ) + + max_length = ( + DEFAULT_MAX_SEQ_LEN + if context.max_length is None + else context.max_length + ) + if ( + next_token_id in self._eos_token_id + or context.current_length > max_length + ): + break + + res[step][request_id] = TextResponse(next_token) + + return res + + def _prepare_initial_token_inputs( + self, + cache_seq_ids: list[int], + context_batch: list[TextContext], + new_sequences: bool, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if new_sequences: + # Process all tokens for new sequences + tokens = [ + torch.tensor(self._cache.tokens[ctx.cache_seq_id]) + for ctx in context_batch + ] + padded = self._tokenizer.pad( + BatchEncoding({"input_ids": tokens}), + padding=True, + return_tensors="pt", + ) + + input_ids = padded["input_ids"].to(self._torch_device) + attention_mask = padded["attention_mask"].to(self._torch_device) + + # Initialize attention patterns + for seq_id, mask in zip(cache_seq_ids, attention_mask): + self._cache.update_attention_pattern(seq_id, mask) + else: + # Process only next tokens for the iterative steps + tokens = [torch.tensor(ctx.next_tokens) for ctx in context_batch] + padded = self._tokenizer.pad( + BatchEncoding({"input_ids": tokens}), + padding=True, + return_tensors="pt", + ) + input_ids = padded["input_ids"].to(self._torch_device) + + # Extend attention patterns by 1 + ones = torch.ones(1, dtype=torch.long, device=self._torch_device) + for seq_id in cache_seq_ids: + pattern = self._cache.attention_patterns[seq_id] + self._cache.update_attention_pattern( + seq_id, + torch.cat([pattern, ones]), + ) + + # Get complete attention mask for all sequences + attention_mask = self._cache.get_attention_mask(cache_seq_ids) + + # Calculate cache position + seq_length = input_ids.size(1) + max_length = attention_mask.size(1) + cache_position = torch.arange( + max_length - seq_length, + max_length, + device=self._torch_device, + ) + + return input_ids, attention_mask, cache_position + + def _prepare_next_token_inputs( + self, + cache_seq_ids: list[int], + next_tokens: Optional[Tensor] = None, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + # Process only next tokens for the iterative steps + encoded_input = BatchEncoding({"input_ids": next_tokens}) + padded = self._tokenizer.pad( + encoded_input, padding=True, return_tensors="pt" + ) + input_ids = padded["input_ids"].to(self._torch_device) + + # Extend attention patterns by 1 + ones = torch.ones(1, dtype=torch.long, device=self._torch_device) + for seq_id in cache_seq_ids: + pattern = self._cache.attention_patterns[seq_id] + self._cache.update_attention_pattern( + seq_id, + torch.cat([pattern, ones]), + ) + + attention_mask = self._cache.get_attention_mask(cache_seq_ids) + + # Calculate cache position + seq_length = input_ids.size(1) + max_length = attention_mask.size(1) + cache_position = torch.arange( + max_length - seq_length, + max_length, + device=self._torch_device, + ) + + return input_ids, attention_mask, cache_position + + def release(self, context: TextContext) -> None: + if context.cache_seq_id not in self._cache.available_slots: + self._cache.release(context.cache_seq_id) + + +class HFEmbeddingsPipeline(EmbeddingsGenerator[TextContext]): + """Generalized token generator pipeline.""" + + def __init__( + self, + pipeline_config: PipelineConfig, + torch_device_type: str, + ) -> None: + self._pipeline_config = pipeline_config + self._torch_device = torch.device(torch_device_type) + self._model = AutoModel.from_pretrained( + pipeline_config.model_path, + trust_remote_code=pipeline_config.trust_remote_code, + ).to(self._torch_device) + self._tokenizer = AutoTokenizer.from_pretrained( + pipeline_config.model_path + ) + + def prepare_initial_token_inputs( + self, + context_batch: list[TextContext], + ) -> tuple[torch.Tensor, torch.Tensor]: + # Get tokens and seq_ids. + tokens = [ctx.next_tokens for ctx in context_batch] + + # Pad tokens for the batch. + padded = self._tokenizer.pad( + BatchEncoding({"input_ids": tokens}), + padding=True, + return_tensors="pt", + pad_to_multiple_of=2, + ) + input_ids = cast(torch.Tensor, padded["input_ids"]).to( + self._torch_device + ) + attention_mask = cast(torch.Tensor, padded["attention_mask"]).to( + self._torch_device + ) + return input_ids, attention_mask + + def encode( + self, batch: dict[str, TextContext] + ) -> dict[str, EmbeddingsResponse]: + """Encodes a batch of text inputs.""" + + # Flatten our batch for consistent indexing. + context_batch = list(batch.values()) + input_ids, attention_mask = self.prepare_initial_token_inputs( + context_batch + ) + + outputs = self._model( + input_ids=input_ids, attention_mask=attention_mask + ) + # Pool the embeddings of together, and copy to cpu. + batch_embeddings = ( + _mean_pooling(outputs, attention_mask).cpu().detach().numpy() + ) + + # Prepare the response. + res: dict[str, Any] = {} + for batch_index, request_id in enumerate(batch.keys()): + request_embeddings = batch_embeddings[batch_index] + res[request_id] = EmbeddingsResponse(request_embeddings) + return res + + +# Taken from the sentence piece transformer: +# https://huggingface.co/sentence-transformers/all-mpnet-base-v2 +# Mean Pooling - Take attention mask into account for correct averaging +def _mean_pooling(model_output, attention_mask): + token_embeddings = model_output[ + 0 + ] # First element of model_output contains all token embeddings + input_mask_expanded = ( + attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() + ) + return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( + input_mask_expanded.sum(1), min=1e-9 + ) diff --git a/src/max/pipelines/hf_utils.py b/src/max/pipelines/hf_utils.py new file mode 100644 index 0000000000..698f62f49a --- /dev/null +++ b/src/max/pipelines/hf_utils.py @@ -0,0 +1,56 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Utilities for interacting with HuggingFace Files/Repos.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +from huggingface_hub import ( + file_exists, + get_hf_file_metadata, + hf_hub_download, + hf_hub_url, +) + + +@dataclass(frozen=True) +class HuggingFaceFile: + """A simple object for tracking huggingface model metadata. + The repo_id will frequently be used to load a tokenizer, + whereas the filename is used to download model weights.""" + + repo_id: str + filename: str + revision: str | None = None + + def download(self, force_download: bool = False) -> Path: + """Download the file and return the file path where the data is saved locally.""" + return Path( + hf_hub_download( + self.repo_id, + self.filename, + revision=self.revision, + force_download=force_download, + ) + ) + + def size(self) -> int | None: + url = hf_hub_url(self.repo_id, self.filename, revision=self.revision) + metadata = get_hf_file_metadata(url) + return metadata.size + + def exists(self) -> bool: + return file_exists(self.repo_id, self.filename, revision=self.revision) diff --git a/src/max/pipelines/interfaces/__init__.py b/src/max/pipelines/interfaces/__init__.py new file mode 100644 index 0000000000..9233e609e6 --- /dev/null +++ b/src/max/pipelines/interfaces/__init__.py @@ -0,0 +1,42 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Top level imports for pipeline interfaces.""" + +from .embeddings_generation import ( + EmbeddingsGenerator, +) +from .tasks import PipelineTask +from .text_generation import ( + PipelineTokenizer, + TokenGenerator, + TokenGeneratorContext, + TokenGeneratorRequest, + TokenGeneratorRequestFunction, + TokenGeneratorRequestMessage, + TokenGeneratorRequestTool, + TokenGeneratorResponseFormat, +) + +__all__ = [ + "PipelineTask", + "PipelineTokenizer", + "TokenGenerator", + "TokenGeneratorContext", + "TokenGeneratorRequest", + "TokenGeneratorRequestFunction", + "TokenGeneratorRequestMessage", + "TokenGeneratorRequestTool", + "TokenGeneratorResponseFormat", + "EmbeddingsGenerator", +] diff --git a/src/max/pipelines/interfaces/embeddings_generation.py b/src/max/pipelines/interfaces/embeddings_generation.py new file mode 100644 index 0000000000..99846df2ed --- /dev/null +++ b/src/max/pipelines/interfaces/embeddings_generation.py @@ -0,0 +1,39 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Interfaces for embeddings generation pipeline behaviors.""" + +from typing import Any, Generic, Protocol, TypeVar, runtime_checkable + +EmbeddingsGeneratorContext = TypeVar("EmbeddingsGeneratorContext") + + +@runtime_checkable +class EmbeddingsGenerator(Generic[EmbeddingsGeneratorContext], Protocol): + """Interface for LLM embeddings-generator models.""" + + def encode( + self, batch: dict[str, EmbeddingsGeneratorContext] + ) -> dict[str, Any]: + """Computes embeddings for a batch of inputs. + + Args: + batch (dict[str, EmbeddingsGeneratorContext]): Batch of contexts to generate + embeddings for. + + Returns: + dict[str, Any]: Dictionary mapping request IDs to their corresponding + embeddings. Each embedding is typically a numpy array or tensor of + floating point values. + """ + ... diff --git a/examples/run-examples.sh b/src/max/pipelines/interfaces/tasks.py old mode 100755 new mode 100644 similarity index 62% rename from examples/run-examples.sh rename to src/max/pipelines/interfaces/tasks.py index 30bc2be5fc..553a4705c1 --- a/examples/run-examples.sh +++ b/src/max/pipelines/interfaces/tasks.py @@ -1,6 +1,5 @@ -#!/usr/bin/env bash # ===----------------------------------------------------------------------=== # -# Copyright (c) 2024, Modular Inc. All rights reserved. +# Copyright (c) 2025, Modular Inc. All rights reserved. # # Licensed under the Apache License v2.0 with LLVM Exceptions: # https://llvm.org/LICENSE.txt @@ -12,14 +11,11 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # -set -euo pipefail +"""Pipeline Tasks.""" -SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) -REPO_ROOT="${SCRIPT_DIR}"/../ -EXAMPLES_DIR="${REPO_ROOT}"/examples +from enum import Enum -BUILD_DIR="${REPO_ROOT}"/build -mkdir -p "${BUILD_DIR}" -# Run the examples using `lit`. -lit -sv "${EXAMPLES_DIR}" +class PipelineTask(str, Enum): + TEXT_GENERATION = "text_generation" + EMBEDDINGS_GENERATION = "embeddings_generation" diff --git a/src/max/pipelines/interfaces/text_generation.py b/src/max/pipelines/interfaces/text_generation.py new file mode 100644 index 0000000000..c2a250a208 --- /dev/null +++ b/src/max/pipelines/interfaces/text_generation.py @@ -0,0 +1,277 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Interfaces for text generation pipeline behaviors.""" + +from dataclasses import dataclass +from typing import ( + Any, + Generic, + List, + Literal, + Optional, + Protocol, + Sequence, + TypedDict, + TypeVar, + Union, + runtime_checkable, +) + + +class TokenGeneratorRequestFunction(TypedDict): + name: str + description: str + parameters: dict + + +class TokenGeneratorRequestTool(TypedDict): + type: str + function: TokenGeneratorRequestFunction + + +class TokenGeneratorResponseFormat(TypedDict): + type: str + json_schema: dict + + +class TokenGeneratorRequestMessage(TypedDict): + role: Literal["system", "user", "assistant"] + content: Union[str, list[dict[str, Any]]] + """Content can be simple string or a list of message parts of different + modalities. + + For example: + + .. code-block:: json + + { + "role": "user", + "content": "What'\''s the weather like in Boston today?" + } + + Or: + + .. code-block:: json + + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What'\''s in this image?" + }, + { + "type": "image_url", + "image_url": { + "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + } + } + ] + } + """ + + +@dataclass(frozen=True) +class TokenGeneratorRequest: + id: str + """ + A unique identifier for the request. This ID can be used to trace and log + the request throughout its lifecycle, facilitating debugging and tracking. + """ + index: int + """ + The sequence order of this request within a batch. This is useful for + maintaining the order of requests when processing multiple requests + simultaneously, ensuring that responses can be matched back to their + corresponding requests accurately. + """ + model_name: str + """ + The name of the model to be used for generating tokens. This should match + the available models on the server and determines the behavior and + capabilities of the response generation. + """ + prompt: Union[str, Sequence[int], None] = None + """ + The prompt to be processed by the model. This field supports legacy + completion APIs and can accept either a string or a sequence of integers + representing token IDs. If not provided, the model may generate output + based on the messages field. + """ + messages: Optional[list[TokenGeneratorRequestMessage]] = None + """ + A list of messages for chat-based interactions. This is used in chat + completion APIs, where each message represents a turn in the conversation. + If provided, the model will generate responses based on these messages. + """ + images: Optional[list[bytes]] = None + """ + A list of image byte arrays that can be included as part of the request. + This field is optional and may be used for multimodal inputs where images + are relevant to the prompt or task. + """ + tools: Optional[list[TokenGeneratorRequestTool]] = None + """ + A list of tools that can be invoked during the generation process. This + allows the model to utilize external functionalities or APIs to enhance its + responses. + """ + response_format: Optional[TokenGeneratorResponseFormat] = None + """ + Specifies the desired format for the model's output. When set, it enables + structured generation, which adheres to the json_schema provided. + """ + max_new_tokens: Optional[int] = None + """ + The maximum number of new tokens to generate in the response. If not set, + the model may generate tokens until it reaches its internal limits or based + on other stopping criteria. + """ + timestamp_ns: int = 0 + """ + The time (in nanoseconds) when the request was received by the server. This + can be useful for performance monitoring and logging purposes. + """ + request_path: str = "/" + """ + The endpoint path for the request. This is typically used for routing and + logging requests within the server infrastructure. + """ + logprobs: int = 0 + """ + The number of top log probabilities to return for each generated token. A value + of 0 means that log probabilities will not be returned. Useful for analyzing + model confidence in its predictions. + """ + echo: bool = False + """ + If set to True, the response will include the original prompt along with the + generated output. This can be useful for debugging or when you want to see how + the input relates to the output. + """ + stop: Optional[Union[str, List[str]]] = None + """ + Optional list of stop expressions (see: https://platform.openai.com/docs/api-reference/chat/create#chat-create-stop) + """ + + def __str__(self) -> str: + txt = f"Id: {self.id}" + if self.max_new_tokens: + txt += f", MaxNewTokens: {self.max_new_tokens}" + return txt + + +TokenGeneratorContext = TypeVar("TokenGeneratorContext") +TokenGeneratorBatchKey = TypeVar("TokenGeneratorBatchKey") + +TokenizerEncoded = TypeVar("TokenizerEncoded") + + +@runtime_checkable +class PipelineTokenizer( + Generic[TokenGeneratorContext, TokenizerEncoded], Protocol +): + """Interface for LLM tokenizers.""" + + @property + def eos(self) -> int: + """The end of sequence token for this tokenizer.""" + ... + + @property + def expects_content_wrapping(self) -> bool: + """If true, this tokenizer expects messages to have a 'content' property. + Text messages are formatted as + { "type" : "text", "content" : "text content"} + instead of, the OpenAI spec. + { "type" : "text", "text": "text content" }. + NOTE: Multimodal messages omit the content property. + Both "image_urls" and "image" content parts are converted to simply + { "type" : "image" } + Their content is provided as byte arrays and by the top level property on + the request object, i.e. "TokenGeneratorRequest.images". + """ + ... + + async def new_context( + self, request: TokenGeneratorRequest + ) -> TokenGeneratorContext: + """Creates a new context from a request object. This is sent to the + worker process once and then cached locally. + + Args: + request (TokenGeneratorRequest): Incoming request. + + Returns: + TokenGeneratorContext: Initialized context. + """ + ... + + async def encode(self, prompt: str) -> TokenizerEncoded: + """Encodes text prompts as tokens. + + Args: + prompt (str): Un-encoded prompt text. + + Raises: + ValueError: If the prompt exceeds the configured maximum length. + + Returns: + TokenizerEncoded: Encoded prompt tokens. + """ + ... + + async def decode( + self, + context: TokenGeneratorContext, + encoded: TokenizerEncoded, + **kwargs, + ) -> str: + """Decodes response tokens to text. + + Args: + context (TokenGeneratorContext): Current generation context. + encoded (TokenizerEncoded): Encoded response tokens. + + Returns: + str: Un-encoded response text. + """ + ... + + +@runtime_checkable +class TokenGenerator(Generic[TokenGeneratorContext], Protocol): + """Interface for LLM token-generator models.""" + + def next_token( + self, batch: dict[str, TokenGeneratorContext], num_steps: int + ) -> list[dict[str, Any]]: + """Computes the next token response for a single batch. + + Args: + batch (dict[str, TokenGeneratorContext]): Batch of contexts. + num_steps int: Number of tokens to generate. + + Returns: + list[dict[str, Any]]: List of encoded responses (indexed by req. ID) + """ + ... + + def release(self, context: TokenGeneratorContext) -> None: + """Releases resources associated with this context. + + Args: + context (TokenGeneratorContext): Finished context. + """ + ... diff --git a/src/max/pipelines/kv_cache/__init__.py b/src/max/pipelines/kv_cache/__init__.py new file mode 100644 index 0000000000..0008512c89 --- /dev/null +++ b/src/max/pipelines/kv_cache/__init__.py @@ -0,0 +1,183 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from typing import Any, Dict, List, Optional, Type + +from max.driver import Device +from max.dtype import DType +from max.engine import InferenceSession + +from .cache_params import KVCacheParams, KVCacheStrategy +from .continuous_batching_cache import ( + ContinuousBatchingKVCache, + ContinuousBatchingKVCacheCollection, + ContinuousBatchingKVCacheCollectionType, + ContinuousBatchingKVCacheManager, + ContinuousBatchingKVCacheType, + FetchContinuousBatchingKVCacheCollection, +) +from .hf import ContinuousHFStaticCache +from .manager import ( + KVCacheInputs, + KVCacheInputsSequence, + KVCacheInputSymbols, + KVCacheManager, + PaddedKVCacheInputs, + RaggedKVCacheInputs, +) +from .naive_cache import NaiveKVCacheManager +from .paged_cache import ( + FetchPagedKVCacheCollection, + PagedKVCacheCollection, + PagedKVCacheManager, + PagedKVCacheType, +) +from .radix_trie import RadixTrie + +CACHE_MANAGER_REGISTRY: dict[KVCacheStrategy, Type[KVCacheManager]] = { + KVCacheStrategy.CONTINUOUS: ContinuousBatchingKVCacheManager, + KVCacheStrategy.NAIVE: NaiveKVCacheManager, + KVCacheStrategy.PAGED: PagedKVCacheManager, +} + + +def load_kv_manager( + params: KVCacheParams, + max_batch_size: Optional[int], + max_seq_len: int, + num_layers: int, + devices: List[Device], + session: InferenceSession, + available_cache_memory: Optional[int] = None, + page_size: Optional[int] = 512, +) -> KVCacheManager: + assert max_batch_size is not None, "Expected max_batch_size to be set" + assert max_batch_size > 0, "max_batch_size must be greater than 0" + if params.cache_strategy == KVCacheStrategy.CONTINUOUS: + return ContinuousBatchingKVCacheManager( + params=params, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + num_layers=num_layers, + devices=devices, + session=session, + ) + elif params.cache_strategy == KVCacheStrategy.NAIVE: + return NaiveKVCacheManager( + params=params, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + num_layers=num_layers, + devices=devices, + session=session, + ) + elif params.cache_strategy == KVCacheStrategy.PAGED: + if page_size is None: + msg = ( + "Missing required argument page_size for KVCacheStrategy.PAGED" + ) + raise ValueError(msg) + + # TODO(KERN-1308) remove this validation as we generalize page_size + if page_size % 128 != 0 or page_size < 128: + msg = "Page size must be a multiple of 128 and at least 128." + raise ValueError(msg) + + if available_cache_memory is None: + msg = "Missing required argument available_cache_memory for KVCacheStrategy.PAGED" + raise ValueError(msg) + + return PagedKVCacheManager( + params=params, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + num_layers=num_layers, + devices=devices, + session=session, + cache_memory=available_cache_memory, + page_size=page_size, + ) + else: + msg = f"cache type: {params.cache_strategy} not supported." + raise ValueError(msg) + + +def estimate_kv_cache_size( + params: KVCacheParams, + max_batch_size: Optional[int], + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, +) -> int: + assert max_batch_size is not None, "Expected max_batch_size to be set" + assert max_batch_size > 0, "max_batch_size must be greater than 0" + if params.cache_strategy not in CACHE_MANAGER_REGISTRY: + msg = f"cache type: {params.cache_strategy} not supported." + raise ValueError(msg) + + return CACHE_MANAGER_REGISTRY[params.cache_strategy].estimated_memory_size( + params=params, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + num_layers=num_layers, + available_cache_memory=available_cache_memory, + devices=devices, + **kwargs, + ) + + +def infer_optimal_batch_size( + params: KVCacheParams, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, +) -> int: + return CACHE_MANAGER_REGISTRY[ + params.cache_strategy + ].infer_optimal_batch_size( + params=params, + max_seq_len=max_seq_len, + num_layers=num_layers, + available_cache_memory=available_cache_memory, + devices=devices, + **kwargs, + ) + + +__all__ = [ + "KVCacheParams", + "KVCacheStrategy", + "ContinuousBatchingKVCache", + "ContinuousBatchingKVCacheCollection", + "ContinuousBatchingKVCacheCollectionType", + "ContinuousBatchingKVCacheManager", + "ContinuousBatchingKVCacheType", + "FetchContinuousBatchingKVCacheCollection", + "FetchPagedKVCacheCollection", + "PagedKVCacheManager", + "PagedKVCacheCollection", + "PagedKVCacheType", + "KVCacheManager", + "KVCacheInputs", + "KVCacheInputsSequence", + "RaggedKVCacheInputs", + "PaddedKVCacheInputs", + "KVCacheInputSymbols", + "NaiveKVCacheManager", + "ContinuousHFStaticCache", + "RadixTrie", +] diff --git a/src/max/pipelines/kv_cache/_utils.py b/src/max/pipelines/kv_cache/_utils.py new file mode 100644 index 0000000000..6942ad7823 --- /dev/null +++ b/src/max/pipelines/kv_cache/_utils.py @@ -0,0 +1,33 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Utility functions for KV cache.""" + +import numpy as np +from max.driver import Tensor + + +def build_max_lengths_tensor( + num_steps: int, max_seq_length: int, max_cache_length: int +) -> Tensor: + # Build a tensor of maximum lengths. Each step slices the first row to + # advance to the values for the next row. + max_lengths_np = np.empty((num_steps, 2), np.uint32) + step_max_seq_length = max_seq_length + step_max_cache_length = max_cache_length + for step in range(num_steps): + max_lengths_np[step, 0] = step_max_seq_length + max_lengths_np[step, 1] = step_max_cache_length + step_max_cache_length += step_max_seq_length + step_max_seq_length = 1 + return Tensor.from_numpy(max_lengths_np) diff --git a/src/max/pipelines/kv_cache/cache_params.py b/src/max/pipelines/kv_cache/cache_params.py new file mode 100644 index 0000000000..6ca3a34491 --- /dev/null +++ b/src/max/pipelines/kv_cache/cache_params.py @@ -0,0 +1,84 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from enum import Enum +from typing import Optional + +from max.dtype import DType + + +class KVCacheStrategy(str, Enum): + MODEL_DEFAULT = "model_default" + NAIVE = "naive" + CONTINUOUS = "continuous" + PAGED = "paged" + + def __str__(self) -> str: + return self.value + + def __repr__(self) -> str: + return self.value + + def kernel_substring(self) -> str: + """Returns the common substring that we include in the kernel name for this caching strategy.""" + if self == KVCacheStrategy.CONTINUOUS: + return "continuous_batching" + return str(self.value) + + def uses_opaque(self) -> bool: + return self != KVCacheStrategy.NAIVE + + +class KVCacheParams: + def __init__( + self, + dtype: DType, + n_kv_heads: int, + head_dim: int, + enable_prefix_caching: bool = False, + cache_strategy: KVCacheStrategy = KVCacheStrategy.CONTINUOUS, + page_size: Optional[int] = None, + n_devices: int = 1, + ): + # Initialize static attributes. + self.dtype = dtype + self.n_kv_heads = n_kv_heads + self.head_dim = head_dim + self.cache_strategy = cache_strategy + self.n_devices = n_devices + self.n_kv_heads_per_device = n_kv_heads // n_devices + self.page_size = page_size + self.enable_prefix_caching = enable_prefix_caching + + # Validate inputs. + if enable_prefix_caching and cache_strategy != KVCacheStrategy.PAGED: + raise ValueError( + "Prefix caching is only supported for paged cache strategy" + ) + if page_size is None and cache_strategy == KVCacheStrategy.PAGED: + raise ValueError("Page size is required for paged cache strategy") + + @property + def dtype_shorthand(self) -> str: + """The textual representation in shorthand of the dtype.""" + return "bf16" if self.dtype == DType.bfloat16 else "f32" + + @property + def static_cache_shape(self) -> tuple[str, str, str, str, str]: + return ( + "num_layers", + "batch_size", + "seq_len", + "n_kv_heads", + "head_dim", + ) diff --git a/src/max/pipelines/kv_cache/continuous_batching_cache.py b/src/max/pipelines/kv_cache/continuous_batching_cache.py new file mode 100644 index 0000000000..b6f75e9c8a --- /dev/null +++ b/src/max/pipelines/kv_cache/continuous_batching_cache.py @@ -0,0 +1,405 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Continuous Batching enabled KV cache for the Transformer leveraging the mo.opaque pattern.""" + +from __future__ import annotations + +from dataclasses import dataclass +from functools import reduce +from operator import mul +from typing import Any, List, cast + +import numpy as np +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import ( + DeviceRef, + TensorType, + TensorValue, + _OpaqueType, + _OpaqueValue, + ops, +) + +from ._utils import build_max_lengths_tensor +from .cache_params import KVCacheParams +from .manager import ( + KVCacheInputs, + KVCacheInputSymbols, + KVCacheManager, + RaggedKVCacheInputs, +) + + +@dataclass +class ContinuousBatchingKVCacheInputSymbols(KVCacheInputSymbols): + kv_blocks: TensorType + cache_lengths: TensorType + lookup_table: TensorType + max_lengths: TensorType + + +class ContinuousBatchingKVCacheType(_OpaqueType): + """Continuous Mojo KV Cache graph type.""" + + def __init__(self) -> None: + """Creates an opaque type containing a continuous batching KV Cache.""" + super().__init__("ContinuousBatchingKVCache") + + +class ContinuousBatchingKVCacheCollectionType(_OpaqueType): + """The graph type for a "view" of the cache for the given sequences in the + batch. + + This object does not own the underlying buffers in k_cache and v_cache, + it's borrowing them from the BlockWrappers in our ContinuousKVCacheManager. + It does own the Pointer[NDBuffer[type, 3]] and valid_lengths buffer + """ + + def __init__(self) -> None: + """Creates an opaque type containing a continuous batching KV cache collection.""" + super().__init__("ContinuousBatchingKVCacheCollection") + + +class ContinuousBatchingKVCache(_OpaqueValue): + """Continuous Mojo KV cache graph value.""" + + +class ContinuousBatchingKVCacheCollection(_OpaqueValue): + """The graph value for a view of the KV cache.""" + + +class FetchContinuousBatchingKVCacheCollection: + def __init__(self, kv_params: KVCacheParams) -> None: + if kv_params.enable_prefix_caching: + raise ValueError( + "Prefix caching is not supported for continuous batching cache." + ) + self.kv_params = kv_params + + def __call__( + self, + blocks: TensorValue, # NDBuffer[type, 6, Self.blocks_shape] + cache_lengths: TensorValue, # NDBuffer[DType.uint32, 1], + lookup_table: TensorValue, # NDBuffer[DType.uint32, 1], + max_lengths: TensorValue, + ) -> ContinuousBatchingKVCacheCollection: + """Constructs a ContinuousBatchingKVCacheCollection for use downstream.""" + + # Explicit validation. + if blocks.dtype != self.kv_params.dtype: + msg = ( + f"expected blocks to be dtype: {self.kv_params.dtype}, got" + f" {blocks.dtype}" + ) + raise ValueError(msg) + + if blocks.rank != 6: + msg = f"expected blocks to be of rank 6, got {blocks.rank}" + raise ValueError(msg) + + # For all tensors other than the blocks tensor, the length should be equivalent + # to batch size, which is unknown within the graph at this stage. + if cache_lengths.dtype != DType.uint32: + msg = ( + "expected cache lengths to be dtype: uint32, got" + f" {cache_lengths.dtype}" + ) + raise ValueError(msg) + + if lookup_table.dtype != DType.uint32: + msg = ( + "expected lookup_table to be dtype: uint32, got" + f" {lookup_table.dtype}" + ) + raise ValueError(msg) + + return ContinuousBatchingKVCacheCollection( + ops.custom( + "mo.kv_collection_ctor.continuous_batching", + values=[ + blocks, + cache_lengths, + lookup_table, + max_lengths, + ], + out_types=[ContinuousBatchingKVCacheCollectionType()], + parameters={ + "num_heads": self.kv_params.n_kv_heads_per_device, + "head_dim": self.kv_params.head_dim, + }, + )[0].opaque + ) + + +class ContinuousBatchingKVCacheManager(KVCacheManager): + def __init__( + self, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + devices: List[Device], + session: InferenceSession, + ) -> None: + super().__init__( + params=params, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + num_layers=num_layers, + devices=devices, + session=session, + is_ragged=True, + ) + + # Allocate memory for the KV cache blocks. + self.blocks: List[Tensor] = [] + for i in range(len(self.devices)): + self.blocks.append( + Tensor.zeros( + self.block_shape(self.max_batch_size), + self.params.dtype, + device=self.devices[i], + ) + ) + + @classmethod + def estimated_memory_size( + cls, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, + ) -> int: + cache_size = ( + reduce( + mul, + cls._block_shape( + params, max_batch_size, max_seq_len, num_layers + ), + ) + * params.dtype.size_in_bytes + ) + lengths_size = max_batch_size * DType.uint32.size_in_bytes + lookup_table_size = max_batch_size * DType.uint32.size_in_bytes + size = cache_size + lengths_size + lookup_table_size + return size * len(devices) + + @classmethod + def infer_optimal_batch_size( + cls, + params: KVCacheParams, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, + ) -> int: + cache_size_per_sequence = ( + reduce( + mul, + cls._block_shape(params, 1, max_seq_len, num_layers), + ) + * params.dtype.size_in_bytes + ) + return int(available_cache_memory // cache_size_per_sequence) + + def _fetch( + self, + seq_ids_and_prompts: dict[int, np.ndarray], + num_steps: int = 1, + ) -> List[KVCacheInputs]: + """Fetches the KV cache state for the given sequence IDs. + + This method retrieves the current cache state for a batch of sequences, including their + cache lengths and lookup information. It's used during token generation to access + previously cached key/value pairs. + + Args: + seq_ids_and_prompts: Dictionary of sequence IDs to fetch cache state for and the + new prompt we plan to add to the cache. Each ID must be within + the max_batch_size and must exist in the current cache. + num_steps: Number of steps to run for multi-step scheduling. + + Returns: + List of tuples for each device containing: + - blocks: Tensor containing the KV cache blocks + - cache_lengths: Tensor of current cache lengths for each sequence + - lookup_table: Tensor mapping sequence IDs to cache positions + - max_lengths: Tensor containing [max_seq_length, max_cache_length] + + Raises: + ValueError: If any seq_id exceeds max_batch_size or doesn't exist in cache + """ + active_batch_size = len(seq_ids_and_prompts) + + # Lookup table and seq_ids are redundant identical tensors. + lookup_table_tensor = Tensor.from_numpy( + np.array(list(seq_ids_and_prompts.keys()), np.uint32) + ) + cache_lengths_np = np.zeros(active_batch_size, np.uint32) + + max_seq_length = 0 + max_cache_length = 0 + + for i, (seq_id, prompt) in enumerate(seq_ids_and_prompts.items()): + if seq_id > self.max_batch_size: + msg = ( + f"seq_id: {seq_id}, beyond max_batch_size, you may" + " want to increase `max_batch_size` in the pipeline" + " config." + ) + raise ValueError(msg) + elif seq_id not in self.cache_lengths: + raise ValueError(f"seq_id: {seq_id} not currently in cache.") + + cache_len = self.cache_lengths[seq_id] + + assert ( + cache_len + len(prompt) + num_steps - 1 <= self.max_seq_len + ), ( + f"seq_id: {seq_id} would overrun the max cache length of {self.max_seq_len} " + f"with {len(prompt)} new tokens and {num_steps} steps. Existing length: {cache_len}" + ) + + cache_lengths_np[i] = cache_len + + # Update the maximum lengths seen so far. + max_seq_length = max(max_seq_length, len(prompt)) + max_cache_length = max(max_cache_length, cache_len) + + cache_lengths = [ + Tensor.from_numpy(cache_lengths_np).to(d) for d in self.devices + ] + lookup_table_tensor_list = [ + lookup_table_tensor.to(self.devices[i]) + for i in range(len(self.devices)) + ] + + # Build a tensor of maximum lengths. Each step slices the first row to + # advance to the values for the next row. + max_lengths_host = build_max_lengths_tensor( + num_steps, max_seq_length, max_cache_length + ) + + result = [ + RaggedKVCacheInputs( + blocks=self.blocks[i], + cache_lengths=cache_lengths[i], + lookup_table=lookup_table_tensor_list[i], + max_lengths=max_lengths_host, + ) + for i in range(len(self.devices)) + ] + return cast(List[KVCacheInputs], result) + + def block_shape(self, n_sequences: int) -> list[int]: + """Returns the shape of the KV cache blocks for the given number of sequences. + + Defines the 6-dimensional shape of the cache blocks used to store key and value + tensors for transformer attention. The dimensions represent: + [n_sequences, 2, num_layers, max_seq_len, n_kv_heads_per_device, head_dim] + where 2 represents separate storage for keys and values. + + Args: + n_sequences: Number of sequences that will be cached + + Returns: + List describing the shape of the cache blocks with dimensions for: + sequences, key/value split, layers, sequence length, attention heads, and head dimension + """ + return self._block_shape( + self.params, + n_sequences, + self.max_seq_len, + self.num_layers, + ) + + @staticmethod + def _block_shape( + params: KVCacheParams, + n_sequences: int, + max_seq_len: int, + num_layers: int, + ) -> list[int]: + return [ + n_sequences, + 2, + num_layers, + max_seq_len, + params.n_kv_heads_per_device, + params.head_dim, + ] + + def input_symbols( + self, + ) -> List[ContinuousBatchingKVCacheInputSymbols]: + """Returns the expected input tensor types for `fetch` on each device. + + Defines the tensor specifications needed by the cache implementation, + including shapes and data types. This is used for graph construction + and validation. + + Returns: + List of ContinuousBatchingKVCacheInputSymbols for each device + containing TensorTypes for: + - KV cache blocks: 6D tensor for storing keys and values + - Cache lengths: 1D tensor tracking sequence lengths + - Lookup table: 1D tensor mapping sequence IDs to cache positions + - Maximum lengths: 2D tensor tracking maximum sequence and cache lengths per step. + """ + return [ + ContinuousBatchingKVCacheInputSymbols( + kv_blocks=TensorType( + self.params.dtype, + shape=[ + "num_blocks", + 2, + "num_layers", + "max_seq_len", + "num_kv_heads", + "head_dim", + ], + device=DeviceRef(self.devices[i].label, self.devices[i].id), + ), + cache_lengths=TensorType( + DType.uint32, + shape=["batch_size"], + device=DeviceRef(self.devices[i].label, self.devices[i].id), + ), + lookup_table=TensorType( + DType.uint32, + shape=["batch_size"], + device=DeviceRef(self.devices[i].label, self.devices[i].id), + ), + # max_lengths (on host) + max_lengths=TensorType( + DType.uint32, + shape=["steps_remaining", 2], + # TODO: This is a hack introduced to remediate a negative side effect + # of the graph compiler changes introduced in #52793. + # We are tricking the compiler into thinking that the max_lengths + # tensor is on the device, even though it is on the host. + # With #52793 graph compiler changes, the compiler would + # insert erroneous device transfers that lead to CUDA_ILLEGAL_ADDRESS + # errors if we don't do this forceful device specification. + device=DeviceRef(self.devices[i].label, self.devices[i].id), + ), + ) + for i in range(len(self.devices)) + ] diff --git a/src/max/pipelines/kv_cache/hf.py b/src/max/pipelines/kv_cache/hf.py new file mode 100644 index 0000000000..7f0a32622c --- /dev/null +++ b/src/max/pipelines/kv_cache/hf.py @@ -0,0 +1,149 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from typing import Any, Optional, Union + +import numpy as np +import torch +from torch import device as torch_device +from transformers import PretrainedConfig, StaticCache + + +class ContinuousHFStaticCache(StaticCache): + def __init__( + self, + config: PretrainedConfig, + max_batch_size: int, + max_seq_len: int, + device: torch_device, + dtype: torch.dtype = torch.float32, + layer_device_map: Optional[ + dict[int, Union[str, torch_device, int]] + ] = None, + ) -> None: + super().__init__( + config, + max_batch_size, + max_seq_len, + device, + dtype, + None, + layer_device_map, + ) + self.max_batch_size = max_batch_size + self.device = device + self._init_slots() + + def _init_slots(self) -> None: + self.available_slots: set[int] = set(range(self.max_batch_size)) + self.active_slots: list[int] = [] + self.attention_patterns: dict[int, torch.Tensor] = {} + self.tokens: dict[int, np.ndarray] = {} + self.cache_position: torch.Tensor = torch.arange( + 0, + len(self.active_slots), + device=self.device, + ) + + def external_claim(self, seq_ids: list[int]) -> None: + if not self.available_slots: + raise RuntimeError("No slots are available for claiming.") + + unavailable_slots = set(seq_ids) - self.available_slots + if unavailable_slots: + raise ValueError( + f"The following seq_ids are already claimed: {unavailable_slots}" + ) + + for seq_id in seq_ids: + self.available_slots.remove(seq_id) + self.attention_patterns[seq_id] = torch.tensor( + [], dtype=torch.long, device=self.device + ) + self.tokens[seq_id] = np.array([]) + + def set_active_slots(self, seq_ids: list[int]) -> None: + self.active_slots = seq_ids + + def set_cache_position(self, cache_position: torch.Tensor): + self.cache_position = cache_position + + def update( + self, + key_states: torch.Tensor, + value_states: torch.Tensor, + layer_idx: int, + cache_kwargs: Optional[dict[str, Any]] = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + cache_position = self.cache_position + + k_out = self.key_cache[layer_idx] + v_out = self.value_cache[layer_idx] + + # Prepare slot indices tensor once + slot_indices = torch.tensor(self.active_slots, device=self.device) + + # Update each sequence individually to handle different shapes correctly + for batch_idx, slot_idx in enumerate(self.active_slots): + k_out[slot_idx].index_copy_( + 1, cache_position, key_states[batch_idx] + ) + v_out[slot_idx].index_copy_( + 1, cache_position, value_states[batch_idx] + ) + + # Return reordered key value cache for the layer + return ( + k_out.index_select(0, slot_indices), + v_out.index_select(0, slot_indices), + ) + + def update_attention_pattern( + self, seq_id: int, attention_mask: torch.Tensor + ) -> None: + self.attention_patterns[seq_id] = attention_mask.to( + device=self.device, dtype=torch.long + ) + + def get_attention_mask(self, seq_ids: list[int]) -> torch.Tensor: + max_len = max( + self.attention_patterns[seq_id].size(0) for seq_id in seq_ids + ) + attention_mask = torch.zeros( + (len(seq_ids), max_len), dtype=torch.long, device=self.device + ) + + for i, seq_id in enumerate(seq_ids): + pattern = self.attention_patterns[seq_id] + attention_mask[i, : pattern.size(0)] = pattern + + return attention_mask + + def release(self, seq_id: int) -> None: + if seq_id in self.available_slots: + raise KeyError( + f"The seq_id {seq_id} is not currently claimed and cannot be released." + ) + + # Zero out cache tensors for the sequence + for layer_idx in range(len(self.key_cache)): + self.key_cache[layer_idx][seq_id].zero_() + self.value_cache[layer_idx][seq_id].zero_() + + self.available_slots.add(seq_id) + del self.attention_patterns[seq_id] + del self.tokens[seq_id] + + def reset(self) -> None: + super().reset() + self._init_slots() diff --git a/src/max/pipelines/kv_cache/manager.py b/src/max/pipelines/kv_cache/manager.py new file mode 100644 index 0000000000..7f124db394 --- /dev/null +++ b/src/max/pipelines/kv_cache/manager.py @@ -0,0 +1,542 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Abstract base class for KVCacheManager for KV Cache.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections import deque +from dataclasses import dataclass +from typing import Any, List, Sequence, cast, final + +import numpy as np +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import DeviceRef, Graph, TensorType, TensorValue + +from .cache_params import KVCacheParams + + +@dataclass +class _FetchMetadata: + """Metadata about sequences that are inflight. + + Inflight refers to sequences that have executed `fetch` but not `step`. + """ + + prompt: np.ndarray + num_steps: int + + +@dataclass +class KVCacheInputs: + """ + A base class that holds KV cache related (Tensor) inputs. + + It is meant to be subclassed by concrete KV cache input types. + + Example: + >>> @dataclass + ... class RaggedKVCacheInputs(KVCacheInputs): + ... blocks: Tensor + ... cache_lengths: Tensor + ... lookup_table: Tensor + ... max_lengths: Tensor + """ + + def __iter__(self): + """Iterates through each Type in order.""" + fields = deque( + getattr(self, field) for field in self.__dataclass_fields__ + ) + while fields: + value = fields.popleft() + if isinstance(value, Sequence) and all( + isinstance(x, KVCacheInputs) for x in value + ): + # Add sequence elements in original order + fields.extendleft([x for x in reversed(value)]) + continue + elif isinstance(value, KVCacheInputs): + fields.extendleft( + reversed( + [ + getattr(value, field) + for field in value.__dataclass_fields__ + ] + ) + ) + continue + else: + yield cast(Tensor, value) + + def __getitem__(self, index): + return list(self)[index] + + def __setitem__(self, index, value): + list(self)[index] = value + + def __len__(self): + count = 0 + # Iterate over all fields in the dataclass. If we run into a sequence of + # KVCacheInputs, we expand and recursively call `len` on the KVCacheInputs + # elements. + for field in self.__dataclass_fields__: + value = getattr(self, field) + if isinstance(value, Sequence) and all( + isinstance(x, KVCacheInputs) for x in value + ): + count += sum(len(x) for x in value) + else: + count += 1 + return count + + +@dataclass +class PaddedKVCacheInputs(KVCacheInputs): + """ + PaddedKVCacheInputs is a class that holds the inputs for + KV cache when used together with padded tensors. + """ + + k_cache: Tensor + v_cache: Tensor + start_pos: Tensor + null_op: Tensor + + +@dataclass +class RaggedKVCacheInputs(KVCacheInputs): + """ + RaggedKVCacheInputs is a class that holds the inputs for + KV cache when used together with ragged tensors. + """ + + blocks: Tensor + cache_lengths: Tensor + lookup_table: Tensor + max_lengths: Tensor + + +@dataclass +class KVCacheInputsSequence(KVCacheInputs): + """ + KVCacheInputsSequence is a sequence of KVCacheInputs. + It is primarily used in our multistep execution to represent batched + KVCacheInputs. + """ + + kv_cache_inputs: Sequence[KVCacheInputs] + + +@dataclass +class KVCacheInputSymbols: + """ + Base class for input symbols for KV cache managers. + + The derived class is responsible for defining the input symbols for the + specific KV cache manager. + + For example, here's a derived class for a text KV cache manager: + >>> @dataclass + ... class ContinuousBatchingKVCacheInputSymbols(KVCacheInputSymbols): + ... kv_blocks: TensorType + ... cache_lengths: TensorType + ... lookup_table: TensorType + ... max_lengths: TensorType + """ + + def __iter__(self): + """Iterates through each Type in order.""" + fields = deque( + getattr(self, field) for field in self.__dataclass_fields__ + ) + while fields: + value = fields.popleft() + if isinstance(value, KVCacheInputSymbols): + fields.extendleft( + reversed( + [ + getattr(value, field) + for field in value.__dataclass_fields__ + ] + ) + ) + continue + else: + yield value + + def __getitem__(self, index): + return list(self)[index] + + +class KVCacheManager(ABC): + def __init__( + self, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + devices: List[Device], + session: InferenceSession, + is_ragged: bool = False, + ) -> None: + self.params = params + self.max_batch_size = max_batch_size + self.max_seq_len = max_seq_len + self.num_layers = num_layers + self.devices = devices + self.session = session + + # Attributes for managing available slots. + self.available = set(range(self.max_batch_size)) + self.cache_lengths: dict[int, int] = {} + + self.is_ragged = is_ragged + increment_cache_lengths_graph = ( + self._create_increment_cache_lengths_graph() + ) + self.increment_cache_lengths_model = session.load( + increment_cache_lengths_graph + ) + self.fetch_metadata: dict[int, _FetchMetadata] = {} + + @classmethod + @abstractmethod + def estimated_memory_size( + cls, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, + ) -> int: + """Returns the estimated total memory usage of the kv cache.""" + ... + + @classmethod + @abstractmethod + def infer_optimal_batch_size( + cls, + params: KVCacheParams, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, + ) -> int: + """Returns the estimated optimal batch size for the kv cache.""" + ... + + @abstractmethod + def _fetch( + self, + seq_ids_and_prompts: dict[int, np.ndarray], + num_steps: int = 1, + ) -> List[KVCacheInputs]: + """Used by `fetch` and should be implemented by child classes.""" + ... + + @final + def fetch( + self, + seq_ids_and_prompts: dict[int, np.ndarray], + num_steps: int = 1, + ) -> List[KVCacheInputs]: + """Returns blocks and other inputs to kv cache kernel for given + sequence ids and prompts.""" + # Call into `_fetch` method implemented by child classes. + # This may trim the prompts in place so the fetch metadata is updated + # afterwards. + res = self._fetch(seq_ids_and_prompts, num_steps) + + # Update the fetch metadata for the given sequence ids and prompts. + for seq_id, prompt in seq_ids_and_prompts.items(): + assert seq_id not in self.fetch_metadata + self.fetch_metadata[seq_id] = _FetchMetadata( + prompt=prompt, + num_steps=num_steps, + ) + + return res + + @abstractmethod + def input_symbols( + self, + ) -> Sequence[KVCacheInputSymbols]: + """Returns the input symbols for the kv cache manager.""" + ... + + def claim(self, n: int) -> List[int]: + """Claims `n` blocks of memory in the cache for incoming requests. + + This returns a list of sequence ids, which identify a sequence's + location within the cache. This sequence id can then be passed + in the fetch function to return the ContinuousBatchingKVCacheCollection + for those sequences. + """ + # TODO we should remove this interface and just use external_claim. + seq_ids = [] + + for _ in range(n): + id = self.available.pop() + seq_ids.append(id) + self.cache_lengths[id] = 0 + + return seq_ids + + def external_claim(self, seq_ids: List[int]) -> None: + """Variant of the above where sequence ids are reserved externally.""" + for seq_id in seq_ids: + self.available.remove(seq_id) + self.cache_lengths[seq_id] = 0 + + def _step( + self, + seq_ids_and_new_tokens: dict[int, np.ndarray], + ) -> None: + """Used by `step` and can optionally be overridden by child classes.""" + ... + + def step(self, seq_ids_and_new_tokens: dict[int, np.ndarray]) -> None: + """Update the `cache_lengths` objects to note that a new + kv projection step has occurred, and that the underlying memory + has been written to. This `cache_lengths` value is then used + downstream in `fetch` to track what section of memory should + be used in the kernels. + """ + # Call into `_step` method possibly overridden by child classes. + self._step(seq_ids_and_new_tokens) + + # Update the cache lengths and delete the fetch metadata for the given + # sequence ids and prompts. + for seq_id, new_tokens in seq_ids_and_new_tokens.items(): + if seq_id not in self.cache_lengths: + raise ValueError(f"seq_id: {seq_id} not in cache.") + + assert seq_id in self.fetch_metadata + metadata = self.fetch_metadata[seq_id] + del self.fetch_metadata[seq_id] + + assert metadata.num_steps == len(new_tokens) + self.cache_lengths[seq_id] += ( + len(metadata.prompt) + metadata.num_steps - 1 + ) + + def release(self, seq_id: int) -> None: + """Release `seq_id` provided, marking this sequence as complete. + This returns the seq_id back to the available pool of cache memory, + allowing it to be reused when a new sequence is claimed. + """ + + if seq_id not in self.cache_lengths: + raise ValueError(f"seq_id: {id} not in cache.") + + self.available.add(seq_id) + del self.cache_lengths[seq_id] + + def contains(self, seq_id: int) -> bool: + return seq_id not in self.slots_remaining + + @property + def slots_remaining(self) -> set[int]: + """The outstanding cache slots available.""" + return self.available + + @property + def max_sequence_length(self) -> int: + """The maximum sequence length in current cache.""" + return max(self.cache_lengths.values()) + + def num_kv_inputs(self) -> int: + """Returns the default number of KV cache inputs for KV managers. + + Subclasses with a different number of KV cache inputs should override + this method and `increment_cache_lengths`. + """ + return 4 + + def increment_cache_lengths( + self, + kv_cache_inputs: List[RaggedKVCacheInputs] | List[PaddedKVCacheInputs], + prev_model_inputs: Any, + ) -> List[RaggedKVCacheInputs] | List[PaddedKVCacheInputs]: + """ + Prepare the inputs for a multistep execution, generally by incrementing + the cache lengths. This should not require a device synchronization, + as this would defeat the purpose of multistep execution. + + This should also not update the cache lengths in our manager, this batch is + still considered in-progress. + """ + if self.is_ragged: + return self._increment_cache_lengths_ragged( + kv_cache_inputs=cast( + list[RaggedKVCacheInputs], kv_cache_inputs + ), + prev_model_inputs=prev_model_inputs, + ) + + return self._increment_cache_lengths_padded( + kv_cache_inputs=cast(list[PaddedKVCacheInputs], kv_cache_inputs), + prev_model_inputs=prev_model_inputs, + ) + + def _increment_cache_lengths_ragged( + self, + kv_cache_inputs: List[RaggedKVCacheInputs], + prev_model_inputs: Any, + ) -> List[RaggedKVCacheInputs]: + """Prepares cache inputs for the next token in multistep execution. + + Updates the cache lengths for the next inference step without requiring device + synchronization or memory copies. This is crucial for maintaining performance + during multi-token generation. + + Args: + kv_cache_inputs: Current cache state tuples (blocks, lengths, lookup, max_lengths) + prev_model_inputs: Previous model inputs including row offsets + + Returns: + Updated cache input tuples with incremented lengths. + """ + blocks = [kv_cache_inputs[i].blocks for i in range(len(self.devices))] + cache_lengths = [ + kv_cache_inputs[i].cache_lengths for i in range(len(self.devices)) + ] + lookup_table = [ + kv_cache_inputs[i].lookup_table for i in range(len(self.devices)) + ] + + # max_lengths is host allocated and the same across all devices. + max_lengths = kv_cache_inputs[0].max_lengths + + # Update the cache_lengths of our batch by the previous sequence length. + updated_cache_lengths = self.increment_cache_lengths_model.execute( + prev_model_inputs.input_row_offsets, *cache_lengths + ) + + # Advance to the next step of the max_lengths tensor. + updated_max_lengths = max_lengths[1:, :] + + # Return our updated batch. + for i in range(len(self.devices)): + updated_cache_length = updated_cache_lengths[i] + assert isinstance(updated_cache_length, Tensor) + kv_cache_inputs[i] = RaggedKVCacheInputs( + blocks=blocks[i], + cache_lengths=updated_cache_length, + lookup_table=lookup_table[i], + max_lengths=updated_max_lengths, + ) + return kv_cache_inputs + + def _increment_cache_lengths_padded( + self, + kv_cache_inputs: List[PaddedKVCacheInputs], + prev_model_inputs: Any, + ) -> List[PaddedKVCacheInputs]: + """ + Prepare the inputs for a multistep execution, generally by incrementing + the cache lengths. This should not require a device synchronization, + as this would defeat the purpose of multistep execution. + + This should also not update the cache lengths in our manager, this batch is + still considered in-progress. + """ + assert len(kv_cache_inputs) == 1 + curr_kv_cache_inputs = kv_cache_inputs[0] + + new_start_pos = self.increment_cache_lengths_model( + curr_kv_cache_inputs.start_pos, prev_model_inputs.tokens + )[0] + assert isinstance(new_start_pos, Tensor) + return [ + PaddedKVCacheInputs( + k_cache=curr_kv_cache_inputs.k_cache, + v_cache=curr_kv_cache_inputs.v_cache, + start_pos=new_start_pos, + null_op=new_start_pos, + ) + ] + + def _create_increment_cache_lengths_graph(self) -> Graph: + """Constructs a graph to increment the cache_lengths argument during multi-step inference. + + It's imperative that this operation occurs entirely on GPU, + otherwise we'll synchronize across devices and incur a latency penalty. + """ + if self.is_ragged: + return self._create_ragged_increment_cache_lengths_graph() + + return self._create_padded_increment_cache_lengths_graph() + + def _create_padded_increment_cache_lengths_graph(self) -> Graph: + start_pos_type = TensorType(DType.int64, shape=[]) + tokens_type = TensorType(DType.int64, shape=["batch_size", "seq_len"]) + with Graph( + "update_start_pos", input_types=[start_pos_type, tokens_type] + ) as graph: + start_pos, tokens = graph.inputs + assert isinstance(start_pos, TensorValue) + assert isinstance(tokens, TensorValue) + graph.output(start_pos + tokens.shape[1]) + + return graph + + def _create_ragged_increment_cache_lengths_graph(self) -> Graph: + input_symbols = self.input_symbols() + cache_lengths_types = [ + input_symbols[i][1] for i in range(len(self.devices)) + ] + + input_row_offsets_type = TensorType( + DType.uint32, + shape=["input_row_offsets_len"], + device=DeviceRef(self.devices[0].label, self.devices[0].id), + ) + + with Graph( + "update_cache_lengths", + input_types=[input_row_offsets_type, *cache_lengths_types], + ) as graph: + inp_row_offset, *cache_lengths = graph.inputs + assert isinstance(inp_row_offset, TensorValue) + # broadcast the inp_row_offset to all devices (naive) + # get rid of this if statement after #51465 merges + if len(self.devices) > 1: + input_row_offsets = [ + inp_row_offset.to(DeviceRef(d.label, d.id)) + for d in self.devices + ] + else: + input_row_offsets = [inp_row_offset] + outputs = [] + for i in range(len(self.devices)): + cache_length = cache_lengths[i] + assert isinstance(cache_length, TensorValue) + right_slice = input_row_offsets[i][1:].rebind( + cache_length.shape + ) + left_slice = input_row_offsets[i][ + : input_row_offsets[i].shape[0] - 1 + ].rebind(cache_length.shape) + increment_amount = right_slice - left_slice + outputs.append(cache_length + increment_amount) + graph.output(*outputs) + + return graph diff --git a/src/max/pipelines/kv_cache/naive_cache.py b/src/max/pipelines/kv_cache/naive_cache.py new file mode 100644 index 0000000000..40ad2d9b5e --- /dev/null +++ b/src/max/pipelines/kv_cache/naive_cache.py @@ -0,0 +1,216 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Naive KV cache for the Transformer.""" + +from dataclasses import dataclass +from functools import reduce +from operator import mul +from typing import Any, List, cast + +import numpy as np +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import BufferType, TensorType + +from .cache_params import KVCacheParams +from .manager import ( + KVCacheInputs, + KVCacheInputSymbols, + KVCacheManager, + PaddedKVCacheInputs, +) + + +@dataclass +class NaiveKVCacheInputSymbols(KVCacheInputSymbols): + k_cache: BufferType + v_cache: BufferType + start_pos: TensorType + null_op: TensorType + + +class NaiveKVCacheManager(KVCacheManager): + def __init__( + self, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + devices: List[Device], + session: InferenceSession, + ) -> None: + assert len(devices) == 1, "Naive caching only supports a single device." + assert params.n_devices == 1, ( + "Naive caching only supports a single device." + ) + if params.enable_prefix_caching: + raise ValueError("Prefix caching is not supported for naive cache.") + super().__init__( + params=params, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + num_layers=num_layers, + devices=devices, + session=session, + is_ragged=False, + ) + + self.keys = Tensor.zeros( + shape=self.cache_shape, + dtype=self.params.dtype, + device=self.devices[0], + ) + + self.values = Tensor.zeros( + shape=self.cache_shape, + dtype=self.params.dtype, + device=self.devices[0], + ) + + @classmethod + def estimated_memory_size( + cls, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, + ) -> int: + return ( + reduce( + mul, + cls._cache_shape( + params, max_batch_size, max_seq_len, num_layers + ), + ) + * params.dtype.size_in_bytes + * 2 + ) + + @classmethod + def infer_optimal_batch_size( + cls, + params: KVCacheParams, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: List[Device], + **kwargs: Any, + ) -> int: + cache_size_per_sequence = ( + reduce( + mul, + cls._cache_shape(params, 1, max_seq_len, num_layers), + ) + * params.dtype.size_in_bytes + * 2 + ) + return int(available_cache_memory // cache_size_per_sequence) + + @property + def cache_shape(self) -> list[int]: + return self._cache_shape( + self.params, + self.max_batch_size, + self.max_seq_len, + self.num_layers, + ) + + @staticmethod + def _cache_shape( + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + ): + return [ + max_seq_len, + num_layers, + max_batch_size, + params.n_kv_heads, + params.head_dim, + ] + + def _fetch( + self, + seq_ids_and_prompts: dict[int, np.ndarray], + num_steps: int = 1, + ) -> List[KVCacheInputs]: + existing_keys = list(self.cache_lengths.keys()) + for i, (seq_id, prompt) in enumerate(seq_ids_and_prompts.items()): + if existing_keys[i] != seq_id: + msg = ( + "seq_ids passed, are different than current inflight" + " batch.Naive Caching currently does not support mutating" + " inflight batches." + ) + raise ValueError(msg) + + total_length = ( + self.cache_lengths[seq_id] + len(prompt) + num_steps - 1 + ) + assert total_length <= self.max_seq_len, ( + f"seq_id: {seq_id} would overrun the max cache length of {self.max_seq_len} " + f"with {len(prompt)} new tokens and {num_steps} steps. Existing length: {self.cache_lengths[seq_id]}" + ) + padded_kv_cache_inputs = [ + PaddedKVCacheInputs( + k_cache=self.keys, + v_cache=self.values, + start_pos=Tensor.scalar( + self.max_sequence_length, DType.int64, self.devices[0] + ), + # TODO: MSDK-1201 - This next variable is not used upstream. + # It is included here, as a placeholder, until we can dynamically + # return a number of tensors from both `fetch` and `input_symbols`. + null_op=Tensor.scalar( + self.max_sequence_length, DType.int64, self.devices[0] + ), + ) + ] + return cast(List[KVCacheInputs], padded_kv_cache_inputs) + + def input_symbols( + self, + ) -> List[NaiveKVCacheInputSymbols]: + return [ + NaiveKVCacheInputSymbols( + k_cache=BufferType( + self.params.dtype, + shape=[ + "max_seq_len", + self.num_layers, + "max_batch_size", + self.params.n_kv_heads, + self.params.head_dim, + ], + ), + v_cache=BufferType( + self.params.dtype, + shape=[ + "max_seq_len", + self.num_layers, + "max_batch_size", + self.params.n_kv_heads, + self.params.head_dim, + ], + ), + start_pos=TensorType(DType.int64, shape=[]), + # null_op - this isnt used for the naive cache + null_op=TensorType(DType.int64, shape=[]), + ) + ] diff --git a/src/max/pipelines/kv_cache/paged_cache.py b/src/max/pipelines/kv_cache/paged_cache.py new file mode 100644 index 0000000000..7c814828be --- /dev/null +++ b/src/max/pipelines/kv_cache/paged_cache.py @@ -0,0 +1,746 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""PagedAttention-enabled KV cache for the Transformer leveraging the mo.opaque pattern.""" + +from __future__ import annotations + +import logging +import math +from dataclasses import dataclass +from functools import reduce +from operator import mul +from typing import Any, Dict, List, Optional, cast + +import numpy as np +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import ( + DeviceRef, + TensorType, + TensorValue, + _OpaqueType, + _OpaqueValue, + ops, +) + +from ._utils import build_max_lengths_tensor +from .cache_params import KVCacheParams +from .manager import ( + KVCacheInputs, + KVCacheInputSymbols, + KVCacheManager, + RaggedKVCacheInputs, +) +from .paged_cache_metadata import PagedCacheMetadata, ceildiv +from .prefix_cache import PrefixCache + +logger = logging.getLogger("max.pipelines") + +PERCENTAGE_BLOCKS_TO_EVICT = 0.05 + + +@dataclass +class PagedCacheInputSymbols(KVCacheInputSymbols): + kv_blocks: TensorType + cache_lengths: TensorType + lookup_table: TensorType + max_lengths: TensorType + + +class PagedKVCacheType(_OpaqueType): + """PagedAttention Mojo KV Cache graph type.""" + + def __init__(self) -> None: + """Creates an opaque type containing a paged KV Cache.""" + super().__init__("PagedKVCache") + + +class PagedKVCacheCollectionType(_OpaqueType): + """The graph type for a "view" of the cache for the given sequences in the + batch. + + This object does not own the underlying buffers in k_cache and v_cache, + it's borrowing them from the BlockWrappers in our ContinuousKVCacheManager. + It does own the Pointer[NDBuffer[type, 3]] and valid_lengths buffer + """ + + def __init__(self) -> None: + """Creates an opaque type containing a paged KV cache collection.""" + super().__init__("PagedKVCacheCollection") + + +class PagedKVCache(_OpaqueValue): + """PagedAttention Mojo KV cache graph value.""" + + +class PagedKVCacheCollection(_OpaqueValue): + """The graph value for a view of the KV cache.""" + + +class FetchPagedKVCacheCollection: + def __init__(self, kv_params: KVCacheParams) -> None: + self.kv_params = kv_params + + def __call__( + self, + blocks: TensorValue, # NDBuffer[type, 6, Self.blocks_shape] + cache_lengths: TensorValue, # NDBuffer[DType.uint32, 1], + lookup_table: TensorValue, # NDBuffer[DType.uint32, 2], + is_cache_empty: TensorValue, + ) -> PagedKVCacheCollection: + """Constructs a PagedKVCacheCollection for use downstream.""" + + # Explicit validation. + if blocks.dtype != self.kv_params.dtype: + msg = ( + f"expected blocks to be dtype: {self.kv_params.dtype}, got" + f" {blocks.dtype}" + ) + raise ValueError(msg) + + if blocks.rank != 6: + msg = f"expected blocks to be of rank 6, got {blocks.rank}" + raise ValueError(msg) + + # For all tensors other than the blocks tensor, the length should be equivalent + # to batch size, which is unknown within the graph at this stage. + if cache_lengths.dtype != DType.uint32: + msg = f"expected cache lengths to be dtype: uint32, got {cache_lengths.dtype}" + raise ValueError(msg) + + if cache_lengths.rank != 1: + msg = f"expected cache lengths to be of rank 1, got {cache_lengths.rank}" + raise ValueError(msg) + + if lookup_table.dtype != DType.uint32: + msg = f"expected lookup_table to be dtype: uint32, got {lookup_table.dtype}" + raise ValueError(msg) + + if lookup_table.rank != 2: + msg = f"expected lookup_table to be of rank 2, got {lookup_table.rank}" + raise ValueError(msg) + + return PagedKVCacheCollection( + ops.custom( + "mo.kv_collection_ctor.paged", + values=[blocks, cache_lengths, lookup_table, is_cache_empty], + out_types=[PagedKVCacheCollectionType()], + parameters={ + "num_heads": self.kv_params.n_kv_heads_per_device, + "head_dim": self.kv_params.head_dim, + "page_size": int(blocks.shape[3]), + }, + )[0].opaque + ) + + +class PagedKVCacheManager(KVCacheManager): + def __init__( + self, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + devices: list[Device], + session: InferenceSession, + cache_memory: int, + page_size: int = 128, + enable_runtime_checks: bool = False, + ): + """ + Args: + params: The KVCacheParams for the given pipeline. + max_batch_size: The maximum number of active + requests that the manager should support. + max_seq_len: The maximum sequence length we will generate. + num_layers: The number of layers in the model. + devices: The devices on which the manager will allocate memory. + session: The inference session to load ops from. + cache_memory: The total amount of memory available for caching. + This is aggregated across all devices. + page_size: The number of tokens that will be stored in a single page. + enable_runtime_checks: Whether to enable runtime correctness checks. + """ + # The number of tokens in a single page. + self.page_size = page_size + + # The number of bytes that a single page will occupy. + single_page_size_bytes = ( + 2 + * num_layers + * params.n_kv_heads_per_device + * params.head_dim + * page_size + * params.dtype.size_in_bytes + ) + + # Normalize cache_memory across all devices. + cache_memory_per_device = cache_memory // len(devices) + + # The total number of pages we'll have per-device. + self.total_num_pages = int( + cache_memory_per_device // single_page_size_bytes + ) + + if self.total_num_pages == 0: + raise RuntimeError( + f"Insufficient cache memory to allocate even a single page.\n" + f"One page requires {single_page_size_bytes} bytes but only {cache_memory_per_device} bytes are available." + ) + + if max_batch_size > self.total_num_pages: + logger.warning( + f"Insufficient cache memory to support a batch containing {max_batch_size} requests with one token per request. " + f"Need to allocate at least {max_batch_size} blocks, but only have enough memory for {self.total_num_pages} blocks. " + f"One page requires {single_page_size_bytes} bytes but only {cache_memory_per_device} bytes are available." + ) + + blocks_needed_for_max_seq_len = ceildiv(max_seq_len, page_size) + if blocks_needed_for_max_seq_len > self.total_num_pages: + logger.warning( + f"Insufficient cache memory to support a batch containing one request at the max sequence length of {max_seq_len} tokens. " + f"Need to allocate at least {blocks_needed_for_max_seq_len} blocks, but only have enough memory for {self.total_num_pages} blocks. " + f"One page requires {single_page_size_bytes} bytes but only {cache_memory_per_device} bytes are available." + ) + + # call our base class constructor + super().__init__( + params=params, + max_batch_size=max_batch_size, + max_seq_len=max_seq_len, + num_layers=num_layers, + devices=devices, + session=session, + is_ragged=True, + ) + + # Initialize the set of available blocks. + self.available_blocks = set(range(self.total_num_pages)) + + # Initialize the blocks for each device. + self.blocks: list[Tensor] = [] + for device in self.devices: + self.blocks.append( + Tensor.zeros( + self.block_shape(), # type: ignore + self.params.dtype, + device=device, + ) + ) + + self.active_requests: Dict[int, PagedCacheMetadata] = {} + + self.prefix_cache: Optional[PrefixCache] = None + if params.enable_prefix_caching: + self.prefix_cache = PrefixCache( + session=session, + page_size=self.page_size, + block_shape=self.block_shape(is_parameterized=True), + dtype=self.params.dtype, + devices=devices, + tensors=self.blocks, + ) + + # Whether to enable runtime correctness checks. These correctness checks + # are expensive and should only be used in tests. + self.enable_runtime_checks = enable_runtime_checks + + def _runtime_check(self) -> None: + if not self.enable_runtime_checks: + return + assert self._count_all_pages() == self.total_num_pages + if self.prefix_cache is None: + return + for seq_id, data in self.active_requests.items(): + self.prefix_cache.validate_req_state_valid( + seq_id, + data.committed_tokens, + data.committed_blocks, + ) + + @property + def cache_hit_rate(self) -> float: + if self.prefix_cache is None: + return 0.0 + return self.prefix_cache.cache_hit_rate + + def alloc_block(self) -> int: + if len(self.available_blocks) == 0 and self.prefix_cache is not None: + blocks_to_evict = self.total_num_pages * PERCENTAGE_BLOCKS_TO_EVICT + blocks_to_evict = int(max(1, blocks_to_evict)) + evicted = self.prefix_cache.evict_blocks(blocks_to_evict) + for block in evicted: + self.available_blocks.add(block) + + if len(self.available_blocks) == 0: + raise RuntimeError( + f"All {self.total_num_pages} KVCache pages have been exhausted! " + "You must restart your process and set a smaller batch size or max seq len." + ) + + block = self.available_blocks.pop() + return block + + def release_block(self, block: int) -> None: + """We can release a block if prefix caching is disabled or if it is not committed. + + If it is committed, it may be in the radix tree and in use by other sequences. + This means it can't be safely released without further checks. + """ + if self.prefix_cache is None: + self.available_blocks.add(block) + return + + # We can only add the block to the available set if it is not committed + # to the prefix cache. + if block not in self.prefix_cache: + self.available_blocks.add(block) + + @classmethod + def _block_size_per_token( + cls, params: KVCacheParams, num_layers: int + ) -> int: + return ( + reduce(mul, cls._block_shape(params, 1, 1, num_layers), 1) + * params.dtype.size_in_bytes + ) + + @classmethod + def estimated_memory_size( + cls, + params: KVCacheParams, + max_batch_size: int, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: list[Device], + **kwargs: Any, + ) -> int: + # Determine how much size is necessary to store the full cache based on max_batch_size and max_seq_len. + # If that's less than available_cache_memory, return that. + # Otherwise, return available_cache_memory. + # This is to prevent over-allocation on devices with a large amount of free memory (e.g. CPUs). + assert params.page_size is not None + block_size_per_token = cls._block_size_per_token( + params, num_layers + ) * len(devices) + + # round up our max_seq_len to the nearest page_size + max_seq_len_round_up = ( + math.ceil(max_seq_len / params.page_size) * params.page_size + ) + size_to_support_full_cache = ( + block_size_per_token * max_batch_size * max_seq_len_round_up + ) + + # return the minimum of the two + return min(available_cache_memory, size_to_support_full_cache) + + @classmethod + def infer_optimal_batch_size( + cls, + params: KVCacheParams, + max_seq_len: int, + num_layers: int, + available_cache_memory: int, + devices: list[Device], + **kwargs: Any, + ) -> int: + # We just hard-code a default of 512 for paged attention. + # The worst case scenario if this is too high is that we'll evict + # requests at an elevated rate. We print warnings in that case so users + # are aware of what needs to be tweaked/changed. + return 512 + + def block_shape( + self, + is_parameterized: bool = False, + ) -> list[int | str]: + return self._block_shape( + self.params, + self.total_num_pages, + self.page_size, + self.num_layers, + is_parameterized, + ) + + @classmethod + def _block_shape( + cls, + params: KVCacheParams, + total_num_pages: int, + page_size: int, + num_layers: int, + is_parameterized: bool = False, + ) -> list[int | str]: + # split k and v caches across a single dim + # 0 = key + # 1 = value + kv_dim = 2 + return [ + num_layers, + kv_dim, + "total_num_pages" if is_parameterized else total_num_pages, + page_size, + params.n_kv_heads_per_device, + params.head_dim, + ] + + def get_num_free_blocks(self) -> int: + if self.prefix_cache is None: + return len(self.available_blocks) + return len(self.available_blocks) + len(self.prefix_cache.stale_blocks) + + def get_num_used_blocks(self) -> int: + return self.total_num_pages - self.get_num_free_blocks() + + def can_fetch( + self, seq_ids_and_prompts: dict[int, np.ndarray], num_steps: int = 1 + ) -> bool: + """Checks if there are sufficient KV pages to run `fetch` on given batch. + + It is OK if some seq_id are not in the cache. We assume the cache lengths + are zero in those cases. + """ + + total_blocks_to_allocate = 0 + all_cache_hit_blocks: set[int] = set() + + for seq_id, prompt in seq_ids_and_prompts.items(): + data = self.active_requests.get( + seq_id, PagedCacheMetadata(self.page_size, self.max_seq_len) + ) + + # Extend the kv cache for given request with any cached prefixes. + cached_blocks: list[int] = [] + if self.prefix_cache is not None: + cached_blocks = self.prefix_cache.get_cached_blocks( + seq_id, prompt + ) + + # Compute the total sequence length and the number of pages required to store it. + total_sequence_length = ( + data.cached_idx + len(prompt) + num_steps - 1 + ) + num_pages_required = ceildiv(total_sequence_length, self.page_size) + + # Compute the number of *new* pages we need to allocate. + blocks_to_allocate = ( + num_pages_required - len(data.blocks) - len(cached_blocks) + ) + + total_blocks_to_allocate += blocks_to_allocate + all_cache_hit_blocks.update(cached_blocks) + + num_evictable_blocks = 0 + if self.prefix_cache is not None: + # the blocks in the prefix cache that will be used by sequences in + # this batch are no longer eligible for eviction / allocation. + num_evictable_blocks = len( + self.prefix_cache.stale_blocks - all_cache_hit_blocks + ) + + num_free_blocks = len(self.available_blocks) + num_evictable_blocks + + return total_blocks_to_allocate <= num_free_blocks + + def get_num_cached_tokens(self, prompt: np.ndarray) -> int: + """Returns the number of tokens in the CE prompt that are found in the + prefix cache. + """ + if self.prefix_cache is None: + return 0 + return self.prefix_cache.get_num_cached_tokens(prompt) + + def _fetch( + self, seq_ids_and_prompts: dict[int, np.ndarray], num_steps: int = 1 + ) -> List[KVCacheInputs]: + """This method identifies available blocks to service the given requests and marks them as inflight. + They're assigned to the request as "in-flight" until step is called. + + Generally the prompt length is n for prefill, and 1 for decode step. Additionally, there is not a + kv entry associated with each token in the prompt. + + When prefix caching is enabled, and KV entries can be retrieved for some tokens in the prompt, the + input `seq_ids_and_prompts` will be modified. Each prompt will be shortened to only include the tokens + for which we do not have a cached KV entry. Note that we will never return a empty prompt. + """ + self._runtime_check() + + max_seq_len_in_batch = -1 + # before we start making any changes, validate that we won't over-write the cache + for batch_idx, (seq_id, prompt) in enumerate( + seq_ids_and_prompts.items() + ): + # Validate there aren't other inflight requests for this sequence. + assert seq_id not in self.fetch_metadata + + # Add prompt and inflight tokens to the token array + data = self.active_requests[seq_id] + data.fetch(prompt, num_steps) + + # Compute the total sequence length + if data.seq_len > max_seq_len_in_batch: + max_seq_len_in_batch = data.seq_len + + assert data.seq_len <= self.max_seq_len, ( + f"seq_id: {seq_id} would overrun the max cache length of {self.max_seq_len} " + f"with {len(prompt)} new tokens. Existing length: {data.cached_idx}" + ) + + max_num_pages = ceildiv(max_seq_len_in_batch, self.page_size) + + # Allocate the buffers containing metadata about the batch. + # [0, total_num_pages) are the valid block ids and total_num_pages + # denotes an unassigned block. + batch_size = len(seq_ids_and_prompts) + lut_table_np = np.full( + (batch_size, max_num_pages), self.total_num_pages, dtype=np.uint32 + ) + cache_lengths_np = np.zeros((batch_size,), dtype=np.uint32) + + # Iterate over requests and query prefix cache + all_cache_hit_blocks: set[int] = set() + for batch_idx, (seq_id, prompt) in enumerate( + seq_ids_and_prompts.items() + ): + # Ensure we've called claim for this sequence id. + if seq_id not in self.active_requests: + raise ValueError(f"seq_id: {seq_id} not in active requests.") + + if self.prefix_cache is not None: + data = self.active_requests[seq_id] + # bump the committed_idx, and possibly the cached_idx + prefix_blocks = self.prefix_cache.fetch( + seq_id, + data, + free_block_fn=self.release_block, + alloc_block_fn=self.alloc_block, + ) + all_cache_hit_blocks.update(prefix_blocks) + # Possibly trim the input prompt. + seq_ids_and_prompts[seq_id] = data.prompt_tokens + + # Determine the number of pages required for each sequence. + max_seq_length = 0 + max_cache_length = 0 + total_sequence_length = 0 + total_blocks_to_allocate = 0 + blocks_to_allocate_by_seq = {} + for batch_idx, (seq_id, prompt) in enumerate( + seq_ids_and_prompts.items() + ): + data = self.active_requests[seq_id] + + # Get the existing cache length for this sequence. + cache_length = data.cached_idx + cache_lengths_np[batch_idx] = cache_length + + # Update the maximum lengths seen so far. + max_seq_length = max(max_seq_length, len(prompt)) + max_cache_length = max(max_cache_length, cache_length) + + # Compute the total sequence length and the number of pages required to store it. + total_sequence_length += data.seq_len + num_pages_required = ceildiv(data.seq_len, self.page_size) + + # Compute the number of *new* pages we need to allocate. + num_new_pages = num_pages_required - len(data.blocks) + assert num_new_pages >= 0 + blocks_to_allocate_by_seq[seq_id] = num_new_pages + total_blocks_to_allocate += num_new_pages + + # Check if we have enough free blocks to service all requests. + num_evictable_blocks = 0 + if self.prefix_cache is not None: + # the blocks in the prefix cache that will be used by sequences in + # this batch are no longer eligible for eviction / allocation. + num_evictable_blocks = len( + self.prefix_cache.stale_blocks - all_cache_hit_blocks + ) + num_free_blocks = len(self.available_blocks) + num_evictable_blocks + if total_blocks_to_allocate > num_free_blocks: + raise RuntimeError( + f"Not enough free blocks to service all {len(seq_ids_and_prompts)} requests.\n" + f"Need an additional {total_blocks_to_allocate} blocks to store KV projections for all {total_sequence_length} tokens.\n" + f"But only {num_free_blocks} out of {self.total_num_pages} cache blocks are available to be allocated.\n" + f"You must restart your process and set a smaller batch size or max sequence length.\n" + ) + + # Allocate additional pages for each request in the batch + for batch_idx, (seq_id, num_new_pages) in enumerate( + blocks_to_allocate_by_seq.items() + ): + data = self.active_requests[seq_id] + + # Assign some new pages to this request. + for _ in range(num_new_pages): + next_block = self.alloc_block() + data.blocks.append(next_block) + + # Populate the lookup table with the new pages. + for i, block_idx in enumerate(data.blocks): + lut_table_np[batch_idx, i] = block_idx + + # Build a tensor of maximum lengths. Each step slices the first row to + # advance to the values for the next row. + max_lengths_host = build_max_lengths_tensor( + num_steps, max_seq_length, max_cache_length + ) + + lut_table_host = Tensor.from_numpy(lut_table_np) + cache_lengths_host = Tensor.from_numpy(cache_lengths_np) + + ret_list = [] + for i, device in enumerate(self.devices): + ret_list.append( + RaggedKVCacheInputs( + blocks=self.blocks[i], + cache_lengths=cache_lengths_host.to(device=device), + lookup_table=lut_table_host.to(device=device), + max_lengths=max_lengths_host, + ) + ) + + self._runtime_check() + + return cast(List[KVCacheInputs], ret_list) + + def input_symbols( + self, + ) -> list[PagedCacheInputSymbols]: + return [ + PagedCacheInputSymbols( + kv_blocks=TensorType( + self.params.dtype, + shape=self.block_shape(is_parameterized=True), + device=DeviceRef(self.devices[i].label, self.devices[i].id), + ), + cache_lengths=TensorType( + DType.uint32, + shape=["batch_size"], + device=DeviceRef(self.devices[i].label, self.devices[i].id), + ), + lookup_table=TensorType( + DType.uint32, + shape=["batch_size", "max_num_pages"], + device=DeviceRef(self.devices[i].label, self.devices[i].id), + ), + max_lengths=TensorType( + DType.uint32, shape=["steps_remaining", 2] + ), + ) + for i in range(len(self.devices)) + ] + + def claim(self, n: int) -> list[int]: + """Claims `n` blocks of memory in the cache for incoming requests. + + This returns a list of sequence ids, which identify a sequence's + location within the cache. This sequence id can then be passed + in the fetch function to return the ContinuousBatchingKVCacheCollection + for those sequences. + """ + seq_ids = super().claim(n) + for seq_id in seq_ids: + self.active_requests[seq_id] = PagedCacheMetadata( + self.page_size, self.max_seq_len + ) + if self.prefix_cache is not None: + for seq_id in seq_ids: + self.prefix_cache.external_claim(seq_id) + return seq_ids + + def external_claim(self, seq_ids: list[int]) -> None: + """Variant of the above where sequence ids are reserved externally.""" + super().external_claim(seq_ids) + for seq_id in seq_ids: + self.active_requests[seq_id] = PagedCacheMetadata( + self.page_size, self.max_seq_len + ) + if self.prefix_cache is not None: + for seq_id in seq_ids: + self.prefix_cache.external_claim(seq_id) + + def _count_all_pages(self) -> int: + available_blocks = self.available_blocks + prefix_cache_blocks = set() + if self.prefix_cache is not None: + prefix_cache_blocks = self.prefix_cache.blocks + uncommitted_blocks = set() + for seq_id in self.active_requests: + uncommitted_blocks.update( + self.active_requests[seq_id].uncommitted_blocks + ) + return len(available_blocks | prefix_cache_blocks | uncommitted_blocks) + + def purge_prefix_cache(self) -> None: + if self.prefix_cache is None: + return + evicted = self.prefix_cache.evict_blocks() + for block in evicted: + self.available_blocks.add(block) + + def release(self, seq_id: int) -> None: + """Release `seq_id` provided, marking this sequence as complete. + This returns the seq_id back to the available pool of cache memory, + allowing it to be reused when a new sequence is claimed. + """ + super().release(seq_id) + data = self.active_requests[seq_id] + + if self.prefix_cache is not None: + self.prefix_cache.release(seq_id) + + for block in data.blocks: + self.release_block(block) + del self.active_requests[seq_id] + + def _step( + self, + seq_ids_and_new_tokens: dict[int, np.ndarray], + ) -> None: + """Update the `cache_lengths` objects to not that a new + kv projection step has occurred, and that the underlying memory + has been written to. This `cache_lengths` value is then used + downstream in `fetch` to track what section of memory should + be used in the kernels. + """ + self._runtime_check() + + for seq_id, new_tokens in seq_ids_and_new_tokens.items(): + if seq_id not in self.active_requests: + raise ValueError(f"seq_id: {seq_id} not in active requests.") + + # Write the new tokens into the token array and bump the cached_idx + data = self.active_requests[seq_id] + data.step(new_tokens) + + if self.prefix_cache is not None: + # Bump the committed_idx + self.prefix_cache.step( + seq_id, + data, + free_block_fn=self.release_block, + ) + + expected_num_pages = ceildiv(data.seq_len, self.page_size) + actual_num_pages = len(data.blocks) + if expected_num_pages != actual_num_pages: + raise ValueError( + f"Mismatch between expected and actual number of pages for seq_id: {seq_id}. Expected: {expected_num_pages}, Actual: {actual_num_pages}" + ) + + self._runtime_check() diff --git a/src/max/pipelines/kv_cache/paged_cache_metadata.py b/src/max/pipelines/kv_cache/paged_cache_metadata.py new file mode 100644 index 0000000000..abeb8f7a40 --- /dev/null +++ b/src/max/pipelines/kv_cache/paged_cache_metadata.py @@ -0,0 +1,171 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""PagedAttention-specific metadata for a single sequence.""" + +from __future__ import annotations + +import numpy as np + + +def ceildiv(n: int, d: int) -> int: + """Compute ceil(n/d) using strictly integer arithmetic.""" + q, r = divmod(n, d) + return q + bool(r) + + +class PagedCacheMetadata: + """Metadata for a single sequence in the paged KV cache. + + Token array layout: + + - Committed tokens have been inserted into the prefix cache. This must be a + multiple of the page size. + - Cached tokens are tokens that have a backing KV projection in the cache. + Uncached tokens will be written to the cache when the model is run. + - Committable tokens are tokens that are not yet committed but have a known + value (i.e. not inflight). We can query the prefix cache for such tokens. + - Inflight tokens are slots allocated for tokens that have not been generated + yet. Such tokens have a filler value of 0. After `fetch`, there should be + `num_steps - 1` inflight tokens. They will be replaced with actual tokens + in `step`. + + committed_idx V seq_len V + +----------------------------------------------------------------+ + | committed | uncommitted | + |----------------------------------------------------------------| + | | committable | | + |----------------------------------------------------------------| + | cached | uncached | + |----------------------------------------------------------------| + | | prompt | inflight | + +----------------------------------------------------------------+ + cached_idx ^ inflight_idx ^ + """ + + def __init__(self, page_size: int, max_seq_len: int) -> None: + self.page_size = page_size + self.committed_idx: int = 0 + self.cached_idx: int = 0 + self.inflight_idx: int = 0 + self.seq_len: int = 0 + self.blocks: list[int] = [] + self.tokens: np.ndarray = np.full((max_seq_len,), 0, dtype=np.int64) + + @property + def committed_blocks(self) -> list[int]: + return self.blocks[: self.committed_idx // self.page_size] + + @property + def uncommitted_blocks(self) -> list[int]: + return self.blocks[self.committed_idx // self.page_size : self.seq_len] + + @property + def committed_tokens(self) -> np.ndarray: + return self.tokens[: self.committed_idx] + + @property + def uncached_tokens(self) -> np.ndarray: + return self.tokens[self.cached_idx : self.seq_len] + + @property + def prompt_tokens(self) -> np.ndarray: + return self.tokens[self.cached_idx : self.inflight_idx] + + @property + def inflight_tokens(self) -> np.ndarray: + return self.tokens[self.inflight_idx : self.seq_len] + + @property + def committable_tokens_aligned(self) -> np.ndarray: + # Return all tokens that are committable and part of a block that only + # contains committable tokens. + inflight_idx = self.inflight_idx + partial_tokens = inflight_idx % self.page_size + if partial_tokens > 0: + inflight_idx -= partial_tokens + return self.tokens[self.committed_idx : inflight_idx] + + @property + def committable_blocks_aligned(self) -> list[int]: + # Returns all blocks that only contain committable tokens. + return self.blocks[ + self.committed_idx // self.page_size : self.inflight_idx + // self.page_size + ] + + @property + def committable_tokens(self) -> np.ndarray: + # Returns all tokens that are committable. + return self.tokens[self.committed_idx : self.inflight_idx] + + @property + def committable_blocks(self) -> list[int]: + # Returns any block that contains at least one committable token. + return self.blocks[ + self.committed_idx // self.page_size : ceildiv( + self.inflight_idx, self.page_size + ) + ] + + def _validate_indices(self): + assert ( + 0 + <= self.committed_idx + <= self.cached_idx + <= self.inflight_idx + <= self.seq_len + ), "The indices must be in the correct order" + assert self.seq_len <= len(self.tokens), ( + "Sequence has exceeded the max sequence length" + ) + assert self.committed_idx % self.page_size == 0, ( + "The committed_idx must be a multiple of the page size since we " + "can't commit a partial page into the prefix cache" + ) + + def fetch(self, prompt: np.ndarray, num_steps: int) -> None: + """Add prompt to token array and reserve space for inflight tokens.""" + self._validate_indices() + assert len(self.prompt_tokens) == 0, ( + "At the start of fetch, there should be no prompt tokens" + ) + assert len(self.inflight_tokens) == 0, ( + "At the start of fetch, there should be no inflight tokens" + ) + assert len(prompt) > 0, ( + "The prompt provided to fetch should be non-empty" + ) + num_inflight_tokens = num_steps - 1 + self.inflight_idx += len(prompt) + self.seq_len += len(prompt) + num_inflight_tokens + self.tokens[self.cached_idx : self.inflight_idx] = prompt + self._validate_indices() + + def step(self, new_tokens: np.ndarray) -> None: + """Write new tokens into inflight token slots. Also update the cached_idx.""" + self._validate_indices() + assert len(self.prompt_tokens) > 0, ( + "We could not have executed the model without at least one prompt token" + ) + num_inflight_tokens = len(new_tokens) - 1 + assert len(self.inflight_tokens) == num_inflight_tokens, ( + "The existing slots for inflight tokens should correspond to all but the last newly generated token" + ) + self.tokens[self.inflight_idx : self.seq_len] = new_tokens[:-1] + self.cached_idx = self.seq_len + self.inflight_idx = self.seq_len + assert len(self.uncached_tokens) == 0, ( + "After step, all tokens should have a backing KV projection in the cache" + ) + self._validate_indices() diff --git a/src/max/pipelines/kv_cache/prefix_cache.py b/src/max/pipelines/kv_cache/prefix_cache.py new file mode 100644 index 0000000000..191f53ffd2 --- /dev/null +++ b/src/max/pipelines/kv_cache/prefix_cache.py @@ -0,0 +1,369 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Prefix cache to enable reuse of KV projections during context encoding with PagedAttention.""" + +from __future__ import annotations + +from typing import Callable, Optional + +import numpy as np +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession +from max.graph import BufferType, DeviceRef, Graph, TensorType, ops + +from .paged_cache_metadata import PagedCacheMetadata +from .radix_trie import RadixTrie, TrieNode + + +def construct_cow_strided_memcpy_graph( + block_shape: list[int | str], dtype: DType, devices: list[Device] +) -> Graph: + """ + Returns a graph for performing COW operations on the KV cache. + """ + assert len(block_shape) == 6 + ds = [DeviceRef(device.label, device.id) for device in devices] + blocks_ty = [BufferType(dtype, shape=block_shape, device=d) for d in ds] + block_src_idx_ty = TensorType(DType.uint32, shape=[]) + block_dst_idx_ty = TensorType(DType.uint32, shape=[]) + num_tokens_ty = TensorType(DType.uint32, shape=[]) + + with Graph( + "mo.kv_collection_cow_strided_memcpy.paged", + input_types=[ + block_dst_idx_ty, + block_src_idx_ty, + num_tokens_ty, + *blocks_ty, + ], + output_types=[], + ) as graph: + block_dst_idx, block_src_idx, num_tokens, *all_blocks = graph.inputs + for blocks in all_blocks: + ops.inplace_custom( + "mo.kv_collection_cow_strided_memcpy.paged", + values=[blocks, block_dst_idx, block_src_idx, num_tokens], + out_types=[], + ) + graph.output() + + return graph + + +class PrefixCache: + def __init__( + self, + session: InferenceSession, + page_size: int, + block_shape: list[int | str], + dtype: DType, + devices: list[Device], + tensors: list[Tensor], + enable_cow: bool = True, + ): + self.page_size = page_size + self.enable_cow = enable_cow + self.radix_trie = RadixTrie(page_size=self.page_size) + self.tensors = tensors + + self.cow_count = 0 + if self.enable_cow and self.page_size > 1: + # Load single op graph for performing memory transfers needed for COW + self.cow_strided_memcpy_graph = session.load( + construct_cow_strided_memcpy_graph( + block_shape, + dtype, + devices, + ), + ) + self.all_tokens = 0 + self.cache_hit_tokens = 0 + + # This is a pointer into the radix trie indicating the prefix of the sequence + # that has been committed into the radix trie. + self.active_requests: dict[int, TrieNode] = {} + + def __contains__(self, block: int) -> bool: + """Check if a block is owned by the prefix cache.""" + return block in self.radix_trie.get_all_blocks() + + def external_claim(self, seq_id: int) -> None: + """Claim a sequence for use by the prefix cache. + + This initializes the cursor in the trie for the given sequence at the + root, indicating that no blocks are committed for this sequence yet. + """ + assert seq_id not in self.active_requests + self.active_requests[seq_id] = self.radix_trie.root + + def release(self, seq_id: int) -> None: + """Release a sequence from the prefix cache. + + This decrements the ref count of committed blocks used by the sequence. + """ + assert seq_id in self.active_requests + node = self.active_requests[seq_id] + self.radix_trie.mark_not_in_use_by(node, seq_id) + del self.active_requests[seq_id] + + @property + def blocks(self) -> set[int]: + """Returns all blocks owned by the prefix cache.""" + return self.radix_trie.get_all_blocks() + + @property + def stale_blocks(self) -> set[int]: + """Returns all blocks that are evictable/stale. + + Stale blocks are those that are not in use by any sequence (refcount == 0) + """ + return self.radix_trie.get_evictable_blocks() + + @property + def cache_hit_rate(self) -> float: + """Returns the prefix cache hit rate.""" + if self.all_tokens == 0: + return 0.0 + assert self.cache_hit_tokens <= self.all_tokens + return self.cache_hit_tokens / self.all_tokens + + def validate_req_state_valid( + self, + seq_id: int, + committed_tokens: np.ndarray, + committed_blocks: list[int], + ): + """Check that the committed tokens and blocks match what was actually + committed into the radix trie.""" + assert seq_id in self.active_requests + node = self.active_requests[seq_id] + # Climb up the trie from the given node, accumulating all the + # prefix tokens and blocks. + tokens, blocks = node.get_prefix_tokens_and_blocks() + assert (tokens == committed_tokens).all() + assert blocks == committed_blocks + + def get_cached_blocks(self, seq_id: int, prompt: np.ndarray) -> list[int]: + """Returns the blocks from the prefix cache that can be reused for the given prompt.""" + node = self.active_requests.get(seq_id, self.radix_trie.root) + # Attempt to match all but the last token in the prompt. This is + # because the model expects a prompt of length at least 1. + _, cached_blocks = self.radix_trie.match_prefix(prompt[:-1], node=node) + return cached_blocks + + def get_num_cached_tokens(self, prompt: np.ndarray) -> int: + """Returns the number of tokens in the CE prompt that are found in the prefix cache.""" + _, prefix_blocks = self.radix_trie.match_prefix(prompt[:-1]) + return len(prefix_blocks) * self.page_size + + def evict_blocks(self, blocks_to_evict: Optional[int] = None) -> list[int]: + """Evict a percentage of all blocks according to a LRU policy on the trie leaves.""" + if blocks_to_evict is None: + blocks_to_evict = len(self.blocks) + return self.radix_trie.evict_blocks(desired_num_evicted=blocks_to_evict) + + def _release_partial_block( + self, + data: PagedCacheMetadata, + free_block_fn: Callable[[int], None], + ) -> None: + """Release the partially cached and uncommitted block. + + There may be a partially cached block if the seq len was not a multiple + of page size after the last `step` operation. We may want to release the + partial block if we can retrieve KV projections for additional tokens + in the block from the cache: + + e.g: + - partial_block b0 = ["I", "love", "to", "dance"] (cached = 2 tokens) + - we have block b1 = ["I", "love", "to", "sing"] (cached = 4 tokens) + in the prefix cache + - we can delete b0 and reuse b1 for the first three tokens for COW + """ + assert data.committed_idx < data.cached_idx + partial_blocks = data.committable_blocks + assert len(partial_blocks) == 1 + free_block_fn(partial_blocks[0]) + data.blocks.pop() + partial_tokens = data.cached_idx - data.committed_idx + assert 0 < partial_tokens < self.page_size + data.cached_idx -= partial_tokens + assert data.committed_idx == data.cached_idx + + def fetch( + self, + seq_id: int, + data: PagedCacheMetadata, + free_block_fn: Callable[[int], None], + alloc_block_fn: Callable[[], int], + ) -> list[int]: + """Extend the kv cache for given request with any cached prefixes. + + This will increment the committed_idx and cached_idx if there is a cache + hit. The prompt will be trimmed in the event that cached_idx is bumped. + """ + # If there is only one committable token, that means that the prompt + # is one token. We cannot reduce the prompt length any further since + # the model expects a prompt of length at least 1. + committable_tokens = data.committable_tokens[:-1] + if len(committable_tokens) == 0: + return [] + + # Query trie for all but last token. + node = self.active_requests[seq_id] + node, prefix_blocks = self.radix_trie.match_prefix( + committable_tokens, node=node + ) + self.active_requests[seq_id] = node + + # Mark the prefix blocks we retrieved from the radix trie cache as + # in use by this sequence so they don't get evicted prematurely. + self.radix_trie.mark_in_use_by(node, seq_id) + + # Update the cache hit rate metrics. + num_cache_hit_tokens = len(prefix_blocks) * self.page_size + self.cache_hit_tokens += num_cache_hit_tokens + self.all_tokens += len(committable_tokens) + + # If there is a block with partially cached tokens, we should release it + # if the cache hit blocks already contain these tokens and more + if data.committed_idx < data.cached_idx and num_cache_hit_tokens > 0: + assert data.committed_idx + num_cache_hit_tokens > data.cached_idx + self._release_partial_block(data, free_block_fn) + + data.blocks.extend(prefix_blocks) + # Bump the committed_idx since we got cache hits + data.committed_idx += num_cache_hit_tokens + data.cached_idx += num_cache_hit_tokens + + if self.enable_cow: + self._fetch_cow(seq_id, data, free_block_fn, alloc_block_fn) + + return prefix_blocks + + def _fetch_cow( + self, + seq_id: int, + data: PagedCacheMetadata, + free_block_fn: Callable[[int], None], + alloc_block_fn: Callable[[], int], + ) -> None: + """Extend the kv cache for given request with any cached prefixes by + copying a portion of the tokens in a committed block to a fresh block. + + This will keep the committed_idx the same, but increment the cached_idx + by between [1, page_size) tokens if we do perform a cow operation. The + prompt will be trimmed in the event that cached_idx is bumped. + """ + assert self.enable_cow + + # If page_size is 1, there is no need to perform COW + if self.page_size == 1: + return + assert self.cow_strided_memcpy_graph is not None + + # Match page_size tokens in the radix trie + committable_tokens = data.committable_tokens[:-1] + if len(committable_tokens) == 0: + return + committable_tokens_cropped = list(committable_tokens[: self.page_size]) + node = self.active_requests[seq_id] + res = node.find_block_with_largest_common_prefix( + committable_tokens_cropped + ) + if res is None: + return + partial_match_block, num_cache_hit_tokens = res + assert 0 < num_cache_hit_tokens < self.page_size + + # No point in performing COW if we have more cached but uncommitted tokens + # in the existing partial block than the matched prefix length. + partial_tokens = data.cached_idx - data.committed_idx + if num_cache_hit_tokens <= partial_tokens: + return + + # If we have a partially cached block, we need to release it before + # appending additional blocks. + if partial_tokens > 0: + assert data.committed_idx + num_cache_hit_tokens > data.cached_idx + self._release_partial_block(data, free_block_fn) + + # Copy prefix_len tokens from partial_match_block to new_block. + new_block = alloc_block_fn() + self.cow_count += 1 + self.cow_strided_memcpy_graph.execute( + new_block, + partial_match_block, + num_cache_hit_tokens, + *self.tensors, + ) + data.blocks.append(new_block) + data.cached_idx += num_cache_hit_tokens + assert len(data.prompt_tokens) > 0 + assert data.cached_idx < data.inflight_idx + + def step( + self, + seq_id: int, + data: PagedCacheMetadata, + free_block_fn: Callable[[int], None], + ) -> None: + """Now that we have written to the inflight blocks, we will try to commit + them to the radix trie. + + This increments the committed_idx. We guarantee that the number of committed + tokens will be a multiple of the page size. There may be some uncommitted + tokens left over due to there being a partial page at the end. Thus the + number of uncommitted tokens will always be less than the page size. + """ + committable_tokens = data.committable_tokens_aligned + node = self.active_requests[seq_id] + node, existing_blocks = self.radix_trie.match_prefix( + committable_tokens, node=node + ) + self.active_requests[seq_id] = node + + # If we computed a kv entry for a token that was already cached, + # we will just release that block we just computed. + for b0, b1 in zip(existing_blocks, data.committable_blocks_aligned): + if b0 != b1: + free_block_fn(b1) + + # Replace the inflight blocks with the existing prefix blocks. + committed_block_idx = data.committed_idx // self.page_size + data.blocks[ + committed_block_idx : committed_block_idx + len(existing_blocks) + ] = existing_blocks + data.committed_idx += len(existing_blocks) * self.page_size + + committable_tokens = data.committable_tokens_aligned + committable_blocks = data.committable_blocks_aligned + assert len(committable_tokens) % self.page_size == 0 + assert ( + len(committable_tokens) == len(committable_blocks) * self.page_size + ) + + # If there are any tokens to commit, insert them into the prefix cache. + node = self.radix_trie.insert( + committable_tokens, + committable_blocks, + node=node, + ) + self.active_requests[seq_id] = node + data.committed_idx += len(committable_tokens) + + # Mark the recently committed blocks as in use by this sequence + # so they don't get evicted prematurely. + self.radix_trie.mark_in_use_by(node, seq_id) diff --git a/src/max/pipelines/kv_cache/radix_trie.py b/src/max/pipelines/kv_cache/radix_trie.py new file mode 100644 index 0000000000..fe848cbe9e --- /dev/null +++ b/src/max/pipelines/kv_cache/radix_trie.py @@ -0,0 +1,479 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, Union + +import numpy as np + +from .simple_trie import SimpleTrie + +TokenId = Any +BlockId = Any +SeqId = int + + +from collections import OrderedDict + + +def _token_prefix_match_len( + tokens0: np.ndarray, tokens1: np.ndarray, page_size: int +) -> int: + """Computes the length of maximum shared prefix of two tokens, aligned by + `page_size`. + + e.g: _token_prefix_match_len(["i", "like", "dogs"], ["i", "like", "cats"], page_size = 1) => 2 + _token_prefix_match_len(["i", "like", "dogs"], ["we", "like", "cats"], page_size = 1) => 0 + _token_prefix_match_len(["i", "like", "dogs"], ["i", "like", "dogs", "and", "cats"], page_size = 1) => 3 + """ + assert len(tokens0) % page_size == 0 + assert len(tokens1) % page_size == 0 + shorter_len = min(len(tokens0), len(tokens1)) + for i in range(0, shorter_len, page_size): + if (tokens0[i : i + page_size] != tokens1[i : i + page_size]).any(): + return i + return shorter_len + + +def _token_to_key(tokens: np.ndarray, page_size: int) -> tuple[TokenId, ...]: + assert len(tokens) >= page_size, ( + f"tokens must be at least page_size ({page_size}) long but is only {len(tokens)} tokens" + ) + return tuple(tokens[:page_size]) + + +class TrieNode: + """A TrieNode consists of a list of tokens and blocks. + + - Tokens are the ids of the tokens in the sequence. + - Blocks are the offsets into the KVCache region that back the KV entries + for a given token. I.e: the page index + """ + + node_id_counter = 0 + + def __init__(self) -> None: + """Constructs a TrieNode.""" + # each node is assigned a unique id to look it up in the lru cache + self.node_id = TrieNode.node_id_counter + TrieNode.node_id_counter += 1 + + self.children: Dict[tuple[TokenId, ...], TrieNode] = {} + # Typically in a map, we would have keys mapping to values. + # To avoid collision with KV cache terminology, we call them tokens and blocks. + # + # Only the root should have empty tokens/blocks + self.tokens: np.ndarray = np.array([]) + self.blocks: List[BlockId] = [] + # Only the root should have a null parent + self.parent: Optional[TrieNode] = None + # Sequences that are using the blocks owned by this trie node + # The node can only be evicted if self.active_seqs is empty + self.active_seqs: Set[SeqId] = set() + # A trie containing only the keys in the self.children dict where each + # key is length exactly page_size + self.key_trie = SimpleTrie() + + def is_leaf(self) -> bool: + """Returns true if the node is a leaf node.""" + return len(self.children) == 0 + + def is_root(self) -> bool: + """Returns true if the node is the root node.""" + return self.parent is None + + def is_evictable(self) -> bool: + """Returns true if the node is evictable.""" + return not self.is_root() and len(self.active_seqs) == 0 + + def find_block_with_largest_common_prefix( + self, target: Sequence[TokenId] + ) -> Optional[Tuple[BlockId, int]]: + """Returns any block in the trie that has the given prefix.""" + res = self.key_trie.find_string_with_largest_common_prefix(target) + if res is None: + return None + key, prefix_len = res + if prefix_len == 0: + return None + assert prefix_len <= len(target) + assert target[:prefix_len] == key[:prefix_len] + return self.children[tuple(key)].blocks[0], prefix_len + + def get_prefix_tokens_and_blocks(self) -> Tuple[np.ndarray, List[BlockId]]: + curr: Optional[TrieNode] = self + tokens: List[TokenId] = [] + blocks: List[BlockId] = [] + while curr is not None: + tokens.extend(curr.tokens[::-1]) + blocks.extend(curr.blocks[::-1]) + curr = curr.parent + return np.array(tokens, dtype=np.int64)[::-1], blocks[::-1] + + +class LRUCache(OrderedDict): + """Least recently used block cache to support O(1) eviction operations.""" + + def __init__(self): + super().__init__() + + def __setitem__(self, key: int, value: TrieNode) -> None: + super().__setitem__(key, value) + super().move_to_end(key) + + def move_to_front(self, key: int) -> None: + super().move_to_end(key, last=False) + + def pop_front(self) -> TrieNode: + return super().popitem(last=False)[-1] + + +class RadixTrie: + """This RadixTrie is specially designed for prefix caching in paged attention. + + The RadixTrie allows for efficient insertion and matching of sequences. It + matches each prefix of tokens in a sequence to its corresponding blocks. + Compared to a naive trie, the RadixTrie allows storing multiple tokens in a + single node for less indirection and faster access. + + Blocks in the RadixTrie should be immutable and committed. If it is in the + RadixTrie, it is eligible for sharing. An inflight or uncommitted block that + is being written to by a sequence should not be in the RadixTrie. + + The RadixTrie allows for an LRU eviction policy for its leaves. We only allow + evictions if no active sequences are using the node. + + Currently, the RadixTrie assumes that the paged KVCache page size is 1. + + This implementation is based off of SGLang: + - https://github.com/sgl-project/sglang/blob/337fe53ac41c68d6f171ef3b446f55eb0e98f77c/python/sglang/srt/mem_cache/radix_cache.py#L58 + """ + + def __init__(self, page_size: int = 1) -> None: + """Constructs a RadixTrie.""" + self.root = TrieNode() + self.page_size = page_size + self.evictable_blocks: set[BlockId] = set() + self.all_blocks: set[BlockId] = set() + + # the lru cache contains each evictable block in the trie + self.lru_cache = LRUCache() + + def _check_node_valid(self, node: TrieNode): + """Rudimentary checks of data structure invariants for TrieNode.""" + if self.root == node: + assert len(node.tokens) == 0 + assert len(node.blocks) == 0 + assert not node.parent + else: + assert len(node.tokens) > 0 + assert len(node.blocks) > 0 + assert node.parent + assert len(node.tokens) % self.page_size == 0 + assert len(node.tokens) // self.page_size == len(node.blocks) + + def insert( + self, + tokens: Union[np.ndarray, List[TokenId]], + blocks: List[BlockId], + node: Optional[TrieNode] = None, + ) -> TrieNode: + """Inserts `tokens` and `blocks` into the trie. + + We assume that each block contains exactly one token so the length of both + input lists must match. + + Args: + tokens: Tokens to insert into trie + blocks: KV cache block for each token + node: Node to begin insertion at. If this is not a leaf node, blocks + in the tree are overwritten. + Return: + trie_node: Node corresponding to end of the sequence where future + generated tokens can be inserted + """ + + if isinstance(tokens, list): + tokens = np.array(tokens) + + def insert_helper( + prev: TrieNode, tokens: np.ndarray, blocks: List[BlockId] + ) -> TrieNode: + if len(tokens) == 0: + return prev + + key = _token_to_key(tokens, self.page_size) + if key not in prev.children: + # insert new node + curr = TrieNode() + curr.parent = prev + curr.tokens = tokens + curr.blocks = blocks + prev.children[key] = curr + prev.key_trie.insert(key) + assert curr.is_evictable() and curr.is_leaf() + self.evictable_blocks.update(blocks) + self.lru_cache[curr.node_id] = curr + self.all_blocks.update(blocks) + + curr = prev.children[key] + prefix_len = _token_prefix_match_len( + curr.tokens, tokens, self.page_size + ) + + if prefix_len == len(curr.tokens) and prefix_len == len(tokens): + return curr + + unmatched_tokens = tokens[prefix_len:] + unmatched_blocks = blocks[prefix_len // self.page_size :] + if prefix_len == len(curr.tokens): + return insert_helper(curr, unmatched_tokens, unmatched_blocks) + + # this means that we got a partial match and must split the curr node + # (prev) -> (curr) + # becomes: + # (prev) -> (parent) -> (child) + (parent, _) = self._split_node(curr, prefix_len) + unmatched_tokens = tokens[prefix_len:] + unmatched_blocks = blocks[prefix_len // self.page_size :] + return insert_helper(parent, unmatched_tokens, unmatched_blocks) + + if len(tokens) % self.page_size != 0: + msg = f"Insertion failed: the number of tokens is not divisible by the page size. len(tokens) == {len(tokens)} but page_size == {self.page_size}." + raise ValueError(msg) + if len(tokens) // self.page_size != len(blocks): + msg = f"Insertion failed: the number of tokens and blocks do not match. len(tokens) // self.page_size == {len(tokens)} // {self.page_size} == {len(tokens) // self.page_size} but len(blocks) == {len(blocks)}." + raise ValueError(msg) + + if node is None: + node = self.root + + if len(tokens) == 0: + return node + + # clone to avoid mutating the original lists + tokens = tokens.copy() + blocks = blocks.copy() + return insert_helper(node, tokens, blocks) + + def match_prefix( + self, + tokens: Union[np.ndarray, List[TokenId]], + node: Optional[TrieNode] = None, + ) -> Tuple[TrieNode, List[BlockId]]: + """Matches the input `tokens` with the contents of the trie. + + Args: + tokens: tokens to search the trie for + node: Node to begin matching at. + Return: + Tuple containing: + - trie_node: Node corresponding to end of matched prefix where + future generated tokens can be inserted. + - block_list: KV cache blocks for matched prefix + """ + if isinstance(tokens, list): + tokens = np.array(tokens) + + def match_prefix_helper( + prev: TrieNode, tokens: np.ndarray, blocks: List[BlockId] + ) -> TrieNode: + if len(tokens) == 0: + return prev + + key = _token_to_key(tokens, self.page_size) + if key not in prev.children: + return prev + + curr = prev.children[key] + prefix_len = _token_prefix_match_len( + curr.tokens, tokens, self.page_size + ) + if prefix_len < len(curr.tokens): + # (prev) -> (curr) + # becomes: + # (prev) -> (parent) -> (child) + (parent, _) = self._split_node(curr, prefix_len) + blocks.extend(parent.blocks) + return parent + else: + blocks.extend(curr.blocks) + return match_prefix_helper(curr, tokens[prefix_len:], blocks) + + if node is None: + node = self.root + blocks: List[BlockId] = [] + + if len(tokens) == 0: + return node, [] + + tokens = tokens[: len(tokens) // self.page_size * self.page_size] + curr = match_prefix_helper(node, tokens, blocks) + return curr, blocks + + def _split_node( + self, node: TrieNode, split_len: int + ) -> Tuple[TrieNode, TrieNode]: + """Splits the provided node into two. + + The resulting parent node receives exactly `split_len` tokens/blocks, and + the child receives the remainder. + + before │ after splitting w/ `split_len` = 2 + │ ┌────────┐ + │ │ ab │ (parent) + ┌────────┐ │ └───▲────┘ + │ abcdef │ │ │ + └────────┘ │ ┌───▼────┐ + │ │ cdef │ (child) + │ └────────┘ + """ + assert node != self.root + assert split_len > 0 + assert split_len % self.page_size == 0 + + parent = TrieNode() + child = node + parent.tokens, child.tokens = ( + child.tokens[:split_len], + child.tokens[split_len:], + ) + parent.blocks, child.blocks = ( + child.blocks[: split_len // self.page_size], + child.blocks[split_len // self.page_size :], + ) + + parent.parent = child.parent + assert parent.parent is not None + assert len(parent.tokens) > 0 + parent_key = _token_to_key(parent.tokens, self.page_size) + parent.parent.children[parent_key] = parent + parent.parent.key_trie.insert(parent_key) + child_key = _token_to_key(child.tokens, self.page_size) + parent.children = {child_key: child} + parent.key_trie.insert(child_key) + self.lru_cache[child.node_id] = child + child.parent = parent + + parent.active_seqs = child.active_seqs.copy() + if parent.is_evictable(): + self.lru_cache[parent.node_id] = parent + + self._check_node_valid(parent) + self._check_node_valid(child) + return (parent, child) + + def mark_in_use_by(self, node: TrieNode, seq_id: SeqId): + """Climb up the trie starting from node, marking each node as being + in use by this seq.""" + + curr = node + while curr != self.root: + assert curr is not None + # optimization: if this node is already marked as using this sequence, + # assume that it is already marked for its parents as well + if seq_id in curr.active_seqs: + break + curr.active_seqs.add(seq_id) + if not curr.is_evictable(): + self.evictable_blocks -= set(curr.blocks) + if curr.node_id in self.lru_cache: + del self.lru_cache[curr.node_id] + assert curr.parent is not None + curr = curr.parent + + def mark_not_in_use_by(self, node: TrieNode, seq_id: SeqId): + """Climb up the trie starting from node, marking each node as no longer + in use by this seq. Since nodes without any users may be eligible for + eviction, we also update its last_access_time.""" + + curr = node + while curr != self.root: + assert curr is not None + assert seq_id in curr.active_seqs + curr.active_seqs.remove(seq_id) + if curr.is_evictable(): + self.evictable_blocks.update(curr.blocks) + self.lru_cache[curr.node_id] = curr + assert curr.parent is not None + curr = curr.parent + + def evict_blocks(self, desired_num_evicted: int) -> List[BlockId]: + """Attempt to evict at most `desired_num_evicted` blocks from trie.""" + evicted_blocks: List[BlockId] = [] + + while len(evicted_blocks) < desired_num_evicted and self.lru_cache: + leaf = self.lru_cache.pop_front() + # we guarantee that the parent node will only be evicted after all + # its children have been evicted. as such, this node we will evict + # is a leaf + assert leaf.is_evictable() and leaf.is_leaf() + key = _token_to_key(leaf.tokens, self.page_size) + num_blocks_to_evict = min( + desired_num_evicted - len(evicted_blocks), + len(leaf.blocks), + ) + assert num_blocks_to_evict > 0 + num_tokens_to_evict = num_blocks_to_evict * self.page_size + blocks_left, blocks_to_evict = ( + leaf.blocks[:-num_blocks_to_evict], + leaf.blocks[-num_blocks_to_evict:], + ) + leaf.blocks = blocks_left + leaf.tokens = leaf.tokens[:-num_tokens_to_evict] + evicted_blocks.extend(blocks_to_evict) + if leaf.blocks: + self.lru_cache[leaf.node_id] = leaf + self.lru_cache.move_to_front(leaf.node_id) + else: + parent = leaf.parent + assert parent is not None + del parent.children[key] + del parent.key_trie[key] + + self.evictable_blocks.difference_update(evicted_blocks) + self.all_blocks.difference_update(evicted_blocks) + if len(evicted_blocks) < desired_num_evicted: + assert not self.evictable_blocks + assert len(self.lru_cache) == 0 + + return evicted_blocks + + def get_all_blocks(self) -> set[BlockId]: + """Returns the total number of blocks in the trie.""" + return self.all_blocks + + def get_evictable_blocks(self) -> set[BlockId]: + """Returns the number of blocks that are eligible for eviction.""" + return self.evictable_blocks + + def pretty_format(self, print_blocks: bool = False) -> List[str]: + """Formats the contents of the trie.""" + + def helper(node: TrieNode, indent: int, lines: List[str]): + for _, child in node.children.items(): + tokens = child.tokens + token_list = tokens.tolist() + if print_blocks: + lines.append(f"{'-' * indent}{token_list} : {child.blocks}") + else: + lines.append(f"{'-' * indent}{token_list}") + helper(child, indent + 2, lines) + + lines: List[str] = [] + helper(self.root, 0, lines) + return lines + + def pretty_print(self, print_blocks: bool = True): + """Prints the contents of the trie.""" + for line in self.pretty_format(print_blocks): + print(line) diff --git a/src/max/pipelines/kv_cache/simple_trie.py b/src/max/pipelines/kv_cache/simple_trie.py new file mode 100644 index 0000000000..3ae86eb6a6 --- /dev/null +++ b/src/max/pipelines/kv_cache/simple_trie.py @@ -0,0 +1,152 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from typing import Any, Optional, Sequence, Tuple + +Key = Any + + +class SimpleNode: + """A node in a simple trie data structure.""" + + def __init__(self): + # Pointer to the children of this node + self.children: dict[Key, SimpleNode] = {} + # Whether this node is the end of a word + self.is_eow: bool = False + + +class SimpleTrie: + """A simple prefix trie with basic set insertion and deletion operations. + + Unlike the RadixTrie, this trie only have one item at each node and does not + map multiple keys to multiple values. Instead, it just stores a set of keys. + """ + + def __init__(self): + self.root = SimpleNode() + + def insert(self, s: Sequence[Key]): + """Inserts a sequence into the trie.""" + curr = self.root + for ch in s: + if ch not in curr.children: + curr.children[ch] = SimpleNode() + curr = curr.children[ch] + curr.is_eow = True + + def _search(self, s: Sequence[Key]) -> Tuple[SimpleNode, list[Key]]: + """Internal helper method to search for a sequence in the trie. + + Args: + s: The sequence to search for + + Returns: + A tuple containing: + - The last node reached during the search + - List of keys matched during the search + """ + curr = self.root + matched: list[Key] = [] + for ch in s: + if ch not in curr.children: + return curr, matched + matched.append(ch) + curr = curr.children[ch] + return curr, matched + + def find_string_with_largest_common_prefix( + self, target: Sequence[Key] + ) -> Optional[Tuple[Sequence[Key], int]]: + """Returns a sequence in the trie that shares the longest common prefix + with the target. + + If a match is found, returns a tuple containing: + - The matched sequence + - Length of the common prefix + Returns None if no match is found + """ + curr, matched = self._search(target) + prefix_len = len(matched) + + if prefix_len == 0: + return None + + # Keep going down left spine to find any string in trie that has s + # as its prefix + s = matched + while not curr.is_eow: + if not curr.children: + return None + ch = next(iter(curr.children)) + curr = curr.children[ch] + s.append(ch) + + return s, prefix_len + + def __contains__(self, s: Sequence[Key]) -> bool: + """Checks if the trie contains the exact sequence.""" + node, matched = self._search(s) + return len(matched) == len(s) and node.is_eow + + def __delitem__(self, s: Sequence[Key]) -> None: + """Deletes a sequence from the trie.""" + + def _remove( + root: Optional[SimpleNode], idx: int + ) -> Optional[SimpleNode]: + if not root: + return None + + if idx == len(s): + assert root.is_eow + root.is_eow = False + if len(root.children) == 0: + del root + root = None + return root + + ch = s[idx] + child = _remove(root.children[ch], idx + 1) + if child is None: + del root.children[ch] + else: + root.children[ch] = child + + if len(root.children) == 0 and not root.is_eow: + del root + root = None + return root + + _remove(self.root, 0) + + def pretty_format(self) -> list[str]: + """Returns a formatted string representation of the trie for debugging.""" + lines: list[str] = [] + + def helper(node: SimpleNode, indent: int): + if node.is_eow: + lines.append(f"{'-' * indent}*") + for ch, child in node.children.items(): + lines.append(f"{'-' * indent}{ch}") + helper(child, indent + 2) + + helper(self.root, 0) + return lines + + def pretty_print(self) -> None: + """Prints a formatted visual representation of the trie for debugging.""" + print("SimpleTrie(") + for line in self.pretty_format(): + print(line) + print(")") diff --git a/src/max/pipelines/nn/__init__.py b/src/max/pipelines/nn/__init__.py new file mode 100644 index 0000000000..22b3b1bac4 --- /dev/null +++ b/src/max/pipelines/nn/__init__.py @@ -0,0 +1,88 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .attention import ( + Attention, + AttentionImpl, + AttentionImplQKV, + AttentionQKV, + AttentionWithoutMask, + AttentionWithRope, + AttentionWithRopeQKV, + AttentionWithRopeV2, + DistributedAttentionImpl, + DistributedAttentionWithRope, + GPTQAttentionWithRope, + NaiveAttentionWithRope, +) +from .comm import Signals +from .conv import Conv1D, Conv2D, Conv3D +from .embedding import Embedding, EmbeddingV2, VocabParallelEmbedding +from .kernels import MHAMaskVariant +from .layer import Layer, LayerV2 +from .linear import MLP, MLPV2, DistributedMLP, GPTQLinearV2, Linear, LinearV2 +from .norm import DistributedRMSNorm, LayerNorm, LayerNormV2, RMSNorm, RMSNormV2 +from .rotary_embedding import OptimizedRotaryEmbedding, RotaryEmbedding +from .sequential import Sequential +from .transformer import ( + DistributedTransformer, + DistributedTransformerBlock, + NaiveTransformer, + NaiveTransformerBlock, + Transformer, + TransformerBlock, +) + +__all__ = [ + "Attention", + "AttentionImpl", + "AttentionImplQKV", + "AttentionQKV", + "AttentionWithRope", + "AttentionWithRopeQKV", + "AttentionWithRopeV2", + "AttentionWithoutMask", + "Conv1D", + "Conv2D", + "Conv3D", + "DistributedAttentionImpl", + "DistributedAttentionWithRope", + "DistributedMLP", + "DistributedRMSNorm", + "DistributedTransformer", + "DistributedTransformerBlock", + "Embedding", + "EmbeddingV2", + "GPTQAttentionWithRope", + "GPTQLinearV2", + "Layer", + "LayerNorm", + "LayerNormV2", + "LayerV2", + "Linear", + "LinearV2", + "MHAMaskVariant", + "MLP", + "MLPV2", + "NaiveAttentionWithRope", + "NaiveTransformer", + "NaiveTransformerBlock", + "OptimizedRotaryEmbedding", + "RMSNorm", + "RMSNormV2", + "RotaryEmbedding", + "Sequential", + "Signals", + "Transformer", + "TransformerBlock", +] diff --git a/src/max/pipelines/nn/_identity.py b/src/max/pipelines/nn/_identity.py new file mode 100644 index 0000000000..eeb038c45a --- /dev/null +++ b/src/max/pipelines/nn/_identity.py @@ -0,0 +1,71 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Utility classes for using objects as keys in data structures.""" + +from collections.abc import MutableMapping, MutableSet + + +# From https://stackoverflow.com/questions/16994307/identityset-in-python +class IdentitySet(MutableSet): + """Set that uses object `id` as keys to support unhashable types.""" + + def __init__(self, iterable=()): + self.map = {} # id -> object + self |= iterable # add elements from iterable to the set (union) + + def __len__(self): + return len(self.map) + + def __iter__(self): + return iter(self.map.values()) + + def __contains__(self, x): + return id(x) in self.map + + def add(self, value): + """Add an element.""" + self.map[id(value)] = value + + def discard(self, value): + """Remove an element. Do not raise an exception if absent.""" + self.map.pop(id(value), None) + + def __repr__(self): + if not self: + return "%s()" % (self.__class__.__name__,) + return "%s(%r)" % (self.__class__.__name__, list(self)) + + +class IdentityMap(MutableMapping): + """Map that uses object `id` as keys to support unhashable types.""" + + def __init__(self): + self.key_map = {} # id -> object + self.value_map = {} # id -> Value + + def __getitem__(self, key): + return self.value_map[id(key)] + + def __setitem__(self, key, value): + self.key_map[id(key)] = key + self.value_map[id(key)] = value + + def __delitem__(self, key): + del self.key_map[id(key)] + del self.value_map[id(key)] + + def __iter__(self): + return iter(self.key_map.values()) + + def __len__(self): + return len(self.key_map) diff --git a/src/max/pipelines/nn/attention/__init__.py b/src/max/pipelines/nn/attention/__init__.py new file mode 100644 index 0000000000..5536ee5871 --- /dev/null +++ b/src/max/pipelines/nn/attention/__init__.py @@ -0,0 +1,44 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""The attention mechanism used within the model.""" + +from .attention import Attention, AttentionQKV +from .attention_with_rope import ( + AttentionWithRope, + AttentionWithRopeQKV, + AttentionWithRopeV2, + DistributedAttentionWithRope, + GPTQAttentionWithRope, +) +from .attention_without_mask import AttentionWithoutMask +from .interfaces import ( + AttentionImpl, + AttentionImplQKV, + DistributedAttentionImpl, +) +from .naive_attention_with_rope import NaiveAttentionWithRope + +__all__ = [ + "Attention", + "AttentionQKV", + "AttentionImpl", + "AttentionImplQKV", + "AttentionWithRope", + "DistributedAttentionImpl", + "DistributedAttentionWithRope", + "AttentionWithRopeQKV", + "AttentionWithoutMask", + "NaiveAttentionWithRope", + "AttentionWithRopeV2", + "GPTQAttentionWithRope", +] diff --git a/src/max/pipelines/nn/attention/attention.py b/src/max/pipelines/nn/attention/attention.py new file mode 100644 index 0000000000..5eb57f2815 --- /dev/null +++ b/src/max/pipelines/nn/attention/attention.py @@ -0,0 +1,157 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""A vanilla opaque KV Cache optimized attention mechanism.""" + +from dataclasses import dataclass +from typing import Union + +from max.dtype import DType +from max.graph import TensorValue, ops +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + PagedKVCacheCollection, +) + +from ..kernels import flash_attention, fused_qkv_matmul +from .interfaces import AttentionImpl, AttentionImplQKV + + +@dataclass +class Attention(AttentionImpl): + def __call__( + self, + x: TensorValue, + kv_collection: Union[ + ContinuousBatchingKVCacheCollection, PagedKVCacheCollection + ], + **kwargs, + ) -> TensorValue: + if isinstance(kv_collection, PagedKVCacheCollection): + raise ValueError( + "Paged attention not supported for Attention on non-ragged tensors." + ) + + if "attention_mask" not in kwargs: + raise ValueError("attention_mask not passed as input to Attention") + attention_mask = kwargs["attention_mask"] + if attention_mask.dtype != x.dtype: + msg = ( + "expected attention_mask and x to have the same dtype, but got" + f" {attention_mask.dtype} and {x.dtype}, respectively." + ) + raise ValueError(msg) + + # Get attributes from inputs + batch_size, seq_len = x.shape[0], x.shape[1] + + # Call into fused qkv matmul. + xq = fused_qkv_matmul( + self.kv_params, + input=x, + wqkv=self.wqkv, + kv_collection=kv_collection, + layer_idx=self.layer_idx, + n_heads=self.n_heads, + ) + + xq = ops.reshape( + xq, + [ + batch_size, + seq_len, + self.n_heads, + self.kv_params.head_dim, + ], + ) + + # Calculate Flash Attention + attn_out = flash_attention( + self.kv_params, + input=xq, + kv_collection=kv_collection, + layer_idx=self.layer_idx, + attention_mask=attention_mask, + valid_lengths=kwargs["valid_lengths"], + scale=self.scale, + ) + + attn_out = ops.reshape(attn_out, shape=[batch_size, seq_len, -1]) + + return self.wo(attn_out) + + +@dataclass +class AttentionQKV(AttentionImplQKV): + def __call__( + self, + x: TensorValue, + kv_collection: Union[ + ContinuousBatchingKVCacheCollection, PagedKVCacheCollection + ], + **kwargs, + ) -> TensorValue: + if isinstance(kv_collection, PagedKVCacheCollection): + raise ValueError( + "Paged attention not supported for Attention on non-ragged tensors." + ) + + if "attention_mask" not in kwargs: + raise ValueError("attention_mask not passed as input to Attention") + attention_mask = kwargs["attention_mask"] + if attention_mask.dtype != x.dtype: + msg = ( + "expected attention_mask and x to have the same dtype, but got" + f" {attention_mask.dtype} and {x.dtype}, respectively." + ) + raise ValueError(msg) + + wqkv = ops.concat((self.wq, self.wk, self.wv)).transpose(0, 1) + wqkv = ops.cast(wqkv, x.dtype) + + # Get attributes from inputs + batch_size, seq_len = x.shape[0], x.shape[1] + + # Call into fused qkv matmul. + xq = fused_qkv_matmul( + self.kv_params, + input=x, + wqkv=wqkv, + kv_collection=kv_collection, + layer_idx=ops.constant(self.layer_idx, DType.uint32), + n_heads=self.n_heads, + ) + + xq = ops.reshape( + xq, + [ + batch_size, + seq_len, + self.n_heads, + self.kv_params.head_dim, + ], + ) + + # Calculate Flash Attention + attn_out = flash_attention( + self.kv_params, + input=xq, + kv_collection=kv_collection, + layer_idx=ops.constant(self.layer_idx, DType.uint32), + attention_mask=attention_mask, + valid_lengths=kwargs["valid_lengths"], + scale=self.scale, + ) + + attn_out = ops.reshape(attn_out, shape=[batch_size, seq_len, -1]) + + return self.wo(attn_out) diff --git a/src/max/pipelines/nn/attention/attention_with_rope.py b/src/max/pipelines/nn/attention/attention_with_rope.py new file mode 100644 index 0000000000..897a14d068 --- /dev/null +++ b/src/max/pipelines/nn/attention/attention_with_rope.py @@ -0,0 +1,626 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""An opaque KV Cache optimized attention mechanism with Rope.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Callable, List, Optional, Union + +from max.dtype import DType +from max.graph import ( + BufferValue, + DeviceRef, + TensorValue, + Weight, + ops, +) +from max.graph.quantization import QuantizationConfig, QuantizationEncoding +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + KVCacheParams, + PagedKVCacheCollection, +) + +from ..kernels import ( + MHAMaskVariant, + flash_attention_ragged, + fused_qk_ragged_rope, + fused_qkv_ragged_matmul, + fused_qkv_ragged_matmul_quantized, +) +from ..layer import LayerV2 +from ..linear import LinearV2 +from ..rotary_embedding import OptimizedRotaryEmbedding +from .clamp import clamp +from .interfaces import ( + AttentionImpl, + AttentionImplQKV, + AttentionImplV2, + DistributedAttentionImpl, +) + + +@dataclass +class AttentionWithRope(AttentionImpl): + # This class will not use the RotaryEmbedding to + # calculate rope, but it already includes a freqs_cis + # calculation, which we will borrow + + rope: OptimizedRotaryEmbedding + bias: Optional[TensorValue] = None + perm_idx: Optional[TensorValue] = None + quantization_config: Optional[QuantizationConfig] = None + + def __call__( + self, + x: TensorValue, + kv_collection: Union[ + ContinuousBatchingKVCacheCollection, PagedKVCacheCollection + ], + **kwargs, + ) -> TensorValue: + # Get attributes from input. + total_seq_len = x.shape[0] + + # Call into fused qkv ragged matmul. + if self.quantization_config: + xq = fused_qkv_ragged_matmul_quantized( + self.kv_params, + input=x, + wqkv=self.wqkv, + input_row_offsets=kwargs["input_row_offsets"], + kv_collection=kv_collection, + layer_idx=self.layer_idx, + n_heads=self.n_heads, + bias=self.bias, + perm_idx=self.perm_idx, + quantization_config=self.quantization_config, + ) + else: + xq = fused_qkv_ragged_matmul( + self.kv_params, + input=x, + wqkv=self.wqkv, + input_row_offsets=kwargs["input_row_offsets"], + kv_collection=kv_collection, + layer_idx=self.layer_idx, + n_heads=self.n_heads, + bias=self.bias, + ) + + # Apply rope. + xq = xq.reshape((-1, self.n_heads, self.kv_params.head_dim)) + + if xq.device is not None: + freqs_cis = ops.cast(self.rope.freqs_cis, xq.dtype).to(xq.device) + else: + freqs_cis = ops.cast(self.rope.freqs_cis, xq.dtype) + + xq = fused_qk_ragged_rope( + self.kv_params, + xq, + kwargs["input_row_offsets"], + kv_collection, + freqs_cis, + self.layer_idx, + interleaved=self.rope.interleaved, + ) + + # Calculate Flash Attention. + attn_out = flash_attention_ragged( + self.kv_params, + input=xq, + kv_collection=kv_collection, + layer_idx=self.layer_idx, + input_row_offsets=kwargs["input_row_offsets"], + mask_variant=MHAMaskVariant.CAUSAL_MASK, + scale=self.scale, + ) + + attn_out = ops.reshape(attn_out, shape=[total_seq_len, -1]) + + return self.wo(attn_out) + + +class AttentionWithRopeV2(AttentionImplV2): + """Implementation of attention that uses the rope frequency. + + `AttentionWithRopeV2` will replace `AttentionWithRope` as we roll out + the new Layer API. + """ + + # This class will not use the RotaryEmbedding to + # calculate rope, but it already includes a freqs_cis + # calculation, which we will borrow + rope: OptimizedRotaryEmbedding + + def __init__( + self, + *, + rope: OptimizedRotaryEmbedding, + num_attention_heads: int, + num_key_value_heads: int, + hidden_size: int, + kv_params: KVCacheParams, + layer_idx: int, + dtype: DType = DType.float32, + device: DeviceRef = DeviceRef.CPU(), + linear_cls: Callable[..., LinearV2] = LinearV2, + stacked_qkv: bool = False, + scale: float | None = None, + has_bias: bool = False, + clip_qkv: float | None = None, + ): + """Initializes the attention layer. + + Args: + rope: The rope layer to borrow the freq_cis value from. + num_attention_heads: The number of attention heads. + num_key_value_heads: Number of key/value heads. + hidden_size: The dimension of the hidden states. + kv_params: KV Cache Params, including the number of kv heads, the head dim, and data type. + layer_idx: The layer number associated with this Attention block. + dtype: DType of the + device: Device to place the weights and run the computation. + linear_cls: Linear class to use for the outputs dense layer. + stacked_qkv: Whether the weights are stacked together. + scale: Value used to scale the results of the attention output. + has_bias: Whether to use an attention bias. + clip_qkv: If provided, the QKV weights are clamped between + `[-clip_qkv, clip_qkv]` + """ + + super().__init__() + self.rope = rope + self.n_heads = num_attention_heads + self.layer_idx = layer_idx + self.kv_params = kv_params + self.has_bias = has_bias + self.scale = ( + scale if scale else math.sqrt(1.0 / self.kv_params.head_dim) + ) + self.clip_qkv = clip_qkv + + if stacked_qkv and clip_qkv: + raise ValueError( + "`clip_qkv` not yet supported when `stack_qkv=True`." + ) + + if not self.kv_params.cache_strategy.uses_opaque(): + raise ValueError( + f"{self.kv_params.cache_strategy} cache strategy, not supported" + " in Attention layer." + ) + + kv_weight_dim = ( + hidden_size // num_attention_heads + ) * num_key_value_heads + + self.stacked_qkv = stacked_qkv + + if stacked_qkv: + # To keep the weight names consistent with the transformers attention, + # the names are suffixed ".weight". + self.qkv_proj = Weight( + name="qkv_proj.weight", + dtype=dtype, + shape=[hidden_size + 2 * kv_weight_dim, hidden_size], + ) + else: + self.q_proj = Weight( + name="q_proj.weight", + dtype=dtype, + shape=[hidden_size, hidden_size], + ) + self.k_proj = Weight( + name="k_proj.weight", + dtype=dtype, + shape=[kv_weight_dim, hidden_size], + ) + self.v_proj = Weight( + name="v_proj.weight", + dtype=dtype, + shape=[kv_weight_dim, hidden_size], + ) + + if has_bias: + assert not stacked_qkv, "Bias is not supported with stacked qkv." + + self.bias_q = Weight( + name="q_proj.bias", dtype=dtype, shape=[hidden_size] + ) + self.bias_k = Weight( + name="k_proj.bias", dtype=dtype, shape=[kv_weight_dim] + ) + self.bias_v = Weight( + name="v_proj.bias", dtype=dtype, shape=[kv_weight_dim] + ) + + self.o_proj = linear_cls( + in_dim=hidden_size, out_dim=hidden_size, dtype=dtype, device=device + ) + + @property + def wqkv(self) -> TensorValue: + """The concatenation of q, k, and v weight vectors.""" + if self.stacked_qkv: + return self.qkv_proj + else: + wq: TensorValue = self.q_proj + wk: TensorValue = self.k_proj + wv: TensorValue = self.v_proj + if self.clip_qkv: + wq = clamp(wq, min=-self.clip_qkv, max=self.clip_qkv) + wk = clamp(wk, min=-self.clip_qkv, max=self.clip_qkv) + wv = clamp(wv, min=-self.clip_qkv, max=self.clip_qkv) + return ops.concat((wq, wk, wv)) + + @property + def wqkv_bias(self) -> TensorValue | None: + """The concatenation of q, k, and v bias weight vectors.""" + if not self.has_bias: + return None + + return ops.concat((self.bias_q, self.bias_k, self.bias_v)) + + def __call__( + self, + x: TensorValue, + kv_collection: Union[ + ContinuousBatchingKVCacheCollection, PagedKVCacheCollection + ], + **kwargs, + ) -> TensorValue: + # Get attributes from input. + total_seq_len = x.shape[0] + + layer_idx = ops.constant(self.layer_idx, DType.uint32) + # Call into fused qkv ragged matmul. + xq = fused_qkv_ragged_matmul( + self.kv_params, + input=x, + wqkv=self.wqkv, + bias=self.wqkv_bias, + input_row_offsets=kwargs["input_row_offsets"], + kv_collection=kv_collection, + layer_idx=layer_idx, + n_heads=self.n_heads, + ) + + # Apply rope. + xq = xq.reshape((-1, self.n_heads, self.kv_params.head_dim)) + + if xq.device is not None: + freqs_cis = ops.cast(self.rope.freqs_cis, xq.dtype).to(xq.device) + else: + freqs_cis = ops.cast(self.rope.freqs_cis, xq.dtype) + + xq = fused_qk_ragged_rope( + self.kv_params, + xq, + kwargs["input_row_offsets"], + kv_collection, + freqs_cis, + layer_idx, + interleaved=self.rope.interleaved, + ) + + # Calculate Flash Attention. + attn_out = flash_attention_ragged( + self.kv_params, + input=xq, + kv_collection=kv_collection, + layer_idx=layer_idx, + input_row_offsets=kwargs["input_row_offsets"], + mask_variant=MHAMaskVariant.CAUSAL_MASK, + scale=self.scale, + ) + + attn_out = ops.reshape(attn_out, shape=[total_seq_len, -1]) + + return self.o_proj(attn_out) + + +class GPTQAttentionWithRope(AttentionWithRopeV2): + """Implementation of the GPT-Q attention layer.""" + + def __init__( + self, + quantization_config: QuantizationConfig, + rope: OptimizedRotaryEmbedding, + num_attention_heads: int, + num_key_value_heads: int, + hidden_size: int, + kv_params: KVCacheParams, + layer_idx: int, + dtype: DType = DType.float32, + device: DeviceRef | None = None, + scale: float | None = None, + linear_cls: Callable[..., LinearV2] = LinearV2, + ): + # Skip AttentionWithRopeV2.__init__ because the weights are created + # differently. + LayerV2.__init__(self) + self.quantization_config = quantization_config + self.rope = rope + self.n_heads = num_attention_heads + self.layer_idx = layer_idx + self.kv_params = kv_params + self.hidden_size = hidden_size + self.device = device + self.scale = ( + scale if scale else math.sqrt(1.0 / self.kv_params.head_dim) + ) + if not self.kv_params.cache_strategy.uses_opaque(): + raise ValueError( + f"{self.kv_params.cache_strategy} cache strategy, not supported" + " in Attention layer." + ) + + self.kv_weight_dim = ( + hidden_size // num_attention_heads + ) * num_key_value_heads + + self.q_proj_qweight = Weight( + name="q_proj.qweight", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + # device=device, + quantization_encoding=QuantizationEncoding.GPTQ, + ) + self.k_proj_qweight = Weight( + name="k_proj.qweight", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + # device=device, + quantization_encoding=QuantizationEncoding.GPTQ, + ) + self.v_proj_qweight = Weight( + name="v_proj.qweight", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + # device=device, + quantization_encoding=QuantizationEncoding.GPTQ, + ) + + self.q_proj_scales = Weight( + name="q_proj.scales", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + # device=device, + quantization_encoding=QuantizationEncoding.GPTQ, + ) + self.k_proj_scales = Weight( + name="k_proj.scales", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + # device=device, + quantization_encoding=QuantizationEncoding.GPTQ, + ) + self.v_proj_scales = Weight( + name="v_proj.scales", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + # device=device, + quantization_encoding=QuantizationEncoding.GPTQ, + ) + self.o_proj = linear_cls( + in_dim=hidden_size, + out_dim=hidden_size, + dtype=dtype, + # device=device, + quantization_encoding=QuantizationEncoding.GPTQ, + ) + + self.perm_idx = None + if quantization_config.desc_act: + self.perm_idx = Weight( + name="q_proj.perm_idx", + dtype=DType.int32, + shape=[hidden_size], + # device=device, + ) + + @property + def wqkv(self) -> TensorValue: + """The concatenation of q, k, and v weight vectors.""" + + # fmt: off + # the `qweight` tensor for a QuantLinear is of type uint32. When allocated as bytes, we reshape the + # uint8 tensor to [cols, rows * 4] so concatenating the uint8 tensors along axis=1 is equivalent to + # concatenating the original uint32 tensors along axis=1. + wq_qweight = ops.reshape(self.q_proj_qweight, (-1, self.hidden_size * 4)) + wk_qweight = ops.reshape(self.k_proj_qweight, (-1, self.kv_weight_dim * 4)) + wv_qweight = ops.reshape(self.v_proj_qweight, (-1, self.kv_weight_dim * 4)) + + wqkv_qweight = ops.reshape( + ops.concat((wq_qweight, wk_qweight, wv_qweight), axis=1), + (-1, self.hidden_size + 2 * self.kv_weight_dim), + ) + # `scales` tensor is in f16/bf16 type, so we reshape the uint8 tensor to [cols, rows * 2]. + wq_scales = ops.reshape(self.q_proj_scales, (-1, self.hidden_size * 2)) + wk_scales = ops.reshape(self.k_proj_scales, (-1, self.kv_weight_dim * 2)) + wv_scales = ops.reshape(self.v_proj_scales, (-1, self.kv_weight_dim * 2)) + + wqkv_scales = ops.reshape( + ops.concat((wq_scales, wk_scales, wv_scales), axis=1), + (-1, self.hidden_size + 2 * self.kv_weight_dim), + ) + # fmt: on + return ops.concat((wqkv_qweight, wqkv_scales), axis=0) + + def __call__( + self, + x: TensorValue, + kv_collection: Union[ + ContinuousBatchingKVCacheCollection, PagedKVCacheCollection + ], + **kwargs, + ) -> TensorValue: + layer_idx = ops.constant(self.layer_idx, DType.uint32) + + # Get attributes from input. + total_seq_len = x.shape[0] + + wqkv = self.wqkv + if self.device: + wqkv = wqkv.to(self.device) + + # Call into fused qkv ragged matmul. + xq = fused_qkv_ragged_matmul_quantized( + self.kv_params, + input=x, + wqkv=wqkv, + input_row_offsets=kwargs["input_row_offsets"], + kv_collection=kv_collection, + layer_idx=layer_idx, + n_heads=self.n_heads, + perm_idx=self.perm_idx, + quantization_config=self.quantization_config, + ) + + # Apply rope. + xq = xq.reshape((-1, self.n_heads, self.kv_params.head_dim)) + + if xq.device is not None: + freqs_cis = ops.cast(self.rope.freqs_cis, xq.dtype).to(xq.device) + else: + freqs_cis = ops.cast(self.rope.freqs_cis, xq.dtype) + + xq = fused_qk_ragged_rope( + self.kv_params, + xq, + kwargs["input_row_offsets"], + kv_collection, + freqs_cis, + layer_idx, + interleaved=self.rope.interleaved, + ) + + # Calculate Flash Attention. + attn_out = flash_attention_ragged( + self.kv_params, + input=xq, + kv_collection=kv_collection, + layer_idx=layer_idx, + input_row_offsets=kwargs["input_row_offsets"], + mask_variant=MHAMaskVariant.CAUSAL_MASK, + scale=self.scale, + ) + attn_out = ops.reshape(attn_out, shape=[total_seq_len, -1]) + + return self.o_proj(attn_out) + + +def distribute_value( + v: TensorValue, devices: List[DeviceRef] +) -> List[TensorValue]: + return [v.to(device) for device in devices] + + +@dataclass +class DistributedAttentionWithRope(DistributedAttentionImpl): + list_of_attentions: List[AttentionWithRope] + devices: list[DeviceRef] + + def __call__( + self, + x: List[TensorValue], + signal_buffers: List[BufferValue], + kv_collections: List[ + ContinuousBatchingKVCacheCollection | PagedKVCacheCollection + ], + **kwargs, + ) -> List[TensorValue]: + input_row_offsets = kwargs["input_row_offsets"] + assert isinstance(input_row_offsets, TensorValue) + input_row_offsets_ = distribute_value(input_row_offsets, self.devices) + + return list( + ops.allreduce.sum( + inputs=[ + self.list_of_attentions[i]( + x[i], + kv_collections[i], + input_row_offsets=input_row_offsets_[i], + ) + for i in range(len(self.devices)) + ], + signal_buffers=signal_buffers, + ) + ) + + +@dataclass +class AttentionWithRopeQKV(AttentionImplQKV): + # This class will not use the RotaryEmbedding to + # calculate rope, but it already includes a freqs_cis + # calculation, which we will borrow + rope: OptimizedRotaryEmbedding + + def __call__( + self, + x: TensorValue, + kv_collection: Union[ + ContinuousBatchingKVCacheCollection, PagedKVCacheCollection + ], + **kwargs, + ) -> TensorValue: + # Get attributes from input. + total_seq_len = x.shape[0] + + wqkv = ops.concat((self.wq, self.wk, self.wv)) + + # Call into fused qkv ragged matmul. + xq = fused_qkv_ragged_matmul( + self.kv_params, + input=x, + wqkv=wqkv, + input_row_offsets=kwargs["input_row_offsets"], + kv_collection=kv_collection, + layer_idx=ops.constant(self.layer_idx, DType.uint32), + n_heads=self.n_heads, + ) + + # Apply rope. + xq = xq.reshape((-1, self.n_heads, self.kv_params.head_dim)) + + # Cast freqs_cis to xq's dtype to match the fused_qk_ragged_rope kernel. + freqs_cis = ops.cast(self.rope.freqs_cis, xq.dtype) + + xq = fused_qk_ragged_rope( + self.kv_params, + xq, + kwargs["input_row_offsets"], + kv_collection, + freqs_cis, + ops.constant(self.layer_idx, DType.uint32), + interleaved=self.rope.interleaved, + ) + + # Calculate Flash Attention. + attn_out = flash_attention_ragged( + self.kv_params, + input=xq, + kv_collection=kv_collection, + layer_idx=ops.constant(self.layer_idx, DType.uint32), + input_row_offsets=kwargs["input_row_offsets"], + mask_variant=MHAMaskVariant.CAUSAL_MASK, + scale=self.scale, + ) + + attn_out = ops.reshape(attn_out, shape=[total_seq_len, -1]) + + return self.wo(attn_out) diff --git a/src/max/pipelines/nn/attention/attention_without_mask.py b/src/max/pipelines/nn/attention/attention_without_mask.py new file mode 100644 index 0000000000..47b368fd53 --- /dev/null +++ b/src/max/pipelines/nn/attention/attention_without_mask.py @@ -0,0 +1,74 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""An opaque KV Cache optimized vanilla attention mechanism, with Mask Variants provided inside the Kernel.""" + +from dataclasses import dataclass +from typing import Union + +from max.graph import TensorValue, ops +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + PagedKVCacheCollection, +) + +from ..kernels import ( + MHAMaskVariant, + flash_attention_ragged, + fused_qkv_ragged_matmul, +) +from .interfaces import AttentionImpl + + +@dataclass +class AttentionWithoutMask(AttentionImpl): + mask_variant: MHAMaskVariant + + def __call__( + self, + x: TensorValue, + kv_collection: Union[ + ContinuousBatchingKVCacheCollection, PagedKVCacheCollection + ], + **kwargs, + ) -> TensorValue: + # Get attributes from input. + total_seq_len = x.shape[0] + + # Call into fused qkv ragged matmul. + xq = fused_qkv_ragged_matmul( + self.kv_params, + input=x, + wqkv=self.wqkv, + input_row_offsets=kwargs["input_row_offsets"], + kv_collection=kv_collection, + layer_idx=self.layer_idx, + n_heads=self.n_heads, + ) + + # Reshape for flash attention. + xq = xq.reshape((-1, self.n_heads, self.kv_params.head_dim)) + + # Calculate Flash Attention. + attn_out = flash_attention_ragged( + self.kv_params, + input=xq, + kv_collection=kv_collection, + layer_idx=self.layer_idx, + input_row_offsets=kwargs["input_row_offsets"], + mask_variant=self.mask_variant, + scale=self.scale, + ) + + attn_out = ops.reshape(attn_out, shape=[total_seq_len, -1]) + + return self.wo(attn_out) diff --git a/src/max/pipelines/nn/attention/clamp.py b/src/max/pipelines/nn/attention/clamp.py new file mode 100644 index 0000000000..86404eb314 --- /dev/null +++ b/src/max/pipelines/nn/attention/clamp.py @@ -0,0 +1,22 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max.graph import TensorValue, ops + + +def clamp(x: TensorValue, min: float, max: float) -> TensorValue: + """Clamps values in `x` to `[min, max]`""" + return ops.min( + ops.max(x, ops.constant(min, x.dtype)), + ops.constant(max, x.dtype), + ) diff --git a/src/max/pipelines/nn/attention/interfaces.py b/src/max/pipelines/nn/attention/interfaces.py new file mode 100644 index 0000000000..ba0303cfb2 --- /dev/null +++ b/src/max/pipelines/nn/attention/interfaces.py @@ -0,0 +1,288 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""General interface for Attention.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass + +from max.graph import ( + BufferValue, + TensorValue, + TensorValueLike, +) +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + KVCacheParams, + PagedKVCacheCollection, +) + +from ..layer import Layer, LayerV2 +from ..linear import Linear + + +@dataclass +class AttentionImpl(Layer, ABC): + """ + A generalized attention interface, that will be used upstream by a general Transformer. + We would expect a seperate subclass, articulating each variation of Attention: + + - AttentionWithRope + - AttentionWithAlibi + - VanillaAttentionWithCausalMask + - ... + + There are a series of shared attributes, however, more may be needed for each individual variant. + For example, we may introduce an OptimizedRotaryEmbedding class for the AttentionWithRope class: + + .. code-block:: python + + @dataclass + class AttentionWithRope(AttentionImpl): + rope: OptimizedRotaryEmbedding + ... + + We expect the ``__call__`` abstractmethod to remain relatively consistent, however the ``**kwargs`` + argument is exposed, allowing you to leverage additional arguments for each particular variant. + For example, we may introduce an VanillaAttentionWithCausalMask class, which includes an attention + mask: + + .. code-block:: python + + @dataclass + class VanillaAttentionWithCausalMask(AttentionImpl): + ... + + def __call__( + self, + x: TensorValueLike, + kv_collection: ContinuousBatchingKVCacheCollection, + valid_lengths: TensorValueLike, + **kwargs, + ) -> tuple[TensorValue, ContinuousBatchingKVCacheCollection]: ... + + if "attn_mask" not in kwargs: + raise ValueError("attn_mask not provided to VanillaAttentionWithCausalMask") + + # Which we can then use the attention mask downstream like so: + op( + attn_mask = kwargs["attn_mask"] + ) + """ + + n_heads: int + """The number of attention heads.""" + + kv_params: KVCacheParams + """KV Cache Params, including the number of kv heads, the head dim, and data type.""" + + layer_idx: TensorValue + """The layer number associated with this Attention block.""" + + wqkv: TensorValue + """The concatenation of q, k, and v weight vectors.""" + + wo: Linear + """A linear layer for the output projection.""" + + scale: float + """The scale factor for the attention.""" + + def __post_init__(self) -> None: + assert self.scale is not None, "scale must be provided to AttentionImpl" + + if not self.kv_params.cache_strategy.uses_opaque(): + raise ValueError( + f"{self.kv_params.cache_strategy} cache strategy, not supported" + " in Attention layer." + ) + + @abstractmethod + def __call__( + self, + x: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection + | PagedKVCacheCollection, + **kwargs, + ) -> TensorValue: ... + + +class AttentionImplV2(LayerV2, ABC): + """A generalized attention interface, that will be used upstream by a general Transformer. + We would expect a separate subclass, articulating each variation of Attention: + + - AttentionWithRope + - AttentionWithAlibi + - VanillaAttentionWithCausalMask + - ... + + `AttentionImplV2` will replace `AttentionImpl` as we roll out changes to the + Layer API. + + There are a series of shared attributes, however, more may be needed for each individual variant. + For example, we may introduce an OptimizedRotaryEmbedding class for the AttentionWithRope class: + + .. code-block:: python + + @dataclass + class AttentionWithRope(AttentionImplV2): + rope: OptimizedRotaryEmbedding + ... + + We expect the ``__call__`` abstractmethod to remain relatively consistent, however the ``**kwargs`` + argument is exposed, allowing you to leverage additional arguments for each particular variant. + For example, we may introduce an VanillaAttentionWithCausalMask class, which includes an attention + mask: + + .. code-block:: python + + class VanillaAttentionWithCausalMask(AttentionImplV2): + ... + + def __call__( + self, + x: TensorValueLike, + kv_collection: ContinuousBatchingKVCacheCollection, + **kwargs, + ) -> tuple[TensorValue, ContinuousBatchingKVCacheCollection]: ... + + if "attn_mask" not in kwargs: + raise ValueError("attn_mask not provided to VanillaAttentionWithCausalMask") + + # Which we can then use the attention mask downstream like so: + op( + attn_mask = kwargs["attn_mask"] + ) + """ + + @abstractmethod + def __call__( + self, + x: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection + | PagedKVCacheCollection, + **kwargs, + ) -> TensorValue: ... + + +@dataclass +class DistributedAttentionImpl(Layer, ABC): + """ + A generalized Distributed attention interface. + """ + + @abstractmethod + def __call__( + self, + x: list[TensorValue], + signal_buffers: list[BufferValue], + kv_collections: list[ + ContinuousBatchingKVCacheCollection | PagedKVCacheCollection + ], + **kwargs, + ) -> list[TensorValue]: ... + + +@dataclass +class AttentionImplQKV(Layer, ABC): + """ + A generalized attention interface, that will be used upstream by a general Transformer. + We would expect a seperate subclass, articulating each variation of Attention: + + - AttentionWithRope + - AttentionWithAlibi + - VanillaAttentionWithCausalMask + - ... + + There are a series of shared attributes, however, more may be needed for each individual variant. + For example, we may introduce an OptimizedRotaryEmbedding class for the AttentionWithRope class: + + .. code-block:: python + + @dataclass + class AttentionWithRope(AttentionImpl): + rope: OptimizedRotaryEmbedding + ... + + We expect the ``__call__`` abstractmethod to remain relatively consistent, however the ``**kwargs`` + argument is exposed, allowing you to leverage additional arguments for each particular variant. + For example, we may introduce an VanillaAttentionWithCausalMask class, which includes an attention + mask: + + .. code-block:: python + + @dataclass + class VanillaAttentionWithCausalMask(AttentionImpl): + ... + + def __call__( + self, + x: TensorValueLike, + kv_collection: ContinuousBatchingKVCacheCollection, + valid_lengths: TensorValueLike, + **kwargs, + ) -> tuple[TensorValue, ContinuousBatchingKVCacheCollection]: ... + + if "attn_mask" not in kwargs: + raise ValueError("attn_mask not provided to VanillaAttentionWithCausalMask") + + # Which we can then use the attention mask downstream like so: + op( + attn_mask = kwargs["attn_mask"] + ) + """ + + n_heads: int + """The number of attention heads.""" + + kv_params: KVCacheParams + """KV Cache Params, including the number of kv heads, the head dim, and data type.""" + + layer_idx: int + """The layer number associated with this Attention block.""" + + wq: TensorValueLike + """The q weight vector.""" + + wk: TensorValueLike + """The k weight vector.""" + + wv: TensorValueLike + """The v weight vector.""" + + wo: Linear + """A linear layer for the output projection.""" + + scale: float + """The scale factor for the attention.""" + + def __post_init__(self) -> None: + assert self.scale is not None, ( + "scale must be provided to AttentionImplQKV" + ) + + if not self.kv_params.cache_strategy.uses_opaque(): + raise ValueError( + f"{self.kv_params.cache_strategy} cache strategy, not supported" + " in Attention layer." + ) + + @abstractmethod + def __call__( + self, + x: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection + | PagedKVCacheCollection, + **kwargs, + ) -> TensorValue: ... diff --git a/src/max/pipelines/nn/attention/naive_attention_with_rope.py b/src/max/pipelines/nn/attention/naive_attention_with_rope.py new file mode 100644 index 0000000000..3f136f167c --- /dev/null +++ b/src/max/pipelines/nn/attention/naive_attention_with_rope.py @@ -0,0 +1,223 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""An attention layer, using only native max graph operations, the naive cache, and ROPE.""" + +from __future__ import annotations + +import math + +from max.dtype import DType +from max.graph import BufferValue, TensorValue, TensorValueLike, ops +from max.pipelines.kv_cache import KVCacheParams + +from ..layer import LayerV2 +from ..linear import Linear, LinearV2 +from ..rotary_embedding import RotaryEmbedding +from .clamp import clamp + + +class NaiveAttentionWithRope(LayerV2): + def __init__( + self, + n_heads: int, + kv_params: KVCacheParams, + dim: int, + wq: Linear | LinearV2, + wk: Linear | LinearV2, + wv: Linear | LinearV2, + wo: Linear | LinearV2, + rope: RotaryEmbedding, + scale: float | None = None, + clip_qkv: float | None = None, + ): + super().__init__() + self.n_heads = n_heads + self.kv_params = kv_params + self.dim = dim + self.q_proj = wq + self.k_proj = wk + self.v_proj = wv + self.o_proj = wo + self.rope = rope + self.scale = ( + scale if scale else math.sqrt(1.0 / self.kv_params.head_dim) + ) + self.clip_qkv = clip_qkv + + if self.kv_params.cache_strategy.uses_opaque(): + raise ValueError( + f"{self.kv_params.cache_strategy} cache strategy, not supported" + " in Attention layer." + ) + + def repeat_kv(self, kv: TensorValue) -> TensorValue: + """Repeats key/value tensors to match the number of query heads.""" + batch = kv.shape[0] + kv = ops.reshape( + kv, + [batch, -1, self.kv_params.n_kv_heads, 1, self.kv_params.head_dim], + ) + + kv = ops.tile( + kv, [1, 1, 1, self.n_heads // self.kv_params.n_kv_heads, 1] + ) + return ops.reshape( + kv, [batch, -1, self.n_heads, self.kv_params.head_dim] + ) + + def attention( + self, + xq: TensorValueLike, + xk: TensorValueLike, + xv: TensorValueLike, + attn_mask: TensorValueLike, + keys: TensorValueLike, + values: TensorValueLike, + ) -> TensorValue: + xq = TensorValue(xq) + xk = TensorValue(xk) + xv = TensorValue(xv) + attn_mask = TensorValue(attn_mask) + keys = TensorValue(keys) + values = TensorValue(values) + + # Broadcast the attention mask across heads. + # Do so in the graph so that the broadcast can be fused downstream ops. + batch, seq_len, post_seq_len = attn_mask.shape + attn_mask = attn_mask.reshape( + (batch, 1, seq_len, post_seq_len) + ).broadcast_to((batch, self.n_heads, seq_len, post_seq_len)) + + keys = keys.transpose(0, 1) + values = values.transpose(0, 1) + + keys = self.repeat_kv(keys) + values = self.repeat_kv(values) + + xq = xq.transpose(1, 2) + keys = keys.transpose(1, 2) + values = values.transpose(1, 2) + + scores = xq @ ops.transpose(keys, 2, 3) + # Note, the graph compiler currently requires the order of operands + # to be `scores * scale` in order to pattern match the fused attention + # operator. + return ( + ops.softmax( + scores * ops.constant(self.scale, dtype=DType.float32) + + attn_mask + ) + @ values + ) + + def __call__( + self, + x: TensorValueLike, + attention_mask: TensorValueLike, + k_cache: BufferValue, + v_cache: BufferValue, + start_pos: TensorValue, + layer_index: int, + ) -> TensorValue: + """Computes attention on x, reusing the KV cache. + + Args: + x: Activations with shape (batch, seq_len, dim). + k_cache: The full keys cache buffer with shape + (max_seq_len, n_layers, max_batch, n_kv_heads, head_dim). + v_cache: The full values cache buffer with shape + (max_seq_len, n_layers, max_batch, n_kv_heads, head_dim). + start_pos: Scalar of the current position in the kv_cache. + + Returns the result of multi-headed self attention on the input. + """ + x = TensorValue(x) + batch, seq_len = x.shape[0], x.shape[1] + # Apply `clip_qkv` before matmuling the weights + if self.clip_qkv: + assert isinstance(self.q_proj.weight, TensorValue) + self.q_proj.weight = clamp( + self.q_proj.weight, min=-self.clip_qkv, max=self.clip_qkv + ) + assert isinstance(self.k_proj.weight, TensorValue) + self.k_proj.weight = clamp( + self.k_proj.weight, min=-self.clip_qkv, max=self.clip_qkv + ) + assert isinstance(self.v_proj.weight, TensorValue) + self.v_proj.weight = clamp( + self.v_proj.weight, min=-self.clip_qkv, max=self.clip_qkv + ) + + xq = self.q_proj(x) + xk = self.k_proj(x) + xv = self.v_proj(x) + + xq = ops.reshape( + xq, [batch, seq_len, self.n_heads, self.kv_params.head_dim] + ) + + xk = ops.reshape( + xk, + [ + batch, + seq_len, + self.kv_params.n_kv_heads, + self.kv_params.head_dim, + ], + ) + xv = ops.reshape( + xv, + [ + batch, + seq_len, + self.kv_params.n_kv_heads, + self.kv_params.head_dim, + ], + ) + + xq = self.rope(xq, start_pos, seq_len) + xk = self.rope(xk, start_pos, seq_len) + + # Write xk and xv back the to cache at start_pos. + # The cache can have a larger max batch size than the current input. + # We slice down to the active batch size. + # cache[start_pos:start_pos+seq_len, layer_index, :batch] = ... + seq_len_val = TensorValue(seq_len) + slice_seq_len = (slice(start_pos, start_pos + seq_len_val), seq_len) + batch_val = TensorValue(batch) + slice_batch = (slice(0, batch_val), batch) + k_cache[slice_seq_len, layer_index, slice_batch] = xk.transpose( + 0, 1 + ).cast(k_cache.dtype) + v_cache[slice_seq_len, layer_index, slice_batch] = xv.transpose( + 0, 1 + ).cast(k_cache.dtype) + + # Then slice the correct keys and values for attention. + # The cache can have a larger max batch size than the current input. + # We slice down to the active batch size. + # ... = cache[0:start_pos+seq_len, layer_index, :batch] + slice_post_seq_len = (slice(0, start_pos + seq_len_val), "post_seq_len") + keys = k_cache[slice_post_seq_len, layer_index, slice_batch].cast( + xq.dtype + ) + values = v_cache[slice_post_seq_len, layer_index, slice_batch].cast( + xq.dtype + ) + + output = ( + self.attention(xq, xk, xv, attention_mask, keys, values) + .transpose(1, 2) + .reshape([batch, seq_len, -1]) + ) + return self.o_proj(output) diff --git a/src/max/pipelines/nn/comm/__init__.py b/src/max/pipelines/nn/comm/__init__.py new file mode 100644 index 0000000000..92288c849d --- /dev/null +++ b/src/max/pipelines/nn/comm/__init__.py @@ -0,0 +1,18 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .allreduce import Signals + +__all__ = [ + "Signals", +] diff --git a/src/max/pipelines/nn/comm/allreduce.py b/src/max/pipelines/nn/comm/allreduce.py new file mode 100644 index 0000000000..3ab64efaa0 --- /dev/null +++ b/src/max/pipelines/nn/comm/allreduce.py @@ -0,0 +1,51 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Allreduce module definitions.""" + +from collections.abc import Iterable + +from max.dtype import DType +from max.graph import BufferType, DeviceRef + + +class Signals: + """Signal buffers used for peer-to-peer communication in allreduce. + + Device code uses these buffers by enabling peer-to-peer access. + Then thread blocks use the buffers to implement barriers for + synchronization, and to hold intermediate communication results. + """ + + NUM_BYTES = (1 + 128) * 1024 * 1024 + """The size of the signal buffers used for communication in allreduce.""" + # NOTE: ``NUM_BYTES`` must stay in sync with the size of the ``Signal`` + # Mojo struct + the size of the intermediate buffer for communication. + + devices: list[DeviceRef] + """List of devices that these signals communicate between.""" + + def __init__(self, devices: Iterable[DeviceRef]) -> None: + """ + Args: + devices: Devices between which these signals communicate. + """ + self.devices = list(devices) + + def input_types(self) -> list[BufferType]: + """Gets graph input types corresponding to these signal buffers.""" + return [ + BufferType( + dtype=DType.uint8, shape=(Signals.NUM_BYTES,), device=dev + ) + for dev in self.devices + ] diff --git a/src/max/pipelines/nn/compute_log_probabilities.py b/src/max/pipelines/nn/compute_log_probabilities.py new file mode 100644 index 0000000000..2914c34582 --- /dev/null +++ b/src/max/pipelines/nn/compute_log_probabilities.py @@ -0,0 +1,101 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from typing import Callable + +import numpy as np +from max.pipelines import LogProbabilities +from scipy.special import log_softmax # type: ignore + + +def compute_log_probabilities( + get_logits_and_samples: Callable[ + [int, bool], (tuple[np.ndarray, np.ndarray] | None) + ], + batch_top_n: list[int], + batch_echo: list[bool], +) -> list[LogProbabilities | None]: + """Computes the log probabilities. + + Args: + get_logits_and_samples: Callable that takes the batch index and an + `echo` bool and returns the logits and sampled tokens for that batch. + Args: + - batch_index is an int between [0, batch_size) + - echo is whether that item was requested to echo the input tokens. + Returns (None if batch item is empty): + - Logits should have shape = (n_tokens, vocab_size). + - Sampled tokens should have shape = (n_tokens). + batch_top_n: Number of top log probabilities to return per input in + the batch. For any element where `top_n == 0`, the + LogProbabilities is skipped. + batch_echo: Whether to include input tokens in the returned log + probabilities. + + Returns: + Computed log probabilities for each item in the batch. + """ + log_probabilities: list[LogProbabilities | None] = [] + for batch, (top_n, echo) in enumerate(zip(batch_top_n, batch_echo)): + if top_n == 0: + log_probabilities.append(None) + continue + + logits_and_samples = get_logits_and_samples(batch, echo) + if not logits_and_samples: + log_probabilities.append(None) + continue + + logits, samples = logits_and_samples + log_probs = log_softmax(logits, axis=-1) + + # Get top n tokens. + top_n_indices = np.argpartition(log_probs, -top_n, axis=-1)[ + ..., -top_n: + ] + + # Get the log probabilities of each sampled token. + sampled_log_probs = np.take_along_axis( + log_probs, samples.reshape(-1, 1), axis=1 + ).reshape(-1) + + # Store the stats for each sample token. + num_tokens = log_probs.shape[0] + token_log_probabilities = [] + top_log_probabilities = [] + for i in range(num_tokens): + token_log_probabilities.append(sampled_log_probs[i].item()) + + # Compute top n log probs. + top_tokens = {} + for n in range(top_n): + top_token = top_n_indices[i][n] + top_token_logits = log_probs[i][top_token] + top_tokens[top_token] = top_token_logits.item() + + # Include sampled token in the top tokens. + sampled_token = samples[i].item() + top_tokens[sampled_token] = sampled_log_probs[i].item() + + top_log_probabilities.append(top_tokens) + + log_probabilities.append( + LogProbabilities( + token_log_probabilities=token_log_probabilities, + top_log_probabilities=top_log_probabilities, + ) + ) + + return log_probabilities diff --git a/src/max/pipelines/nn/conv.py b/src/max/pipelines/nn/conv.py new file mode 100644 index 0000000000..b7cda54f3e --- /dev/null +++ b/src/max/pipelines/nn/conv.py @@ -0,0 +1,177 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +from max.graph import TensorValue, TensorValueLike, Weight, ops + +from .layer import Layer + + +@dataclass +class Conv2D(Layer): + """A 2D convolution over an input signal composed of several input + planes. + """ + + filter: TensorValueLike + bias: Optional[TensorValueLike] = None + + stride: Union[int, Tuple[int, int]] = (1, 1) + padding: Union[int, Tuple[int, int, int, int]] = (0, 0, 0, 0) + dilation: Union[int, Tuple[int, int]] = (1, 1) + groups: int = 1 + + def __call__(self, x: TensorValue) -> TensorValue: + # These need to be casted as the underlying ops.conv2d call + # expects them to only be tuple types. + if isinstance(self.stride, int): + self.stride = (self.stride, self.stride) + + if isinstance(self.padding, int): + self.padding = ( + self.padding, + self.padding, + self.padding, + self.padding, + ) + + if isinstance(self.dilation, int): + self.dilation = (self.dilation, self.dilation) + + if ( + isinstance(self.filter, Weight) + and self.filter.quantization_encoding is not None + ): + raise ValueError("Conv1D not implemented with weight quantization.") + return ops.conv2d( + x, + self.filter, + self.stride, + self.dilation, + self.padding, + self.groups, + self.bias, + ) + + +@dataclass +class Conv1D(Layer): + """A 1D convolution over an input signal composed of several input + planes. + """ + + filter: TensorValueLike # [kernel_size, in_channels, out_channels] + bias: Optional[TensorValueLike] = None + + stride: int = 1 + padding: int = 0 + dilation: int = 1 + groups: int = 1 + + def __call__(self, x: TensorValueLike) -> TensorValue: + """ + Args: + x: a tensor of shape [batch_size, length, in_channels] + + Returns: + a tensor of shape [batch_size, new_length, out_channels] + new_length = ((length + 2 * padding - (kernel_size - 1) - 1) / stride) + 1 + """ + # TODO(GEX-327): Support Conv1D in mo rather than implementing it using Conv2D. + # Reshape [batch_size, length, in_channels] to [batch_size, height=1, length, in_channels]. + x = ops.unsqueeze(x, 1) + # Reshape [kernel_size, in_channels, out_channels] to [height=1, kernel_size, in_channels, out_channels]. + filter = ops.unsqueeze(self.filter, 0) + if ( + isinstance(self.filter, Weight) + and self.filter.quantization_encoding is not None + ): + raise ValueError("Conv1D not implemented with weight quantization.") + else: + output = ops.conv2d( + x, + filter, + (1, self.stride), + (1, self.dilation), + (0, 0, self.padding, self.padding), + self.groups, + self.bias, + ) + # Reshape [batch_size, height=1, new_length, out_channels] to [batch_size, new_length, out_channels]. + return ops.squeeze(output, 1) + + +@dataclass +class Conv3D(Layer): + """A 3D convolution over an input signal composed of several input + planes. + """ + + filter: TensorValueLike # [depth, height, width, in_channels / num_groups, out_channels] + bias: Optional[TensorValueLike] = None # [out_channels] + + stride: Union[int, Tuple[int, int, int]] = (1, 1, 1) + padding: Union[int, Tuple[int, int, int, int, int, int]] = ( + 0, + 0, + 0, + 0, + 0, + 0, + ) + dilation: Union[int, Tuple[int, int, int]] = (1, 1, 1) + groups: int = 1 + + def __call__(self, x: TensorValueLike) -> TensorValue: + """ + Args: + x: a tensor of shape [batch_size, length, in_channels] + + Returns: + a tensor of shape [batch_size, new_length, out_channels] + new_length = ((length + 2 * padding - (kernel_size - 1) - 1) / stride) + 1 + """ + # These need to be casted as the underlying ops.conv2d call + # expects them to only be tuple types. + if isinstance(self.stride, int): + self.stride = (self.stride, self.stride, self.stride) + + if isinstance(self.padding, int): + self.padding = ( + self.padding, + self.padding, + self.padding, + self.padding, + self.padding, + self.padding, + ) + + if isinstance(self.dilation, int): + self.dilation = (self.dilation, self.dilation, self.dilation) + + if ( + isinstance(self.filter, Weight) + and self.filter.quantization_encoding is not None + ): + raise ValueError("Conv3D not implemented with weight quantization.") + return ops.conv3d( + x, + self.filter, + self.stride, + self.dilation, + self.padding, + self.groups, + self.bias, + ) diff --git a/src/max/pipelines/nn/embedding.py b/src/max/pipelines/nn/embedding.py new file mode 100644 index 0000000000..fceaa845d3 --- /dev/null +++ b/src/max/pipelines/nn/embedding.py @@ -0,0 +1,255 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Optional + +from max.dtype import DType +from max.graph import ( + BufferValue, + DeviceRef, + TensorValue, + TensorValueLike, + Weight, + ops, +) +from max.graph.quantization import QuantizationEncoding + +from .layer import Layer, LayerV2 + + +@dataclass +class Embedding(Layer): + weights: TensorValueLike + + def __call__(self, indices: TensorValueLike) -> TensorValue: + result = ops.gather(self.weights, indices, axis=0) + if ( + isinstance(self.weights, Weight) + and self.weights.quantization_encoding is not None + ): + result = ops.dequantize(self.weights.quantization_encoding, result) + return result + + +class EmbeddingV2(LayerV2): + """ + A lookup table for embedding integer indices into dense vectors. + + This layer maps each integer index to a dense vector of fixed size. + Embedding weights are stored on the CPU but are moved to the specified + device during the model init phase. + + Example: + + .. code-block:: python + + embedding_layer = EmbeddingV2( + vocab_size=1000, + hidden_dim=256, + dtype=DType.float32, + device=DeviceRef.GPU(), + name="embeddings", + ) + + # Token indices of shape: [batch, ..., num_indices]. + token_indices: TensorValueLike + embeddings = embedding_layer(token_indices) + """ + + weight: Weight + """The embedding weight matrix stored on the CPU. + Model init moves weights to the device specified in :obj:`device`.""" + + device: DeviceRef | None + """The device on which embedding lookup is performed.""" + + def __init__( + self, + vocab_size: int, + hidden_dim: int, + dtype: DType, + device: DeviceRef | None = None, + quantization_encoding: Optional[QuantizationEncoding] = None, + name: Optional[str] = None, + ) -> None: + """Initializes the embedding layer with the given arguments. + + Args: + vocab_size: The number of unique items in the vocabulary. + Indices must be in the range ``[0, vocab_size)``. + hidden_dim: The dimensionality of each embedding vector. + dtype: The data type of the embedding weights. + device: The device where embedding lookups are executed. + Model init transfers the initially CPU-resident weights to this + device. + name: The name identifier for the embedding weight matrix. + """ + super().__init__() + + self.device = device + self.weight = Weight( + name or "weight", + dtype, + shape=(vocab_size, hidden_dim), + device=DeviceRef.CPU() if self.device else None, + quantization_encoding=quantization_encoding, + ) + + def __call__(self, indices: TensorValueLike) -> TensorValue: + """Embeds the input indices by looking up corresponding vectors. + + Args: + indices: A tensor of integer indices to look up. + Each index must be in the range ``[0, vocab_size)``. + + Returns: + A tensor containing the embeddings corresponding to the input + indices. + The result resides on the device specified in :obj:`device`. + """ + weight = self.weight.to(self.device) if self.device else self.weight + result = ops.gather( + TensorValue(weight), + indices, + axis=0, + ) + if self.weight.quantization_encoding is not None: + result = ops.dequantize(self.weight.quantization_encoding, result) + return result + + +class VocabParallelEmbedding(LayerV2): + """ + A lookup table for embedding integer indices into dense vectors. + + This layer works like `nn.Embedding` except the embedding table is sharded + on the vocabulary dimension across all devices. + + Example: + + .. code-block:: python + + embedding_layer = VocabParallelEmbedding( + vocab_size=1000, + hidden_dim=256, + dtype=DType.float32, + device=[DeviceRef.GPU(0), DeviceRef.GPU(1)], + name="embeddings", + ) + + # Token indices of shape: [batch, ..., num_indices]. + token_indices: TensorValueLike + embeddings = embedding_layer(token_indices) + """ + + def __init__( + self, + vocab_size: int, + hidden_dim: int, + dtype: DType, + devices: list[DeviceRef], + quantization_encoding: Optional[QuantizationEncoding] = None, + name: Optional[str] = None, + ): + """ + Args: + vocab_size: The number of unique items in the vocabulary. + Indices must be in the range ``[0, vocab_size)``. + hidden_dim: The dimensionality of each embedding vector. + dtype: The data type of the embedding weights. + devices: The devices where embedding lookups are executed. + Model init transfers the initially CPU-resident weights to this + device. + name: The name identifier for the embedding weight matrix. + """ + super().__init__() + self.vocab_size = vocab_size + self.devices = devices + + self.num_devices = len(self.devices) + self.shard_size = math.ceil(self.vocab_size / self.num_devices) + + # The weight is loaded in with a single op, then copied to each device + # in __call__. + self.weight = Weight( + name or "weight", + dtype, + shape=(vocab_size, hidden_dim), + device=DeviceRef.CPU(), + quantization_encoding=quantization_encoding, + ) + + def __call__( + self, indices: TensorValueLike, signal_buffers: list[BufferValue] + ) -> list[TensorValue]: + """Embeds the input indices by looking up corresponding vectors. + + Args: + indices: A tensor of integer indices to look up. + Each index must be in the range ``[0, vocab_size)``. + + Returns: + A tensor containing the embeddings corresponding to the input + indices. + The result resides on the device specified in :obj:`device`. + """ + # Shard the weight onto each device. + input = TensorValue(indices) + outputs = [ + self._per_device_call(input, n) for n in range(self.num_devices) + ] + return ops.allreduce.sum(outputs, signal_buffers) + + def _per_device_call( + self, indices: TensorValue, device_idx: int + ) -> TensorValue: + """Computes the embeddings for the input indices, for a single device.""" + # Copy a shard from the embedding weights onto the device. + device = self.devices[device_idx] + vocab_start_index = self.shard_size * device_idx + vocab_end_index = min( + self.shard_size * (device_idx + 1), self.vocab_size + ) + embedding_shard = self.weight[vocab_start_index:vocab_end_index].to( + device + ) + + indices = indices.to(device) + # Process indices so that all tokens are between 0 and the shard size. + + # Set up mask so that the 1=tokens within range, 0=tokens out of range. + input_mask = ops.logical_and( + indices >= vocab_start_index, indices < vocab_end_index + ) + + # Tokens that are out of this range are masked out. + indices -= vocab_start_index + + # Apply mask to avoid searching for out-of-bound tokens + indices *= input_mask + + result = ops.gather( + embedding_shard, + indices, + axis=0, + ) + if self.weight.quantization_encoding is not None: + result = ops.dequantize(self.weight.quantization_encoding, result) + result *= ops.cast( + ops.unsqueeze(input_mask, 1), result.dtype + ) # Apply input mask again + return result diff --git a/src/max/pipelines/nn/hooks/__init__.py b/src/max/pipelines/nn/hooks/__init__.py new file mode 100644 index 0000000000..b39c9375e4 --- /dev/null +++ b/src/max/pipelines/nn/hooks/__init__.py @@ -0,0 +1,20 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .print_hook import PrintHook +from .torch_print_hook import TorchPrintHook + +__all__ = [ + "PrintHook", + "TorchPrintHook", +] diff --git a/src/max/pipelines/nn/hooks/base_print_hook.py b/src/max/pipelines/nn/hooks/base_print_hook.py new file mode 100644 index 0000000000..4241cd88bb --- /dev/null +++ b/src/max/pipelines/nn/hooks/base_print_hook.py @@ -0,0 +1,159 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import dataclasses +import os +from abc import ABC, abstractmethod +from collections import Counter +from dataclasses import dataclass +from typing import Any + +from max.pipelines.nn._identity import IdentityMap + + +@dataclass +class LayerInfo: + layer_name: str + call_count: int = 0 + + +class BasePrintHook(ABC): + """Base hook for printing values. + + This class defines a `__call__` method that prints the inputs and outputs. + Only layers that have been saved with `hook.add_layer` will be printed. + + Abstract methods: + - print_value(name, value) -> bool: Override this method to print the value, + and return whether the print was successful. + + Properties: + - export_path: The base path to write values into (can be None). The full + exported path is "{export_path}/{step}". + + Methods: + - __call__(layer, args, kwargs, outputs): Calls `self.print_value`. + - add_layer(layer, name): Stores the name of a layer. + - step(): Can be called to increment the step and update the export path. + - remove(): To be used to remove the hook and run some cleanup steps. + - summarize(): Summarize the total number of tensors printed at each step. + """ + + def __init__(self, export_path: str | None = None): + self._known_layers = IdentityMap() # Maps layer -> LayerInfo + self._export_path = export_path + self._current_step = 0 + # Keep a counter for creating unique layer names. + self._layer_counter: dict[str, int] = Counter() + + # Maps step number -> [list of printed tensors] + self._recorded_prints: dict[int, list[str]] = {} + + def add_layer(self, layer, name): + self._known_layers[layer] = LayerInfo(name) + + @property + def export_path(self) -> str | None: + if self._export_path is None: + return None + return os.path.join(self._export_path, str(self._current_step)) + + def step(self): + self._current_step += 1 + + # Update export path. + if export_path := self.export_path: + os.makedirs(export_path, exist_ok=True) + + # Reset layer call counts. + for info in self._known_layers.values(): + info.call_count = 0 + + def __call__(self, layer, args, kwargs, outputs): + """Print all TensorValues.""" + if layer not in self._known_layers: + # If layer is not yet named, use the class name. + layer_cls = type(layer).__name__ + self._layer_counter[layer_cls] += 1 + layer_name = layer_cls + if self._layer_counter[layer_cls] > 1: + layer_name = f"{layer_name}{self._layer_counter[layer_cls]}" + self._known_layers[layer] = LayerInfo(layer_name) + + # Update call count and get layer name. + info = self._known_layers[layer] + info.call_count += 1 + if info.call_count > 1: + debug_name = f"{info.layer_name}({info.call_count})" + else: + debug_name = info.layer_name + + # Print input args and kwargs + for n, arg in enumerate(args): + self.print_and_record(f"{debug_name}-input_{n}", arg) + for key, value in kwargs.items(): + self.print_and_record(f"{debug_name}-input_{key}", value) + + # Print outputs. + if self.print_and_record(f"{debug_name}-output", outputs): + pass + elif isinstance(outputs, (list, tuple)): + for n, out in enumerate(outputs): + self.print_and_record(f"{debug_name}-output_{n}", out) + elif dataclasses.is_dataclass(outputs): + for field in dataclasses.fields(outputs): + self.print_and_record( + f"{debug_name}-output_{field.name}", + getattr(outputs, field.name), + ) + else: + print( + f"Was not able to write outputs from {debug_name} (output type" + f" was {type(outputs)})" + ) + + def print_and_record(self, name: str, value: Any) -> bool: + """Runs self.print_value and records the tensor if printed.""" + print_success = self.print_value(name, value) + if print_success: + if self._current_step not in self._recorded_prints: + self._recorded_prints[self._current_step] = [] + self._recorded_prints[self._current_step].append(name) + return print_success + + @abstractmethod + def print_value(self, name: str, value: Any) -> bool: + """Prints a value, and returns whether the print is successful.""" + raise NotImplementedError + + def summarize(self): + action = "Printed" + if self.export_path: + action = "Saved" + + tensors_printed = False + for step, tensors in self._recorded_prints.items(): + print(f"{action} {len(tensors)} tensors for step {step}.") + tensors_printed = tensors_printed or bool(len(tensors)) + + if not tensors_printed: + print("No tensors exported.") + elif self._export_path: + print(f"Tensors exported to {self._export_path}") + + def remove(self): + # Clean up export_path if it's empty. + if (export_path := self.export_path) and not os.listdir(export_path): + os.rmdir(export_path) diff --git a/src/max/pipelines/nn/hooks/identity.py b/src/max/pipelines/nn/hooks/identity.py new file mode 100644 index 0000000000..eeb038c45a --- /dev/null +++ b/src/max/pipelines/nn/hooks/identity.py @@ -0,0 +1,71 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Utility classes for using objects as keys in data structures.""" + +from collections.abc import MutableMapping, MutableSet + + +# From https://stackoverflow.com/questions/16994307/identityset-in-python +class IdentitySet(MutableSet): + """Set that uses object `id` as keys to support unhashable types.""" + + def __init__(self, iterable=()): + self.map = {} # id -> object + self |= iterable # add elements from iterable to the set (union) + + def __len__(self): + return len(self.map) + + def __iter__(self): + return iter(self.map.values()) + + def __contains__(self, x): + return id(x) in self.map + + def add(self, value): + """Add an element.""" + self.map[id(value)] = value + + def discard(self, value): + """Remove an element. Do not raise an exception if absent.""" + self.map.pop(id(value), None) + + def __repr__(self): + if not self: + return "%s()" % (self.__class__.__name__,) + return "%s(%r)" % (self.__class__.__name__, list(self)) + + +class IdentityMap(MutableMapping): + """Map that uses object `id` as keys to support unhashable types.""" + + def __init__(self): + self.key_map = {} # id -> object + self.value_map = {} # id -> Value + + def __getitem__(self, key): + return self.value_map[id(key)] + + def __setitem__(self, key, value): + self.key_map[id(key)] = key + self.value_map[id(key)] = value + + def __delitem__(self, key): + del self.key_map[id(key)] + del self.value_map[id(key)] + + def __iter__(self): + return iter(self.key_map.values()) + + def __len__(self): + return len(self.key_map) diff --git a/src/max/pipelines/nn/hooks/print_hook.py b/src/max/pipelines/nn/hooks/print_hook.py new file mode 100644 index 0000000000..845a6c0c3a --- /dev/null +++ b/src/max/pipelines/nn/hooks/print_hook.py @@ -0,0 +1,91 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Print hook for MAX Pipeline models.""" + +from __future__ import annotations + +import logging +import os +from collections import deque +from typing import Any, Optional, Tuple + +from max.graph import TensorValue +from max.pipelines.nn._identity import IdentitySet +from max.pipelines.nn.layer import Layer, add_layer_hook, clear_hooks + +from .base_print_hook import BasePrintHook + +logger = logging.getLogger("max.pipelines") + + +class PrintHook(BasePrintHook): + """Hook that prints/saves layer tensor inputs and outputs. + + This class must be initialized added before the graph is built so the + print ops can be added to the graph. + """ + + def __init__(self, export_path: Optional[str] = None): + super().__init__(export_path=export_path) + add_layer_hook(self) + if export_path is not None: + logger.warning( + "Export path is currently not supported. Values will be printed" + " to stdout with COMPACT format." + ) + + def name_layers(self, model: Layer): + """Create names for all layers in the model based on nested attributes.""" + for layer, name in _walk_layers(model): + self.add_layer(layer, name) + + @property + def export_path(self) -> Optional[str]: + if self._export_path is None: + return None + return os.path.join(self._export_path, str(self._current_step)) + + def print_value(self, name: str, value: Any) -> bool: + if isinstance(value, TensorValue): + value.print(name) + return True + return False + + def remove(self): + super().remove() + clear_hooks() # TODO: Add individual hook remover. + + def __del__(self): + self.summarize() + + +_SUPPORTED_TYPES = (Layer, list, tuple) + + +def _walk_layers(model): + """Walks through model and yields all layers with generated names.""" + seen = IdentitySet() + seen.add(model) + queue: deque[Tuple[Any, str]] = deque([(model, "model")]) + + while queue: + obj, name = queue.popleft() + if isinstance(obj, Layer): + yield obj, name + for k, v in obj.__dict__.items(): + if v not in seen or isinstance(v, _SUPPORTED_TYPES): + queue.append((v, f"{name}.{k}")) + elif isinstance(obj, (list, tuple)): + for n, v in enumerate(obj): + if v not in seen or isinstance(v, _SUPPORTED_TYPES): + queue.append((v, f"{name}.{n}")) diff --git a/src/max/pipelines/nn/hooks/torch_print_hook.py b/src/max/pipelines/nn/hooks/torch_print_hook.py new file mode 100644 index 0000000000..1decc2677c --- /dev/null +++ b/src/max/pipelines/nn/hooks/torch_print_hook.py @@ -0,0 +1,69 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Print hook for torch models.""" + +from __future__ import annotations + +import os +from typing import Any + +import torch + +from .base_print_hook import BasePrintHook + + +class TorchPrintHook(BasePrintHook): + """A torch-compatible print hook.""" + + _handle: torch.utils.hooks.RemovableHandle + """A handle used to remove the forward hook registered by this class.""" + + def __init__(self, export_path: str | None = None): + super().__init__(export_path) + self._handle = torch.nn.modules.module.register_module_forward_hook( + self + ) + + if export_path := self.export_path: + os.makedirs(export_path, exist_ok=True) + + def name_layers(self, model: torch.nn.Module): + """Create names for all layers in the model.""" + for module_name, module in model.named_modules(): + name = f"model.{module_name}" if module_name else "model" + self.add_layer(module, name) + + @property + def export_path(self) -> str | None: + if self._export_path is None: + return None + return os.path.join(self._export_path, str(self._current_step)) + + def __call__(self, module, args, outputs) -> None: # type: ignore + super().__call__(module, args, kwargs={}, outputs=outputs) + + def print_value(self, name: str, value: Any) -> bool: + if isinstance(value, torch.Tensor): + if export_path := self.export_path: + full_path = f"{export_path}/{name}.pt" + torch.save(value, full_path) + else: + print(name, value) + return True + return False + + def remove(self): + super().remove() + + if self._handle: + self._handle.remove() diff --git a/src/max/pipelines/nn/kernels.py b/src/max/pipelines/nn/kernels.py new file mode 100644 index 0000000000..476491da17 --- /dev/null +++ b/src/max/pipelines/nn/kernels.py @@ -0,0 +1,910 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""Helper functions for wrapping custom kv cache/attention related ops.""" + +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum + +import numpy as np +from max.dtype import DType +from max.graph import Dim, TensorType, TensorValue, TensorValueLike, ops +from max.graph.quantization import QuantizationConfig +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + KVCacheParams, + KVCacheStrategy, + PagedKVCacheCollection, +) + + +def fused_qkv_ragged_matmul( + kv_params: KVCacheParams, + input: TensorValue, + input_row_offsets: TensorValue, + wqkv: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection | PagedKVCacheCollection, + layer_idx: TensorValue, + n_heads: int, + bias: TensorValue | None = None, +) -> TensorValue: + """Computes fused query, key, and value projections with ragged input. + + `input` and `input_row_offsets` are used together to implement the ragged + tensor. + `input_row_offsets` indicates where each batch starts and ends in `input` + + Raises: + ValueError: on input shapes/dtypes that are invalid for the kernel. + """ + if input.dtype != wqkv.dtype: + msg = ( + "expected input and wqkv to have the same dtype, but got" + f" {input.dtype} and {wqkv.dtype}, respectively." + ) + raise ValueError(msg) + + input_rank_expected = 2 + if input.rank != input_rank_expected: + msg = f"expected input to have rank {input_rank_expected}, was {input.rank}" + raise ValueError(msg) + + if input_row_offsets.dtype != DType.uint32: + msg = ( + "expected input_row_offsets to have dtype uint32, was" + f" {input_row_offsets.dtype}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected layer_idx to have dtype uint32, was {layer_idx.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy not in { + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.PAGED, + }: + msg = f"unsupported cache strategy for fused_qkv_ragged_matmul: {kv_params.cache_strategy}" + raise ValueError(msg) + + parameters: dict[str, int | str | DType] = { + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + } + if kv_params.cache_strategy == KVCacheStrategy.PAGED: + assert kv_params.page_size is not None + parameters["page_size"] = int(kv_params.page_size) + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + + if bias: + op_name = f"mo.fused_qkv_matmul.ragged.{cache_strategy_str}.bias" + + return ops.inplace_custom( + op_name, + values=[ + input, + input_row_offsets, + wqkv, + kv_collection, + layer_idx, + bias, + ], + out_types=[ + TensorType( + dtype=input.dtype, + shape=input.shape[:-1] + [n_heads * kv_params.head_dim], + device=input.device, + ) + ], + parameters=parameters, + )[0].tensor + + op_name = f"mo.fused_qkv_matmul.ragged.{cache_strategy_str}" + + return ops.inplace_custom( + op_name, + values=[input, input_row_offsets, wqkv, kv_collection, layer_idx], + out_types=[ + TensorType( + dtype=input.dtype, + shape=input.shape[:-1] + [n_heads * kv_params.head_dim], + device=input.device, + ) + ], + parameters=parameters, + )[0].tensor + + +def fused_qkv_ragged_matmul_quantized( + kv_params: KVCacheParams, + input: TensorValue, + input_row_offsets: TensorValue, + wqkv: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection | PagedKVCacheCollection, + layer_idx: TensorValue, + n_heads: int, + quantization_config: QuantizationConfig, + perm_idx: TensorValue | None = None, + bias: TensorValue | None = None, +) -> TensorValue: + """Computes fused query, key, and value projections with ragged input and + quantized weight matrices. A `quantization_config` must be provided. + + `input` and `input_row_offsets` are used together to implement the ragged + tensor. + `input_row_offsets` indicates where each batch starts and ends in `input` + + Raises: + ValueError: on input shapes/dtypes that are invalid for the kernel. + """ + + input_rank_expected = 2 + if input.rank != input_rank_expected: + msg = f"expected input to have rank {input_rank_expected}, was {input.rank}" + raise ValueError(msg) + + if input_row_offsets.dtype != DType.uint32: + msg = ( + "expected input_row_offsets to have dtype uint32, was" + f" {input_row_offsets.dtype}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected layer_idx to have dtype uint32, was {layer_idx.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy not in { + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.PAGED, + }: + msg = f"unsupported cache strategy for fused_qkv_ragged_matmul: {kv_params.cache_strategy}" + raise ValueError(msg) + + # In the group-wise quantization scheme, every `group_size` quantized weights + # share the same scale. If `has_zp` is `True`, there is also a group-wise zero + # point that need to be substracted from the quantized weights. + # Since the new extensibility API doesn't currently support `bool` type parameters, + # we pass `has_zp` as an interger (`has_zp_int`). + # For GPTQ, `has_zp_int` will always be 0. + parameters: dict[str, int | str | DType] = { + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + "group_size": quantization_config.group_size, + "has_zp_int": 0, + } + if perm_idx: + input = ops.gather(input, TensorValue(perm_idx), axis=1) + wqkv = ops.custom( + "GPTQ_gpu_repack_b4_g128_desc_act", + list((wqkv, perm_idx)), + out_types=[ + TensorType( + DType.uint8, + ((wqkv.shape[1], wqkv.shape[0])), + ) + ], + )[0].tensor + else: + wqkv = ops.custom( + "GPTQ_gpu_repack_b4_g128", + list((wqkv,)), + out_types=[ + TensorType( + DType.uint8, + ((wqkv.shape[1], wqkv.shape[0])), + ) + ], + )[0].tensor + + if kv_params.cache_strategy == KVCacheStrategy.PAGED: + assert kv_params.page_size is not None + parameters["page_size"] = int(kv_params.page_size) + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + + args = [input, input_row_offsets, wqkv, kv_collection, layer_idx] + if bias: + args.append(bias) + bias_name_str = "bias." + else: + bias_name_str = "" + + op_name = f"mo.fused_qkv_matmul.ragged.{cache_strategy_str}.{bias_name_str}quantized" + + return ops.inplace_custom( + op_name, + values=args, + out_types=[ + TensorType( + dtype=input.dtype, + shape=input.shape[:-1] + [n_heads * kv_params.head_dim], + device=input.device, + ) + ], + parameters=parameters, + )[0].tensor + + +def fused_qkv_matmul( + kv_params: KVCacheParams, + input: TensorValue, + wqkv: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection, + layer_idx: TensorValue, + n_heads: int, +) -> TensorValue: + """Computes fused query, key and value projections.""" + if input.dtype != wqkv.dtype: + msg = ( + "expected input and wqkv to have the same dtype, but got" + f" {input.dtype} and {wqkv.dtype}, respectively." + ) + raise ValueError(msg) + + input_rank_expected = 3 + if input.rank != input_rank_expected: + msg = f"expected input to have rank {input_rank_expected}, was {input.rank}" + raise ValueError(msg) + + wqkv_rank_expected = 2 + if wqkv.rank != wqkv_rank_expected: + msg = ( + f"expected wqkv to have rank {wqkv_rank_expected}, was {wqkv.rank}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected layer_idx to have dtype uint32, was {layer_idx.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy != KVCacheStrategy.CONTINUOUS: + msg = f"unsupported cache strategy for fused_qkv_matmul: {kv_params.cache_strategy}" + raise ValueError(msg) + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + op_name = f"mo.fused_qkv_matmul.padded.{cache_strategy_str}" + + return ops.inplace_custom( + op_name, + values=[input, wqkv, kv_collection, layer_idx], + out_types=[ + TensorType( + dtype=input.dtype, + shape=input.shape[:-1] + [n_heads * kv_params.head_dim], + device=input.device, + ) + ], + parameters={ + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + }, + )[0].tensor + + +def matmul_kv_cache_ragged( + kv_params: KVCacheParams, + hidden_states: TensorValue, + input_row_offsets: TensorValue, + weight: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection, + layer_idx: int | np.integer, +) -> None: + """Computes key and value projections with ragged input. + + `hidden_states` and `input_row_offsets` are used together to + implement the ragged tensor. + `input_row_offsets` indicates where each batch starts and ends in `input` + """ + if hidden_states.dtype != weight.dtype: + msg = ( + "expected hidden_states and weight to have the same dtype, but got" + f" {hidden_states.dtype} and {weight.dtype}, respectively." + ) + raise ValueError(msg) + + hidden_states_rank_expected = 2 + if hidden_states.rank != hidden_states_rank_expected: + msg = ( + "expected hidden_states to have rank " + f"{hidden_states_rank_expected}, was {hidden_states.rank}" + ) + raise ValueError(msg) + + if input_row_offsets.dtype != DType.uint32: + msg = ( + "expected input_row_offsets to have dtype uint32, was" + f" {input_row_offsets.dtype}" + ) + raise ValueError(msg) + + if kv_params.cache_strategy != KVCacheStrategy.CONTINUOUS: + msg = f"unsupported cache strategy for matmul_kv_cache_ragged: {kv_params.cache_strategy}" + raise ValueError(msg) + + parameters: dict[str, int | str | DType] = { + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + } + if kv_params.cache_strategy == KVCacheStrategy.PAGED: + assert kv_params.page_size is not None + parameters["page_size"] = kv_params.page_size + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + op_name = f"mo.kv_matmul.ragged.{cache_strategy_str}" + + ops.inplace_custom( + name=op_name, + values=[ + hidden_states, + input_row_offsets, + weight, + kv_collection, + ops.constant(layer_idx, DType.uint32), + ], + parameters=parameters, + ) + + +def fused_qk_ragged_rope( + kv_params: KVCacheParams, + input: TensorValue, + input_row_offsets: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection | PagedKVCacheCollection, + freqs_cis: TensorValue, + layer_idx: TensorValue, + interleaved: bool = True, +) -> TensorValue: + """Computes fused query-key attention with rotary positional encodings and ragged inputs. + + `input` and `input_row_offsets` are used together to implement the ragged tensor. + `input_row_offsets` indicates where each batch starts and ends in `input` + """ + + if input.dtype != freqs_cis.dtype: + msg = ( + "expected input and freqs_cis to share a dtype, but got" + f" {input.dtype} and {freqs_cis.dtype} respectively" + ) + raise ValueError(msg) + + if input_row_offsets.dtype != DType.uint32: + msg = ( + "expected input_row_offsets to have dtype uint32, was" + f" {input_row_offsets.dtype}" + ) + + if layer_idx.dtype != DType.uint32: + msg = f"expected layer_idx to have dtype uint32, was {layer_idx.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy not in { + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.PAGED, + }: + msg = f"unsupported cache strategy for fused_qk_ragged_rope: {kv_params.cache_strategy}" + raise ValueError(msg) + + parameters: dict[str, bool | int | str | DType] = { + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + "interleaved": interleaved, + } + if kv_params.cache_strategy == KVCacheStrategy.PAGED: + assert kv_params.page_size is not None + parameters["page_size"] = kv_params.page_size + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + op_name = f"mo.fused_qk_rope.ragged.{cache_strategy_str}" + + return ops.inplace_custom( + op_name, + values=[input, input_row_offsets, kv_collection, freqs_cis, layer_idx], + out_types=[ + TensorType( + dtype=input.dtype, shape=input.shape, device=input.device + ) + ], + parameters=parameters, + )[0].tensor + + +def fused_qk_rope( + kv_params: KVCacheParams, + input: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection, + freqs_cis_2d: TensorValue, + layer_idx: TensorValue, + interleaved: bool = True, +) -> TensorValue: + """Computes fused query-key attention with rotary positional encodings.""" + input_rank_expected = 4 + if input.rank != input_rank_expected: + msg = ( + f"expected input of rank {input_rank_expected} but got {input.rank}" + ) + raise ValueError(msg) + + freqs_cis_rank_expected = 2 + if freqs_cis_2d.rank != freqs_cis_rank_expected: + msg = ( + f"expected freqs_cis_2d of rank {freqs_cis_rank_expected} but got " + f"{freqs_cis_2d.rank}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected uint32 layer_idx but got {layer_idx.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy != KVCacheStrategy.CONTINUOUS: + msg = f"unsupported cache strategy for fused_qk_rope: {kv_params.cache_strategy}" + raise ValueError(msg) + + parameters: dict[str, bool | int | str | DType] = { + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + "interleaved": interleaved, + } + if kv_params.cache_strategy == KVCacheStrategy.PAGED: + assert kv_params.page_size is not None + parameters["page_size"] = kv_params.page_size + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + op_name = f"mo.fused_qk_rope.padded.{cache_strategy_str}" + + return ops.inplace_custom( + op_name, + values=[input, kv_collection, freqs_cis_2d, layer_idx], + out_types=[ + TensorType( + dtype=input.dtype, shape=input.shape, device=input.device + ) + ], + parameters=parameters, + )[0].tensor + + +def flash_attention( + kv_params: KVCacheParams, + input: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection, + layer_idx: TensorValue, + attention_mask: TensorValue, + valid_lengths: TensorValue, + scale: float, +) -> TensorValue: + """Computes flash attention provided the mo.opaque KV Cache.""" + input_rank_expected = 4 + if input.rank != input_rank_expected: + msg = ( + f"expected input of rank {input_rank_expected} but got {input.rank}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected uint32 layer_idx but got {layer_idx.dtype}" + raise ValueError(msg) + + if attention_mask.dtype != input.dtype: + msg = ( + f"expected attention mask dtype {attention_mask.dtype} to match " + f"the input's dtype {input.dtype}" + ) + raise ValueError(msg) + + if valid_lengths.dtype != DType.uint32: + msg = f"expected uint32 valid_lengths but got {valid_lengths.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy != KVCacheStrategy.CONTINUOUS: + msg = f"unsupported cache strategy for flash_attention: {kv_params.cache_strategy}" + raise ValueError(msg) + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + op_name = f"mo.mha.padded.{cache_strategy_str}.tensor_mask.no_pos" + + return ops.inplace_custom( + op_name, + values=[ + input, + kv_collection, + layer_idx, + attention_mask, + valid_lengths, + # NOTE: The scale argument to the flash attention kernel is + # constrained to float32. + ops.constant(scale, dtype=DType.float32), + ], + out_types=[ + TensorType( + dtype=input.dtype, shape=input.shape, device=input.device + ) + ], + parameters={ + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + }, + )[0].tensor + + +def flash_attention_with_causal_mask( + kv_params: KVCacheParams, + input: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection, + layer_idx: TensorValue, + valid_lengths: TensorValue, + scale: float, +) -> TensorValue: + """Computes flash attention provided the mo.opaque KV Cache. + Notably, materializes the causal mask within the kernel.""" + + if input.shape[0] != valid_lengths.shape[0]: + msg = ( + "expected batch size of input, to equal length of valid_lengths" + f" got batch size of input ({input.shape[0]}), length of" + f" valid_lengths ({valid_lengths.shape[0]})" + ) + raise ValueError(msg) + + if input.dtype != kv_params.dtype: + msg = ( + f"expected input to be dtype: {kv_params.dtype}, got {input.dtype}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected uint32 layer_idx but got {layer_idx.dtype}" + raise ValueError(msg) + + if valid_lengths.dtype != DType.uint32: + msg = f"expected uint32 valid_lengths but got {valid_lengths.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy != KVCacheStrategy.CONTINUOUS: + msg = f"unsupported cache strategy for flash_attention_with_causal_mask: {kv_params.cache_strategy}" + raise ValueError(msg) + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + op_name = f"mo.mha.padded.{cache_strategy_str}.causal_mask.no_pos" + + return ops.inplace_custom( + op_name, + values=[ + input, + kv_collection, + layer_idx, + valid_lengths, + # NOTE: The scale argument to flash attention is constrained to float32. + ops.constant(scale, dtype=DType.float32), + ], + out_types=[ + TensorType( + dtype=input.dtype, shape=input.shape, device=input.device + ) + ], + parameters={ + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + }, + )[0].tensor + + +@dataclass +class MHAMaskConfig: + attention_mask_variant: AttentionMaskVariant + positional_encoding_variant: PositionalEncodingVariant + + +class AttentionMaskVariant(str, Enum): + NULL_MASK = "null_mask" + CAUSAL_MASK = "causal_mask" + TENSOR_MASK = "tensor_mask" + + +class PositionalEncodingVariant(str, Enum): + NO_POS = "no_pos" + ALIBI_POS = "alibi_pos" + + +class MHAMaskVariant(str, Enum): + CAUSAL_MASK = 0 + CAUSAL_ALIBI_MASK = 1 + NULL_MASK = 2 + + +_MHA_MASK_CONFIG_DICT = { + MHAMaskVariant.CAUSAL_MASK: MHAMaskConfig( + attention_mask_variant=AttentionMaskVariant.CAUSAL_MASK, + positional_encoding_variant=PositionalEncodingVariant.NO_POS, + ), + MHAMaskVariant.CAUSAL_ALIBI_MASK: MHAMaskConfig( + attention_mask_variant=AttentionMaskVariant.CAUSAL_MASK, + positional_encoding_variant=PositionalEncodingVariant.ALIBI_POS, + ), + MHAMaskVariant.NULL_MASK: MHAMaskConfig( + attention_mask_variant=AttentionMaskVariant.NULL_MASK, + positional_encoding_variant=PositionalEncodingVariant.NO_POS, + ), +} + + +def flash_attention_ragged( + kv_params: KVCacheParams, + input: TensorValue, + input_row_offsets: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection | PagedKVCacheCollection, + layer_idx: TensorValue, + mask_variant: MHAMaskVariant, + scale: float, +) -> TensorValue: + """Computes flash (self) attention provided the `!mo.opaque` KV Cache. + + Notably, this materializes the attention mask (dependent on MHAMaskVariant) + within the kernel. + `input` and `input_row_offsets` are used together to implement the ragged + tensor. + `input_row_offsets` indicates where each batch starts and ends in `input` + + Note that this is self attention and the KV sequence length is + assumed to be equal to the Q sequence length. + For KV sequence length != Q sequence length, use `cross_attention_ragged`. + """ + input_rank_expected = 3 + if input.rank != input_rank_expected: + msg = ( + f"expected input of rank {input_rank_expected} but got {input.rank}" + ) + raise ValueError(msg) + + if input.dtype != kv_params.dtype: + msg = ( + f"expected input to be dtype: {kv_params.dtype}, got {input.dtype}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected uint32 layer_idx but got {layer_idx.dtype}" + raise ValueError(msg) + + if input_row_offsets.dtype != DType.uint32: + msg = f"expected uint32 input_row_offsets but got {input_row_offsets.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy not in { + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.PAGED, + }: + msg = f"unsupported cache strategy for flash_attention_ragged: {kv_params.cache_strategy}" + raise ValueError(msg) + + parameters: dict[str, int | str | DType] = { + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + } + if kv_params.cache_strategy == KVCacheStrategy.PAGED: + assert kv_params.page_size is not None + parameters["page_size"] = kv_params.page_size + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + mha_mask_config = _MHA_MASK_CONFIG_DICT[mask_variant] + op_name = f"mo.mha.ragged.{cache_strategy_str}.{str(mha_mask_config.attention_mask_variant.value)}.{str(mha_mask_config.positional_encoding_variant.value)}" + + return ops.inplace_custom( + op_name, + values=[ + input, + input_row_offsets, + kv_collection, + layer_idx, + # NOTE: The scale argument to flash attention is constrained to float32. + ops.constant(scale, dtype=DType.float32), + ], + out_types=[ + TensorType( + dtype=input.dtype, shape=input.shape, device=input.device + ) + ], + parameters=parameters, + )[0].tensor + + +def cross_attention_ragged( + kv_params: KVCacheParams, + input: TensorValue, + input_row_offsets: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection | PagedKVCacheCollection, + layer_idx: TensorValue, + mask_variant: MHAMaskVariant, + kv_input_row_offsets: TensorValue, + q_max_seq_len: TensorValue, + scale: float, +) -> TensorValue: + """Computes cross attention provided the `!mo.opaque` KV Cache. + + Notably, this materializes the attention mask (dependent on MHAMaskVariant) + within the kernel. + `input` and `input_row_offsets` are used together to implement the ragged + tensor. + `input_row_offsets` indicates where each batch starts and ends in `input` + + attention, `kv_input_row_offsets` represents the KV sequence length. + """ + input_rank_expected = 3 + if input.rank != input_rank_expected: + msg = ( + f"expected input of rank {input_rank_expected} but got {input.rank}" + ) + raise ValueError(msg) + + if input.dtype != kv_params.dtype: + msg = ( + f"expected input to be dtype: {kv_params.dtype}, got {input.dtype}" + ) + raise ValueError(msg) + + if layer_idx.dtype != DType.uint32: + msg = f"expected uint32 layer_idx but got {layer_idx.dtype}" + raise ValueError(msg) + + if input_row_offsets.dtype != DType.uint32: + msg = f"expected uint32 input_row_offsets but got {input_row_offsets.dtype}" + raise ValueError(msg) + + if kv_params.cache_strategy not in { + KVCacheStrategy.CONTINUOUS, + KVCacheStrategy.PAGED, + }: + msg = f"unsupported cache strategy for cross_attention_ragged: {kv_params.cache_strategy}" + raise ValueError(msg) + + if q_max_seq_len and (q_max_seq_len.dtype != DType.uint32): + msg = ( + "expected q_max_seq_len to be uint32 but got {q_max_seq_len.dtype}" + ) + raise ValueError(msg) + + parameters: dict[str, int | str | DType] = { + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + } + if kv_params.cache_strategy == KVCacheStrategy.PAGED: + assert kv_params.page_size is not None + parameters["page_size"] = kv_params.page_size + + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + mha_mask_config = _MHA_MASK_CONFIG_DICT[mask_variant] + op_name = f"mo.cross_attention.ragged.{cache_strategy_str}.{str(mha_mask_config.attention_mask_variant.value)}.{str(mha_mask_config.positional_encoding_variant.value)}" + + return ops.inplace_custom( + op_name, + values=[ + input, + input_row_offsets, + # Plumb in the query max sequence length for cross attention. + # For self attention this is the same as the KV max seq len stored + # on the kv_collection, but that isn't the case for cross attention. + q_max_seq_len, + kv_input_row_offsets, + kv_collection, + layer_idx, + # NOTE: The scale argument to flash attention is constrained to float32. + ops.constant(scale, dtype=DType.float32), + ], + out_types=[ + TensorType( + dtype=input.dtype, shape=input.shape, device=input.device + ) + ], + parameters=parameters, + )[0].tensor + + +def swish_glu( + a: TensorValueLike, b0: TensorValueLike, b1: TensorValueLike +) -> TensorValue: + """Computes swish(a@b0.t()) * (a@b1.t())""" + a = TensorValue(a) + b0 = TensorValue(b0) + b1 = TensorValue(b1) + a_rank_expected = 2 + if a.rank != a_rank_expected: + msg = f"expected a to have rank {a_rank_expected}, was {a.rank}" + raise ValueError(msg) + + b0_rank_expected = 2 + if b0.rank != b0_rank_expected: + msg = f"expected b0 to have rank {b0_rank_expected}, was {b0.rank}" + raise ValueError(msg) + + b1_rank_expected = 2 + if b1.rank != b1_rank_expected: + msg = f"expected b1 to have rank {b1_rank_expected}, was {b1.rank}" + raise ValueError(msg) + + m = a.shape[0] + n = b0.shape[0] + if b0.shape[1] != a.shape[1]: + msg = f"a.shape[1] == {a.shape[1]} != {b0.shape[1]} == b0.shape[1]" + raise ValueError(msg) + + if b0.shape != b1.shape: + msg = f"b0.shape == {b0.shape} != {b1.shape} == b1.shape" + raise ValueError(msg) + + if a.dtype != b0.dtype or a.dtype != b1.dtype: + msg = ( + "Element types of all arguments must be equal, but received" + f" {a.dtype}, {b0.dtype}, and {b1.dtype}." + ) + raise ValueError(msg) + + return ops.custom( + "swishGLU", + values=[a, b0, b1], + out_types=[ + TensorType( + dtype=a.dtype, + shape=[m, n], + device=a.device, + ) + ], + )[0].tensor + + +def rms_norm_key_cache( + kv_params: KVCacheParams, + kv_collection: ContinuousBatchingKVCacheCollection, + gamma: TensorValue, + epsilon: float | np.floating, + layer_idx: int | np.integer, + total_seq_len: Dim, + input_row_offsets: TensorValue, +) -> None: + """Computes RMSNorm on the _new_ entries in the KVCache. + + Currently, the KVCacheT class itself isn't aware of the new cache entries + until cache length increment, which happens after model forward. + So use `input_row_offsets` to do this bookkeeping. + """ + cache_strategy_str = kv_params.cache_strategy.kernel_substring() + op_name = f"mo.rms_norm_kv_cache.ragged.{cache_strategy_str}" + + gamma_rank_expected = 1 + if gamma.rank != gamma_rank_expected: + msg = ( + f"expected gamma of rank {gamma_rank_expected} but got {gamma.rank}" + ) + raise ValueError(msg) + + if input_row_offsets.dtype != DType.uint32: + msg = f"expected uint32 input_row_offsets but got {input_row_offsets.dtype}" + raise ValueError(msg) + + ops.inplace_custom( + op_name, + values=[ + kv_collection, + gamma, + ops.constant(epsilon, gamma.dtype), + ops.constant(layer_idx, DType.uint32), + ops.cast(TensorValue.from_dim(total_seq_len), DType.uint32), + input_row_offsets, + ], + parameters={ + "num_heads": kv_params.n_kv_heads_per_device, + "head_dim": kv_params.head_dim, + }, + ) diff --git a/src/max/pipelines/nn/layer/__init__.py b/src/max/pipelines/nn/layer/__init__.py new file mode 100644 index 0000000000..d3a8fc44a9 --- /dev/null +++ b/src/max/pipelines/nn/layer/__init__.py @@ -0,0 +1,30 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .layer import ( + Layer, + LayerV2, + add_layer_hook, + clear_hooks, + recursive_named_layers, +) +from .layer_list import LayerList + +__all__ = [ + "Layer", + "LayerV2", + "add_layer_hook", + "clear_hooks", + "recursive_named_layers", + "LayerList", +] diff --git a/src/max/pipelines/nn/layer/layer.py b/src/max/pipelines/nn/layer/layer.py new file mode 100644 index 0000000000..3b76c479b6 --- /dev/null +++ b/src/max/pipelines/nn/layer/layer.py @@ -0,0 +1,423 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +import difflib +import threading +from abc import ABC, abstractmethod +from collections import deque +from dataclasses import dataclass +from functools import wraps +from inspect import signature +from typing import ( + Any, + Callable, + Dict, + Iterable, + Mapping, + Tuple, + Union, + get_args, +) + +import numpy as np +from max.driver import DLPackArray, Tensor +from max.dtype import DType +from max.graph import DeviceRef, ShapeLike, TensorValue, Weight +from max.graph.quantization import QuantizationEncoding +from max.graph.weights import WeightData + +from .._identity import IdentitySet + +DLPackCompatible = Union[DLPackArray, np.ndarray] + + +@dataclass +class ShardingStrategy: + """Defines how to load and shard a weight onto multiple devices.""" + + host_device: DeviceRef + shard_value: Callable[[Weight], tuple[TensorValue, ...]] + + +class Layer: + """Base Layer class. + + Currently, only functionality is for adding hooks to the call function of + each layer to support testing, debugging or profiling. + """ + + def __init_subclass__(cls): + if cls.__name__ == "LayerV2": + # LayerV2 subclasses Layer, but we don't want to apply + # _call_with_hooks to it. + return + # Check `__dict__` instead of `hasattr` because `hasattr` passes on + # subclasses that don't implement the method. + if "__call__" in cls.__dict__: + setattr(cls, "__call__", _call_with_hooks(cls.__dict__["__call__"])) + + def __call__(self, *args, **kwargs): + """Defines the forward function of this layer. + + Subclasses must override this function. There is no exact signature that a + call function must follow, but inputs/outputs should generally be + `max.graph.TensorValue`. Non-`TensorValue` inputs are fine, but + cannot be updated once the graph is built. + """ + + +class LayerV2(Layer, ABC): + """(new) Base class for model layers with weight and device management. + + This will be merged with the above class once all layers have been moved to + V2. + """ + + def __init__(self): + # `__init__` may be called if `__setattr__` is called before + # `super().__init__()`. So, to avoid resetting the values, first + # check to see if the layer has been initialized before. + if not hasattr(self, "_sublayers"): + self._sublayers: dict[str, LayerV2] = {} + self._layer_weights: dict[str, Weight] = {} + self._weight_values: dict[str, DLPackCompatible] = {} + self._shared_weights: dict[str, Weight] = {} + + # Update device list and sharding strategy to a singular object + # similar to a partition spec. + self._devices: tuple[DeviceRef, ...] = () + self._sharding_strategy: ShardingStrategy | None = None + + def __setattr__(self, name, value): + try: + if isinstance(value, LayerV2): + self._sublayers[name] = value + if self._devices or self._sharding_strategy: + value.to( + *self._devices, + sharding_strategy=self._sharding_strategy, + ) + elif isinstance(value, Weight): + self._layer_weights[value.name] = value + except AttributeError: + # The layer didn't call `super().__init__()` first thing. + LayerV2.__init__(self) + self.__setattr__(name, value) + return + super().__setattr__(name, value) + + def __repr__(self): + # TODO: Make this pretty + return f"{type(self).__name__}({len(self.sublayers)} layers, {len(self.layer_weights)} weights)" + + def to( + self, + *devices: DeviceRef, + sharding_strategy: ShardingStrategy | None = None, + ) -> None: + if len(self._devices) > 1 and not sharding_strategy: + raise ValueError( + "Must provide a sharding strategy if multiple devices are provided." + ) + + for _, layer in recursive_named_layers(self): + layer._devices = devices + layer._sharding_strategy = sharding_strategy + + @property + def layer_weights(self) -> dict[str, Weight]: + return self._layer_weights + + def __delattr__(self, name: str): + self._sublayers.pop(name, None) + self._layer_weights.pop(name, None) + self._shared_weights.pop(name, None) + super().__delattr__(name) + + def set_shared_weight(self, name: str, weight: Weight): + setattr(self, name, weight) + self._shared_weights[name] = weight + + @property + def sublayers(self) -> dict[str, LayerV2]: + return self._sublayers + + def load_state_dict( + self, + state_dict: Mapping[str, DLPackCompatible | WeightData], + *, + override_quantization_encoding: bool = False, + ) -> None: + """Sets the values of all weights in this model. + + Args: + state_dict: A map from weight name to a numpy array or + max.driver.Tensor. + override_quantization_encoding: Whether to override the weight + quantization based on the loaded value. + + Raises: + Error if any weight in the model is not present in the state dict. + """ + for name, layer in recursive_named_layers(self): + weight_prefix = f"{name}." if name else "" + for weight_name, weight in layer.layer_weights.items(): + # Skip the shared weights, since their values are loaded with + # the original layers. + if weight_name in layer._shared_weights: + continue + full_weight_name = f"{weight_prefix}{weight_name}" + if (data := state_dict.get(full_weight_name)) is not None: + if isinstance(data, WeightData): + data = _array_from_weight_loader( + weight, + data, + override_quantization_encoding, + full_weight_name, + ).data + else: + _validate_weight_value(weight, data, full_weight_name) + self._weight_values[full_weight_name] = data + weight.name = full_weight_name + else: + msg = f"Could not find weight '{full_weight_name}'. " + if possible_match := difflib.get_close_matches( + full_weight_name, state_dict.keys(), n=1 + ): + msg += f" Did you mean '{possible_match[0]}'?" + raise ValueError() + + def state_dict( + self, auto_initialize: bool = True + ) -> dict[str, DLPackCompatible]: + """Returns values of all weights in the model. + + The values returned are the same as the values set in `load_state_dict`. + If `load_state_dict` has not been called and none of the weights have + values, then they are initialized to zero. + + Args: + auto_initialize: Determines whether to initialize weights to zero if + the weight value has not been loaded. If this is False, a + ValueError is raised if an uninitialized weight is found. + + Returns: + Map from weight name to the weight value (can be numpy array or + max.driver.Tensor). + """ + + state_dict = {} + for full_weight_name, weight in self.raw_state_dict().items(): + if (data := self._weight_values.get(full_weight_name)) is None: + if not auto_initialize: + raise ValueError( + f"Weight '{full_weight_name}' was not initialized." + ) + data = self._weight_values[full_weight_name] = Tensor.zeros( + weight.shape.static_dims, weight.dtype + ) + state_dict[full_weight_name] = data + return state_dict + + def raw_state_dict(self) -> dict[str, Weight]: + """Returns all weights objects in the model. + Unlike `state_dict`, this returns `max.graph.Weight` objects instead of + the assigned values. Some parameters inside the `Weight` can be + configured before a graph is built. Do not change these attributes after + building a graph: + + - Weight.shape + - Weight.dtype + - Weight.quantization_encoding + - Weight.device + - Weight.align + + Returns: + Map from weight name to the `max.graph.Weight` object. + """ + state_dict = {} + for name, layer in recursive_named_layers(self): + prefix = f"{name}." if name else "" + + for weight_name, weight in layer.layer_weights.items(): + state_dict[f"{prefix}{weight_name}"] = weight + return state_dict + + @abstractmethod + def __call__(self, *args, **kwargs): + """Defines the forward function of this layer. + + Subclasses must override this function. There is no exact signature that a + call function must follow, but inputs/outputs should generally be + `max.graph.TensorValue`. Non-`TensorValue` inputs are fine, but + cannot be updated once the graph is built. + """ + + +def _array_from_weight_loader( + weight: Weight, + data: WeightData, + override_quantization_encoding: bool, + name: str, +) -> WeightData: + """Processes and validates the data from WeightData.""" + + if weight.quantization_encoding == QuantizationEncoding.GPTQ: + # Load all weights with GPTQ quantization as uint8. + # Store the original shape and dtype of the weight (used in layers like + # GPTLinear). + weight.original_dtype_and_shape = (data.dtype, data.shape) + data = data.view(DType.uint8) + else: + data = data.astype(weight.dtype) + + if weight.quantization_encoding: + # TODO: Set the quantized weight shape correctly when initializing the + # weight. For now, we trust that the value loaded from the checkpoint + # has the correct shape. + weight._shape = data.shape + elif weight.shape != data.shape: + raise ValueError( + f"Value provided to weight '{name}' had different shape" + f" (expected={weight.shape}, actual={data.shape})" + ) + + if weight.quantization_encoding != data.quantization_encoding: + if override_quantization_encoding: + weight.quantization_encoding = data.quantization_encoding + # We don't raise an error if `override_quantization_encoding` is `False` + # because in some cases the data is not aware of its own quantization + # type (e.g. data loaded from GPTQ Safetensors do not have a + # quantization label) + return data.astype(weight.dtype) + + +def _get_value_shape_dtype(value: DLPackCompatible) -> tuple[ShapeLike, DType]: + if isinstance(value, Tensor): + shape = value.shape + dtype = value.dtype + elif isinstance(value, np.ndarray): + shape = value.shape + dtype = DType.from_numpy(value.dtype) + else: + # `from_dlpack` does not copy the data. + value_tensor = Tensor.from_dlpack(value) + shape = value_tensor.shape + dtype = value_tensor.dtype + + return shape, dtype + + +def _validate_weight_value(weight: Weight, value: Any, name: str) -> None: + if not isinstance(value, get_args(DLPackCompatible)): + raise ValueError( + f"The class type of '{name}' value ({type(value)}) is not an array " + "type that we understand. Please use a numpy array or max.driver.Tensor." + ) + + shape, dtype = _get_value_shape_dtype(value) + + diffs = [] + weight_shape = tuple(weight.shape.static_dims) + if shape != weight_shape: + diffs.append(f"shape (expected={weight_shape}, actual={shape})") + if dtype != weight.dtype: + diffs.append(f"dtype (expected={weight.dtype}, actual={dtype})") + if diffs: + diff_str = " and ".join(diffs) + raise ValueError( + f"Value provided to weight '{name}' had different {diff_str}." + ) + + +def recursive_named_layers( + parent: LayerV2, prefix: str = "" +) -> Iterable[tuple[str, LayerV2]]: + """Recursively walks through the layers and generates names.""" + seen = IdentitySet() + queue: deque[tuple[str, LayerV2]] = deque() + queue.append((prefix, parent)) + + while queue: + name, layer = queue.popleft() + if layer in seen: + continue + seen.add(layer) + + yield (name, layer) + prefix = f"{name}." if name else "" + for local_name, layer in layer.sublayers.items(): + queue.append((f"{prefix}{local_name}", layer)) + + +_LOCAL = threading.local() +_LAYER_HOOKS = _LOCAL._layer_hooks = [] + + +def add_layer_hook( + fn: Callable[[Layer, Tuple[Any, ...], Dict[str, Any], Any], Any], +) -> None: + """Adds a hook to call a function after each layer's `__call__`. + + The function will be passed four inputs: the layer, input_args, + input_kwargs and outputs. The function can either return `None` or new + outputs that will replace the layer returned outputs. + + Note that input and outputs contain graph Values, which show limited + information (like shape and dtype). You can still see the computed values + if you include the Value in the `graph.output` op, or call `value.print`. + + Example of printing debug inputs: + + .. code-block:: python + + def print_info(layer, args, kwargs, outputs): + print("Layer:", type(layer).__name__) + print("Input args:", args) + print("Input kwargs:", kwargs) + print("Outputs:", outputs) + return outputs + + add_layer_hook(print_info) + """ + _LAYER_HOOKS.append(fn) + + +def clear_hooks(): + """Remove all hooks.""" + _LAYER_HOOKS.clear() + + +def _call_with_hooks(call_fn): + @wraps(call_fn) + def __call_with_hooks(layer, *args, **kwargs): + # Hide this wrapper from rich traceback. + _rich_traceback_omit = True + + outputs = call_fn(layer, *args, **kwargs) + # Use the inspect lib to ensure that args and kwargs are passed + # to the hook as defined in the function signature. + bound_args = signature(call_fn).bind(layer, *args, **kwargs) + for hook in _LAYER_HOOKS: + # Call the hook. Note that the first argument in `bound_args.args` + # is the layer, so it is skipped. + hook_outputs = hook( + layer, bound_args.args[1:], bound_args.kwargs, outputs + ) + if hook_outputs is not None: + outputs = hook_outputs + return outputs + + return __call_with_hooks diff --git a/src/max/pipelines/nn/layer/layer_list.py b/src/max/pipelines/nn/layer/layer_list.py new file mode 100644 index 0000000000..1f0aad4d82 --- /dev/null +++ b/src/max/pipelines/nn/layer/layer_list.py @@ -0,0 +1,77 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from typing import Sequence + +from max.graph import TensorValue + +from .layer import Layer, LayerV2 + + +class LayerList(LayerV2): + """Stores a list of layers. + + Can be used as a regular python list.""" + + def __init__(self, layers: Sequence[Layer]): + super().__init__() + self.layers = list(layers) + + # Only assign `LayerV2` objects to Sequential.sublayers. We ensure that + # the V2 functionality (getting sublayers) is correct by throwing + # an error in the `sublayers` property if any layer is still V1. + for n, layer in enumerate(layers): + if isinstance(layer, LayerV2): + self._sublayers[str(n)] = layer + + @property + def sublayers(self) -> dict[str, LayerV2]: + if len(self._sublayers) != len(self.layers): + raise ValueError( + "Not all layers in this Sequential object have " + "been migrated to V2." + ) + return super().sublayers + + def __len__(self): + return len(self.layers) + + def __getitem__(self, i: int) -> Layer: + return self.layers[i] + + def __delitem__(self, i: int): + del self.layers[i] + + def __setitem__(self, i: int, layer: Layer): + self.layers[i] = layer + + def insert(self, i, layer: Layer): + self.layers.insert(i, layer) + + def append(self, layer: Layer): + self.layers.append(layer) + + def extend(self, layer: Layer): + self.layers.append(layer) + + def __str__(self): + return str(self.layers) + + def __iter__(self): + return iter(self.layers) + + def __call__(self, *args, **kwargs) -> TensorValue: + x = self.layers[0](*args, **kwargs) + for layer in self.layers[1:]: + x = layer(x) + return x diff --git a/src/max/pipelines/nn/linear.py b/src/max/pipelines/nn/linear.py new file mode 100644 index 0000000000..dd6a625af0 --- /dev/null +++ b/src/max/pipelines/nn/linear.py @@ -0,0 +1,555 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Multi-layer Perceptron.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional + +import numpy as np +from max.dtype import DType +from max.graph import ( + BufferValue, + DeviceRef, + TensorValue, + TensorValueLike, + Weight, + ops, +) +from max.graph.quantization import QuantizationConfig, QuantizationEncoding +from max.graph.weights import Weights + +from .kernels import swish_glu +from .layer import Layer, LayerV2 + + +class LinearV2(LayerV2): + """ + Applies a linear transformation to incoming data: :math:`y = xW^T + b`. + + This layer implements a fully connected layer where inputs are multiplied + by a weight matrix and optionally added with a bias vector. + Both weights and bias initially reside on CPU, and the model init phase + moves them to :obj:`device`. + + Example: + + .. code-block:: python + + linear_layer = LinearV2( + in_dim=256, + out_dim=128, + dtype=DType.float32, + device=DeviceRef.GPU(), + name="linear", + has_bias=True + ) + + # Input tensor of shape: [batch, ..., 256] + input_tensor: TensorValue + output = linear_layer(input_tensor) + """ + + weight: Weight | TensorValue + """The weight matrix stored on CPU with shape (out_dim, in_dim). + Model init transposes the weight and moves it to :obj:`device`.""" + + bias: Weight | None = None + """The optional bias vector stored on CPU with shape (out_dim,). + Model init moves the bias to :obj:`device` if present.""" + + device: DeviceRef | None + """The device where matrix operations are performed.""" + + def __init__( + self, + in_dim: int, + out_dim: int, + dtype: DType, + device: DeviceRef | None = None, + has_bias: bool = False, + quantization_encoding: Optional[QuantizationEncoding] = None, + name: Optional[str] = None, + ) -> None: + """Initializes the linear layer with weights and optional bias. + + Args: + in_dim: The dimensionality of the input space. + out_dim: The dimensionality of the output space. + dtype: The data type for both weights and bias. + device: The target device for computation. + Weights remain on CPU until moved during computation. + name: Base name for weights (appended with ``.weight`` and + ``.bias`` if applicable). + has_bias: When :obj:`True`, adds a bias vector to the layer. + Defaults to :obj:`False`. + """ + super().__init__() + + self.device = device + + self.weight = Weight( + name=f"{name}.weight" if name else "weight", + dtype=dtype, + shape=(out_dim, in_dim), + device=DeviceRef.CPU() if self.device else None, + quantization_encoding=quantization_encoding, + ) + self.quantization_encoding = quantization_encoding + + if has_bias: + self.bias = Weight( + name=f"{name}.bias" if name else "bias", + dtype=dtype, + shape=(out_dim,), + device=DeviceRef.CPU() if self.device else None, + quantization_encoding=quantization_encoding, + ) + + def __call__(self, x: TensorValue) -> TensorValue: + """Applies a linear transformation to the input data. + + Args: + x: Input tensor of shape ``(..., in_dim)``. + The last dimension must match the layer's ``in_dim``. + The input tensor must reside on :obj:`device`. + + Returns: + Output tensor of shape ``(..., out_dim)``. + The result resides on the device specified in :obj:`device`. + + Raises: + ValueError: If the last dimension of ``x`` doesn't match ``in_dim``. + """ + weight: TensorValue = self.weight + if self.device: + weight = weight.to(self.device) + + if self.quantization_encoding: + res = ops.qmatmul( + self.quantization_encoding, + None, + x, + weight, + ) + else: + res = x @ weight.T + if self.bias is not None: + bias = self.bias.to(self.device) if self.device else self.bias + res += bias + return res + + +def _allocate_if_needed(value: Weights | Weight, dtype, shape) -> Weight: + if isinstance(value, Weight): + return value + else: + return value.weight.allocate(dtype, shape) + + +@dataclass +class Linear(Layer): + """A unified linear layer that delegates to either regular or quantized implementation.""" + + weight: TensorValueLike + bias: Optional[TensorValueLike] = None + + def __call__(self, x: TensorValue) -> TensorValue: + weight = TensorValue(self.weight) + res = x @ weight.T + if self.bias is not None: + res += self.bias + return res + + @classmethod + def create( + cls, + dtype: DType, + quantization_encoding: Optional[QuantizationEncoding], + in_features: int, + out_features: int, + weights: Weights | Weight, + bias: Optional[Weights | Weight] = None, + quantization_config: Optional[QuantizationConfig] = None, + ) -> "Linear": + """Factory method to create a Linear layer with appropriate implementation.""" + if not quantization_encoding: + weight = _allocate_if_needed( + weights, dtype, [in_features, out_features] + ) + bias_weight = ( + _allocate_if_needed(bias, dtype, [out_features]) + if bias + else None + ) + return Linear(weight=weight, bias=bias_weight) + else: + return QLinear._create( + dtype, + quantization_encoding, + in_features, + out_features, + weights, + bias, + quantization_config, + ) + + +@dataclass +class QLinear(Linear): + """A quantized fully connected layer.""" + + # Because Linear.bias is optional and Linear is a dataclass and we inherit from Linear, all our fields must be optional even if it doesn't make logical sense + quantization_encoding: QuantizationEncoding | None = None + + @classmethod + def _create( + cls, + dtype: DType, + quantization_encoding: QuantizationEncoding, + in_features: int, + out_features: int, + weights: Weights | Weight, + bias: Optional[Weights | Weight], + quantization_config: Optional[QuantizationConfig], + ) -> "Linear": + if quantization_encoding != QuantizationEncoding.GPTQ: + weight = _allocate_if_needed( + weights, dtype, [in_features, out_features] + ) + bias_weight = ( + _allocate_if_needed(bias, dtype, [out_features]) + if bias + else None + ) + return QLinear( + weight=weight, + bias=bias_weight, + # GGUF weights can have different quantization per weight + quantization_encoding=weight.quantization_encoding, + ) + else: + return GPTQLinear._create( + dtype, + quantization_encoding, + in_features, + out_features, + weights, + bias, + quantization_config, + ) + + def __call__(self, x: TensorValue) -> TensorValue: + assert self.quantization_encoding is not None + weight = TensorValue(self.weight) + res = ops.qmatmul( + self.quantization_encoding, + None, + x, + weight, + ) + if self.bias is not None: + res += TensorValue(self.bias) + return res + + +@dataclass +class GPTQLinear(QLinear): + "A Linear layer for GPTQ encoding" + + # Because QLinear has optional fields, so must we, since we subclass QLinear + quantization_config: QuantizationConfig | None = None + perm_idx: Optional[TensorValueLike] | None = None + + @classmethod + def _create( + cls, + dtype: DType, + quantization_encoding: QuantizationEncoding, + in_features: int, + out_features: int, + weights: Weights | Weight, + bias: Optional[Weights | Weight], + quantization_config: Optional[QuantizationConfig], + ) -> "Linear": + """Internal method to create a Linear layer from GPTQ weights.""" + + assert quantization_config, ( + "QuantizationConfig must be provided for GPTQLinear" + ) + + assert quantization_config.sym, "GPTQ with sym=False is not supported." + + desc_act = quantization_config.desc_act + + perm_idx = None + + if isinstance(weights, Weights) and weights.qweight.exists(): + orig_quantized_weights = [weights.qweight, weights.scales] + quantized_weights = [] + for idx, qw in enumerate(orig_quantized_weights): + orig = qw.allocate() + # TODO(AITLIB-135): allocate_as_bytes is only available for + # safetensors. This isn't a problem right now because gptq is + # only present for safetensors + weight_bytes = qw.allocate_as_bytes() # type: ignore + assert len(orig.shape) == 2 + reshaped = ops.reshape( + weight_bytes, + (orig.shape[0] * orig.dtype.size_in_bytes, orig.shape[1]), + ).transpose(0, 1) + quantized_weights.append(reshaped) + + weight = ops.concat( + (quantized_weights[0], quantized_weights[1]), axis=1 + ).transpose(0, 1) + + if desc_act: + perm_idx = weights.g_idx.allocate( + DType.int32, + [out_features], + ) + # hack: argsort the perm_idx array + weights._allocated[perm_idx.name] = np.argsort( # type: ignore + weights._allocated[perm_idx.name] # type: ignore + ).astype(np.int32) + + return GPTQLinear( + weight=weight, + bias=None, + quantization_encoding=quantization_encoding, + quantization_config=quantization_config, + perm_idx=perm_idx, + ) + + else: + weight = _allocate_if_needed( + weights, DType.bfloat16, [in_features, out_features] + ) + bias_weight = ( + _allocate_if_needed(bias, dtype, [out_features]) + if bias + else None + ) + return Linear(weight, bias_weight) + + def __call__(self, x: TensorValue) -> TensorValue: + assert self.quantization_encoding is not None + weight = TensorValue(self.weight) + if self.perm_idx is not None: + perm_idx = TensorValue(self.perm_idx) + res = ops.qmatmul( + self.quantization_encoding, + self.quantization_config, + ops.gather(x, perm_idx, axis=(x.rank - 1)), + weight, + perm_idx, + ) + else: + res = ops.qmatmul( + self.quantization_encoding, + self.quantization_config, + x, + weight, + ) + if self.bias is not None: + res += TensorValue(self.bias) + return res + + +@dataclass +class GPTQLinearV2(LinearV2): + "A Linear layer for GPTQ encoding" + + def __init__( + self, + in_dim: int, + out_dim: int, + dtype: DType, + device: DeviceRef | None = None, + has_bias: bool = False, + quantization_encoding: Optional[QuantizationEncoding] = None, + quantization_config: Optional[QuantizationConfig] = None, + ) -> None: + """Initializes the linear layer with weights and optional bias with + GPTQ quantization. + + Args: + in_dim: The dimensionality of the input space. + out_dim: The dimensionality of the output space. + dtype: The data type for both weights and bias. + device: The target device for computation. + Weights remain on CPU until moved during computation. + has_bias: When :obj:`True`, adds a bias vector to the layer. + Defaults to :obj:`False`. + quantization_encoding: The quantization encoding of the weights. + quantization_config: Extra config for the weight quantization. + """ + del out_dim, dtype # Unused. + if has_bias: + raise ValueError("has_bias=True is not supported in GPTQLinear.") + + # Skip LinearV2 initialization. + LayerV2.__init__(self) + self.device = device + self.qweight = Weight( + name="qweight", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + device=DeviceRef.CPU() if device else None, + quantization_encoding=quantization_encoding, + ) + self.scales = Weight( + name="scales", + dtype=DType.uint8, + shape=(1, 1), # Shape will be overridden at load_state_dict. + device=DeviceRef.CPU() if device else None, + quantization_encoding=quantization_encoding, + ) + + assert quantization_config, ( + "QuantizationConfig must be provided for GPTQLinear" + ) + assert quantization_config.sym, "GPTQ with sym=False is not supported." + + self.quantization_config = quantization_config + + desc_act = self.quantization_config.desc_act + self.perm_idx = None + if desc_act: + self.perm_idx = Weight( + "perm_idx", + DType.int32, + [in_dim], + device=DeviceRef.CPU() if device else None, + ) + + def __call__(self, x: TensorValue) -> TensorValue: + assert self.qweight.quantization_encoding is not None + qweight_dtype, qweight_shape = self.qweight.original_dtype_and_shape + qweight = ops.reshape( + self.qweight, + (qweight_shape[0] * qweight_dtype.size_in_bytes, qweight_shape[1]), + ).transpose(0, 1) + + scales_dtype, scales_shape = self.scales.original_dtype_and_shape + scales = ops.reshape( + self.scales, + (scales_shape[0] * scales_dtype.size_in_bytes, scales_shape[1]), + ).transpose(0, 1) + weight = ops.concat((qweight, scales), axis=1).transpose(0, 1) + if self.device: + weight = weight.to(self.device) + if self.perm_idx is not None: + perm_idx: TensorValue = self.perm_idx + if self.device: + perm_idx = perm_idx.to(self.device) + + res = ops.qmatmul( + self.qweight.quantization_encoding, + self.quantization_config, + ops.gather(x, self.perm_idx, axis=(x.rank - 1)), + weight, + self.perm_idx, + ) + else: + res = ops.qmatmul( + self.qweight.quantization_encoding, + None, + x, + weight, + ) + if self.bias is not None: + res += TensorValue(self.bias) + return res + + +@dataclass +class MLP(Layer): + """ + Simple multi-layer perceptron composed of three linear layers. + Uses SiLU activation function. + """ + + gate_proj: Linear + down_proj: Linear + up_proj: Linear + + def __call__(self, x: TensorValueLike) -> TensorValue: + if ( + self.gate_proj.bias is None + and self.up_proj.bias is None + and TensorValue(x).rank == 2 + and TensorValue(x).device is not None + and TensorValue(x).device != DeviceRef.CPU() + and False # GEX-1476: This causes elaboration errors - disable swish_glu pathway. + ): + return self.down_proj( + swish_glu( + x, + self.gate_proj.weight, + self.up_proj.weight, + ) + ) + + return self.down_proj(ops.silu(self.gate_proj(x)) * self.up_proj(x)) # type: ignore + + +@dataclass +class MLPV2(LayerV2): + """ + Simple multi-layer perceptron composed of three linear layers. + Uses SiLU activation function. + """ + + gate_proj: LinearV2 + down_proj: LinearV2 + up_proj: LinearV2 + + def __post_init__(self): + super().__init__() + + def __call__(self, x: TensorValueLike) -> TensorValue: + if ( + self.gate_proj.bias is None + and self.up_proj.bias is None + and TensorValue(x).rank == 2 + and TensorValue(x).device is not None + and TensorValue(x).device != DeviceRef.CPU() + and False # GEX-1476: This causes elaboration errors - disable swish_glu pathway. + ): + return self.down_proj( + swish_glu( + x, + self.gate_proj.weight, + self.up_proj.weight, + ) + ) + + return self.down_proj( + ops.silu(self.gate_proj(x)) * self.up_proj(x) # type: ignore + ) + + +@dataclass +class DistributedMLP(Layer): + list_of_mlps: list[MLP] + num_devices: int + + def __call__( + self, x: list[TensorValue], signal_buffers: list[BufferValue] + ) -> list[TensorValue]: + mlp_outs = [self.list_of_mlps[i](x[i]) for i in range(self.num_devices)] + return ops.allreduce.sum(mlp_outs, signal_buffers) diff --git a/src/max/pipelines/nn/norm/__init__.py b/src/max/pipelines/nn/norm/__init__.py new file mode 100644 index 0000000000..6c5ff6c0b1 --- /dev/null +++ b/src/max/pipelines/nn/norm/__init__.py @@ -0,0 +1,23 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .layer_norm import LayerNorm, LayerNormV2 +from .rms_norm import DistributedRMSNorm, RMSNorm, RMSNormV2 + +__all__ = [ + "LayerNorm", + "LayerNormV2", + "RMSNorm", + "RMSNormV2", + "DistributedRMSNorm", +] diff --git a/src/max/pipelines/nn/norm/layer_norm.py b/src/max/pipelines/nn/norm/layer_norm.py new file mode 100644 index 0000000000..9619d19ced --- /dev/null +++ b/src/max/pipelines/nn/norm/layer_norm.py @@ -0,0 +1,75 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Layer Normalization layer.""" + +from __future__ import annotations + +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import TensorValue, Weight, ops + +from ..layer import Layer, LayerV2 + + +@dataclass +class LayerNorm(Layer): + """Layer normalization block.""" + + weight: Weight + bias: Weight | None = None + eps: float = 1e-6 + + def __call__(self, input: TensorValue): + # TODO: AIPIPE-95 Replace with a broadcasting rmo.layer_norm + bias = ( + ops.cast(self.bias, input.dtype) + if self.bias + # If bias wasn't passed then use bias-less layer norm (beta = 0). + else ops.broadcast_to( + ops.constant(0.0, input.dtype), shape=(input.shape[-1],) + ) + ) + return ops.layer_norm( + input, + gamma=ops.cast(self.weight, input.dtype), + beta=bias, + epsilon=self.eps, + ) + + +class LayerNormV2(LayerV2): + """Layer normalization block.""" + + def __init__(self, dims: int, eps: float = 1e-5, use_bias=True): + self.weight = Weight("weight", DType.float32, (dims,)) + self.bias = Weight("bias", DType.float32, (dims,)) if use_bias else None + self.eps = eps + + def __call__(self, input: TensorValue): + # TODO: AIPIPE-95 Replace with a broadcasting rmo.layer_norm + bias = ( + ops.cast(self.bias, input.dtype) + if self.bias + # If bias wasn't passed then use bias-less layer norm (beta = 0). + else ops.broadcast_to( + ops.constant(0.0, input.dtype), shape=(input.shape[-1],) + ) + ) + return ops.layer_norm( + input, + gamma=ops.cast(self.weight, input.dtype), + beta=bias, + epsilon=self.eps, + ) diff --git a/src/max/pipelines/nn/norm/rms_norm.py b/src/max/pipelines/nn/norm/rms_norm.py new file mode 100644 index 0000000000..f6e4b455c3 --- /dev/null +++ b/src/max/pipelines/nn/norm/rms_norm.py @@ -0,0 +1,64 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Normalization layer.""" + +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import ( + DeviceRef, + TensorType, + TensorValue, + TensorValueLike, + Weight, + ops, +) + +from ..layer import Layer, LayerV2 + + +@dataclass +class RMSNorm(Layer): + weight: TensorValueLike + eps: float = 1e-6 + + def __call__(self, x: TensorValue) -> TensorValue: + return ops.custom( + "rms_norm", + [x, ops.cast(self.weight, x.dtype), ops.cast(self.eps, x.dtype)], + [TensorType(dtype=x.dtype, shape=x.shape, device=x.device)], + )[0].tensor + + +@dataclass +class DistributedRMSNorm(Layer): + rms_norms: list[RMSNorm] + devices: list[DeviceRef] + + def __call__(self, xs: list[TensorValue]) -> list[TensorValue]: + return [self.rms_norms[i](xs[i]) for i in range(len(self.devices))] + + +class RMSNormV2(LayerV2): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.weight = Weight("weight", DType.float32, [dim]) + self.eps = eps + + def __call__(self, x: TensorValue) -> TensorValue: + return ops.custom( + "rms_norm", + [x, ops.cast(self.weight, x.dtype), ops.cast(self.eps, x.dtype)], + [TensorType(dtype=x.dtype, shape=x.shape, device=x.device)], + )[0].tensor diff --git a/src/max/pipelines/nn/rotary_embedding.py b/src/max/pipelines/nn/rotary_embedding.py new file mode 100644 index 0000000000..ae622d8dad --- /dev/null +++ b/src/max/pipelines/nn/rotary_embedding.py @@ -0,0 +1,156 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""The rope embedding used within the model.""" + +from dataclasses import dataclass +from functools import cached_property +from typing import Optional + +import numpy as np +from max.dtype import DType +from max.graph import Dim, DimLike, TensorValue, TensorValueLike, ops + +from .layer import LayerV2 + + +@dataclass +class RotaryEmbedding(LayerV2): + """ + RotaryEmbedding layer to calculate and apply the frequency tensor for complex exponentials. + """ + + dim: DimLike + n_heads: int + theta: float + """Hyperparameter used to control the frequency scaling of the sinusoidal components of the embeddings.""" + max_seq_len: int + """The maximum sequence length for model's input.""" + rope_scaling: Optional[np.ndarray] = None + """Scaling factor for the positional frequencies.""" + _freqs_cis: Optional[TensorValueLike] = None + interleaved: bool = True + + def __post_init__(self): + super().__init__() + + def freqs_cis_base(self) -> TensorValue: + """ + Computes the frequency tensor for complex exponentials (cis) + for a given seq_len. Tensor is scaled with theta parameter. + Required to apply Rotary Position Embedding (RoPE) to tensor. + See 'Roformer: Enhanced Transformer with Rotary Embedding' + (arxiv.org/pdf/2104.09864). + + Returns: + The frequency tensor for complex exponentials with shape + (max_seq_len * 2, dim//(2 * n_heads), 2) + """ + if self._freqs_cis is None: + n = self.dim // self.n_heads # type: ignore + # Note: using float64 to avoid an overflow on the exponential, then converting back to float32. + iota = ops.range( + ops.constant(0, DType.float64), + ops.constant(n - 1, DType.float64), # type: ignore + ops.constant(2, DType.float64), + out_dim=n // 2, + ) + if self.rope_scaling is not None: + iota = iota * self.rope_scaling + freqs = ops.cast(1.0 / (self.theta ** (iota / n)), DType.float32) + t = ops.range( + ops.constant(0, DType.float32), + ops.constant(self.max_seq_len * 2.0, DType.float32), + ops.constant(1, DType.float32), + out_dim=self.max_seq_len * 2, + ) + freqs = ops.outer(t, freqs) + self._freqs_cis = ops.stack( + [ops.cos(freqs), ops.sin(freqs)], axis=-1 + ) + return TensorValue(self._freqs_cis) + + @cached_property + def freqs_cis(self) -> TensorValue: + self._freqs_cis = self.freqs_cis_base() + return self._freqs_cis + + def __call__( + self, x: TensorValueLike, start_pos: TensorValue, seq_len: Dim + ) -> TensorValue: + """Applies rotary positional embeddings (RoPE) to `x`. + + Args: + x: Activation tensor with shape (batch, seq_len, n_kv_heads, head_dim). + start_pos: starting position of input tensor + seq_len: length of input tensor + + Returns: + Input activation tensor with rotary positional embeddings applied and + the same shape as `x`. + """ + v = TensorValue(x) + + if self.interleaved: + complex = ops.as_interleaved_complex(v) + x_re = complex[..., 0] + x_im = complex[..., 1] + else: + head_dim = v.shape[-1] + head_dim_val = TensorValue(head_dim) + half_dim = head_dim // 2 + half_dim_val = TensorValue(half_dim) + slice_re = (slice(0, half_dim_val), half_dim) + slice_im = (slice(half_dim_val, head_dim_val), half_dim) + x_re = v[..., slice_re] + x_im = v[..., slice_im] + + seq_len_val = TensorValue(seq_len) + freqs_cis_sliced = self.freqs_cis[ + (slice(start_pos, start_pos + seq_len_val), seq_len), + ] + # TODO(MSDK-1188): Ideally this cast would happen inside of the cached + # self.freqs_cis property instead of here, but complex.dtype is not + # known at that point. + freqs_cis_sliced = ops.cast(freqs_cis_sliced, v.dtype) + + freqs_cis_bcast = ops.unsqueeze(ops.unsqueeze(freqs_cis_sliced, 1), 0) + + freqs_re = freqs_cis_bcast[..., 0] + freqs_im = freqs_cis_bcast[..., 1] + + rope_re = (x_re * freqs_re) - (x_im * freqs_im) + rope_im = (x_re * freqs_im) + (x_im * freqs_re) + + if self.interleaved: + rope_complex = ops.stack([rope_re, rope_im], axis=-1) + else: + rope_complex = ops.concat((rope_re, rope_im), axis=-1) + + # Cast back to the activations dtype, which may differ from + # freqs_cis's dtype. + return ops.cast(ops.reshape(rope_complex, v.shape), v.dtype) + + +@dataclass +class OptimizedRotaryEmbedding(RotaryEmbedding): + """ + Optimized version of RotaryEmbedding using 2D frequency tensor representation. + """ + + @cached_property + def freqs_cis(self): + freqs = self.freqs_cis_base() + d1, d2, d3 = freqs.shape + new_f_shape = [d1.dim, d2.dim * d3.dim] # type: ignore + self._freqs_cis = ops.reshape(freqs, new_f_shape) + return self._freqs_cis diff --git a/src/max/pipelines/nn/sequential.py b/src/max/pipelines/nn/sequential.py new file mode 100644 index 0000000000..0d75fb976f --- /dev/null +++ b/src/max/pipelines/nn/sequential.py @@ -0,0 +1,20 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""A General sequential layer, each layer is executed with the outputs of the previous.""" + +from .layer import LayerList + + +class Sequential(LayerList): + """A sequential stack of layers where each layer is called by the outputs + of the previous layer.""" diff --git a/src/max/pipelines/nn/transformer/__init__.py b/src/max/pipelines/nn/transformer/__init__.py new file mode 100644 index 0000000000..02c70b1c3c --- /dev/null +++ b/src/max/pipelines/nn/transformer/__init__.py @@ -0,0 +1,29 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +"""The transformer mechanism used within the model.""" + +from .distributed_transformer import ( + DistributedTransformer, + DistributedTransformerBlock, +) +from .naive_transformer import NaiveTransformer, NaiveTransformerBlock +from .transformer import Transformer, TransformerBlock + +__all__ = [ + "DistributedTransformer", + "DistributedTransformerBlock", + "NaiveTransformer", + "NaiveTransformerBlock", + "Transformer", + "TransformerBlock", +] diff --git a/src/max/pipelines/nn/transformer/distributed_transformer.py b/src/max/pipelines/nn/transformer/distributed_transformer.py new file mode 100644 index 0000000000..0172b003f0 --- /dev/null +++ b/src/max/pipelines/nn/transformer/distributed_transformer.py @@ -0,0 +1,149 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from dataclasses import dataclass + +from max.dtype import DType +from max.graph import BufferValue, DeviceRef, TensorValue, TensorValueLike, ops +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + KVCacheParams, + PagedKVCacheCollection, +) + +from ..attention.interfaces import DistributedAttentionImpl +from ..embedding import VocabParallelEmbedding +from ..layer import Layer +from ..linear import DistributedMLP, LinearV2 +from ..norm import DistributedRMSNorm, LayerNorm, RMSNorm + + +# TODO (pavan): clean up duplicate instances of distribute_value, shard_col_value, +# shard_row_value across the codebase into a multi gpu utils file +def distribute_value(v, devices: list[DeviceRef]): + return [v.to(device) for device in devices] + + +@dataclass +class DistributedTransformerBlock(Layer): + """Stack of Attention, FeedForward, and RMSNorm layers.""" + + attention: DistributedAttentionImpl + mlp: DistributedMLP + attention_norm: DistributedRMSNorm + mlp_norm: DistributedRMSNorm + devices: list[DeviceRef] + + def __call__( + self, + xs: list[TensorValue], + signal_buffers: list[BufferValue], + kv_collections: list[ + ContinuousBatchingKVCacheCollection | PagedKVCacheCollection + ], + **kwargs, + ) -> list[TensorValue]: + attn_outs = self.attention( + self.attention_norm(xs), signal_buffers, kv_collections, **kwargs + ) + + hs = [x + attn_out for x, attn_out in zip(xs, attn_outs)] + mlp_outs = self.mlp(self.mlp_norm(hs), signal_buffers) + hs = [h + mlp_out for h, mlp_out in zip(hs, mlp_outs)] + + return hs + + +@dataclass +class DistributedTransformer(Layer): + """Transformer model consisting for TransformerBlock layers.""" + + dim: int + n_heads: int + layers: list[DistributedTransformerBlock] + norm: RMSNorm | LayerNorm + output: LinearV2 + embedding: VocabParallelEmbedding + kv_params: KVCacheParams + kv_collection_constructor: ( + FetchContinuousBatchingKVCacheCollection | FetchPagedKVCacheCollection + ) + devices: list[DeviceRef] + all_logits: bool = False + + def __call__( + self, + tokens: TensorValueLike, + signal_buffers: list[BufferValue], + kv_cache_inputs_per_dev: list[tuple[TensorValue, ...]], + **kwargs, + ) -> tuple[TensorValue, ...]: + h = self.embedding(tokens, signal_buffers) + + kv_collections = [ + self.kv_collection_constructor(*kv_cache_inputs) + for kv_cache_inputs in kv_cache_inputs_per_dev + ] + + for _, layer in enumerate(self.layers): + h = layer(h, signal_buffers, kv_collections, **kwargs) + + h0 = h[0] # All the outputs are the same here. + if self.all_logits: + # When echo is enabled, the logits of the input tokens are + # returned. + logits = ops.cast(self.output(self.norm(h0)), DType.float32) + if "input_row_offsets" in kwargs: + # For ragged tensors gather the last tokens from packed dim 0. + input_row_offsets: TensorValueLike = kwargs["input_row_offsets"] + last_token_indices = input_row_offsets[1:] - 1 # type: ignore + last_token_logits = ops.gather( + logits, last_token_indices, axis=0 + ) + else: + # For padded tensors, use `gather_nd`. + # Unsqueeze since `gather_nd` expects a static last dim. + valid_lengths: TensorValueLike = kwargs["valid_lengths"] + last_token_logits = ops.gather_nd( + logits, + indices=ops.unsqueeze(valid_lengths - 1, -1), # type: ignore + batch_dims=1, + ) + return (last_token_logits, logits) + else: + # Otherwise, only return the logits for the last non-pad token + # (right-padded). + if "input_row_offsets" in kwargs: + # For ragged tensors gather the last tokens from packed dim 0. + input_row_offsets = kwargs["input_row_offsets"] + last_token_indices = input_row_offsets[1:] - 1 # type: ignore + # Should be: last_token = h[last_token_indices] + last_token = ops.gather(h0, last_token_indices, axis=0) + else: + # For padded tensors, use `gather_nd`. + # Unsqueeze since `gather_nd` expects a static last dim. + valid_lengths = kwargs["valid_lengths"] + last_token = ops.gather_nd( + h0, + indices=ops.unsqueeze(valid_lengths - 1, -1), # type: ignore + batch_dims=1, + ) + + # Always return float32 logits, no matter the activation type + return ( + ops.cast(self.output(self.norm(last_token)), DType.float32), + ) diff --git a/src/max/pipelines/nn/transformer/naive_transformer.py b/src/max/pipelines/nn/transformer/naive_transformer.py new file mode 100644 index 0000000000..ae3dadbb2d --- /dev/null +++ b/src/max/pipelines/nn/transformer/naive_transformer.py @@ -0,0 +1,139 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from typing import Callable + +from max.dtype import DType +from max.graph import TensorValue, TensorValueLike, ops + +from ..attention import NaiveAttentionWithRope +from ..embedding import Embedding, EmbeddingV2 +from ..layer import Layer, LayerList, LayerV2 +from ..linear import Linear, LinearV2 + + +class NaiveTransformerBlock(LayerV2): + """Max-Graph Only Stack of Attention, FeedForward, and RMSNorm layers.""" + + def __init__( + self, + attention: NaiveAttentionWithRope, + mlp: Layer, + attention_norm: Layer, + mlp_norm: Layer, + residual_multiplier: float = 1.0, + ): + super().__init__() + self.self_attn = attention + self.mlp = mlp + self.input_layernorm = attention_norm + self.post_attention_layernorm = mlp_norm + self.residual_multiplier = residual_multiplier + + def __call__( + self, + x: TensorValue, + attention_mask: TensorValueLike, + k_cache: TensorValueLike, + v_cache: TensorValueLike, + start_pos: TensorValue, + layer_index: int, + ) -> tuple[TensorValue, TensorValue, TensorValue]: + residual_multiplier = ops.constant(self.residual_multiplier, x.dtype) + attn_out = self.self_attn( + self.input_layernorm(x), + attention_mask, + k_cache, # type: ignore + v_cache, # type: ignore + start_pos, + layer_index, + ) + + if self.residual_multiplier != 1.0: + attn_out = attn_out * residual_multiplier + + h = x + attn_out + mlp = self.mlp(self.post_attention_layernorm(h)) + if self.residual_multiplier != 1.0: + mlp = mlp * residual_multiplier + + return h + mlp + + +class NaiveTransformer(LayerV2): + """Max-Graph only model consisting of NaiveTransformerBlock layers.""" + + def __init__( + self, + dim: int, + n_heads: int, + layers: list[NaiveTransformerBlock], + norm: Layer, + output: Linear | LinearV2, + theta: float, + embedding: Embedding | EmbeddingV2, + output_type: DType | None = None, + embedding_multiplier: float = 1.0, + logits_postprocessor: Callable[[TensorValue], TensorValue] + | None = None, + ): + super().__init__() + self.dim = dim + self.n_heads = n_heads + self.layers = LayerList(layers) + self.norm = norm + self.lm_head = output + self.theta = theta + self.embed_tokens = embedding + self.output_type = output_type + self.embedding_multiplier = embedding_multiplier + self.logits_postprocessor = logits_postprocessor + + def _apply_logits_postprocessor( + self, output: tuple[TensorValue] + ) -> tuple[TensorValue]: + if self.logits_postprocessor is None: + return output + return tuple(self.logits_postprocessor(elem) for elem in output) # type:ignore + + def __call__( + self, + tokens: TensorValueLike, + attention_mask: TensorValueLike, + k_cache: TensorValueLike, + v_cache: TensorValueLike, + start_pos: TensorValueLike, + ) -> tuple[TensorValue]: + h = self.embed_tokens(tokens) + + if self.embedding_multiplier != 1.0: + h = h * ops.constant(self.embedding_multiplier, h.dtype) + + for i in range(len(self.layers)): + h = self.layers[i]( + h, + attention_mask, + k_cache, + v_cache, + start_pos, + i, + ) + + output = self.lm_head(self.norm(h)) + if self.output_type is not None: + casted_output = ops.cast(output, self.output_type) + return self._apply_logits_postprocessor((casted_output,)) + + return self._apply_logits_postprocessor((output,)) diff --git a/src/max/pipelines/nn/transformer/transformer.py b/src/max/pipelines/nn/transformer/transformer.py new file mode 100644 index 0000000000..c0ba441975 --- /dev/null +++ b/src/max/pipelines/nn/transformer/transformer.py @@ -0,0 +1,162 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Callable + +from max.dtype import DType +from max.graph import TensorValue, TensorValueLike, ops +from max.pipelines.kv_cache import ( + ContinuousBatchingKVCacheCollection, + FetchContinuousBatchingKVCacheCollection, + FetchPagedKVCacheCollection, + KVCacheParams, + PagedKVCacheCollection, +) + +from ..attention.interfaces import ( + AttentionImpl, + AttentionImplQKV, + AttentionImplV2, +) +from ..embedding import Embedding, EmbeddingV2 +from ..layer import Layer, LayerList, LayerV2 +from ..linear import Linear, LinearV2 + + +class TransformerBlock(LayerV2): + """Stack of Attention, FeedForward, and RMSNorm layers.""" + + def __init__( + self, + attention: AttentionImpl | AttentionImplV2 | AttentionImplQKV, + mlp: Layer, + attention_norm: Layer, + mlp_norm: Layer, + residual_multiplier: float = 1.0, + ): + super().__init__() + self.self_attn = attention + self.mlp = mlp + self.input_layernorm = attention_norm + self.post_attention_layernorm = mlp_norm + self.residual_multiplier = residual_multiplier + + def __call__( + self, + x: TensorValue, + kv_collection: ContinuousBatchingKVCacheCollection + | PagedKVCacheCollection, + **kwargs, + ) -> TensorValue: + residual_multiplier = ops.constant(self.residual_multiplier, x.dtype) + attn_out = self.self_attn( + self.input_layernorm(x), + kv_collection, + **kwargs, + ) + + if self.residual_multiplier != 1.0: + attn_out = attn_out * residual_multiplier + + h = x + attn_out + mlp = self.mlp(self.post_attention_layernorm(h)) + if self.residual_multiplier != 1.0: + mlp = mlp * residual_multiplier + + return h + mlp + + +class Transformer(LayerV2): + """Transformer model consisting for TransformerBlock layers.""" + + def __init__( + self, + dim: int, + n_heads: int, + layers: list[TransformerBlock], + norm: Layer, + output: Linear | LinearV2, + embedding: Embedding | EmbeddingV2, + kv_params: KVCacheParams, + kv_collection_constructor: ( + FetchContinuousBatchingKVCacheCollection + | FetchPagedKVCacheCollection + ), + all_logits: bool = False, + embedding_multiplier: float = 1.0, + logits_postprocessor: Callable[[TensorValue], TensorValue] + | None = None, + ): + super().__init__() + self.dim = dim + self.n_heads = n_heads + self.layers = LayerList(layers) + self.norm = norm + self.lm_head = output + self.embed_tokens = embedding + self.kv_params = kv_params + self.kv_collection_constructor = kv_collection_constructor + self.all_logits = all_logits + self.embedding_multiplier = embedding_multiplier + self.logits_postprocessor = logits_postprocessor + + def _apply_logits_postprocessor( + self, output: tuple[TensorValue, ...] + ) -> tuple[TensorValue, ...]: + if self.logits_postprocessor is None: + return output + return tuple(self.logits_postprocessor(elem) for elem in output) + + def __call__( + self, + tokens: TensorValueLike, + kv_cache_inputs: Sequence[TensorValue], + **kwargs, + ) -> tuple[TensorValue, ...]: + # TODO: Split into a ragged and non-ragged version. + h = self.embed_tokens(tokens) + + if self.embedding_multiplier != 1.0: + h = h * ops.constant(self.embedding_multiplier, h.dtype) + + kv_collection = self.kv_collection_constructor(*kv_cache_inputs) + + for _, layer in enumerate(self.layers): + h = layer(h, kv_collection, **kwargs) + + normalized = self.norm(h) + + if "input_row_offsets" in kwargs: + # Ragged inputs/activations + last_indices = kwargs["input_row_offsets"][1:] - 1 + last_tokens = ops.gather(normalized, last_indices, axis=0) + else: + # Dense padded inputs/activations + valid_lengths = kwargs["valid_lengths"] + # TODO: Remove once `gather_nd` works with nonstatic last dims. + indices = ops.unsqueeze(valid_lengths - 1, -1) + last_tokens = ops.gather_nd(normalized, indices, batch_dims=1) + + # Always return float32 logits, no matter the activation type. + last_token_logits = ops.cast(self.lm_head(last_tokens), DType.float32) + + if self.all_logits: + all_logits = ops.cast(self.lm_head(normalized), DType.float32) + return self._apply_logits_postprocessor( + (last_token_logits, all_logits) + ) + + return self._apply_logits_postprocessor((last_token_logits,)) diff --git a/src/max/pipelines/pipeline.py b/src/max/pipelines/pipeline.py new file mode 100644 index 0000000000..822602fb07 --- /dev/null +++ b/src/max/pipelines/pipeline.py @@ -0,0 +1,739 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# mypy: disable-error-code="import-not-found" +"""HF Token Generation Pipeline""" + +from __future__ import annotations + +import logging +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import ( + Any, + Generic, + List, + Optional, + Protocol, + Sequence, + Type, + TypeVar, + runtime_checkable, +) + +import torch +from max.driver import Device, Tensor +from max.dtype import DType +from max.engine import InferenceSession +from max.pipelines.kv_cache import ( + KVCacheInputs, + KVCacheInputsSequence, + infer_optimal_batch_size, +) +from max.profiler import Tracer, traced +from transformers import AutoTokenizer + +from .config import PipelineConfig +from .context import InputContext +from .interfaces import TokenGenerator +from .kv_cache import KVCacheManager, KVCacheParams +from .response import LogProbabilities, TextResponse +from .sampling import token_sampler + +try: + import xgrammar as xgr + + # This retrieves the last logger handler added + # which presumably is the one initialized in xgrammar + # and removes it, this stops our server logging from + # doubling up. + logger = logging.getLogger() + handler = logger.handlers[-1] + logger.removeHandler(handler) +except ImportError: + pass + +logger = logging.getLogger("max.pipelines") + +ARCH_SAFE_VRAM_USAGE_LIMIT = { + "DeepseekCoder": 0.96, + "ExaoneForCausalLM": 0.96, + "LlamaForCausalLM": 0.96, + "MistralForCausalLM": 0.96, +} + + +def upper_bounded_default(upper_bound: int, default: int | None) -> int: + """ + Given an upper bound and an optional default value, returns a final value + that cannot exceed the upper bound. + + Args: + default: The default value to use, or None to use the upper bound. + upper_bound: The upper bound to use. + + Raises: + ValueError: If the provided default value exceeds the upper bound. + + Returns: + The final value. + """ + if default is None: + return upper_bound + elif default > upper_bound: + raise ValueError( + f"default value provided ({default}) exceeds the upper bound ({upper_bound})" + ) + return default + + +class ModelInputs: + """ + Base class for model inputs. + Use this class to encapsulate inputs for your model. + You may store any number of dataclass fields + + Example: + >>> class ReplitInputs(ModelInputs): + ... tokens: Tensor + ... input_row_offsets: Tensor + ... + ... def __init__(self, tokens: Tensor, input_row_offsets: Tensor): + ... self.tokens = tokens + ... self.input_row_offsets = input_row_offsets + ... + >>> # Create tensors + >>> tokens = Tensor.zeros((1, 2, 3), DType.int64) + >>> input_row_offsets = Tensor.zeros((1, 1, 1), DType.int64) + >>> # Initialize inputs + >>> inputs = ReplitInputs(tokens=tokens, input_row_offsets=input_row_offsets) + >>> # Access tensors + >>> list(inputs) == [tokens, input_row_offsets] + True + """ + + +@dataclass(frozen=True) +class ModelOutputs: + next_token_logits: Tensor | None = None + """Logits for just the next token.""" + + logits: Tensor | None = None + """Logits for the entire token sequence.""" + + +T = TypeVar("T", bound=InputContext) + + +class PipelineModel(ABC, Generic[T]): + """A pipeline model with setup, input preparation and execution methods.""" + + _MAX_DEFAULT_BATCH_SIZE = 4096 + _MIN_DEFAULT_BATCH_SIZE = 1 + + def __init__( + self, pipeline_config: PipelineConfig, session: InferenceSession + ) -> None: + self.pipeline_config = pipeline_config + + if isinstance(self, KVCacheMixin): + self.kv_manager = self.load_kv_manager( + session, pipeline_config._available_cache_memory + ) + + @classmethod + @abstractmethod + def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + """Calculate the optimal max sequence length for the model. + Models are expected to implement this method. + + Example: + >>> class MistralModel(PipelineModel): + ... @classmethod + ... def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: + ... try: + ... return upper_bounded_default( + ... upper_bound=pipeline_config.huggingface_config.max_seq_len, + ... default=pipeline_config.max_length, + ... ) + ... except ValueError as e: + ... msg = ( + ... "Unable to infer max_length for Mistral, the provided " + ... f"max_length ({pipeline_config.max_length}) exceeds the " + ... f"model's max_seq_len " + ... f"({pipeline_config.huggingface_config.max_seq_len})." + ... ) + ... raise ValueError(msg) from e + ... + """ + raise NotImplementedError( + "PipelineModel must implement calculate_max_seq_len" + ) + + @classmethod + @abstractmethod + def get_kv_params(cls, pipeline_config: PipelineConfig) -> KVCacheParams: + """Returns the KV cache params for the pipeline model.""" + ... + + @classmethod + @abstractmethod + def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: + """Returns the number of layers for the pipeline model.""" + ... + + @classmethod + def infer_optimal_batch_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + ) -> int: + """Returns the estimated optimal batch size to run the model + given current memory constraints.""" + if not issubclass(cls, KVCacheMixin): + # we rely on the KVCache setup to know optimal batch size. + # If we don't have that, default to BS=1. + return 1 + elif ( + len(pipeline_config.devices) == 1 + and pipeline_config.devices[0].is_host + ): + # batching on CPU is generally not useful, so we hard-code a batch size of 1. + return 1 + + # TODO we should map HF configs to a unified MAX Config object + # this would help avoid these excessive calls to class methods. + n_layers = cls.get_num_layers(pipeline_config) + kv_params = cls.get_kv_params(pipeline_config) + inferred_batch_size = infer_optimal_batch_size( + params=kv_params, + max_seq_len=cls.calculate_max_seq_len(pipeline_config), + num_layers=n_layers, + available_cache_memory=available_cache_memory, + devices=pipeline_config.devices, + ) + + # clamp the floor of the inferred batch size to 1 and the ceiling to 4096 + inferred_batch_size = max( + cls._MIN_DEFAULT_BATCH_SIZE, + min(inferred_batch_size, cls._MAX_DEFAULT_BATCH_SIZE), + ) + return inferred_batch_size + + @classmethod + def estimate_weights_size(cls, pipeline_config: PipelineConfig) -> int: + """Calculates the estimated memory consumption of our model.""" + + # TODO move this logic to the PipelineModel instead of PipelineConfig class. + # Better yet, make this more accurate by loading and measuring memory consumption + # after we load the model + return pipeline_config.weights_size() + + @abstractmethod + def execute( + self, + model_inputs: ModelInputs, + # TODO(zheng): This should be tucked inside ModelInputs in the future. + kv_cache_inputs: KVCacheInputs | None = None, + ) -> ModelOutputs: + """Executes the graph with the given inputs. + + Args: + model_inputs: The model inputs to execute, containing tensors and any other + required data for model execution. + kv_cache_inputs: The kv cache inputs to execute, containing tensors and any other + required data for model execution. + + Returns: + ModelOutputs containing the pipeline's output tensors. + + This is an abstract method that must be implemented by concrete PipelineModels + to define their specific execution logic. + """ + + @abstractmethod + def prepare_initial_token_inputs( + self, context_batch: Sequence[T] + ) -> ModelInputs: + """Prepares the initial inputs to be passed to `.execute()`. + + The inputs and functionality of this method can vary per model. + For example, the model inputs could include: + - Encoded tensors + - A unique IDs for each tensor if this model uses a KV Cache manager. + + This function would batch the encoded tensors, claim a slot in the kv + cache if the ID hasn't been seen before, and return the inputs and + caches as a list of tensors.""" + ... + + @abstractmethod + def prepare_next_token_inputs( + self, + next_tokens: Tensor, + prev_model_inputs: ModelInputs, + ) -> ModelInputs: + """Prepares the secondary inputs to be passed to `.execute()`. + + While `prepare_initial_token_inputs` is responsible for managing the initial inputs. + This function is responsible for updating the inputs, for each step in a multi-step execution pattern. + """ + ... + + def compute_log_probabilities( + self, + model_inputs: ModelInputs, + model_outputs: ModelOutputs, + next_tokens: Tensor, + batch_top_n: list[int], + batch_echo: list[bool], + ) -> list[LogProbabilities | None] | None: + """Optional method that can be overridden to compute log probabilities. + + Args: + model_inputs: Inputs to the model returned by + `prepare_*_token_inputs()`. + model_outputs: Outputs returned by `execute()`. + next_tokens: Sampled tokens. Should have shape=[batch size] + batch_top_n: Number of top log probabilities to return per input in + the batch. For any element where `top_n == 0`, the + LogProbabilities is skipped. + batch_echo: Whether to include input tokens in the returned log + probabilities. + + Returns: + List of log probabilities. + """ + raise NotImplementedError( + f"Log probabilities not implemented for {type(self)}." + ) + + +@runtime_checkable +class KVCacheMixin(Protocol): + def load_kv_manager( + self, + session: InferenceSession, + available_cache_memory: Optional[int], + ) -> KVCacheManager: + """Provided a PipelineConfig and InferenceSession, loads the KV manager. + + Args: + session: Inference session to compile and init the KV cache. + available_cache_memory: Amount of memory available to the KV cache, + in bytes. + + Returns: + Either a single KV cache manager or a tuple of KV cache managers: + one per input modality. + """ + ... + + @classmethod + @abstractmethod + def estimate_kv_cache_size( + cls, + pipeline_config: PipelineConfig, + available_cache_memory: int, + devices: list[Device], + ) -> int: + """Estimates the size of the kv cache in bytes.""" + ... + + +class TextGenerationPipeline(TokenGenerator[T]): + """Generalized token generator pipeline.""" + + def __init__( + self, + pipeline_config: PipelineConfig, + pipeline_model: Type[PipelineModel], + # TODO: This should be removed. + eos_token_id: int, + ) -> None: + self._pipeline_config = pipeline_config + + # Expand eos tokens if more are provided in pipeline_config + if "eos_token_id" in pipeline_config.huggingface_config: + eos_tokens = pipeline_config.huggingface_config.eos_token_id + if isinstance(eos_tokens, int): + if eos_tokens != eos_token_id: + msg = f"eos_token_id provided in huggingface config ({eos_tokens}), does not match provided eos_token_id ({eos_token_id}), using provided eos_token_id" + logger.warning(msg) + + self._eos_token_id = set([eos_tokens]) + elif isinstance(eos_tokens, list): + if eos_token_id in eos_tokens: + self._eos_token_id = set(eos_tokens) + else: + self._eos_token_id = set([eos_token_id]) + else: + msg = f"eos_token_id in huggingface_config, is neither int or list: {eos_tokens}" + logger.warning(msg) + self._eos_token_id = set([eos_token_id]) + + else: + self._eos_token_id = set([eos_token_id]) + + # Create a grammar compiler if constrained decoding is enabled + self.vocab_size = None + if pipeline_config.enable_structured_output: + tokenizer = AutoTokenizer.from_pretrained( + pipeline_config.model_path + ) + self.vocab_size = len(tokenizer) + tokenizer_info = xgr.TokenizerInfo.from_huggingface( + tokenizer, + vocab_size=self.vocab_size, + ) + + self._grammar_compiler = xgr.GrammarCompiler(tokenizer_info) + + # Initialize Session. + session = InferenceSession(devices=self._pipeline_config.devices) + + # Enable profiling if enabled. + session.gpu_profiling(self._pipeline_config.gpu_profiling) + + # Use experimental kernels if enabled by env var `USE_EXPERIMENTAL_KERNELS`. + session._use_experimental_kernels( + self._pipeline_config.use_experimental_kernels + ) + + # Load model. + self._pipeline_model = pipeline_model( + pipeline_config=self._pipeline_config, session=session + ) + + # Load sampler. + self._sampler = session.load( + token_sampler(self._pipeline_config.sampling_params), + ) + + def calculate_num_steps( + self, + num_steps: int, + context: T, + ) -> int: + max_seq_len = self._pipeline_model.calculate_max_seq_len( + self._pipeline_config + ) + # this is effectively: max_seq_len - (num_tokens_in_kv_cache + num_new_tokens) - num_new_tokens + num_available_steps = max_seq_len - ( + context.current_length - context.active_length + ) + if num_available_steps <= 0: + raise ValueError( + f"Request {context.cache_seq_id} length ({context.current_length}) is larger than or equal to the configured max_length ({max_seq_len})" + ) + + return ( + num_steps + if num_available_steps > num_steps + else num_available_steps + ) + + @traced + def prepare_batch( + self, + batch: list[T], + num_steps: int, + ) -> tuple[ModelInputs, List[KVCacheInputs], int, Optional[torch.Tensor]]: + tracer: Tracer = Tracer("prepare_batch") + + if self._pipeline_config.enable_structured_output: + assert self.vocab_size is not None + bitmask = torch.ones( + xgr.get_bitmask_shape( + len(batch), + self.vocab_size, + ), + dtype=torch.int32, + ) + else: + bitmask = None + + seq_ids_and_prompts = {} + seq_ids_and_untrimmed_lengths = {} + tracer.next("claim_cache_rows") + for i, context in enumerate(batch): + # Initialize a matcher if needed + if context.json_schema and context.matcher is None: + if not self._pipeline_config.enable_structured_output: + msg = "json_schema provided but constrained decoding is not enabled." + raise ValueError(msg) + + try: + compiled_grammar = ( + self._grammar_compiler.compile_json_schema( + context.json_schema, + any_whitespace=False, + ) + ) + matcher = xgr.GrammarMatcher(compiled_grammar) + context.set_matcher(matcher) + except Exception as e: + msg = f"Json schema provided in request cannot be compiled to valid grammar. \ + Please update your json schema to produce valid structured output. From XGrammar: {e}" + logger.warning(msg) + # I am removing the json_schema, so it doesn't try to load the grammar repeatedly. + context.json_schema = None # type: ignore + + # Claim cache rows for context. + if not self._pipeline_model.kv_manager.contains( + context.cache_seq_id + ): + self._pipeline_model.kv_manager.external_claim( + [context.cache_seq_id] + ) + + # Gather tokens and untrimmed lengths. + seq_ids_and_prompts[context.cache_seq_id] = context.next_tokens + seq_ids_and_untrimmed_lengths[context.cache_seq_id] = ( + context.active_length + ) + + # Update num_steps. + num_steps = self.calculate_num_steps(num_steps, context) + + # Update bitmask + if ( + self._pipeline_config.enable_structured_output + and context.matcher + ): + context.matcher.fill_next_token_bitmask(bitmask, index=i) + + # `fetch` mutates the seq_ids_and_prompts input in place when tokens are + # retrieved from the cache. This shortens the prompt in the event that + # some tokens have backing KV cache entries. + tracer.next("fetch_kv_cache") + kv_cache_inputs = self._pipeline_model.kv_manager.fetch( + seq_ids_and_prompts, num_steps + ) + + # Update the context with the new possibly shortened prompt. + tracer.next("trim_prompt") + for context in batch: + untrimmed_length = seq_ids_and_untrimmed_lengths[ + context.cache_seq_id + ] + trimmed_length = len(seq_ids_and_prompts[context.cache_seq_id]) + bump_length = untrimmed_length - trimmed_length + if bump_length > 0: + context.bump_token_indices( + start_idx=bump_length, + ) + + return ( + self._pipeline_model.prepare_initial_token_inputs(batch), + kv_cache_inputs, + num_steps, + bitmask, + ) + + @traced + def sample_logits( + self, + logits: Tensor, + prev_tokens: Tensor, + bitmask: Optional[Tensor], + ) -> tuple[Tensor, Tensor]: + if bitmask is not None: + a, b = self._sampler(logits, prev_tokens, bitmask)[:2] + else: + a, b = self._sampler( + logits, + prev_tokens, + )[:2] + assert isinstance(a, Tensor) + assert isinstance(b, Tensor) + return (a, b) + + @traced + def next_token( + self, + batch: dict[str, T], + num_steps: int, + ) -> list[dict[str, Any]]: + """Provided a batch, process batch inputs, execute the graph for num_steps in a multi-step scenario, + then decode the tokens holistically and return the list of decoded tokens. + """ + tracer: Tracer = Tracer("compute_parameters") + + # Flatten our batch for consistent indexing. + context_batch = list(batch.values()) + + # # Get extra compute parameters for each input. + batch_top_n = [context.log_probabilities for context in context_batch] + compute_log_probabilities = any(batch_top_n) + batch_echo: list[bool] = [ + context.log_probabilities_echo for context in context_batch + ] + + # Prepare the batch. + model_inputs, batched_kv_cache_inputs, num_steps, bitmask = ( + self.prepare_batch(context_batch, num_steps) + ) + + # Multistep execution loop. + tracer.next("allocate_generated_tokens") + generated_tokens = Tensor.zeros( + (len(context_batch), 0), + dtype=DType.int64, + device=self._pipeline_config.devices[0], + ) + + curr_step_inputs = model_inputs + batch_log_probabilities = [] + tracer.next(f"multistep_execution_loop_{num_steps}_steps") + for i in range(num_steps): + tracer.push(f"step_{i}") + + # Execute the model and get next tokens. + model_outputs = self._pipeline_model.execute( + model_inputs=curr_step_inputs, + kv_cache_inputs=KVCacheInputsSequence( + kv_cache_inputs=batched_kv_cache_inputs, + ), + ) + assert model_outputs.next_token_logits is not None + next_token_logits = model_outputs.next_token_logits + + if bitmask is not None: + assert self.vocab_size is not None + bits = 2 ** torch.arange(32, dtype=torch.int32) + bitmask = (bitmask.unsqueeze(-1) & bits) != 0 + bitmask = bitmask.reshape( + len(context_batch), + -1, + ).to(torch.bool) + bitmask = bitmask[:, 0 : self.vocab_size] + + bitmask = Tensor.from_dlpack(bitmask).to( + self._pipeline_config.devices[0] + ) + + # Sample next token. + tracer.next("sample_next_token") + new_tokens, new_generated_tokens = self.sample_logits( + next_token_logits, + generated_tokens, + bitmask, + ) + + assert isinstance(new_tokens, Tensor) + assert isinstance(new_generated_tokens, Tensor) + generated_tokens = new_generated_tokens + + if compute_log_probabilities: + try: + tracer.next("compute_log_probabilities") + batch_log_probabilities.append( + self._pipeline_model.compute_log_probabilities( + curr_step_inputs, + model_outputs, + new_tokens, + batch_top_n, + batch_echo, + ) + ) + except NotImplementedError: + logger.warning( + "Unable to compute log probabilities for" + f" {self._pipeline_config.model_path}" + ) + batch_log_probabilities.append(None) + # Check if we're on our last iteration. If so, skip preparing the next batch + if i == num_steps - 1: + tracer.pop() # pops f"step_{i}" + break + # Prepare inputs for the next token in multistep execution + tracer.next("increment_cache_lengths") # pops sample_next_token + # Unpack model inputs for execute() call by getting all fields + batched_kv_cache_inputs = ( + self._pipeline_model.kv_manager.increment_cache_lengths( + batched_kv_cache_inputs, # type: ignore + curr_step_inputs, + ) + ) + tracer.next("prepare_next_token_inputs") # pops inc_cache_lengths + curr_step_inputs = self._pipeline_model.prepare_next_token_inputs( + new_tokens, curr_step_inputs + ) + tracer.pop() # pops step_{i} + + # Do the copy to host for each token generated. + tracer.next( + "generated_tokens.to(CPU())" + ) # pops multistep_execution_loop_steps + generated_tokens_host = generated_tokens.to_numpy() + + # Actually update the cache lengths in our kv_cache manager + tracer.next("kv_manager.step") # pops generated_tokens.to(CPU()) + seq_ids_and_new_tokens = { + ctx.cache_seq_id: generated_tokens_host[i] + for i, ctx in enumerate(context_batch) + } + self._pipeline_model.kv_manager.step(seq_ids_and_new_tokens) + tracer.pop() # pops kv_manager.step + + # Prepare the response, pruning away completed requests as we go. + res: list[dict[str, Any]] = [{} for _ in range(num_steps)] + tracer.push("prepare_response") + for batch_index, (request_id, context) in enumerate(batch.items()): + step = 0 + while step < num_steps: + # Convert to a Python scalar to improve serialization performance. + next_token = int(generated_tokens_host[batch_index, step]) + + # Write this token into our pre-allocated tokens array. + context.update( + new_token=next_token, + ) + + max_length = upper_bounded_default( + upper_bound=self._pipeline_model.calculate_max_seq_len( + self._pipeline_config + ), + default=context.max_length, + ) + + # The current length is incremented above, during context.update + # As such, if we are already at the max length, exiting here + # would cause us to miss updating the request. + # As such, we overrun here by 1, ensuring that the context object + # tracks special tokens like eos_token_id appropriately for benchmarking + # and other uses, but that they are not returned in the request. + if ( + next_token in self._eos_token_id + or context.current_length > max_length + ): + step += 1 + break + + # Set up TextResponse + log_probs: Optional[LogProbabilities] = None + if compute_log_probabilities and ( + log_probs_for_step := batch_log_probabilities[step] + ): + log_probs = log_probs_for_step[batch_index] + + # Removing the positional arguments here, go about 100us faster. + res[step][request_id] = TextResponse(next_token, log_probs) + + step += 1 + + return res + + def release(self, context: T) -> None: + """Mark the context as complete, releasing the cache slot from the KV manager.""" + self._pipeline_model.kv_manager.release(context.cache_seq_id) diff --git a/src/max/pipelines/registry.py b/src/max/pipelines/registry.py new file mode 100644 index 0000000000..312b0d1e79 --- /dev/null +++ b/src/max/pipelines/registry.py @@ -0,0 +1,1045 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Model registry, for tracking various model variants.""" + +from __future__ import annotations + +import functools +import logging +import os +from io import StringIO +from typing import Callable, Optional, Type, Union, cast + +import torch +from max.graph.weights import WeightsAdapter + +from .config import ( + PipelineConfig, + PipelineEngine, + RopeType, + SupportedEncoding, + WeightsFormat, +) +from .embeddings_pipeline import EmbeddingsPipeline +from .hf_pipeline import HFEmbeddingsPipeline, HFTextGenerationPipeline +from .interfaces import ( + EmbeddingsGenerator, + PipelineTask, + PipelineTokenizer, + TokenGenerator, +) +from .kv_cache import KVCacheStrategy +from .pipeline import KVCacheMixin, PipelineModel, TextGenerationPipeline +from .tokenizer import TextAndVisionTokenizer, TextTokenizer + +logger = logging.getLogger("max.pipelines") + +# Store a map of checkpoint encodings that can be cast to another dtype while +# keeping similar results. Maps the requested encoding to an acceptable +# alternate checkpoint encoding. +_ALTERNATE_ENCODINGS = { + SupportedEncoding.float32: SupportedEncoding.bfloat16, + SupportedEncoding.bfloat16: SupportedEncoding.float32, +} + +_PIPELINE_TASK_MAP = { + PipelineTask.TEXT_GENERATION: TextGenerationPipeline, + PipelineTask.EMBEDDINGS_GENERATION: EmbeddingsPipeline, +} + + +_HF_PIPELINE_TASK_MAP: dict[ + PipelineTask, type[HFTextGenerationPipeline] | type[HFEmbeddingsPipeline] +] = { + PipelineTask.TEXT_GENERATION: HFTextGenerationPipeline, + PipelineTask.EMBEDDINGS_GENERATION: HFEmbeddingsPipeline, +} + + +def _to_human_memory_size(bytes: int) -> str: + """Convert bytes to human readable memory size.""" + KiB = 1024 + MiB = KiB * 1024 + GiB = MiB * 1024 + TiB = GiB * 1024 + if bytes > TiB: + return f"{bytes / TiB:.2f} TiB" + if bytes > GiB: + return f"{bytes / GiB:.2f} GiB" + if bytes > MiB: + return f"{bytes / MiB:.2f} MiB" + return f"{bytes / KiB:.2f} KiB" + + +class SupportedArchitecture: + def __init__( + self, + name: str, + example_repo_ids: list[str], + default_encoding: SupportedEncoding, + supported_encodings: dict[SupportedEncoding, list[KVCacheStrategy]], + pipeline_model: Type[PipelineModel], + task: PipelineTask, + tokenizer: Type[Union[TextTokenizer, TextAndVisionTokenizer]], + default_weights_format: WeightsFormat, + rope_type: RopeType = RopeType.none, + weight_adapters: dict[WeightsFormat, WeightsAdapter] | None = None, + ): + """Initializes a model architecture supported by MAX pipelines. + + New architectures should be registered into the `PipelineRegistry`. + + args: + name: Architecture name. + example_repo_ids: HuggingFace repo_id which runs this architecture. + default_encoding: Default encoding for the model. + supported_encodings: Alternate encodings supported. + pipeline_model: PipelineModel class that defines the model graph + and execution. + task: Which pipeline task should the model run with. + tokenizer: Tokenizer used to preprocess model inputs. + default_weights_format: The weights format used in `pipeline_model`. + weight_converters: A dictionary of weight loaders to use if the + input checkpoint has a different format than the default. + """ + self.name = name + self.example_repo_ids = example_repo_ids + self.default_encoding = default_encoding + self.supported_encodings = supported_encodings + self.pipeline_model = pipeline_model + self.tokenizer = tokenizer + self.default_weights_format = default_weights_format + self.rope_type = rope_type + self.weight_adapters = weight_adapters or {} + self.task = task + + +class PipelineRegistry: + def __init__(self, architectures: list[SupportedArchitecture]): + self.architectures = {arch.name: arch for arch in architectures} + + def register(self, architecture: SupportedArchitecture): + """Add new architecture to registry.""" + if architecture.name in self.architectures: + msg = f"Refusing to override existing architecture for '{architecture.name}'" + raise ValueError(msg) + + self.architectures[architecture.name] = architecture + + def architecture_details( + self, pipeline_config: PipelineConfig + ) -> Optional[SupportedArchitecture]: + """Return architecture details for pipeline_config if available, None if not found.""" + + # If no architecture is provided in the pipeline_config, we have nothing to retrieve. + if not pipeline_config.architecture: + return None + + # If the engine is not provided or MAX, we should retrieve the architecture and validate it. + if ( + not pipeline_config.engine + or pipeline_config.engine == PipelineEngine.MAX + ): + if pipeline_config.architecture in self.architectures: + return self.architectures[pipeline_config.architecture] + else: + return None + else: + return None + + def validate_pipeline_config( + self, pipeline_config: PipelineConfig + ) -> PipelineConfig: + """Update pipeline config with appropriate values if not provided. + If invalid config is provided, error out with detailed reason.""" + + # This will update the architecture, and engine if no architecture is available. + pipeline_config.update_architecture() + + # This will retrieve the architecture, if we support it. + arch = self.architecture_details(pipeline_config) + + # If nothing is provided, we should not update any more params. + # Instead, fall back to the HuggingFace engine. + if not arch and pipeline_config.engine == PipelineEngine.MAX: + msg = ( + "optimized architecture not available for" + f" '{pipeline_config.architecture}', failing as engine is provided as 'MAX'" + ) + raise ValueError(msg) + + elif not arch: + msg = ( + "optimized architecture not available for" + f" '{pipeline_config.architecture}' falling back to" + " HuggingFace." + ) + logger.warning(msg) + pipeline_config.engine = PipelineEngine.HUGGINGFACE + return pipeline_config + + # The remainder of this function, assumes we have both a valid model_path, + # and a SupportedArchitecture. We should then validate the details of the existing architecture + # and fallback to HuggingFace if needed. + + # If weight_path and quantization_encoding are provided, verify that they are consistent. + huggingface_weights_repo = pipeline_config.huggingface_weights_repo() + if ( + pipeline_config.weight_path + and pipeline_config.quantization_encoding + # Cannot validate quantization_encoding for pytorch. + and pipeline_config.weights_format != WeightsFormat.pytorch + ): + # Get the encoding of the first weight path file. + if os.path.exists(pipeline_config.weight_path[0]): + file_encoding = SupportedEncoding.parse_from_file_name( + str(pipeline_config.weight_path[0]) + ) + else: + file_encoding = huggingface_weights_repo.encoding_for_file( + pipeline_config.weight_path[0] + ) + + if file_encoding: + if file_encoding != pipeline_config.quantization_encoding: + msg = f"weight_path provided '{pipeline_config.weight_path[0]}' has an inconsistent encoding '{file_encoding}' than quantization_encoding provided '{pipeline_config.quantization_encoding}'. Please update one." + raise ValueError(msg) + # If weight path is not None, infer the quantization_encoding from the weight_path. + elif ( + pipeline_config.weight_path + and not pipeline_config.quantization_encoding + and pipeline_config.weights_format != WeightsFormat.pytorch + ): + if os.path.exists(pipeline_config.weight_path[0]): + # Not currently supported. Infer encoding from local path. + if pipeline_config.weight_path[0].suffix == ".safetensors": + msg = "If a local safetensors file is provided, please provide a quantization_encoding." + raise ValueError(msg) + + if encoding := SupportedEncoding.parse_from_file_name( + str(pipeline_config.weight_path[0]) + ): + msg = f"encoding inferred from weights file: {encoding}" + logger.debug(msg) + pipeline_config.quantization_encoding = encoding + + else: + if encoding := huggingface_weights_repo.encoding_for_file( + pipeline_config.weight_path[0] + ): + msg = f"encoding inferred from weights file: {encoding}" + logger.debug(msg) + pipeline_config.quantization_encoding = encoding + else: + msg = f"encoding cannot be inferred from weights file: {pipeline_config.weight_path[0]}, please pass a quantization_encoding explictly." + raise ValueError(msg) + elif not pipeline_config.quantization_encoding: + # Check if the repo only has one quantization_encoding. + supported_encodings = huggingface_weights_repo.supported_encodings + if len(supported_encodings) == 1: + msg = f"huggingface repo only has '{supported_encodings[0]}' weights, using '{supported_encodings[0]}'" + logger.debug(msg) + pipeline_config.quantization_encoding = supported_encodings[0] + elif ( + not pipeline_config.devices[0].is_host + ) and SupportedEncoding.bfloat16 in arch.supported_encodings: + # TODO(AITLIB-137): replace this with more full featured logic. + # If we are running on an accelerator and the quantiziation encoding is not set, override to bfloat16. + pipeline_config.quantization_encoding = ( + SupportedEncoding.bfloat16 + ) + else: + msg = f"encoding not provided, using default encoding of {arch.default_encoding}" + logger.debug(msg) + pipeline_config.quantization_encoding = arch.default_encoding + # by this point, the quantization_encoding must be provided. verify it is supported. + if ( + pipeline_config.quantization_encoding + not in arch.supported_encodings + ): + if pipeline_config.engine == PipelineEngine.MAX: + msg = f"quantization_encoding of '{pipeline_config.quantization_encoding}' not supported by MAX engine, unable to run with engine = 'max'." + raise ValueError(msg) + + else: + msg = f"quantization_encoding of '{pipeline_config.quantization_encoding}' not supported by MAX engine, falling back to HuggingFace." + logger.warning(msg) + pipeline_config.engine = PipelineEngine.HUGGINGFACE + return pipeline_config + + # Check that the quantization encoding is supported on the specified + # devices. + for device_spec in pipeline_config.device_specs: + if not pipeline_config.quantization_encoding.supported_on( + device_spec + ): + raise ValueError( + f"{pipeline_config.quantization_encoding} is not supported on {device_spec.device_type}. " + "Please use the flag --devices=cpu or --devices=gpu to configure the device." + ) + + pipeline_config.finalize_encoding_config() + + # Pass weight adapters to the PipelineConfig. + pipeline_config._weight_adapters = arch.weight_adapters + + # We should now have a valid quantization_encoding, and possibly a weight_path. + # If no weight_path is provided, we should grab the default. + if not pipeline_config.weight_path: + # Retrieve the default files for each weights format. + + # Get alternate encoding (e.g. if float32 is requested and there are + # only bfloat16 weights, allow retrieving the bfloat16 weights + # because they can be cast to float32). + alternate_encoding = _ALTERNATE_ENCODINGS.get( + pipeline_config.quantization_encoding + ) + + weight_files = huggingface_weights_repo.files_for_encoding( + encoding=pipeline_config.quantization_encoding, + alternate_encoding=alternate_encoding, + ) + + if default_weight_files := weight_files.get( + arch.default_weights_format, [] + ): + pipeline_config.weight_path = default_weight_files + elif weight_files: + # Load any available weight file. + pipeline_config.weight_path = next(iter(weight_files.values())) + + if not pipeline_config.weight_path: + if pipeline_config.quantization_encoding not in [ + SupportedEncoding.bfloat16, + SupportedEncoding.float32, + ]: + msg = f"compatible weights cannot be found for '{pipeline_config.quantization_encoding}' in 'gguf' format, in the provided repo: '{huggingface_weights_repo.repo_id}'" + raise ValueError(msg) + else: + msg = f"compatible weights cannot be found for '{pipeline_config.quantization_encoding}'" + raise ValueError(msg) + + # Check supported_cache_strategy + supported_cache_strategies = arch.supported_encodings.get( + pipeline_config.quantization_encoding, [] + ) + if ( + pipeline_config.cache_strategy == KVCacheStrategy.MODEL_DEFAULT + and supported_cache_strategies + ): + default_strategy = supported_cache_strategies[0] + msg = f"default cache_strategy of '{default_strategy}' enabled" + logger.debug(msg) + + pipeline_config.cache_strategy = default_strategy + elif ( + supported_cache_strategies + and pipeline_config.cache_strategy not in supported_cache_strategies + ): + supported_strategy = supported_cache_strategies[0] + + msg = f"cache_strategy = '{pipeline_config.cache_strategy}' not supported for '{pipeline_config.quantization_encoding}', using '{supported_strategy}' cache strategy." + logger.warning(msg) + + pipeline_config.cache_strategy = supported_strategy + + # Assume at this point, an architecture, + # a model_path and weight_paths are available. + assert pipeline_config.weight_path, "weight_path must be provided." + for path in pipeline_config.weight_path: + # Check if file exists locally. + if not os.path.exists(path): + # If does not exist locally, verify that it exists on Huggingface. + if not huggingface_weights_repo.file_exists(str(path)): + msg = ( + f"weight_path: '{path}' does not exist locally, and" + f" '{pipeline_config.model_path}/{path}' does" + " not exist on HuggingFace." + ) + raise ValueError(msg) + + if pipeline_config.rope_type is None: + pipeline_config.rope_type = arch.rope_type + + self._estimate_memory_footprint(pipeline_config, arch) + + # If we pass validation ensure, the engine is set as MAX. + pipeline_config.engine = PipelineEngine.MAX + return pipeline_config + + def _estimate_memory_footprint( + self, + pipeline_config: PipelineConfig, + arch: SupportedArchitecture, + ): + model_cls = arch.pipeline_model + + try: + free_memory = int( + sum(d.stats["free_memory"] for d in pipeline_config.devices) + ) + except Exception as e: + logger.warning( + "Unable to estimate memory footprint of model, can't query device stats: " + + str(e) + ) + if not pipeline_config.max_batch_size: + pipeline_config.max_batch_size = 1 + if not pipeline_config.max_length: + pipeline_config.max_length = model_cls.calculate_max_seq_len( + pipeline_config + ) + return + + model_weights_size = model_cls.estimate_weights_size(pipeline_config) + + total_size = model_weights_size + available_kv_cache_memory = max(0, free_memory - model_weights_size) + available_kv_cache_memory = int( + available_kv_cache_memory + * pipeline_config.device_memory_utilization + ) + + user_provided_max_length = pipeline_config.max_length is not None + user_provided_max_batch_size = ( + pipeline_config.max_batch_size is not None + ) + if not user_provided_max_length: + pipeline_config.max_length = model_cls.calculate_max_seq_len( + pipeline_config + ) + + if not user_provided_max_batch_size: + pipeline_config.max_batch_size = self._infer_optimal_batch_size( + pipeline_config, model_cls, available_kv_cache_memory + ) + + actual_kv_cache_size = self._calculate_kv_cache_size( + model_cls, + pipeline_config, + available_kv_cache_memory, + ) + + pipeline_config._available_cache_memory = actual_kv_cache_size + + total_size += actual_kv_cache_size + + # If the model is too large to fit in memory, and the user did not + # specify a max_length, try to infer a value that would fit. + if total_size > free_memory and not user_provided_max_length: + original_max_length = pipeline_config.max_length + ( + found_valid_max_length, + inferred_max_length, + _, + ) = self._find_valid_max_length( + pipeline_config, + model_cls, + available_kv_cache_memory, + user_provided_max_batch_size, + ) + + if found_valid_max_length: + logger.warning( + f"Truncated model's default max_length from {original_max_length} to {inferred_max_length} to fit in memory." + ) + pipeline_config.max_length = inferred_max_length + actual_kv_cache_size = self._calculate_kv_cache_size( + model_cls, + pipeline_config, + available_kv_cache_memory, + ) + total_size = model_weights_size + actual_kv_cache_size + + if free_memory: + free_memory_str = f" / {_to_human_memory_size(free_memory)} free" + + weights_str = "" + if model_weights_size: + weights_str = f"\n\t Weights: {_to_human_memory_size(model_weights_size)}" + + if not user_provided_max_length: + max_length_str = f"Auto-inferred max sequence length: {pipeline_config.max_length}" + else: + max_length_str = ( + f"Current max sequence length: {pipeline_config.max_length}" + ) + + if not user_provided_max_batch_size: + max_batch_size_str = f"Auto-inferred max batch size: {pipeline_config.max_batch_size}" + else: + max_batch_size_str = ( + f"Current max batch size: {pipeline_config.max_batch_size}" + ) + + logging_str = ( + "\n" + f"\n\tEstimated memory consumption:" + f"{weights_str}" + f"\n\t KVCache allocation: {_to_human_memory_size(actual_kv_cache_size)}" + f"\n\t Total estimated: {_to_human_memory_size(model_weights_size + actual_kv_cache_size)} used{free_memory_str}" + f"\n\t{max_length_str}" + f"\n\t{max_batch_size_str}\n" + ) + logger.info(logging_str) + vram_usage_limit_scale = 0.95 + + if isinstance(free_memory, (int, float)): + if total_size > free_memory: + self._raise_oom_error( + pipeline_config, + user_provided_max_length, + user_provided_max_batch_size, + model_cls, + total_size, + free_memory, + available_kv_cache_memory, + model_weights_size, + ) + + elif total_size > vram_usage_limit_scale * free_memory: + logger.warning( + "Estimated model and kv cache memory use nears available memory. You may experience errors." + ) + + def _raise_oom_error( + self, + pipeline_config: PipelineConfig, + user_provided_max_length: bool, + user_provided_max_batch_size: bool, + model_cls: Type[PipelineModel], + total_size: int, + original_free_memory: int, + available_kv_cache_memory: int, + weights_size: int, + ) -> None: + """If we've determined the current configuration won't fit in device memory, + this method provides a friendly error message suggesting a viable configuration. + + The approach is to: + 1. Binary search max_length until we find a setting that works + 2. If user provided max_batch_size, binary search that too + 3. Generate appropriate suggestions based on this truth table: + + max_length + +----------------------+--------------------------+ + | set by user | set to default | + +----------------+======================+==========================+ + | set by user ║ Recommend both | Recommend max_batch_size | + max_batch_size +----------------+----------------------+--------------------------+ + | set to default ║ Recommend max_length | Recommend both | + +----------------+----------------------+--------------------------+ + """ + if weights_size > original_free_memory: + raise RuntimeError( + "Weights size exceeds available memory. Try running a smaller model, using a smaller precision, or using a device with more memory." + ) + + original_max_length = cast(int, pipeline_config.max_length) + original_max_batch_size = cast(int, pipeline_config.max_batch_size) + + # Find valid configurations through binary search + ( + found_valid_max_length, + inferred_max_length, + inferred_max_length_compatible_batch_size, + ) = self._find_valid_max_length( + pipeline_config, + model_cls, + available_kv_cache_memory, + user_provided_max_batch_size, + ) + + pipeline_config.max_batch_size = original_max_batch_size + + found_valid_max_batch_size, inferred_max_batch_size = ( + self._find_valid_batch_size( + pipeline_config, + model_cls, + available_kv_cache_memory, + original_max_length, + user_provided_max_batch_size, + ) + ) + + # Generate error message with suggestions + error_msg = self._generate_oom_error_message( + total_size=total_size, + original_free_memory=original_free_memory, + user_provided_max_length=user_provided_max_length, + user_provided_max_batch_size=user_provided_max_batch_size, + found_valid_max_length=found_valid_max_length, + found_valid_max_batch_size=found_valid_max_batch_size, + inferred_max_length=inferred_max_length, + inferred_max_batch_size=inferred_max_batch_size, + inferred_max_length_compatible_batch_size=inferred_max_length_compatible_batch_size, + original_max_length=original_max_length, + ) + + raise RuntimeError(error_msg) + + def _find_valid_max_length( + self, + pipeline_config: PipelineConfig, + model_cls: Type[PipelineModel], + available_kv_cache_memory: int, + user_provided_max_batch_size: bool, + ) -> tuple[bool, int, int]: + """Binary search to find a valid max_length configuration. + + Returns: + Tuple containing: + - found_valid_max_length: Whether a valid max_length was found + - inferred_max_length: The suggested max_length value + - inferred_max_length_compatible_batch_size: Compatible batch size for the max_length + """ + assert pipeline_config.max_length is not None + assert pipeline_config.max_batch_size is not None + + found_valid_max_length = False + lower = 1 + upper = pipeline_config.max_length + inferred_max_length = upper + + while not found_valid_max_length: + inferred_max_length = (lower + upper) // 2 + pipeline_config.max_length = inferred_max_length + + if not user_provided_max_batch_size: + pipeline_config.max_batch_size = self._infer_optimal_batch_size( + pipeline_config, model_cls, available_kv_cache_memory + ) + + kv_cache_size = self._calculate_kv_cache_size( + model_cls, pipeline_config, available_kv_cache_memory + ) + + if lower > upper: + break + elif upper - lower <= 1: + if kv_cache_size <= available_kv_cache_memory: + found_valid_max_length = True + break + + if kv_cache_size > available_kv_cache_memory: + upper = inferred_max_length - 1 + else: + lower = inferred_max_length + return ( + found_valid_max_length, + inferred_max_length, + pipeline_config.max_batch_size, + ) + + def _find_valid_batch_size( + self, + pipeline_config: PipelineConfig, + model_cls: Type[PipelineModel], + available_kv_cache_memory: int, + original_max_length: int, + user_provided_max_batch_size: bool, + ) -> tuple[bool, int]: + """Binary search to find a valid batch size configuration. + + Returns: + Tuple containing: + - found_valid_max_batch_size: Whether a valid batch size was found + - inferred_max_batch_size: The suggested batch size value. + If the user did not provide a batch size, this will be -1. + """ + if not user_provided_max_batch_size: + return False, -1 + + found_valid_max_batch_size = False + pipeline_config.max_length = original_max_length + inferred_max_batch_size = cast(int, pipeline_config.max_batch_size) + lower = 1 + upper = cast(int, pipeline_config.max_batch_size) + + while not found_valid_max_batch_size: + inferred_max_batch_size = (lower + upper) // 2 + pipeline_config.max_batch_size = inferred_max_batch_size + + kv_cache_size = self._calculate_kv_cache_size( + model_cls, pipeline_config, available_kv_cache_memory + ) + + if lower > upper: + break + elif upper - lower <= 1: + if kv_cache_size <= available_kv_cache_memory: + found_valid_max_batch_size = True + break + + if kv_cache_size > available_kv_cache_memory: + upper = inferred_max_batch_size - 1 + else: + lower = inferred_max_batch_size + + return found_valid_max_batch_size, inferred_max_batch_size + + def _calculate_kv_cache_size( + self, + model_cls: Type[PipelineModel], + pipeline_config: PipelineConfig, + available_kv_cache_memory: int, + ) -> int: + """Calculate the KV cache size for the current configuration.""" + if issubclass(model_cls, KVCacheMixin): + return model_cls.estimate_kv_cache_size( + pipeline_config=pipeline_config, + available_cache_memory=available_kv_cache_memory, + devices=pipeline_config.devices, + ) + return 0 + + def _generate_oom_error_message( + self, + total_size: int, + original_free_memory: int, + user_provided_max_length: bool, + user_provided_max_batch_size: bool, + found_valid_max_length: bool, + found_valid_max_batch_size: bool, + inferred_max_length: int, + inferred_max_batch_size: int, + inferred_max_length_compatible_batch_size: int, + original_max_length: int, + ) -> str: + """Generate an appropriate error message based on the configuration state.""" + free_memory_str = ( + f" / {_to_human_memory_size(original_free_memory)} free" + if original_free_memory + else "" + ) + + msg = StringIO() + msg.write( + f"Estimated model and kv cache memory use exceeds available memory ({_to_human_memory_size(total_size)} {free_memory_str}). Try " + ) + + if not found_valid_max_length and not found_valid_max_batch_size: + msg.write( + "reducing --max-length or --max-batch-size, finding a smaller model, or using a device with more memory." + ) + + elif user_provided_max_length: + self._add_user_provided_max_length_suggestions( + msg, + user_provided_max_batch_size, + found_valid_max_length, + found_valid_max_batch_size, + inferred_max_length, + inferred_max_batch_size, + inferred_max_length_compatible_batch_size, + ) + else: + self._add_default_max_length_suggestions( + msg, + user_provided_max_batch_size, + found_valid_max_length, + found_valid_max_batch_size, + inferred_max_length, + inferred_max_batch_size, + inferred_max_length_compatible_batch_size, + original_max_length, + ) + + msg.write(".") + return msg.getvalue() + + def _add_user_provided_max_length_suggestions( + self, + msg: StringIO, + user_provided_max_batch_size: bool, + found_valid_max_length: bool, + found_valid_max_batch_size: bool, + inferred_max_length: int, + inferred_max_batch_size: int, + inferred_max_length_compatible_batch_size: int, + ) -> None: + """Add error message suggestions when user provided max_length. + + This handles the top row of the truth table from the _raise_oom_error docstring. + + Args: + msg: StringIO buffer to write message to + user_provided_max_batch_size: Whether user provided batch size + found_valid_max_length: Whether valid max_length was found + found_valid_max_batch_size: Whether valid batch size was found + inferred_max_length: Suggested max_length value + inferred_max_batch_size: Suggested batch size value + inferred_max_length_compatible_batch_size: Compatible batch size for max_length + """ + if not user_provided_max_batch_size: + if found_valid_max_length: + msg.write( + f"reducing --max-length to {inferred_max_length} " + f"(supports batch size of {inferred_max_length_compatible_batch_size})" + ) + else: + msg.write("reducing --max-length or --max-batch-size") + else: + if found_valid_max_length: + msg.write( + f"reducing --max-length to {inferred_max_length} and " + f"--max-batch-size to {inferred_max_length_compatible_batch_size})" + ) + + if found_valid_max_batch_size: + if found_valid_max_length: + msg.write(" or ") + msg.write( + f"reducing --max-batch-size to {inferred_max_batch_size}" + ) + + def _add_default_max_length_suggestions( + self, + msg: StringIO, + user_provided_max_batch_size: bool, + found_valid_max_length: bool, + found_valid_max_batch_size: bool, + inferred_max_length: int, + inferred_max_batch_size: int, + inferred_max_length_compatible_batch_size: int, + original_max_length: int, + ) -> None: + """Add error message suggestions when max_length was set to default. + + This handles the bottom row of the truth table from the _raise_oom_error docstring. + + Args: + msg: StringIO buffer to write message to + user_provided_max_batch_size: Whether user provided batch size + found_valid_max_length: Whether valid max_length was found + found_valid_max_batch_size: Whether valid batch size was found + inferred_max_length: Suggested max_length value + inferred_max_batch_size: Suggested batch size value + inferred_max_length_compatible_batch_size: Compatible batch size for max_length + original_max_length: Original max_length value before modifications + """ + if not user_provided_max_batch_size: + if found_valid_max_length: + msg.write( + f"setting --max-length to {inferred_max_length} and " + f"--max-batch-size to {inferred_max_length_compatible_batch_size})" + ) + + if found_valid_max_batch_size: + if found_valid_max_length: + msg.write(" or ") + msg.write( + f"setting --max-batch-size to {inferred_max_batch_size}" + ) + + else: + if found_valid_max_batch_size: + msg.write( + f"reducing --max-batch-size to {inferred_max_batch_size}" + ) + if found_valid_max_length: + if found_valid_max_batch_size: + msg.write(" or ") + msg.write( + f"setting --max-length to {inferred_max_length} " + f"(currently defaulted to {original_max_length})" + ) + + def _infer_optimal_batch_size( + self, + pipeline_config: PipelineConfig, + model_cls: Type[PipelineModel], + available_kv_cache_memory: int, + ) -> int: + return model_cls.infer_optimal_batch_size( + pipeline_config, + available_kv_cache_memory, + ) + + def _load_logging_message( + self, + pipeline_config: PipelineConfig, + tokenizer_type: Type[PipelineTokenizer], + pipeline_name: str, + pipeline_model: str, + factory: bool, + ): + weight_path = ",\n ".join( + [ + f" {path}" + for path in pipeline_config.weight_path + ] + ) + factory_str = "factory" if factory else "" + + weights_repo_str = ( + f"\n weights_repo_id: {pipeline_config._weights_repo_id}" + if pipeline_config._weights_repo_id + else "" + ) + + devices_str = ", ".join( + f"{d.label}[{d.id}]" for d in pipeline_config.devices + ) + message = f""" + + Loading {tokenizer_type.__name__} and {pipeline_name}({pipeline_model}) {factory_str} for: + engine: {pipeline_config.engine} + architecture: {pipeline_config.architecture} + devices: {devices_str} + model_path: {pipeline_config.model_path}{weights_repo_str} + huggingface_revision: {pipeline_config.huggingface_revision} + quantization_encoding: {pipeline_config.quantization_encoding} + cache_strategy: {pipeline_config.cache_strategy} + weight_path: [ + {weight_path} + ] + """ + + return message + + def _set_hf_pipeline_defaults( + self, pipeline_config: PipelineConfig + ) -> PipelineConfig: + if pipeline_config.max_batch_size is None: + pipeline_config.max_batch_size = 1 + # HF pipelines always use custom continuous cache + pipeline_config.cache_strategy = KVCacheStrategy.CONTINUOUS + return pipeline_config + + def retrieve_factory( + self, + pipeline_config: PipelineConfig, + task: PipelineTask = PipelineTask.TEXT_GENERATION, + ) -> tuple[ + PipelineTokenizer, + Callable[[], TokenGenerator | EmbeddingsGenerator], + ]: + tokenizer: PipelineTokenizer + pipeline_factory: Callable[[], TokenGenerator | EmbeddingsGenerator] + + # Validate pipeline_config, and update missing values. + pipeline_config = self.validate_pipeline_config(pipeline_config) + if pipeline_config.engine == PipelineEngine.MAX: + # Keep MyPy happy. + assert pipeline_config.architecture is not None + + pipeline_class = _PIPELINE_TASK_MAP[task] + + # MAX pipeline + arch = self.architectures[pipeline_config.architecture] + logger.info( + self._load_logging_message( + pipeline_config=pipeline_config, + tokenizer_type=arch.tokenizer, + pipeline_model=arch.pipeline_model.__name__, + pipeline_name=pipeline_class.__name__, + factory=True, + ) + ) + + max_length = arch.pipeline_model.calculate_max_seq_len( + pipeline_config + ) + + # Old Mistral model like Mistral-7B-Instruct-v0.3 uses LlamaTokenizer + # and suffers from the whitespace decoding bug. So, we enable the fix + # for only MistralModel in order to avoid any issues with performance + # for rest of the models. This can be applied more generically once + # we have more time verifying this for all the models. + # More information: + # https://linear.app/modularml/issue/AIPIPE-197/add-support-for-mistral-7b-instruct-v03 + # TODO: remove this pipeline_model.__name__ check + if ( + arch.pipeline_model.__name__ in ("MistralModel", "Phi3Model") + and arch.tokenizer is TextTokenizer + ): + text_tokenizer = cast(Type[TextTokenizer], arch.tokenizer) + tokenizer = text_tokenizer( + pipeline_config.model_path, + revision=pipeline_config.huggingface_revision, + max_length=max_length, + max_new_tokens=pipeline_config.max_new_tokens, + trust_remote_code=pipeline_config.trust_remote_code, + enable_llama_whitespace_fix=True, + ) + else: + tokenizer = arch.tokenizer( + pipeline_config.model_path, + revision=pipeline_config.huggingface_revision, + max_length=max_length, + max_new_tokens=pipeline_config.max_new_tokens, + trust_remote_code=pipeline_config.trust_remote_code, + ) + + pipeline_factory = functools.partial( + pipeline_class, + pipeline_config=pipeline_config, + pipeline_model=arch.pipeline_model, + eos_token_id=tokenizer.eos, + ) + else: + pipeline_config = self._set_hf_pipeline_defaults(pipeline_config) + hf_pipeline_class = _HF_PIPELINE_TASK_MAP[task] + + torch_device_type = str(pipeline_config.device_specs[0].device_type) + if pipeline_config.device_specs[0].device_type == "gpu": + torch_device_type = "cuda" + torch.multiprocessing.set_start_method("spawn", force=True) + + # Generalized pipeline + tokenizer = TextTokenizer( + pipeline_config.model_path, + revision=pipeline_config.huggingface_revision, + max_length=pipeline_config.max_length, + max_new_tokens=pipeline_config.max_new_tokens, + trust_remote_code=pipeline_config.trust_remote_code, + enable_llama_whitespace_fix=True, + ) + logger.info( + self._load_logging_message( + pipeline_config=pipeline_config, + tokenizer_type=TextTokenizer, + pipeline_model="", + pipeline_name=hf_pipeline_class.__name__, + factory=True, + ) + ) + pipeline_factory = functools.partial( + hf_pipeline_class, + pipeline_config=pipeline_config, + torch_device_type=torch_device_type, + ) + + if tokenizer.eos is None: + msg = "tokenizer.eos value is None, tokenizer configuration is incomplete." + raise ValueError(msg) + + return tokenizer, pipeline_factory + + def retrieve( + self, + pipeline_config: PipelineConfig, + task: PipelineTask = PipelineTask.TEXT_GENERATION, + ) -> tuple[PipelineTokenizer, TokenGenerator | EmbeddingsGenerator]: + tokenizer, pipeline_factory = self.retrieve_factory( + pipeline_config, task + ) + return tokenizer, pipeline_factory() + + def reset(self) -> None: + self.architectures.clear() + + +PIPELINE_REGISTRY = PipelineRegistry([]) diff --git a/src/max/pipelines/requirements.txt b/src/max/pipelines/requirements.txt new file mode 100644 index 0000000000..708e101b5b --- /dev/null +++ b/src/max/pipelines/requirements.txt @@ -0,0 +1,38 @@ +# This was deleted as part of pipeline refactor +# https://github.com/modularml/modular/commit/d7e54f4a11b8f9a29e883d204dcacea90513db82#diff-b47636dfb4ba8723a713bfc288fdb324fa6e048067c7bbad0195795583edb21e +# But this is needed by benchmarking to setup the environment +# Adding this back for now to unblock. +click >= 8.1.7 +fastapi >= 0.111.0 +gguf >= 0.10.0 +requests >= 2.32.3 +sentencepiece >= 0.2.0 +tokenizers >= 0.19.1 +# PyTorch 2.5.1 with cuda support +torch == 2.5.1 +torchvision == 0.20.1 +torchaudio == 2.5.1 +transformers >= 4.44.2 +safetensors >= 0.4.5 +psutil + +# serve +hf-transfer>=0.1.9 +httpx>=0.28.1,<0.29 +huggingface-hub>=0.24.0 +opentelemetry-api>=1.29.0 +opentelemetry-exporter-otlp-proto-http>=1.27.0 +opentelemetry-exporter-prometheus>=0.48b0 +opentelemetry-sdk>=1.29.0 +pillow>=10.3.0 +prometheus-async>=22.2.0 +prometheus-client>=0.21.0 +pydantic-settings>=2.7.1 +pydantic +pyinstrument>=5.0.1 +python-json-logger>=2.0.7 +sentinel>=0.3.0 +sse-starlette>=2.1.2 +tokenizers>=0.19.0 +uvicorn>=0.34.0 +uvloop>=0.21.0 \ No newline at end of file diff --git a/src/max/pipelines/response.py b/src/max/pipelines/response.py new file mode 100644 index 0000000000..6154570afc --- /dev/null +++ b/src/max/pipelines/response.py @@ -0,0 +1,102 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Standardized response object for Pipeline Inference.""" + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np + + +class LogProbabilities: + """Log probabilities for an individual output token. + + Attributes: + token_log_probabilities (list[float]): Probabilities of each token. + top_log_probabilities (list[dict[int, float]]): Top tokens and their corresponding probabilities. + + """ + + def __init__( + self, + token_log_probabilities: list[float], + top_log_probabilities: list[dict[int, float]], + ) -> None: + self.token_log_probabilities = token_log_probabilities + self.top_log_probabilities = top_log_probabilities + + def __eq__(self, other: object) -> bool: + if not isinstance(other, LogProbabilities): + return False + + if len(self.token_log_probabilities) != len( + other.token_log_probabilities + ): + return False + + if not all( + a == b + for a, b in zip( + self.token_log_probabilities, other.token_log_probabilities + ) + ): + return False + + if len(self.top_log_probabilities) != len(other.top_log_probabilities): + return False + + if not all( + a == b + for a, b in zip( + self.top_log_probabilities, other.top_log_probabilities + ) + ): + return False + + return True + + +class TextResponse: + """A base class for model response, specifically for Text model variants. + + Attributes: + next_token (int | str): Encoded predicted next token. + log_probabilities (LogProbabilities | None): Log probabilities of each output token. + + """ + + def __init__( + self, + next_token: int | str, + log_probabilities: LogProbabilities | None = None, + ) -> None: + self.next_token = next_token + self.log_probabilities = log_probabilities + + def __eq__(self, value: object) -> bool: + if not isinstance(value, TextResponse): + return False + + return ( + self.next_token == value.next_token + and self.log_probabilities == value.log_probabilities + ) + + +@dataclass +class EmbeddingsResponse: + """Container for the response from embeddings pipeline.""" + + embeddings: np.ndarray diff --git a/src/max/pipelines/sampling.py b/src/max/pipelines/sampling.py new file mode 100644 index 0000000000..eda8e6e895 --- /dev/null +++ b/src/max/pipelines/sampling.py @@ -0,0 +1,95 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +"""Token sampling algorithms.""" + +from max.dtype import DType +from max.graph import Dim, Graph, Shape, TensorType, TensorValue, ops +from max.pipelines import SamplingParams + + +def _bitmask_sampler(sampling_params: SamplingParams) -> Graph: + logits_in_type = TensorType( + sampling_params.in_dtype, ["batch", "vocab_size"] + ) + prev_tokens_type = TensorType(DType.int64, ["batch", "num_prev_steps"]) + bitmask_type = TensorType(DType.bool, ["batch", "vocab_size"]) + + with Graph( + "bitmask_sampler", + input_types=[logits_in_type, prev_tokens_type, bitmask_type], + ) as graph: + # Deconstruct inputs and cast. + logits, prev_tokens, bitmask = (val.tensor for val in graph.inputs) + logits = ops.cast(logits, sampling_params.out_dtype) + + # Mask the logits out. + logits = ops.select( + bitmask, logits, ops.constant(-10000, dtype=DType.float32) + ) + + # Apply top_k or argmax sampling. + shape = Shape(logits.shape) + shape[-1] = Dim(1) + tokens = ops.custom( + "topk_fused_sampling", + [ + ops.constant(sampling_params.top_k, dtype=DType.int64), + logits, + ], + [TensorType(DType.int64, shape)], + )[0] + assert isinstance(tokens, TensorValue) + + all_tokens = ops.concat([prev_tokens, tokens], -1) + tokens = ops.squeeze(tokens, -1) + graph.output(tokens, all_tokens) + + return graph + + +def _vanilla_sampler(sampling_params: SamplingParams) -> Graph: + logits_in_type = TensorType( + sampling_params.in_dtype, ["batch", "vocab_size"] + ) + prev_tokens_type = TensorType(DType.int64, ["batch", "num_prev_steps"]) + with Graph( + "token_sampler", input_types=[logits_in_type, prev_tokens_type] + ) as graph: + logits, prev_tokens = (val.tensor for val in graph.inputs) + logits = ops.cast(logits, sampling_params.out_dtype) + + shape = Shape(logits.shape) + shape[-1] = Dim(1) + tokens = ops.custom( + "topk_fused_sampling", + [ + ops.constant(sampling_params.top_k, dtype=DType.int64), + logits, + ], + [TensorType(DType.int64, shape)], + )[0] + assert isinstance(tokens, TensorValue) + + all_tokens = ops.concat([prev_tokens, tokens], -1) + tokens = ops.squeeze(tokens, -1) + graph.output(tokens, all_tokens) + + return graph + + +def token_sampler(sampling_params: SamplingParams) -> Graph: + if sampling_params.enable_structured_output: + return _bitmask_sampler(sampling_params) + else: + return _vanilla_sampler(sampling_params) diff --git a/src/max/pipelines/tokenizer.py b/src/max/pipelines/tokenizer.py new file mode 100644 index 0000000000..03f4b73ae7 --- /dev/null +++ b/src/max/pipelines/tokenizer.py @@ -0,0 +1,518 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# mypy: disable-error-code="import-not-found" +"""Implementations of provided tokenizers.""" + +from __future__ import annotations + +import asyncio +import io +import json +import logging +from typing import Optional, Sequence, Union, cast + +import numpy as np +import torch +from PIL import Image +from transformers import ( + AutoProcessor, + AutoTokenizer, + CodeLlamaTokenizer, + CodeLlamaTokenizerFast, + LlamaTokenizer, + LlamaTokenizerFast, + PreTrainedTokenizer, + PreTrainedTokenizerFast, +) + +from .context import TextAndVisionContext, TextContext +from .interfaces import ( + PipelineTokenizer, + TokenGeneratorContext, + TokenGeneratorRequest, + TokenGeneratorRequestMessage, + TokenGeneratorRequestTool, +) + +logger = logging.getLogger("max.pipelines") + + +class IdentityPipelineTokenizer( + PipelineTokenizer[TokenGeneratorContext, str], +): + @property + def eos(self) -> int: + return 0 + + @property + def expects_content_wrapping(self) -> bool: + return False + + async def encode(self, prompt: str) -> str: + return prompt + + async def decode( + self, + context: TokenGeneratorContext, + encoded: str, + **kwargs, + ) -> str: + if isinstance(encoded, str): + return encoded + return "" + + +class PreTrainedPipelineTokenizer( + PipelineTokenizer[TokenGeneratorContext, np.ndarray] +): + def __init__( + self, + delegate: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], + ) -> None: + assert isinstance( + delegate, (PreTrainedTokenizer, PreTrainedTokenizerFast) + ) + self.delegate = delegate + + def apply_chat_template( + self, messages: list[TokenGeneratorRequestMessage] + ) -> str: + try: + templated_message = self.delegate.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + return cast(str, templated_message) + except Exception: + msg = ( + "apply_chat_template failed for" + " PreTrainedTokenGeneratorTokenizer" + ) + logger.warning(msg) + return "\n".join([str(message["content"]) for message in messages]) + + @property + def eos(self) -> int: + return self.delegate.eos_token_id + + @property + def expects_content_wrapping(self) -> bool: + return False + + async def encode(self, prompt: str) -> np.ndarray: + return np.array(self.delegate.encode(prompt)) + + async def decode( + self, + context: TokenGeneratorContext, + encoded: np.ndarray, + **kwargs, + ) -> str: + return self.delegate.decode(encoded, **kwargs) + + +def max_tokens_to_generate( + prompt_size: int, + max_length: int | None, + max_new_tokens: int | None = None, +) -> int | None: + """Returns the max number of new tokens to generate.""" + if max_length is None: + return max_new_tokens + _difference_between_max_and_prompt = max(max_length - prompt_size, 0) + if max_new_tokens is None: + return _difference_between_max_and_prompt + return min(max_new_tokens, _difference_between_max_and_prompt) + + +async def run_with_default_executor(fn, *args): + loop = asyncio.get_running_loop() + return await loop.run_in_executor(None, fn, *args) + + +class TextTokenizer(PipelineTokenizer[TextContext, np.ndarray]): + """Encapsulates creation of TextContext and specific token encode/decode logic.""" + + def __init__( + self, + model_path: str, + *, + revision: str | None = None, + max_length: int | None = None, + max_new_tokens: int | None = None, + trust_remote_code: bool = False, + enable_llama_whitespace_fix: bool = False, + ) -> None: + self.model_path = model_path + self.max_length = max_length + self.max_new_tokens = max_new_tokens + + self.delegate = AutoTokenizer.from_pretrained( + model_path, + revision=revision, + trust_remote_code=trust_remote_code, + # If `max_length` is None, the max length will be taken + # from the HuggingFace tokenizer_config. + model_max_length=max_length, + ) + + # configure Llama whitespace fix if needed + self._enable_llama_whitespace_fix = ( + enable_llama_whitespace_fix and self._is_llama_tokenizer + ) + ( + self._llama_whitespace_fix_dummy_token_id, + self._llama_whitespace_fix_dummy_token_len, + ) = self._llama_whitespace_fix_dummy_token + + def apply_chat_template( + self, + messages: list[TokenGeneratorRequestMessage], + tools: Optional[list[TokenGeneratorRequestTool]], + ) -> str: + try: + templated_message = self.delegate.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + tools=tools, + ) + return cast(str, templated_message) + except Exception: + msg = ( + "apply_chat_template failed for" + f" TextTokenizer({self.model_path})" + ) + logger.warning(msg) + return "\n".join([str(message["content"]) for message in messages]) + + @property + def eos(self) -> int: + return self.delegate.eos_token_id + + @property + def expects_content_wrapping(self) -> bool: + return False + + async def encode(self, prompt: Union[str, Sequence[int]]) -> np.ndarray: + """Transform the provided prompt into a token array.""" + + encoded_prompt: np.ndarray + if isinstance(prompt, str): + # Note: the underlying tokenizer may not be thread safe in some cases, see https://github.com/huggingface/tokenizers/issues/537 + # Add a standard (non-async) lock in the executor thread if needed. + encoded_prompt = await run_with_default_executor( + self.delegate.encode, prompt + ) + max_length = self.max_length or self.delegate.model_max_length + if max_length and len(encoded_prompt) > max_length: + raise ValueError( + f"Input string is larger than tokenizer's max length ({len(encoded_prompt)} > {max_length})." + ) + else: + encoded_prompt = np.array(list(prompt)) + + return encoded_prompt + + async def decode( + self, context: TextContext, encoded: np.ndarray, **kwargs + ) -> str: + """Transformer a provided encoded token array, back into readable text.""" + # Sometimes, encoded comes in as an int so, make it np array + if isinstance(encoded, int): + encoded = np.array(encoded) + + # There is an issue where Llama tokenizer strips leading spaces + # if a single token is decoded at a time. This is a temporary + # fix until the issue resolved on the Tokenizers side. + # More information: + # https://github.com/huggingface/transformers/issues/31643 + # https://github.com/Lightning-AI/litgpt/pull/1559 + if self._enable_llama_whitespace_fix and encoded.size == 1: + return self._decode_with_llama_whitespace_fix(encoded, **kwargs) + + return self.delegate.decode(encoded, **kwargs) + + async def new_context(self, request: TokenGeneratorRequest) -> TextContext: + """Create a new TextContext object, leveraging necessary information like + cache_seq_id and prompt from TokenGeneratorRequest.""" + + prompt: Union[str, list[int]] + if request.prompt is not None: + if isinstance(request.prompt, str): + prompt = str(request.prompt) + else: + prompt = [int(t) for t in request.prompt] + elif request.messages is not None: + prompt = self.apply_chat_template(request.messages, request.tools) + else: + raise ValueError(f"{request} does not provide messages or prompt.") + + encoded_prompt = await self.encode(prompt) + + # TODO(zheng): We should probably just make max_new_tokens an optional + # instead of -1. + max_new_tokens = None + if request.max_new_tokens is not None: + max_new_tokens = request.max_new_tokens + elif self.max_new_tokens != -1: + max_new_tokens = self.max_new_tokens + + max_gen_tokens = max_tokens_to_generate( + len(encoded_prompt), + self.max_length, + max_new_tokens, + ) + + json_schema = ( + json.dumps(request.response_format.get("json_schema", None)) + if request.response_format + else None + ) + context = TextContext( + prompt=prompt, + cache_seq_id=request.index, + max_length=len(encoded_prompt) + max_gen_tokens + if max_gen_tokens is not None + else None, + tokens=np.array(encoded_prompt), + log_probabilities=request.logprobs, + log_probabilities_echo=request.echo, + json_schema=json_schema, + ) + return context + + @property + def _is_llama_tokenizer(self) -> bool: + tokenizers = ( + LlamaTokenizer, + LlamaTokenizerFast, + CodeLlamaTokenizer, + CodeLlamaTokenizerFast, + ) + return isinstance(self.delegate, tokenizers) + + @property + def _llama_whitespace_fix_dummy_token(self) -> tuple[int, int]: + dummy_token_id = 33 # \x1e + dummy_token_decoded = self.delegate.decode([dummy_token_id]) + return dummy_token_id, len(dummy_token_decoded) + + def _decode_with_llama_whitespace_fix( + self, encoded: np.ndarray, **kwargs + ) -> str: + decoded = self.delegate.decode( + np.insert(encoded, 0, self._llama_whitespace_fix_dummy_token_id), + **kwargs, + ) + return decoded[self._llama_whitespace_fix_dummy_token_len :] + + +class TextAndVisionTokenizer( + PipelineTokenizer[TextAndVisionContext, np.ndarray] +): + """Encapsulates creation of TextContext and specific token encode/decode logic.""" + + def __init__( + self, + model_path: str, + *, + revision: str | None = None, + max_length: int | None = None, + max_new_tokens: int | None = None, + trust_remote_code: bool = False, + ) -> None: + self.model_path = model_path + self.max_length = max_length + self.max_new_tokens = max_new_tokens + + self.delegate = AutoTokenizer.from_pretrained( + model_path, + revision=revision, + trust_remote_code=trust_remote_code, + # If `max_length` is None, the max length will be taken + # from the HuggingFace tokenizer_config. + model_max_length=max_length, + ) + self.processor = AutoProcessor.from_pretrained( + model_path, + revision=revision, + trust_remote_code=trust_remote_code, + ) + + def _wrap_str_message_content( + self, messages: list[TokenGeneratorRequestMessage] + ) -> list[TokenGeneratorRequestMessage]: + # Wrap string type values of "content" key with "type": "text" and its + # value. For example, if the message is {"content": "Hello, world!"}, + # it will be wrapped with {"type": "text", "text": "Hello, world!"}. + # This is a workaround for LlamaVision's chat template: + # https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct/blob/main/chat_template.json + for message in messages: + if isinstance(message["content"], str): + message["content"] = [ + {"type": "text", "text": message["content"]} + ] + elif isinstance(message["content"], list): + for content in message["content"]: + if "content" in content and content["type"] == "text": + content["text"] = content.pop("content") + return messages + + def apply_chat_template( + self, messages: list[TokenGeneratorRequestMessage] + ) -> str: + # TODO: Refactor this. + if self.model_path == "meta-llama/Llama-3.2-11B-Vision-Instruct": + messages = self._wrap_str_message_content(messages) + try: + templated_message = self.processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + return cast(str, templated_message) + except Exception as e: + msg = "apply_chat_template failed for TextAndVisionTokenizer" + logger.warning(msg) + logger.warning(str(e)) + prompt = [] + for message in messages: + if isinstance(message["content"], str): + prompt.append(message["content"]) + elif isinstance(message["content"], list): + for content in message["content"]: + if content["type"] == "text": + if "text" in content: + prompt.append(content["text"]) + else: + prompt.append(content["content"]) + return "\n".join(prompt) + + @property + def eos(self) -> int: + return self.delegate.eos_token_id + + @property + def expects_content_wrapping(self) -> bool: + return True + + async def encode(self, prompt: Union[str, Sequence[int]]) -> np.ndarray: + """Transform the provided prompt into a token array.""" + + encoded_prompt: np.ndarray + if isinstance(prompt, str): + # Note: the underlying tokenizer may not be thread safe in some cases, see https://github.com/huggingface/tokenizers/issues/537 + # Add a standard (non-async) lock in the executor thread if needed. + encoded_prompt = await run_with_default_executor( + self.delegate.encode, prompt + ) + max_length = self.max_length or self.delegate.model_max_length + if max_length and len(encoded_prompt) > max_length: + raise ValueError( + f"Input string is larger than tokenizer's max length ({len(encoded_prompt)} > {max_length})." + ) + else: + encoded_prompt = np.array(list(prompt)) + + return encoded_prompt + + async def decode( + self, context: TextAndVisionContext, encoded: np.ndarray, **kwargs + ) -> str: + """Transformer a provided encoded token array, back into readable text.""" + return self.delegate.decode(encoded, **kwargs) + + async def new_context( + self, request: TokenGeneratorRequest + ) -> TextAndVisionContext: + """Create a new TextAndVisionContext object, leveraging necessary information like + cache_seq_id and prompt from TokenGeneratorRequest.""" + prompt: Union[str, Sequence[int]] + if request.prompt is not None: + prompt = request.prompt + elif request.messages is not None: + prompt = self.apply_chat_template(request.messages) + else: + msg = f"{request} does not provide messages or prompt." + raise ValueError(msg) + + # Load images. + images = ( + [ + Image.open(io.BytesIO(image_data)) + for image_data in request.images + ] + if request.images + else None + ) + # PixtralProcessor returns a list of torch tensors. + # LlamaVision returns a np Array. + inputs = self.processor( + text=prompt, + images=images, + ) + + if "input_ids" not in inputs: + msg = "input_ids not provided in AutoProcessor output, please ensure you are using the correct processor for multi-modal inputs." + raise ValueError(msg) + encoded_prompt = np.array(inputs["input_ids"][0]) + + # TODO(zheng): We should probably just make max_new_tokens an optional + # instead of -1. + max_new_tokens = None + if request.max_new_tokens is not None: + max_new_tokens = request.max_new_tokens + elif self.max_new_tokens != -1: + max_new_tokens = self.max_new_tokens + + max_gen_tokens = max_tokens_to_generate( + encoded_prompt.shape[0], + self.max_length, + max_new_tokens, + ) + + extra_model_args = dict() + + if images is not None: + if "pixel_values" not in inputs: + msg = "pixel_values not provided in AutoProcessor output, please ensure you are using the correct processor for multi-modal inputs." + raise ValueError(msg) + pixel_values = inputs["pixel_values"][0] + if isinstance(pixel_values, list): + pixel_values = [ + tensor.numpy() if torch.is_tensor(tensor) else tensor + for tensor in pixel_values + ] + if "aspect_ratio_ids" in inputs: + extra_model_args["aspect_ratio_ids"] = inputs.aspect_ratio_ids + if "aspect_ratio_mask" in inputs: + extra_model_args["aspect_ratio_mask"] = inputs.aspect_ratio_mask + else: + pixel_values = [] + + json_schema = ( + json.dumps(request.response_format.get("json_schema", None)) + if request.response_format + else None + ) + + context = TextAndVisionContext( + prompt=prompt, + pixel_values=pixel_values, + extra_model_args=extra_model_args, + cache_seq_id=request.index, + tokens=encoded_prompt, + max_length=encoded_prompt.shape[0] + max_gen_tokens + if max_gen_tokens is not None + else None, + json_schema=json_schema, + ) + return context diff --git a/src/max/pixi.toml b/src/max/pixi.toml new file mode 100644 index 0000000000..6c2812dc86 --- /dev/null +++ b/src/max/pixi.toml @@ -0,0 +1,17 @@ +[project] +name = "max-pipelines-example" +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["osx-arm64", "linux-aarch64", "linux-64"] +description = "MAX Pipelines" +version = "0.5.0" +authors = ["Modular Inc "] + +[dependencies] +max-pipelines = "*" + +[tasks] +llama3 = "max-pipelines llama3" +replit = "max-pipelines replit" +mistral = "max-pipelines mistral" +generate = "max-pipelines generate" +serve = "max-pipelines serve" diff --git a/tutorials/README.md b/tutorials/README.md new file mode 100644 index 0000000000..d5bca75fb9 --- /dev/null +++ b/tutorials/README.md @@ -0,0 +1,4 @@ +# MAX tutorials finished code + +This directory contains the "finished" code for tutorials you can read at +[docs.modular.com/max/tutorials](https://docs.modular.com/max/tutorials). diff --git a/tutorials/deploy-aws-kubernetes/README.md b/tutorials/deploy-aws-kubernetes/README.md new file mode 100644 index 0000000000..b319416118 --- /dev/null +++ b/tutorials/deploy-aws-kubernetes/README.md @@ -0,0 +1,6 @@ +# Tutorial: Deploy a model with Kubernetes and Helm + +This is the client code for the [Deploy a model with Kubernetes and +Helm](https://docs.modular.com/max/tutorials/deploy-aws-kubernetes) tutorial. + +Please refer to that page for more information. diff --git a/tutorials/deploy-aws-kubernetes/client.py b/tutorials/deploy-aws-kubernetes/client.py new file mode 100644 index 0000000000..076bc3c525 --- /dev/null +++ b/tutorials/deploy-aws-kubernetes/client.py @@ -0,0 +1,63 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# suppress extraneous logging +import os + +os.environ["TRANSFORMERS_VERBOSITY"] = "critical" +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +import numpy as np +import tritonclient.http as httpclient +from transformers import AutoTokenizer + +text = "Paris is the [MASK] of France." + +# Create a triton client +triton_client = httpclient.InferenceServerClient(url="127.0.0.1:8000") + +# Preprocess input statement +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") +inputs = tokenizer( + text, + return_tensors="np", + return_token_type_ids=True, + padding="max_length", + truncation=True, + max_length=128, +) + +# Set the input data +triton_inputs = [ + httpclient.InferInput("input_ids", inputs["input_ids"].shape, "INT32"), + httpclient.InferInput( + "attention_mask", inputs["attention_mask"].shape, "INT32" + ), + httpclient.InferInput( + "token_type_ids", inputs["token_type_ids"].shape, "INT32" + ), +] +triton_inputs[0].set_data_from_numpy(inputs["input_ids"].astype(np.int32)) +triton_inputs[1].set_data_from_numpy(inputs["attention_mask"].astype(np.int32)) +triton_inputs[2].set_data_from_numpy(inputs["token_type_ids"].astype(np.int32)) + +# Executing +output = triton_client.infer("bert-base-uncased", triton_inputs) + +# Post-processing +masked_index = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero()[1] +logits = output.as_numpy("result0")[0, masked_index, :] +predicted_token_ids = logits.argmax(axis=-1) +predicted_text = tokenizer.decode(predicted_token_ids) +output_text = text.replace("[MASK]", predicted_text) +print(output_text) diff --git a/tutorials/deploy-cloudformation-sagemaker/README.md b/tutorials/deploy-cloudformation-sagemaker/README.md new file mode 100644 index 0000000000..9cd602e606 --- /dev/null +++ b/tutorials/deploy-cloudformation-sagemaker/README.md @@ -0,0 +1,7 @@ +# Tutorial: Deploy a model with AWS CloudFormation + +This is the client code for the [Deploy a model with with AWS +CloudFormation](https://docs.modular.com/max/tutorials/deploy-cloudformation-sagemaker) +tutorial. + +Please refer to that page for more information. diff --git a/tutorials/deploy-cloudformation-sagemaker/client.py b/tutorials/deploy-cloudformation-sagemaker/client.py new file mode 100644 index 0000000000..8e39608aa4 --- /dev/null +++ b/tutorials/deploy-cloudformation-sagemaker/client.py @@ -0,0 +1,116 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +# suppress extraneous logging +import os + +os.environ["TRANSFORMERS_VERBOSITY"] = "critical" + +import json + +import boto3 +import numpy as np +import transformers +from botocore.config import Config + +config = Config(region_name="us-east-1") +client = boto3.client("sagemaker-runtime", config=config) + +# NOTE: Paste your endpoint here +endpoint_name = "YOUR-ENDPOINT-GOES-HERE" + +text = "The quick brown fox jumped over the lazy dog." + +tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased") +inputs = tokenizer( + text, padding="max_length", max_length=128, return_tensors="pt" +) + +# Convert tensor inputs to list for payload +input_ids = inputs["input_ids"].tolist()[0] +attention_mask = inputs["attention_mask"].tolist()[0] +token_type_ids = inputs["token_type_ids"].tolist()[0] + +payload = { + "inputs": [ + { + "name": "input_ids", + "shape": [1, 128], + "datatype": "INT32", + "data": input_ids, + }, + { + "name": "attention_mask", + "shape": [1, 128], + "datatype": "INT32", + "data": attention_mask, + }, + { + "name": "token_type_ids", + "shape": [1, 128], + "datatype": "INT32", + "data": token_type_ids, + }, + ] +} + +http_response = client.invoke_endpoint( + EndpointName=endpoint_name, + ContentType="application/octet-stream", + Body=json.dumps(payload), +) +response = json.loads(http_response["Body"].read().decode("utf8")) +outputs = response["outputs"] + + +def softmax(logits): + exp_logits = np.exp(logits - np.max(logits)) + return exp_logits / exp_logits.sum(axis=-1, keepdims=True) + + +# Process the output +for output in outputs: + logits = output["data"] + logits = np.array(logits).reshape(output["shape"]) + + print(f"Logits shape: {logits.shape}") + + if ( + len(logits.shape) == 3 + ): # Shape [batch_size, sequence_length, num_classes] + token_probabilities = softmax(logits) + predicted_classes = np.argmax(token_probabilities, axis=-1) + + print(f"Predicted classes shape: {predicted_classes.shape}") + print( + f"Predicted class indices range: {np.min(predicted_classes)}," + f" {np.max(predicted_classes)}" + ) + + # Map predicted indices to tokens + predicted_tokens = tokenizer.convert_ids_to_tokens(predicted_classes[0]) + + # Pair each input token with its predicted token + input_tokens = tokenizer.convert_ids_to_tokens(input_ids) + token_pairs = list(zip(input_tokens, predicted_tokens)) + + print("Predicted Token Pairs:") + print("-" * 45) + print("| {:<20} | {:<18} |".format("Input Token", "Predicted Token")) + print("-" * 45) + for input_token, predicted_token in token_pairs: + if input_token != "[PAD]": # Exclude padding tokens + print( + "| {:<20} | {:<18} |".format(input_token, predicted_token) + ) + print("-" * 45) diff --git a/tutorials/hello-magic/.gitattributes b/tutorials/hello-magic/.gitattributes new file mode 100644 index 0000000000..07fe41c52c --- /dev/null +++ b/tutorials/hello-magic/.gitattributes @@ -0,0 +1,2 @@ +# GitHub syntax highlighting +pixi.lock linguist-language=YAML linguist-generated=true diff --git a/tutorials/hello-magic/.gitignore b/tutorials/hello-magic/.gitignore new file mode 100644 index 0000000000..bd58f98a84 --- /dev/null +++ b/tutorials/hello-magic/.gitignore @@ -0,0 +1,5 @@ +# pixi environments +.pixi +*.egg-info +# magic environments +.magic diff --git a/tutorials/hello-magic/hello.mojo b/tutorials/hello-magic/hello.mojo new file mode 100644 index 0000000000..7630652a0d --- /dev/null +++ b/tutorials/hello-magic/hello.mojo @@ -0,0 +1,16 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + + +def main(): + print("hello, magic!") diff --git a/tutorials/hello-magic/local/.gitattributes b/tutorials/hello-magic/local/.gitattributes new file mode 100644 index 0000000000..07fe41c52c --- /dev/null +++ b/tutorials/hello-magic/local/.gitattributes @@ -0,0 +1,2 @@ +# GitHub syntax highlighting +pixi.lock linguist-language=YAML linguist-generated=true diff --git a/tutorials/hello-magic/local/.gitignore b/tutorials/hello-magic/local/.gitignore new file mode 100644 index 0000000000..bd58f98a84 --- /dev/null +++ b/tutorials/hello-magic/local/.gitignore @@ -0,0 +1,5 @@ +# pixi environments +.pixi +*.egg-info +# magic environments +.magic diff --git a/tutorials/hello-magic/local/__init__.mojo b/tutorials/hello-magic/local/__init__.mojo new file mode 100644 index 0000000000..ccf277134c --- /dev/null +++ b/tutorials/hello-magic/local/__init__.mojo @@ -0,0 +1,14 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from .zero import zero diff --git a/tutorials/hello-magic/local/mojoproject.toml b/tutorials/hello-magic/local/mojoproject.toml new file mode 100644 index 0000000000..27267f0813 --- /dev/null +++ b/tutorials/hello-magic/local/mojoproject.toml @@ -0,0 +1,13 @@ +[project] +platforms = ["osx-arm64", "linux-64"] +version = "0.0.0" +authors = ["Modular "] +channels = ["pytorch", "conda-forge", "https://conda.modular.com/max-nightly/"] +description = "Add a short description here" +name = "local" + +[tasks] + +[dependencies] +max = ">=24.4.0,<25" +pytorch = ">=2.5.1,<3" diff --git a/tutorials/hello-magic/local/zero.mojo b/tutorials/hello-magic/local/zero.mojo new file mode 100644 index 0000000000..83cbaecd3c --- /dev/null +++ b/tutorials/hello-magic/local/zero.mojo @@ -0,0 +1,23 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from python import Python, PythonObject + + +def zero() -> PythonObject: + torch = Python.import_module("torch") + return torch.zeros(1) + + +def main(): + print(zero()) diff --git a/tutorials/hello-magic/main.py b/tutorials/hello-magic/main.py new file mode 100644 index 0000000000..d6f77a6dbe --- /dev/null +++ b/tutorials/hello-magic/main.py @@ -0,0 +1,47 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import subprocess + +from fastapi import FastAPI, HTTPException + +app = FastAPI() + + +@app.get("/") +def root(): + return {"message": "Hello, magic!"} + + +@app.get("/zero") +def zero(): + try: + p = subprocess.Popen( + ["magic", "run", "mojo", "local/zero.mojo"], + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + ) + while True: + output = p.stdout.readline() + if output == "" and p.poll() is not None: + raise HTTPException( + status_code=500, detail="Failed to produce zero" + ) + + return {"message": f"answer is {output}"} + + except subprocess.SubprocessError: + raise HTTPException( + status_code=500, detail="Failed to execute subprocess" + ) diff --git a/tutorials/hello-magic/mojoproject.toml b/tutorials/hello-magic/mojoproject.toml new file mode 100644 index 0000000000..201ce3b96e --- /dev/null +++ b/tutorials/hello-magic/mojoproject.toml @@ -0,0 +1,30 @@ +[project] +authors = ["Modular "] +channels = ["pytorch", "conda-forge", "https://conda.modular.com/max-nightly/"] +description = "Add a short description here" +name = "hello-magic" +platforms = ["osx-arm64", "linux-64"] +version = "0.1.0" + +[tasks] +hello = "mojo hello.mojo" +dev-server = "fastapi dev main.py" +format = "mojo format ." + +[dependencies] +max = ">=24.4.0,<25" +python = ">=3.8,<3.12" +pytorch = ">=2.5.1,<3" + +[pypi-dependencies] +fastapi = ">=0.115.0" + +[feature.test.tasks] +test = "pytest" + +[feature.test.pypi-dependencies] +pytest = ">=8.3.2, <9" + +[environments] +default = { solve-group = "default" } +test = { features = ["test"], solve-group = "default" } diff --git a/tutorials/helm/max-openai-api/.helmignore b/tutorials/helm/max-openai-api/.helmignore new file mode 100644 index 0000000000..7c4ce2fca1 --- /dev/null +++ b/tutorials/helm/max-openai-api/.helmignore @@ -0,0 +1,19 @@ +.DS_Store +# Common VCS dirs +.git/ +.gitignore +.bzr/ +.bzrignore +.hg/ +.hgignore +.svn/ +# Common backup files +*.swp +*.bak +*.tmp +*~ +# Various IDEs +.project +.idea/ +*.tmproj +bin \ No newline at end of file diff --git a/tutorials/helm/max-openai-api/Chart.yaml b/tutorials/helm/max-openai-api/Chart.yaml new file mode 100644 index 0000000000..c930088dd6 --- /dev/null +++ b/tutorials/helm/max-openai-api/Chart.yaml @@ -0,0 +1,28 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +git apiVersion: v2 +appVersion: "0.1.0" +description: The MAX platform unifies the leading AI development frameworks (TensorFlow, PyTorch, ONNX) and hardware backends in order to simplify deployment for AI production teams and accelerate innovation for AI developers. +name: max-openai-api-chart +home: https://www.modular.com/ +keywords: + - machine learning + - inference +sources: + - https://github.com/modular/max +maintainers: + - name: Modular team + email: max-feedback@modular.com + url: https://github.com/modular/max +version: 0.1.0 diff --git a/tutorials/helm/max-openai-api/README.md b/tutorials/helm/max-openai-api/README.md new file mode 100644 index 0000000000..5af9a9380c --- /dev/null +++ b/tutorials/helm/max-openai-api/README.md @@ -0,0 +1,193 @@ + + + +# MAX OpenAI API Helm chart + +The MAX platform unifies the leading AI development frameworks (TensorFlow, PyTorch, ONNX) and hardware backends in order to simplify deployment for AI production teams and accelerate innovation for AI developers. + +**Homepage:** + +## Source Code + +* + +## Usage + +### Installing the chart + +To install this chart using Helm 3, run the following command: + +```console +helm install max-openai-api oci://registry-1.docker.io/modular/max-openai-api-chart \ + --version \ + --set huggingfaceRepoId= + --set maxServe.maxLength=512 \ + --set maxServe.maxBatchSize=16 \ + --set envSecret.HF_TOKEN= \ + --set env.HF_HUB_ENABLE_HF_TRANSFER=1 \ + --wait +``` + +The command deploys MAX OpenAI API on the Kubernetes cluster in the default configuration. The Values reference section below lists the parameters that can be configured during installation. + +### Upgrading the chart + +To upgrade the chart with the release name `max-openai-api`: + +```console +helm upgrade max-openai-api oci://registry-1.docker.io/modular/max-openai-api-chart +``` + +### Uninstalling the chart + +To uninstall/delete the `max-openai-api` deployment: + +```console +helm delete max-openai-api +``` + +### End-to-end example that provisions an K8s cluster and installs MAX OpenAI API + +To provision a k8s cluster via `eksctl` and then install MAX OpenAI API, run the following commands: + +```console +# provision a k8s cluster (takes 10-15 minutes) +eksctl create cluster \ + --name max-openai-api-demo \ + --region us-east-1 \ + --node-type g5.4xlarge \ + --nodes 1 + +# create a k8s namespace +kubectl create namespace max-openai-api-demo + +# deploy MAX OpenAI API via helm chart (takes 10 minutes) +helm install max-openai-api oci://registry-1.docker.io/modular/max-openai-api-chart \ + --version \ + --namespace max-openai-api-demo \ + --set huggingfaceRepoId=modularai/Llama-3.1-8B-Instruct-GGUF + --set maxServe.maxLength=512 \ + --set maxServe.maxBatchSize=16 \ + --set envSecret.HF_TOKEN= \ + --set env.HF_HUB_ENABLE_HF_TRANSFER=1 \ + --timeout 10m0s \ + --wait + +# forward the remote k8s port to the local network to access the service locally +# the command is blocking and takes the terminal +# user another terminal for subsequent curl and ctrl-c to stop the port forwarding +POD_NAME=$(kubectl get pods --namespace max-openai-api-demo -l "app.kubernetes.io/name=max-openai-api-chart,app.kubernetes.io/instance=max-openai-api" -o jsonpath="{.items[0].metadata.name}") +CONTAINER_PORT=$(kubectl get pod --namespace max-openai-api-demo $POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}") +kubectl port-forward $POD_NAME 8000:$CONTAINER_PORT --namespace max-openai-api-demo & + +# test the service +curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "modularai/Llama-3.1-8B-Instruct-GGUF", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who won the world series in 2020?"} + ] + }' + +# uninstall MAX OpenAI API +helm uninstall max-openai-api --namespace max-openai-api-demo + +# Delete the namespace +kubectl delete namespace max-openai-api-demo + +# delete the k8s cluster +eksctl delete cluster \ + --name max-openai-api-demo \ + --region us-east-1 +``` + +## Values + +| Key | Type | Default | Description | +|-----|------|---------|-------------| +| affinity | object | `{}` | Affinity to be added to all deployments | +| env | object | `{}` | Environment variables that will be passed into pods | +| envFromSecret | string | `"{{ template \"max.fullname\" . }}-env"` | The name of the secret which we will use to populate env vars in deployed pods This can be useful for secret keys, etc. | +| envFromSecrets | list | `[]` | This can be a list of templated strings | +| envRaw | list | `[]` | Environment variables in RAW format that will be passed into pods | +| envSecret | object | `{}` | Environment variables to pass as secrets | +| fullnameOverride | string | `nil` | Provide a name to override the full names of resources | +| image.pullPolicy | string | `"IfNotPresent"` | | +| image.repository | string | `"modular/max-openai-api"` | | +| image.tag | string | `"latest"` | | +| imagePullSecrets | list | `[]` | | +| inferenceServer.affinity | object | `{}` | Affinity to be added to inferenceServer deployment | +| inferenceServer.args | list | See `values.yaml` | Arguments to pass to the node entrypoint. If defined it overwrites the default args value set by .Values.max-serve | +| inferenceServer.autoscaling.enabled | bool | `false` | | +| inferenceServer.autoscaling.maxReplicas | int | `2` | | +| inferenceServer.autoscaling.minReplicas | int | `1` | | +| inferenceServer.autoscaling.targetCPUUtilizationPercentage | int | `80` | | +| inferenceServer.containerSecurityContext | object | `{}` | | +| inferenceServer.deploymentAnnotations | object | `{}` | Annotations to be added to inferenceServer deployment | +| inferenceServer.deploymentLabels | object | `{}` | Labels to be added to inferenceServer deployment | +| inferenceServer.env | object | `{}` | | +| inferenceServer.extraContainers | list | `[]` | Launch additional containers into inferenceServer pod | +| inferenceServer.livenessProbe.failureThreshold | int | `3` | | +| inferenceServer.livenessProbe.httpGet.path | string | `"/v1/health"` | | +| inferenceServer.livenessProbe.httpGet.port | string | `"http"` | | +| inferenceServer.livenessProbe.initialDelaySeconds | int | `1` | | +| inferenceServer.livenessProbe.periodSeconds | int | `15` | | +| inferenceServer.livenessProbe.successThreshold | int | `1` | | +| inferenceServer.livenessProbe.timeoutSeconds | int | `1` | | +| inferenceServer.nodeSelector | object | `{}` | NodeSelector to be added to inferenceServer deployment | +| inferenceServer.podAnnotations | object | `{}` | Annotations to be added to inferenceServer pods | +| inferenceServer.podLabels | object | `{}` | Labels to be added to inferenceServer pods | +| inferenceServer.podSecurityContext | object | `{}` | | +| inferenceServer.readinessProbe.failureThreshold | int | `3` | | +| inferenceServer.readinessProbe.httpGet.path | string | `"/v1/health"` | | +| inferenceServer.readinessProbe.httpGet.port | string | `"http"` | | +| inferenceServer.readinessProbe.initialDelaySeconds | int | `1` | | +| inferenceServer.readinessProbe.periodSeconds | int | `15` | | +| inferenceServer.readinessProbe.successThreshold | int | `1` | | +| inferenceServer.readinessProbe.timeoutSeconds | int | `1` | | +| inferenceServer.replicaCount | int | `1` | | +| inferenceServer.resources | object | `{}` | Resource settings for the inferenceServer pods - these settings overwrite existing values from the global resources object defined above. | +| inferenceServer.startupProbe.failureThreshold | int | `60` | | +| inferenceServer.startupProbe.httpGet.path | string | `"/v1/health"` | | +| inferenceServer.startupProbe.httpGet.port | string | `"http"` | | +| inferenceServer.startupProbe.initialDelaySeconds | int | `1` | | +| inferenceServer.startupProbe.periodSeconds | int | `5` | | +| inferenceServer.startupProbe.successThreshold | int | `1` | | +| inferenceServer.startupProbe.timeoutSeconds | int | `1` | | +| inferenceServer.strategy | object | `{}` | | +| inferenceServer.tolerations | list | `[]` | Tolerations to be added to inferenceServer deployment | +| inferenceServer.topologySpreadConstraints | list | `[]` | TopologySpreadConstrains to be added to inferenceServer deployments | +| inferenceServer.volumeMounts | list | `[]` | Volumes to mount into inferenceServer pod | +| inferenceServer.volumes | list | `[]` | Volumes to mount into inferenceServer pod | +| ingress.annotations | object | `{}` | | +| ingress.enabled | bool | `false` | | +| ingress.extraHostsRaw | list | `[]` | | +| ingress.hosts | list | `[]` | | +| ingress.ingressClassName | string | `nil` | | +| ingress.path | string | `"/"` | | +| ingress.pathType | string | `"ImplementationSpecific"` | | +| ingress.tls | list | `[]` | | +| maxServe | object | `{"cacheStrategy":"continuous","huggingfaceRepoId":"modularai/Llama-3.1-8B-Instruct-GGUF","maxBatchSize":"250","maxLength":"2048","maxNumSteps":"10"}` | MAX Serve arguments | +| nameOverride | string | `nil` | Provide a name to override the name of the chart | +| nodeSelector | object | `{}` | NodeSelector to be added to all deployments | +| resources | object | `{}` | | +| runAsUser | int | `0` | User ID directive. This user must have enough permissions to run the bootstrap script Running containers as root is not recommended in production. Change this to another UID - e.g. 1000 to be more secure | +| service.annotations | object | `{}` | | +| service.loadBalancerIP | string | `nil` | | +| service.ports[0].name | string | `"http"` | | +| service.ports[0].port | int | `8000` | | +| service.ports[0].protocol | string | `"TCP"` | | +| service.ports[0].targetPort | int | `8000` | | +| service.type | string | `"ClusterIP"` | | +| serviceAccount.annotations | object | `{}` | | +| serviceAccount.create | bool | `false` | Create custom service account for MAX Serving. If create: true and serviceAccountName is not provided, `max.fullname` will be used. | +| serviceAccountName | string | `nil` | Specify service account name to be used | +| tolerations | list | `[]` | Tolerations to be added to all deployments | +| topologySpreadConstraints | list | `[]` | TopologySpreadConstraints to be added to all deployments | +| volumeMounts | list | `[]` | | +| volumes | list | `[]` | | diff --git a/tutorials/helm/max-openai-api/README.md.gotmpl b/tutorials/helm/max-openai-api/README.md.gotmpl new file mode 100644 index 0000000000..f64ee8a86c --- /dev/null +++ b/tutorials/helm/max-openai-api/README.md.gotmpl @@ -0,0 +1,112 @@ + + + +# MAX OpenAI API Helm chart + +{{ template "chart.deprecationWarning" . }} + +{{ template "chart.description" . }} + +{{ template "chart.homepageLine" . }} + +{{ template "chart.sourcesSection" . }} + +## Usage + +### Installing the chart + +To install this chart using Helm 3, run the following command: + +```console +helm install max-openai-api oci://registry-1.docker.io/modular/max-openai-api-chart \ + --version \ + --set huggingfaceRepoId= + --set maxServe.maxLength=512 \ + --set maxServe.maxBatchSize=16 \ + --set envSecret.HF_TOKEN= \ + --set env.HF_HUB_ENABLE_HF_TRANSFER=1 \ + --wait +``` + +The command deploys MAX OpenAI API on the Kubernetes cluster in the default configuration. The Values reference section below lists the parameters that can be configured during installation. + +### Upgrading the chart + +To upgrade the chart with the release name `max-openai-api`: + +```console +helm upgrade max-openai-api oci://registry-1.docker.io/modular/max-openai-api-chart +``` + +### Uninstalling the chart + +To uninstall/delete the `max-openai-api` deployment: + +```console +helm delete max-openai-api +``` + +### End-to-end example that provisions an K8s cluster and installs MAX OpenAI API + +To provision a k8s cluster via `eksctl` and then install MAX OpenAI API, run the following commands: + +```console +# provision a k8s cluster (takes 10-15 minutes) +eksctl create cluster \ + --name max-openai-api-demo \ + --region us-east-1 \ + --node-type g5.4xlarge \ + --nodes 1 + +# create a k8s namespace +kubectl create namespace max-openai-api-demo + +# deploy MAX OpenAI API via helm chart (takes 10 minutes) +helm install max-openai-api oci://registry-1.docker.io/modular/max-openai-api-chart \ + --version \ + --namespace max-openai-api-demo \ + --set huggingfaceRepoId=modularai/Llama-3.1-8B-Instruct-GGUF + --set maxServe.maxLength=512 \ + --set maxServe.maxBatchSize=16 \ + --set envSecret.HF_TOKEN= \ + --set env.HF_HUB_ENABLE_HF_TRANSFER=1 \ + --timeout 10m0s \ + --wait + +# forward the remote k8s port to the local network to access the service locally +# the command is blocking and takes the terminal +# user another terminal for subsequent curl and ctrl-c to stop the port forwarding +POD_NAME=$(kubectl get pods --namespace max-openai-api-demo -l "app.kubernetes.io/name=max-openai-api-chart,app.kubernetes.io/instance=max-openai-api" -o jsonpath="{.items[0].metadata.name}") +CONTAINER_PORT=$(kubectl get pod --namespace max-openai-api-demo $POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}") +kubectl port-forward $POD_NAME 8000:$CONTAINER_PORT --namespace max-openai-api-demo & + +# test the service +curl -N http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "modularai/Llama-3.1-8B-Instruct-GGUF", + "stream": true, + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who won the world series in 2020?"} + ] + }' + +# uninstall MAX OpenAI API +helm uninstall max-openai-api --namespace max-openai-api-demo + +# Delete the namespace +kubectl delete namespace max-openai-api-demo + +# delete the k8s cluster +eksctl delete cluster \ + --name max-openai-api-demo \ + --region us-east-1 +``` + + +{{ template "chart.requirementsSection" . }} + +{{ template "chart.valuesSection" . }} diff --git a/tutorials/helm/max-openai-api/templates/NOTES.txt b/tutorials/helm/max-openai-api/templates/NOTES.txt new file mode 100644 index 0000000000..b6ed1cdc8d --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/NOTES.txt @@ -0,0 +1,22 @@ +1. Get the application URL by running these commands: +{{- if .Values.ingress.enabled }} + {{- range .Values.ingress.hosts }} + http{{ if $.Values.ingress.tls }}s{{ end }}://{{ . }}{{ $.Values.ingress.path }} + {{- end }} +{{- else if contains "NodePort" .Values.service.type }} + NODE_PORT=$(kubectl get --namespace {{ .Release.Namespace }} -o jsonpath="{.spec.ports[0].nodePort}" services {{ template "max.fullname" . }}) + NODE_IP=$(kubectl get nodes --namespace {{ .Release.Namespace }} -o jsonpath="{.items[0].status.addresses[0].address}") + echo http://$NODE_IP:$NODE_PORT +{{- else if contains "LoadBalancer" .Values.service.type }} + NOTE: It may take a few minutes for the LoadBalancer IP to be available. + You can watch the status of by running 'kubectl get svc -w {{ template "max.fullname" . }}' + SERVICE_IP=$(kubectl get svc --namespace {{ .Release.Namespace }} {{ template "max.fullname" . }} -o jsonpath='{.status.loadBalancer.ingress[0].ip}') + echo http://$SERVICE_IP:{{ .Values.service.port }} +{{- else if contains "ClusterIP" .Values.service.type }} + POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "app.kubernetes.io/name={{ template "max.name" . }},app.kubernetes.io/instance={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}") + CONTAINER_PORT=$(kubectl get pod --namespace {{ .Release.Namespace }} $POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}") + echo "The application is available at the following DNS name from within your cluster:" + echo "{{ .Release.Name }}.{{ .Release.Namespace }}.svc.cluster.local:$CONTAINER_PORT" + echo "Or use the following command to forward ports and visit it locally at http://127.0.0.1:8000" + echo "kubectl port-forward $POD_NAME 8000:$CONTAINER_PORT --namespace {{ .Release.Namespace }}" +{{- end }} diff --git a/tutorials/helm/max-openai-api/templates/_helpers.tpl b/tutorials/helm/max-openai-api/templates/_helpers.tpl new file mode 100644 index 0000000000..47a043060a --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/_helpers.tpl @@ -0,0 +1,43 @@ +{{/* vim: set filetype=mustache: */}} +{{/* +Expand the name of the chart. +*/}} +{{- define "max.name" -}} + {{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" -}} +{{- end -}} + +{{/* +Create a default fully qualified app name. +We truncate at 63 chars because some Kubernetes name fields are limited to this (by the DNS naming spec). +If release name contains chart name it will be used as a full name. +*/}} +{{- define "max.fullname" -}} + {{- if .Values.fullnameOverride -}} + {{- .Values.fullnameOverride | trunc 63 | trimSuffix "-" -}} + {{- else -}} + {{- $name := default .Chart.Name .Values.nameOverride -}} + {{- if contains $name .Release.Name -}} + {{- .Release.Name | trunc 63 | trimSuffix "-" -}} + {{- else -}} + {{- printf "%s-%s" .Release.Name $name | trunc 63 | trimSuffix "-" -}} + {{- end -}} + {{- end -}} +{{- end -}} + +{{/* +Create the name of the service account to use +*/}} +{{- define "max.serviceAccountName" -}} + {{- if .Values.serviceAccount.create -}} + {{- default (include "max.fullname" .) .Values.serviceAccountName -}} + {{- else -}} + {{- default "default" .Values.serviceAccountName -}} + {{- end -}} +{{- end -}} + +{{/* +Create chart name and version as used by the chart label. +*/}} +{{- define "max.chart" -}} + {{- printf "%s-%s" .Chart.Name .Chart.Version | replace "+" "_" | trunc 63 | trimSuffix "-" -}} +{{- end -}} diff --git a/tutorials/helm/max-openai-api/templates/deployment.yaml b/tutorials/helm/max-openai-api/templates/deployment.yaml new file mode 100644 index 0000000000..9ce59c5c55 --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/deployment.yaml @@ -0,0 +1,181 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ template "max.fullname" . }} + namespace: {{ .Release.Namespace }} + labels: + app.kubernetes.io/name: {{ template "max.name" . }} + helm.sh/chart: {{ template "max.chart" . }} + app.kubernetes.io/managed-by: {{ .Release.Service }} + app.kubernetes.io/instance: {{ .Release.Name }} + {{- if .Values.inferenceServer.deploymentLabels }} + {{- toYaml .Values.inferenceServer.deploymentLabels | nindent 4 }} + {{- end }} + {{- if .Values.inferenceServer.deploymentAnnotations }} + annotations: {{- toYaml .Values.inferenceServer.deploymentAnnotations | nindent 4 }} + {{- end }} +spec: + {{- if not .Values.inferenceServer.autoscaling.enabled }} + replicas: {{ .Values.inferenceServer.replicaCount }} + {{- end }} + {{- if .Values.inferenceServer.strategy }} + strategy: {{- toYaml .Values.inferenceServer.strategy | nindent 4 }} + {{- end }} + selector: + matchLabels: + app.kubernetes.io/name: {{ template "max.name" . }} + app.kubernetes.io/instance: {{ .Release.Name }} + template: + metadata: + annotations: + # Force reload on changes + checksum/envSecret: {{ .Values.envSecret | toYaml | sha256sum }} + {{- if .Values.inferenceServer.podAnnotations }} + {{- toYaml .Values.inferenceServer.podAnnotations | nindent 8 }} + {{- end }} + labels: + app.kubernetes.io/name: {{ template "max.name" . }} + app.kubernetes.io/instance: {{ .Release.Name }} + {{- if .Values.inferenceServer.podLabels }} + {{- toYaml .Values.inferenceServer.podLabels | nindent 8 }} + {{- end }} + spec: + {{- if or (.Values.serviceAccount.create) (.Values.serviceAccountName) }} + serviceAccountName: {{ template "max.serviceAccountName" . }} + {{- end }} + securityContext: + runAsUser: {{ .Values.runAsUser }} + {{- if .Values.inferenceServer.podSecurityContext }} + {{- toYaml .Values.inferenceServer.podSecurityContext | nindent 8 }} + {{- end }} + {{- if .Values.inferenceServer.initContainers }} + initContainers: {{- tpl (toYaml .Values.inferenceServer.initContainers) . | nindent 6 }} + {{- end }} + containers: + - name: {{ .Chart.Name }} + image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}" + imagePullPolicy: {{ .Values.image.pullPolicy }} + {{- if .Values.inferenceServer.containerSecurityContext }} + securityContext: {{- toYaml .Values.inferenceServer.containerSecurityContext | nindent 12 }} + {{- end }} + {{- if .Values.inferenceServer.args }} + args: {{- toYaml .Values.inferenceServer.args | nindent 12 }} + {{- else }} + args: [ + "--model-path={{ .Values.maxServe.huggingfaceRepoId }}", + "--max-length={{ .Values.maxServe.maxLength }}", + "--max-batch-size={{ .Values.maxServe.maxBatchSize }}", + "--cache-strategy={{ .Values.maxServe.cacheStrategy }}", + "--max-num-steps={{ .Values.maxServe.maxNumSteps }}"] + {{- end }} + env: + {{- range $key, $value := .Values.env }} + - name: {{ $key | quote}} + value: {{ $value | quote }} + {{- end }} + {{- range $key, $value := .Values.inferenceServer.env }} + - name: {{ $key | quote}} + value: {{ $value | quote }} + {{- end }} + {{- if .Values.envRaw }} + {{- toYaml .Values.envRaw | nindent 12 }} + {{- end }} + envFrom: + - secretRef: + name: {{ tpl .Values.envFromSecret . | quote }} + {{- range .Values.envFromSecrets }} + - secretRef: + name: {{ tpl . $ | quote }} + {{- end }} + {{- if or .Values.volumeMounts .Values.inferenceServer.volumeMounts }} + volumeMounts: + {{- with .Values.volumeMounts }} + {{- tpl (toYaml .) $ | nindent 12 -}} + {{- end }} + {{- with .Values.inferenceServer.volumeMounts }} + {{- tpl (toYaml .) $ | nindent 12 -}} + {{- end }} + {{- end }} + ports: + {{- range .Values.service.ports }} + - name: {{ .name }} + containerPort: {{ .targetPort }} + {{- end }} + {{- if .Values.inferenceServer.startupProbe }} + startupProbe: {{- .Values.inferenceServer.startupProbe | toYaml | nindent 12 }} + {{- end }} + {{- if .Values.inferenceServer.readinessProbe }} + readinessProbe: {{- .Values.inferenceServer.readinessProbe | toYaml | nindent 12 }} + {{- end }} + {{- if .Values.inferenceServer.livenessProbe }} + livenessProbe: {{- .Values.inferenceServer.livenessProbe | toYaml | nindent 12 }} + {{- end }} + resources: + {{- if .Values.inferenceServer.resources }} + {{- toYaml .Values.inferenceServer.resources | nindent 12 }} + {{- else }} + {{- toYaml .Values.resources | nindent 12 }} + {{- end }} + {{- if .Values.inferenceServer.extraContainers }} + {{- toYaml .Values.inferenceServer.extraContainers | nindent 8 }} + {{- end }} + {{- if or .Values.nodeSelector .Values.inferenceServer.nodeSelector }} + nodeSelector: + {{- with .Values.nodeSelector }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.inferenceServer.nodeSelector }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- end }} + {{- if or .Values.affinity .Values.inferenceServer.affinity }} + affinity: + {{- with .Values.affinity }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.inferenceServer.affinity }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- end }} + {{- if or .Values.topologySpreadConstraints .Values.inferenceServer.topologySpreadConstraints }} + topologySpreadConstraints: + {{- with .Values.topologySpreadConstraints }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.inferenceServer.topologySpreadConstraints }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- end }} + {{- if or .Values.tolerations .Values.inferenceServer.tolerations }} + tolerations: + {{- with .Values.tolerations }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.inferenceServer.tolerations }} + {{- toYaml . | nindent 8 }} + {{- end }} + {{- end }} + {{- if .Values.imagePullSecrets }} + imagePullSecrets: {{- toYaml .Values.imagePullSecrets | nindent 8 }} + {{- end }} + {{- if or .Values.volumes .Values.inferenceServer.volumes }} + volumes: + {{- with .Values.volumes }} + {{- tpl (toYaml .) $ | nindent 8 -}} + {{- end }} + {{- with .Values.inferenceServer.volumes }} + {{- tpl (toYaml .) $ | nindent 8 -}} + {{- end }} + {{- end }} diff --git a/tutorials/helm/max-openai-api/templates/hpa.yaml b/tutorials/helm/max-openai-api/templates/hpa.yaml new file mode 100644 index 0000000000..ab5626d79a --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/hpa.yaml @@ -0,0 +1,47 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## +{{- if .Values.inferenceServer.autoscaling.enabled }} +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: {{ include "max.fullname" . }}-hpa + labels: + app.kubernetes.io/name: {{ template "max.name" . }} + helm.sh/chart: {{ template "max.chart" . }} + app.kubernetes.io/managed-by: {{ .Release.Service }} + app.kubernetes.io/instance: {{ .Release.Name }} +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: {{ include "max.fullname" . }} + minReplicas: {{ .Values.inferenceServer.autoscaling.minReplicas }} + maxReplicas: {{ .Values.inferenceServer.autoscaling.maxReplicas }} + metrics: + {{- if .Values.inferenceServer.autoscaling.targetCPUUtilizationPercentage }} + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: {{ .Values.inferenceServer.autoscaling.targetCPUUtilizationPercentage }} + {{- end }} + {{- if .Values.inferenceServer.autoscaling.targetMemoryUtilizationPercentage }} + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: {{ .Values.inferenceServer.autoscaling.targetMemoryUtilizationPercentage }} + {{- end }} +{{- end }} diff --git a/tutorials/helm/max-openai-api/templates/ingress.yaml b/tutorials/helm/max-openai-api/templates/ingress.yaml new file mode 100644 index 0000000000..e02fc3f027 --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/ingress.yaml @@ -0,0 +1,57 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## +{{ if .Values.ingress.enabled -}} +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: {{ template "max.fullname" . }} + namespace: {{ .Release.Namespace }} + labels: + app.kubernetes.io/name: {{ template "max.name" . }} + helm.sh/chart: {{ template "max.chart" . }} + app.kubernetes.io/managed-by: {{ .Release.Service }} + app.kubernetes.io/instance: {{ .Release.Name }} + {{- with .Values.ingress.annotations }} + annotations: {{- toYaml . | nindent 4 }} + {{- end }} +spec: + {{- if .Values.ingress.ingressClassName }} + ingressClassName: {{ .Values.ingress.ingressClassName }} + {{- end }} + {{- if .Values.ingress.tls }} + tls: + {{- range .Values.ingress.tls }} + - hosts: + {{- range .hosts }} + - {{ . }} + {{- end }} + secretName: {{ .secretName }} + {{- end }} + {{- end }} + rules: + {{- range .Values.ingress.hosts }} + - host: {{ . }} + http: + paths: + - path: {{ $.Values.ingress.path }} + pathType: {{ $.Values.ingress.pathType }} + backend: + service: + name: {{ template "max.fullname" . }} + port: + name: http + {{- end }} + {{- if .Values.ingress.extraHostsRaw }} + {{- toYaml .Values.ingress.extraHostsRaw | nindent 4 }} + {{- end }} +{{- end }} diff --git a/tutorials/helm/max-openai-api/templates/secret-env.yaml b/tutorials/helm/max-openai-api/templates/secret-env.yaml new file mode 100644 index 0000000000..1f4649686e --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/secret-env.yaml @@ -0,0 +1,29 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## +apiVersion: v1 +kind: Secret +metadata: + name: {{ template "max.fullname" . }}-env + namespace: {{ .Release.Namespace }} + labels: + app.kubernetes.io/name: {{ template "max.name" . }} + helm.sh/chart: {{ template "max.chart" . }} + app.kubernetes.io/managed-by: {{ .Release.Service }} + app.kubernetes.io/instance: {{ .Release.Name }} +type: Opaque +stringData: + {{- if .Values.envSecret }} + {{- range $key, $value := .Values.envSecret }} + {{ $key }}: {{ $value | quote }} + {{- end }} + {{- end }} diff --git a/tutorials/helm/max-openai-api/templates/service.yaml b/tutorials/helm/max-openai-api/templates/service.yaml new file mode 100644 index 0000000000..3f7d148592 --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/service.yaml @@ -0,0 +1,34 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +apiVersion: v1 +kind: Service +metadata: + name: {{ template "max.fullname" . }} + namespace: {{ .Release.Namespace }} + labels: + app.kubernetes.io/name: {{ template "max.name" . }} + helm.sh/chart: {{ template "max.chart" . }} + app.kubernetes.io/instance: {{ .Release.Name }} + {{- with .Values.service.annotations }} + annotations: {{- toYaml . | nindent 4 }} + {{- end }} +spec: + type: {{ .Values.service.type }} + ports: {{- .Values.service.ports | toYaml | nindent 2 }} + selector: + app.kubernetes.io/name: {{ template "max.name" . }} + app.kubernetes.io/instance: {{ .Release.Name }} + {{- if .Values.service.loadBalancerIP }} + loadBalancerIP: {{ .Values.service.loadBalancerIP }} + {{- end }} diff --git a/tutorials/helm/max-openai-api/templates/serviceaccount.yaml b/tutorials/helm/max-openai-api/templates/serviceaccount.yaml new file mode 100644 index 0000000000..f1aee92905 --- /dev/null +++ b/tutorials/helm/max-openai-api/templates/serviceaccount.yaml @@ -0,0 +1,28 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +{{- if .Values.serviceAccount.create }} +apiVersion: v1 +kind: ServiceAccount +metadata: + name: {{ include "max.serviceAccountName" . }} + namespace: {{ .Release.Namespace }} + labels: + app.kubernetes.io/name: {{ template "max.name" . }} + helm.sh/chart: {{ template "max.chart" . }} + app.kubernetes.io/managed-by: {{ .Release.Service }} + app.kubernetes.io/instance: {{ .Release.Name }} + {{- if .Values.serviceAccount.annotations }} + annotations: {{- toYaml .Values.serviceAccount.annotations | nindent 4 }} + {{- end }} +{{- end -}} diff --git a/tutorials/helm/max-openai-api/values.yaml b/tutorials/helm/max-openai-api/values.yaml new file mode 100644 index 0000000000..d87d815cfe --- /dev/null +++ b/tutorials/helm/max-openai-api/values.yaml @@ -0,0 +1,239 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +# Default values for the MAX Serving Helm chart. + +# A README is automatically generated from this file to document it, using helm-docs (see https://github.com/norwoodj/helm-docs) +# To update it, install helm-docs and run helm-docs from the root of this chart + +# -- Provide a name to override the name of the chart +nameOverride: ~ +# -- Provide a name to override the full names of resources +fullnameOverride: ~ + +# -- User ID directive. This user must have enough permissions to run the bootstrap script +# Running containers as root is not recommended in production. Change this to another UID - e.g. 1000 to be more secure +runAsUser: 0 + +# -- Specify service account name to be used +serviceAccountName: ~ +serviceAccount: + # -- Create custom service account for MAX Serving. If create: true and serviceAccountName is not provided, `max.fullname` will be used. + create: false + annotations: {} + +# -- Environment variables that will be passed into pods +env: {} + +# -- Environment variables in RAW format that will be passed into pods +envRaw: [] + +# -- The name of the secret which we will use to populate env vars in deployed pods +# This can be useful for secret keys, etc. +envFromSecret: '{{ template "max.fullname" . }}-env' +# -- This can be a list of templated strings +envFromSecrets: [] + +# -- Environment variables to pass as secrets +envSecret: + {} + # # AWS security credentials: https://docs.aws.amazon.com/IAM/latest/UserGuide/security-creds.html + # AWS_ACCESS_KEY_ID: ... + # AWS_SECRET_ACCESS_KEY: ... + # AWS_SESSION_TOKEN: ... + # # Google API Keys + # GOOGLE_KEY: ... + # GOOGLE_SECRET: ... + +volumes: + [] + # - name: customConfig + # configMap: + # name: '{{ template "max.fullname" . }}-custom-config' + # - name: additionalSecret + # secret: + # secretName: my-secret + # defaultMode: 0600 + +volumeMounts: + [] + # - name: customConfig + # mountPath: /mnt/config + # readOnly: true + # - name: additionalSecret: + # mountPath: /mnt/secret + +image: + repository: "docker.modular.com/modular/max-openai-api" + tag: "latest" + pullPolicy: IfNotPresent + +# -- MAX Serve arguments +maxServe: + huggingfaceRepoId: modularai/Llama-3.1-8B-Instruct-GGUF + maxLength: "2048" + maxBatchSize: "250" + cacheStrategy: "continuous" + maxNumSteps: "10" + +imagePullSecrets: [] + +service: + type: ClusterIP + # you may want to have a headless service if use grpc protocol + # https://kubernetes.io/docs/concepts/services-networking/service/#headless-services + # https://github.com/grpc/grpc/blob/57dacad8c761a12de3e5eafa45f190015c73cccb/doc/load-balancing.md + # clusterIP: None + ports: + - name: http + protocol: TCP + port: 8000 + targetPort: 8000 + annotations: + {} + # cloud.google.com/load-balancer-type: "Internal" + loadBalancerIP: ~ + +ingress: + enabled: false + ingressClassName: ~ + annotations: + {} + # kubernetes.io/tls-acme: "true" + ## Extend timeout to allow long running queries. + # nginx.ingress.kubernetes.io/proxy-connect-timeout: "300" + # nginx.ingress.kubernetes.io/proxy-read-timeout: "300" + # nginx.ingress.kubernetes.io/proxy-send-timeout: "300" + path: / + pathType: ImplementationSpecific + hosts: [] + tls: [] + extraHostsRaw: [] + # - secretName: chart-example-tls + # hosts: + # - chart-example.local + +resources: + {} + # We do not specify default resources and leave this as a conscious + # choice for the user. If you do want to specify resources, uncomment the following + # lines, adjust them as necessary, and remove the curly braces after 'resources:'. + # The limits below will apply to all MAX Serving components. To set individual resource limitations refer to the pod specific values below. + # The pod specific values will overwrite anything that is set here. + # limits: + # cpu: 100m + # memory: 128Mi + # requests: + # cpu: 100m + # memory: 128Mi + +# MAX Serving node configuration +inferenceServer: + replicaCount: 1 + autoscaling: + enabled: false + minReplicas: 1 + maxReplicas: 2 + targetCPUUtilizationPercentage: 80 + # targetMemoryUtilizationPercentage: 80 + + # -- Arguments to pass to the node entrypoint. If defined it overwrites the default args value set by .Values.max-serve + # @default -- See `values.yaml` + args: [] + # - "--model-path=modularai/Llama-3.1-8B-Instruct-GGUF", + # - "--max-length=2048", + # - "--max-batch-size=250", + # - "--cache-strategy=continuous", + # - "--max-num-steps=10", + + env: {} + # Limit number of CPUs that MAX Serving and its Inference Engine can use + # MODULAR_AsyncRT_CPU_MAX: 16 + # -- Volumes to mount into inferenceServer pod + volumes: [] + # -- Volumes to mount into inferenceServer pod + volumeMounts: [] + # -- Launch additional containers into inferenceServer pod + extraContainers: [] + # -- Annotations to be added to inferenceServer deployment + deploymentAnnotations: {} + # -- Labels to be added to inferenceServer deployment + deploymentLabels: {} + # -- NodeSelector to be added to inferenceServer deployment + nodeSelector: {} + # -- Tolerations to be added to inferenceServer deployment + tolerations: [] + # -- Affinity to be added to inferenceServer deployment + affinity: {} + # -- TopologySpreadConstrains to be added to inferenceServer deployments + topologySpreadConstraints: [] + # -- Annotations to be added to inferenceServer pods + podAnnotations: {} + # -- Labels to be added to inferenceServer pods + podLabels: {} + startupProbe: + httpGet: + path: /v1/health + port: http + initialDelaySeconds: 1 + timeoutSeconds: 1 + failureThreshold: 60 + periodSeconds: 5 + successThreshold: 1 + livenessProbe: + httpGet: + path: /v1/health + port: http + initialDelaySeconds: 1 + timeoutSeconds: 1 + failureThreshold: 3 + periodSeconds: 15 + successThreshold: 1 + readinessProbe: + httpGet: + path: /v1/health + port: http + initialDelaySeconds: 1 + timeoutSeconds: 1 + failureThreshold: 3 + periodSeconds: 15 + successThreshold: 1 + # -- Resource settings for the inferenceServer pods - these settings overwrite existing values from the global resources object defined above. + resources: + {} + # limits: + # cpu: 100m + # memory: 128Mi + # requests: + # cpu: 100m + # memory: 128Mi + podSecurityContext: {} + containerSecurityContext: {} + strategy: + {} + # type: RollingUpdate + # rollingUpdate: + # maxSurge: 25% + # maxUnavailable: 25% + +# -- NodeSelector to be added to all deployments +nodeSelector: {} + +# -- Tolerations to be added to all deployments +tolerations: [] + +# -- Affinity to be added to all deployments +affinity: {} + +# -- TopologySpreadConstraints to be added to all deployments +topologySpreadConstraints: [] diff --git a/tutorials/max-graph-api/.gitattributes b/tutorials/max-graph-api/.gitattributes new file mode 100644 index 0000000000..07fe41c52c --- /dev/null +++ b/tutorials/max-graph-api/.gitattributes @@ -0,0 +1,2 @@ +# GitHub syntax highlighting +pixi.lock linguist-language=YAML linguist-generated=true diff --git a/tutorials/max-graph-api/.gitignore b/tutorials/max-graph-api/.gitignore new file mode 100644 index 0000000000..1ef4bed3a8 --- /dev/null +++ b/tutorials/max-graph-api/.gitignore @@ -0,0 +1,7 @@ +# pixi environments +.pixi +*.egg-info +# magic environments +.magic +# MNIST data +data diff --git a/tutorials/max-graph-api/README.md b/tutorials/max-graph-api/README.md new file mode 100644 index 0000000000..a96f37787e --- /dev/null +++ b/tutorials/max-graph-api/README.md @@ -0,0 +1,7 @@ +# Tutorial: MAX Graph API + +This repository contains the code for +[Get started with MAX Graph](https://docs.modular.com/max/tutorials/get-started-with-max-graph) +tutorial. + +Please refer to that page for more information. diff --git a/tutorials/max-graph-api/add.mojo b/tutorials/max-graph-api/add.mojo new file mode 100644 index 0000000000..ffedee6d3d --- /dev/null +++ b/tutorials/max-graph-api/add.mojo @@ -0,0 +1,44 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from max import engine +from max.graph import Graph, TensorType, Type +from max.tensor import Tensor + + +def main(): + # 1. define add graph + graph = Graph( + in_types=List[Type]( + TensorType(DType.float32, 1), TensorType(DType.float32, 1) + ) + ) + out = graph[0] + graph[1] + graph.output(out) + graph.verify() + print("finall graph:", graph) + # 2. load and compile the graph + session = engine.InferenceSession() + model = session.load(graph) + print("input names are:") + for input_name in model.get_model_input_names(): + print(input_name[]) + + # 3. Execute / run the graph with some inputs + print("set some input values:") + input0 = Tensor[DType.float32](List[Float32](1.0)) + print("input0:", input0) + input1 = Tensor[DType.float32](List[Float32](1.0)) + print("input1:", input1) + ret = model.execute("input0", input0^, "input1", input1^) + print("result:", ret.get[DType.float32]("output0")) diff --git a/tutorials/max-graph-api/add.png b/tutorials/max-graph-api/add.png new file mode 100644 index 0000000000000000000000000000000000000000..46a8ad114c6a98a368acca0f3483b548d4073f9e GIT binary patch literal 16220 zcmcJ$Wn5I<`!@=RFrdH;p>zxl0wN_KJwu1I(ug1((hY)?QqtWZjdXWNOE(D8t(5e$ zxc~R>Ja5kLbDkH^dBM!E_g-tSb=A7!dj-E#l)}L##YRCv!I6@k zYcomEXV=$=F&hVQe|J}AVkWYaL(&sIi+1N4R{c(dl@W18bTjO%zf-yBdB*?l!2Yx> zVAzc^{IdCA(c*e?qh`qY_S6RFT?S*S%LsO)CdI)>LVJ64qe7w9^(f0Q4#T+;Caz>3A7;Fz(X@qa32Le zKGr0Ee=9p`AoCNu`Qk~5bLeZb8t|Mh{*R6GS&LF0i;}ur*KO6b z*%0QSL*jTU*Hu`>g40~WCywUnCpGi$*UwsRo6g75Zr)lq?C~Cbc-+`pRNlv85{Z07 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zaCJSHd-W=s_pv~s6~w1!P#0F}cL?~27an9~j&!C-ApV;RAJ2EXxsQqFqeDEFZ3)k| zpRr#r&e~xo2G2IvQ987`0JJy;KG*&sHe1$j!h4h2#+gtvlS3{#F|3@YbdnW{&C$62p6PW@3bv&h<^}A?m&m3kIQ8* z6*NwV+l!x>J5g?^FD>fOi6bPg)99Rk#j@1))`cyK>C3oTMg)@(a7oo}Xciu~`dpCa zoi~~6M{yPDBA8-gPzBhC#&i+u z8NA(I4P3kOymmM#53(bK&>{uu3h(9%#WkSjcBDkC-Z@NDnlH$(%|%NrTYh@Z{0ZWn z&j>^zD?$}`AjL1GLd4JRQ98gbrJE5FyurbyyMuypCPn3LMD4wuZL|1jHdL1axR1^i z_S}s2t*3m_2&tQ0cXH-nGKM!TP$LwiKgbCl?vKrnz#bK*;g1y>d`VMbfP zqL>6Gz^7wc)dytW;)NjcJ-c~i?3;Z$HH-6n{3Y78K?-S)1}!sOJ*sgtsvT_YSd@qN zcfpB>3}K<*kGU*vbYq{OQ9Y7lCI_9N^xQ5x+^P2%4-1xl_B2^&^i9Fo%*g6ubF7P# zrbH52y!X_MPsnc=&gfj6oI|;xdDaT}q=r10VDMTyZ+8lT?_fIz7Q?X`RI1_6YBc?p znV=&uYs%a{7-^XOdp2#%?Ycc8I^BUQ@4%hC*PJUp2JO}*M6TuOO~m@at7CR-Pel&) z7EK&I-9;fFlQr9qnSDcCs=b%ybmF}!MgF`m7B$foE89QAn(rnz2>VFK98aX~8T8$@k2ibcoK_P&k_O+e6gBZeP2JW6urqt)y? z4u`KOkl@)QR7#28I9ym7Yo68f#qop!#2uETQo?M*oV{epPHGEVcY-q(h~ACC1cEw9#MqU;<DQpBbe3NDMC?EUU0<=RXd@Sz^v0DRt@W|m{T}D= zm2WW?_~~SNAIewy$GSNrc@AqtWUtN0u&lhg5APXM{;iL zUy_b4iBP8unTr$$7K2=TI%g9-BD521VhACBPri!mZ_9Uf!oi*{oL)Cm2F}kfX4VY7 u0lQ#-w5raRX@JeLlbO7||5Cv%{&= PythonObject: + np = py.import_module("numpy") + fin = py.evaluate('open("model_weights.npy", mode="rb")') + model_weights = np.load( + fin, allow_pickle=True + ).item() # note this is of type PythonObject + fin.close() + print("python type of model_weights:", py.type(model_weights)) + for item in model_weights.items(): + print(item[0], item[1].shape, py.type(item[1])) + + return model_weights + + +@always_inline +fn numpy_data_pointer[ + type: DType +](numpy_array: PythonObject) raises -> UnsafePointer[Scalar[type]]: + return numpy_array.__array_interface__["data"][0].unsafe_get_as_pointer[ + type + ]() + + +@always_inline +fn memcpy_from_numpy(array: PythonObject, tensor: Tensor) raises: + src = numpy_data_pointer[tensor.type](array) + dst = tensor._ptr + length = tensor.num_elements() + memcpy(dst, src, length) + + +@always_inline +fn numpy_to_tensor[type: DType](array: PythonObject) raises -> Tensor[type]: + shape = List[Int]() + array_shape = array.shape + for dim in array_shape: + shape.append(dim) + + out = Tensor[type](shape) + memcpy_from_numpy(array, out) + return out^ + + +def build_mnist_graph( + fc1w: Tensor[DType.float32], + fc1b: Tensor[DType.float32], + fc2w: Tensor[DType.float32], + fc2b: Tensor[DType.float32], +) -> Graph: + # Note: "batch" is a symbolic dim which is known ahead of time vs dynamic dim + graph = Graph(TensorType(DType.float32, "batch", 28 * 28)) + # PyTorch linear is defined as: x W^T + b so we need to transpose the weights + fc1 = ( + graph[0] @ ops.transpose(graph.constant(fc1w), 1, 0) + ) + graph.constant(fc1b) + + relu = ops.relu(fc1) + + fc2 = (relu @ ops.transpose(graph.constant(fc2w), 1, 0)) + graph.constant( + fc2b + ) + out = ops.softmax(fc2) # adding explicit softmax for inference prob + graph.output(out) + graph.verify() + return graph + + +def load_mnist_test_data() -> PythonObject: + torchvision = py.import_module("torchvision") + test_dataset = torchvision.datasets.MNIST( + root="./data", train=False, transform=None, download=False + ) + return test_dataset + + +def preprocess(image: PythonObject) -> PythonObject: + transforms = py.import_module("torchvision.transforms") + image_tensor = transforms.ToTensor()(image) + image_tensor_normalized = transforms.Normalize((0.5,), (0.5,))(image_tensor) + reshaped_image = image_tensor_normalized.reshape(1, 28 * 28).numpy() + return reshaped_image + + +def argmax(t: Tensor) -> List[Int]: + var res = List[Int](capacity=t.dim(0)) + for i in range(t.dim(0)): + var max_val = Scalar[t.type].MIN + var max_idx = 0 + for j in range(t.dim(1)): + if t[i, j] > max_val: + max_val = t[i, j] + max_idx = j + res.append(max_idx) + return res + + +def main(): + weights_dict = load_model_weights() + fc1w = numpy_to_tensor[DType.float32](weights_dict["fc1.weight"]) + fc1b = numpy_to_tensor[DType.float32](weights_dict["fc1.bias"]) + fc2w = numpy_to_tensor[DType.float32](weights_dict["fc2.weight"]) + fc2b = numpy_to_tensor[DType.float32](weights_dict["fc2.bias"]) + + mnist_graph = build_mnist_graph(fc1w^, fc1b^, fc2w^, fc2b^) + session = engine.InferenceSession() + model = session.load(mnist_graph) + + for name in model.get_model_input_names(): + print("input:", name[]) + + for name in model.get_model_output_names(): + print("output:", name[]) + + correct = 0 + total = 0 + # use batch size of 1 in this example + test_dataset = load_mnist_test_data() + for i in range(len(test_dataset)): + item = test_dataset[i] + image = item[0] + label = item[1] + + preprocessed_image = preprocess(image) + + output = model.execute("input0", preprocessed_image) + probs = output.get[DType.float32]("output0") + + predicted = argmax(probs)[0] + + correct += Int(predicted == label) + total += 1 + + print( + "Accuracy of the network on the 10000 test images:", + 100 * correct / total, + "%", + ) diff --git a/tutorials/max-graph-api/mnist.png 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zl>p!p7V}l@MVZO|G+# zBJdNxUO`uBwuZZEno$c?n21-*KsSp?a|I(h7pGuzM9D$qw_ydicuyB$iwhoU;c^=N zKz568i3BF=97KNE;y7bw6djyTbFf(#SE$XU=+g+kdI!8#@$zSW)=NqRjdi}hDhzXq z{iXhY>blmKrouQp6)Pgc6^NHFVoHlVt@2t(b8%d{i5F7voI*{fsW>&!G^PzN*>svF zYlZ2i;dG@_L}wYL6;0DFR!)QbpjLwEhZ6Up=JfQO=xgUMIPW>{`||rezvtpbus`@| z>3St|xwu%5V(az6fB`fexGU4@O@@gx37FF3M#Q={hxHAHIRiF=MwE36y9-iorD5so z`IMA}H2;SA-g7F}IgTVpMn;TXjm~PxhSY#-v03LWH2ZCRd}ib{`J;k0QA#d&QKTzjK*K+wy%w1n zElCe@>CKI*&C_ET6^E(>5M4ZW`ga{;?>1A2bqOa)1z>MTktn;Z?%Nt1)UEURW2V3w zTFBq!Xo9zdb2hDmw%`{7*dkxpyaOV^{Tw8&Z2r%Yq3$BU&$3oG4_o7O0JG#T+CWLO z2=&Y0t571diK0ert89+=0fI1^Dd|-16;Z1^@`iirCB@AnV;wKp=)V8kFG??H8NuY{ USZ(#5HdhXwsE8P{?{sSAA5nrzTmS$7 literal 0 HcmV?d00001 diff --git a/tutorials/max-graph-api/mnist.py b/tutorials/max-graph-api/mnist.py new file mode 100644 index 0000000000..eb7f2a971e --- /dev/null +++ b/tutorials/max-graph-api/mnist.py @@ -0,0 +1,123 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import random + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torchvision +import torchvision.transforms as transforms + + +def set_seeds(seed=42): + """Sets seed on CPU""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +set_seeds() + + +class Model(nn.Module): + def __init__(self, input_size, hidden_size, num_classes): + super().__init__() + self.fc1 = nn.Linear(input_size, hidden_size) + self.relu = nn.ReLU() + self.fc2 = nn.Linear(hidden_size, num_classes) + + def forward(self, x): + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + return x + + +def main(): + input_size = 28 * 28 + hidden_size = 512 + num_classes = 10 + num_epochs = 5 + learning_rate = 0.001 + + preprocess = transforms.Compose( + [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))] + ) + + train_dataset = torchvision.datasets.MNIST( + root="./data", train=True, transform=preprocess, download=True + ) + test_dataset = torchvision.datasets.MNIST( + root="./data", train=False, transform=preprocess, download=True + ) + + train_loader = torch.utils.data.DataLoader( + dataset=train_dataset, batch_size=128, shuffle=True + ) + test_loader = torch.utils.data.DataLoader( + dataset=test_dataset, batch_size=128, shuffle=False + ) + + model = Model(input_size, hidden_size, num_classes) + + loss_fn = nn.CrossEntropyLoss() + optimizer = optim.RMSprop(model.parameters(), lr=learning_rate) + + total_steps = len(train_loader) + for epoch in range(num_epochs): + for i, (images, labels) in enumerate(train_loader): + images = images.reshape(-1, 28 * 28) + outputs = model(images) + loss = loss_fn(outputs, labels) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if (i + 1) % 100 == 0: + print( + f"Epoch [{epoch + 1}/{num_epochs}], Step" + f" [{i + 1}/{total_steps}], Loss: {loss.item():.4f}" + ) + + model.eval() + + with torch.no_grad(): + correct = 0 + total = 0 + for images, labels in test_loader: + images = images.reshape(-1, 28 * 28) + outputs = model(images) + probs = F.softmax(outputs, dim=1) + predicted = torch.argmax(probs, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + print( + "Accuracy of the network on the 10000 test images:" + f" {100 * correct / total} %" + ) + + # save weights in numpy binary format + weights = {} + for name, param in model.named_parameters(): + weights[name] = param.detach().cpu().numpy() + + np.save("model_weights.npy", weights) + return + + +if __name__ == "__main__": + main() diff --git a/tutorials/max-graph-api/mojoproject.toml b/tutorials/max-graph-api/mojoproject.toml new file mode 100644 index 0000000000..7a073dbfb4 --- /dev/null +++ b/tutorials/max-graph-api/mojoproject.toml @@ -0,0 +1,19 @@ +[project] +authors = ["Modular "] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/", "pytorch"] +description = "Add a short description here" +name = "max-graph-api" +platforms = ["osx-arm64", "linux-64"] +version = "0.0.0" + +[tasks] +add = "mojo add.mojo" +matmul = "mojo matmul.mojo" +mnist = "python mnist.py && mojo mnist.mojo" +test = { depends-on = ["add", "matmul", "mnist"] } + +[dependencies] +max = "~=24.6" +pytorch = { version = "==2.4.0", channel = "pytorch" } +torchvision = { version = ">=0.19.0,<0.20", channel = "pytorch" } +numpy = "<2.0" diff --git a/tutorials/max-graph-python/.mypy.ini b/tutorials/max-graph-python/.mypy.ini new file mode 100644 index 0000000000..1215375ed9 --- /dev/null +++ b/tutorials/max-graph-python/.mypy.ini @@ -0,0 +1,2 @@ +[mypy] +ignore_missing_imports = True \ No newline at end of file diff --git a/tutorials/max-graph-python/README.md b/tutorials/max-graph-python/README.md new file mode 100644 index 0000000000..4972b568c6 --- /dev/null +++ b/tutorials/max-graph-python/README.md @@ -0,0 +1,60 @@ +# Basic example for MAX Graph API in Python + +This is an example of building a model with the MAX Graph API in Python and +execute it with MAX Engine. + +## Usage + +First, install [Magic](https://docs.modular.com/magic/). + +Then run the `magic run addition` command from the terminal: + +```sh +magic run addition +``` + +You should see the following output: + +```output +input names are: +name: input0, shape: [1], dtype: DType.float32 +name: input1, shape: [1], dtype: DType.float32 +result: [2.] +``` + +## Tests + +To run tests, use the following command: + +```sh +magic run pytest +``` + +You should see the following output: + +```output +==================== test session starts ==================== +platform darwin -- Python 3.12.7, pytest-8.3.3, pluggy-1.5.0 +rootdir: /max-repo +configfile: pyproject.toml +collected 6 items + +tests/test_addition.py ...... [100%] + +===================== 6 passed in 6.94s ===================== +``` + +## Lint and format + +To lint with `mypy` run the following command: + +```sh +magic run mypy +``` + +To format the python files: + +```sh +magic run black +magic run isort +``` diff --git a/tutorials/max-graph-python/pyproject.toml b/tutorials/max-graph-python/pyproject.toml new file mode 100644 index 0000000000..c07431b62f --- /dev/null +++ b/tutorials/max-graph-python/pyproject.toml @@ -0,0 +1,33 @@ +[project] +authors = [{name = "Modular", email = "hello@modular.com"}] +dependencies = [] +description = "Add two numbers" +name = "max_ops" +requires-python = ">=3.11,<3.13" +version = "0.1.0" + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.pixi.project] +channels = ["conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["osx-arm64", "linux-64"] + +[tool.pixi.pypi-dependencies] +max_ops = { path = ".", editable = true } + +[tool.pixi.tasks] +addition = "python3 src/max_ops/addition.py" +mypy = "mypy . --config .mypy.ini" +black = "black src/max_ops/addition.py tests/test_addition.py" +isort = "isort src/max_ops/addition.py tests/test_addition.py" +test = { depends-on = ["addition"] } + + +[tool.pixi.dependencies] +max = "~=24.6" +numpy = "<2.0" +pytest = ">=8.3.3,<9" +black = ">=24.10.0,<25" +mypy = ">=1.13.0,<2" diff --git a/tutorials/max-graph-python/src/max_ops/__init__.py b/tutorials/max-graph-python/src/max_ops/__init__.py new file mode 100644 index 0000000000..75c4f824f2 --- /dev/null +++ b/tutorials/max-graph-python/src/max_ops/__init__.py @@ -0,0 +1,12 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # diff --git a/tutorials/max-graph-python/src/max_ops/addition.py b/tutorials/max-graph-python/src/max_ops/addition.py new file mode 100644 index 0000000000..4528d4a549 --- /dev/null +++ b/tutorials/max-graph-python/src/max_ops/addition.py @@ -0,0 +1,51 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from typing import Any + +import numpy as np +from max import engine +from max.dtype import DType +from max.graph import Graph, TensorType, ops + + +def add_tensors(a: np.ndarray, b: np.ndarray) -> dict[str, Any]: + # 1. Build the graph + input_type = TensorType(dtype=DType.float32, shape=(1,)) + with Graph( + "simple_add_graph", input_types=(input_type, input_type) + ) as graph: + lhs, rhs = graph.inputs + out = ops.add(lhs, rhs) + graph.output(out) + print("final graph:", graph) + + # 2. Create an inference session + session = engine.InferenceSession() + model = session.load(graph) + + for tensor in model.input_metadata: + print( + f"name: {tensor.name}, shape: {tensor.shape}, dtype: {tensor.dtype}" + ) + + # 3. Execute the graph + ret = model.execute(a, b)[0] + print("result:", ret) + return ret + + +if __name__ == "__main__": + input0 = np.array([1.0], dtype=np.float32) + input1 = np.array([1.0], dtype=np.float32) + add_tensors(input0, input1) diff --git a/tutorials/max-graph-python/tests/__init__.py b/tutorials/max-graph-python/tests/__init__.py new file mode 100644 index 0000000000..75c4f824f2 --- /dev/null +++ b/tutorials/max-graph-python/tests/__init__.py @@ -0,0 +1,12 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # diff --git a/tutorials/max-serve-cloud-configs/aws/max-serve-aws.yaml b/tutorials/max-serve-cloud-configs/aws/max-serve-aws.yaml new file mode 100644 index 0000000000..cf63426766 --- /dev/null +++ b/tutorials/max-serve-cloud-configs/aws/max-serve-aws.yaml @@ -0,0 +1,250 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +AWSTemplateFormatVersion: '2010-09-09' +Description: CloudFormation template to deploy MAX Serve on an EC2 instance. + +Parameters: + InstanceType: + Type: String + Default: g5.4xlarge + AllowedValues: + - g5.4xlarge + Description: EC2 instance type for the MAX Serve deployment. + + AmiId: + Type: AWS::EC2::Image::Id + Default: ami-02769e6d1f6a88067 + Description: AMI ID for Deep Learning Base OSS Nvidia Driver AMI (Amazon Linux 2) in us-east-1. + + HuggingFaceHubToken: + Type: String + NoEcho: true + Description: HuggingFace Hub API Token for accessing models. + + HuggingFaceRepoId: + Type: String + Default: modularai/Llama-3.1-8B-Instruct-GGUF + Description: Hugging Face Repository ID for the Model. + +Resources: + MaxServeInstanceProfile: + Type: AWS::IAM::InstanceProfile + Properties: + Roles: + - !Ref MaxServeInstanceRole + + MaxServeInstanceRole: + Type: AWS::IAM::Role + Properties: + AssumeRolePolicyDocument: + Version: '2012-10-17' + Statement: + - Effect: Allow + Principal: + Service: + - ec2.amazonaws.com + Action: + - sts:AssumeRole + ManagedPolicyArns: + - arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly + - arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore + - arn:aws:iam::aws:policy/CloudWatchAgentServerPolicy + Policies: + - PolicyName: CloudWatchLogsAccess + PolicyDocument: + Version: '2012-10-17' + Statement: + - Effect: Allow + Action: + - logs:CreateLogStream + - logs:PutLogEvents + - logs:DescribeLogStreams + Resource: !Sub 'arn:aws:logs:${AWS::Region}:${AWS::AccountId}:log-group:/aws/ec2/${AWS::StackName}-logs:*' + + MaxServeLogGroup: + Type: AWS::Logs::LogGroup + DeletionPolicy: Delete + UpdateReplacePolicy: Delete + Properties: + LogGroupName: !Sub '/aws/ec2/${AWS::StackName}-logs' + RetentionInDays: 1 + + MaxServeSecurityGroup: + Type: AWS::EC2::SecurityGroup + Properties: + GroupDescription: Enable HTTP access on port 80 and SSH on port 22 + SecurityGroupIngress: + - IpProtocol: tcp + FromPort: 80 + ToPort: 80 + CidrIp: 0.0.0.0/0 + - IpProtocol: tcp + FromPort: 22 + ToPort: 22 + CidrIp: 0.0.0.0/0 + + MaxServeInstance: + Type: AWS::EC2::Instance + Properties: + InstanceType: !Ref InstanceType + ImageId: !Ref AmiId + SecurityGroupIds: + - !Ref MaxServeSecurityGroup + IamInstanceProfile: !Ref MaxServeInstanceProfile + BlockDeviceMappings: + - DeviceName: /dev/xvda + Ebs: + VolumeSize: 100 + VolumeType: gp3 + DeleteOnTermination: true + UserData: + 'Fn::Base64': !Sub | + #!/bin/bash + set -xe # Enable detailed logging + # Redirect all output to a log file for debugging + exec > >(tee /var/log/user-data.log|logger -t user-data -s 2>/dev/console) 2>&1 + + echo "Starting user data script execution..." + + # Install CloudWatch agent first + echo "Installing CloudWatch agent..." + sudo yum install -y amazon-cloudwatch-agent + + # Create log files and directory with proper permissions + sudo mkdir -p /var/log/max-serve + sudo touch /var/log/max-serve/container.log + sudo chmod 644 /var/log/max-serve/container.log + sudo chown root:root /var/log/max-serve/container.log + + # Configure CloudWatch agent early + cat < /opt/aws/amazon-cloudwatch-agent/etc/amazon-cloudwatch-agent.json + { + "agent": { + "metrics_collection_interval": 60, + "run_as_user": "root" + }, + "logs": { + "logs_collected": { + "files": { + "collect_list": [ + { + "file_path": "/var/log/messages", + "log_group_name": "/aws/ec2/${AWS::StackName}-logs", + "log_stream_name": "instance-logs", + "timestamp_format": "%b %d %H:%M:%S", + "timezone": "UTC" + }, + { + "file_path": "/var/log/max-serve/container.log", + "log_group_name": "/aws/ec2/${AWS::StackName}-logs", + "log_stream_name": "instance-logs", + "timestamp_format": "%Y-%m-%d %H:%M:%S", + "timezone": "UTC" + }, + { + "file_path": "/var/log/user-data.log", + "log_group_name": "/aws/ec2/${AWS::StackName}-logs", + "log_stream_name": "instance-logs", + "timestamp_format": "%Y-%m-%d %H:%M:%S", + "timezone": "UTC" + } + ] + } + }, + "force_flush_interval": 15 + } + } + EOF + + # Start the CloudWatch agent + sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl -a fetch-config -m ec2 -c file:/opt/aws/amazon-cloudwatch-agent/etc/amazon-cloudwatch-agent.json -s + sudo systemctl enable amazon-cloudwatch-agent + sudo systemctl start amazon-cloudwatch-agent + + # Verify CloudWatch agent is running + sudo systemctl status amazon-cloudwatch-agent + + # Continue with Docker installation and rest of the setup + echo "Installing docker..." + sudo yum update -y + sudo yum install -y docker aws-cfn-bootstrap + sudo systemctl enable docker + sudo systemctl start docker + sudo usermod -a -G docker ec2-user + + # Verify docker is running + echo "Checking docker status..." + sudo systemctl status docker + docker --version + + # Install NVIDIA Container Toolkit + echo "Installing NVIDIA Container Toolkit..." + distribution=$(. /etc/os-release;echo $ID$VERSION_ID) + curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo + sudo yum clean expire-cache + sudo yum install -y nvidia-docker2 + sudo systemctl restart docker + + # Verify NVIDIA docker installation + echo "Checking NVIDIA docker installation..." + nvidia-smi + docker info | grep -i nvidia + + # Pull and run the MAX Serve container + echo "Pulling and running MAX Serve container..." + # Add error checking for docker pull + if ! sudo docker pull docker.modular.com/modular/max-openai-api:latest; then + echo "Failed to pull container image" + /opt/aws/bin/cfn-signal -e 1 --stack ${AWS::StackName} --resource MaxServeInstance --region ${AWS::Region} + exit 1 + fi + + sudo docker images + + # Start the container and capture logs + CONTAINER_ID=$(sudo docker run -d \ + --env "HF_TOKEN=${HuggingFaceHubToken}" \ + --env "HF_HUB_ENABLE_HF_TRANSFER=1" \ + -v /home/ec2-user/.cache/huggingface:/root/.cache/huggingface \ + --gpus 1 \ + -p 80:8000 \ + --ipc=host \ + docker.modular.com/modular/max-openai-api:latest \ + --model-path ${HuggingFaceRepoId}) + + if [ $? -ne 0 ]; then + echo "Failed to start container" + /opt/aws/bin/cfn-signal -e 1 --stack ${AWS::StackName} --resource MaxServeInstance --region ${AWS::Region} + exit 1 + fi + + # Start following container logs in the background + sudo docker logs -f $CONTAINER_ID > /var/log/max-serve/container.log 2>&1 & + + # Verify container is running + echo "Checking container status..." + if ! sudo docker ps | grep max-openai-api; then + echo "Container is not running" + /opt/aws/bin/cfn-signal -e 1 --stack ${AWS::StackName} --resource MaxServeInstance --region ${AWS::Region} + exit 1 + fi + +Outputs: + InstanceId: + Description: Instance ID of the EC2 instance + Value: !Ref MaxServeInstance + + PublicDNS: + Description: Public DNS of the EC2 instance + Value: !GetAtt MaxServeInstance.PublicDnsName diff --git a/tutorials/max-serve-cloud-configs/aws/notify.sh b/tutorials/max-serve-cloud-configs/aws/notify.sh new file mode 100644 index 0000000000..4d4b241536 --- /dev/null +++ b/tutorials/max-serve-cloud-configs/aws/notify.sh @@ -0,0 +1,106 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +REGION=$1 +STACK_NAME=$2 +PUBLIC_IP=$3 +MAX_WAIT_MINUTES=30 +START_TIME=$(date +%s) +LOG_GROUP="/aws/ec2/$STACK_NAME-logs" + +fetch_logs() { + local stream_name=$1 + local stream_type=$2 + local limit=$3 + echo "=== $stream_type Logs ===" + + if [ -n "$limit" ]; then + aws logs get-log-events \ + --log-group-name "$LOG_GROUP" \ + --log-stream-name "$stream_name" \ + --limit $limit \ + --region $REGION \ + --query 'events[*].[timestamp,message]' \ + --output text + else + aws logs get-log-events \ + --log-group-name "$LOG_GROUP" \ + --log-stream-name "$stream_name" \ + --start-time $(($(date +%s) - 60))000 \ + --region $REGION \ + --query 'events[*].[timestamp,message]' \ + --output text + fi + echo "====================" +} + +check_server_status() { + echo "🔍 Checking server health..." + + if echo "$EC2_LOGS" | grep -q "Pulling from docker.modular.com/modular/max-openai-api"; then + echo "⏳ Docker image is still being pulled..." + return 1 + fi + + if curl -s -f "http://$PUBLIC_IP/v1/health" >/dev/null; then + echo "✅ Server health check passed!" + return 0 + fi + + if echo "$EC2_LOGS" | grep -q "Building\|Compiling\|Starting download of model"; then + echo "⏳ Server is initializing (compiling model)..." + return 1 + fi + + echo "⏳ Server still starting up..." + return 1 +} + +echo "🔍 Starting monitoring for MAX server (max wait: ${MAX_WAIT_MINUTES} minutes)..." + +while true; do + current_time=$(date +%s) + elapsed_minutes=$(((current_time - START_TIME) / 60)) + + if [ $elapsed_minutes -ge $MAX_WAIT_MINUTES ]; then + echo "❌ Timeout after ${MAX_WAIT_MINUTES} minutes. Server might still be starting up." + exit 1 + fi + + EC2_LOG_STREAM=$(aws logs describe-log-streams \ + --log-group-name "$LOG_GROUP" \ + --log-stream-name-prefix "instance-logs" \ + --region $REGION \ + --query "logStreams[0].logStreamName" \ + --output text) + + echo "⏳ Checking logs... (${elapsed_minutes}/${MAX_WAIT_MINUTES} minutes)" + + if [ "$EC2_LOG_STREAM" != "None" ]; then + echo "📜 Instance Logs:" + EC2_LOGS=$(fetch_logs "$EC2_LOG_STREAM" "Instance" 50) + if check_server_status "$EC2_LOGS"; then + echo "✅ Server is ready! (took ${elapsed_minutes} minutes)" + echo "📋 Latest logs:" + echo "$EC2_LOGS" + exit 0 + fi + else + echo "⏳ Logs not yet available..." + fi + + echo "⏳ Server still starting up... checking again in 60 seconds" + echo "-------------------------------------------" + sleep 60 +done diff --git a/tutorials/max-serve-cloud-configs/azure/max-serve-azure.json b/tutorials/max-serve-cloud-configs/azure/max-serve-azure.json new file mode 100644 index 0000000000..554d50d8f0 --- /dev/null +++ b/tutorials/max-serve-cloud-configs/azure/max-serve-azure.json @@ -0,0 +1,252 @@ +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", + "contentVersion": "1.0.0.0", + "metadata": { + "_generator": { + "name": "Modular Inc.", + "copyright": "Copyright (c) 2025, Modular Inc. All rights reserved.", + "license": "Licensed under the Apache License v2.0 with LLVM Exceptions: https://llvm.org/LICENSE.txt", + "disclaimer": "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + } + }, + "parameters": { + "adminUsername": { + "type": "string", + "metadata": { + "description": "Admin username for the virtual machine." + } + }, + "adminPassword": { + "type": "securestring", + "metadata": { + "description": "Admin password for the virtual machine." + } + }, + "vmSize": { + "type": "string", + "defaultValue": "Standard_NV36ads_A10_v5", + "metadata": { + "description": "Size of the virtual machine." + } + }, + "osDiskSizeGB": { + "type": "int", + "defaultValue": 128, + "metadata": { + "description": "OS disk size in GB." + } + }, + "vnetAddressPrefix": { + "type": "string", + "defaultValue": "10.0.0.0/16", + "metadata": { + "description": "Address space for the virtual network." + } + }, + "subnetAddressPrefix": { + "type": "string", + "defaultValue": "10.0.0.0/24", + "metadata": { + "description": "Subnet address space." + } + }, + "startupScript": { + "type": "string", + "metadata": { + "description": "Base64-encoded startup script." + } + }, + "location": { + "type": "string", + "defaultValue": "westus3", + "metadata": { + "description": "Location for all resources." + } + } + }, + "resources": [ + { + "type": "Microsoft.Network/virtualNetworks", + "apiVersion": "2021-03-01", + "name": "maxServeVNet", + "location": "[parameters('location')]", + "properties": { + "addressSpace": { + "addressPrefixes": [ + "[parameters('vnetAddressPrefix')]" + ] + }, + "subnets": [ + { + "name": "maxServeSubnet", + "properties": { + "addressPrefix": "[parameters('subnetAddressPrefix')]" + } + } + ] + } + }, + { + "type": "Microsoft.Network/publicIPAddresses", + "apiVersion": "2021-03-01", + "name": "maxServePublicIP", + "location": "[parameters('location')]", + "properties": { + "publicIPAllocationMethod": "Dynamic" + } + }, + { + "type": "Microsoft.Network/networkSecurityGroups", + "apiVersion": "2021-02-01", + "name": "maxServeNSG", + "location": "[parameters('location')]", + "properties": { + "securityRules": [ + { + "name": "allowHTTP", + "properties": { + "priority": 100, + "protocol": "Tcp", + "access": "Allow", + "direction": "Inbound", + "sourceAddressPrefix": "*", + "sourcePortRange": "*", + "destinationAddressPrefix": "*", + "destinationPortRange": "80", + "description": "Allow HTTP traffic on port 80" + } + }, + { + "name": "allowSSH", + "properties": { + "priority": 200, + "protocol": "Tcp", + "access": "Allow", + "direction": "Inbound", + "sourceAddressPrefix": "*", + "sourcePortRange": "*", + "destinationAddressPrefix": "*", + "destinationPortRange": "22", + "description": "Allow SSH traffic on port 22" + } + }, + { + "name": "allowOutbound", + "properties": { + "priority": 300, + "protocol": "Tcp", + "access": "Allow", + "direction": "Outbound", + "sourceAddressPrefix": "*", + "sourcePortRange": "*", + "destinationAddressPrefix": "*", + "destinationPortRange": "*", + "description": "Allow all outbound traffic" + } + } + ] + } + }, + { + "type": "Microsoft.Network/networkInterfaces", + "apiVersion": "2021-03-01", + "name": "maxServeNIC", + "location": "[parameters('location')]", + "dependsOn": [ + "[resourceId('Microsoft.Network/publicIPAddresses', 'maxServePublicIP')]", + "[resourceId('Microsoft.Network/virtualNetworks', 'maxServeVNet')]", + "[resourceId('Microsoft.Network/networkSecurityGroups', 'maxServeNSG')]" + ], + "properties": { + "ipConfigurations": [ + { + "name": "ipconfig1", + "properties": { + "subnet": { + "id": "[concat(resourceId('Microsoft.Network/virtualNetworks', 'maxServeVNet'), '/subnets/maxServeSubnet')]" + }, + "privateIPAllocationMethod": "Dynamic", + "publicIPAddress": { + "id": "[resourceId('Microsoft.Network/publicIPAddresses', 'maxServePublicIP')]" + } + } + } + ], + "networkSecurityGroup": { + "id": "[resourceId('Microsoft.Network/networkSecurityGroups', 'maxServeNSG')]" + } + } + }, + { + "type": "Microsoft.Compute/virtualMachines", + "apiVersion": "2021-03-01", + "name": "maxServeVM", + "location": "[parameters('location')]", + "dependsOn": [ + "[resourceId('Microsoft.Network/networkInterfaces', 'maxServeNIC')]" + ], + "plan": { + "name": "nvaie_gpu_1_gen2", + "publisher": "nvidia", + "product": "nvidia-ai-enterprise" + }, + "properties": { + "hardwareProfile": { + "vmSize": "[parameters('vmSize')]" + }, + "osProfile": { + "computerName": "maxServeVM", + "adminUsername": "[parameters('adminUsername')]", + "adminPassword": "[parameters('adminPassword')]" + }, + "storageProfile": { + "imageReference": { + "publisher": "nvidia", + "offer": "nvidia-ai-enterprise", + "sku": "nvaie_gpu_1_gen2", + "version": "24.07.03" + }, + "osDisk": { + "createOption": "FromImage", + "managedDisk": { + "storageAccountType": "Standard_LRS" + }, + "diskSizeGB": "[parameters('osDiskSizeGB')]" + } + }, + "networkProfile": { + "networkInterfaces": [ + { + "id": "[resourceId('Microsoft.Network/networkInterfaces', 'maxServeNIC')]" + } + ] + } + } + }, + { + "type": "Microsoft.Compute/virtualMachines/extensions", + "apiVersion": "2021-03-01", + "name": "maxServeVM/customScriptExtension", + "location": "[resourceGroup().location]", + "dependsOn": [ + "[resourceId('Microsoft.Compute/virtualMachines', 'maxServeVM')]" + ], + "properties": { + "publisher": "Microsoft.Azure.Extensions", + "type": "CustomScript", + "typeHandlerVersion": "2.1", + "autoUpgradeMinorVersion": true, + "settings": { + "fileUris": [], + "script": "[parameters('startupScript')]" + } + } + } + ], + "outputs": { + "vmName": { + "type": "string", + "value": "[reference('maxServeVM').osProfile.computerName]" + } + } +} diff --git a/tutorials/max-serve-cloud-configs/azure/notify.sh b/tutorials/max-serve-cloud-configs/azure/notify.sh new file mode 100644 index 0000000000..c038b8b10a --- /dev/null +++ b/tutorials/max-serve-cloud-configs/azure/notify.sh @@ -0,0 +1,113 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +RESOURCE_GROUP=$1 +VM_PASSWORD=$2 +PUBLIC_IP=$3 +VM_NAME=maxServeVM +MAX_WAIT_MINUTES=30 +START_TIME=$(date +%s) + +fetch_logs() { + echo "=== Azure VM Logs ===" + echo "📋 Attempting to fetch logs from VM at ${PUBLIC_IP}..." + + LOGS=$(sshpass -p "$VM_PASSWORD" ssh -o StrictHostKeyChecking=no -o ConnectTimeout=50 -v azureuser@$PUBLIC_IP " + # First check if container is running + CONTAINER_ID=\$(sudo docker ps -q -f ancestor=docker.modular.com/modular/max-openai-api:latest) + if [ -n \"\$CONTAINER_ID\" ]; then + echo '=== Docker Container Found ===' + sudo docker logs \$CONTAINER_ID + else + echo '=== Checking Deployment Logs ===' + # Check deployment logs if container isn't running yet + sudo cat /var/log/azure/custom-script/handler.log 2>/dev/null || echo 'No handler.log'; + sudo cat /var/lib/waagent/custom-script/download/0/stdout 2>/dev/null || echo 'No stdout'; + sudo cat /var/lib/waagent/custom-script/download/0/stderr 2>/dev/null || echo 'No stderr'; + fi + " 2>&1) + + if [ $? -ne 0 ]; then + echo "❌ Failed to connect to VM or fetch logs" + echo "Debug info: $LOGS" + return 1 + fi + + echo "$LOGS" + echo "====================" +} + +check_server_status() { + local logs=$1 + echo "🔍 Checking logs for server status..." + + if echo "$logs" | grep -q "Pulling from docker.modular.com/modular/max-openai-api"; then + echo "⏳ Docker image is still being pulled..." + return 1 + fi + + if curl -s -f "http://$PUBLIC_IP/v1/health" >/dev/null; then + echo "✅ Server health check passed!" + return 0 + fi + + if echo "$logs" | grep -q "Building\|Compiling\|Starting download of model"; then + echo "⏳ Server is initializing (compiling model)..." + return 1 + fi + + echo "⏳ Server still starting up..." + return 1 +} + +echo "🔍 Starting monitoring for MAX server (max wait: ${MAX_WAIT_MINUTES} minutes)..." + +if ! az vm show -g "$RESOURCE_GROUP" -n "$VM_NAME" &>/dev/null; then + echo "❌ VM not found. Please check if VM exists in resource group ${RESOURCE_GROUP}" + exit 1 +fi + +while true; do + current_time=$(date +%s) + elapsed_minutes=$(((current_time - START_TIME) / 60)) + + if [ $elapsed_minutes -ge $MAX_WAIT_MINUTES ]; then + echo "❌ Timeout after ${MAX_WAIT_MINUTES} minutes. Server might still be starting up." + echo "👉 You can manually check the logs by:" + echo " ssh azureuser@$PUBLIC_IP" + echo " sudo cat /var/log/azure/custom-script/handler.log" + echo " sudo cat /var/lib/waagent/custom-script/download/0/stdout" + echo " sudo cat /var/lib/waagent/custom-script/download/0/stderr" + exit 1 + fi + + echo "⏳ Checking logs... (${elapsed_minutes}/${MAX_WAIT_MINUTES} minutes)" + + LOGS=$(fetch_logs) + + if [ -n "$LOGS" ]; then + if check_server_status "$LOGS"; then + echo "✅ Server is ready! (took ${elapsed_minutes} minutes)" + echo "📋 Latest logs:" + echo "$LOGS" + exit 0 + fi + else + echo "⏳ Logs not yet available..." + fi + + echo "⏳ Server still starting up... checking again in 60 seconds" + echo "-------------------------------------------" + sleep 60 +done diff --git a/tutorials/max-serve-cloud-configs/gcp/max-serve-gcp.jinja b/tutorials/max-serve-cloud-configs/gcp/max-serve-gcp.jinja new file mode 100644 index 0000000000..7f5a20dfec --- /dev/null +++ b/tutorials/max-serve-cloud-configs/gcp/max-serve-gcp.jinja @@ -0,0 +1,120 @@ +{# +===----------------------------------------------------------------------=== +Copyright (c) 2025, Modular Inc. All rights reserved. + +Licensed under the Apache License v2.0 with LLVM Exceptions: +https://llvm.org/LICENSE.txt + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +===----------------------------------------------------------------------=== +#} + +resources: +# Main compute instance +- name: {{ properties['instanceName'] }} + type: compute.v1.instance + properties: + zone: {{ properties['zone'] }} + machineType: zones/{{ properties['zone'] }}/machineTypes/{{ properties['machineType'] }} + guestAccelerators: + - acceleratorType: zones/{{ properties['zone'] }}/acceleratorTypes/{{ properties['acceleratorType'] }} + acceleratorCount: {{ properties['acceleratorCount'] }} + disks: + - deviceName: boot + boot: true + autoDelete: true + initializeParams: + sourceImage: projects/deeplearning-platform-release/global/images/{{ properties['sourceImage'] }} + diskSizeGb: 100 # disk space in GB + networkInterfaces: + - network: global/networks/default + accessConfigs: + - name: External NAT + type: ONE_TO_ONE_NAT + serviceAccounts: + - email: default + scopes: + - https://www.googleapis.com/auth/cloud-platform + scheduling: + preemptible: false + onHostMaintenance: TERMINATE # Disables live migration for GPU instances + automaticRestart: true + metadata: + items: + - key: startup-script + value: | + #!/bin/bash + set -xe # Enable detailed logging + curl -sSO https://dl.google.com/cloudagents/add-logging-agent-repo.sh + sudo bash add-logging-agent-repo.sh + + # Update and install dependencies + sudo apt-get update + sudo apt-get install -y google-fluentd curl apt-transport-https ca-certificates gnupg lsb-release software-properties-common + + # Configure Stackdriver logging + sudo service google-fluentd start + sudo systemctl enable google-fluentd + + # Install the NVIDIA drivers if not installed + if [ ! -f /opt/google/cuda-installer ]; then + sudo /opt/deeplearning/install-driver.sh + fi + + # Add Docker GPG key and Docker repository + curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg + echo \ + "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \ + $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null + + # Install Docker + sudo apt-get update + sudo apt-get install -y docker-ce docker-ce-cli containerd.io + + # Add NVIDIA Docker repository + distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ + && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ + && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list + + # Install NVIDIA container runtime + sudo apt-get update + sudo apt-get install -y nvidia-container-toolkit + sudo systemctl restart docker + + # Add user to docker group + sudo usermod -aG docker $(whoami) + + docker run \ + --env "HF_TOKEN={{ properties['huggingFaceHubToken'] }}" \ + --env "HF_HUB_ENABLE_HF_TRANSFER=1" \ + -v $HOME/.cache/huggingface:/root/.cache/huggingface \ + --gpus 1 \ + -p 80:8000 \ + --ipc host \ + docker.modular.com/modular/max-openai-api:latest \ + --model-path {{ properties['huggingFaceRepoId'] }} + +# Add firewall rule directly in template +- name: allow-http + type: compute.v1.firewall + properties: + network: global/networks/default + sourceRanges: ["0.0.0.0/0"] + targetTags: ["http-server"] + allowed: + - IPProtocol: tcp + ports: ["80"] + +# Outputs section to output public IP and instance details +outputs: +- name: instanceName + value: $(ref.{{ properties['instanceName'] }}.name) + description: Name of the GCP Compute instance. + +- name: instancePublicIP + value: $(ref.{{ properties['instanceName'] }}.networkInterfaces[0].accessConfigs[0].natIP) + description: Public IP address of the GCP Compute instance. diff --git a/tutorials/max-serve-cloud-configs/gcp/notify.sh b/tutorials/max-serve-cloud-configs/gcp/notify.sh new file mode 100644 index 0000000000..89e7262f62 --- /dev/null +++ b/tutorials/max-serve-cloud-configs/gcp/notify.sh @@ -0,0 +1,103 @@ +#!/bin/bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +PROJECT_ID=$1 +INSTANCE_NAME=$2 +ZONE=$3 +PUBLIC_IP=$4 +MAX_WAIT_MINUTES=30 +START_TIME=$(date +%s) + +fetch_logs() { + local instance_id=$1 + local limit=${2:-50} + echo "=== GCP Instance Logs ===" + + echo "Querying logs for instance_id: ${instance_id}" + + gcloud logging read \ + "resource.type=gce_instance AND \ + resource.labels.instance_id=${instance_id} AND \ + jsonPayload.message:*" \ + --project="${PROJECT_ID}" \ + --format="table(timestamp,jsonPayload.message)" \ + --limit=10 + + echo "====================" +} + +check_server_status() { + local logs=$1 + echo "🔍 Checking server health..." + + if echo "$logs" | grep -q "Pulling from docker.modular.com/modular/max-openai-api"; then + echo "⏳ Docker image is still being pulled..." + return 1 + fi + + if curl -s -f "http://${PUBLIC_IP}/v1/health" >/dev/null; then + echo "✅ Server health check passed!" + return 0 + fi + + if echo "$logs" | grep -q "Building\|Compiling\|Starting download of model"; then + echo "⏳ Server is initializing (compiling model)..." + return 1 + fi + + echo "⏳ Server still starting up..." + return 1 +} + +echo "🔍 Starting monitoring for MAX server (max wait: ${MAX_WAIT_MINUTES} minutes)..." + +# Get instance ID +INSTANCE_ID=$(gcloud compute instances describe "${INSTANCE_NAME}" \ + --zone="${ZONE}" \ + --project="${PROJECT_ID}" \ + --format="value(id)") + +if [ -z "$INSTANCE_ID" ]; then + echo "❌ Failed to get instance ID. Please check if instance exists." + exit 1 +fi + +while true; do + current_time=$(date +%s) + elapsed_minutes=$(((current_time - START_TIME) / 60)) + + if [ $elapsed_minutes -ge $MAX_WAIT_MINUTES ]; then + echo "❌ Timeout after ${MAX_WAIT_MINUTES} minutes. Server might still be starting up." + exit 1 + fi + + echo "⏳ Checking logs... (${elapsed_minutes}/${MAX_WAIT_MINUTES} minutes)" + + LOGS=$(fetch_logs "$INSTANCE_ID") + + if [ -n "$LOGS" ]; then + if check_server_status "$LOGS"; then + echo "✅ Server is ready! (took ${elapsed_minutes} minutes)" + echo "📋 Latest logs:" + echo "$LOGS" + exit 0 + fi + else + echo "⏳ Logs not yet available..." + fi + + echo "⏳ Server still starting up... checking again in 60 seconds" + echo "-------------------------------------------" + sleep 60 +done diff --git a/tutorials/run-embeddings-with-max-serve/.gitattributes b/tutorials/run-embeddings-with-max-serve/.gitattributes new file mode 100644 index 0000000000..8f61a8e774 --- /dev/null +++ b/tutorials/run-embeddings-with-max-serve/.gitattributes @@ -0,0 +1,2 @@ +# SCM syntax highlighting +pixi.lock linguist-language=YAML linguist-generated=true diff --git a/tutorials/run-embeddings-with-max-serve/.gitignore b/tutorials/run-embeddings-with-max-serve/.gitignore new file mode 100644 index 0000000000..9279c11d44 --- /dev/null +++ b/tutorials/run-embeddings-with-max-serve/.gitignore @@ -0,0 +1,6 @@ + +# pixi environments +.pixi +*.egg-info +# magic environments +.magic diff --git a/tutorials/run-embeddings-with-max-serve/README.md b/tutorials/run-embeddings-with-max-serve/README.md new file mode 100644 index 0000000000..4fc60548b3 --- /dev/null +++ b/tutorials/run-embeddings-with-max-serve/README.md @@ -0,0 +1,16 @@ +# Embeddings endpoint + +A document management system that uses embeddings and clustering to organize and +search through documents semantically. + +1. Start the embeddings endpoint: + + ```bash + max-pipelines serve --model-path=sentence-transformers/all-mpnet-base-v2 + ``` + +1. Run the system: + + ```bash + python -m embeddings.kb_system + ``` diff --git a/tutorials/run-embeddings-with-max-serve/pyproject.toml b/tutorials/run-embeddings-with-max-serve/pyproject.toml new file mode 100644 index 0000000000..ff683e51a7 --- /dev/null +++ b/tutorials/run-embeddings-with-max-serve/pyproject.toml @@ -0,0 +1,25 @@ +[project] +authors = [{name = "Modular", email = "hello@modular.com"}] +dependencies = [] +description = "Run an embeddings model using the all-mpnet-base-v2 model" +name = "embeddings" +requires-python = ">= 3.11" +version = "0.1.0" + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.pixi.project] +channels = ["https://conda.modular.com/max-nightly", "https://conda.modular.com/max", "https://repo.prefix.dev/modular-community", "conda-forge"] +platforms = ["osx-arm64"] + +[tool.pixi.pypi-dependencies] +embeddings = { path = ".", editable = true } + +[tool.pixi.tasks] + +[tool.pixi.dependencies] +numpy = ">=2.2.2,<3" +scikit-learn = ">=1.6.1,<2" +requests = ">=2.32.3,<3" diff --git a/tutorials/run-embeddings-with-max-serve/src/embeddings/__init__.py b/tutorials/run-embeddings-with-max-serve/src/embeddings/__init__.py new file mode 100644 index 0000000000..75c4f824f2 --- /dev/null +++ b/tutorials/run-embeddings-with-max-serve/src/embeddings/__init__.py @@ -0,0 +1,12 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # diff --git a/tutorials/run-embeddings-with-max-serve/src/embeddings/kb_system.py b/tutorials/run-embeddings-with-max-serve/src/embeddings/kb_system.py new file mode 100644 index 0000000000..5363aec1d1 --- /dev/null +++ b/tutorials/run-embeddings-with-max-serve/src/embeddings/kb_system.py @@ -0,0 +1,196 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import logging +from functools import lru_cache +from typing import Dict, List, Tuple + +import numpy as np +import requests +from sklearn.cluster import KMeans +from sklearn.metrics.pairwise import cosine_similarity + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class SmartKnowledgeBase: + def __init__(self, endpoint: str = "http://localhost:8000/v1/embeddings"): + self.endpoint = endpoint + self.documents: List[str] = [] + self.doc_titles: List[str] = [] + self.embeddings: np.ndarray = None + self.clusters: Dict[int, List[int]] = {} + + def _get_embedding( + self, texts: List[str], max_retries: int = 3 + ) -> np.ndarray: + """Get embeddings with retry logic.""" + for attempt in range(max_retries): + try: + response = requests.post( + self.endpoint, + headers={"Content-Type": "application/json"}, + json={ + "input": texts, + "model": "sentence-transformers/all-mpnet-base-v2", + }, + timeout=5, + ).json() + return np.array( + [item["embedding"] for item in response["data"]] + ) + except Exception as e: + if attempt == max_retries - 1: + raise Exception( + f"Failed to get embeddings after {max_retries} attempts: {e}" + ) + logger.warning(f"Attempt {attempt + 1} failed, retrying...") + + @lru_cache(maxsize=1000) + def _get_embedding_cached(self, text: str) -> np.ndarray: + """Cached version for single text embedding.""" + return self._get_embedding([text])[0] + + def add_document(self, title: str, content: str): + """Add a single document with title.""" + self.doc_titles.append(title) + self.documents.append(content) + + # Update embeddings + if len(self.documents) == 1: + self.embeddings = self._get_embedding([content]) + else: + self.embeddings = np.vstack( + [self.embeddings, self._get_embedding([content])] + ) + + # Recluster if we have enough documents + if len(self.documents) >= 3: + self._cluster_documents() + + def _cluster_documents(self, n_clusters: int = None): + """Cluster documents into topics.""" + if n_clusters is None: + n_clusters = max(2, len(self.documents) // 5) + + n_clusters = min(n_clusters, len(self.documents)) + kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit( + self.embeddings + ) + + self.clusters = {} + for i in range(n_clusters): + self.clusters[i] = np.where(kmeans.labels_ == i)[0].tolist() + + def search( + self, query: str, top_k: int = 3 + ) -> List[Tuple[str, str, float]]: + """Find documents most similar to the query.""" + query_embedding = self._get_embedding_cached(query) + similarities = cosine_similarity([query_embedding], self.embeddings)[0] + top_indices = np.argsort(similarities)[-top_k:][::-1] + return [ + (self.doc_titles[i], self.documents[i], similarities[i]) + for i in top_indices + ] + + def get_topic_documents(self, topic_id: int) -> List[Tuple[str, str]]: + """Get all documents in a topic cluster.""" + return [ + (self.doc_titles[i], self.documents[i]) + for i in self.clusters.get(topic_id, []) + ] + + def suggest_topics( + self, query: str, top_k: int = 2 + ) -> List[Tuple[int, float]]: + query_embedding = self._get_embedding_cached(query) + topic_similarities = [] + + for topic_id, doc_indices in self.clusters.items(): + topic_embeddings = self.embeddings[doc_indices] + similarity = cosine_similarity( + [query_embedding], topic_embeddings + ).max() + topic_similarities.append((topic_id, similarity)) # Remove [0] + + return sorted(topic_similarities, key=lambda x: x[1], reverse=True)[ + :top_k + ] + + +# Example usage +if __name__ == "__main__": + # Initialize knowledge base + kb = SmartKnowledgeBase() + + # Add technical documentation + kb.add_document( + "Password Reset Guide", + "To reset your password: 1. Click 'Forgot Password' 2. Enter your email " + "3. Follow the reset link 4. Create a new password meeting security requirements", + ) + + kb.add_document( + "Account Security", + "Secure your account by enabling 2FA, using a strong password, and regularly " + "monitoring account activity. Enable login notifications for suspicious activity.", + ) + + kb.add_document( + "Billing Overview", + "Your billing cycle starts on the 1st of each month. View charges, update " + "payment methods, and download invoices from the Billing Dashboard.", + ) + + kb.add_document( + "Payment Methods", + "We accept credit cards, PayPal, and bank transfers. Update payment methods " + "in Billing Settings. New payment methods are verified with a $1 hold.", + ) + + kb.add_document( + "Installation Guide", + "Install by downloading the appropriate package for your OS. Run with admin " + "privileges. Follow prompts to select installation directory and components.", + ) + + kb.add_document( + "System Requirements", + "Minimum: 8GB RAM, 2GB storage, Windows 10/macOS 11+. Recommended: 16GB RAM, " + "4GB storage, SSD, modern multi-core processor for optimal performance.", + ) + + # Example 1: Search for password-related help + print("\nSearching for password help:") + results = kb.search("How do I change my password?") + for title, content, score in results: + print(f"\nTitle: {title}") + print(f"Relevance: {score:.2f}") + print(f"Content: {content[:100]}...") + + # Example 2: Get topic suggestions + print("\nGetting topics for billing query:") + query = "Where can I update my credit card?" + topics = kb.suggest_topics(query) + for topic_id, relevance in topics: + print(f"\nTopic {topic_id} (Relevance: {relevance:.2f}):") + for title, content in kb.get_topic_documents(topic_id): + print(f"- {title}: {content[:50]}...") + + # Example 3: Get all documents in a topic + print("\nAll documents in Topic 0:") + for title, content in kb.get_topic_documents(0): + print(f"\nTitle: {title}") + print(f"Content: {content[:100]}...") diff --git a/tutorials/run-onnx-with-python/.gitignore b/tutorials/run-onnx-with-python/.gitignore new file mode 100644 index 0000000000..bf7158d955 --- /dev/null +++ b/tutorials/run-onnx-with-python/.gitignore @@ -0,0 +1,2 @@ +.magic +resnet50.onnx diff --git a/tutorials/run-onnx-with-python/README.md b/tutorials/run-onnx-with-python/README.md new file mode 100644 index 0000000000..a4503b0232 --- /dev/null +++ b/tutorials/run-onnx-with-python/README.md @@ -0,0 +1,21 @@ +# Tutorial: Run an ONNX model with Python + +This is the finished project for the [Run an ONNX model with +Python](https://docs.modular.com/max/tutorials/run-onnx-with-python) tutorial. + +Please refer to that page for more information. + +If you just want to run this code, use these commands (first [install +magic](https://docs.modular.com/magic/#install-magic)): + +```sh +git clone https://github.com/modular/max.git + +cd max/tutorials/run-onnx-with-python + +magic shell + +python3 download-model.py + +python3 run.py +``` diff --git a/tutorials/run-onnx-with-python/download-model.py b/tutorials/run-onnx-with-python/download-model.py new file mode 100644 index 0000000000..286369fc46 --- /dev/null +++ b/tutorials/run-onnx-with-python/download-model.py @@ -0,0 +1,47 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import torch +from torch.onnx import export +from transformers import ResNetForImageClassification + +# The HuggingFace model name and exported file name +HF_MODEL_NAME = "microsoft/resnet-50" +MODEL_PATH = "resnet50.onnx" + + +def main(): + # Load the ResNet model from HuggingFace in evaluation mode + model = ResNetForImageClassification.from_pretrained(HF_MODEL_NAME) + model.eval() + + # Create random input for tracing, then export the model to ONNX + dummy_input = torch.randn(1, 3, 224, 224) + export( + model, + dummy_input, + MODEL_PATH, + opset_version=11, + input_names=["pixel_values"], + output_names=["output"], + dynamic_axes={ + "pixel_values": {0: "batch_size"}, + "output": {0: "batch_size"}, + }, + ) + + print(f"Model saved as {MODEL_PATH}") + + +if __name__ == "__main__": + main() diff --git a/tutorials/run-onnx-with-python/pyproject.toml b/tutorials/run-onnx-with-python/pyproject.toml new file mode 100644 index 0000000000..b754655b7f --- /dev/null +++ b/tutorials/run-onnx-with-python/pyproject.toml @@ -0,0 +1,36 @@ +[project] +dependencies = [] +description = "Tutorial: Run an ONNX model with Python" +name = "onnx-tutorial" +requires-python = ">=3.9,<3.13" +version = "0.1.0" + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.pixi.project] +channels = ["pytorch", "conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["linux-64", "osx-arm64"] + +[tool.pixi.pypi-dependencies] +onnx_tutorial = { path = ".", editable = true } + +[tool.pixi.dependencies] +max = "~=24.6" +pytorch = "==2.4.0" +numpy = "<2.0" +onnx = "==1.16.0" +transformers = "==4.40.1" +datasets = "==2.18" +pillow = "==10.3.0" +libjpeg-turbo = { version = "==3.0.0", channel = "conda-forge" } + +[tool.pixi.feature.test.tasks] +test = "python3 download-model.py && python3 run.py" + +[tool.pixi.environments] +test = { features = ["test"] } + +[tool.pixi.feature.test.dependencies] +python-dateutil = "==2.9.0" diff --git a/tutorials/run-onnx-with-python/run.py b/tutorials/run-onnx-with-python/run.py new file mode 100644 index 0000000000..ef84a1b096 --- /dev/null +++ b/tutorials/run-onnx-with-python/run.py @@ -0,0 +1,50 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import numpy as np +from datasets import load_dataset +from max import engine +from transformers import AutoImageProcessor, AutoModelForImageClassification + +# The HuggingFace model name and exported file name +HF_MODEL_NAME = "microsoft/resnet-50" +MODEL_PATH = "resnet50.onnx" + + +def main(): + dataset = load_dataset("huggingface/cats-image", trust_remote_code=True) + image = dataset["test"]["image"][0] + # optionally, save the image to see it yourself: + # image.save("cat.png") + + image_processor = AutoImageProcessor.from_pretrained(HF_MODEL_NAME) + inputs = image_processor(image, return_tensors="np") + + print("Keys:", inputs.keys()) + print("Shape:", inputs["pixel_values"].shape) + + session = engine.InferenceSession() + model = session.load(MODEL_PATH) + outputs = model.execute_legacy(**inputs) + + print("Output shape:", outputs["output"].shape) + + predicted_label = np.argmax(outputs["output"], axis=-1)[0] + hf_model = AutoModelForImageClassification.from_pretrained(HF_MODEL_NAME) + predicted_class = hf_model.config.id2label[predicted_label] + + print(f"Prediction: {predicted_class}") + + +if __name__ == "__main__": + main() diff --git a/tutorials/run-onnx-with-python/src/onnx_tutorial/__init__.py b/tutorials/run-onnx-with-python/src/onnx_tutorial/__init__.py new file mode 100644 index 0000000000..1d49ac09ac --- /dev/null +++ b/tutorials/run-onnx-with-python/src/onnx_tutorial/__init__.py @@ -0,0 +1,12 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # diff --git a/tutorials/run-torchscript-with-python/.gitignore b/tutorials/run-torchscript-with-python/.gitignore new file mode 100644 index 0000000000..5254ad982c --- /dev/null +++ b/tutorials/run-torchscript-with-python/.gitignore @@ -0,0 +1,2 @@ +.magic +roberta.torchscript diff --git a/tutorials/run-torchscript-with-python/README.md b/tutorials/run-torchscript-with-python/README.md new file mode 100644 index 0000000000..639dc85912 --- /dev/null +++ b/tutorials/run-torchscript-with-python/README.md @@ -0,0 +1,22 @@ +# Tutorial: Run a TorchScript model with Python + +This is the finished project for the [Run a TorchScript model with +Python](https://docs.modular.com/max/tutorials/run-torchscript-with-python) +tutorial. + +Please refer to that page for more information. + +If you just want to run this code, use these commands (first [install +magic](https://docs.modular.com/magic/#install-magic)): + +```sh +git clone https://github.com/modular/max.git + +cd max/tutorials/run-torchscript-with-python + +magic shell + +python3 download-model.py + +python3 run.py +``` diff --git a/tutorials/run-torchscript-with-python/download-model.py b/tutorials/run-torchscript-with-python/download-model.py new file mode 100644 index 0000000000..bcd91ba410 --- /dev/null +++ b/tutorials/run-torchscript-with-python/download-model.py @@ -0,0 +1,43 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from pathlib import Path + +import torch +from transformers import AutoModelForSequenceClassification + +# The HuggingFace model name and exported file name +HF_MODEL_NAME = "cardiffnlp/twitter-roberta-base-emotion-multilabel-latest" +MODEL_PATH = Path("roberta.torchscript") + + +def main(): + # Load the ResNet model from HuggingFace in evaluation mode + hf_model = AutoModelForSequenceClassification.from_pretrained(HF_MODEL_NAME) + + # Convert the model to TorchScript + batch = 1 + seqlen = 128 + input_spec = { + "input_ids": torch.zeros((batch, seqlen), dtype=torch.int64), + "attention_mask": torch.zeros((batch, seqlen), dtype=torch.int64), + } + with torch.no_grad(): + traced_model = torch.jit.trace( + hf_model, example_kwarg_inputs=dict(input_spec), strict=False + ) + torch.jit.save(traced_model, MODEL_PATH) + + +if __name__ == "__main__": + main() diff --git a/tutorials/run-torchscript-with-python/pyproject.toml b/tutorials/run-torchscript-with-python/pyproject.toml new file mode 100644 index 0000000000..f4b1f5772e --- /dev/null +++ b/tutorials/run-torchscript-with-python/pyproject.toml @@ -0,0 +1,31 @@ +[project] +dependencies = [] +description = "Tutorial: Run a TorchScript model with Python" +name = "torchscript-tutorial" +requires-python = ">=3.11,<3.13" +version = "0.1.0" + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.pixi.project] +channels = ["pytorch", "conda-forge", "https://conda.modular.com/max-nightly/"] +platforms = ["linux-64", "osx-arm64"] + +[tool.pixi.pypi-dependencies] +torchscript_tutorial = { path = ".", editable = true } + +[tool.pixi.dependencies] +max = "~=24.6" +pytorch = "==2.4.0" +transformers = "==4.40.1" + +[tool.pixi.feature.test.tasks] +test = "python3 download-model.py && python3 run.py" + +[tool.pixi.environments] +test = { features = ["test"] } + +[tool.pixi.feature.test.dependencies] +python-dateutil = "==2.9.0" diff --git a/tutorials/run-torchscript-with-python/run.py b/tutorials/run-torchscript-with-python/run.py new file mode 100644 index 0000000000..0a9c75b4d3 --- /dev/null +++ b/tutorials/run-torchscript-with-python/run.py @@ -0,0 +1,76 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from pathlib import Path + +import torch +from max import engine +from max.dtype import DType +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +# The HuggingFace model name and TorchScript file name +HF_MODEL_NAME = "cardiffnlp/twitter-roberta-base-emotion-multilabel-latest" +MODEL_PATH = Path("roberta.torchscript") + + +def main(): + batch = 1 + seqlen = 128 + input_spec = { + "input_ids": torch.zeros((batch, seqlen), dtype=torch.int64), + "attention_mask": torch.zeros((batch, seqlen), dtype=torch.int64), + } + + # We use the same `input_spec` that we used to trace the model (download-model.py) + input_spec_list = [ + engine.TorchInputSpec(shape=tensor.size(), dtype=DType.int64) + for tensor in input_spec.values() + ] + + session = engine.InferenceSession() + model = session.load(MODEL_PATH, input_specs=input_spec_list) + + for tensor in model.input_metadata: + print( + f"name: {tensor.name}, shape: {tensor.shape}, dtype: {tensor.dtype}" + ) + + # The model input + text_input = ( + "There are many exciting developments in the field of AI" + " Infrastructure!" + ) + + # Tokenize the input + tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_NAME) + inputs = tokenizer( + text_input, + return_tensors="pt", + padding="max_length", + truncation=True, + max_length=seqlen, + ) + print(inputs) + + outputs = model.execute_legacy(**inputs) + + # Extract class prediction from output + hf_model = AutoModelForSequenceClassification.from_pretrained(HF_MODEL_NAME) + predicted_class_id = outputs["result0"]["logits"].argmax(axis=-1)[0] + classification = hf_model.config.id2label[predicted_class_id] + + print(f"The sentiment is: {classification}") + + +if __name__ == "__main__": + main() diff --git a/tutorials/run-torchscript-with-python/src/torchscript_tutorial/__init__.py b/tutorials/run-torchscript-with-python/src/torchscript_tutorial/__init__.py new file mode 100644 index 0000000000..1d49ac09ac --- /dev/null +++ b/tutorials/run-torchscript-with-python/src/torchscript_tutorial/__init__.py @@ -0,0 +1,12 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # From 44a9f7af205fe50d02fc0dd05ceb5ac4513bd26e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Thomas=20B=C3=B8rstad?= Date: Wed, 26 Feb 2025 19:49:08 +0100 Subject: [PATCH 467/509] [Pipelines] Fix bug in quantized version of LinearV2 (#56733) This PR fixes an issue where quantized Llama would get NaNs at the output. This likely means that there's a difference between `self.quantization_encoding` and `self.weight. self.quantization_encoding`, perhaps the weight's encoding will change at a later time? This perhaps means that there are some deeper problems. This quick fix at least fixes the pipelines logits verification --------- Co-authored-by: Kathy Wu MODULAR_ORIG_COMMIT_REV_ID: 74f1da31cc9f3433714a025ab4a613171545d559 --- .../architectures/llama3/naive_llama3.py | 4 +++- .../nn/attention/attention_with_rope.py | 2 +- .../nn/attention/naive_attention_with_rope.py | 17 ----------------- src/max/pipelines/nn/{attention => }/clamp.py | 0 src/max/pipelines/nn/linear.py | 13 +++++++++---- 5 files changed, 13 insertions(+), 23 deletions(-) rename src/max/pipelines/nn/{attention => }/clamp.py (100%) diff --git a/src/max/pipelines/architectures/llama3/naive_llama3.py b/src/max/pipelines/architectures/llama3/naive_llama3.py index c79badcbde..d171f926cb 100644 --- a/src/max/pipelines/architectures/llama3/naive_llama3.py +++ b/src/max/pipelines/architectures/llama3/naive_llama3.py @@ -219,6 +219,7 @@ def __init__( dtype=dtype, device=device, quantization_encoding=quantization_encoding, + clip_weight=clip_qkv, ), wv=linear_cls( in_dim=hidden_size, @@ -226,6 +227,7 @@ def __init__( dtype=dtype, device=device, quantization_encoding=quantization_encoding, + clip_weight=clip_qkv, ), wq=linear_cls( in_dim=hidden_size, @@ -233,6 +235,7 @@ def __init__( dtype=dtype, device=device, quantization_encoding=quantization_encoding, + clip_weight=clip_qkv, ), wo=linear_cls( in_dim=hidden_size, @@ -243,7 +246,6 @@ def __init__( ), rope=rope, scale=scale, - clip_qkv=clip_qkv, ) diff --git a/src/max/pipelines/nn/attention/attention_with_rope.py b/src/max/pipelines/nn/attention/attention_with_rope.py index 897a14d068..a9d4c70fb8 100644 --- a/src/max/pipelines/nn/attention/attention_with_rope.py +++ b/src/max/pipelines/nn/attention/attention_with_rope.py @@ -33,6 +33,7 @@ PagedKVCacheCollection, ) +from ..clamp import clamp from ..kernels import ( MHAMaskVariant, flash_attention_ragged, @@ -43,7 +44,6 @@ from ..layer import LayerV2 from ..linear import LinearV2 from ..rotary_embedding import OptimizedRotaryEmbedding -from .clamp import clamp from .interfaces import ( AttentionImpl, AttentionImplQKV, diff --git a/src/max/pipelines/nn/attention/naive_attention_with_rope.py b/src/max/pipelines/nn/attention/naive_attention_with_rope.py index 3f136f167c..8900378460 100644 --- a/src/max/pipelines/nn/attention/naive_attention_with_rope.py +++ b/src/max/pipelines/nn/attention/naive_attention_with_rope.py @@ -23,7 +23,6 @@ from ..layer import LayerV2 from ..linear import Linear, LinearV2 from ..rotary_embedding import RotaryEmbedding -from .clamp import clamp class NaiveAttentionWithRope(LayerV2): @@ -38,7 +37,6 @@ def __init__( wo: Linear | LinearV2, rope: RotaryEmbedding, scale: float | None = None, - clip_qkv: float | None = None, ): super().__init__() self.n_heads = n_heads @@ -52,7 +50,6 @@ def __init__( self.scale = ( scale if scale else math.sqrt(1.0 / self.kv_params.head_dim) ) - self.clip_qkv = clip_qkv if self.kv_params.cache_strategy.uses_opaque(): raise ValueError( @@ -143,20 +140,6 @@ def __call__( """ x = TensorValue(x) batch, seq_len = x.shape[0], x.shape[1] - # Apply `clip_qkv` before matmuling the weights - if self.clip_qkv: - assert isinstance(self.q_proj.weight, TensorValue) - self.q_proj.weight = clamp( - self.q_proj.weight, min=-self.clip_qkv, max=self.clip_qkv - ) - assert isinstance(self.k_proj.weight, TensorValue) - self.k_proj.weight = clamp( - self.k_proj.weight, min=-self.clip_qkv, max=self.clip_qkv - ) - assert isinstance(self.v_proj.weight, TensorValue) - self.v_proj.weight = clamp( - self.v_proj.weight, min=-self.clip_qkv, max=self.clip_qkv - ) xq = self.q_proj(x) xk = self.k_proj(x) diff --git a/src/max/pipelines/nn/attention/clamp.py b/src/max/pipelines/nn/clamp.py similarity index 100% rename from src/max/pipelines/nn/attention/clamp.py rename to src/max/pipelines/nn/clamp.py diff --git a/src/max/pipelines/nn/linear.py b/src/max/pipelines/nn/linear.py index dd6a625af0..c09c5e8d74 100644 --- a/src/max/pipelines/nn/linear.py +++ b/src/max/pipelines/nn/linear.py @@ -31,6 +31,7 @@ from max.graph.quantization import QuantizationConfig, QuantizationEncoding from max.graph.weights import Weights +from .clamp import clamp from .kernels import swish_glu from .layer import Layer, LayerV2 @@ -62,7 +63,7 @@ class LinearV2(LayerV2): output = linear_layer(input_tensor) """ - weight: Weight | TensorValue + weight: Weight """The weight matrix stored on CPU with shape (out_dim, in_dim). Model init transposes the weight and moves it to :obj:`device`.""" @@ -82,6 +83,7 @@ def __init__( has_bias: bool = False, quantization_encoding: Optional[QuantizationEncoding] = None, name: Optional[str] = None, + clip_weight: Optional[float] = None, ) -> None: """Initializes the linear layer with weights and optional bias. @@ -99,6 +101,7 @@ def __init__( super().__init__() self.device = device + self.clip_weight = clip_weight self.weight = Weight( name=f"{name}.weight" if name else "weight", @@ -107,7 +110,6 @@ def __init__( device=DeviceRef.CPU() if self.device else None, quantization_encoding=quantization_encoding, ) - self.quantization_encoding = quantization_encoding if has_bias: self.bias = Weight( @@ -137,9 +139,12 @@ def __call__(self, x: TensorValue) -> TensorValue: if self.device: weight = weight.to(self.device) - if self.quantization_encoding: + if self.clip_weight: + weight = clamp(weight, -self.clip_weight, self.clip_weight) + + if self.weight.quantization_encoding: res = ops.qmatmul( - self.quantization_encoding, + self.weight.quantization_encoding, None, x, weight, From bdd80f93d705e29766bee4c0e503338e132efa99 Mon Sep 17 00:00:00 2001 From: kcaverly Date: Wed, 26 Feb 2025 15:44:13 -0500 Subject: [PATCH 468/509] [Pipelines] Harden TextResponse and next_token interface This puts explicit types on next token TextResponse objects, strengthening the interface for downstream changes. MODULAR_ORIG_COMMIT_REV_ID: 7cd459270f39d0813e421ed9387674901a7b2a69 --- src/max/pipelines/__init__.py | 4 +++- src/max/pipelines/embeddings_pipeline.py | 3 +-- src/max/pipelines/hf_pipeline.py | 14 +++++++++----- src/max/pipelines/interfaces/__init__.py | 4 ++++ src/max/pipelines/{ => interfaces}/response.py | 0 src/max/pipelines/interfaces/text_generation.py | 6 ++++-- src/max/pipelines/pipeline.py | 8 +++----- 7 files changed, 24 insertions(+), 15 deletions(-) rename src/max/pipelines/{ => interfaces}/response.py (100%) diff --git a/src/max/pipelines/__init__.py b/src/max/pipelines/__init__.py index 3020fb1d21..33c02c405a 100644 --- a/src/max/pipelines/__init__.py +++ b/src/max/pipelines/__init__.py @@ -29,8 +29,11 @@ from .hf_utils import HuggingFaceFile from .interfaces import ( EmbeddingsGenerator, + EmbeddingsResponse, + LogProbabilities, PipelineTask, PipelineTokenizer, + TextResponse, TokenGenerator, TokenGeneratorContext, TokenGeneratorRequest, @@ -47,7 +50,6 @@ upper_bounded_default, ) from .registry import PIPELINE_REGISTRY, SupportedArchitecture -from .response import EmbeddingsResponse, LogProbabilities, TextResponse from .tokenizer import ( IdentityPipelineTokenizer, PreTrainedPipelineTokenizer, diff --git a/src/max/pipelines/embeddings_pipeline.py b/src/max/pipelines/embeddings_pipeline.py index 85ed8932ec..fa609ef7f1 100644 --- a/src/max/pipelines/embeddings_pipeline.py +++ b/src/max/pipelines/embeddings_pipeline.py @@ -22,9 +22,8 @@ from .config import PipelineConfig from .context import InputContext -from .interfaces import EmbeddingsGenerator +from .interfaces import EmbeddingsGenerator, EmbeddingsResponse from .pipeline import PipelineModel -from .response import EmbeddingsResponse T = TypeVar("T", bound=InputContext) diff --git a/src/max/pipelines/hf_pipeline.py b/src/max/pipelines/hf_pipeline.py index 0d03506701..52f25f6985 100644 --- a/src/max/pipelines/hf_pipeline.py +++ b/src/max/pipelines/hf_pipeline.py @@ -17,7 +17,7 @@ import logging import warnings -from typing import Any, Optional, cast +from typing import Optional, cast import numpy as np import torch @@ -31,9 +31,13 @@ from .config import PipelineConfig from .context import TextContext -from .interfaces import EmbeddingsGenerator, TokenGenerator +from .interfaces import ( + EmbeddingsGenerator, + EmbeddingsResponse, + TextResponse, + TokenGenerator, +) from .kv_cache import ContinuousHFStaticCache -from .response import EmbeddingsResponse, TextResponse logger = logging.getLogger("max.pipelines") @@ -179,7 +183,7 @@ def next_token( generated_tokens = generated_tokens.cpu() # Prepare the response, pruning away completed requests as we go. - res: list[dict[str, Any]] = [{} for i in range(num_steps)] + res: list[dict[str, TextResponse]] = [{} for i in range(num_steps)] for batch_idx, (request_id, context) in enumerate(batch.items()): for step in range(num_steps): next_token_id = generated_tokens[batch_idx, step].item() @@ -362,7 +366,7 @@ def encode( ) # Prepare the response. - res: dict[str, Any] = {} + res: dict[str, EmbeddingsResponse] = {} for batch_index, request_id in enumerate(batch.keys()): request_embeddings = batch_embeddings[batch_index] res[request_id] = EmbeddingsResponse(request_embeddings) diff --git a/src/max/pipelines/interfaces/__init__.py b/src/max/pipelines/interfaces/__init__.py index 9233e609e6..5e0b009eea 100644 --- a/src/max/pipelines/interfaces/__init__.py +++ b/src/max/pipelines/interfaces/__init__.py @@ -16,6 +16,7 @@ from .embeddings_generation import ( EmbeddingsGenerator, ) +from .response import EmbeddingsResponse, LogProbabilities, TextResponse from .tasks import PipelineTask from .text_generation import ( PipelineTokenizer, @@ -29,8 +30,11 @@ ) __all__ = [ + "EmbeddingsResponse", + "LogProbabilities", "PipelineTask", "PipelineTokenizer", + "TextResponse", "TokenGenerator", "TokenGeneratorContext", "TokenGeneratorRequest", diff --git a/src/max/pipelines/response.py b/src/max/pipelines/interfaces/response.py similarity index 100% rename from src/max/pipelines/response.py rename to src/max/pipelines/interfaces/response.py diff --git a/src/max/pipelines/interfaces/text_generation.py b/src/max/pipelines/interfaces/text_generation.py index c2a250a208..ac7b64d1c7 100644 --- a/src/max/pipelines/interfaces/text_generation.py +++ b/src/max/pipelines/interfaces/text_generation.py @@ -28,6 +28,8 @@ runtime_checkable, ) +from .response import TextResponse + class TokenGeneratorRequestFunction(TypedDict): name: str @@ -256,7 +258,7 @@ class TokenGenerator(Generic[TokenGeneratorContext], Protocol): def next_token( self, batch: dict[str, TokenGeneratorContext], num_steps: int - ) -> list[dict[str, Any]]: + ) -> list[dict[str, TextResponse]]: """Computes the next token response for a single batch. Args: @@ -264,7 +266,7 @@ def next_token( num_steps int: Number of tokens to generate. Returns: - list[dict[str, Any]]: List of encoded responses (indexed by req. ID) + list[dict[str, TextResponse]]: List of encoded responses (indexed by req. ID) """ ... diff --git a/src/max/pipelines/pipeline.py b/src/max/pipelines/pipeline.py index 822602fb07..054d6f66c8 100644 --- a/src/max/pipelines/pipeline.py +++ b/src/max/pipelines/pipeline.py @@ -19,7 +19,6 @@ from abc import ABC, abstractmethod from dataclasses import dataclass from typing import ( - Any, Generic, List, Optional, @@ -44,9 +43,8 @@ from .config import PipelineConfig from .context import InputContext -from .interfaces import TokenGenerator +from .interfaces import LogProbabilities, TextResponse, TokenGenerator from .kv_cache import KVCacheManager, KVCacheParams -from .response import LogProbabilities, TextResponse from .sampling import token_sampler try: @@ -563,7 +561,7 @@ def next_token( self, batch: dict[str, T], num_steps: int, - ) -> list[dict[str, Any]]: + ) -> list[dict[str, TextResponse]]: """Provided a batch, process batch inputs, execute the graph for num_steps in a multi-step scenario, then decode the tokens holistically and return the list of decoded tokens. """ @@ -687,7 +685,7 @@ def next_token( tracer.pop() # pops kv_manager.step # Prepare the response, pruning away completed requests as we go. - res: list[dict[str, Any]] = [{} for _ in range(num_steps)] + res: list[dict[str, TextResponse]] = [{} for _ in range(num_steps)] tracer.push("prepare_response") for batch_index, (request_id, context) in enumerate(batch.items()): step = 0 From 39857d9afab81ff481b63e0b5d7615e824bc34e2 Mon Sep 17 00:00:00 2001 From: kcaverly Date: Wed, 26 Feb 2025 16:27:12 -0500 Subject: [PATCH 469/509] [Pipelines] Introduce TextGenerationResponse and TextGenerationStatus These objects will be used in the following PR to harden completion tracking and enable variable length token outputs. MODULAR_ORIG_COMMIT_REV_ID: c8691328e36643a38b43217b338f062bbf1400d0 --- src/max/pipelines/interfaces/__init__.py | 10 +++++++++- src/max/pipelines/interfaces/response.py | 25 ++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/src/max/pipelines/interfaces/__init__.py b/src/max/pipelines/interfaces/__init__.py index 5e0b009eea..804d0d8d6c 100644 --- a/src/max/pipelines/interfaces/__init__.py +++ b/src/max/pipelines/interfaces/__init__.py @@ -16,7 +16,13 @@ from .embeddings_generation import ( EmbeddingsGenerator, ) -from .response import EmbeddingsResponse, LogProbabilities, TextResponse +from .response import ( + EmbeddingsResponse, + LogProbabilities, + TextGenerationResponse, + TextGenerationStatus, + TextResponse, +) from .tasks import PipelineTask from .text_generation import ( PipelineTokenizer, @@ -43,4 +49,6 @@ "TokenGeneratorRequestTool", "TokenGeneratorResponseFormat", "EmbeddingsGenerator", + "TextGenerationResponse", + "TextGenerationStatus", ] diff --git a/src/max/pipelines/interfaces/response.py b/src/max/pipelines/interfaces/response.py index 6154570afc..45808c9d72 100644 --- a/src/max/pipelines/interfaces/response.py +++ b/src/max/pipelines/interfaces/response.py @@ -16,6 +16,7 @@ from __future__ import annotations from dataclasses import dataclass +from enum import Enum import numpy as np @@ -100,3 +101,27 @@ class EmbeddingsResponse: """Container for the response from embeddings pipeline.""" embeddings: np.ndarray + + +class TextGenerationStatus(str, Enum): + ACTIVE = "active" + END_OF_SEQUENCE = "end_of_sequence" + MAXIMUM_LENGTH = "maximum_length" + + @property + def is_done(self) -> bool: + return self is not TextGenerationStatus.ACTIVE + + +class TextGenerationResponse: + def __init__( + self, tokens: list[TextResponse], final_status: TextGenerationStatus + ) -> None: + self.tokens = tokens + self.final_status = final_status + + def is_done(self) -> bool: + return self.final_status.is_done + + def append_token(self, token: TextResponse) -> None: + self.tokens.append(token) From 5f1d2d298b13ddd347c7da39aa9d498597e409f9 Mon Sep 17 00:00:00 2001 From: Frank <139076616+frspokan@users.noreply.github.com> Date: Wed, 26 Feb 2025 14:52:29 -0800 Subject: [PATCH 470/509] [Serve] Prevent busted version of OTEL prometheus pkg from installing (#56291) They accidentally published a broken package 1.12.0rc1 which is getting installed in the serving container (but not Bazel). The latest good version is 0.51b0. MODULAR_ORIG_COMMIT_REV_ID: 412ae88ccea58f10c4fe0648484ccc4a8f84882b --- src/max/pipelines/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/max/pipelines/requirements.txt b/src/max/pipelines/requirements.txt index 708e101b5b..dc050dddf6 100644 --- a/src/max/pipelines/requirements.txt +++ b/src/max/pipelines/requirements.txt @@ -22,7 +22,7 @@ httpx>=0.28.1,<0.29 huggingface-hub>=0.24.0 opentelemetry-api>=1.29.0 opentelemetry-exporter-otlp-proto-http>=1.27.0 -opentelemetry-exporter-prometheus>=0.48b0 +opentelemetry-exporter-prometheus<1.0.0 opentelemetry-sdk>=1.29.0 pillow>=10.3.0 prometheus-async>=22.2.0 From efe686e8a1d587352dda1ac93e5bcc08f825c2dd Mon Sep 17 00:00:00 2001 From: Brendan Duke Date: Wed, 26 Feb 2025 18:51:38 -0500 Subject: [PATCH 471/509] [SDK] Add a Signals.buffers method Add a `Signals.buffers` method as a convenient way of passing runtime signal buffers (as driver tensors). MODULAR_ORIG_COMMIT_REV_ID: 2c6f6fcf46a31b40a0de921781ae1db2a3ecf3c1 --- src/max/pipelines/nn/comm/allreduce.py | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/src/max/pipelines/nn/comm/allreduce.py b/src/max/pipelines/nn/comm/allreduce.py index 3ab64efaa0..40d7838049 100644 --- a/src/max/pipelines/nn/comm/allreduce.py +++ b/src/max/pipelines/nn/comm/allreduce.py @@ -14,8 +14,9 @@ from collections.abc import Iterable +from max.driver import Accelerator, Tensor from max.dtype import DType -from max.graph import BufferType, DeviceRef +from max.graph import BufferType, DeviceKind, DeviceRef class Signals: @@ -32,14 +33,31 @@ class Signals: # Mojo struct + the size of the intermediate buffer for communication. devices: list[DeviceRef] - """List of devices that these signals communicate between.""" + """List of graph devices that these signals communicate between.""" def __init__(self, devices: Iterable[DeviceRef]) -> None: """ Args: - devices: Devices between which these signals communicate. + num_gpus: Number of GPUs involved in the allreduce. """ - self.devices = list(devices) + # Convert the iterable to a list since we iterate over it twice. + devices = list(devices) + if not all(dev.device_type == DeviceKind.GPU for dev in devices): + msg = "peer-to-peer signals should be constructed for accelerators" + raise TypeError(msg) + + self.devices = devices + + def buffers(self) -> list[Tensor]: + """Allocates and returns buffers used for communication in allreduce.""" + return [ + Tensor.zeros( + shape=(Signals.NUM_BYTES,), + dtype=DType.uint8, + device=Accelerator(id=dev.id), + ) + for dev in self.devices + ] def input_types(self) -> list[BufferType]: """Gets graph input types corresponding to these signal buffers.""" From f004ee3153e0276c4c216be8f2426eedb25c6886 Mon Sep 17 00:00:00 2001 From: Zheng Hao Tan Date: Wed, 26 Feb 2025 16:42:03 -0800 Subject: [PATCH 472/509] [Pipelines][KV Cache] Wrap KVCacheInputs under ModelInputs This tucks all `ModelInputs` into one coherent object and can be passed into an `.execute()` function like `model_outputs = execute(model_inputs)` MODULAR_ORIG_COMMIT_REV_ID: bea806b8c14c77d5548d1e9df2002d03138829a3 --- .../pipelines/architectures/llama3/model.py | 31 +++++++++---- .../llama_vision/llama_vision.py | 36 +++++++++++---- .../pipelines/architectures/mistral/model.py | 15 +++--- .../pipelines/architectures/mpnet/model.py | 10 ++-- .../architectures/pixtral/pixtral.py | 12 +++-- .../pipelines/architectures/qwen2/model.py | 29 ++++++++---- .../pipelines/architectures/replit/model.py | 17 +++++-- src/max/pipelines/embeddings_pipeline.py | 8 ++-- src/max/pipelines/pipeline.py | 46 +++++++++++-------- 9 files changed, 136 insertions(+), 68 deletions(-) diff --git a/src/max/pipelines/architectures/llama3/model.py b/src/max/pipelines/architectures/llama3/model.py index eeff3afa16..deb5de0c32 100644 --- a/src/max/pipelines/architectures/llama3/model.py +++ b/src/max/pipelines/architectures/llama3/model.py @@ -77,6 +77,7 @@ def __init__( tokens: np.ndarray | Tensor, input_row_offsets_or_attn_mask: np.ndarray | Tensor, signal_buffers: list[Tensor], + kv_cache_inputs: KVCacheInputs | None = None, ) -> None: """ Args: @@ -89,6 +90,7 @@ def __init__( self.tokens = tokens self.input_row_offsets_or_attn_mask = input_row_offsets_or_attn_mask self.signal_buffers = signal_buffers + self.kv_cache_inputs = kv_cache_inputs @property def input_row_offsets(self) -> np.ndarray | Tensor: @@ -164,16 +166,14 @@ def get_num_layers(cls, pipeline_config: PipelineConfig) -> int: def execute( self, model_inputs: ModelInputs, - kv_cache_inputs: KVCacheInputs | None = None, ) -> ModelOutputs: model_inputs = cast(Llama3Inputs, model_inputs) - if kv_cache_inputs is None: - kv_cache_inputs = () # type: ignore + curr_kv_cache_inputs = model_inputs.kv_cache_inputs or () model_outputs = self.model.execute( model_inputs.tokens, model_inputs.input_row_offsets_or_attn_mask, *model_inputs.signal_buffers, - *kv_cache_inputs, # type: ignore + *curr_kv_cache_inputs, copy_inputs_to_device=( not self.pipeline_config.cache_strategy.uses_opaque() ), @@ -190,7 +190,9 @@ def execute( ) def _prepare_ragged_initial_token_inputs( - self, context_batch: Sequence[TextContext] + self, + context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> Llama3Inputs: # Get input_row_offsets: start and end position of each batch in the # combined total_seq_len dimension. @@ -210,10 +212,13 @@ def _prepare_ragged_initial_token_inputs( input_row_offsets ).to(self.pipeline_config.devices[0]), signal_buffers=self.signal_buffers, + kv_cache_inputs=kv_cache_inputs, ) def _prepare_padded_initial_token_inputs( - self, context_batch: Sequence[TextContext] + self, + context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> Llama3Inputs: # Get tokens and seq_ids tokens = [ctx.next_tokens for ctx in context_batch] @@ -231,16 +236,23 @@ def _prepare_padded_initial_token_inputs( tokens=next_tokens_batch, input_row_offsets_or_attn_mask=attn_mask, signal_buffers=self.signal_buffers, + kv_cache_inputs=kv_cache_inputs, ) def prepare_initial_token_inputs( - self, context_batch: Sequence[TextContext] + self, + context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> Llama3Inputs: """Prepare the inputs for the first pass in multistep execution.""" if self.pipeline_config.cache_strategy.uses_opaque(): - return self._prepare_ragged_initial_token_inputs(context_batch) + return self._prepare_ragged_initial_token_inputs( + context_batch, kv_cache_inputs + ) else: - return self._prepare_padded_initial_token_inputs(context_batch) + return self._prepare_padded_initial_token_inputs( + context_batch, kv_cache_inputs + ) def _prepare_ragged_next_token_inputs( self, @@ -256,6 +268,7 @@ def _prepare_ragged_next_token_inputs( tokens=next_tokens, input_row_offsets_or_attn_mask=next_row_offsets, signal_buffers=self.signal_buffers, + kv_cache_inputs=prev_model_inputs.kv_cache_inputs, ) def prepare_next_token_inputs( diff --git a/src/max/pipelines/architectures/llama_vision/llama_vision.py b/src/max/pipelines/architectures/llama_vision/llama_vision.py index 3dc23904eb..accd5f919a 100644 --- a/src/max/pipelines/architectures/llama_vision/llama_vision.py +++ b/src/max/pipelines/architectures/llama_vision/llama_vision.py @@ -433,6 +433,7 @@ def __init__( pixel_values: Tensor | None = None, aspect_ratio_ids: Tensor | None = None, aspect_ratio_mask: Tensor | None = None, + kv_cache_inputs: KVCacheInputs | None = None, ): self.input_id_values = input_id_values self.input_row_offsets = input_row_offsets @@ -441,6 +442,7 @@ def __init__( self._pixel_values = pixel_values self._aspect_ratio_ids = aspect_ratio_ids self._aspect_ratio_mask = aspect_ratio_mask + self.kv_cache_inputs = kv_cache_inputs def __post_init__(self) -> None: """Validate consistency between vision fields. @@ -486,7 +488,10 @@ def aspect_ratio_mask(self) -> Tensor: return self._aspect_ratio_mask def update_for_next_token( - self, next_tokens: Tensor, next_row_offsets: Tensor + self, + next_tokens: Tensor, + next_row_offsets: Tensor, + kv_cache_inputs: KVCacheInputs | None = None, ) -> LlamaVisionInputs: """Updates next_tokens and row_offsets after an initial step.""" return LlamaVisionInputs( @@ -498,6 +503,7 @@ def update_for_next_token( pixel_values=None, aspect_ratio_ids=None, aspect_ratio_mask=None, + kv_cache_inputs=kv_cache_inputs, ) @@ -879,6 +885,7 @@ def _prepare_vision_inputs( def prepare_initial_token_inputs( self, context_batch: Sequence[TextAndVisionContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> LlamaVisionInputs: """Creates tensors of token and image inputs, if applicable.""" if self.pipeline_config.cache_strategy != KVCacheStrategy.CONTINUOUS: @@ -963,6 +970,7 @@ def initial_prompt_missing_image(ctx: TextAndVisionContext) -> bool: pixel_values=pixel_values, aspect_ratio_ids=aspect_ratio_ids, aspect_ratio_mask=aspect_ratio_mask, + kv_cache_inputs=kv_cache_inputs, ) def prepare_next_token_inputs( @@ -980,15 +988,17 @@ def prepare_next_token_inputs( : prev_inputs.input_row_offsets.shape[0] ] - return prev_inputs.update_for_next_token(next_tokens, next_row_offsets) + return prev_inputs.update_for_next_token( + next_tokens, next_row_offsets, prev_inputs.kv_cache_inputs + ) def execute( self, model_inputs: ModelInputs, - # TODO(zheng): This should be folded as KVCacheInputs into ModelInputs. - kv_cache_inputs: Optional[KVCacheInputs] = None, ) -> ModelOutputs: - assert kv_cache_inputs is not None + assert model_inputs.kv_cache_inputs is not None, ( + "Llama Vision has KV cache inputs" + ) # batch_size * num_concurrent_media * max_num_tiles * num_patches # are set to 0 here to imitate a dummy tensor (used in text-only mode). cross_attention_states = Tensor.zeros( @@ -1009,11 +1019,19 @@ def execute( cross_attention_states = exec_result all_kv_cache_inputs: list[Tensor] = [] - if isinstance(kv_cache_inputs, MultimodalKVCacheInputs): - all_kv_cache_inputs.extend(kv_cache_inputs.text_kv_cache_inputs) - all_kv_cache_inputs.extend(kv_cache_inputs.vision_kv_cache_inputs) + if isinstance(model_inputs.kv_cache_inputs, MultimodalKVCacheInputs): + all_kv_cache_inputs.extend( + model_inputs.kv_cache_inputs.text_kv_cache_inputs + ) + all_kv_cache_inputs.extend( + model_inputs.kv_cache_inputs.vision_kv_cache_inputs + ) + elif isinstance(model_inputs.kv_cache_inputs, KVCacheInputs): + all_kv_cache_inputs = list(model_inputs.kv_cache_inputs) else: - all_kv_cache_inputs = list(kv_cache_inputs) + raise ValueError( + f"Unsupported kv_cache_inputs type: {type(model_inputs.kv_cache_inputs)}" + ) model_outputs = self.language_model.execute( cross_attention_states, diff --git a/src/max/pipelines/architectures/mistral/model.py b/src/max/pipelines/architectures/mistral/model.py index 69ba410ffa..357cd5b132 100644 --- a/src/max/pipelines/architectures/mistral/model.py +++ b/src/max/pipelines/architectures/mistral/model.py @@ -57,9 +57,11 @@ def __init__( self, input_tokens: Tensor, input_row_offsets: Tensor, + kv_cache_inputs: KVCacheInputs | None = None, ) -> None: self.input_tokens = input_tokens self.input_row_offsets = input_row_offsets + self.kv_cache_inputs = kv_cache_inputs class MistralModel(PipelineModel[TextContext]): @@ -69,20 +71,16 @@ def __init__( super().__init__(pipeline_config, session) self.model = self.load_model(session) - def execute( - self, - model_inputs: ModelInputs, - kv_cache_inputs: KVCacheInputs | None = None, - ) -> ModelOutputs: + def execute(self, model_inputs: ModelInputs) -> ModelOutputs: """Runs the graph.""" model_inputs = cast(MistralInputs, model_inputs) - assert kv_cache_inputs is not None, ( + assert model_inputs.kv_cache_inputs is not None, ( "Mistral has KV cache inputs, but none were provided" ) model_outputs = self.model.execute( model_inputs.input_tokens, model_inputs.input_row_offsets, - *kv_cache_inputs, + *model_inputs.kv_cache_inputs, copy_inputs_to_device=False, ) assert isinstance(model_outputs[0], Tensor) @@ -91,6 +89,7 @@ def execute( def prepare_initial_token_inputs( self, context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> MistralInputs: # Get tokens and seq ids tokens = [ctx.next_tokens for ctx in context_batch] @@ -113,6 +112,7 @@ def prepare_initial_token_inputs( return MistralInputs( input_tokens=next_tokens_batch, input_row_offsets=input_row_offsets, + kv_cache_inputs=kv_cache_inputs, ) def prepare_next_token_inputs( @@ -126,6 +126,7 @@ def prepare_next_token_inputs( return MistralInputs( input_tokens=next_tokens, input_row_offsets=next_row_offsets, + kv_cache_inputs=prev_model_inputs.kv_cache_inputs, ) @classmethod diff --git a/src/max/pipelines/architectures/mpnet/model.py b/src/max/pipelines/architectures/mpnet/model.py index 72180fe367..7a1df30d8f 100644 --- a/src/max/pipelines/architectures/mpnet/model.py +++ b/src/max/pipelines/architectures/mpnet/model.py @@ -61,6 +61,8 @@ def __init__( ) -> None: self.next_tokens_batch = next_tokens_batch self.attention_mask = attention_mask + # MPNet does not have KV cache inputs. + self.kv_cache_inputs = None class MPNetPipelineModel(PipelineModel[TextContext]): @@ -103,13 +105,8 @@ def calculate_max_seq_len(cls, pipeline_config: PipelineConfig) -> int: ) raise ValueError(msg) from e - def execute( - self, - model_inputs: ModelInputs, - kv_cache_inputs: KVCacheInputs | None = None, - ) -> ModelOutputs: + def execute(self, model_inputs: ModelInputs) -> ModelOutputs: model_inputs = cast(MPNetInputs, model_inputs) - assert kv_cache_inputs is None, "MPNet does not have KV cache inputs" model_outputs = self.model.execute( model_inputs.next_tokens_batch, model_inputs.attention_mask, @@ -121,6 +118,7 @@ def execute( def prepare_initial_token_inputs( self, context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> MPNetInputs: # Get tokens and seq_ids. tokens = [ctx.next_tokens for ctx in context_batch] diff --git a/src/max/pipelines/architectures/pixtral/pixtral.py b/src/max/pipelines/architectures/pixtral/pixtral.py index 31f4e13682..95102c3d89 100644 --- a/src/max/pipelines/architectures/pixtral/pixtral.py +++ b/src/max/pipelines/architectures/pixtral/pixtral.py @@ -61,11 +61,13 @@ def __init__( input_row_offsets: Tensor, pixel_values: Tensor | None = None, attention_mask: Tensor | None = None, + kv_cache_inputs: KVCacheInputs | None = None, ): self.input_ids = input_ids self.input_row_offsets = input_row_offsets self._pixel_values = pixel_values self._attention_mask = attention_mask + self.kv_cache_inputs = kv_cache_inputs @property def has_vision_inputs(self) -> bool: @@ -99,8 +101,6 @@ def __init__( def execute( self, model_inputs: ModelInputs, - # TODO(zheng): This should be folded as KVCacheInputs into ModelInputs. - kv_cache_inputs: KVCacheInputs | None = None, ) -> ModelOutputs: model_inputs = cast(PixtralInputs, model_inputs) if model_inputs.has_vision_inputs: @@ -119,14 +119,14 @@ def execute( ], dtype=self.pipeline_config.dtype, ).to(self.pipeline_config.devices[0]) - assert kv_cache_inputs is not None, ( + assert model_inputs.kv_cache_inputs is not None, ( "Pixtral has KV cache inputs, but none were provided" ) model_outputs = self.language_model.execute( model_inputs.input_ids, image_embeds, model_inputs.input_row_offsets, - *kv_cache_inputs, + *model_inputs.kv_cache_inputs, copy_inputs_to_device=False, ) assert not self.pipeline_config.enable_echo @@ -136,6 +136,7 @@ def execute( def prepare_initial_token_inputs( self, context_batch: Sequence[TextAndVisionContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> PixtralInputs: # Input row offset type: ["input_row_offsets_len"], UInt32 input_row_offsets = Tensor.from_numpy( @@ -182,11 +183,13 @@ def prepare_initial_token_inputs( input_row_offsets=input_row_offsets, pixel_values=pixel_values, attention_mask=attention_mask, + kv_cache_inputs=kv_cache_inputs, ) return PixtralInputs( input_ids=input_ids, input_row_offsets=input_row_offsets, + kv_cache_inputs=kv_cache_inputs, ) def prepare_next_token_inputs( @@ -204,6 +207,7 @@ def prepare_next_token_inputs( return PixtralInputs( input_ids=next_tokens, input_row_offsets=next_row_offsets, + kv_cache_inputs=prev_model_inputs.kv_cache_inputs, ) @classmethod diff --git a/src/max/pipelines/architectures/qwen2/model.py b/src/max/pipelines/architectures/qwen2/model.py index 526234d031..3d26a468d1 100644 --- a/src/max/pipelines/architectures/qwen2/model.py +++ b/src/max/pipelines/architectures/qwen2/model.py @@ -62,9 +62,11 @@ def __init__( self, tokens: Tensor, input_row_offsets_or_attn_mask: Tensor, + kv_cache_inputs: KVCacheInputs | None = None, ) -> None: self.tokens = tokens self.input_row_offsets_or_attn_mask = input_row_offsets_or_attn_mask + self.kv_cache_inputs = kv_cache_inputs @property def input_row_offsets(self) -> Tensor: @@ -83,15 +85,13 @@ def __init__( def execute( self, model_inputs: ModelInputs, - kv_cache_inputs: KVCacheInputs | None = None, ) -> ModelOutputs: - assert kv_cache_inputs is not None - model_inputs = cast(Qwen2Inputs, model_inputs) + assert model_inputs.kv_cache_inputs is not None model_outputs = self.model.execute( model_inputs.tokens, model_inputs.input_row_offsets_or_attn_mask, - *kv_cache_inputs, + *model_inputs.kv_cache_inputs, copy_inputs_to_device=( not self.pipeline_config.cache_strategy.uses_opaque() ), @@ -113,7 +113,9 @@ def execute( return ModelOutputs(next_token_logits=model_outputs[0]) def _prepare_ragged_initial_token_inputs( - self, context_batch: Sequence[TextContext] + self, + context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> Qwen2Inputs: # Get input_row_offsets: start and end position of each batch in the # combined total_seq_len dimension. @@ -132,10 +134,13 @@ def _prepare_ragged_initial_token_inputs( input_row_offsets_or_attn_mask=Tensor.from_numpy( input_row_offsets ).to(self.pipeline_config.devices[0]), + kv_cache_inputs=kv_cache_inputs, ) def _prepare_padded_initial_token_inputs( - self, context_batch: Sequence[TextContext] + self, + context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> Qwen2Inputs: # Get tokens and seq_ids tokens = [ctx.next_tokens for ctx in context_batch] @@ -156,18 +161,24 @@ def _prepare_padded_initial_token_inputs( input_row_offsets_or_attn_mask=Tensor.from_numpy(attn_mask).to( self.pipeline_config.devices[0] ), + kv_cache_inputs=kv_cache_inputs, ) # Ignored type due to challenge with Interface implementation and mypy rules. def prepare_initial_token_inputs( self, context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> Qwen2Inputs: """Prepare the inputs for the first pass in multistep execution.""" if self.pipeline_config.cache_strategy.uses_opaque(): - return self._prepare_ragged_initial_token_inputs(context_batch) + return self._prepare_ragged_initial_token_inputs( + context_batch, kv_cache_inputs + ) else: - return self._prepare_padded_initial_token_inputs(context_batch) + return self._prepare_padded_initial_token_inputs( + context_batch, kv_cache_inputs + ) def _prepare_ragged_next_token_inputs( self, @@ -182,6 +193,7 @@ def _prepare_ragged_next_token_inputs( return Qwen2Inputs( tokens=next_tokens, input_row_offsets_or_attn_mask=next_row_offsets, + kv_cache_inputs=prev_model_inputs.kv_cache_inputs, ) def _prepare_padded_next_token_inputs( @@ -206,6 +218,7 @@ def _prepare_padded_next_token_inputs( input_row_offsets_or_attn_mask=Tensor.from_numpy(attn_mask).to( self.pipeline_config.devices[0] ), + kv_cache_inputs=prev_model_inputs.kv_cache_inputs, ) def prepare_next_token_inputs( diff --git a/src/max/pipelines/architectures/replit/model.py b/src/max/pipelines/architectures/replit/model.py index 211d63a840..3c2a42a208 100644 --- a/src/max/pipelines/architectures/replit/model.py +++ b/src/max/pipelines/architectures/replit/model.py @@ -61,9 +61,11 @@ def __init__( self, tokens: Tensor, input_row_offsets: Tensor, + kv_cache_inputs: KVCacheInputs | None = None, ) -> None: self.tokens = tokens self.input_row_offsets = input_row_offsets + self.kv_cache_inputs = kv_cache_inputs class ReplitModel(PipelineModel[TextContext]): @@ -80,14 +82,16 @@ def __init__( def execute( self, model_inputs: ModelInputs, - kv_cache_inputs: KVCacheInputs | None = None, ) -> ModelOutputs: model_inputs = cast(ReplitInputs, model_inputs) - assert kv_cache_inputs is not None, "Replit has KV cache inputs" + # keep mypy happy. + assert model_inputs.kv_cache_inputs is not None, ( + "Replit has KV cache inputs" + ) model_outputs = self.model.execute( model_inputs.tokens, model_inputs.input_row_offsets, - *kv_cache_inputs, + *model_inputs.kv_cache_inputs, copy_inputs_to_device=False, ) if self.pipeline_config.enable_echo: @@ -105,6 +109,7 @@ def execute( def prepare_initial_token_inputs( self, context_batch: Sequence[TextContext], + kv_cache_inputs: KVCacheInputs | None = None, ) -> ReplitInputs: # Get input_row_offsets: start and end position of each batch in the # combined total_seq_len dimension. @@ -116,6 +121,10 @@ def prepare_initial_token_inputs( # Create a ragged token vector of length: sum(len(t) for t in tokens). tokens = np.concatenate([ctx.next_tokens for ctx in context_batch]) + if kv_cache_inputs is None: + raise ValueError( + "Replit has KV cache inputs, but got None instead." + ) return ReplitInputs( tokens=Tensor.from_numpy(tokens).to( self.pipeline_config.devices[0] @@ -123,6 +132,7 @@ def prepare_initial_token_inputs( input_row_offsets=Tensor.from_numpy(input_row_offsets).to( self.pipeline_config.devices[0] ), + kv_cache_inputs=kv_cache_inputs, ) def prepare_next_token_inputs( @@ -136,6 +146,7 @@ def prepare_next_token_inputs( return ReplitInputs( tokens=next_tokens, input_row_offsets=next_row_offsets, + kv_cache_inputs=prev_model_inputs.kv_cache_inputs, ) @classmethod diff --git a/src/max/pipelines/embeddings_pipeline.py b/src/max/pipelines/embeddings_pipeline.py index fa609ef7f1..0c2bb35e8f 100644 --- a/src/max/pipelines/embeddings_pipeline.py +++ b/src/max/pipelines/embeddings_pipeline.py @@ -59,13 +59,13 @@ def encode(self, batch: dict[str, T]) -> dict[str, EmbeddingsResponse]: tracer.next("prepare_initial_token_inputs") # Prepare inputs for the first token in multistep execution. model_inputs = self._pipeline_model.prepare_initial_token_inputs( - context_batch + context_batch=context_batch, + kv_cache_inputs=None, ) tracer.next("execute") - model_outputs = self._pipeline_model.execute( - model_inputs=model_inputs, kv_cache_inputs=None - ) + model_outputs = self._pipeline_model.execute(model_inputs) + assert model_outputs.logits # Do the copy to host for each token generated. tracer.next("logits.to(CPU())") diff --git a/src/max/pipelines/pipeline.py b/src/max/pipelines/pipeline.py index 054d6f66c8..b42e9b7f48 100644 --- a/src/max/pipelines/pipeline.py +++ b/src/max/pipelines/pipeline.py @@ -20,7 +20,6 @@ from dataclasses import dataclass from typing import ( Generic, - List, Optional, Protocol, Sequence, @@ -119,6 +118,8 @@ class ModelInputs: True """ + kv_cache_inputs: KVCacheInputs | None = None + @dataclass(frozen=True) class ModelOutputs: @@ -240,16 +241,12 @@ def estimate_weights_size(cls, pipeline_config: PipelineConfig) -> int: def execute( self, model_inputs: ModelInputs, - # TODO(zheng): This should be tucked inside ModelInputs in the future. - kv_cache_inputs: KVCacheInputs | None = None, ) -> ModelOutputs: """Executes the graph with the given inputs. Args: model_inputs: The model inputs to execute, containing tensors and any other required data for model execution. - kv_cache_inputs: The kv cache inputs to execute, containing tensors and any other - required data for model execution. Returns: ModelOutputs containing the pipeline's output tensors. @@ -260,7 +257,9 @@ def execute( @abstractmethod def prepare_initial_token_inputs( - self, context_batch: Sequence[T] + self, + context_batch: Sequence[T], + kv_cache_inputs: KVCacheInputs | None = None, ) -> ModelInputs: """Prepares the initial inputs to be passed to `.execute()`. @@ -268,7 +267,8 @@ def prepare_initial_token_inputs( For example, the model inputs could include: - Encoded tensors - A unique IDs for each tensor if this model uses a KV Cache manager. - + - kv_cache_inputs: The kv cache inputs required for the model. This + should be None if the model does not use KV Cache. This function would batch the encoded tensors, claim a slot in the kv cache if the ID hasn't been seen before, and return the inputs and caches as a list of tensors.""" @@ -445,7 +445,7 @@ def prepare_batch( self, batch: list[T], num_steps: int, - ) -> tuple[ModelInputs, List[KVCacheInputs], int, Optional[torch.Tensor]]: + ) -> tuple[ModelInputs, int, Optional[torch.Tensor]]: tracer: Tracer = Tracer("prepare_batch") if self._pipeline_config.enable_structured_output: @@ -532,8 +532,12 @@ def prepare_batch( ) return ( - self._pipeline_model.prepare_initial_token_inputs(batch), - kv_cache_inputs, + self._pipeline_model.prepare_initial_token_inputs( + context_batch=batch, + kv_cache_inputs=KVCacheInputsSequence( + kv_cache_inputs=kv_cache_inputs + ), + ), num_steps, bitmask, ) @@ -578,8 +582,8 @@ def next_token( ] # Prepare the batch. - model_inputs, batched_kv_cache_inputs, num_steps, bitmask = ( - self.prepare_batch(context_batch, num_steps) + model_inputs, num_steps, bitmask = self.prepare_batch( + context_batch, num_steps ) # Multistep execution loop. @@ -599,9 +603,6 @@ def next_token( # Execute the model and get next tokens. model_outputs = self._pipeline_model.execute( model_inputs=curr_step_inputs, - kv_cache_inputs=KVCacheInputsSequence( - kv_cache_inputs=batched_kv_cache_inputs, - ), ) assert model_outputs.next_token_logits is not None next_token_logits = model_outputs.next_token_logits @@ -656,13 +657,22 @@ def next_token( break # Prepare inputs for the next token in multistep execution tracer.next("increment_cache_lengths") # pops sample_next_token - # Unpack model inputs for execute() call by getting all fields - batched_kv_cache_inputs = ( + + assert isinstance( + curr_step_inputs.kv_cache_inputs, KVCacheInputsSequence + ), ( + "prepare_batch instantiates and passes this as a KVCacheInputsSequence" + ) + assert isinstance( + curr_step_inputs.kv_cache_inputs.kv_cache_inputs, list + ), "increment_cache_lengths instantiates and passes this as a list" + curr_step_inputs.kv_cache_inputs.kv_cache_inputs = ( self._pipeline_model.kv_manager.increment_cache_lengths( - batched_kv_cache_inputs, # type: ignore + curr_step_inputs.kv_cache_inputs.kv_cache_inputs, curr_step_inputs, ) ) + tracer.next("prepare_next_token_inputs") # pops inc_cache_lengths curr_step_inputs = self._pipeline_model.prepare_next_token_inputs( new_tokens, curr_step_inputs From 4c20b5768e8672e512b0115d92580e45423627ed Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 26 Feb 2025 20:53:38 -0500 Subject: [PATCH 473/509] [max-examples] Add dedicated benchmark file for custom ops This moves the benchmark out of the top_k.mojo kernel and into a dedicated file, which imports the custom op, sets up ManagedTensorSlices and passes them to the `execute` function. This also enabled comparing GPU to CPU results. Added a couple of ctors to `DeviceContextPtr` to enable passing `Device` and `DeviceContext` from CPU/GPU. Working on adding matmul to the same benchmark file, and allowing the user to select specific benchmarks by passing an argument e.g. `magic run benchmark top_k`. Currently looks like: ``` ------------------------------------------------------------- | name | met (ms) | iters | GFLOPS/s | GElems/s | ------------------------------------------------------------- | top_k_custom/cpu | 2.291646 | 524 | 2.094 | 0.418 | | top_k_custom/gpu | 0.138753 | 8648 | 34.593 | 6.918 | ------------------------------------------------------------- ``` MODULAR_ORIG_COMMIT_REV_ID: bb0e346e4ff84e04830d7385ae48b75bc20a41f0 --- examples/custom_ops/README.md | 6 ++ examples/custom_ops/benchmarks.mojo | 135 ++++++++++++++++++++++++ examples/custom_ops/kernels/top_k.mojo | 137 ++++++------------------- examples/custom_ops/mojoproject.toml | 3 +- examples/custom_ops/top_k.py | 16 --- 5 files changed, 176 insertions(+), 121 deletions(-) create mode 100644 examples/custom_ops/benchmarks.mojo diff --git a/examples/custom_ops/README.md b/examples/custom_ops/README.md index 31825508ca..b3ff1d9910 100644 --- a/examples/custom_ops/README.md +++ b/examples/custom_ops/README.md @@ -53,3 +53,9 @@ The execution has two phases: first a `kernels.mojopkg` is compiled from the custom Mojo kernel, and then the graph is constructed and run in Python. The inference session is pointed to the `kernels.mojopkg` in order to load the custom operations. + +You can also run benchmarks to compare the performance of your GPU to your CPU: + +```sh +magic run benchmark +``` diff --git a/examples/custom_ops/benchmarks.mojo b/examples/custom_ops/benchmarks.mojo new file mode 100644 index 0000000000..c768958981 --- /dev/null +++ b/examples/custom_ops/benchmarks.mojo @@ -0,0 +1,135 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +from kernels import TopK +from gpu.host import DeviceContext +from utils import IndexList +from max.driver.device import cpu_device +from max.tensor import ( + ManagedTensorSlice, + InputTensor, + OutputTensor, + StaticTensorSpec, +) +from random import rand +from memory import UnsafePointer +from runtime.asyncrt import DeviceContextPtr +from benchmark import ThroughputMeasure, BenchId, BenchMetric, Bench, Bencher +from bit import log2_floor +from sys import sizeof, has_nvidia_gpu_accelerator, has_amd_gpu_accelerator +from memory import AddressSpace + + +def top_k(): + alias batch_size = 30_000 + alias K = 32 + alias els = batch_size * K + alias rank = 2 + alias shape = IndexList[rank](batch_size, K) + alias val_dtype = DType.float32 + alias idx_dtype = DType.int32 + + # Slightly better performance compared to `create_unknown`. Using global + # address space doesn't improve perf for GPU. + alias val_spec = StaticTensorSpec[val_dtype, rank]( + shape=(batch_size, K), + strides=(K, 1), + alignment=sizeof[val_dtype](), + address_space=AddressSpace.GENERIC, + exclusive=True, + in_lambda=None, + out_lambda=None, + ) + alias idx_spec = StaticTensorSpec[idx_dtype, rank]( + shape=(batch_size, K), + strides=(K, 1), + alignment=sizeof[idx_dtype](), + address_space=AddressSpace.GENERIC, + exclusive=True, + in_lambda=None, + out_lambda=None, + ) + + var in_vals = UnsafePointer[Scalar[val_dtype]].alloc(els) + var out_vals = UnsafePointer[Scalar[val_dtype]].alloc(els) + var out_idxs = UnsafePointer[Scalar[idx_dtype]].alloc(els) + + rand(in_vals, els) + + @parameter + @always_inline + fn bench_cpu(mut b: Bencher) raises: + var cpu_ctx_ptr = cpu_device().unsafe_ptr() + + @parameter + @always_inline + fn run_bench() raises: + TopK.execute[K=K, target="cpu"]( + out_vals=OutputTensor[static_spec=val_spec](out_vals, shape), + out_idxs=OutputTensor[static_spec=idx_spec](out_idxs, shape), + in_vals=InputTensor[static_spec=val_spec](in_vals, shape), + ctx=cpu_ctx_ptr, + ) + + b.iter[run_bench]() + + var flops = ThroughputMeasure(BenchMetric.flops, els * log2_floor(K)) + var elements = ThroughputMeasure(BenchMetric.elements, els) + + var b = Bench() + b.bench_function[bench_cpu](BenchId("top_k_custom", "cpu"), flops, elements) + + @parameter + if has_nvidia_gpu_accelerator() or has_amd_gpu_accelerator(): + var gpu_ctx = DeviceContext() + var dev_in_vals = gpu_ctx.enqueue_create_buffer[val_dtype](els) + var dev_out_vals = gpu_ctx.enqueue_create_buffer[val_dtype](els) + var dev_out_idxs = gpu_ctx.enqueue_create_buffer[idx_dtype](els) + gpu_ctx.copy(dev_in_vals, in_vals) + + @parameter + @always_inline + fn bench_gpu(mut b: Bencher) raises: + @parameter + @always_inline + fn kernel_launch() raises: + TopK.execute[K=K, target="gpu"]( + out_vals=OutputTensor[static_spec=val_spec]( + dev_out_vals.unsafe_ptr(), shape + ), + out_idxs=OutputTensor[static_spec=idx_spec]( + dev_out_idxs.unsafe_ptr(), shape + ), + in_vals=InputTensor[static_spec=val_spec]( + dev_in_vals.unsafe_ptr(), shape + ), + ctx=gpu_ctx, + ) + + b.iter[kernel_launch]() + + b.bench_function[bench_gpu]( + BenchId("top_k_custom", "gpu"), flops, elements + ) + _ = gpu_ctx + + b.config.verbose_metric_names = False + print(b) + + in_vals.free() + out_vals.free() + out_idxs.free() + + +def main(): + top_k() diff --git a/examples/custom_ops/kernels/top_k.mojo b/examples/custom_ops/kernels/top_k.mojo index 7a03e23d18..0035212533 100644 --- a/examples/custom_ops/kernels/top_k.mojo +++ b/examples/custom_ops/kernels/top_k.mojo @@ -13,17 +13,13 @@ from math import iota from sys import alignof, sizeof, num_physical_cores - from algorithm import parallelize_over_rows from bit import log2_floor from compiler import register -from gpu import WARP_SIZE, barrier -import gpu.warp as warp +from gpu import WARP_SIZE, barrier, warp from gpu.memory import AddressSpace, external_memory from max.tensor import ManagedTensorSlice from memory import Span -from benchmark import Bench, Bencher, BenchId, BenchMetric, ThroughputMeasure - from utils.index import IndexList from utils.numerics import min_or_neg_inf @@ -63,11 +59,14 @@ struct TopK: ctx: DeviceContextPtr, ) raises: constrained[rank == 2, "rank must be 2"]() + constrained[ + not (target == "gpu" and K > WARP_SIZE), + "K can't be larger than warp size", + ]() + var shape = in_vals.shape() var batch_size = shape[0] - var dev_ctx = ctx.get_device_context() - print("Executing on device:", dev_ctx.name()) @parameter fn top_k_gpu[ @@ -124,106 +123,36 @@ struct TopK: top_k_sram[index].val = min_or_neg_inf[type]() @parameter - fn top_k_cpu(start_idx: Int, end_idx: Int): - for row_idx in range(start_idx, end_idx): - var offset = (row_idx * K) - iota(out_idxs.unsafe_ptr() + offset, K) - - @parameter - fn val_greater_than(lhs: Int32, rhs: Int32) -> Bool: - return ( - in_vals[row_idx, Int(lhs)] > in_vals[row_idx, Int(rhs)] - ) - - sort[val_greater_than](Span(out_idxs.unsafe_ptr() + offset, K)) - - for i in range(K): - var sorted_idx = Int(out_idxs[row_idx, i]) - out_vals[row_idx, i] = in_vals[row_idx, sorted_idx] - - if batch_size <= 10: - - @parameter - if target == "gpu": - # This is a simplified version that only works for K being under - # the warp size. The MAX "mo.top_k" op supports any K and does - # another reduction after each warp has reduced its values. - if K >= WARP_SIZE: - raise Error( - "[top_k_custom] K=", - K, - " but must be less than the WARP_SIZE=", - WARP_SIZE, - ) - - if K < WARP_SIZE: - dev_ctx.enqueue_function[top_k_gpu[K]]( - out_vals, - out_idxs, - in_vals, - grid_dim=batch_size, # One block per batch - block_dim=K, # One thread per K - shared_mem_bytes=K * sizeof[TopKElement[type]](), - ) - else: - # Set grain size to 1 to put each batch in a separate task - parallelize_over_rows[top_k_cpu](shape, 1, grain_size=1) - - # Everything below is for benchmarking when running a stress test + if target == "gpu": + dev_ctx.enqueue_function[top_k_gpu[K]]( + out_vals, + out_idxs, + in_vals, + grid_dim=batch_size, # One block per batch + block_dim=K, # One thread per K + shared_mem_bytes=K * sizeof[TopKElement[type]](), + ) else: - var bench = Bench() @parameter - @always_inline - fn bench_gpu(mut b: Bencher, shape: IndexList[rank]) raises: - @parameter - @always_inline - fn kernel_launch(dev_ctx: DeviceContext) raises: - dev_ctx.enqueue_function[top_k_gpu[K]]( - out_vals, - out_idxs, - in_vals, - grid_dim=batch_size, # One block per batch - block_dim=K, # One thread per K - shared_mem_bytes=K * sizeof[TopKElement[type]](), + fn top_k_cpu(start_idx: Int, end_idx: Int): + for row_idx in range(start_idx, end_idx): + var offset = (row_idx * K) + iota(out_idxs.unsafe_ptr() + offset, K) + + @parameter + fn val_greater_than(lhs: Int32, rhs: Int32) -> Bool: + return ( + in_vals[row_idx, Int(lhs)] + > in_vals[row_idx, Int(rhs)] + ) + + sort[val_greater_than]( + Span(out_idxs.unsafe_ptr() + offset, K) ) - b.iter_custom[kernel_launch](ctx.get_device_context()) + for i in range(K): + var sorted_idx = Int(out_idxs[row_idx, i]) + out_vals[row_idx, i] = in_vals[row_idx, sorted_idx] - @parameter - @always_inline - fn bench_cpu(mut b: Bencher) raises: - var grain = 1 - # Split job up evenly across physical cores on large batch - if batch_size > 1000: - grain = batch_size // num_physical_cores() - - @parameter - fn run_bench(): - parallelize_over_rows[top_k_cpu](shape, 1, grain_size=grain) - - b.iter[run_bench]() - - var els = ThroughputMeasure( - BenchMetric.elements, shape.flattened_length() - ) - var flops = ThroughputMeasure( - BenchMetric.flops, shape.flattened_length() * log2_floor(K) - ) - - # Only benchmark GPU if it's available - @parameter - if target == "gpu": - bench.bench_with_input[IndexList[rank], bench_gpu]( - BenchId("top_k_custom", "gpu"), shape, els, flops - ) - - # TODO: Always benchmark CPU to compare with GPU - else: - bench.bench_function[bench_cpu]( - BenchId("top_k_custom", "cpu"), els, flops - ) - - bench.config.verbose_metric_names = False - bench.config.verbose_timing = True - print(bench) + parallelize_over_rows[top_k_cpu](shape, axis=1, grain_size=1) diff --git a/examples/custom_ops/mojoproject.toml b/examples/custom_ops/mojoproject.toml index d110891084..f2a6f222ee 100644 --- a/examples/custom_ops/mojoproject.toml +++ b/examples/custom_ops/mojoproject.toml @@ -15,7 +15,8 @@ top_k = { cmd = "python top_k.py", depends-on = ["package"] } fused_attention = { cmd = "python fused_attention.py", depends-on = ["package"] } matrix_multiplication = { cmd = "python matrix_multiplication.py", depends-on = ["package"] } histogram = { cmd = "python histogram.py", depends-on = ["package"] } -test = { depends-on = ["addition", "mandelbrot", "vector_addition", "top_k", "fused_attention", "matrix_multiplication"] } +benchmark = { cmd = "mojo benchmarks.mojo", depends-on = ["package"] } +test = { depends-on = ["addition", "mandelbrot", "vector_addition", "top_k", "fused_attention", "matrix_multiplication", "benchmark"] } [dependencies] python = ">=3.9,<3.13" diff --git a/examples/custom_ops/top_k.py b/examples/custom_ops/top_k.py index 887fd444c0..3a77d793f7 100644 --- a/examples/custom_ops/top_k.py +++ b/examples/custom_ops/top_k.py @@ -105,15 +105,6 @@ def main(): parser = argparse.ArgumentParser( description="Top-K sampling with custom ops" ) - parser.add_argument( - "--stress-test", - type=int, - default=0, - help=( - "Number of times to repeat input for stress testing. " - "On NVIDIA A100, 250_000_000 takes less than 0.5ms and uses over 75%% of its memory." - ), - ) parser.add_argument( "--cpu", action="store_true", @@ -135,10 +126,6 @@ def main(): # Get probabilities of next word for each word in the `word_predictions` list probabilities = frequencies.next_word_probabilities(word_predictions) - # If stress testing, repeat the input the specified number of times - if args.stress_test > 0: - probabilities = np.repeat(probabilities, args.stress_test, axis=0) - batch_size = len(probabilities) K = frequencies.max_next_words @@ -183,9 +170,6 @@ def main(): values, indices = model.execute(input_tensor) - if args.stress_test > 0: - return - # Copy values and indices back to the CPU to be read. assert isinstance(values, Tensor) values = values.to(CPU()) From 390a755e7f5a440d391746e8f63f6f2b26e5e837 Mon Sep 17 00:00:00 2001 From: akirchhoff-modular Date: Wed, 26 Feb 2025 18:06:58 -0800 Subject: [PATCH 474/509] [KVCache] Clean up iterator logic of KVCacheInputs & KVCacheInputSymbols Both of these were using a deque to flatten out contained tensors and tensor types, respectively. This avoids recursion, but the structure of data they're iterating over is not very deep, so there is not much risk of stack overflow. Use "yield from" to make this more concise, and use a type guard to clean up some of the `isinstance` uses. Type annotations added where necessary or helpful. I also removed `__setitem__` since it was both broken and didn't seem to be used anywhere. MODULAR_ORIG_COMMIT_REV_ID: a0261de3f8e9aaf384c13e65119cfe5cbe0e01dc --- src/max/pipelines/kv_cache/manager.py | 89 ++++++++++++--------------- 1 file changed, 41 insertions(+), 48 deletions(-) diff --git a/src/max/pipelines/kv_cache/manager.py b/src/max/pipelines/kv_cache/manager.py index 7f124db394..1e1ad0040b 100644 --- a/src/max/pipelines/kv_cache/manager.py +++ b/src/max/pipelines/kv_cache/manager.py @@ -16,15 +16,25 @@ from __future__ import annotations from abc import ABC, abstractmethod -from collections import deque from dataclasses import dataclass -from typing import Any, List, Sequence, cast, final +from typing import ( + Any, + Iterator, + List, + Sequence, + Type, + TypeVar, + cast, + final, + overload, +) import numpy as np from max.driver import Device, Tensor from max.dtype import DType from max.engine import InferenceSession from max.graph import DeviceRef, Graph, TensorType, TensorValue +from typing_extensions import TypeGuard from .cache_params import KVCacheParams @@ -40,6 +50,13 @@ class _FetchMetadata: num_steps: int +_T = TypeVar("_T") + + +def _is_sequence_of(x: Any, ty: Type[_T]) -> TypeGuard[Sequence[_T]]: + return isinstance(x, Sequence) and all(isinstance(item, ty) for item in x) + + @dataclass class KVCacheInputs: """ @@ -56,48 +73,35 @@ class KVCacheInputs: ... max_lengths: Tensor """ - def __iter__(self): + def __iter__(self) -> Iterator[Tensor]: """Iterates through each Type in order.""" - fields = deque( - getattr(self, field) for field in self.__dataclass_fields__ - ) - while fields: - value = fields.popleft() - if isinstance(value, Sequence) and all( - isinstance(x, KVCacheInputs) for x in value - ): - # Add sequence elements in original order - fields.extendleft([x for x in reversed(value)]) - continue - elif isinstance(value, KVCacheInputs): - fields.extendleft( - reversed( - [ - getattr(value, field) - for field in value.__dataclass_fields__ - ] - ) - ) - continue + for field in self.__dataclass_fields__: + value = getattr(self, field) + if isinstance(value, KVCacheInputs): + yield from value + elif _is_sequence_of(value, KVCacheInputs): + for item in value: + yield from item else: yield cast(Tensor, value) - def __getitem__(self, index): - return list(self)[index] + @overload + def __getitem__(self, index: int) -> Tensor: ... + + @overload + def __getitem__(self, index: slice) -> Sequence[Tensor]: ... - def __setitem__(self, index, value): - list(self)[index] = value + def __getitem__(self, index: Any) -> Any: + return list(self)[index] - def __len__(self): + def __len__(self) -> int: count = 0 # Iterate over all fields in the dataclass. If we run into a sequence of # KVCacheInputs, we expand and recursively call `len` on the KVCacheInputs # elements. for field in self.__dataclass_fields__: value = getattr(self, field) - if isinstance(value, Sequence) and all( - isinstance(x, KVCacheInputs) for x in value - ): + if _is_sequence_of(value, KVCacheInputs): count += sum(len(x) for x in value) else: count += 1 @@ -158,27 +162,16 @@ class KVCacheInputSymbols: ... max_lengths: TensorType """ - def __iter__(self): + def __iter__(self) -> Iterator[Any]: """Iterates through each Type in order.""" - fields = deque( - getattr(self, field) for field in self.__dataclass_fields__ - ) - while fields: - value = fields.popleft() + for field in self.__dataclass_fields__: + value = getattr(self, field) if isinstance(value, KVCacheInputSymbols): - fields.extendleft( - reversed( - [ - getattr(value, field) - for field in value.__dataclass_fields__ - ] - ) - ) - continue + yield from value else: yield value - def __getitem__(self, index): + def __getitem__(self, index) -> Any: return list(self)[index] From 69403856b96b63a573a509972cc4f208753b899b Mon Sep 17 00:00:00 2001 From: akirchhoff-modular Date: Wed, 26 Feb 2025 18:14:37 -0800 Subject: [PATCH 475/509] [Pipelines] Qualify huggingface_hub accesses (NFC) This makes it easier to search to see where a network call may be being made. MODULAR_ORIG_COMMIT_REV_ID: f28696fda1657afd909348c577bf22f28764c000 --- src/max/pipelines/config.py | 51 ++++++++++++++----------------------- 1 file changed, 19 insertions(+), 32 deletions(-) diff --git a/src/max/pipelines/config.py b/src/max/pipelines/config.py index d254ca7c0b..b71b604777 100644 --- a/src/max/pipelines/config.py +++ b/src/max/pipelines/config.py @@ -28,25 +28,10 @@ from pathlib import Path from typing import Any, Optional, Union, cast +import huggingface_hub import torch -from huggingface_hub import ( - HfFileSystem, - file_exists, - get_hf_file_metadata, - hf_hub_download, - hf_hub_url, - model_info, - repo_exists, -) from huggingface_hub import constants as hf_hub_constants -from huggingface_hub.hf_api import ModelInfo -from huggingface_hub.utils import ( - EntryNotFoundError, - GatedRepoError, - HfHubHTTPError, - RepositoryNotFoundError, - RevisionNotFoundError, -) +from huggingface_hub import errors as hf_hub_errors from huggingface_hub.utils import tqdm as hf_tqdm from max.driver import CPU, Accelerator, Device, DeviceSpec, accelerator_count from max.dtype import DType @@ -209,17 +194,17 @@ def _repo_exists_with_retry(repo_id: str) -> bool: for attempt, delay_in_seconds in enumerate(retry_delays_in_seconds): try: - return repo_exists(repo_id) + return huggingface_hub.repo_exists(repo_id) except ( - RepositoryNotFoundError, - GatedRepoError, - RevisionNotFoundError, - EntryNotFoundError, + hf_hub_errors.RepositoryNotFoundError, + hf_hub_errors.GatedRepoError, + hf_hub_errors.RevisionNotFoundError, + hf_hub_errors.EntryNotFoundError, ) as e: # Forward these specific errors to the user logger.error(f"Hugging Face repository error: {str(e)}") raise - except HfHubHTTPError as e: + except hf_hub_errors.HfHubHTTPError as e: if attempt == max_attempts - 1: logger.error( f"Failed to connect to Hugging Face Hub after {max_attempts} attempts: {str(e)}" @@ -265,13 +250,15 @@ def __repr__(self) -> str: return self.repo_id @cached_property - def info(self) -> ModelInfo: + def info(self) -> huggingface_hub.ModelInfo: if self.repo_type == RepoType.local: raise ValueError( "using model info, on local repos is not supported." ) elif self.repo_type == RepoType.online: - return model_info(self.repo_id, files_metadata=False) + return huggingface_hub.model_info( + self.repo_id, files_metadata=False + ) else: raise ValueError(f"Unsupported repo type: {self.repo_type}") @@ -293,7 +280,7 @@ def weight_files(self) -> dict[WeightsFormat, list[str]]: os.path.join(self.repo_id, pytorch_search_pattern) ) elif self.repo_type == RepoType.online: - fs = HfFileSystem() + fs = huggingface_hub.HfFileSystem() safetensor_paths = cast( list[str], fs.glob(f"{self.repo_id}/{safetensor_search_pattern}"), @@ -337,8 +324,8 @@ def weight_files(self) -> dict[WeightsFormat, list[str]]: def size_of(self, filename: str) -> Union[int, None]: if self.repo_type == RepoType.online: - url = hf_hub_url(self.repo_id, filename) - metadata = get_hf_file_metadata(url) + url = huggingface_hub.hf_hub_url(self.repo_id, filename) + metadata = huggingface_hub.get_hf_file_metadata(url) return metadata.size raise NotImplementedError("not implemented for non-online repos.") @@ -503,11 +490,11 @@ def files_for_encoding( return gguf_files def file_exists(self, filename: str) -> bool: - return file_exists(self.repo_id, filename) + return huggingface_hub.file_exists(self.repo_id, filename) def download(self, filename: str, force_download: bool = False) -> Path: return Path( - hf_hub_download( + huggingface_hub.hf_hub_download( self.repo_id, filename, force_download=force_download ) ) @@ -749,7 +736,7 @@ def __post_init__(self) -> None: file_name = "/".join(path_pieces[2:]) if self.model_path != "" and repo_id == self.model_path: path = Path(file_name) - elif file_exists(repo_id, file_name): + elif huggingface_hub.file_exists(repo_id, file_name): self._weights_repo_id = repo_id path = Path(file_name) elif self.model_path == "": @@ -1006,7 +993,7 @@ def download_weights(self) -> None: self.weight_path = list( thread_map( lambda filename: Path( - hf_hub_download( + huggingface_hub.hf_hub_download( weights_repo_id, str(filename), revision=self.huggingface_revision, From 82953520c45fa62f6bae540b8c41a70ee60a0e98 Mon Sep 17 00:00:00 2001 From: Brian Zhang Date: Wed, 26 Feb 2025 18:50:12 -0800 Subject: [PATCH 476/509] [MAX] Format bytes into human readable strings in kv cache err messages (#56791) Stacked PRs: * __->__#56791 --- --- --- ### [MAX] Format bytes into human readable strings in kv cache err messages Add a new support subpackage that contains these few helpers. I leave DType where it currently is context: [Internal link] Closes E2EOPT-70 MODULAR_ORIG_COMMIT_REV_ID: 5ff6caa4f2cf53bae77bf8ee3fb366c8d227f294 --- src/max/pipelines/kv_cache/paged_cache.py | 13 ++++++++--- src/max/pipelines/registry.py | 28 ++++++----------------- 2 files changed, 17 insertions(+), 24 deletions(-) diff --git a/src/max/pipelines/kv_cache/paged_cache.py b/src/max/pipelines/kv_cache/paged_cache.py index 7c814828be..ab33d3335e 100644 --- a/src/max/pipelines/kv_cache/paged_cache.py +++ b/src/max/pipelines/kv_cache/paged_cache.py @@ -34,6 +34,7 @@ _OpaqueValue, ops, ) +from max.support.human_readable_formatter import to_human_readable_bytes from ._utils import build_max_lengths_tensor from .cache_params import KVCacheParams @@ -194,17 +195,23 @@ def __init__( cache_memory_per_device // single_page_size_bytes ) + single_page_size_bytes_str = to_human_readable_bytes( + single_page_size_bytes + ) + cache_memory_per_device_str = to_human_readable_bytes( + cache_memory_per_device + ) if self.total_num_pages == 0: raise RuntimeError( f"Insufficient cache memory to allocate even a single page.\n" - f"One page requires {single_page_size_bytes} bytes but only {cache_memory_per_device} bytes are available." + f"One page requires {single_page_size_bytes_str} but only {cache_memory_per_device_str} are available." ) if max_batch_size > self.total_num_pages: logger.warning( f"Insufficient cache memory to support a batch containing {max_batch_size} requests with one token per request. " f"Need to allocate at least {max_batch_size} blocks, but only have enough memory for {self.total_num_pages} blocks. " - f"One page requires {single_page_size_bytes} bytes but only {cache_memory_per_device} bytes are available." + f"One page requires {single_page_size_bytes_str} but only {cache_memory_per_device_str} are available." ) blocks_needed_for_max_seq_len = ceildiv(max_seq_len, page_size) @@ -212,7 +219,7 @@ def __init__( logger.warning( f"Insufficient cache memory to support a batch containing one request at the max sequence length of {max_seq_len} tokens. " f"Need to allocate at least {blocks_needed_for_max_seq_len} blocks, but only have enough memory for {self.total_num_pages} blocks. " - f"One page requires {single_page_size_bytes} bytes but only {cache_memory_per_device} bytes are available." + f"One page requires {single_page_size_bytes} but only {cache_memory_per_device} are available." ) # call our base class constructor diff --git a/src/max/pipelines/registry.py b/src/max/pipelines/registry.py index 312b0d1e79..2e180772f2 100644 --- a/src/max/pipelines/registry.py +++ b/src/max/pipelines/registry.py @@ -23,6 +23,7 @@ import torch from max.graph.weights import WeightsAdapter +from max.support.human_readable_formatter import to_human_readable_bytes from .config import ( PipelineConfig, @@ -67,21 +68,6 @@ } -def _to_human_memory_size(bytes: int) -> str: - """Convert bytes to human readable memory size.""" - KiB = 1024 - MiB = KiB * 1024 - GiB = MiB * 1024 - TiB = GiB * 1024 - if bytes > TiB: - return f"{bytes / TiB:.2f} TiB" - if bytes > GiB: - return f"{bytes / GiB:.2f} GiB" - if bytes > MiB: - return f"{bytes / MiB:.2f} MiB" - return f"{bytes / KiB:.2f} KiB" - - class SupportedArchitecture: def __init__( self, @@ -463,11 +449,11 @@ def _estimate_memory_footprint( total_size = model_weights_size + actual_kv_cache_size if free_memory: - free_memory_str = f" / {_to_human_memory_size(free_memory)} free" + free_memory_str = f" / {to_human_readable_bytes(free_memory)} free" weights_str = "" if model_weights_size: - weights_str = f"\n\t Weights: {_to_human_memory_size(model_weights_size)}" + weights_str = f"\n\t Weights: {to_human_readable_bytes(model_weights_size)}" if not user_provided_max_length: max_length_str = f"Auto-inferred max sequence length: {pipeline_config.max_length}" @@ -487,8 +473,8 @@ def _estimate_memory_footprint( "\n" f"\n\tEstimated memory consumption:" f"{weights_str}" - f"\n\t KVCache allocation: {_to_human_memory_size(actual_kv_cache_size)}" - f"\n\t Total estimated: {_to_human_memory_size(model_weights_size + actual_kv_cache_size)} used{free_memory_str}" + f"\n\t KVCache allocation: {to_human_readable_bytes(actual_kv_cache_size)}" + f"\n\t Total estimated: {to_human_readable_bytes(model_weights_size + actual_kv_cache_size)} used{free_memory_str}" f"\n\t{max_length_str}" f"\n\t{max_batch_size_str}\n" ) @@ -719,14 +705,14 @@ def _generate_oom_error_message( ) -> str: """Generate an appropriate error message based on the configuration state.""" free_memory_str = ( - f" / {_to_human_memory_size(original_free_memory)} free" + f" / {to_human_readable_bytes(original_free_memory)} free" if original_free_memory else "" ) msg = StringIO() msg.write( - f"Estimated model and kv cache memory use exceeds available memory ({_to_human_memory_size(total_size)} {free_memory_str}). Try " + f"Estimated model and kv cache memory use exceeds available memory ({to_human_readable_bytes(total_size)} {free_memory_str}). Try " ) if not found_valid_max_length and not found_valid_max_batch_size: From 8ae8a5c73210b9f90419755de38f74e8abb2c449 Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Wed, 26 Feb 2025 22:11:20 -0500 Subject: [PATCH 477/509] [max-examples] Add matmul benchmark to custom ops Results on A100 at 1028x1028x1028: ``` ------------------------------------------------------------------------ | name | met (ms)| iters | GFLOPS/s | GElems/s | ------------------------------------------------------------------------ | cpu/naive | 1345.17 | 2 | 1.6144302 | 0.0007856 | | gpu/naive | 2.84457 | 421 | 763.44951 | 0.3715082 | | gpu/coalescing | 0.65844 | 1822 | 3298.2149 | 1.6049707 | | gpu/tiled | 0.62589 | 1917 | 3469.7502 | 1.6884429 | | gpu/tiled_register | 0.40628 | 2953 | 5345.2100 | 2.6010754 | | gpu/block_tiled | 0.37401 | 3207 | 5806.3713 | 2.8254848 | | gpu/block_tiled_vectorized | 0.37404 | 3208 | 5805.9318 | 2.8252709 | ------------------------------------------------------------------------ ``` MODULAR_ORIG_COMMIT_REV_ID: e42b94e1862195d335533a790b6e0ed4b8d3f184 --- examples/custom_ops/benchmarks.mojo | 137 +++++++++++++++++++++++++++- 1 file changed, 136 insertions(+), 1 deletion(-) diff --git a/examples/custom_ops/benchmarks.mojo b/examples/custom_ops/benchmarks.mojo index c768958981..c9bdc86245 100644 --- a/examples/custom_ops/benchmarks.mojo +++ b/examples/custom_ops/benchmarks.mojo @@ -11,7 +11,8 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # -from kernels import TopK +from kernels.top_k import TopK +from kernels.matrix_multiplication import MatrixMultiplication from gpu.host import DeviceContext from utils import IndexList from max.driver.device import cpu_device @@ -131,5 +132,139 @@ def top_k(): out_idxs.free() +def matmul(): + alias M = 1028 + alias K = 1028 + alias N = 1028 + alias FLOPS = M * N * (2 * K - 1) + + alias rank = 2 + alias a_shape = IndexList[rank](M, K) + alias b_shape = IndexList[rank](K, N) + alias c_shape = IndexList[rank](M, N) + + alias a_els = a_shape.flattened_length() + alias b_els = b_shape.flattened_length() + alias c_els = c_shape.flattened_length() + + alias dtype = DType.float32 + + alias a_spec = StaticTensorSpec[dtype, rank]( + shape=(M, K), + strides=(K, 1), + alignment=sizeof[dtype](), + address_space=AddressSpace.GENERIC, + exclusive=True, + in_lambda=None, + out_lambda=None, + ) + alias b_spec = StaticTensorSpec[dtype, rank]( + shape=(K, N), + strides=(N, 1), + alignment=sizeof[dtype](), + address_space=AddressSpace.GENERIC, + exclusive=True, + in_lambda=None, + out_lambda=None, + ) + alias c_spec = StaticTensorSpec[dtype, rank]( + shape=(M, N), + strides=(N, 1), + alignment=sizeof[dtype](), + address_space=AddressSpace.GENERIC, + exclusive=True, + in_lambda=None, + out_lambda=None, + ) + + var a_ptr = UnsafePointer[Scalar[dtype]].alloc(a_els) + var b_ptr = UnsafePointer[Scalar[dtype]].alloc(b_els) + var c_ptr = UnsafePointer[Scalar[dtype]].alloc(c_els) + + var a = InputTensor[static_spec=a_spec](a_ptr, a_shape) + var b = InputTensor[static_spec=b_spec](b_ptr, b_shape) + var c = OutputTensor[static_spec=c_spec](c_ptr, c_shape) + + rand(a_ptr, a_els) + rand(b_ptr, b_els) + + var cpu_ctx_ptr = cpu_device().unsafe_ptr() + var bench = Bench() + var flops = ThroughputMeasure(BenchMetric.flops, FLOPS) + var elements = ThroughputMeasure(BenchMetric.elements, M * N) + + @parameter + @always_inline + fn bench_cpu(mut bencher: Bencher) raises: + @parameter + @always_inline + fn run_bench() raises: + MatrixMultiplication["naive"].execute[target="cpu"]( + c, + a, + b, + cpu_ctx_ptr, + ) + + bencher.iter[run_bench]() + + bench.bench_function[bench_cpu](BenchId("cpu", "naive"), flops, elements) + + @parameter + if has_nvidia_gpu_accelerator() or has_amd_gpu_accelerator(): + var gpu_ctx = DeviceContext() + var a_dev = gpu_ctx.enqueue_create_buffer[dtype](a_els) + var b_dev = gpu_ctx.enqueue_create_buffer[dtype](b_els) + var c_dev = gpu_ctx.enqueue_create_buffer[dtype](c_els) + var c = InputTensor[static_spec=c_spec](c_dev.unsafe_ptr(), c_shape) + var a = OutputTensor[static_spec=a_spec](a_dev.unsafe_ptr(), a_shape) + var b = OutputTensor[static_spec=b_spec](b_dev.unsafe_ptr(), b_shape) + gpu_ctx.copy(a_dev, a_ptr) + gpu_ctx.copy(b_dev, b_ptr) + + @parameter + def bench_matmul_kernel[impl: StringLiteral](): + @parameter + @always_inline + fn bench_gpu(mut bench: Bencher) raises: + @parameter + @always_inline + fn kernel_launch(gpu_ctx: DeviceContext) raises: + MatrixMultiplication[impl].execute[target="gpu"]( + c, + a, + b, + gpu_ctx, + ) + + var gpu_ctx = DeviceContext() + bench.iter_custom[kernel_launch](gpu_ctx) + _ = gpu_ctx + + bench.bench_function[bench_gpu]( + BenchId("gpu", impl), flops, elements + ) + + bench_matmul_kernel["naive"]() + bench_matmul_kernel["coalescing"]() + bench_matmul_kernel["tiled"]() + bench_matmul_kernel["tiled_register"]() + bench_matmul_kernel["block_tiled"]() + bench_matmul_kernel["block_tiled_vectorized"]() + _ = gpu_ctx + _ = a_dev + _ = b_dev + _ = c_dev + + bench.config.verbose_metric_names = False + print(bench) + + a_ptr.free() + b_ptr.free() + c_ptr.free() + + +# TODO: arg parsing to select benchmarks def main(): top_k() + matmul() From e543f249c2c28b6adc9471019dbc202e5b312f65 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Thomas=20B=C3=B8rstad?= Date: Thu, 27 Feb 2025 14:42:32 +0100 Subject: [PATCH 478/509] ] [max-examples] Fix DeviceContext lifetime in top_k benchmark Moves the cpu_device() context creation outside the benchmark function and keeps it alive until the end of the scope to prevent use-after-free errors. This prevents the example from segfaulting on Mac OS [ MODULAR_ORIG_COMMIT_REV_ID: 37177ca86aa143cc4dd405dd7253e6f218ddb18e --- examples/custom_ops/benchmarks.mojo | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/custom_ops/benchmarks.mojo b/examples/custom_ops/benchmarks.mojo index c9bdc86245..f02bb8b731 100644 --- a/examples/custom_ops/benchmarks.mojo +++ b/examples/custom_ops/benchmarks.mojo @@ -67,11 +67,11 @@ def top_k(): rand(in_vals, els) + var cpu_ctx_ptr = cpu_device().unsafe_ptr() + @parameter @always_inline fn bench_cpu(mut b: Bencher) raises: - var cpu_ctx_ptr = cpu_device().unsafe_ptr() - @parameter @always_inline fn run_bench() raises: @@ -127,6 +127,7 @@ def top_k(): b.config.verbose_metric_names = False print(b) + _ = cpu_ctx_ptr in_vals.free() out_vals.free() out_idxs.free() From 5f2570fa100418dcce60108b8d9337ea1115cf89 Mon Sep 17 00:00:00 2001 From: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Date: Thu, 27 Feb 2025 08:04:22 -0600 Subject: [PATCH 479/509] [External] [stdlib] Rename `List.size` to `List._len` and refactor usage of the field to use the public API (#56692) [External] [stdlib] Rename `List.size` to `List._len` Make the data member `size` of `List` private by prefixing with an `_`. Rename it to `len` while we're here. Adjust call sites to use public APIs where possible. Details: - Part of https://github.com/modular/mojo/issues/3511 - To prepare for any future implementation details than can change under the hood for `List`. Co-authored-by: martinvuyk <110240700+martinvuyk@users.noreply.github.com> Closes modularml/mojo#3814 MODULAR_ORIG_COMMIT_REV_ID: c2ac41d4c543b01619814ce0cd58fd8ba15ac9e9 --- mojo/docs/changelog.md | 36 ++++++ mojo/stdlib/src/builtin/file.mojo | 4 +- mojo/stdlib/src/builtin/string_literal.mojo | 15 +-- mojo/stdlib/src/collections/list.mojo | 111 ++++++++---------- .../src/collections/string/_unicode.mojo | 26 ++-- .../src/collections/string/codepoint.mojo | 2 +- .../stdlib/src/collections/string/format.mojo | 5 +- .../stdlib/src/collections/string/string.mojo | 81 +++++-------- .../src/collections/string/string_slice.mojo | 12 +- mojo/stdlib/src/memory/span.mojo | 2 +- mojo/stdlib/src/pathlib/path.mojo | 2 +- .../test_list_getitem_invalid_index.mojo | 2 +- 12 files changed, 141 insertions(+), 157 deletions(-) diff --git a/mojo/docs/changelog.md b/mojo/docs/changelog.md index c223bb1bd0..8f7363e0de 100644 --- a/mojo/docs/changelog.md +++ b/mojo/docs/changelog.md @@ -215,4 +215,40 @@ ctx.enqueue_function(compiled_func, grid_dim=1, block_dim=1) ### ❌ Removed +- Direct access to `List.size` has been removed. Use the public API instead. + + Examples: + + Extending a List: + + ```mojo + base_data = List[Byte](1, 2, 3) + + data_list = List[Byte](4, 5, 6) + ext_data_list = base_data.copy() + ext_data_list.extend(data_list) # [1, 2, 3, 4, 5, 6] + + data_span = Span(List[Byte](4, 5, 6)) + ext_data_span = base_data.copy() + ext_data_span.extend(data_span) # [1, 2, 3, 4, 5, 6] + + data_vec = SIMD[DType.uint8, 4](4, 5, 6, 7) + ext_data_vec_full = base_data.copy() + ext_data_vec_full.extend(data_vec) # [1, 2, 3, 4, 5, 6, 7] + + ext_data_vec_partial = base_data.copy() + ext_data_vec_partial.extend(data_vec, count=3) # [1, 2, 3, 4, 5, 6] + ``` + + Slicing and extending a list efficiently: + + ```mojo + base_data = List[Byte](1, 2, 3, 4, 5, 6) + n4_n5 = Span(base_data)[3:5] + extra_data = Span(List[Byte](8, 10)) + end_result = List[Byte](capacity=len(n4_n5) + len(extra_data)) + end_result.extend(n4_n5) + end_result.extend(extra_data) # [4, 5, 8, 10] + ``` + ### 🛠️ Fixed diff --git a/mojo/stdlib/src/builtin/file.mojo b/mojo/stdlib/src/builtin/file.mojo index 9325034705..e3ba22bf27 100644 --- a/mojo/stdlib/src/builtin/file.mojo +++ b/mojo/stdlib/src/builtin/file.mojo @@ -129,7 +129,7 @@ struct FileHandle: """Reads data from a file and sets the file handle seek position. If size is left as the default of -1, it will read to the end of the file. Setting size to a number larger than what's in the file will set - String.size to the total number of bytes, and read all the data. + the String length to the total number of bytes, and read all the data. Args: size: Requested number of bytes to read (Default: -1 = EOF). @@ -273,7 +273,7 @@ struct FileHandle: """Reads data from a file and sets the file handle seek position. If size is left as default of -1, it will read to the end of the file. Setting size to a number larger than what's in the file will be handled - and set the List.size to the total number of bytes in the file. + and set the List length to the total number of bytes in the file. Args: size: Requested number of bytes to read (Default: -1 = EOF). diff --git a/mojo/stdlib/src/builtin/string_literal.mojo b/mojo/stdlib/src/builtin/string_literal.mojo index aabd2b7678..da60f9515c 100644 --- a/mojo/stdlib/src/builtin/string_literal.mojo +++ b/mojo/stdlib/src/builtin/string_literal.mojo @@ -411,17 +411,10 @@ struct StringLiteral( # inline the string slice constructor to work around an elaborator # memory leak. # return self.as_string_slice() - var string = String() - var length = self.byte_length() - var buffer = String._buffer_type() - var new_capacity = length + 1 - buffer._realloc(new_capacity) - buffer.size = new_capacity - var data: UnsafePointer[UInt8] = self.unsafe_ptr() - memcpy(buffer.data, data, length) - (buffer.data + length).init_pointee_move(0) - string._buffer = buffer^ - return string + var buffer = String._buffer_type(capacity=self.byte_length() + 1) + buffer.extend(self.as_bytes()) + buffer.append(0) + return String(buffer=buffer^) @no_inline fn __repr__(self) -> String: diff --git a/mojo/stdlib/src/collections/list.mojo b/mojo/stdlib/src/collections/list.mojo index 21416c95dd..1aa7051342 100644 --- a/mojo/stdlib/src/collections/list.mojo +++ b/mojo/stdlib/src/collections/list.mojo @@ -98,7 +98,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # Fields var data: UnsafePointer[T] """The underlying storage for the list.""" - var size: Int + var _len: Int """The number of elements in the list.""" var capacity: Int """The amount of elements that can fit in the list without resizing it.""" @@ -110,7 +110,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( fn __init__(out self): """Constructs an empty list.""" self.data = UnsafePointer[T]() - self.size = 0 + self._len = 0 self.capacity = 0 fn copy(self) -> Self: @@ -131,7 +131,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( capacity: The requested capacity of the list. """ self.data = UnsafePointer[T].alloc(capacity) - self.size = 0 + self._len = 0 self.capacity = capacity fn __init__(out self, owned *values: T): @@ -161,7 +161,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # Do not destroy the elements when their backing storage goes away. __disable_del elements - self.size = length + self._len = length fn __init__(out self, span: Span[T]): """Constructs a list from the a Span of values. @@ -184,7 +184,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( capacity: The capacity of the list. """ self.data = ptr - self.size = length + self._len = length self.capacity = capacity fn __moveinit__(out self, owned existing: Self): @@ -194,7 +194,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( existing: The existing list. """ self.data = existing.data - self.size = existing.size + self._len = existing._len self.capacity = existing.capacity fn __copyinit__(out self, existing: Self): @@ -212,7 +212,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( @parameter if not hint_trivial_type: - for i in range(self.size): + for i in range(len(self)): (self.data + i).destroy_pointee() self.data.free() @@ -369,13 +369,14 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # Trait implementations # ===-------------------------------------------------------------------===# + @always_inline("nodebug") fn __len__(self) -> Int: """Gets the number of elements in the list. Returns: The number of elements in the list. """ - return self.size + return self._len fn __bool__(self) -> Bool: """Checks whether the list has any elements or not. @@ -411,7 +412,9 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Returns: A string representation of the list. """ - var output = String() + # at least 1 byte per item e.g.: [a, b, c, d] = 4 + 2 * 3 + [] + null + var l = len(self) + var output = String(capacity=l + 2 * (l - 1) * Int(l > 1) + 3) self.write_to(output) return output^ @@ -478,11 +481,12 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( fn _realloc(mut self, new_capacity: Int): var new_data = UnsafePointer[T].alloc(new_capacity) - _move_pointee_into_many_elements[hint_trivial_type]( - dest=new_data, - src=self.data, - size=self.size, - ) + @parameter + if hint_trivial_type: + memcpy(new_data, self.data, len(self)) + else: + for i in range(len(self)): + (self.data + i).move_pointee_into(new_data + i) if self.data: self.data.free() @@ -499,10 +503,10 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( If there is no capacity left, resizes to twice the current capacity. Except for 0 capacity where it sets 1. """ - if self.size >= self.capacity: + if self._len >= self.capacity: self._realloc(self.capacity * 2 | Int(self.capacity == 0)) self._unsafe_next_uninit_ptr().init_pointee_move(value^) - self.size += 1 + self._len += 1 fn insert(mut self, i: Int, owned value: T): """Inserts a value to the list at the given index. @@ -512,7 +516,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( i: The index for the value. value: The value to insert. """ - debug_assert(i <= self.size, "insert index out of range") + debug_assert(i <= len(self), "insert index out of range") var normalized_idx = i if i < 0: @@ -573,7 +577,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # visible outside this function if a `__moveinit__()` constructor were # to throw (not currently possible AFAIK though) part way through the # logic below. - other.size = 0 + other._len = 0 var dest_ptr = self.data + len(self) @@ -590,7 +594,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( # Update the size now that all new elements have been moved into this # list. - self.size = final_size + self._len = final_size fn extend[ D: DType, // @@ -606,9 +610,9 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Notes: If there is no capacity left, resizes to `len(self) + value.size`. """ - self.reserve(self.size + value.size) + self.reserve(self._len + value.size) self._unsafe_next_uninit_ptr().store(value) - self.size += value.size + self._len += value.size fn extend[ D: DType, // @@ -627,10 +631,10 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( If there is no capacity left, resizes to `len(self) + count`. """ debug_assert(count <= value.size, "count must be <= value.size") - self.reserve(self.size + count) + self.reserve(self._len + count) var v_ptr = UnsafePointer.address_of(value).bitcast[Scalar[D]]() memcpy(self._unsafe_next_uninit_ptr(), v_ptr, count) - self.size += count + self._len += count fn extend[ D: DType, // @@ -646,9 +650,9 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Notes: If there is no capacity left, resizes to `len(self) + len(value)`. """ - self.reserve(self.size + len(value)) + self.reserve(self._len + len(value)) memcpy(self._unsafe_next_uninit_ptr(), value.unsafe_ptr(), len(value)) - self.size += len(value) + self._len += len(value) fn pop(mut self, i: Int = -1) -> T: """Pops a value from the list at the given index. @@ -659,17 +663,17 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Returns: The popped value. """ - debug_assert(-len(self) <= i < len(self), "pop index out of range") + debug_assert(-self._len <= i < self._len, "pop index out of range") var normalized_idx = i if i < 0: - normalized_idx += len(self) + normalized_idx += self._len var ret_val = (self.data + normalized_idx).take_pointee() - for j in range(normalized_idx + 1, self.size): + for j in range(normalized_idx + 1, self._len): (self.data + j).move_pointee_into(self.data + j - 1) - self.size -= 1 - if self.size * 4 < self.capacity: + self._len -= 1 + if self._len * 4 < self.capacity: if self.capacity > 1: self._realloc(self.capacity // 2) return ret_val^ @@ -698,13 +702,13 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( new_size: The new size. value: The value to use to populate new elements. """ - if new_size <= self.size: + if new_size <= self._len: self.resize(new_size) else: self.reserve(new_size) - for i in range(self.size, new_size): + for i in range(self._len, new_size): (self.data + i).init_pointee_copy(value) - self.size = new_size + self._len = new_size fn resize(mut self, new_size: Int): """Resizes the list to the given new size. @@ -715,16 +719,16 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( Args: new_size: The new size. """ - if self.size < new_size: + if len(self) < new_size: abort( "You are calling List.resize with a new_size bigger than the" " current size. If you want to make the List bigger, provide a" " value to fill the new slots with. If not, make sure the new" " size is smaller than the current size." ) - for i in range(new_size, self.size): + for i in range(new_size, len(self)): (self.data + i).destroy_pointee() - self.size = new_size + self._len = new_size self.reserve(new_size) fn reverse(mut self): @@ -831,9 +835,9 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( fn clear(mut self): """Clears the elements in the list.""" - for i in range(self.size): + for i in range(self._len): (self.data + i).destroy_pointee() - self.size = 0 + self._len = 0 fn steal_data(mut self) -> UnsafePointer[T]: """Take ownership of the underlying pointer from the list. @@ -843,7 +847,7 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( """ var ptr = self.data self.data = UnsafePointer[T]() - self.size = 0 + self._len = 0 self.capacity = 0 return ptr @@ -891,11 +895,11 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( else: var normalized_idx = Int(idx) debug_assert( - -self.size <= normalized_idx < self.size, + -self._len <= normalized_idx < self._len, "index: ", normalized_idx, - " is out of bounds for `List` of size: ", - self.size, + " is out of bounds for `List` of length: ", + self._len, ) if normalized_idx < 0: normalized_idx += len(self) @@ -1048,24 +1052,24 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( - This pointer MUST not be used to read or write memory beyond the allocated capacity of this list. - This pointer may not be used to initialize non-contiguous elements. - - Ensure that `List.size` is updated to reflect the new number of + - Ensure that `List._len` is updated to reflect the new number of initialized elements, otherwise elements may be unexpectedly overwritten or not destroyed correctly. """ debug_assert( - self.capacity > 0 and self.capacity > self.size, + self.capacity > 0 and self.capacity > self._len, ( "safety violation: Insufficient capacity to retrieve pointer to" " next uninitialized element" ), ) - return self.data + self.size + return self.data + self._len fn _cast_hint_trivial_type[ hint_trivial_type: Bool ](owned self) -> List[T, hint_trivial_type]: - var size = self.size + var size = self._len var capacity = self.capacity # TODO: Why doesn't `__disable_del self` work here? @@ -1078,18 +1082,3 @@ struct List[T: CollectionElement, hint_trivial_type: Bool = False]( fn _clip(value: Int, start: Int, end: Int) -> Int: return max(start, min(value, end)) - - -fn _move_pointee_into_many_elements[ - T: CollectionElement, //, hint_trivial_type: Bool -](dest: UnsafePointer[T], src: UnsafePointer[T], size: Int): - @parameter - if hint_trivial_type: - memcpy( - dest=dest.bitcast[Int8](), - src=src.bitcast[Int8](), - count=size * sizeof[T](), - ) - else: - for i in range(size): - (src + i).move_pointee_into(dest + i) diff --git a/mojo/stdlib/src/collections/string/_unicode.mojo b/mojo/stdlib/src/collections/string/_unicode.mojo index 4ec5cd280e..04613c373e 100644 --- a/mojo/stdlib/src/collections/string/_unicode.mojo +++ b/mojo/stdlib/src/collections/string/_unicode.mojo @@ -183,22 +183,21 @@ fn to_lowercase(s: StringSlice) -> String: ) var lowercase_char_opt = _get_lowercase_mapping(rune_and_size[0]) if lowercase_char_opt is None: - memcpy( - output._unsafe_next_uninit_ptr(), - input + input_offset, - rune_and_size[1], + output.extend( + Span[Byte, s.origin]( + ptr=input + input_offset, length=rune_and_size[1] + ) ) - output.size += rune_and_size[1] else: var lower_char: Codepoint = lowercase_char_opt.unsafe_value() - output.size += lower_char.unsafe_write_utf8( + output._len += lower_char.unsafe_write_utf8( output._unsafe_next_uninit_ptr() ) input_offset += rune_and_size[1] # Check if we need to reserve additional capacity. - if output.size >= output.capacity - 5: + if len(output) >= output.capacity - 5: output.reserve( output.capacity + _estimate_needed_size(s.byte_length() - input_offset) @@ -238,21 +237,20 @@ fn to_uppercase(s: StringSlice) -> String: ) for char_idx in range(count): var char: Codepoint = uppercase_replacement_chars[char_idx] - output.size += char.unsafe_write_utf8( + output._len += char.unsafe_write_utf8( output._unsafe_next_uninit_ptr() ) else: - memcpy( - output._unsafe_next_uninit_ptr(), - input + input_offset, - rune_and_size[1], + output.extend( + Span[Byte, s.origin]( + ptr=input + input_offset, length=rune_and_size[1] + ) ) - output.size += rune_and_size[1] input_offset += rune_and_size[1] # Check if we need to reserve additional capacity. - if output.size >= output.capacity - 5: + if len(output) >= output.capacity - 5: output.reserve( output.capacity + _estimate_needed_size(s.byte_length() - input_offset) diff --git a/mojo/stdlib/src/collections/string/codepoint.mojo b/mojo/stdlib/src/collections/string/codepoint.mojo index 934d74ce49..1974d40318 100644 --- a/mojo/stdlib/src/collections/string/codepoint.mojo +++ b/mojo/stdlib/src/collections/string/codepoint.mojo @@ -286,7 +286,7 @@ struct Codepoint(CollectionElement, EqualityComparable, Intable, Stringable): var buffer = List[Byte](capacity=char_len + 1) _ = self.unsafe_write_utf8(buffer.unsafe_ptr()) buffer.unsafe_ptr()[char_len] = 0 - buffer.size = char_len + 1 + buffer._len = char_len + 1 return String(buffer=buffer^) # ===-------------------------------------------------------------------===# diff --git a/mojo/stdlib/src/collections/string/format.mojo b/mojo/stdlib/src/collections/string/format.mojo index d1e8d34411..ec2935b9e5 100644 --- a/mojo/stdlib/src/collections/string/format.mojo +++ b/mojo/stdlib/src/collections/string/format.mojo @@ -147,9 +147,8 @@ struct _FormatCurlyEntry(CollectionElement, CollectionElementNew): entries, size_estimation = Self._create_entries(fmt_src, len_pos_args) var fmt_len = fmt_src.byte_length() var buf = String._buffer_type(capacity=fmt_len + size_estimation) - buf.size = 1 - buf.unsafe_set(0, 0) - var res = String(buf^) + buf.append(0) + var res = String(buffer=buf^) var offset = 0 var ptr = fmt_src.unsafe_ptr() alias S = StringSlice[StaticConstantOrigin] diff --git a/mojo/stdlib/src/collections/string/string.mojo b/mojo/stdlib/src/collections/string/string.mojo index 304d33b6cc..85e121f004 100644 --- a/mojo/stdlib/src/collections/string/string.mojo +++ b/mojo/stdlib/src/collections/string/string.mojo @@ -748,7 +748,7 @@ struct String( bytes: The byte span to write to this String. Must NOT be null terminated. """ - self._iadd[False](bytes) + self._iadd(bytes) fn write[*Ts: Writable](mut self, *args: *Ts): """Write a sequence of Writable arguments to the provided Writer. @@ -841,7 +841,7 @@ struct String( if buff[-1]: buff.append(0) - return String(buff^) + return String(buffer=buff^) # ===------------------------------------------------------------------=== # # Operator dunders @@ -861,10 +861,7 @@ struct String( """ # TODO(#933): implement this for unicode when we support llvm intrinsic evaluation at compile time var normalized_idx = normalize_index["String"](idx, len(self)) - var buf = Self._buffer_type(capacity=1) - buf.append(self._buffer[normalized_idx]) - buf.append(0) - return String(buf^) + return String(buffer=Self._buffer_type(self._buffer[normalized_idx], 0)) fn __getitem__(self, span: Slice) -> String: """Gets the sequence of characters at the specified positions. @@ -889,14 +886,12 @@ struct String( ) ) - var buffer = Self._buffer_type() - var result_len = len(r) - buffer.resize(result_len + 1, 0) + var buffer = Self._buffer_type(capacity=len(r) + 1) var ptr = self.unsafe_ptr() - for i in range(result_len): - buffer[i] = ptr[r[i]] - buffer[result_len] = 0 - return Self(buffer^) + for i in r: + buffer.append(ptr[i]) + buffer.append(0) + return String(buffer=buffer^) @always_inline fn __eq__(self, other: String) -> Bool: @@ -982,27 +977,19 @@ struct String( return not (self < rhs) @staticmethod - fn _add[rhs_has_null: Bool](lhs: Span[Byte], rhs: Span[Byte]) -> String: + fn _add(lhs: Span[Byte], rhs: Span[Byte]) -> String: var lhs_len = len(lhs) var rhs_len = len(rhs) - var lhs_ptr = lhs.unsafe_ptr() - var rhs_ptr = rhs.unsafe_ptr() alias S = StringSlice[ImmutableAnyOrigin] if lhs_len == 0: - return String(S(ptr=rhs_ptr, length=rhs_len)) + return String(S(ptr=rhs.unsafe_ptr(), length=rhs_len)) elif rhs_len == 0: - return String(S(ptr=lhs_ptr, length=lhs_len)) - var sum_len = lhs_len + rhs_len - var buffer = Self._buffer_type(capacity=sum_len + 1) - var ptr = buffer.unsafe_ptr() - memcpy(ptr, lhs_ptr, lhs_len) - memcpy(ptr + lhs_len, rhs_ptr, rhs_len + Int(rhs_has_null)) - buffer.size = sum_len + 1 - - @parameter - if not rhs_has_null: - ptr[sum_len] = 0 - return Self(buffer^) + return String(S(ptr=lhs.unsafe_ptr(), length=lhs_len)) + var buffer = Self._buffer_type(capacity=lhs_len + rhs_len + 1) + buffer.extend(lhs) + buffer.extend(rhs) + buffer.append(0) + return String(buffer=buffer^) @always_inline fn __add__(self, other: StringSlice) -> String: @@ -1014,7 +1001,7 @@ struct String( Returns: The new constructed string. """ - return Self._add[False](self.as_bytes(), other.as_bytes()) + return Self._add(self.as_bytes(), other.as_bytes()) @always_inline fn __radd__(self, other: StringSlice) -> String: @@ -1026,27 +1013,17 @@ struct String( Returns: The new constructed string. """ - return Self._add[True](other.as_bytes(), self.as_bytes()) + return Self._add(other.as_bytes(), self.as_bytes()) - fn _iadd[has_null: Bool](mut self, other: Span[Byte]): - var s_len = self.byte_length() + fn _iadd(mut self, other: Span[Byte]): var o_len = len(other) - var o_ptr = other.unsafe_ptr() - if s_len == 0: - alias S = StringSlice[ImmutableAnyOrigin] - self = String(S(ptr=o_ptr, length=o_len)) - return - elif o_len == 0: + if o_len == 0: return - var sum_len = s_len + o_len - self._buffer.reserve(sum_len + 1) - var s_ptr = self.unsafe_ptr() - memcpy(s_ptr + s_len, o_ptr, o_len + Int(has_null)) - self._buffer.size = sum_len + 1 - - @parameter - if not has_null: - s_ptr[sum_len] = 0 + self._buffer.reserve(self.byte_length() + o_len + 1) + if len(self._buffer) > 0: + _ = self._buffer.pop() + self._buffer.extend(other) + self._buffer.append(0) @always_inline fn __iadd__(mut self, other: StringSlice): @@ -1055,7 +1032,7 @@ struct String( Args: other: The string to append. """ - self._iadd[False](other.as_bytes()) + self._iadd(other.as_bytes()) @deprecated("Use `str.codepoints()` or `str.codepoint_slices()` instead.") fn __iter__(self) -> CodepointSliceIter[__origin_of(self)]: @@ -1366,11 +1343,7 @@ struct String( Returns: The pointer to the underlying memory. """ - var ptr = self.unsafe_ptr() - self._buffer.data = UnsafePointer[UInt8]() - self._buffer.size = 0 - self._buffer.capacity = 0 - return ptr + return self._buffer.steal_data() fn count(self, substr: StringSlice) -> Int: """Return the number of non-overlapping occurrences of substring diff --git a/mojo/stdlib/src/collections/string/string_slice.mojo b/mojo/stdlib/src/collections/string/string_slice.mojo index e1c6bf41ca..7ec78dc993 100644 --- a/mojo/stdlib/src/collections/string/string_slice.mojo +++ b/mojo/stdlib/src/collections/string/string_slice.mojo @@ -939,15 +939,11 @@ struct StringSlice[mut: Bool, //, origin: Origin[mut]]( The string concatenated `n` times. """ - var len_self = self.byte_length() - var count = len_self * n + 1 - var buf = String._buffer_type(capacity=count) - buf.size = count - var b_ptr = buf.unsafe_ptr() + var buffer = List[Byte](capacity=self.byte_length() * n + 1) for i in range(n): - memcpy(b_ptr + len_self * i, self.unsafe_ptr(), len_self) - b_ptr[count - 1] = 0 - return String(buf^) + buffer.extend(self.as_bytes()) + buffer.append(0) + return String(buffer=buffer) # ===------------------------------------------------------------------===# # Methods diff --git a/mojo/stdlib/src/memory/span.mojo b/mojo/stdlib/src/memory/span.mojo index 290c720f61..70a0482a52 100644 --- a/mojo/stdlib/src/memory/span.mojo +++ b/mojo/stdlib/src/memory/span.mojo @@ -166,7 +166,7 @@ struct Span[ list: The list to which the span refers. """ self._data = list.data.address_space_cast[address_space]() - self._len = list.size + self._len = list._len @always_inline @implicit diff --git a/mojo/stdlib/src/pathlib/path.mojo b/mojo/stdlib/src/pathlib/path.mojo index 38f8683dc5..ce5cff99fa 100644 --- a/mojo/stdlib/src/pathlib/path.mojo +++ b/mojo/stdlib/src/pathlib/path.mojo @@ -151,7 +151,7 @@ struct Path( Returns: A string representation of the path. """ - return String.write(self) + return self.path @always_inline fn __bool__(self) -> Bool: diff --git a/mojo/stdlib/test/collections/test_list_getitem_invalid_index.mojo b/mojo/stdlib/test/collections/test_list_getitem_invalid_index.mojo index 34ef22d26c..09bb5c0a45 100644 --- a/mojo/stdlib/test/collections/test_list_getitem_invalid_index.mojo +++ b/mojo/stdlib/test/collections/test_list_getitem_invalid_index.mojo @@ -17,7 +17,7 @@ # CHECK-FAIL-LABEL: test_fail_list_index fn main(): print("== test_fail_list_index") - # CHECK-FAIL: index: 4 is out of bounds for `List` of size: 3 + # CHECK-FAIL: index: 4 is out of bounds for `List` of length: 3 nums = List[Int](1, 2, 3) print(nums[4]) From 87b50fcbd0fe7a321b4fac191866d146bac732a9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Thomas=20B=C3=B8rstad?= Date: Thu, 27 Feb 2025 16:38:34 +0100 Subject: [PATCH 480/509] [Pipelines] Remove dependency on scipy (#56734) MODULAR_ORIG_COMMIT_REV_ID: c39bb49bc0f7d0f5207646ac54bf86e325f62553 --- .../pipelines/nn/compute_log_probabilities.py | 33 ++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/src/max/pipelines/nn/compute_log_probabilities.py b/src/max/pipelines/nn/compute_log_probabilities.py index 2914c34582..150ae74479 100644 --- a/src/max/pipelines/nn/compute_log_probabilities.py +++ b/src/max/pipelines/nn/compute_log_probabilities.py @@ -17,7 +17,38 @@ import numpy as np from max.pipelines import LogProbabilities -from scipy.special import log_softmax # type: ignore + + +def log_softmax(x: np.ndarray, axis: int = -1) -> np.ndarray: + """Compute the logarithm of the softmax function. + + This implementation uses the identity log(softmax(x)) = x - log(sum(exp(x))) + with numerical stability improvements to prevent overflow/underflow. + + Args: + x: Input array + axis: Axis to compute values along + + Returns: + Array with same shape as x, representing log(softmax(x)) + """ + # Subtract max value for numerical stability (prevents exp overflow) + x_max = np.amax(x, axis=axis, keepdims=True) + + # Compute exp(x - x_max) which is now safe from overflow + shifted_x = x - x_max + exp_shifted = np.exp(shifted_x) + + # Suppress -inf warnings from log(0) + # This can happen when input contains extreme negative values (-inf), + # which become 0 after exp() operation + with np.errstate(divide="ignore"): + sum_exp = np.sum(exp_shifted, axis=axis, keepdims=True) + log_sum_exp = np.log(sum_exp) + + # Final result: x - x_max - log(sum(exp(x - x_max))) + # This is mathematically equivalent to log(softmax(x)) + return shifted_x - log_sum_exp def compute_log_probabilities( From 56813a471e25a2be23c0aaf13938b84399268214 Mon Sep 17 00:00:00 2001 From: Patrick Dougherty Date: Thu, 27 Feb 2025 09:44:48 -0600 Subject: [PATCH 481/509] [CI] Update scripts for repo changes MODULAR_ORIG_COMMIT_REV_ID: c83157382bee3bb3eae3aa90ddb4921903febfcf --- examples/mojo/lit.cfg.py | 71 +++++++++++++++++++ examples/mojo/run-examples.sh | 25 +++++++ .../standard_library_tests_and_examples.yml | 1 + mojo/pixi.toml | 2 +- 4 files changed, 98 insertions(+), 1 deletion(-) create mode 100644 examples/mojo/lit.cfg.py create mode 100755 examples/mojo/run-examples.sh diff --git a/examples/mojo/lit.cfg.py b/examples/mojo/lit.cfg.py new file mode 100644 index 0000000000..f4bc0fc233 --- /dev/null +++ b/examples/mojo/lit.cfg.py @@ -0,0 +1,71 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +import os +from pathlib import Path + +import lit.formats +import lit.llvm + +config.test_format = lit.formats.ShTest(True) + +# name: The name of this test suite. +config.name = "Mojo Public Examples" + +# suffixes: A list of file extensions to treat as test files. +# TODO: Enable notebooks +config.suffixes = [".mojo", ".🔥"] + +config.excludes = [ + # No RUN: directive, just bare examples + "hello_interop.mojo", + "matmul.mojo", +] + [path.name for path in os.scandir("../examples/mojo") if path.is_dir()] + +# Have the examples run in the build directory. +# The `run-examples.sh` script creates the build directory. +build_root = Path.cwd().parent.parent / "mojo" / "build" + +# Execute the examples inside this part of the build +# directory to avoid polluting the source tree. +config.test_exec_root = build_root / "examples" / "mojo" + +# test_source_root: The root path where tests are located. +config.test_source_root = Path(__file__).parent.resolve() + +# Substitute %mojo for just `mojo` itself. +config.substitutions.insert(0, ("%mojo", "mojo")) + +pre_built_packages_path = os.environ.get( + "MODULAR_MOJO_NIGHTLY_IMPORT_PATH", + Path(os.environ["MODULAR_HOME"]) + / "pkg" + / "packages.modular.com_nightly_mojo" + / "lib" + / "mojo", +) + +os.environ[ + "MODULAR_MOJO_NIGHTLY_IMPORT_PATH" +] = f"{build_root},{pre_built_packages_path}" + +# Pass through several environment variables +# to the underlying subprocesses that run the tests. +lit.llvm.initialize(lit_config, config) +lit.llvm.llvm_config.with_system_environment( + [ + "MODULAR_HOME", + "MODULAR_MOJO_NIGHTLY_IMPORT_PATH", + "PATH", + ] +) diff --git a/examples/mojo/run-examples.sh b/examples/mojo/run-examples.sh new file mode 100755 index 0000000000..323239e1a6 --- /dev/null +++ b/examples/mojo/run-examples.sh @@ -0,0 +1,25 @@ +#!/usr/bin/env bash +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +set -euo pipefail + +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) +REPO_ROOT="${SCRIPT_DIR}"/../../ +EXAMPLES_DIR="${REPO_ROOT}"/examples/mojo + +BUILD_DIR="${REPO_ROOT}"/mojo/build +mkdir -p "${BUILD_DIR}" + +# Run the examples using `lit`. +lit -sv "${EXAMPLES_DIR}" diff --git a/mojo/.github/workflows/standard_library_tests_and_examples.yml b/mojo/.github/workflows/standard_library_tests_and_examples.yml index 2f13005fb3..14a4f3dc45 100644 --- a/mojo/.github/workflows/standard_library_tests_and_examples.yml +++ b/mojo/.github/workflows/standard_library_tests_and_examples.yml @@ -62,6 +62,7 @@ jobs: - name: Run standard library tests and examples env: MOJO_ENABLE_ASSERTIONS_IN_TESTS: ${{ matrix.mojo-enable-assertions }} + working-directory: mojo run: | magic run --frozen tests magic run --frozen examples diff --git a/mojo/pixi.toml b/mojo/pixi.toml index 37772a1b5e..5a4c015d34 100644 --- a/mojo/pixi.toml +++ b/mojo/pixi.toml @@ -7,7 +7,7 @@ platforms = ["linux-64", "linux-aarch64", "osx-arm64"] [tasks] build = "./stdlib/scripts/build-stdlib.sh" tests = "./stdlib/scripts/run-tests.sh" -examples = "./examples/run-examples.sh" +examples = "../examples/mojo/run-examples.sh" benchmarks = { cmd = ["./stdlib/scripts/run-benchmarks.sh"], env = { MODULAR_MOJO_NIGHTLY_IMPORT_PATH = "$CONDA_PREFIX/lib/mojo" } } [dependencies] From 479c0eb1f1ee53769dec2003c439401ad48dbc8b Mon Sep 17 00:00:00 2001 From: Ewa Matejska Date: Thu, 27 Feb 2025 08:04:56 -0800 Subject: [PATCH 482/509] [CI][Docs] Updating readmes and .github for merge of max/mojo repos MODULAR_ORIG_COMMIT_REV_ID: 5d22e1cb3a7fece09567d5460ccd51c18b9d587f --- .github/CODEOWNERS | 18 +++++ .github/ISSUE_TEMPLATE/CODEOWNERS | 5 -- .github/ISSUE_TEMPLATE/magic_issue.yaml | 61 +++++++++++++++++ .github/ISSUE_TEMPLATE/max_bug_report.yaml | 2 +- .../ISSUE_TEMPLATE/max_builds_bug_report.yaml | 2 +- .../ISSUE_TEMPLATE/max_feature_request.yaml | 2 +- .github/ISSUE_TEMPLATE/mojo_bug_report.yaml | 61 +++++++++++++++++ .../ISSUE_TEMPLATE/mojo_feature_request.yaml | 63 +++++++++++++++++ .github/PULL_REQUEST_TEMPLATE.md | 10 +++ .github/workflows/check_pr_target.yml | 28 ++++++++ .github/workflows/check_pr_title.yml | 53 +++++++++++++++ .../standard_library_tests_and_examples.yml | 68 +++++++++++++++++++ .github/workflows/test_pre_commit.yml | 52 ++++++++++++++ README.md | 8 ++- examples/README.md | 5 ++ examples/mojo/README.md | 2 +- examples/mojo/check_mod.py | 2 +- mojo/CONTRIBUTING.md | 14 ++-- mojo/README.md | 18 ++--- mojo/proposals/byte-as-uint8.md | 2 +- mojo/proposals/inferred-parameters.md | 2 +- mojo/proposals/lifetimes-keyword-renaming.md | 2 +- mojo/proposals/ref-convention.md | 4 +- mojo/proposals/remove-let-decls.md | 10 +-- mojo/stdlib/README.md | 2 +- mojo/stdlib/docs/development.md | 4 +- mojo/stdlib/src/builtin/simd.mojo | 2 +- mojo/stdlib/src/collections/linked_list.mojo | 2 +- mojo/stdlib/test/builtin/test_issue_1004.mojo | 2 +- mojo/stdlib/test/builtin/test_issue_1505.mojo | 2 +- mojo/stdlib/test/builtin/test_simd.mojo | 4 +- mojo/stdlib/test/collections/test_list.mojo | 2 +- 32 files changed, 466 insertions(+), 48 deletions(-) create mode 100644 .github/CODEOWNERS delete mode 100644 .github/ISSUE_TEMPLATE/CODEOWNERS create mode 100644 .github/ISSUE_TEMPLATE/magic_issue.yaml create mode 100644 .github/ISSUE_TEMPLATE/mojo_bug_report.yaml create mode 100644 .github/ISSUE_TEMPLATE/mojo_feature_request.yaml create mode 100644 .github/PULL_REQUEST_TEMPLATE.md create mode 100644 .github/workflows/check_pr_target.yml create mode 100644 .github/workflows/check_pr_title.yml create mode 100644 .github/workflows/standard_library_tests_and_examples.yml create mode 100644 .github/workflows/test_pre_commit.yml diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000000..97ce248c98 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,18 @@ +# Codeowners for MAX repo. +# Every line is a file pattern that is followed by one or more code owners. +# Order is important; the last matching pattern takes the most precedence. + +# Standard Library Sources +/mojo/stdlib/ @modular/stdlib + +# Documentation +/mojo/docs/ @modular/mojo-docs + +/mojo/docs/changelog.md @modular/stdlib + +/mojo/stdlib/docs/ @modular/mojo-docs @modular/stdlib + +# Examples +/examples/mojo/ @jackos + +* @modularml/max-code-reviewers diff --git a/.github/ISSUE_TEMPLATE/CODEOWNERS b/.github/ISSUE_TEMPLATE/CODEOWNERS deleted file mode 100644 index 303c1ec52f..0000000000 --- a/.github/ISSUE_TEMPLATE/CODEOWNERS +++ /dev/null @@ -1,5 +0,0 @@ -# Codeowners for MAX repo. -# Every line is a file pattern that is followed by one or more code owners. -# Order is important; the last matching pattern takes the most precedence. - -* @modularml/max-code-reviewers diff --git a/.github/ISSUE_TEMPLATE/magic_issue.yaml b/.github/ISSUE_TEMPLATE/magic_issue.yaml new file mode 100644 index 0000000000..0f5ce15b62 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/magic_issue.yaml @@ -0,0 +1,61 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: Magic issue report +description: Create an issue to help us improve Magic +title: "[Magic]" +labels: + - "magic" +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out a bug report! + + Please provide a descriptive title above and fill in the following fields. + + - type: textarea + id: Description + attributes: + label: Bug description + description: Describe the bug you encountered and what you expected to happen. + value: | + ### Actual behavior + + + ### Expected behavior + + validations: + required: true + + - type: textarea + id: Steps + attributes: + label: Steps to reproduce + description: Provide the specific steps to reproduce the issue. + value: | + - Provide a relevant code snippet or a link to the code that did not work as expected. + - If applicable, add screenshots to help explain the problem. + - If using the Playground, name the pre-existing notebook that failed and the steps that led to failure. + - Include anything else that might help us debug the issue. + validations: + required: true + + - type: textarea + id: Context + attributes: + label: System information + description: What version of Mojo are you using? + value: | + - Provide the system information by running `magic info`. + - Provide version information for MAX (includes Mojo) by pasting the output of `magic list max`. diff --git a/.github/ISSUE_TEMPLATE/max_bug_report.yaml b/.github/ISSUE_TEMPLATE/max_bug_report.yaml index 573a7069e2..097602640a 100644 --- a/.github/ISSUE_TEMPLATE/max_bug_report.yaml +++ b/.github/ISSUE_TEMPLATE/max_bug_report.yaml @@ -15,7 +15,7 @@ name: MAX bug report description: Create a bug report to help us improve MAX title: "[BUG]: " labels: - - bug + - "bug,max" body: - type: markdown attributes: diff --git a/.github/ISSUE_TEMPLATE/max_builds_bug_report.yaml b/.github/ISSUE_TEMPLATE/max_builds_bug_report.yaml index 0c43f15bac..65faa039e5 100644 --- a/.github/ISSUE_TEMPLATE/max_builds_bug_report.yaml +++ b/.github/ISSUE_TEMPLATE/max_builds_bug_report.yaml @@ -15,7 +15,7 @@ name: MAX Builds model bug report description: Report a bug with a model title: "[BUG]: " labels: - - bug + - max-builds body: - type: markdown attributes: diff --git a/.github/ISSUE_TEMPLATE/max_feature_request.yaml b/.github/ISSUE_TEMPLATE/max_feature_request.yaml index 57e6716fb8..b9a61bd61a 100644 --- a/.github/ISSUE_TEMPLATE/max_feature_request.yaml +++ b/.github/ISSUE_TEMPLATE/max_feature_request.yaml @@ -15,7 +15,7 @@ name: MAX feature request description: Suggest an enhancement for MAX title: "[Feature Request]" labels: - - enhancement + - "enhancement,max" body: - type: markdown attributes: diff --git a/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml b/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml new file mode 100644 index 0000000000..57b19d978c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/mojo_bug_report.yaml @@ -0,0 +1,61 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: Mojo bug report +description: Create a bug report to help us improve Mojo +title: "[BUG]" +labels: + - "bug,mojo" +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out a bug report! + + Please provide a descriptive title above and fill in the following fields. + + - type: textarea + id: Description + attributes: + label: Bug description + description: Describe the bug you encountered and what you expected to happen. + value: | + ### Actual behavior + + + ### Expected behavior + + validations: + required: true + + - type: textarea + id: Steps + attributes: + label: Steps to reproduce + description: Provide the specific steps to reproduce the issue. + value: | + - Provide a relevant code snippet or a link to the code that did not work as expected. + - If applicable, add screenshots to help explain the problem. + - If using the Playground, name the pre-existing notebook that failed and the steps that led to failure. + - Include anything else that might help us debug the issue. + validations: + required: true + + - type: textarea + id: Context + attributes: + label: System information + description: What version of Mojo are you using? + value: | + - Provide the system information by running `magic info`. + - Provide version information for MAX (includes Mojo) by pasting the output of `magic list max`. diff --git a/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml b/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml new file mode 100644 index 0000000000..a5ddc32a58 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/mojo_feature_request.yaml @@ -0,0 +1,63 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + +name: Mojo feature request +description: Suggest an enhancement for Mojo +title: "[Feature Request]" +labels: + - "enhancement,mojo" +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to suggest a Mojo enhancement! + + Please enter a concise title above and fill out the following fields. + + - type: checkboxes + id: Roadmap + attributes: + label: Review Mojo's priorities + description: Please take a look at our roadmap before you file a new feature request. + options: + - label: I have read the [roadmap and priorities](https://docs.modular.com/mojo/roadmap.html#overall-priorities) and I believe this request falls within the priorities. + required: true + + - type: markdown + attributes: + value: | + If the request is out of the published roadmap and priorities, please start a [discussion](https://forum.modular.com/) in the Modular Forum to get feedback from the team. + + - type: textarea + id: Request + attributes: + label: What is your request? + description: Describe how you'd like us to improve Mojo. + validations: + required: true + + - type: textarea + id: Motivation + attributes: + label: What is your motivation for this change? + description: Describe the problem that your feature seeks to address (what is the value to the product/user?). + validations: + required: true + + - type: textarea + id: Description + attributes: + label: Any other details? + description: Perhaps some minimum functional attributes the implementation should include, or other context about your feature. + validations: + required: false diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000..2cd5f0bf30 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,10 @@ + diff --git a/.github/workflows/check_pr_target.yml b/.github/workflows/check_pr_target.yml new file mode 100644 index 0000000000..4e333baf2a --- /dev/null +++ b/.github/workflows/check_pr_target.yml @@ -0,0 +1,28 @@ +name: Check PR target branch + +on: + pull_request: + types: [opened, edited, synchronize, reopened, ready_for_review] + +permissions: + contents: read + pull-requests: read + +jobs: + check-pr-target: + name: Check PR target branch + + runs-on: ubuntu-latest + timeout-minutes: 10 + + defaults: + run: + shell: bash + + steps: + - name: Fail if not targeting main branch + if: ${{ github.base_ref != 'main' }} + run: | + echo "PRs must be targeted to merge to the main branch!" + echo "PR is currently targeting: \"${{ github.base_ref }}\"" + exit 1 diff --git a/.github/workflows/check_pr_title.yml b/.github/workflows/check_pr_title.yml new file mode 100644 index 0000000000..9b2f320a15 --- /dev/null +++ b/.github/workflows/check_pr_title.yml @@ -0,0 +1,53 @@ +name: Check PR title + +on: + pull_request: + # By default, a workflow only runs when a pull_request's activity type is opened, synchronize, or reopened. We + # explicitly override here so that PR titles are re-linted when the PR text content is edited. + # + # Possible values: https://help.github.com/en/actions/reference/events-that-trigger-workflows#pull-request-event-pull_request + types: [opened, edited, reopened, synchronize, ready_for_review] + +jobs: + check-pr-title: + name: Check PR title format + + permissions: + pull-requests: write + + runs-on: ubuntu-latest + timeout-minutes: 5 + + steps: + - uses: morrisoncole/pr-lint-action@51f3cfabaf5d46f94e54524214e45685f0401b2a + if: github.actor != 'dependabot[bot]' + with: + title-regex: "^(Revert \")?(\\[\\S.*\\]\\s?)+\\s+[a-zA-Z`].*" + repo-token: "${{ secrets.GITHUB_TOKEN }}" + on-failed-regex-fail-action: true + on-failed-regex-create-review: true + on-failed-regex-request-changes: true + on-failed-regex-comment: + "The PR title does not conform to the '[\\] Title' format. + Please update the PR title. + \n\n + Typical [\\] values include: + \n\n + * `[stdlib]` — indicates a change to the Mojo standard library code\n + * `[docs]` — indicates a change to the documentation + \n\n + It's okay to include multiple labels on a PR that + affect multiple areas of work. + \n\n + Thank you for contributing to Mojo!🔥 + \n\n + + You can also use a tool like www.regex101.com to see why your PR + title fails to conform. Use + ``` + %regex% + ``` + as the regex to test and ```${{ github.event.pull_request.title }}``` + as the test string." + on-succeeded-regex-dismiss-review-comment: + "All good now, thanks!🫸🫷" diff --git a/.github/workflows/standard_library_tests_and_examples.yml b/.github/workflows/standard_library_tests_and_examples.yml new file mode 100644 index 0000000000..980e1e8678 --- /dev/null +++ b/.github/workflows/standard_library_tests_and_examples.yml @@ -0,0 +1,68 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +name: Test stdlib and examples +on: + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + workflow_dispatch: + +permissions: + contents: read + pull-requests: read + +jobs: + test-examples: + name: with ${{ matrix.os }} and assertions=${{ matrix.mojo-enable-assertions }} + strategy: + fail-fast: false + matrix: + os: ["ubuntu-latest", "macos-14"] + mojo-enable-assertions: [0, 1] + + runs-on: ${{ matrix.os }} + timeout-minutes: 30 + + defaults: + run: + shell: bash + env: + DEBIAN_FRONTEND: noninteractive + + steps: + - name: Checkout repo + uses: actions/checkout@v4 + + - name: Download Magic CLI + run: | + curl -ssL https://magic.modular.com/cfba4c92-2390-4b86-93de-04b2f47114d5 | bash + # Add magic to PATH + echo "$HOME/.modular/bin" >> $GITHUB_PATH + + - name: Install build tools (Linux) + if: ${{ matrix.os == 'ubuntu-latest' }} + run: | + ./mojo/stdlib/scripts/install-build-tools-linux.sh + + - name: Install build tools (macOS) + if: ${{ matrix.os == 'macos-14' }} + run: | + ./mojo/stdlib/scripts/install-build-tools-macos.sh + + - name: Run standard library tests and examples + env: + MOJO_ENABLE_ASSERTIONS_IN_TESTS: ${{ matrix.mojo-enable-assertions }} + working-directory: mojo + run: | + magic run --frozen tests + magic run --frozen examples diff --git a/.github/workflows/test_pre_commit.yml b/.github/workflows/test_pre_commit.yml new file mode 100644 index 0000000000..99a2ddf73d --- /dev/null +++ b/.github/workflows/test_pre_commit.yml @@ -0,0 +1,52 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2024, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # + +name: Run pre-commit +on: + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + workflow_dispatch: + +permissions: + contents: read + pull-requests: read + +jobs: + lint: + runs-on: "ubuntu-latest" + timeout-minutes: 30 + + defaults: + run: + shell: bash + env: + DEBIAN_FRONTEND: noninteractive + + steps: + - name: Checkout repo + uses: actions/checkout@v4 + + - name: Download Magic CLI + run: | + curl -ssL https://magic.modular.com/cfba4c92-2390-4b86-93de-04b2f47114d5 | bash + # Add magic to PATH + echo "$HOME/.modular/bin" >> $GITHUB_PATH + + - name: Install pre-commit + run: | + pip install pre-commit + pre-commit install + + - name: Run pre-commit + run: magic run pre-commit run --all-files + diff --git a/README.md b/README.md index 92d8013410..24507ed98d 100644 --- a/README.md +++ b/README.md @@ -85,9 +85,13 @@ The MAX container image is available in the ## Contributing Thanks for your interest in contributing to this repository! -We are not accepting pull requests yet. -However, we welcome your bug reports. If you have a bug, please file an issue +We accept contributions to the [Mojo standard library](./mojo). +Please see the [Contribution Guide](mojo/CONTRIBUTING.md) for instructions. + +We are not accepting contributions for other parts of the repository. + +We also welcome your bug reports. If you have a bug, please file an issue [here](https://github.com/modular/max/issues/new/choose). If you need support, the [Discord](https://discord.gg/modular) diff --git a/examples/README.md b/examples/README.md index 0722ca6ec4..b4f2e87907 100644 --- a/examples/README.md +++ b/examples/README.md @@ -3,6 +3,11 @@ These examples demonstrate the power and flexibility of [MAX](https://docs.modular.com/max/). They include: +## [Mojo code examples](mojo/) + +A collection of sample programs written in the +[Mojo](https://docs.modular.com/mojo/manual/) programming language. + ## [Custom GPU and CPU operations in Mojo](custom_ops/) The [MAX Graph API](https://docs.modular.com/max/graph/) provides a powerful diff --git a/examples/mojo/README.md b/examples/mojo/README.md index 6c651cd92d..f40ecbc1ca 100644 --- a/examples/mojo/README.md +++ b/examples/mojo/README.md @@ -16,7 +16,7 @@ After you have set up a Mojo programming environment, you can then use `git` to clone this repository of Mojo samples using the command below: ```bash -git clone https://github.com/modular/mojo.git +git clone https://github.com/modular/max.git ``` ## Running diff --git a/examples/mojo/check_mod.py b/examples/mojo/check_mod.py index e2f2719906..36049a4fd7 100644 --- a/examples/mojo/check_mod.py +++ b/examples/mojo/check_mod.py @@ -18,7 +18,7 @@ FIX = """ ------------------------------------------------------------------------- fix following the steps here: - https://github.com/modular/mojo/issues/1085#issuecomment-1771403719 + https://github.com/modular/max/issues/1085#issuecomment-1771403719 ------------------------------------------------------------------------- """ diff --git a/mojo/CONTRIBUTING.md b/mojo/CONTRIBUTING.md index 4c39f7729a..14e6ee1a9a 100644 --- a/mojo/CONTRIBUTING.md +++ b/mojo/CONTRIBUTING.md @@ -210,7 +210,7 @@ which represents the most recent nightly build. Before you start your first pull request, please complete this checklist: - Read this entire contributor guide. -- Read the [Code of Conduct](./CODE_OF_CONDUCT.md). +- Read the [Code of Conduct](../CODE_OF_CONDUCT.md). #### Evaluate and get buy-in on the change @@ -222,7 +222,7 @@ for more details. #### Fork and clone the repo -Go to the [Mojo repo](https://github.com/modular/mojo) and click the fork +Go to the [MAX repo](https://github.com/modular/max) and click the fork button: ![Create Fork](stdlib/docs/images/create-fork.png) @@ -230,14 +230,14 @@ button: Clone your forked repo locally with the command: ```bash -git clone git@github.com:[your-username]/mojo.git -cd mojo +git clone git@github.com:[your-username]/max.git +cd max/mojo ``` Add the upstream remote and fetch it: ```bash -git remote add upstream git@github.com:modular/mojo.git +git remote add upstream git@github.com:modular/max.git git fetch upstream ``` @@ -311,10 +311,10 @@ You'll see a link to create a PR: ```plaintext remote: Create a pull request for 'my-fix-pr' on GitHub by visiting: -remote: https://github.com/[your-username]/mojo/pull/new/my-fix-pr +remote: https://github.com/[your-username]/max/pull/new/my-fix-pr ``` -It should automatically set the base branch to the upstream `modular/mojo/main`, +It should automatically set the base branch to the upstream `modular/max/main`, but if it doesn't, you can set it manually. Now fill out the details: diff --git a/mojo/README.md b/mojo/README.md index 2a4878c789..8e4682b38b 100644 --- a/mojo/README.md +++ b/mojo/README.md @@ -26,11 +26,11 @@ This repo includes source code for: This repo has two primary branches: -- The [`stable`](https://github.com/modular/mojo/tree/stable) branch, which +- The [`stable`](https://github.com/modular/max/tree/stable) branch, which is in sync with the last stable released version of Mojo. Use the examples here if you’re using a [release build of Mojo](#latest-released). -- The [`main`](https://github.com/modular/mojo/tree/main) branch, which +- The [`main`](https://github.com/modular/max/tree/main) branch, which is in sync with the Mojo nightly build and subject to breakage. Use this branch for [contributions](./CONTRIBUTING.md), or if you're using the latest [nightly build of Mojo](#latest-nightly). @@ -79,7 +79,7 @@ When you clone this repo, you'll be on the `main` branch by default, which includes code matching the latest nightly build: ```bash -git clone https://github.com/modular/mojo.git +git clone https://github.com/modular/max.git ``` If you want to instead see the source from the most recent stable @@ -88,13 +88,13 @@ release, then you can switch to the `stable` branch. ## Contributing When you want to report issues or request features, [please create a GitHub -issue here](https://github.com/modular/mojo/issues). +issue here](https://github.com/modular/max/issues). See [here](./CONTRIBUTING.md) for guidelines on filing good bugs. -We welcome contributions to this repo on the -[`main`](https://github.com/modular/mojo/tree/main) +We welcome contributions to this repo for mojo on the +[`main`](https://github.com/modular/max/tree/main) branch. If you’d like to contribute to Mojo, please first read our [Contributor -Guide](https://github.com/modular/mojo/blob/main/CONTRIBUTING.md). +Guide](https://github.com/modular/max/blob/main/mojo/CONTRIBUTING.md). For more general questions or to chat with other Mojo developers, check out our [Discord](https://discord.gg/modular). @@ -108,6 +108,6 @@ MAX and Mojo usage and distribution are licensed under the ## Thanks to our contributors - - + + diff --git a/mojo/proposals/byte-as-uint8.md b/mojo/proposals/byte-as-uint8.md index 9881574ccd..03d4f77189 100644 --- a/mojo/proposals/byte-as-uint8.md +++ b/mojo/proposals/byte-as-uint8.md @@ -40,5 +40,5 @@ and 78 results for `DTypePointer[DType.int8]`. Replacing `DTypePointer[DType.int8]` with `DTypePointer[DType.uint8]` and `Pointer[Int8]` with `Pointer[UInt8]` on case by case bases is a substantial refactoring effort, but it will prevent a certain class of logical bugs (see -). As it is a breaking change in +). As it is a breaking change in sense of API design, it is sensible to do the refactoring as soon as possible. diff --git a/mojo/proposals/inferred-parameters.md b/mojo/proposals/inferred-parameters.md index f8eb6bf5c1..0e46dc0c1a 100644 --- a/mojo/proposals/inferred-parameters.md +++ b/mojo/proposals/inferred-parameters.md @@ -49,7 +49,7 @@ scalar_param[DType.int32, Int32()]() # 'dt' parameter is required This has been requested multiple times in various forms, especially given the new autoparameterization feature. The current tracking feature request: -- +- ## Proposal diff --git a/mojo/proposals/lifetimes-keyword-renaming.md b/mojo/proposals/lifetimes-keyword-renaming.md index 3b783202e7..b39636e616 100644 --- a/mojo/proposals/lifetimes-keyword-renaming.md +++ b/mojo/proposals/lifetimes-keyword-renaming.md @@ -2,7 +2,7 @@ Date: October 2024 -Previous revision: [[June 2023](https://github.com/modular/mojo/blob/f8d7cb8ba4c21ec3fbc87e21609b3fd56cab695f/proposals/lifetimes-keyword-renaming.md)] +Previous revision: [[June 2023](https://github.com/modular/max/blob/f8d7cb8ba4c21ec3fbc87e21609b3fd56cab695f/proposals/lifetimes-keyword-renaming.md)] The design of the Mojo references subsystem is starting to come together. To finalize the major points, it helps to come back and re-evaluate several early diff --git a/mojo/proposals/ref-convention.md b/mojo/proposals/ref-convention.md index 53b608e51e..f3c86e107c 100644 --- a/mojo/proposals/ref-convention.md +++ b/mojo/proposals/ref-convention.md @@ -12,7 +12,7 @@ Mojo’s safe references have evolved and iterated a lot. From the initial compiler re-plumbing that made memory-only types possible, to threading lifetimes through everything with the introduction of `!lit.ref` to the development of a “user-space” Reference type, to the recent discussions about -[adding automatic dereference to Reference](https://github.com/modular/mojo/discussions/2594), +[adding automatic dereference to Reference](https://github.com/modular/max/discussions/2594), we’ve been iteratively improving the model with a goal of ending up with something powerful and explainable. @@ -42,7 +42,7 @@ Along the way, we’ve had a number of challenges to address: to `Reference`. It would be awesome to clarify this. 5. We still need to - [reconsider which keywords](https://github.com/modular/mojo/blob/main/proposals/lifetimes-keyword-renaming.md) + [reconsider which keywords](https://github.com/modular/max/blob/main/mojo/proposals/lifetimes-keyword-renaming.md) to use for argument conventions. The `inout` keyword, for example, is problematic because it works with types that are not movable or copyable. The callee doesn’t actually move things in and out, it takes a mutable reference. diff --git a/mojo/proposals/remove-let-decls.md b/mojo/proposals/remove-let-decls.md index 1d1f8a3692..2fda1bc30b 100644 --- a/mojo/proposals/remove-let-decls.md +++ b/mojo/proposals/remove-let-decls.md @@ -1,6 +1,6 @@ # Simplifying Mojo🔥 - let's get rid of `let` -Chris Lattner, Dec 5, 2023, Status: **Accepted**, [discussion thread](https://github.com/modular/mojo/discussions/1456#discussioncomment-8358722) +Chris Lattner, Dec 5, 2023, Status: **Accepted**, [discussion thread](https://github.com/modular/max/discussions/1456#discussioncomment-8358722) Mojo is still a new language, and is rapidly evolving. We’re learning a lot from other languages, but Mojo poses its own set of tradeoffs that indicate a @@ -10,7 +10,7 @@ One of the early decisions made in Mojo's development is that it adopts the `let` and `var` design point that Swift uses. This whitepaper argues that we should switch to a simpler model by jettisoning `let` and just retaining `var` (and implicit Python-style variable declarations in `def`). This has also been -[suggested by the community](https://github.com/modular/mojo/issues/1205). +[suggested by the community](https://github.com/modular/max/issues/1205). Note that immutability and value semantics remain an important part of the Mojo design, this is just about removing "named immutable variables". Immutable @@ -31,7 +31,7 @@ variables aren't a core programming concept, and not something required to achieve Mojo's goals. 2. The naming of `let` caused a lot of early [heat and -debate](https://github.com/modular/mojo/discussions/120). Other programming +debate](https://github.com/modular/max/discussions/120). Other programming languages have a wide range of design points (e.g. `const` in C/C++ and Javascript) and there is a divergence of naming for all these things: `let`, `val`, `const`, etc, etc. @@ -43,7 +43,7 @@ three concepts going around: `alias`, `let`, and `var`. Most of the uses of 4. Both Swift and Rust encourage immutable values - Swift (and currently Mojo) warn about unneeded mutability, Rust makes mutability more verbose (`let mut`), and some propose that Mojo [make mutability more -verbose](https://github.com/modular/mojo/issues/451). This cuts very hard +verbose](https://github.com/modular/max/issues/451). This cuts very hard against a lot of the design center of Python, which doesn’t even have this concept at all: it would be weird to make it the default, but if we don’t, then why bother having it? @@ -112,7 +112,7 @@ This would eliminate a bunch of complexity in the compiler as well: like: “`let x: Int; x = 1; use(x); x = 2; use(x)`” even though the original lifetime of the first “`x=1`” naturally ended and “`x`” is uninitialized before being assigned to. This has always been a design smell, and it - [doesn’t work right](https://github.com/modular/mojo/issues/1414). + [doesn’t work right](https://github.com/modular/max/issues/1414). This proposal will not affect runtime performance at all as far as we know. diff --git a/mojo/stdlib/README.md b/mojo/stdlib/README.md index d64144c02d..8a9891f02d 100644 --- a/mojo/stdlib/README.md +++ b/mojo/stdlib/README.md @@ -54,6 +54,6 @@ See the license file in the repository for more details. ## Support For any inquiries, bug reports, or feature requests, please [open an -issue](https://github.com/modular/mojo/issues) on the GitHub repository. See +issue](https://github.com/modular/max/issues) on the GitHub repository. See the [Mojo contributor guide](../CONTRIBUTING.md) for guidelines on filing good bugs. diff --git a/mojo/stdlib/docs/development.md b/mojo/stdlib/docs/development.md index 37cfb5182e..a306c57515 100644 --- a/mojo/stdlib/docs/development.md +++ b/mojo/stdlib/docs/development.md @@ -107,7 +107,7 @@ You can do the same for a directory with All the tests should pass on the `main` branch with the nightly Mojo compiler. If you've pulled the latest changes and they're still failing please [open a GitHub -issue](https://github.com/modular/mojo/issues/new?assignees=&labels=bug%2Cmojo&projects=&template=mojo_bug_report.yaml&title=%5BBUG%5D). +issue](https://github.com/modular/max/issues). ### Running a subset of the Standard Library Unit Tests @@ -135,7 +135,7 @@ disabled, you can set the environment variable `MOJO_ENABLE_ASSERTIONS_IN_TESTS=0`. If you run into any issues when running the tests, -[please file an issue](https://github.com/modular/mojo/issues) and we’ll take +[please file an issue](https://github.com/modular/max/issues) and we’ll take a look. ## Formatting changes diff --git a/mojo/stdlib/src/builtin/simd.mojo b/mojo/stdlib/src/builtin/simd.mojo index 018d8f5f25..3a39d1c5ed 100644 --- a/mojo/stdlib/src/builtin/simd.mojo +++ b/mojo/stdlib/src/builtin/simd.mojo @@ -2256,7 +2256,7 @@ struct SIMD[type: DType, size: Int]( # Not an overload of shuffle because there is ambiguity # with fn shuffle[*mask: Int](self, other: Self) -> Self: - # TODO: move to the utils directory - see https://github.com/modular/mojo/issues/3477 + # TODO: move to the utils directory - see https://github.com/modular/max/issues/3477 @always_inline fn _dynamic_shuffle[ mask_size: Int, // diff --git a/mojo/stdlib/src/collections/linked_list.mojo b/mojo/stdlib/src/collections/linked_list.mojo index 55c49adc18..6c145d10fa 100644 --- a/mojo/stdlib/src/collections/linked_list.mojo +++ b/mojo/stdlib/src/collections/linked_list.mojo @@ -203,7 +203,7 @@ struct LinkedList[ self._tail = node # Do not destroy the elements when their backing storage goes away. - # FIXME(https://github.com/modular/mojo/issues/3969) this is leaking! + # FIXME(https://github.com/modular/max/issues/3969) this is leaking! __disable_del elements self._size = length diff --git a/mojo/stdlib/test/builtin/test_issue_1004.mojo b/mojo/stdlib/test/builtin/test_issue_1004.mojo index e80c944316..391205de72 100644 --- a/mojo/stdlib/test/builtin/test_issue_1004.mojo +++ b/mojo/stdlib/test/builtin/test_issue_1004.mojo @@ -11,7 +11,7 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -# Test for https://github.com/modular/mojo/issues/1004 +# Test for https://github.com/modular/max/issues/1004 from testing import assert_equal diff --git a/mojo/stdlib/test/builtin/test_issue_1505.mojo b/mojo/stdlib/test/builtin/test_issue_1505.mojo index 712c1d9c0c..43eb2b764d 100644 --- a/mojo/stdlib/test/builtin/test_issue_1505.mojo +++ b/mojo/stdlib/test/builtin/test_issue_1505.mojo @@ -11,7 +11,7 @@ # limitations under the License. # ===----------------------------------------------------------------------=== # # RUN: %mojo %s -# Test for https://github.com/modular/mojo/issues/1505 +# Test for https://github.com/modular/max/issues/1505 from random import random_ui64 diff --git a/mojo/stdlib/test/builtin/test_simd.mojo b/mojo/stdlib/test/builtin/test_simd.mojo index 184627c5b7..e48370e2d1 100644 --- a/mojo/stdlib/test/builtin/test_simd.mojo +++ b/mojo/stdlib/test/builtin/test_simd.mojo @@ -119,7 +119,7 @@ def test_convert_simd_to_string(): var c: SIMD[DType.index, 8] = 7 assert_equal(String(c), "[7, 7, 7, 7, 7, 7, 7, 7]") - # TODO: uncomment when https://github.com/modular/mojo/issues/2353 is fixed + # TODO: uncomment when https://github.com/modular/max/issues/2353 is fixed # assert_equal(String(UInt32(-1)), "4294967295") assert_equal(String(UInt64(-1)), "18446744073709551615") @@ -129,7 +129,7 @@ def test_convert_simd_to_string(): assert_equal(String(UInt64(16646288086500911323)), "16646288086500911323") - # https://github.com/modular/mojo/issues/556 + # https://github.com/modular/max/issues/556 assert_equal( String( SIMD[DType.uint64, 4]( diff --git a/mojo/stdlib/test/collections/test_list.mojo b/mojo/stdlib/test/collections/test_list.mojo index b7c3cecf7a..a7a6e30f0f 100644 --- a/mojo/stdlib/test/collections/test_list.mojo +++ b/mojo/stdlib/test/collections/test_list.mojo @@ -574,7 +574,7 @@ def test_no_extra_copies_with_sugared_set_by_field(): # Ensure correct behavior of __copyinit__ # as reported in GH issue 27875 internally and -# https://github.com/modular/mojo/issues/1493 +# https://github.com/modular/max/issues/1493 def test_list_copy_constructor(): var vec = List[Int](capacity=1) var vec_copy = vec From d35951e21b5b17ac6c8a16b6b8b00598a8a4a291 Mon Sep 17 00:00:00 2001 From: Dan Moldovan <26628547+mdanatg@users.noreply.github.com> Date: Thu, 27 Feb 2025 11:12:34 -0500 Subject: [PATCH 483/509] Remove stale readme files. These are largely superseded by https://github.com/modular/max/tree/main/src/max/README.md MODULAR_ORIG_COMMIT_REV_ID: a63ce6c37bb4971ed67b17033d29b4b172eda196 --- src/max/pipelines/README.md | 119 ---------------- .../pipelines/architectures/mistral/README.md | 129 ----------------- .../pipelines/architectures/pixtral/README.md | 134 ------------------ .../pipelines/architectures/qwen2/README.md | 120 ---------------- .../pipelines/architectures/replit/README.md | 119 ---------------- 5 files changed, 621 deletions(-) delete mode 100644 src/max/pipelines/README.md delete mode 100644 src/max/pipelines/architectures/mistral/README.md delete mode 100644 src/max/pipelines/architectures/pixtral/README.md delete mode 100644 src/max/pipelines/architectures/qwen2/README.md delete mode 100644 src/max/pipelines/architectures/replit/README.md diff --git a/src/max/pipelines/README.md b/src/max/pipelines/README.md deleted file mode 100644 index 160952f64b..0000000000 --- a/src/max/pipelines/README.md +++ /dev/null @@ -1,119 +0,0 @@ -# MAX Pipelines - -These are end-to-end pipelines that demonstrate the power of -[MAX](https://docs.modular.com/max/) for accelerating common AI workloads, and -more. Each of the supported pipelines can be served via an OpenAI-compatible -endpoint. - -MAX can also serve most PyTorch-based large language models that are -present on Hugging Face, although not at the same performance as native MAX -Graph versions. - -## Pipelines - -Highly optimized MAX Graph implementations exist for several core model -architectures. These include: - -- [Llama 3.1](llama3): A text completion pipeline using the Llama 3.1 model, -implemented using the MAX Graph API. This pipeline contains everything -needed to run a self-hosted large language model in the `LlamaForCausalLM` -family with state-of-the-art serving throughput. -- [Mistral](architectures/mistral): Support for the `MistralForCausalLM` family -of text completion models, by default using the Mistral NeMo 12B model. This pipeline -has been tuned for performance using the MAX Graph API. -- [Replit Code](architectures/replit): Code generation via the -Replit Code V1.5 3B model, implemented using the MAX Graph API. - -Instructions for how to run each pipeline can be found in their respective -subdirectories, along with all configuration parameters. A shared driver is -used to execute the pipelines. - -## Usage - -The easiest way to try out any of the pipelines is with our Magic command-line -tool. - -1. Install Magic on macOS and Ubuntu with this command: - - ```shell - curl -ssL https://magic.modular.com | bash - ``` - - Then run the source command that's printed in your terminal. - - To see the available commands, you can run `magic --help`. - [Learn more about Magic here](https://docs.modular.com/magic). - -2. Clone the MAX examples repository: - - If you don't already have a local clone of this repository, create one via: - - ```shell - git clone https://github.com/modular/max.git - ``` - - The following instructions assume that you're present within this - directory, and you can change to it after cloning: - - ```shell - cd max/src/max/ - ``` - -3. Now run one of the text completion demos with any of following commands: - - ```shell - magic run generate --model-path=modularai/llama-3.1 --prompt "Why is the sky blue?" - magic run generate --model-path=modularai/replit-code-1.5 --prompt "def fibonacci(n):" - magic run generate --model-path=mistralai/Mistral-Nemo-Instruct-2407 --prompt "Why is the sky blue?" - ``` - -4. Host a chat completion endpoint via MAX Serve. - - MAX Serve provides functionality to host performant OpenAI compatible - endpoints using the FastAPI framework. - - Finetuned weights hosted on Hugging Face can be used with one - of these optimized pipeline architectures when serving via the `serve` - command: - - ```shell - magic run serve --model-path=modularai/llama-3.1 - ``` - - A request can then be submitted via a cURL command. - - ```shell - curl -N http://localhost:8000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "modularai/llama-3.1", - "stream": true, - "messages": [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Who won the world series in 2020?"} - ] - }' - ``` - -## Verified Hugging Face model architectures - -If you provide a repository ID for a Hugging Face large language model -that does not currently have an optimized MAX Graph implementation, MAX -falls back to serving a PyTorch eager version of the model. - -The following table lists the model architectures tested to work with MAX. - -| **Architecture** | **Example Model** | -| --- | --- | -| AquilaForCausalLM | BAAI/Aquila-7B | -| ChatGLMModel | THUDM/chatglm3-6b | -| GPT2LMHeadModel | openai-community/gpt2 | -| GPTJForCausalLM | EleutherAI/gpt-j-6b | -| LlamaForCausalLM | meta-llama/Llama-3.2-3B-Instruct | -| LlamaForCausalLM | Skywork/Skywork-o1-Open-Llama-3.1-8B | -| LlamaForCausalLM | deepseek-ai/deepseek-coder-1.3b-instruct | -| PhiForCausalLM | microsoft/phi-2 | -| Phi3ForCausalLM | microsoft/Phi-3-mini-4k-instruct | -| GraniteForCausalLM | ibm-granite/granite-3.1-8b-instruct | -| Qwen2ForCausalLM | Qwen/Qwen2.5-1.5B-Instruct | -| OlmoForCausalLM | allenai/OLMo-1B-0724-hf | diff --git a/src/max/pipelines/architectures/mistral/README.md b/src/max/pipelines/architectures/mistral/README.md deleted file mode 100644 index 4603761d44..0000000000 --- a/src/max/pipelines/architectures/mistral/README.md +++ /dev/null @@ -1,129 +0,0 @@ -# Mistral NeMo - -**Language:** Python - -**API**: MAX Graph - -This pipeline provides optimized support for the `MistralForCausalLM` family -of large language models, as exemplified by the Mistral NeMo 12B text -completion model. The model itself has been constructed in Python -using the [MAX Graph API](https://docs.modular.com/max/graph/). - -The MAX Graph API provides an accessible interface to the construction of -flexible accelerated compute graphs, which are then optimized by the MAX -Engine's advanced graph compiler. This pipeline showcases how a large language -model can be fully defined using Python and MAX Graphs and then compiled for -optimal inference performance via the MAX Engine. - -## Model - -[Mistral NeMo](https://mistral.ai/news/mistral-nemo/) is an open source large -language model released by [Mistral AI](https://mistral.ai) in partnership -with NVIDIA. The weight checkpoints have been released under an Apache 2.0 -license. - -The default settings for this pipeline use the NeMo 12B set of pretrained -weights in the `bfloat16` encoding. - -## Usage - -The easiest way to try out this pipeline is with our Magic command-line tool. - -1. Install Magic on macOS and Ubuntu with this command: - - ```shell - curl -ssL https://magic.modular.com | bash - ``` - - Then run the source command that's printed in your terminal. - - To see the available commands, you can run `magic --help`. - [Learn more about Magic here](https://docs.modular.com/magic). - -2. Clone the MAX examples repository: - - If you don't already have a local clone of this repository, create one via: - - ```shell - git clone https://github.com/modular/max.git - ``` - - The following instructions assume that you're present within this pipeline's - directory, and you can change to it after cloning: - - ```shell - cd max/src/max/ - ``` - -3. Now run the Mistral text completion demo with the following command: - - ```shell - magic run mistral --prompt "I believe the meaning of life is" - ``` - -4. Host a chat completion endpoint via MAX Serve. - - MAX Serve provides functionality to host performant OpenAI compatible - endpoints using the FastAPI framework. - - You can configure the pipeline to be hosted by using the `--serve` argument. - For example: - - ```shell - magic run mistral --serve - ``` - - A request can be submitted via a cURL command. - - ```shell - curl -N http://localhost:8000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "mistralai/Mistral-Nemo-Instruct-2407", - "stream": true, - "messages": [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Who won the world series in 2020?"} - ] - }' - ``` - - Additionally, finetuned weights hosted on Hugging Face for any compatible - `MistralForCausalLM` model can be used with this optimized architecture - via the `serve` command: - - ```shell - magic run serve --model-path=mistralai/Ministral-8B-Instruct-2410 - ``` - -## Options - -The following command-line options are available to customize operation of the -pipeline: - -- `--max-length`: Controls the maximum length of the text sequence - (includes the input tokens). - (Default value: 512) -- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 - value is provided, the model will continue to generate tokens for the entire - context length. (Default value: -1) -- `--prompt`: The text prompt to use for further generation. -- `--quantization-encoding`: The encoding to use for a datatype that can be - quantized to a low bits per weight format. The options for quantized formats - will download and cache default weights, but `float32` requires the use of - `--weight-path` to specify locally downloaded full-precision weights for use - in the model. - Valid values: `bfloat16`, `float32`. - (Default value: `float32`). -- `--save-to-serialized-model-path`: If specified, writes the serialized model - to this path. -- `--serialized-model-path`: If specified, tries to load a serialized model - from this path. -- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. -- `--weight-path`: Overrides the default URL, and allows for an - already-downloaded pretrained weight file to be used with the model. -- `--max-batch-size`: Specifies the maximum batch size to be used. - Default is 1. -- `--devices`: Specifies the device(s) to use for the model. This can optionally - be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". - Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/pixtral/README.md b/src/max/pipelines/architectures/pixtral/README.md deleted file mode 100644 index 4911c4ac85..0000000000 --- a/src/max/pipelines/architectures/pixtral/README.md +++ /dev/null @@ -1,134 +0,0 @@ -# Pixtral - -**Language:** Python - -**API**: MAX Graph - -This pipeline provides optimized support for the -`LlavaForConditionalGeneration` family of multimodal models, as exemplified -by the Pixtral multimodal text generation model. The model itself has -been constructed in Python using the -[MAX Graph API](https://docs.modular.com/max/graph/). - -The MAX Graph API provides an accessible interface to the construction of -flexible accelerated compute graphs, which are then optimized by the MAX -Engine's advanced graph compiler. This pipeline showcases how a multimodal -model can be fully defined using Python and MAX Graphs and then compiled for -optimal inference performance via the MAX Engine. - -> [!NOTE] -> This pipeline is under active development, and while many layers have been -> implemented, the entire pipeline is not fully functional at present. - -## Model - -[Pixtral](https://mistral.ai/news/pixtral-12b/) -is an open source multimodal model released by Mistral AI. It handles both text -and image input, and allows for text generation based on those multimodal -inputs. The underlying architecture provides support for the broader -`LlavaForConditionalGeneration` family of models. - -The default settings for this pipeline use the 12B set of pretrained weights in -the `bfloat16` encoding. - -## Usage - -The easiest way to try out this pipeline is with our Magic command-line tool. - -1. Install Magic on macOS and Ubuntu with this command: - - ```shell - curl -ssL https://magic.modular.com | bash - ``` - - Then run the source command that's printed in your terminal. - - To see the available commands, you can run `magic --help`. - [Learn more about Magic here](https://docs.modular.com/magic). - -2. Clone the MAX examples repository: - - If you don't already have a local clone of this repository, create one via: - - ```shell - git clone https://github.com/modular/max.git - ``` - - The following instructions assume that you're present within this pipeline's - directory, and you can change to it after cloning: - - ```shell - cd max/src/max/pipelines - ``` - -3. Host a multimodal chat completion endpoint via MAX Serve. - - MAX Serve provides functionality to host performant OpenAI compatible - endpoints using the FastAPI framework. - - You can configure the pipeline to be hosted by using the `serve` command. - Weights hosted on Hugging Face for any compatible - `LlavaForConditionalGeneration` model can be used with this optimized - architecture. For example: - - ```shell - magic run serve --model-path mistral-community/pixtral-12b - ``` - - A request can be submitted via a cURL command. - - ```shell - curl -N http://localhost:8000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "mistral-community/pixtral-12b", - "stream": true, - "messages": [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": [ - {"type": "text", "text": "What is in this image?"}, - {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"}} - ]} - ] - }' - ``` - -## Options - -The following command-line options are available to customize operation of the -pipeline: - -- `--model-path`: Specify the repository ID of a Hugging Face model - repository to use. This is used to load tokenizers, architectures and model - weights. -- `--force-download`: Specify whether to force a download of configuration - files and weights even if they already exist in the local cache. Set this - if you want to ensure you have the correct version of the model. -- `--max-batch-size`: Specifies the maximum batch size to be used. - Default is 1. -- `--max-ce-batch-size`: Set the maximum cache size reserved for a single - context encoding batch. The effective limit will be the lesser of this value - and `max-cache-batch-size`. - Default is 32. -- `--max-length`: Controls the maximum length of the text sequence (includes the - input tokens). Defaults to huggingface model config's max sequence length. -- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 - value is provided, the model will continue to generate tokens for the entire - context length. (Default value: -1) -- `--quantization-encoding`: The encoding to use for a datatype that can be - quantized to a low bits per weight format. - Valid values: `q4_0`, `q4_k`, `q6_k`, `bfloat16`, `float32`. - (Default value: `bfloat16`). -- `--save-to-serialized-model-path`: If specified, writes the serialized model - to this path. -- `--serialized-model-path`: If specified, tries to load a serialized model - from this path. -- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. -- `--trust-remote-code`: Indicate whether to allow custom modeling files from - Hugging Face repositories. Set this to true with caution, as it may - introduce security risks. -- `--weight-path`: Overrides the default URL, and allows for an - already-downloaded pretrained weight file to be used with the model. -- `--devices`: Specifies the device(s) to use for the model. This can optionally - be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". - Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/qwen2/README.md b/src/max/pipelines/architectures/qwen2/README.md deleted file mode 100644 index d42abdc47d..0000000000 --- a/src/max/pipelines/architectures/qwen2/README.md +++ /dev/null @@ -1,120 +0,0 @@ -# Qwen 2.5 - -**Language:** Python - -**API**: MAX Graph - -This pipeline provides optimized support for the `Qwen2ForCausalLM` family -of large language models, as exemplified by the Qwen 2.5 7B Instruct text -completion model. The model itself has been constructed in Python -using the [MAX Graph API](https://docs.modular.com/max/graph/). - -The MAX Graph API provides an accessible interface to the construction of -flexible accelerated compute graphs, which are then optimized by the MAX -Engine's advanced graph compiler. This pipeline showcases how a large language -model can be fully defined using Python and MAX Graphs and then compiled for -optimal inference performance via the MAX Engine. - -## Model - -[Qwen 2.5](https://qwenlm.github.io/blog/qwen2.5/) is an open source large -language model released by [Qwen AI](https://qwenlm.github.io/). The weight -checkpoints have been released under an Apache 2.0 license. - -The default settings for this pipeline use the 7B Instruct set of pretrained -weights in the `bfloat16` encoding. - -## Usage - -The easiest way to try out this pipeline is with our Magic command-line tool. - -1. Install Magic on macOS and Ubuntu with this command: - - ```shell - curl -ssL https://magic.modular.com | bash - ``` - - Then run the source command that's printed in your terminal. - - To see the available commands, you can run `magic --help`. - [Learn more about Magic here](https://docs.modular.com/magic). - -2. Clone the MAX examples repository: - - If you don't already have a local clone of this repository, create one via: - - ```shell - git clone https://github.com/modular/max.git - ``` - - The following instructions assume that you're present within this pipeline's - directory, and you can change to it after cloning: - - ```shell - cd max/src/max/ - ``` - -3. Now run the Qwen text completion demo with the following command: - - ```shell - magic run qwen --prompt "I believe the meaning of life is" - ``` - -4. Host a chat completion endpoint via MAX Serve. - - MAX Serve provides functionality to host performant OpenAI compatible - endpoints using the FastAPI framework. - - You can configure the pipeline to be hosted by using the `--serve` argument. - For example: - - ```shell - magic run qwen --serve - ``` - - A request can be submitted via a cURL command. - - ```shell - curl -N http://localhost:8000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "Qwen/Qwen2.5-7B-Instruct", - "stream": true, - "messages": [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Who won the world series in 2020?"} - ] - }' - ``` - - Additionally, finetuned weights hosted on Hugging Face for any compatible - `Qwen2ForCausalLM` model can be used with this optimized architecture - via the `serve` command: - - ```shell - magic run serve --model-path=Qwen/Qwen2.5-7B-Instruct - ``` - -## Options - -The following command-line options are available to customize operation of the -pipeline: - -- `--max-length`: Controls the maximum length of the text sequence - (includes the input tokens). -- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 - value is provided, the model will continue to generate tokens for the entire - context length. (Default value: -1) -- `--prompt`: The text prompt to use for further generation. -- `--save-to-serialized-model-path`: If specified, writes the serialized model - to this path. -- `--serialized-model-path`: If specified, tries to load a serialized model - from this path. -- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. -- `--weight-path`: Overrides the default URL, and allows for an - already-downloaded pretrained weight file to be used with the model. -- `--max-batch-size`: Specifies the maximum batch size to be used. - Default is 1. -- `--devices`: Specifies the device(s) to use for the model. This can optionally - be "cpu", "gpu", or a comma-separated list of GPU ID(s) like "gpu-0,gpu-1". - Defaults to the first available GPU, or "cpu" if no GPUs are available. diff --git a/src/max/pipelines/architectures/replit/README.md b/src/max/pipelines/architectures/replit/README.md deleted file mode 100644 index a77f156e3a..0000000000 --- a/src/max/pipelines/architectures/replit/README.md +++ /dev/null @@ -1,119 +0,0 @@ -# Replit Code V1.5 3B - -**Language:** Python - -**API**: MAX Graph - -This pipeline demonstrates code completion from an initial prompt using -Replit's Code V1.5 3B large language model. The model itself has been -constructed in Python using the -[MAX Graph API](https://docs.modular.com/max/graph/). - -The MAX Graph API provides an accessible interface to the construction of -flexible accelerated compute graphs, which are then optimized by the MAX -Engine's advanced graph compiler. This pipeline showcases how a large language -model can be fully defined using Python and MAX Graphs and then compiled for -optimal inference performance via the MAX Engine. - -## Model - -[Replit Code](https://blog.replit.com/replit-code-v1_5) is an open source code -generation model trained on permissively licensed code and released by -[Replit](https://replit.com). The V1.5, 3B variant is the basis for this -implementation, and weights are -[obtained via Hugging Face](https://huggingface.co/replit/replit-code-v1-3b). - -## Usage - -The easiest way to try out this pipeline is with our Magic command-line tool. - -1. Install Magic on macOS and Ubuntu with this command: - - ```shell - curl -ssL https://magic.modular.com | bash - ``` - - Then run the source command that's printed in your terminal. - - To see the available commands, you can run `magic --help`. - [Learn more about Magic here](https://docs.modular.com/magic). - -2. Clone the MAX examples repository: - - If you don't already have a local clone of this repository, create one via: - - ```shell - git clone https://github.com/modular/max.git - ``` - - The following instructions assume that you're present within this pipeline's - directory, and you can change to it after cloning: - - ```shell - cd max/src/max/ - ``` - -3. Now run the Replit code completion demo with the following command: - - ```shell - magic run replit --prompt "def fibonacci(n):" - ``` - -4. Host a code completion endpoint via MAX Serve. - - MAX Serve provides functionality to host performant OpenAI compatible - endpoints using the FastAPI framework. - - You can configure the pipeline to be hosted by using the `--serve` argument. - For example: - - ```shell - magic run replit --serve - ``` - - A request can be submitted via a cURL command. - - ```shell - curl -N http://localhost:8000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "replit/replit-code-v1_5-3b", - "stream": true, - "messages": [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "def fibonacci(n)"} - ] - }' - ``` - -## Options - -The following command-line options are available to customize operation of the -pipeline: - -- `--max-length`: Controls the maximum length of the text sequence - (includes the input tokens). - (Default value: 512) -- `--max-new-tokens`: The maximum number of new tokens to generate. If a -1 - value is provided, the model will continue to generate tokens for the entire - context length. (Default value: -1) -- `--prompt`: The text prompt to use for further generation. -- `--quantization-encoding`: The encoding to use for a datatype that can be - quantized to a low bits per weight format. The options for quantized formats - will download and cache default weights, but `float32` requires the use of - `--weight-path` to specify locally downloaded full-precision weights for use - in the model. - Valid values: `bfloat16`, `float32`. - (Default value: `float32`). -- `--save-to-serialized-model-path`: If specified, writes the serialized model - to this path. -- `--serialized-model-path`: If specified, tries to load a serialized model - from this path. -- `--top-k`: Limits the sampling to the K most probable tokens. Default is 1. -- `--weight-path`: Overrides the default URL, and allows for an - already-downloaded pretrained weight file to be used with the model. -- `--max-batch-size`: Specifies the maximum batch size to be used. - Default is 1. -- `--devices`: Specifies the device(s) to use for the model. This can optionally - be "cpu" or a comma-separated list of GPU ID(s). Defaults to the first - available GPU. From 98333f260bbf490d0f2034e923b42b2cf83c85df Mon Sep 17 00:00:00 2001 From: Scott Main Date: Thu, 27 Feb 2025 08:58:53 -0800 Subject: [PATCH 484/509] Update all GitHub links for `mojo`->`max` repo change MODULAR_ORIG_COMMIT_REV_ID: e3c60edfa481cae4a17f91c23cf9d1080d4bd030 --- mojo/docs/README.md | 2 +- mojo/docs/changelog-released.md | 662 +++++++++++++------------- mojo/docs/changelog.md | 2 +- mojo/docs/faq.md | 4 +- mojo/docs/manual/basics.mdx | 10 +- mojo/docs/manual/get-started.mdx | 4 +- mojo/docs/manual/operators.mdx | 6 +- mojo/docs/manual/parameters/index.mdx | 2 +- mojo/docs/manual/values/lifetimes.mdx | 4 +- mojo/docs/roadmap.md | 4 +- mojo/docs/tools/debugging.mdx | 6 +- mojo/docs/tools/testing.mdx | 13 +- 12 files changed, 361 insertions(+), 358 deletions(-) diff --git a/mojo/docs/README.md b/mojo/docs/README.md index 5200bc5cfe..d5abf29c53 100644 --- a/mojo/docs/README.md +++ b/mojo/docs/README.md @@ -16,7 +16,7 @@ Be aware that we don't provide tools to generate a preview of the website, because the Mojo docs are built along with other content that's not included in this repo. As such, we recommend you preview your edits in an IDE that can render Markdown and MDX files, such as VS Code, including the -[VS Code environment in GitHub](https://github.dev/modular/mojo/blob/main/). +[VS Code environment in GitHub](https://github.dev/modular/max/blob/main/). For more information about how to contribute, see the [Contributor Guide](../CONTRIBUTING.md) diff --git a/mojo/docs/changelog-released.md b/mojo/docs/changelog-released.md index 13cca39ddf..82fa4aa7a8 100644 --- a/mojo/docs/changelog-released.md +++ b/mojo/docs/changelog-released.md @@ -572,25 +572,25 @@ in [Update a package](/magic#update-a-package) to update the `max` package. - The command `mojo debug --vscode` now sets the current working directory properly. -- [Issue #3796](https://github.com/modular/mojo/issues/3796) - Compiler crash +- [Issue #3796](https://github.com/modular/max/issues/3796) - Compiler crash handling `for`-`else` statement. -- [Issue #3540](https://github.com/modular/mojo/issues/3540) - Using named +- [Issue #3540](https://github.com/modular/max/issues/3540) - Using named output slot breaks trait conformance -- [Issue #3617](https://github.com/modular/mojo/issues/3617) - Can't generate +- [Issue #3617](https://github.com/modular/max/issues/3617) - Can't generate the constructors for a type wrapping `!lit.ref` - The Mojo Language Server doesn't crash anymore on empty `__init__.mojo` files. - [Issue #3826](https://github.com/modular/mojo/issues/3826). + [Issue #3826](https://github.com/modular/max/issues/3826). -- [Issue #3935](https://github.com/modular/mojo/issues/3935) - Confusing OOM +- [Issue #3935](https://github.com/modular/max/issues/3935) - Confusing OOM error when using `Tuple.get()` incorrectly. -- [Issue #3955](https://github.com/modular/mojo/issues/3955) - Unexpected +- [Issue #3955](https://github.com/modular/max/issues/3955) - Unexpected copy behavior with `def` arguments in loops -- [Issue #3960](https://github.com/modular/mojo/issues/3960) - Infinite `for` +- [Issue #3960](https://github.com/modular/max/issues/3960) - Infinite `for` loop ## v24.6 (2024-12-17) @@ -617,7 +617,7 @@ detailed information in the following sections: (`ref [a, b]`). For details, see [Language changes](#24-6-language-changes). For background information and rationale on the name change see - [the proposal](https://github.com/modular/mojo/issues/3623). For more + [the proposal](https://github.com/modular/max/issues/3623). For more information on origins, see [Lifetimes, origins and references](/mojo/manual/values/lifetimes) in the Mojo Manual. @@ -699,7 +699,7 @@ detailed information in the following sections: eventually be deprecated and removed. This was [discussed extensively in a public - proposal](https://github.com/modular/mojo/issues/3623). For more + proposal](https://github.com/modular/max/issues/3623). For more information, see [Named results](/nightly/mojo/manual/functions#named-results) in the Mojo Manual. @@ -922,7 +922,7 @@ detailed information in the following sections: - Mojo can now interpret simple LLVM intrinsics in parameter expressions, enabling things like `count_leading_zeros` to work at compile time: - [Issue #933](https://github.com/modular/mojo/issues/933). + [Issue #933](https://github.com/modular/max/issues/933). - Introduced the `@explicit_destroy` annotation, the `__disable_del` keyword, the `UnknownDestructibility` trait, and the `ImplicitlyDestructible` keyword, @@ -1041,8 +1041,8 @@ detailed information in the following sections: 'strong reference' pointer. - Support for multi-dimensional indexing and slicing for `PythonObject` - (PR [#3549](https://github.com/modular/mojo/pull/3549), - PR [#3583](https://github.com/modular/mojo/pull/3583)). + (PR [#3549](https://github.com/modular/max/pull/3549), + PR [#3583](https://github.com/modular/max/pull/3583)). ```mojo var np = Python.import_module("numpy") @@ -1055,7 +1055,7 @@ detailed information in the following sections: - Added [`PythonObject.__contains__()`](/mojo/stdlib/python/python_object/PythonObject#__contains__). - ([PR #3101](https://github.com/modular/mojo/pull/3101)) + ([PR #3101](https://github.com/modular/max/pull/3101)) Example usage: @@ -1087,7 +1087,7 @@ detailed information in the following sections: semantics to Rust's [`Box<>`](https://doc.rust-lang.org/std/boxed/struct.Box.html) and C++'s [`std::unique_ptr`](https://en.cppreference.com/w/cpp/memory/unique_ptr). - ([PR #3524](https://github.com/modular/mojo/pull/3524)) + ([PR #3524](https://github.com/modular/max/pull/3524)) - `Arc` has been renamed to [`ArcPointer`](/mojo/stdlib/memory/arc/ArcPointer), for consistency with `OwnedPointer`. @@ -1117,7 +1117,7 @@ detailed information in the following sections: [`FloatableRaising`](/mojo/stdlib/builtin/floatable/FloatableRaising) traits to denote types that can be converted to a `Float64` value using the builtin `float` function. Made `SIMD` and `FloatLiteral` conform to the `Floatable` - trait. ([PR #3163](https://github.com/modular/mojo/pull/3163)) + trait. ([PR #3163](https://github.com/modular/max/pull/3163)) ```mojo fn foo[F: Floatable](v: F): @@ -1135,7 +1135,7 @@ detailed information in the following sections: - Introduced the [`random.shuffle()`](/mojo/stdlib/random/random/shuffle) function for randomizing the elements of a `List`. - ([PR #3327](https://github.com/modular/mojo/pull/3327)) + ([PR #3327](https://github.com/modular/max/pull/3327)) Example: @@ -1153,20 +1153,20 @@ detailed information in the following sections: - [`Slice.step`](/mojo/stdlib/builtin/builtin_slice/Slice#fields) is now an `Optional[Int]`, matching the optionality of `slice.step` in Python. - ([PR #3160](https://github.com/modular/mojo/pull/3160)) + ([PR #3160](https://github.com/modular/max/pull/3160)) - There is now a [`Byte`](/mojo/stdlib/builtin/simd/#aliases) alias to better express intent when working with a pack of bits. - ([PR #3670](https://github.com/modular/mojo/pull/3670)). + ([PR #3670](https://github.com/modular/max/pull/3670)). - Expanded [`os.path`](/mojo/stdlib/os/path/path/) with new functions: - - `os.path.expandvars()`: Expands environment variables in a path ([PR #3735](https://github.com/modular/mojo/pull/3735)). + - `os.path.expandvars()`: Expands environment variables in a path ([PR #3735](https://github.com/modular/max/pull/3735)). - `os.path.splitroot()`: Split a path into drive, root and tail. - ([PR #3780](https://github.com/modular/mojo/pull/3780)). + ([PR #3780](https://github.com/modular/max/pull/3780)). - Added a [`reserve()`](/mojo/stdlib/collections/string/String#reserve) method and new constructor to the `String` struct to allocate additional capacity. - ([PR #3755](https://github.com/modular/mojo/pull/3755)). + ([PR #3755](https://github.com/modular/max/pull/3755)). - A new [`StringLiteral.get[some_stringable]()`](/mojo/stdlib/builtin/string_literal/StringLiteral#get) @@ -1192,7 +1192,7 @@ detailed information in the following sections: - [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) now implements `split()` which can be used to split a `StringRef` into a `List[StringRef]` by a - delimiter. ([PR #2705](https://github.com/modular/mojo/pull/2705)) + delimiter. ([PR #2705](https://github.com/modular/max/pull/2705)) - [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) is now representable so `repr(StringRef("hello"))` will return `StringRef('hello')`. @@ -1251,7 +1251,7 @@ detailed information in the following sections: - The VS Code extension now has the `mojo.run.focusOnTerminalAfterLaunch` setting, which controls whether to focus on the terminal used by the `Mojo: Run Mojo File` command or on the editor after launch. - [Issue #3532](https://github.com/modular/mojo/issues/3532). + [Issue #3532](https://github.com/modular/max/issues/3532). - The VS Code extension now has the `mojo.SDK.additionalSDKs` setting, which allows the user to provide a list of MAX SDKs that the extension can use when @@ -1316,40 +1316,40 @@ detailed information in the following sections: - Lifetime tracking is now fully field sensitive, which makes the uninitialized variable checker more precise. -- [Issue #1310](https://github.com/modular/mojo/issues/1310) - Mojo permits +- [Issue #1310](https://github.com/modular/max/issues/1310) - Mojo permits the use of any constructor for implicit conversions -- [Issue #1632](https://github.com/modular/mojo/issues/1632) - Mojo produces +- [Issue #1632](https://github.com/modular/max/issues/1632) - Mojo produces weird error when inout function is used in non mutating function -- [Issue #3444](https://github.com/modular/mojo/issues/3444) - Raising init +- [Issue #3444](https://github.com/modular/max/issues/3444) - Raising init causing use of uninitialized variable -- [Issue #3544](https://github.com/modular/mojo/issues/3544) - Known +- [Issue #3544](https://github.com/modular/max/issues/3544) - Known mutable `ref` argument are not optimized as `noalias` by LLVM. -- [Issue #3559](https://github.com/modular/mojo/issues/3559) - VariadicPack +- [Issue #3559](https://github.com/modular/max/issues/3559) - VariadicPack doesn't extend the lifetimes of the values it references. -- [Issue #3627](https://github.com/modular/mojo/issues/3627) - Compiler +- [Issue #3627](https://github.com/modular/max/issues/3627) - Compiler overlooked exclusivity violation caused by `ref [MutableAnyOrigin] T` -- [Issue #3710](https://github.com/modular/mojo/issues/3710) - Mojo frees +- [Issue #3710](https://github.com/modular/max/issues/3710) - Mojo frees memory while reference to it is still in use. -- [Issue #3805](https://github.com/modular/mojo/issues/3805) - Crash When +- [Issue #3805](https://github.com/modular/max/issues/3805) - Crash When Initializing !llvm.ptr. -- [Issue #3816](https://github.com/modular/mojo/issues/3816) - Ternary +- [Issue #3816](https://github.com/modular/max/issues/3816) - Ternary if-operator doesn't propagate origin information. -- [Issue #3815](https://github.com/modular/mojo/issues/3815) - +- [Issue #3815](https://github.com/modular/max/issues/3815) - [BUG] Mutability not preserved when taking the union of two origins. -- [Issue #3829](https://github.com/modular/mojo/issues/3829) - Poor error +- [Issue #3829](https://github.com/modular/max/issues/3829) - Poor error message when invoking a function pointer upon an argument of the wrong origin -- [Issue #3830](https://github.com/modular/mojo/issues/3830) - Failures +- [Issue #3830](https://github.com/modular/max/issues/3830) - Failures emitting register RValues to ref arguments. - The VS Code extension now auto-updates its private copy of the MAX SDK. @@ -1555,7 +1555,7 @@ detailed information in the following sections: The Mojo compiler now always passes the "new value" being set using the last keyword argument of the `__setitem__()`, e.g. turning `yourType[1, 2] = 3` into `yourType.__setitem__(1, 2, val=3)`. This fixes - [Issue #248](https://github.com/modular/mojo/issues/248). + [Issue #248](https://github.com/modular/max/issues/248). - Mojo context managers used in regions of code that may raise no longer need to define a "conditional" exit function in the form of @@ -1682,7 +1682,7 @@ detailed information in the following sections: - Added the builtin [`input()`](/mojo/stdlib/builtin/io/input) function, which behaves the same as Python. - ([PR #3392](https://github.com/modular/mojo/pull/3392)) + ([PR #3392](https://github.com/modular/max/pull/3392)) ```mojo name = input("Enter your name: ") @@ -1693,7 +1693,7 @@ detailed information in the following sections: There is a known issue when running the `input()` function with JIT compilation (see issue - [#3479](https://github.com/modular/mojo/issues/3479)). + [#3479](https://github.com/modular/max/issues/3479)). - [`print()`](/mojo/stdlib/builtin/io/print) now requires that its arguments conform to the [`Formattable`](/mojo/stdlib/utils/format/Formattable) trait. @@ -1766,12 +1766,12 @@ detailed information in the following sections: - Added [`TemporaryDirectory`](/mojo/stdlib/tempfile/tempfile/TemporaryDirectory) in module `tempfile`. - ([PR 2743](https://github.com/modular/mojo/pull/2743)) + ([PR 2743](https://github.com/modular/max/pull/2743)) - Added [`NamedTemporaryFile`](/mojo/stdlib/tempfile/tempfile/NamedTemporaryFile) in module `tempfile`. - ([PR 2762](https://github.com/modular/mojo/pull/2762)) + ([PR 2762](https://github.com/modular/max/pull/2762)) - [`String`](/mojo/stdlib/collections/string/String) and friends: @@ -1780,7 +1780,7 @@ detailed information in the following sections: - Added the [`String.format()`](/mojo/stdlib/collections/string/String#format) method. - ([PR #2771](https://github.com/modular/mojo/pull/2771)) + ([PR #2771](https://github.com/modular/max/pull/2771)) Supports automatic and manual indexing of `*args`. @@ -1801,7 +1801,7 @@ detailed information in the following sections: - [`String.format()`](/mojo/stdlib/collections/string/String#format) now supports conversion flags `!s` and `!r`, allowing for `str()` and `repr()` conversions within format strings. - ([PR #3279](https://github.com/modular/mojo/pull/3279)) + ([PR #3279](https://github.com/modular/max/pull/3279)) Example: @@ -1818,14 +1818,14 @@ detailed information in the following sections: [`ljust()`](/mojo/stdlib/collections/string/String#ljust), and [`center()`](/mojo/stdlib/collections/string/String#center) methods to return a justified string based on width and fillchar. ([PR - #3278](https://github.com/modular/mojo/pull/3278)) + #3278](https://github.com/modular/max/pull/3278)) - The [`atol()`](/mojo/stdlib/collections/string/atol) function now correctly supports leading underscores, (e.g.`atol("0x_ff", 0)`), when the appropriate base is specified or inferred (base 0). non-base-10 integer literals as per Python's [Integer Literals](). - ([PR #3180](https://github.com/modular/mojo/pull/3180)) + ([PR #3180](https://github.com/modular/max/pull/3180)) - Added the [`unsafe_cstr_ptr()`](/mojo/stdlib/collections/string/String#unsafe_cstr_ptr) @@ -1843,7 +1843,7 @@ detailed information in the following sections: and [`StringSlice.__len__()`](/mojo/stdlib/utils/string_slice/StringSlice#__len__) now does return the Unicode codepoints length. - ([PR #2960](https://github.com/modular/mojo/pull/2960)) + ([PR #2960](https://github.com/modular/max/pull/2960)) - Added a new [`StaticString`](/mojo/stdlib/utils/string_slice/#aliases) type alias. This can be used in place of @@ -1987,7 +1987,7 @@ detailed information in the following sections: assert_equal(str(stacked), "[1 2 3 4 5 6]") ``` - ([PR #3264](https://github.com/modular/mojo/pull/3264)) + ([PR #3264](https://github.com/modular/max/pull/3264)) - Accessing local Python modules with [`Python.add_to_path(".")`](/mojo/stdlib/python/python/Python#add_to_path) @@ -2002,7 +2002,7 @@ detailed information in the following sections: - [`List`](/mojo/stdlib/collections/list/List) values are now equality comparable with `==` and `!=` when their element type is equality - comparable. ([PR #3195](https://github.com/modular/mojo/pull/3195)) + comparable. ([PR #3195](https://github.com/modular/max/pull/3195)) - [`Optional`](/mojo/stdlib/collections/optional/Optional) values are now equality comparable with `==` and `!=` when their element type is equality @@ -2010,22 +2010,22 @@ detailed information in the following sections: - Added a new [`Counter`](/mojo/stdlib/collections/counter/Counter) dictionary-like type, matching most of the features of the Python one. - ([PR #2910](https://github.com/modular/mojo/pull/2910)) + ([PR #2910](https://github.com/modular/max/pull/2910)) - [`Dict`](/mojo/stdlib/collections/dict/Dict) now implements [`setdefault()`](/mojo/stdlib/collections/dict/Dict#setdefault), which gets a value from the dictionary by key, or sets it to a default if it doesn't exist. - ([PR #2803](https://github.com/modular/mojo/pull/2803)) + ([PR #2803](https://github.com/modular/max/pull/2803)) - `Dict` now supports [`popitem()`](/mojo/stdlib/collections/dict/Dict#popitem), which removes and returns the last item in the `Dict`. - ([PR #2701](https://github.com/modular/mojo/pull/2701)) + ([PR #2701](https://github.com/modular/max/pull/2701)) - Added a [`Dict.__init__()`](/mojo/stdlib/collections/dict/Dict#__init__) overload to specify initial capacity. - ([PR #3171](https://github.com/modular/mojo/pull/3171)) + ([PR #3171](https://github.com/modular/max/pull/3171)) The capacity has to be a power of two and greater than or equal to 8. @@ -2040,7 +2040,7 @@ detailed information in the following sections: - `ListLiteral` now supports [`__contains__()`](/mojo/stdlib/builtin/builtin_list/ListLiteral#__contains__). - ([PR #3251](https://github.com/modular/mojo/pull/3251)) + ([PR #3251](https://github.com/modular/max/pull/3251)) - Filesystem and environment utilities: @@ -2122,7 +2122,7 @@ detailed information in the following sections: - Added the [`Identifiable`](/mojo/stdlib/builtin/identifiable/Identifiable) trait, used to describe types that implement the `__is__()` and `__isnot__()` trait methods. - ([PR #2807](https://github.com/modular/mojo/pull/2807)) + ([PR #2807](https://github.com/modular/max/pull/2807)) - Types conforming to [`Boolable`](/mojo/stdlib/builtin/bool/Boolable) (that is, those implementing `__bool__()`) no longer implicitly convert to `Bool`. @@ -2164,7 +2164,7 @@ detailed information in the following sections: - Added the [`oct()`](/mojo/stdlib/builtin/format_int/oct) builtin function for formatting an integer in octal. - ([PR #2914](https://github.com/modular/mojo/pull/2914)) + ([PR #2914](https://github.com/modular/max/pull/2914)) - Added the [`assert_is()`](/mojo/stdlib/testing/testing/assert_is) and [`assert_is_not()`](/mojo/stdlib/testing/testing/assert_is_not) test @@ -2172,7 +2172,7 @@ detailed information in the following sections: - The [`math`](/mojo/stdlib/math/constants/) package now includes the `pi`, `e`, and `tau` constants (Closes Issue - [#2135](https://github.com/modular/mojo/issues/2135)). + [#2135](https://github.com/modular/max/issues/2135)). - The [`ulp`](/mojo/stdlib/math/math/ulp) function from `numerics` has been moved to the `math` module. @@ -2181,7 +2181,7 @@ detailed information in the following sections: [`bit_reverse()`](/mojo/stdlib/bit/bit/bit_reverse), [`byte_swap()`](/mojo/stdlib/bit/bit/byte_swap), and [`pop_count()`](/mojo/stdlib/bit/bit/pop_count) for the `Int` type. - ([PR #3150](https://github.com/modular/mojo/pull/3150)) + ([PR #3150](https://github.com/modular/max/pull/3150)) - A few `bit` functions have been renamed for clarity: @@ -2193,7 +2193,7 @@ detailed information in the following sections: `OptionalReg[Int]` for `start` and `end` and implements a constructor which accepts optional values. `Slice._has_end()` has also been removed since a Slice with no end is now represented by an empty `Slice.end` option. - ([PR #2495](https://github.com/modular/mojo/pull/2495)) + ([PR #2495](https://github.com/modular/max/pull/2495)) ```mojo var s = Slice(1, None, 2) @@ -2305,28 +2305,28 @@ detailed information in the following sections: - Mojo now prints `ref` arguments and results in generated documentation correctly. -- [#1734](https://github.com/modular/mojo/issues/1734) - Calling +- [#1734](https://github.com/modular/max/issues/1734) - Calling `__copyinit__` on self causes crash. -- [#3142](https://github.com/modular/mojo/issues/3142) - [QoI] Confusing +- [#3142](https://github.com/modular/max/issues/3142) - [QoI] Confusing `__setitem__` method is failing with a "must be mutable" error. -- [#248](https://github.com/modular/mojo/issues/248) - [Feature] Enable +- [#248](https://github.com/modular/max/issues/248) - [Feature] Enable `__setitem__` to take variadic arguments -- [#3065](https://github.com/modular/mojo/issues/3065) - Fix incorrect behavior +- [#3065](https://github.com/modular/max/issues/3065) - Fix incorrect behavior of `SIMD.__int__` on unsigned types -- [#3045](https://github.com/modular/mojo/issues/3045) - Disable implicit SIMD +- [#3045](https://github.com/modular/max/issues/3045) - Disable implicit SIMD conversion routes through `Bool` -- [#3126](https://github.com/modular/mojo/issues/3126) - [BUG] List doesn't +- [#3126](https://github.com/modular/max/issues/3126) - [BUG] List doesn't work at compile time. -- [#3237](https://github.com/modular/mojo/issues/3237) - [BUG] Difference +- [#3237](https://github.com/modular/max/issues/3237) - [BUG] Difference between `__getitem__` and `[.]` operator. -- [#3336](https://github.com/modular/mojo/issues/3336) - Fix outdated +- [#3336](https://github.com/modular/max/issues/3336) - Fix outdated references to `let` in REPL documentation. - The VS Code extension no longer caches the information of the selected @@ -2545,7 +2545,7 @@ Big themes for this release: - Added built-in [`repr()`](/mojo/stdlib/builtin/repr/repr) function and [`Representable`](/mojo/stdlib/builtin/repr/Representable) trait. - ([PR #2361](https://github.com/modular/mojo/pull/2361)) + ([PR #2361](https://github.com/modular/max/pull/2361)) - Added the [`Indexer`](/mojo/stdlib/builtin/int/Indexer) trait to denote types that implement the `__index__()` method which allows these types to be @@ -2592,7 +2592,7 @@ Big themes for this release: print(a[AlwaysZero()]) # works and prints 1 ``` - ([PR #2685](https://github.com/modular/mojo/pull/2685)) + ([PR #2685](https://github.com/modular/max/pull/2685)) - Added traits allowing user-defined types to be supported by various built-in and math functions. @@ -2670,14 +2670,14 @@ Big themes for this release: [`rstrip()`](/mojo/stdlib/collections/string/String#rstrip) can now remove custom characters other than whitespace. In addition, there are now several useful aliases for whitespace, ASCII lower/uppercase, and so on. - ([PR #2555](https://github.com/modular/mojo/pull/2555)) + ([PR #2555](https://github.com/modular/max/pull/2555)) - `String` now has a [`splitlines()`](/mojo/stdlib/collections/string/String#splitlines) method, which allows splitting strings at line boundaries. This method supports [universal newlines](https://docs.python.org/3/glossary.html#term-universal-newlines) and provides an option to retain or remove the line break characters. - ([PR #2810](https://github.com/modular/mojo/pull/2810)) + ([PR #2810](https://github.com/modular/max/pull/2810)) - `InlinedString` has been renamed to [`InlineString`](/mojo/stdlib/utils/inline_string/InlineString) to be @@ -2686,12 +2686,12 @@ Big themes for this release: - [`StringRef`](/mojo/stdlib/utils/stringref/StringRef) now implements [`strip()`](/mojo/stdlib/utils/stringref/StringRef#strip), which can be used to remove leading and trailing whitespace. - ([PR #2683](https://github.com/modular/mojo/pull/2683)) + ([PR #2683](https://github.com/modular/max/pull/2683)) - `StringRef` now implements [`startswith()`](/mojo/stdlib/utils/stringref/StringRef#startswith) and [`endswith()`](/mojo/stdlib/utils/stringref/StringRef#endswith). - ([PR #2710](https://github.com/modular/mojo/pull/2710)) + ([PR #2710](https://github.com/modular/max/pull/2710)) - Added a new [`StringSlice`](/mojo/stdlib/utils/string_slice/StringSlice) type, to replace uses of the unsafe `StringRef` type in standard library @@ -2743,7 +2743,7 @@ Big themes for this release: - Added a built-in [`sort()`](/mojo/stdlib/builtin/sort/sort) function for lists of elements that conform to the [`ComparableCollectionElement`](/mojo/stdlib/builtin/value/ComparableCollectionElement) - trait.([PR #2609](https://github.com/modular/mojo/pull/2609)) + trait.([PR #2609](https://github.com/modular/max/pull/2609)) - [`int()`](/mojo/stdlib/builtin/int/int-function) can now take a string and a specified base to parse an integer from a @@ -2751,17 +2751,17 @@ Big themes for this release: specified, the string will be parsed as if it was an integer literal, with the base determined by whether the string contains the prefix `"0x"`, `"0o"`, or `"0b"`. - ([PR #2273](https://github.com/modular/mojo/pull/2273), - fixes [#2274](https://github.com/modular/mojo/issues/2274)) + ([PR #2273](https://github.com/modular/max/pull/2273), + fixes [#2274](https://github.com/modular/max/issues/2274)) - Added the [`bin()`](/mojo/stdlib/builtin/format_int/bin) built-in function to convert integral types into their binary string representation. - ([PR #2603](https://github.com/modular/mojo/pull/2603)) + ([PR #2603](https://github.com/modular/max/pull/2603)) - Added the [`atof()`](/mojo/stdlib/collections/string/atof) built-in function, which can convert a `String` to a `float64`. - ([PR #2649](https://github.com/modular/mojo/pull/2649)) + ([PR #2649](https://github.com/modular/max/pull/2649)) - You can now use the built-in [`any()`](/mojo/stdlib/builtin/bool/any) and [`all()`](/mojo/stdlib/builtin/bool/all) functions to check for truthy @@ -2769,7 +2769,7 @@ Big themes for this release: `size=1`, You must explicitly use these to get the truthy value of a SIMD vector with more than one element. This avoids common bugs around implicit conversion of `SIMD` to `Bool`. - ([PR #2600](https://github.com/modular/mojo/pull/2600)) + ([PR #2600](https://github.com/modular/max/pull/2600)) For example: @@ -2784,10 +2784,10 @@ Big themes for this release: - [`object`](/mojo/stdlib/builtin/object/) now implements all the bitwise operators. - ([PR #2324](https://github.com/modular/mojo/pull/2324)) + ([PR #2324](https://github.com/modular/max/pull/2324)) - [`Tuple`](/mojo/stdlib/builtin/tuple/Tuple) now supports `__contains__()`. - ([PR #2709](https://github.com/modular/mojo/pull/2709)) For example: + ([PR #2709](https://github.com/modular/max/pull/2709)) For example: ```mojo var x = Tuple(1, 2, True) @@ -2837,12 +2837,12 @@ Big themes for this release: Note that `List` doesn't conform to the `Stringable` trait yet so you cannot use `str(my_list)` yet. - ([PR #2673](https://github.com/modular/mojo/pull/2673)) + ([PR #2673](https://github.com/modular/max/pull/2673)) - `List` has a simplified syntax to call the [`count()`](/mojo/stdlib/collections/list/List#count) method: `my_list.count(x)`. - ([PR #2675](https://github.com/modular/mojo/pull/2675)) + ([PR #2675](https://github.com/modular/max/pull/2675)) - `List()` now supports `__contains__()`, so you can now use lists with the `in` operator: @@ -2851,84 +2851,84 @@ Big themes for this release: if x in my_list: ``` - ([PR #2667](https://github.com/modular/mojo/pull/2667)) + ([PR #2667](https://github.com/modular/max/pull/2667)) - `List` now has an [`unsafe_get()`](/mojo/stdlib/collections/list/List#unsafe_get) to get the reference to an element without bounds check or wraparound for negative indices. Note that this method is unsafe. Use with caution. - [PR #2800](https://github.com/modular/mojo/pull/2800)) + [PR #2800](https://github.com/modular/max/pull/2800)) - Added a [`fromkeys()`](/mojo/stdlib/collections/dict/Dict#fromkeys) method to `Dict` to return a `Dict` with the specified keys and values. - ([PR 2622](https://github.com/modular/mojo/pull/2622)) + ([PR 2622](https://github.com/modular/max/pull/2622)) - Added a [`clear()`](/mojo/stdlib/collections/dict/Dict#clear) method to - `Dict`. ([PR 2627](https://github.com/modular/mojo/pull/2627)) + `Dict`. ([PR 2627](https://github.com/modular/max/pull/2627)) - `Dict` now supports [`reversed()`](/mojo/stdlib/builtin/reversed/reversed) for its `items()` and `values()` iterators. - ([PR #2340](https://github.com/modular/mojo/pull/2340)) + ([PR #2340](https://github.com/modular/max/pull/2340)) - `Dict` now has a simplified conversion to `String` with `my_dict.__str__()`. Note that `Dict` does not conform to the `Stringable` trait so `str(my_dict)` is not possible yet. - ([PR #2674](https://github.com/modular/mojo/pull/2674)) + ([PR #2674](https://github.com/modular/max/pull/2674)) - `Dict` now implements [`get(key)`](/mojo/stdlib/collections/dict/Dict#get) and `get(key, default)` functions. - ([PR #2519](https://github.com/modular/mojo/pull/2519)) + ([PR #2519](https://github.com/modular/max/pull/2519)) - Added a temporary `__get_ref(key)` method to `Dict`, allowing you to get a `Reference` to a dictionary value. - Added a new [`InlineList`](/mojo/stdlib/collections/inline_list/InlineList) type, a stack-allocated list with a static maximum size. - ([PR 2587#](https://github.com/modular/mojo/pull/2587)) - ([PR #2703](https://github.com/modular/mojo/pull/2703)) + ([PR 2587#](https://github.com/modular/max/pull/2587)) + ([PR #2703](https://github.com/modular/max/pull/2703)) - Added a new [`Span`](/mojo/stdlib/memory/span/Span) type for taking slices of contiguous collections. - ([PR #2595](https://github.com/modular/mojo/pull/2595)) + ([PR #2595](https://github.com/modular/max/pull/2595)) - [`os`](/mojo/stdlib/os/os/) module: - The `os` module now provides functionality for adding and removing directories using [`mkdir()`](/mojo/stdlib/os/os/mkdir) and [`rmdir()`](/mojo/stdlib/os/os/rmdir). - ([PR #2430](https://github.com/modular/mojo/pull/2430)) + ([PR #2430](https://github.com/modular/max/pull/2430)) - Added the [`os.path.getsize()`](/mojo/stdlib/os/path/path/getsize) function, which gives the size in bytes of the file identified by the path. - ([PR 2626](https://github.com/modular/mojo/pull/2626)) + ([PR 2626](https://github.com/modular/max/pull/2626)) - Added [`os.path.join()`](/mojo/stdlib/os/path/path/join) function. - ([PR 2792](https://github.com/modular/mojo/pull/2792)) + ([PR 2792](https://github.com/modular/max/pull/2792)) - Added a new [`tempfile`](/mojo/stdlib/tempfile/tempfile/) module, with `gettempdir()` and `mkdtemp()` functions. - ([PR 2742](https://github.com/modular/mojo/pull/2742)) + ([PR 2742](https://github.com/modular/max/pull/2742)) - [`SIMD`](/mojo/stdlib/builtin/simd/SIMD) type: - Added [`SIMD.shuffle()`](/mojo/stdlib/builtin/simd/SIMD#shuffle) with `IndexList` mask. - ([PR #2315](https://github.com/modular/mojo/pull/2315)) + ([PR #2315](https://github.com/modular/max/pull/2315)) - [`SIMD.__bool__()`](/mojo/stdlib/builtin/simd/SIMD#__bool__) is constrained such that it only works when `size` is `1`. For SIMD vectors with more than one element, use [`any()`](/mojo/stdlib/builtin/bool/any) or [`all()`](/mojo/stdlib/builtin/bool/all). - ([PR #2502](https://github.com/modular/mojo/pull/2502)) + ([PR #2502](https://github.com/modular/max/pull/2502)) - The [`SIMD.reduce_or()`](/mojo/stdlib/builtin/simd/SIMD#reduce_or) and [`SIMD.reduce_and()`](/mojo/stdlib/builtin/simd/SIMD#reduce_and) methods are now bitwise operations, and support integer types. - ([PR #2671](https://github.com/modular/mojo/pull/2671)) + ([PR #2671](https://github.com/modular/max/pull/2671)) - Added [`SIMD.__repr__()`](/mojo/stdlib/builtin/simd/SIMD#__repr__) to get the verbose string representation of `SIMD` types. - ([PR #2728](https://github.com/modular/mojo/pull/2728)) + ([PR #2728](https://github.com/modular/max/pull/2728)) - [`math`](/mojo/stdlib/math/math/) package: @@ -2964,7 +2964,7 @@ Big themes for this release: - [`math.gcd()`](/mojo/stdlib/math/math/gcd) now works on negative inputs, and like Python's implementation, accepts a variadic list of integers. New overloads for a `List` or `Span`of integers are also added. - ([PR #2777](https://github.com/modular/mojo/pull/2777)) + ([PR #2777](https://github.com/modular/max/pull/2777)) - Async and coroutines: @@ -2988,12 +2988,12 @@ Big themes for this release: that works on memory-only types. Compare with the existing [`StaticTuple`](/mojo/stdlib/utils/static_tuple/StaticTuple) type, which is conceptually an array type, but only works on `AnyTrivialRegType`. - ([PR #2294](https://github.com/modular/mojo/pull/2294)) + ([PR #2294](https://github.com/modular/max/pull/2294)) - The [`base64`](/mojo/stdlib/base64/) package now includes encoding and decoding support for both the Base64 and Base16 encoding schemes. - ([PR #2364](https://github.com/modular/mojo/pull/2364)) - ([PR #2584](https://github.com/modular/mojo/pull/2584)) + ([PR #2364](https://github.com/modular/max/pull/2364)) + ([PR #2584](https://github.com/modular/max/pull/2584)) - The `take()` function in [`Variant`](/mojo/stdlib/utils/variant/Variant) and [`Optional`](/mojo/stdlib/collections/optional/Optional) has been renamed to @@ -3010,7 +3010,7 @@ Big themes for this release: - `infinity` and `NaN` are now correctly handled in [`testing.assert_almost_equal()`](/mojo/stdlib/testing/testing/assert_almost_equal) and an `inf` function has been added to `utils/numerics.mojo`. - ([PR #2375](https://github.com/modular/mojo/pull/2375)) + ([PR #2375](https://github.com/modular/max/pull/2375)) ### Tooling changes @@ -3124,16 +3124,16 @@ Big themes for this release: #### 🛠️ Fixed -- [#1837](https://github.com/modular/mojo/issues/1837) Fix self-referential +- [#1837](https://github.com/modular/max/issues/1837) Fix self-referential variant crashing the compiler. -- [#2363](https://github.com/modular/mojo/issues/2363) Fix LSP crashing on +- [#2363](https://github.com/modular/max/issues/2363) Fix LSP crashing on simple trait definitions. -- [#1787](https://github.com/modular/mojo/issues/1787) Fix error when using +- [#1787](https://github.com/modular/max/issues/1787) Fix error when using `//` on `FloatLiteral` in alias expression. - Made several improvements to dictionary performance. Dicts with integer keys are most heavily affected, but large dicts and dicts with large values will also see large improvements. -- [#2692](https://github.com/modular/mojo/issues/2692) Fix `assert_raises` +- [#2692](https://github.com/modular/max/issues/2692) Fix `assert_raises` to include calling location. ### Special thanks @@ -3322,21 +3322,21 @@ Special thanks to our community contributors: - `pop(index)` for removing an element at a particular index. By default, `List.pop()` removes the last element in the list. ([@LJ-9801](https://github.com/LJ-9801), fixes - [#2017](https://github.com/modular/mojo/issues/2017)) + [#2017](https://github.com/modular/max/issues/2017)) - `resize(new_size)` for resizing the list without the need to specify an additional value. ([@mikowals](https://github.com/mikowals), fixes - [#2133](https://github.com/modular/mojo/issues/2133)) + [#2133](https://github.com/modular/max/issues/2133)) - `insert(index, value)` for inserting a value at a specified index into the `List`. ([@whym1here](https://github.com/whym1here), fixes - [#2134](https://github.com/modular/mojo/issues/2134)) + [#2134](https://github.com/modular/max/issues/2134)) - A new constructor `List(ptr, size, capacity)` to to avoid needing to do a deep copy of an existing contiguous memory allocation when constructing a new `List`. ([@StandinKP](https://github.com/StandinKP), fixes - [#2170](https://github.com/modular/mojo/issues/2170)) + [#2170](https://github.com/modular/max/issues/2170)) - [`Dict`](/mojo/stdlib/collections/dict/Dict) now has a `update()` method to update keys/values from another `Dict`. @@ -3377,7 +3377,7 @@ Special thanks to our community contributors: ([@helehex](https://github.com/helehex) and [@jayzhan211](https://github.com/jayzhan211), contributes towards - [#2325](https://github.com/modular/mojo/issues/2325)) + [#2325](https://github.com/modular/max/issues/2325)) - [`Optional`](/mojo/stdlib/collections/optional/Optional) now implements `__is__` and `__isnot__` methods so that you can compare an `Optional` with @@ -3524,7 +3524,7 @@ Special thanks to our community contributors: [`chr`](/mojo/stdlib/collections/string/chr) functions have been improved to accept any Unicode character. ([@mzaks](https://github.com/mzaks), contributes towards - [#1616](https://github.com/modular/mojo/issues/1616)) + [#1616](https://github.com/modular/max/issues/1616)) - [`atol()`](/mojo/stdlib/collections/string/atol) now handles whitespace. The `atol()`function is used internally by `String.__int__()`, so @@ -3533,7 +3533,7 @@ Special thanks to our community contributors: - [`SIMD`](/mojo/stdlib/builtin/simd/SIMD) now implements the `__rmod__()` method. ([@bgreni](https://github.com/bgreni), fixes - [#1482](https://github.com/modular/mojo/issues/1482)) + [#1482](https://github.com/modular/max/issues/1482)) - [`bool(None)`](/mojo/stdlib/builtin/bool/bool-function) is now implemented. ([@zhoujingya](https://github.com/zhoujingya)) @@ -3568,7 +3568,7 @@ Special thanks to our community contributors: division, modulo, and left and right shift operators, including the in-place and reverse variants. ([@LJ-9801](https://github.com/LJ-9801), fixes - [#2224](https://github.com/modular/mojo/issues/2224)) + [#2224](https://github.com/modular/max/issues/2224)) - Added checked arithmetic operations for `SIMD` integers. @@ -3600,7 +3600,7 @@ Special thanks to our community contributors: - Added [`os.remove()`](/mojo/stdlib/os/os/remove) and [`os.unlink()`](/mojo/stdlib/os/os/unlink) for deleting files. ([@artemiogr97](https://github.com/artemiogr97), fixes - [#2306](https://github.com/modular/mojo/issues/2306)) + [#2306](https://github.com/modular/max/issues/2306)) #### 🦋 Changed @@ -3620,15 +3620,15 @@ Special thanks to our community contributors: ``` ([@lsh](https://github.com/lsh), fixes - [#2179](https://github.com/modular/mojo/issues/2179)) + [#2179](https://github.com/modular/max/issues/2179)) -- Per the accepted community proposal, - [Standardize the representation of byte sequence as a sequence of unsigned - 8-bit integers](https://github.com/modular/mojo/blob/main/proposals/byte-as-uint8.md), - began transition to using `UInt8` by changing the data pointer of `Error` - to `DTypePointer[DType.uint8]`. - ([@gabrieldemarmiesse](https://github.com/gabrieldemarmiesse), contributes - towards [#2317](https://github.com/modular/mojo/issues/2317)) +- Per the accepted community proposal, [Standardize the representation of byte +sequence as a sequence of unsigned 8-bit +integers](https://github.com/modular/max/blob/main/mojo/proposals/byte-as-uint8.md), +began transition to using `UInt8` by changing the data pointer of `Error` to +`DTypePointer[DType.uint8]`. +([@gabrieldemarmiesse](https://github.com/gabrieldemarmiesse), contributes +towards [#2317](https://github.com/modular/max/issues/2317)) - Continued transition to `UnsafePointer` from the legacy `Pointer` type in various standard library APIs and internals. @@ -3700,56 +3700,56 @@ Special thanks to our community contributors: #### 🛠️ Fixed -- [#516](https://github.com/modular/mojo/issues/516) and - [#1817](https://github.com/modular/mojo/issues/1817) and many others, e.g. +- [#516](https://github.com/modular/max/issues/516) and + [#1817](https://github.com/modular/max/issues/1817) and many others, e.g. "Can't create a function that returns two strings." -- [#1178](https://github.com/modular/mojo/issues/1178) (os/kern) failure (5). +- [#1178](https://github.com/modular/max/issues/1178) (os/kern) failure (5). -- [#1609](https://github.com/modular/mojo/issues/1609) alias with +- [#1609](https://github.com/modular/max/issues/1609) alias with `DynamicVector[Tuple[Int]]` fails. -- [#1987](https://github.com/modular/mojo/issues/1987) Defining `main` +- [#1987](https://github.com/modular/max/issues/1987) Defining `main` in a Mojo package is an error, for now. This is not intended to work yet, erroring for now will help to prevent accidental undefined behavior. -- [#1215](https://github.com/modular/mojo/issues/1215) and - [#1949](https://github.com/modular/mojo/issues/1949) The Mojo LSP server no +- [#1215](https://github.com/modular/max/issues/1215) and + [#1949](https://github.com/modular/max/issues/1949) The Mojo LSP server no longer cuts off hover previews for functions with functional arguments, parameters, or results. -- [#1901](https://github.com/modular/mojo/issues/1901) Fixed Mojo LSP and +- [#1901](https://github.com/modular/max/issues/1901) Fixed Mojo LSP and documentation generation handling of inout arguments. -- [#1913](https://github.com/modular/mojo/issues/1913) - `0__` no longer +- [#1913](https://github.com/modular/max/issues/1913) - `0__` no longer crashes the Mojo parser. -- [#1924](https://github.com/modular/mojo/issues/1924) JIT debugging on Mac +- [#1924](https://github.com/modular/max/issues/1924) JIT debugging on Mac has been fixed. -- [#1941](https://github.com/modular/mojo/issues/1941) Mojo variadic arguments +- [#1941](https://github.com/modular/max/issues/1941) Mojo variadic arguments don't work with non-trivial register-only types. -- [#1963](https://github.com/modular/mojo/issues/1963) `a!=0` is now parsed +- [#1963](https://github.com/modular/max/issues/1963) `a!=0` is now parsed and formatted correctly by `mojo format`. -- [#1676](https://github.com/modular/mojo/issues/1676) Fix a crash related to +- [#1676](https://github.com/modular/max/issues/1676) Fix a crash related to `@value` decorator and structs with empty body. -- [#1917](https://github.com/modular/mojo/issues/1917) Fix a crash after +- [#1917](https://github.com/modular/max/issues/1917) Fix a crash after syntax error during tuple creation. -- [#2006](https://github.com/modular/mojo/issues/2006) The Mojo LSP now +- [#2006](https://github.com/modular/max/issues/2006) The Mojo LSP now properly supports signature types with named arguments and parameters. -- [#2007](https://github.com/modular/mojo/issues/2007) and - [#1997](https://github.com/modular/mojo/issues/1997) The Mojo LSP no longer +- [#2007](https://github.com/modular/max/issues/2007) and + [#1997](https://github.com/modular/max/issues/1997) The Mojo LSP no longer crashes on certain types of closures. -- [#1675](https://github.com/modular/mojo/issues/1675) Ensure `@value` +- [#1675](https://github.com/modular/max/issues/1675) Ensure `@value` decorator fails gracefully after duplicate field error. -- [#2068](https://github.com/modular/mojo/issues/2068) +- [#2068](https://github.com/modular/max/issues/2068) Fix `SIMD.reduce()` for size_out == 2. ([@soraros](https://github.com/soraros)) @@ -3762,7 +3762,7 @@ This release doesn't include any changes to Mojo. ### 🔥 Legendary - The Mojo standard library is now open source! Check out the - [README](https://github.com/modular/mojo/blob/main/stdlib/README.md) + [README](https://github.com/modular/max/blob/main/mojo/stdlib/README.md) for everything you need to get started. - Structs and other nominal types are now allowed to implicitly conform to @@ -3831,7 +3831,7 @@ This release doesn't include any changes to Mojo. - `let` declarations now produce a compile time error instead of a warning, our next step in [removing let - declarations](https://github.com/modular/mojo/blob/main/proposals/remove-let-decls.md). + declarations](https://github.com/modular/max/blob/main/mojo/proposals/remove-let-decls.md). The compiler still recognizes the `let` keyword for now in order to produce a good error message, but that will be removed in subsequent releases. @@ -4135,27 +4135,27 @@ fixed in a future release. #### 🛠️ Fixed -- [#1362](https://github.com/modular/mojo/issues/1362) - Parameter inference +- [#1362](https://github.com/modular/max/issues/1362) - Parameter inference now recursively matches function types. -- [#951](https://github.com/modular/mojo/issues/951) - Functions that were +- [#951](https://github.com/modular/max/issues/951) - Functions that were both `async` and `@always_inline` incorrectly errored. -- [#1858](https://github.com/modular/mojo/issues/1858) - Trait with parametric +- [#1858](https://github.com/modular/max/issues/1858) - Trait with parametric methods regression. -- [#1892](https://github.com/modular/mojo/issues/1892) - Forbid unsupported +- [#1892](https://github.com/modular/max/issues/1892) - Forbid unsupported decorators on traits. -- [#1735](https://github.com/modular/mojo/issues/1735) - Trait-typed values +- [#1735](https://github.com/modular/max/issues/1735) - Trait-typed values are incorrectly considered equal. -- [#1909](https://github.com/modular/mojo/issues/1909) - Crash due to nested +- [#1909](https://github.com/modular/max/issues/1909) - Crash due to nested import in unreachable block. -- [#1921](https://github.com/modular/mojo/issues/1921) - Parser crashes +- [#1921](https://github.com/modular/max/issues/1921) - Parser crashes binding Reference to lvalue with subtype lifetime. -- [#1945](https://github.com/modular/mojo/issues/1945) - `Optional[T].or_else()` +- [#1945](https://github.com/modular/max/issues/1945) - `Optional[T].or_else()` should return `T` instead of `Optional[T]`. -- [#1940](https://github.com/modular/mojo/issues/1940) - Constrain +- [#1940](https://github.com/modular/max/issues/1940) - Constrain `math.copysign` to floating point or integral types. -- [#1838](https://github.com/modular/mojo/issues/1838) - Variadic `print` +- [#1838](https://github.com/modular/max/issues/1838) - Variadic `print` does not work when specifying `end=""` -- [#1826](https://github.com/modular/mojo/issues/1826) - The `SIMD.reduce` +- [#1826](https://github.com/modular/max/issues/1826) - The `SIMD.reduce` methods correctly handle edge cases where `size_out >= size`. ## v24.1.1 (2024-03-18) @@ -4509,7 +4509,7 @@ installation issues. Otherwise it is functionally identical to Mojo 24.1. ### 🦋 Changed - As another step towards [removing let - declarations](https://github.com/modular/mojo/blob/main/proposals/remove-let-decls.md) + declarations](https://github.com/modular/max/blob/main/mojo/proposals/remove-let-decls.md) we have removed support for let declarations inside the compiler. To ease migration, we parse `let` declarations as a `var` declaration so your code won't break. We emit a warning about this, but please switch your code to @@ -4609,72 +4609,72 @@ experience without dedicated sugar. ### 🛠️ Fixed -- [#435](https://github.com/modular/mojo/issues/435) +- [#435](https://github.com/modular/max/issues/435) Structs with Self type don't always work. -- [#1540](https://github.com/modular/mojo/issues/1540) +- [#1540](https://github.com/modular/max/issues/1540) Crash in register_passable self referencing struct. -- [#1664](https://github.com/modular/mojo/issues/1664) - Improve error +- [#1664](https://github.com/modular/max/issues/1664) - Improve error message when `StaticTuple` is constructed with a negative size for the number of elements. -- [#1679](https://github.com/modular/mojo/issues/1679) - crash on SIMD of zero +- [#1679](https://github.com/modular/max/issues/1679) - crash on SIMD of zero elements. - Various crashes on invalid code: - [#1230](https://github.com/modular/mojo/issues/1230), - [#1699](https://github.com/modular/mojo/issues/1699), - [#1708](https://github.com/modular/mojo/issues/1708) -- [#1223](https://github.com/modular/mojo/issues/1223) - Crash when parametric + [#1230](https://github.com/modular/max/issues/1230), + [#1699](https://github.com/modular/max/issues/1699), + [#1708](https://github.com/modular/max/issues/1708) +- [#1223](https://github.com/modular/max/issues/1223) - Crash when parametric function is passed as (runtime) argument. The parser now errors out instead. -- [#1530](https://github.com/modular/mojo/issues/1530) - Crash during +- [#1530](https://github.com/modular/max/issues/1530) - Crash during diagnostic emission for parameter deduction failure. -- [#1538](https://github.com/modular/mojo/issues/1538) and [#1607]( - https://github.com/modular/mojo/issues/1607) - Crash when returning type +- [#1538](https://github.com/modular/max/issues/1538) and [#1607]( + https://github.com/modular/max/issues/1607) - Crash when returning type value instead of instance of expected type. This is a common mistake and the error now includes a hint to point users to the problem. -- [#1613](https://github.com/modular/mojo/issues/1613) - Wrong type name in +- [#1613](https://github.com/modular/max/issues/1613) - Wrong type name in error for incorrect `self` argument type in trait method declaration. -- [#1670](https://github.com/modular/mojo/issues/1670) - Crash on implicit +- [#1670](https://github.com/modular/max/issues/1670) - Crash on implicit conversion in a global variable declaration. -- [#1741](https://github.com/modular/mojo/issues/1741) - Mojo documentation +- [#1741](https://github.com/modular/max/issues/1741) - Mojo documentation generation doesn't show `inout`/`owned` on variadic arguments. -- [#1621](https://github.com/modular/mojo/issues/1621) - VS Code does not +- [#1621](https://github.com/modular/max/issues/1621) - VS Code does not highlight `raises` and `capturing` in functional type expressions. -- [#1617](https://github.com/modular/mojo/issues/1617) - VS Code does not +- [#1617](https://github.com/modular/max/issues/1617) - VS Code does not highlight `fn` in specific contexts. -- [#1740](https://github.com/modular/mojo/issues/1740) - LSP shows unrelated +- [#1740](https://github.com/modular/max/issues/1740) - LSP shows unrelated info when hovering over a struct. -- [#1238](https://github.com/modular/mojo/issues/1238) - File shadows Mojo +- [#1238](https://github.com/modular/max/issues/1238) - File shadows Mojo package path. -- [#1429](https://github.com/modular/mojo/issues/1429) - Crash when using +- [#1429](https://github.com/modular/max/issues/1429) - Crash when using nested import statement. -- [#1322](https://github.com/modular/mojo/issues/1322) - Crash when missing +- [#1322](https://github.com/modular/max/issues/1322) - Crash when missing types in variadic argument. -- [#1314](https://github.com/modular/mojo/issues/1314) - Typecheck error when +- [#1314](https://github.com/modular/max/issues/1314) - Typecheck error when binding alias to parametric function with default argument. -- [#1248](https://github.com/modular/mojo/issues/1248) - Crash when importing +- [#1248](https://github.com/modular/max/issues/1248) - Crash when importing from file the same name as another file in the search path. -- [#1354](https://github.com/modular/mojo/issues/1354) - Crash when importing +- [#1354](https://github.com/modular/max/issues/1354) - Crash when importing from local package. -- [#1488](https://github.com/modular/mojo/issues/1488) - Crash when setting +- [#1488](https://github.com/modular/max/issues/1488) - Crash when setting generic element field. -- [#1476](https://github.com/modular/mojo/issues/1476) - Crash in interpreter +- [#1476](https://github.com/modular/max/issues/1476) - Crash in interpreter when calling functions in parameter context. -- [#1537](https://github.com/modular/mojo/issues/1537) - Crash when copying +- [#1537](https://github.com/modular/max/issues/1537) - Crash when copying parameter value. -- [#1546](https://github.com/modular/mojo/issues/1546) - Modify nested vector +- [#1546](https://github.com/modular/max/issues/1546) - Modify nested vector element crashes parser. -- [#1558](https://github.com/modular/mojo/issues/1558) - Invalid import causes +- [#1558](https://github.com/modular/max/issues/1558) - Invalid import causes parser to crash. -- [#1562](https://github.com/modular/mojo/issues/1562) - Crash when calling +- [#1562](https://github.com/modular/max/issues/1562) - Crash when calling parametric type member function. -- [#1577](https://github.com/modular/mojo/issues/1577) - Crash when using +- [#1577](https://github.com/modular/max/issues/1577) - Crash when using unresolved package as a variable. -- [#1579](https://github.com/modular/mojo/issues/1579) - Member access into +- [#1579](https://github.com/modular/max/issues/1579) - Member access into type instances causes a crash. -- [#1602](https://github.com/modular/mojo/issues/1602) - Interpreter failure +- [#1602](https://github.com/modular/max/issues/1602) - Interpreter failure when constructing strings at compile time. -- [#1696](https://github.com/modular/mojo/issues/1696) - Fixed an issue that +- [#1696](https://github.com/modular/max/issues/1696) - Fixed an issue that caused syntax highlighting to occasionally fail. -- [#1549](https://github.com/modular/mojo/issues/1549) - Fixed an issue when +- [#1549](https://github.com/modular/max/issues/1549) - Fixed an issue when the shift amount is out of range in `SIMD.shift_left` and `SIMD.shift_right`. ## v0.7.0 (2024-01-25) @@ -5020,7 +5020,7 @@ experience without dedicated sugar. - The `simd_width` and `dtype` parameters of `polynomial_evaluate` have been switched. Based on the request in - [#1587](https://github.com/modular/mojo/issues/1587), the + [#1587](https://github.com/modular/max/issues/1587), the `polynomial_evaluate` function has also been extended so that the `coefficients` parameter can take either a either a [`StaticTuple`](/mojo/stdlib/utils/static_tuple/StaticTuple) or a @@ -5031,50 +5031,50 @@ experience without dedicated sugar. ### 🛠️ Fixed -- [#1595](https://github.com/modular/mojo/issues/1595) - Improve error message +- [#1595](https://github.com/modular/max/issues/1595) - Improve error message when trying to materialize `IntLiteral` in runtime code. - Raising an error from the initializer of a memory-only type now works correctly in the presence of complex control flow. Previously Mojo could run the destructor on `self` before it was initialized when exiting with an error. -- [#1096](https://github.com/modular/mojo/issues/1096) - Improve warning +- [#1096](https://github.com/modular/max/issues/1096) - Improve warning messages for dead code in conditionals like `or` expressions. -- [#1419](https://github.com/modular/mojo/issues/1419) - Fix assertion failure +- [#1419](https://github.com/modular/max/issues/1419) - Fix assertion failure with uninitialized lattice values. -- [#1402](https://github.com/modular/mojo/issues/1402) - Fix movable trait not +- [#1402](https://github.com/modular/max/issues/1402) - Fix movable trait not detected on recursive struct implemented with `AnyPointer`. -- [#1399](https://github.com/modular/mojo/issues/1399) - Fix parser crash when +- [#1399](https://github.com/modular/max/issues/1399) - Fix parser crash when a parameter type in a struct that implements a trait is misspelled. -- [#1152](https://github.com/modular/mojo/issues/1152) - Allow mutable `self` +- [#1152](https://github.com/modular/max/issues/1152) - Allow mutable `self` argument when overloading operators using dunder methods. -- [#1493](https://github.com/modular/mojo/issues/1493) - Fix crash in +- [#1493](https://github.com/modular/max/issues/1493) - Fix crash in `DynamicVector` copy constructor in certain situations. -- [#1316](https://github.com/modular/mojo/issues/1316) - The `benchmark.keep` +- [#1316](https://github.com/modular/max/issues/1316) - The `benchmark.keep` function now properly handles vector types. -- [#1505](https://github.com/modular/mojo/issues/1505) - The `simd.shuffle` +- [#1505](https://github.com/modular/max/issues/1505) - The `simd.shuffle` operation now works on 64 element permutations. -- [#1355](https://github.com/modular/mojo/issues/1355) - Fix `String.find()` +- [#1355](https://github.com/modular/max/issues/1355) - Fix `String.find()` returning wrong value when starting index is non-zero. -- [#1367](https://github.com/modular/mojo/issues/1367) - Fix `String.replace()` +- [#1367](https://github.com/modular/max/issues/1367) - Fix `String.replace()` returning incorrect results for multi-character search strings. -- [#1535](https://github.com/modular/mojo/issues/1535) - Invalid error `field +- [#1535](https://github.com/modular/max/issues/1535) - Invalid error `field 'w.x.y' destroyed out of the middle of a value, preventing the overall value from being destroyed`. -- [#1475](https://github.com/modular/mojo/issues/1475) - Assertion failure in +- [#1475](https://github.com/modular/max/issues/1475) - Assertion failure in nested loop. -- [#1591](https://github.com/modular/mojo/issues/1591) - Assertion failure +- [#1591](https://github.com/modular/max/issues/1591) - Assertion failure when using `AnyType` struct member. -- [#1503](https://github.com/modular/mojo/issues/1503) - Rename the mojo build +- [#1503](https://github.com/modular/max/issues/1503) - Rename the mojo build of LLDB to `mojo-lldb`, to prevent name collisions with the system's LLDB. -- [#1542](https://github.com/modular/mojo/issues/1542) - `@unroll` does not +- [#1542](https://github.com/modular/max/issues/1542) - `@unroll` does not accept alias as unroll factor. -- [#1443](https://github.com/modular/mojo/issues/1443) - Compiler crash on +- [#1443](https://github.com/modular/max/issues/1443) - Compiler crash on variadic list of traits. -- [#1604](https://github.com/modular/mojo/issues/1604) - Variable of trivial +- [#1604](https://github.com/modular/max/issues/1604) - Variable of trivial type not destroyed by transferring ownership. -- [#1341](https://github.com/modular/mojo/issues/1341) - Segmentation fault +- [#1341](https://github.com/modular/max/issues/1341) - Segmentation fault when passing closures around. -- [#217](https://github.com/modular/mojo/issues/217) - Closure state is +- [#217](https://github.com/modular/max/issues/217) - Closure state is stack allocated. ## v0.6.1 (2023-12-18) @@ -5136,23 +5136,23 @@ experience without dedicated sugar. ### 🛠️ Fixed -- [#1421](https://github.com/modular/mojo/issues/1421) - Fixed a crash when +- [#1421](https://github.com/modular/max/issues/1421) - Fixed a crash when using Tuples in the REPL. -- [#222](https://github.com/modular/mojo/issues/222) - Generate an error +- [#222](https://github.com/modular/max/issues/222) - Generate an error for obviously self recursive functions. -- [#1408](https://github.com/modular/mojo/issues/1408) - Fix overload +- [#1408](https://github.com/modular/max/issues/1408) - Fix overload resolution when candidates can return generic types. -- [#1413](https://github.com/modular/mojo/issues/1413) and - [#1395](https://github.com/modular/mojo/issues/1395) - Do not crash when +- [#1413](https://github.com/modular/max/issues/1413) and + [#1395](https://github.com/modular/max/issues/1395) - Do not crash when re-declaring a builtin declaration. -- [#1307](https://github.com/modular/mojo/issues/1307) - Fix compatibility of +- [#1307](https://github.com/modular/max/issues/1307) - Fix compatibility of function signatures that only differ in default argument values. -- [#1380](https://github.com/modular/mojo/issues/1380) - Fix printing +- [#1380](https://github.com/modular/max/issues/1380) - Fix printing of empty `String`. ## v0.6.0 (2023-12-04) @@ -5287,7 +5287,7 @@ experience without dedicated sugar. manual (now deprecated). Plus, the entire Mojo Manual and other Mojo docs are now [open-sourced on - GitHub](https://github.com/modular/mojo/tree/main/docs), and we'd love + GitHub](https://github.com/modular/max/tree/main/mojo/docs), and we'd love to accept contributions to help us improve them! - Mojo now supports partial automatic parameterization: when a function is @@ -5455,7 +5455,7 @@ the previous "read to EOF" behavior when size is negative. - The Mojo Language Server now implements the Document Symbols request. IDEs use this to provide support for **Outline View** and **Go to Symbol**. This - addresses [Issue #960](https://github.com/modular/mojo/issues/960). + addresses [Issue #960](https://github.com/modular/max/issues/960). - The Mojo Language Server now shows documentation when code completing modules or packages in `import` statements. @@ -5519,31 +5519,31 @@ the previous "read to EOF" behavior when size is negative. ### 🛠️ Fixed -- [#734](https://github.com/modular/mojo/issues/734) - Consumption of struct +- [#734](https://github.com/modular/max/issues/734) - Consumption of struct works only for types with a `__del__` method. -- [#910](https://github.com/modular/mojo/issues/910) - Parser crash when +- [#910](https://github.com/modular/max/issues/910) - Parser crash when using memory-only generic type as return of function that `raise`s. -- [#1060](https://github.com/modular/mojo/issues/1060) - Mojo happily parses +- [#1060](https://github.com/modular/max/issues/1060) - Mojo happily parses code that has messed up indentation -- [#1159](https://github.com/modular/mojo/issues/1159) - The language server +- [#1159](https://github.com/modular/max/issues/1159) - The language server doesn't warn about bad return type. -- [#1166](https://github.com/modular/mojo/issues/1166) - warning: unreachable +- [#1166](https://github.com/modular/max/issues/1166) - warning: unreachable code after return statement with context manager -- [#1098](https://github.com/modular/mojo/issues/1098) - The language server +- [#1098](https://github.com/modular/max/issues/1098) - The language server doesn't highlight properties of PythonObjects correctly. -- [#1153](https://github.com/modular/mojo/issues/1153) - The language server +- [#1153](https://github.com/modular/max/issues/1153) - The language server crashes when parsing an invalid multi-nested module import. -- [#1236](https://github.com/modular/mojo/issues/1236) - The language server +- [#1236](https://github.com/modular/max/issues/1236) - The language server doesn't show autocomplete in if statements. -- [#1246](https://github.com/modular/mojo/issues/1246) - Warning diagnostics +- [#1246](https://github.com/modular/max/issues/1246) - Warning diagnostics are transient in the presence of caching. ### Known Issue @@ -5798,32 +5798,32 @@ the previous "read to EOF" behavior when size is negative. ### 🛠️ Fixed -- [#532](https://github.com/modular/mojo/issues/532) - Compiler optimizing +- [#532](https://github.com/modular/max/issues/532) - Compiler optimizing while True loop away -- [#760](https://github.com/modular/mojo/issues/760) - Compilation error: +- [#760](https://github.com/modular/max/issues/760) - Compilation error: 'hlcf.for.yield' op specifies 0 branch inputs but target expected 1 along control-flow edge from here -- [#849](https://github.com/modular/mojo/issues/849) - The `Tensor` type is +- [#849](https://github.com/modular/max/issues/849) - The `Tensor` type is now initialized with zeros at construction time. -- [#912](https://github.com/modular/mojo/issues/912) - Invalid load for +- [#912](https://github.com/modular/max/issues/912) - Invalid load for `__get_address_as_lvalue`. -- [#916](https://github.com/modular/mojo/issues/916) - Parser crash when +- [#916](https://github.com/modular/max/issues/916) - Parser crash when specifying default values for `inout` arguments. -- [#943](https://github.com/modular/mojo/issues/943) - Mojo hangs if you +- [#943](https://github.com/modular/max/issues/943) - Mojo hangs if you use continue in the nested loop -- [#957](https://github.com/modular/mojo/issues/957) - Parser crash when a +- [#957](https://github.com/modular/max/issues/957) - Parser crash when a function call with variadic arguments of a memory-only type is evaluated at compile time. -- [#990](https://github.com/modular/mojo/issues/990) - Fixes rounding +- [#990](https://github.com/modular/max/issues/990) - Fixes rounding issue with floor division with negative numerator. -- [#1018](https://github.com/modular/mojo/issues/1018) - In some cases the +- [#1018](https://github.com/modular/max/issues/1018) - In some cases the sort function was returning invalid results. This release fixes some of these corner cases. -- [#1010](https://github.com/modular/mojo/issues/1010) - Initializing tensor +- [#1010](https://github.com/modular/max/issues/1010) - Initializing tensor in alias declaration results in crash. -- [#1110](https://github.com/modular/mojo/issues/1110) - The `time.now()` +- [#1110](https://github.com/modular/max/issues/1110) - The `time.now()` function now returns nanoseconds across all operating systems. -- [#1115](https://github.com/modular/mojo/issues/1115) - cannot load +- [#1115](https://github.com/modular/max/issues/1115) - cannot load non-register passable type into SSA register. ## v0.4.0 for Mac (2023-10-19) @@ -6002,33 +6002,33 @@ the previous "read to EOF" behavior when size is negative. ### 🛠️ Fixed -- [#794](https://github.com/modular/mojo/issues/794) - Parser crash when +- [#794](https://github.com/modular/max/issues/794) - Parser crash when using the `in` operator. -- [#936](https://github.com/modular/mojo/issues/936) - The `Int` constructor +- [#936](https://github.com/modular/max/issues/936) - The `Int` constructor now accepts other `Int` instances. -- [#921](https://github.com/modular/mojo/issues/921) - Better error message +- [#921](https://github.com/modular/max/issues/921) - Better error message when running `mojo` on a module with no `main` function. -- [#556](https://github.com/modular/mojo/issues/556) - UInt64s are now +- [#556](https://github.com/modular/max/issues/556) - UInt64s are now printed correctly. -- [#804](https://github.com/modular/mojo/issues/804) - Emit error instead of +- [#804](https://github.com/modular/max/issues/804) - Emit error instead of crashing when passing variadic arguments of unsupported types. -- [#833](https://github.com/modular/mojo/issues/833) - Parser crash when +- [#833](https://github.com/modular/max/issues/833) - Parser crash when assigning module value. -- [#752](https://github.com/modular/mojo/issues/752) - Parser crash when +- [#752](https://github.com/modular/max/issues/752) - Parser crash when calling async def. -- [#711](https://github.com/modular/mojo/issues/711) - The overload resolution +- [#711](https://github.com/modular/max/issues/711) - The overload resolution logic now correctly prioritizes instance methods over static methods (if candidates are an equally good match otherwise), and no longer crashed if a static method has a `Self` type as its first argument. -- [#859](https://github.com/modular/mojo/issues/859) - Fix confusing error and +- [#859](https://github.com/modular/max/issues/859) - Fix confusing error and documentation of the `rebind` builtin. -- [#753](https://github.com/modular/mojo/issues/753) - Direct use of LLVM +- [#753](https://github.com/modular/max/issues/753) - Direct use of LLVM dialect produces strange errors in the compiler. -- [#926](https://github.com/modular/mojo/issues/926) - Fixes an issue that +- [#926](https://github.com/modular/max/issues/926) - Fixes an issue that occurred when a function with a return type of `StringRef` raised an error. When the function raised an error, it incorrectly returned the string value of that error. -- [#536](https://github.com/modular/mojo/issues/536) - Report More information +- [#536](https://github.com/modular/max/issues/536) - Report More information on python exception. ## v0.3.1 (2023-09-28) @@ -6039,15 +6039,15 @@ installing the previous versions of the SDK, this release may be for you. ### 🛠️ Fixed -- [#538](https://github.com/modular/mojo/issues/538) - Installation hangs +- [#538](https://github.com/modular/max/issues/538) - Installation hangs during the testing phase. This issue occurs on machines with a low number of CPU cores, such as free AWS EC2 instances and GitHub Codespaces. -- [#590](https://github.com/modular/mojo/issues/590) - Installation fails +- [#590](https://github.com/modular/max/issues/590) - Installation fails with a “failed to run python” message. -- [#672](https://github.com/modular/mojo/issues/672) - Language server hangs +- [#672](https://github.com/modular/max/issues/672) - Language server hangs on code completion. Related to #538, this occurs on machines with a low number of CPU cores. -- [#913](https://github.com/modular/mojo/issues/913) - In the REPL and Jupyter +- [#913](https://github.com/modular/max/issues/913) - In the REPL and Jupyter notebooks, inline comments were being parsed incorrectly. ## v0.3.0 (2023-09-21) @@ -6149,7 +6149,7 @@ Code](https://marketplace.visualstudio.com/items?itemName=modular-mojotools.vsco - The Mojo Language Server now supports top-level code completions, enabling completion when typing a reference to a variable, type, etc. This resolves - [#679](https://github.com/modular/mojo/issues/679). + [#679](https://github.com/modular/max/issues/679). - The Mojo REPL now colorizes the resultant variables to help distinguish input expressions from the output variables. @@ -6188,19 +6188,19 @@ Code](https://marketplace.visualstudio.com/items?itemName=modular-mojotools.vsco ### 🛠️ Fixed -- [#503](https://github.com/modular/mojo/issues/503) - Improve error message +- [#503](https://github.com/modular/max/issues/503) - Improve error message for failure lowering `kgen.param.constant`. -- [#554](https://github.com/modular/mojo/issues/554) - Alias of static tuple +- [#554](https://github.com/modular/max/issues/554) - Alias of static tuple fails to expand. -- [#500](https://github.com/modular/mojo/issues/500) - Call expansion failed +- [#500](https://github.com/modular/max/issues/500) - Call expansion failed due to verifier error. -- [#422](https://github.com/modular/mojo/issues/422) - Incorrect comment +- [#422](https://github.com/modular/max/issues/422) - Incorrect comment detection in multiline strings. -- [#729](https://github.com/modular/mojo/issues/740) - Improve messaging on +- [#729](https://github.com/modular/max/issues/740) - Improve messaging on how to exit the REPL. -- [#756](https://github.com/modular/mojo/issues/756) - Fix initialization +- [#756](https://github.com/modular/max/issues/756) - Fix initialization errors of the VS Code extension. -- [#575](https://github.com/modular/mojo/issues/575) - Build LLDB/REPL with +- [#575](https://github.com/modular/max/issues/575) - Build LLDB/REPL with libedit for a nicer editing experience in the terminal. ## v0.2.1 (2023-09-07) @@ -6263,17 +6263,17 @@ All earlier releases were considered version 0.1. been changed to enable type inference. Now it's possible to write `rotate_right[shift_val](simd_val)` and have the `dtype` and `simd_width` inferred from the argument. This addresses - [Issue #528](https://github.com/modular/mojo/issues/528). + [Issue #528](https://github.com/modular/max/issues/528). ### 🛠️ Fixed - Fixed a bug causing the parser to crash when the `with` statement was written without a colon. - This addresses [Issue #529](https://github.com/modular/mojo/issues/529). + This addresses [Issue #529](https://github.com/modular/max/issues/529). - Incorrect imports no longer crash when there are other errors at the top level of a module. This fixes [Issue - #531](https://github.com/modular/mojo/issues/531). + #531](https://github.com/modular/max/issues/531). ## August 2023 @@ -6348,15 +6348,15 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed - Fixed issues with print formatting for `DType.uint16` and `DType.int16`. -- [Issue #499](https://github.com/modular/mojo/issues/499) - Two new +- [Issue #499](https://github.com/modular/max/issues/499) - Two new `rotate_right` and `rotate_left` functions have been added to the SIMD module. -- [Issue #429](https://github.com/modular/mojo/issues/429) - You can now +- [Issue #429](https://github.com/modular/max/issues/429) - You can now construct a `Bool` from a `SIMD` type whose element-type is `DType.bool`. -- [Issue #350](https://github.com/modular/mojo/issues/350) - Confusing Matrix +- [Issue #350](https://github.com/modular/max/issues/350) - Confusing Matrix implementation -- [Issue #349](https://github.com/modular/mojo/issues/349) - Missing load_tr +- [Issue #349](https://github.com/modular/max/issues/349) - Missing load_tr in struct Matrix -- [Issue #501](https://github.com/modular/mojo/issues/501) - Missing syntax +- [Issue #501](https://github.com/modular/max/issues/501) - Missing syntax error messages in Python expressions. ### 2023-08-09 @@ -6420,7 +6420,7 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #53](https://github.com/modular/mojo/issues/53) - `Int` now +- [Issue #53](https://github.com/modular/max/issues/53) - `Int` now implements true division with the `/` operator. Similar to Python, this returns a 64-bit floating point number. The corresponding in-place operator, `/=`, has the same semantics as `//=`. @@ -6460,10 +6460,10 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #437](https://github.com/modular/mojo/issues/437) - Range can now +- [Issue #437](https://github.com/modular/max/issues/437) - Range can now be instantiated with a PythonObject. -- [Issue #288](https://github.com/modular/mojo/issues/288) - Python strings +- [Issue #288](https://github.com/modular/max/issues/288) - Python strings can now be safely copied. ### 2023-07-20 @@ -6472,7 +6472,7 @@ All earlier releases were considered version 0.1. - Mojo now includes a `Limits` module, which contains functions to get the max and min values representable by a type, as requested in [Issue - #51](https://github.com/modular/mojo/issues/51). The following functions + #51](https://github.com/modular/max/issues/51). The following functions moved from `Math` to `Limits`: `inf()`, `neginf()`, `isinf()`, `isfinite()`. - Mojo decorators are now distinguished between "signature" and "body" @@ -6539,14 +6539,14 @@ All earlier releases were considered version 0.1. - Reverted the feature from 2023-02-13 that allowed unqualified struct members. Use the `Self` keyword to conveniently access struct members with bound parameters instead. This was required to fix - [Issue #260](https://github.com/modular/mojo/issues/260). + [Issue #260](https://github.com/modular/max/issues/260). - Updated the RayTracing notebook: added step 5 to create specular lighting for more realistic images and step 6 to add a background image. #### 🛠️ Fixed -- [Issue #260](https://github.com/modular/mojo/issues/260) - Definitions +- [Issue #260](https://github.com/modular/max/issues/260) - Definitions inside structs no longer shadow definitions outside of struct definitions. ### 2023-07-12 @@ -6583,7 +6583,7 @@ All earlier releases were considered version 0.1. - The walrus operator now works in if/while statements without parentheses, e.g. `if x := function():`. -- [Issue #428](https://github.com/modular/mojo/issues/428) - The +- [Issue #428](https://github.com/modular/max/issues/428) - The `FloatLiteral` and `SIMD` types now support conversion to `Int` via the `to_int` or `__int__` method calls. The behavior matches that of Python, which rounds towards zero. @@ -6613,21 +6613,21 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #253](https://github.com/modular/mojo/issues/253) - Issue +- [Issue #253](https://github.com/modular/max/issues/253) - Issue when accessing a struct member alias without providing parameters. -- [Issue #404](https://github.com/modular/mojo/issues/404) - The docs now use +- [Issue #404](https://github.com/modular/max/issues/404) - The docs now use `snake_case` for variable names, which more closely conforms to Python's style. -- [Issue #379](https://github.com/modular/mojo/issues/379) - Tuple +- [Issue #379](https://github.com/modular/max/issues/379) - Tuple limitations have been addressed and multiple return values are now supported, even without parentheses. -- [Issue #347](https://github.com/modular/mojo/issues/347) - Tuples no longer +- [Issue #347](https://github.com/modular/max/issues/347) - Tuples no longer require parentheses. -- [Issue #320](https://github.com/modular/mojo/issues/320) - Python objects +- [Issue #320](https://github.com/modular/max/issues/320) - Python objects are now traversable via `for` loops. ## June 2023 @@ -6653,18 +6653,18 @@ All earlier releases were considered version 0.1. #### 🛠️ Fixed -- [Issue #229](https://github.com/modular/mojo/issues/229) - Issue when +- [Issue #229](https://github.com/modular/max/issues/229) - Issue when throwing an exception from `__init__` before all fields are initialized. -- [Issue #74](https://github.com/modular/mojo/issues/74) - Struct +- [Issue #74](https://github.com/modular/max/issues/74) - Struct definition with recursive reference crashes. -- [Issue #285](https://github.com/modular/mojo/issues/285) - The +- [Issue #285](https://github.com/modular/max/issues/285) - The [`TargetInfo`](/mojo/stdlib/sys/info) module now includes `is_little_endian()` and `is_big_endian()` to check if the target host uses either little or big endian. -- [Issue #254](https://github.com/modular/mojo/issues/254) - Parameter name +- [Issue #254](https://github.com/modular/max/issues/254) - Parameter name shadowing in nested scopes is now handled correctly. ### 2023-06-21 @@ -6721,14 +6721,14 @@ only in declared parameter names, e.g. the following now works correctly: fn foobar[w: Int, S: DType]() -> SIMD[S, w]: ... ``` -- [Issue #219](https://github.com/modular/mojo/issues/219) - Issue when +- [Issue #219](https://github.com/modular/max/issues/219) - Issue when redefining a function and a struct defined in the same cell. -- [Issue #355](https://github.com/modular/mojo/issues/355) - The loop order +- [Issue #355](https://github.com/modular/max/issues/355) - The loop order in the Matmul notebook for Python and naive mojo have been reordered for consistency. The loop order now follows (M, K, N) ordering. -- [Issue #309](https://github.com/modular/mojo/issues/309) - Use snake case +- [Issue #309](https://github.com/modular/max/issues/309) - Use snake case naming within the testing package and move the asserts out of the TestSuite struct. @@ -6750,11 +6750,11 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #354](https://github.com/modular/mojo/issues/354) - Returning a tuple +- [Issue #354](https://github.com/modular/max/issues/354) - Returning a tuple doesn't work even with parens. -- [Issue #365](https://github.com/modular/mojo/issues/365) - Copy-paste error +- [Issue #365](https://github.com/modular/max/issues/365) - Copy-paste error in `FloatLiteral` docs. -- [Issue #357](https://github.com/modular/mojo/issues/357) - Crash when +- [Issue #357](https://github.com/modular/max/issues/357) - Crash when missing input parameter to variadic parameter struct member function. ### 2023-06-07 @@ -6771,7 +6771,7 @@ only in declared parameter names, e.g. the following now works correctly: - Mojo Playground no longer includes the following Python packages (due to size, compute costs, and - [environment complications](https://github.com/modular/mojo/issues/300)): + [environment complications](https://github.com/modular/max/issues/300)): `torch`, `tensorflow`, `keras`, `transformers`. #### 🦋 Changed @@ -6779,19 +6779,19 @@ only in declared parameter names, e.g. the following now works correctly: - The data types and scalar names now conform to the naming convention used by numpy. So we use `Int32` instead of `SI32`, similarly using `Float32` instead of `F32`. Closes - [Issue #152](https://github.com/modular/mojo/issues/152). + [Issue #152](https://github.com/modular/max/issues/152). #### 🛠️ Fixed -- [Issue #287](https://github.com/modular/mojo/issues/287) - computed +- [Issue #287](https://github.com/modular/max/issues/287) - computed lvalues don't handle raising functions correctly -- [Issue #318](https://github.com/modular/mojo/issues/318) - Large integers +- [Issue #318](https://github.com/modular/max/issues/318) - Large integers are not being printed correctly -- [Issue #326](https://github.com/modular/mojo/issues/326) - Float modulo +- [Issue #326](https://github.com/modular/max/issues/326) - Float modulo operator is not working as expected -- [Issue #282](https://github.com/modular/mojo/issues/282) - Default arguments +- [Issue #282](https://github.com/modular/max/issues/282) - Default arguments are not working as expected -- [Issue #271](https://github.com/modular/mojo/issues/271) - Confusing error +- [Issue #271](https://github.com/modular/max/issues/271) - Confusing error message when converting between function types with different result semantics ## May 2023 @@ -6801,7 +6801,7 @@ only in declared parameter names, e.g. the following now works correctly: #### ⭐️ New - Mojo Playground now includes the following Python packages (in response to - [popular demand](https://github.com/modular/mojo/discussions/173)): + [popular demand](https://github.com/modular/max/discussions/173)): `torch`, `tensorflow`, `polars`, `opencv-python`, `keras`, `Pillow`, `plotly`, `seaborn`, `sympy`, `transformers`. @@ -6865,9 +6865,9 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #231](https://github.com/modular/mojo/issues/231) - Unexpected error +- [Issue #231](https://github.com/modular/max/issues/231) - Unexpected error when a Python expression raises an exception -- [Issue #119](https://github.com/modular/mojo/issues/119) - The REPL fails +- [Issue #119](https://github.com/modular/max/issues/119) - The REPL fails when a python variable is redefined ### 2023-05-24 @@ -6892,39 +6892,39 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #204](https://github.com/modular/mojo/issues/204) - Mojo REPL +- [Issue #204](https://github.com/modular/max/issues/204) - Mojo REPL crash when returning a String at compile-time -- [Issue #143](https://github.com/modular/mojo/issues/143) - synthesized +- [Issue #143](https://github.com/modular/max/issues/143) - synthesized init in `@register_passable` type doesn't get correct convention. -- [Issue #201](https://github.com/modular/mojo/issues/201) - String literal +- [Issue #201](https://github.com/modular/max/issues/201) - String literal concatenation is too eager. -- [Issue #209](https://github.com/modular/mojo/issues/209) - [QoI] Terrible +- [Issue #209](https://github.com/modular/max/issues/209) - [QoI] Terrible error message trying to convert a type to itself. -- [Issue #32](https://github.com/modular/mojo/issues/32) - Include struct +- [Issue #32](https://github.com/modular/max/issues/32) - Include struct fields in docgen -- [Issue #50](https://github.com/modular/mojo/issues/50) - Int to string +- [Issue #50](https://github.com/modular/max/issues/50) - Int to string conversion crashes due to buffer overflow -- [Issue #132](https://github.com/modular/mojo/issues/132) - PythonObject +- [Issue #132](https://github.com/modular/max/issues/132) - PythonObject `to_int` method has a misleading name -- [Issue #189](https://github.com/modular/mojo/issues/189) - PythonObject bool +- [Issue #189](https://github.com/modular/max/issues/189) - PythonObject bool conversion is incorrect -- [Issue #65](https://github.com/modular/mojo/issues/65) - Add SIMD +- [Issue #65](https://github.com/modular/max/issues/65) - Add SIMD constructor from Bool -- [Issue #153](https://github.com/modular/mojo/issues/153) - Meaning of +- [Issue #153](https://github.com/modular/max/issues/153) - Meaning of `Time.now` function result is unclear -- [Issue #165](https://github.com/modular/mojo/issues/165) - Type in +- [Issue #165](https://github.com/modular/max/issues/165) - Type in `Pointer.free` documentation -- [Issue #210](https://github.com/modular/mojo/issues/210) - Parameter results +- [Issue #210](https://github.com/modular/max/issues/210) - Parameter results cannot be declared outside top-level in function -- [Issue #214](https://github.com/modular/mojo/issues/214) - Pointer offset +- [Issue #214](https://github.com/modular/max/issues/214) - Pointer offset calculations at compile-time are incorrect -- [Issue #115](https://github.com/modular/mojo/issues/115) - Float printing +- [Issue #115](https://github.com/modular/max/issues/115) - Float printing does not include the right number of digits -- [Issue #202](https://github.com/modular/mojo/issues/202) - +- [Issue #202](https://github.com/modular/max/issues/202) - `kgen.unreachable` inside nested functions is illegal -- [Issue #235](https://github.com/modular/mojo/issues/235) - Crash when +- [Issue #235](https://github.com/modular/max/issues/235) - Crash when register passable struct field is not register passable -- [Issue #237](https://github.com/modular/mojo/issues/237) - Parameter +- [Issue #237](https://github.com/modular/max/issues/237) - Parameter closure sharp edges are not documented ### 2023-05-16 @@ -6939,16 +6939,16 @@ only in declared parameter names, e.g. the following now works correctly: #### 🛠️ Fixed -- [Issue #98](https://github.com/modular/mojo/issues/98): +- [Issue #98](https://github.com/modular/max/issues/98): Incorrect error with lifetime tracking in loop. -- [Issue #49](https://github.com/modular/mojo/issues/49): Type inference +- [Issue #49](https://github.com/modular/max/issues/49): Type inference issue (?) in 'ternary assignment' operation (FloatLiteral vs. 'SIMD[f32, 1]'). -- [Issue #48](https://github.com/modular/mojo/issues/48): +- [Issue #48](https://github.com/modular/max/issues/48): and/or don't work with memory-only types. -- [Issue #11](https://github.com/modular/mojo/issues/11): `setitem` Support +- [Issue #11](https://github.com/modular/max/issues/11): `setitem` Support for `PythonObject`. ### 2023-05-11 @@ -6966,7 +6966,7 @@ only in declared parameter names, e.g. the following now works correctly: - The "byref" syntax with the `&` sigil has changed to use an `inout` keyword to be more similar to the `borrowed` and `owned` syntax in arguments. - Please see [Issue #7](https://github.com/modular/mojo/issues/7) for more + Please see [Issue #7](https://github.com/modular/max/issues/7) for more information. - Optimized the Matrix multiplication implementation in the notebook. @@ -6983,10 +6983,10 @@ from "consume" to "transfer." - Fixed missing overloads for `Testing.assertEqual` so that they work on `Integer` and `String` values. -- [Issue #6](https://github.com/modular/mojo/issues/6): +- [Issue #6](https://github.com/modular/max/issues/6): Playground stops evaluating cells when a simple generic is defined. -- [Issue #18](https://github.com/modular/mojo/issues/18): +- [Issue #18](https://github.com/modular/max/issues/18): Memory leak in Python interoperability was removed. ### 2023-05-02 diff --git a/mojo/docs/changelog.md b/mojo/docs/changelog.md index 8f7363e0de..750d44f331 100644 --- a/mojo/docs/changelog.md +++ b/mojo/docs/changelog.md @@ -105,7 +105,7 @@ what we publish. The same statement will now raise an error informing the user their indices are invalid. -- Added an iterator to `LinkedList` ([PR #4005](https://github.com/modular/mojo/pull/4005)) +- Added an iterator to `LinkedList` ([PR #4005](https://github.com/modular/max/pull/4005)) - `LinkedList.__iter__()` to create a forward iterator. - `LinkedList.__reversed__()` for a backward iterator. diff --git a/mojo/docs/faq.md b/mojo/docs/faq.md index 2061c83564..93da03c653 100644 --- a/mojo/docs/faq.md +++ b/mojo/docs/faq.md @@ -148,7 +148,7 @@ at Modular to develop AI algorithms, but you can use it for other things like HPC, data transformations, writing pre/post processing operations, and much more. For examples of how Mojo can be used for other general programming tasks, see our [Mojo -examples](https://github.com/modular/mojo/tree/main/examples). +examples](https://github.com/modular/max/tree/main/examples/mojo). ### Is Mojo interpreted or compiled? @@ -285,7 +285,7 @@ and does not require login. using buttons in the Playground toolbar. - There might be some bugs. Please [report issues and feedback on - GitHub](https://github.com/modular/mojo/issues/new/choose). + GitHub](https://github.com/modular/max/issues/new/choose). ### What are the license terms for the SDK? diff --git a/mojo/docs/manual/basics.mdx b/mojo/docs/manual/basics.mdx index 4020b3eb96..ea9c44180c 100644 --- a/mojo/docs/manual/basics.mdx +++ b/mojo/docs/manual/basics.mdx @@ -450,11 +450,15 @@ Otherwise, here are some other resources to check out: * See [Get started with Mojo](/mojo/manual/get-started) for a hands-on tutorial that will get you up and running with Mojo. -* If you want to experiment with some code, clone [the Mojo - repo](https://github.com/modular/mojo/) to try our code examples: +* If you want to experiment with some code, clone [our GitHub + repo](https://github.com/modular/max/) to try our code examples: ```sh - git clone https://github.com/modular/mojo.git + git clone https://github.com/modular/max.git + ``` + + ```sh + cd max/examples/mojo ``` * To see all the available Mojo APIs, check out the [Mojo standard library diff --git a/mojo/docs/manual/get-started.mdx b/mojo/docs/manual/get-started.mdx index 799ebbc78d..2c7ac86f54 100644 --- a/mojo/docs/manual/get-started.mdx +++ b/mojo/docs/manual/get-started.mdx @@ -2,7 +2,7 @@ title: "Get started with Mojo" sidebar_label: "Tutorial: Get started with Mojo" description: "Install Mojo and learn the language basics by building a complete Mojo program" -github_url: https://github.com/modular/mojo/tree/main/examples/life +github_url: https://github.com/modular/max/tree/main/examples/mojo/life image: /images/artwork/mojo-get-started.jpg --- @@ -37,7 +37,7 @@ This tutorial might be a little long because there's a lot to learn, but we tried to keep the explanations simple, and we included links along the way for you to go learn more about each topic. If you just want to see the finished code, you can [get it on -GitHub](https://github.com/modular/mojo/tree/main/examples/life). +GitHub](https://github.com/modular/max/tree/main/examples/mojo/life). System requirements: diff --git a/mojo/docs/manual/operators.mdx b/mojo/docs/manual/operators.mdx index b3f9e8fd00..22a52dd56e 100644 --- a/mojo/docs/manual/operators.mdx +++ b/mojo/docs/manual/operators.mdx @@ -1,7 +1,7 @@ --- title: Operators, expressions, and dunder methods sidebar_label: Operators and expressions -github_url: https://github.com/modular/mojo/tree/main/examples/operators +github_url: https://github.com/modular/max/tree/main/examples/mojo/operators --- Mojo includes a variety of operators for manipulating values of different types. @@ -1071,8 +1071,8 @@ operations like printing complex values. We'll also allow mixing `Complex` and `Float64` values in arithmetic expressions to produce a `Complex` result. This example builds our `Complex` struct incrementally. You can also find the -[complete example in the public Mojo GitHub -repo](https://github.com/modular/mojo/tree/main/examples/operators). +[complete example in the public GitHub +repo](https://github.com/modular/max/tree/main/examples/mojo/operators). :::note diff --git a/mojo/docs/manual/parameters/index.mdx b/mojo/docs/manual/parameters/index.mdx index 22f588a94a..3c1b82c475 100644 --- a/mojo/docs/manual/parameters/index.mdx +++ b/mojo/docs/manual/parameters/index.mdx @@ -140,7 +140,7 @@ Mojo's support for generics is still early. You can write generic functions like this using traits and parameters. You can also write generic collections like `List` and `Dict`. If you're interested in learning how these types work, you can find the source code for the standard library collection types -[on GitHub](https://github.com/modular/mojo/blob/nightly/stdlib/src/collections/). +[on GitHub](https://github.com/modular/max/blob/main/mojo/stdlib/src/collections/). ## Parameterized structs diff --git a/mojo/docs/manual/values/lifetimes.mdx b/mojo/docs/manual/values/lifetimes.mdx index bd96d45b95..5d9bb37c10 100644 --- a/mojo/docs/manual/values/lifetimes.mdx +++ b/mojo/docs/manual/values/lifetimes.mdx @@ -424,8 +424,8 @@ for i in range(len(nums)): ``` (You can find the code for the -`List` iterator in the [Mojo -repo](https://github.com/modular/mojo/blob/main/stdlib/src/collections/list.mojo#L63).) +`List` iterator in the [public +repo](https://github.com/modular/max/blob/main/mojo/stdlib/src/collections/list.mojo#L63).) #### Parametric mutability of return values diff --git a/mojo/docs/roadmap.md b/mojo/docs/roadmap.md index 05e5bf94da..754c0acdc7 100644 --- a/mojo/docs/roadmap.md +++ b/mojo/docs/roadmap.md @@ -29,7 +29,7 @@ In the near-term, we will **not** prioritize "general goodness" work such as: - Tackling broad Python ecosystem challenges like packaging. If you have encountered any bugs with current Mojo behavior, please -[submit an issue on GitHub](https://github.com/modular/mojo/issues). +[submit an issue on GitHub](https://github.com/modular/max/issues). If you have ideas about how to improve the core Mojo features, we prefer that you first look for similar topics or start a new conversation about it @@ -148,7 +148,7 @@ When we get here, we will discuss what the right default is: for example, is full Python hash-table dynamism the default? Or do we use a more efficient model by default (e.g. vtable-based dispatch and explicitly declared stored properties) and allow opt'ing into dynamism with a `@dynamic` decorator on the -class. More discussion is [in this proposal](https://github.com/modular/mojo/blob/main/proposals/mojo-and-dynamism.md). +class. More discussion is [in this proposal](https://github.com/modular/max/blob/main/mojo/proposals/mojo-and-dynamism.md). ## C/C++ Interop diff --git a/mojo/docs/tools/debugging.mdx b/mojo/docs/tools/debugging.mdx index 2a761c6f88..50befddbed 100644 --- a/mojo/docs/tools/debugging.mdx +++ b/mojo/docs/tools/debugging.mdx @@ -24,9 +24,9 @@ in VS Code. There are several ways to start a debug session in VS Code. -To start debugging, you'll need to have a Mojo project to debug. There are -a number of examples ranging from simple to complex in the [Mojo repo on -GitHub](https://github.com/modular/mojo). +To start debugging, you'll need to have a Mojo project to debug. There are a +number of examples ranging from simple to complex in [our GitHub +repo](https://github.com/modular/max/tree/main/examples/mojo). :::note **VS Code veteran?** diff --git a/mojo/docs/tools/testing.mdx b/mojo/docs/tools/testing.mdx index 34dc575ce1..8861c554fe 100644 --- a/mojo/docs/tools/testing.mdx +++ b/mojo/docs/tools/testing.mdx @@ -2,7 +2,7 @@ title: Testing sidebar_position: 2 description: Testing Mojo programs. -github_url: https://github.com/modular/mojo/tree/main/examples/testing +github_url: https://github.com/modular/max/tree/main/examples/mojo/testing --- Mojo includes a framework for developing and executing unit tests. The Mojo @@ -96,9 +96,8 @@ its error message. organize them into test files. - [The `mojo test` command](#the-mojo-test-command) describes how to execute and collect lists of tests. -- The public [GitHub repo](https://github.com/modular/mojo/tree/main) contains - an [example - project](https://github.com/modular/mojo/tree/main/examples/testing) to +- Our GitHub repo contains an [example + project](https://github.com/modular/max/tree/main/examples/mojo/testing) to demonstrate unit testing. Several of the examples shown later are based on this project. @@ -291,7 +290,7 @@ the following: If needed, you can optionally use the `-I` option one or more times to append additional paths to the list of directories searched to import Mojo modules and packages. Consider the [example testing -project](https://github.com/modular/mojo/tree/main/examples/testing) in +project](https://github.com/modular/max/tree/main/examples/mojo/testing) in GitHub, which has the following directory structure: ```output @@ -363,7 +362,7 @@ By including the `--collect-only` or `--co` option, you can use `mojo test` to discover and print a list of tests. Consider the [example testing -project](https://github.com/modular/mojo/tree/main/examples/testing) +project](https://github.com/modular/max/tree/main/examples/mojo/testing) directory structure shown in the [Running tests](#running-tests) section. The following command produces a list of all of the tests defined in the `test` directory hierarchy. @@ -434,7 +433,7 @@ output shown): You can also produce JSON output for test collection as well. Consider the [example testing -project](https://github.com/modular/mojo/tree/main/examples/testing) +project](https://github.com/modular/max/tree/main/examples/mojo/testing) directory structure shown in the [Running tests](#running-tests) section. The following command collects a list in JSON format of all of the tests defined in the `test` directory hierarchy: From e9be43eb64d6899e0fa54b1af5b29f86695e9ce7 Mon Sep 17 00:00:00 2001 From: kcaverly Date: Thu, 27 Feb 2025 12:08:06 -0500 Subject: [PATCH 485/509] [Pipelines] Refactor next_token to return dict[str, TextGenerationResponse] This PR refactors the `next_token` interface to enable variable token length responses from the pipeline on a per-request basis. Instead of returning a `list[dict[str, TextResponse]]` and implicitly identifying request completion based on the keys returned in the dictionary. This PR refactors `next_token` to return a `dict[str, TextGenerationResponse]`, in which the dictionary keys align with the request ids provided. The newly introduced `TextGenerationResponse` includes variable token length arrays, and explicitly states the final status (`TextGenerationStatus`: ACTIVE, MAXIMUM_LENGTH, or END_OF_SEQUENCE).This hardens our interface to completion tracking, enables more complex decoding strategies, and provides the server the opportunity to provide a corrected `finish_reason`. MODULAR_ORIG_COMMIT_REV_ID: fa8c7ff5af9470d25d3f86c966c633f783cc37ec --- src/max/entrypoints/cli/generate.py | 30 +++++----- src/max/pipelines/hf_pipeline.py | 29 +++++++--- src/max/pipelines/interfaces/response.py | 20 +++++-- .../pipelines/interfaces/text_generation.py | 4 +- src/max/pipelines/pipeline.py | 56 ++++++++++++------- 5 files changed, 89 insertions(+), 50 deletions(-) diff --git a/src/max/entrypoints/cli/generate.py b/src/max/entrypoints/cli/generate.py index 97718673a8..ec90472b7c 100644 --- a/src/max/entrypoints/cli/generate.py +++ b/src/max/entrypoints/cli/generate.py @@ -76,22 +76,22 @@ async def stream_text_to_console( num_steps=num_steps, ) - for response in responses: - if req_id not in response: - # next_token is expected to omit the return if - # it encounters eos. + for request_idx, response in responses.items(): + if response.is_done: generate_again = False - break - - encoded_text = response[req_id].next_token - response_text = await tokenizer.decode(context, encoded_text) - if metrics: - if first_token: - first_token = False - metrics.signpost("first_token") - metrics.new_token() - if print_tokens: - print(response_text, end="", flush=True) + + for text_response in response.tokens: + encoded_text = text_response.next_token + response_text = await tokenizer.decode( + context, encoded_text + ) + if metrics: + if first_token: + first_token = False + metrics.signpost("first_token") + metrics.new_token() + if print_tokens: + print(response_text, end="", flush=True) # Yield to the event loop. If at no other point (e.g. # tokenizer.decode which we await earlier does not yield to the diff --git a/src/max/pipelines/hf_pipeline.py b/src/max/pipelines/hf_pipeline.py index 52f25f6985..ff252ceec0 100644 --- a/src/max/pipelines/hf_pipeline.py +++ b/src/max/pipelines/hf_pipeline.py @@ -34,6 +34,8 @@ from .interfaces import ( EmbeddingsGenerator, EmbeddingsResponse, + TextGenerationResponse, + TextGenerationStatus, TextResponse, TokenGenerator, ) @@ -119,7 +121,7 @@ def __init__( def next_token( self, batch: dict[str, TextContext], num_steps: int - ) -> list[dict[str, TextResponse]]: + ) -> dict[str, TextGenerationResponse]: """Provided a batch, process batch inputs, execute the graph for num_steps in a multi-step scenario, then decode the tokens holistically and return the list of decoded tokens. """ @@ -183,8 +185,10 @@ def next_token( generated_tokens = generated_tokens.cpu() # Prepare the response, pruning away completed requests as we go. - res: list[dict[str, TextResponse]] = [{} for i in range(num_steps)] + res: dict[str, TextGenerationResponse] = {} for batch_idx, (request_id, context) in enumerate(batch.items()): + status = TextGenerationStatus.ACTIVE + res[request_id] = TextGenerationResponse([], status) for step in range(num_steps): next_token_id = generated_tokens[batch_idx, step].item() @@ -200,13 +204,22 @@ def next_token( if context.max_length is None else context.max_length ) - if ( - next_token_id in self._eos_token_id - or context.current_length > max_length - ): - break - res[step][request_id] = TextResponse(next_token) + if next_token_id in self._eos_token_id: + status = TextGenerationStatus.END_OF_SEQUENCE + res[request_id].update_status(status) + elif context.current_length > max_length: + status = TextGenerationStatus.MAXIMUM_LENGTH + res[request_id].update_status(status) + elif context.current_length == max_length: + res[request_id].append_token(TextResponse(next_token)) + status = TextGenerationStatus.MAXIMUM_LENGTH + res[request_id].update_status(status) + else: + res[request_id].append_token(TextResponse(next_token)) + + if status.is_done: + break return res diff --git a/src/max/pipelines/interfaces/response.py b/src/max/pipelines/interfaces/response.py index 45808c9d72..573326871b 100644 --- a/src/max/pipelines/interfaces/response.py +++ b/src/max/pipelines/interfaces/response.py @@ -117,11 +117,23 @@ class TextGenerationResponse: def __init__( self, tokens: list[TextResponse], final_status: TextGenerationStatus ) -> None: - self.tokens = tokens - self.final_status = final_status + self._tokens = tokens + self._final_status = final_status + @property def is_done(self) -> bool: - return self.final_status.is_done + return self._final_status.is_done + + @property + def tokens(self) -> list[TextResponse]: + return self._tokens + + @property + def final_status(self) -> TextGenerationStatus: + return self._final_status def append_token(self, token: TextResponse) -> None: - self.tokens.append(token) + self._tokens.append(token) + + def update_status(self, status: TextGenerationStatus) -> None: + self._final_status = status diff --git a/src/max/pipelines/interfaces/text_generation.py b/src/max/pipelines/interfaces/text_generation.py index ac7b64d1c7..e329e0829b 100644 --- a/src/max/pipelines/interfaces/text_generation.py +++ b/src/max/pipelines/interfaces/text_generation.py @@ -28,7 +28,7 @@ runtime_checkable, ) -from .response import TextResponse +from .response import TextGenerationResponse class TokenGeneratorRequestFunction(TypedDict): @@ -258,7 +258,7 @@ class TokenGenerator(Generic[TokenGeneratorContext], Protocol): def next_token( self, batch: dict[str, TokenGeneratorContext], num_steps: int - ) -> list[dict[str, TextResponse]]: + ) -> dict[str, TextGenerationResponse]: """Computes the next token response for a single batch. Args: diff --git a/src/max/pipelines/pipeline.py b/src/max/pipelines/pipeline.py index b42e9b7f48..d1c951a3ab 100644 --- a/src/max/pipelines/pipeline.py +++ b/src/max/pipelines/pipeline.py @@ -42,7 +42,13 @@ from .config import PipelineConfig from .context import InputContext -from .interfaces import LogProbabilities, TextResponse, TokenGenerator +from .interfaces import ( + LogProbabilities, + TextGenerationResponse, + TextGenerationStatus, + TextResponse, + TokenGenerator, +) from .kv_cache import KVCacheManager, KVCacheParams from .sampling import token_sampler @@ -565,7 +571,7 @@ def next_token( self, batch: dict[str, T], num_steps: int, - ) -> list[dict[str, TextResponse]]: + ) -> dict[str, TextGenerationResponse]: """Provided a batch, process batch inputs, execute the graph for num_steps in a multi-step scenario, then decode the tokens holistically and return the list of decoded tokens. """ @@ -695,11 +701,12 @@ def next_token( tracer.pop() # pops kv_manager.step # Prepare the response, pruning away completed requests as we go. - res: list[dict[str, TextResponse]] = [{} for _ in range(num_steps)] + res: dict[str, TextGenerationResponse] = {} tracer.push("prepare_response") for batch_index, (request_id, context) in enumerate(batch.items()): - step = 0 - while step < num_steps: + status = TextGenerationStatus.ACTIVE + res[request_id] = TextGenerationResponse([], status) + for step in range(num_steps): # Convert to a Python scalar to improve serialization performance. next_token = int(generated_tokens_host[batch_index, step]) @@ -715,19 +722,6 @@ def next_token( default=context.max_length, ) - # The current length is incremented above, during context.update - # As such, if we are already at the max length, exiting here - # would cause us to miss updating the request. - # As such, we overrun here by 1, ensuring that the context object - # tracks special tokens like eos_token_id appropriately for benchmarking - # and other uses, but that they are not returned in the request. - if ( - next_token in self._eos_token_id - or context.current_length > max_length - ): - step += 1 - break - # Set up TextResponse log_probs: Optional[LogProbabilities] = None if compute_log_probabilities and ( @@ -735,10 +729,30 @@ def next_token( ): log_probs = log_probs_for_step[batch_index] - # Removing the positional arguments here, go about 100us faster. - res[step][request_id] = TextResponse(next_token, log_probs) + # Update status + # If its eos, dont add it to the token array. + if next_token in self._eos_token_id: + status = TextGenerationStatus.END_OF_SEQUENCE + res[request_id].update_status(status) + elif context.current_length == max_length: + status = TextGenerationStatus.MAXIMUM_LENGTH + res[request_id].append_token( + TextResponse(next_token, log_probs) + ) + res[request_id].update_status(status) + # This practically, should not be hit, as once the context object + # reaches the max_length, we should break from this current loop. + # TODO: Explore cleaning up max length checks. + elif context.current_length > max_length: + status = TextGenerationStatus.MAXIMUM_LENGTH + res[request_id].update_status(status) + else: + res[request_id].append_token( + TextResponse(next_token, log_probs) + ) - step += 1 + if status.is_done: + break return res From ab72aae5ae7e363837ad6f135ac8c5516ff55059 Mon Sep 17 00:00:00 2001 From: Patrick Dougherty Date: Thu, 27 Feb 2025 12:09:37 -0600 Subject: [PATCH 486/509] [CI] Add pre-commit-config to internal source of truth MODULAR_ORIG_COMMIT_REV_ID: 14e2678c27e07378a49cdfcae41feae5b7c27fb6 --- .pre-commit-config.yaml | 26 +++++++++++++++++++++++++ mojo/stdlib/scripts/check-docstrings.py | 2 +- 2 files changed, 27 insertions(+), 1 deletion(-) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000..b04addfbb6 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + - repo: local + hooks: + - id: mojo-format + name: mojo-format + entry: mojo format mojo + language: system + files: '\.(mojo|🔥|py)$' + stages: [commit] + - id: check-docstrings + name: check-docstrings + entry: python3 ./mojo/stdlib/scripts/check-docstrings.py + language: system + pass_filenames: false + stages: [commit] + - id: check-license + name: check-license + entry: mojo mojo/stdlib/scripts/check_licenses.mojo + language: system + files: '\.(mojo|🔥|py)$' + stages: [commit] + - repo: https://github.com/igorshubovych/markdownlint-cli + rev: v0.40.0 + hooks: + - id: markdownlint + args: ['--config', 'mojo/stdlib/scripts/.markdownlint.yaml', '--fix'] diff --git a/mojo/stdlib/scripts/check-docstrings.py b/mojo/stdlib/scripts/check-docstrings.py index ddba7bb42c..1eb77ea27e 100644 --- a/mojo/stdlib/scripts/check-docstrings.py +++ b/mojo/stdlib/scripts/check-docstrings.py @@ -27,7 +27,7 @@ def main(): "--diagnose-missing-doc-strings", "-o", "/dev/null", - "./stdlib/src", + "./mojo/stdlib/src", ] result = subprocess.run(command, capture_output=True) if result.stderr or result.returncode != 0: From 99bbe159934c260bd1aa77e997a89d1c3d287e3b Mon Sep 17 00:00:00 2001 From: modularbot Date: Thu, 27 Feb 2025 22:33:19 +0000 Subject: [PATCH 487/509] [Release] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022718 --- examples/custom_ops/magic.lock | 2182 ++++ examples/gpu_functions/magic.lock | 2182 ++++ examples/graph-api/magic.lock | 2182 ++++ .../inference/bert-c-torchscript/magic.lock | 7455 ++++++++++++++ .../bert-mojo-torchscript/magic.lock | 7480 ++++++++++++++ .../bert-python-torchscript/magic.lock | 7480 ++++++++++++++ .../resnet50-python-torchscript/magic.lock | 8098 +++++++++++++++ .../stable-diffusion-mojo-onnx/magic.lock | 8173 +++++++++++++++ .../stable-diffusion-python-onnx/magic.lock | 8272 +++++++++++++++ .../inference/yolo-python-onnx/magic.lock | 2220 ++++ examples/mojo/life/magic.lock | 5402 ++++++++++ examples/mojo/magic.lock | 2185 ++++ examples/mojo/operators/magic.lock | 2182 ++++ examples/mojo/testing/magic.lock | 2182 ++++ examples/notebooks/magic.lock | 7828 +++----------- examples/offline-inference/magic.lock | 9001 +++++++++++++++++ mojo/magic.lock | 2194 ++++ src/max/magic.lock | 9001 +++++++++++++++++ tutorials/hello-magic/local/magic.lock | 2333 +++++ tutorials/hello-magic/magic.lock | 2901 ++++++ tutorials/max-graph-api/magic.lock | 4414 ++++++++ 21 files changed, 99104 insertions(+), 6243 deletions(-) create mode 100644 examples/custom_ops/magic.lock create mode 100644 examples/gpu_functions/magic.lock create mode 100644 examples/graph-api/magic.lock create mode 100644 examples/inference/bert-c-torchscript/magic.lock create mode 100644 examples/inference/bert-mojo-torchscript/magic.lock create mode 100644 examples/inference/bert-python-torchscript/magic.lock create mode 100644 examples/inference/resnet50-python-torchscript/magic.lock create mode 100644 examples/inference/stable-diffusion-mojo-onnx/magic.lock create mode 100644 examples/inference/stable-diffusion-python-onnx/magic.lock create mode 100644 examples/inference/yolo-python-onnx/magic.lock create mode 100644 examples/mojo/life/magic.lock create mode 100644 examples/mojo/magic.lock create mode 100644 examples/mojo/operators/magic.lock create mode 100644 examples/mojo/testing/magic.lock create mode 100644 examples/offline-inference/magic.lock create mode 100644 mojo/magic.lock create mode 100644 src/max/magic.lock create mode 100644 tutorials/hello-magic/local/magic.lock create mode 100644 tutorials/hello-magic/magic.lock create mode 100644 tutorials/max-graph-api/magic.lock diff --git a/examples/custom_ops/magic.lock b/examples/custom_ops/magic.lock new file mode 100644 index 0000000000..9473097321 --- /dev/null +++ b/examples/custom_ops/magic.lock @@ -0,0 +1,2182 @@ +version: 6 +environments: + default: + channels: + - url: https://conda.anaconda.org/conda-forge/ + - url: https://conda.modular.com/max-nightly/ + packages: + linux-64: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh707e725_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.7.2-pyh31011fe_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2 + - 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name: Run pre-commit - run: magic run pre-commit run --all-files + run: magic run --manifest-path mojo pre-commit run --all-files diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b04addfbb6..59ab737652 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,9 +3,9 @@ repos: hooks: - id: mojo-format name: mojo-format - entry: mojo format mojo + entry: mojo format language: system - files: '\.(mojo|🔥|py)$' + files: '^(mojo|examples/mojo).*\.(mojo|🔥|py)$' stages: [commit] - id: check-docstrings name: check-docstrings @@ -17,7 +17,7 @@ repos: name: check-license entry: mojo mojo/stdlib/scripts/check_licenses.mojo language: system - files: '\.(mojo|🔥|py)$' + files: '^(mojo|examples/mojo).*\.(mojo|🔥|py)$' stages: [commit] - repo: https://github.com/igorshubovych/markdownlint-cli rev: v0.40.0 From 093b209f4a47202e49af7bbc4f640ad33553928e Mon Sep 17 00:00:00 2001 From: weiwei chen Date: Thu, 27 Feb 2025 13:11:20 -0500 Subject: [PATCH 489/509] [stdlib] Enable interpreter mode for `memcpy`. - [x] Enable simplified interpreter mode for memcpy (for fast interpreting). - [x] Add test. MODULAR_ORIG_COMMIT_REV_ID: 534b00b142ebca0b294d2bf8466f3ad37b47d7b9 --- .github/workflows/test_pre_commit.yml | 2 +- .pre-commit-config.yaml | 6 +++--- mojo/stdlib/src/memory/memory.mojo | 20 +++++++++++++++----- 3 files changed, 19 insertions(+), 9 deletions(-) diff --git a/.github/workflows/test_pre_commit.yml b/.github/workflows/test_pre_commit.yml index 8e65864e5a..99a2ddf73d 100644 --- a/.github/workflows/test_pre_commit.yml +++ b/.github/workflows/test_pre_commit.yml @@ -48,5 +48,5 @@ jobs: pre-commit install - name: Run pre-commit - run: magic run --manifest-path mojo pre-commit run --all-files + run: magic run pre-commit run --all-files diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 59ab737652..b04addfbb6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,9 +3,9 @@ repos: hooks: - id: mojo-format name: mojo-format - entry: mojo format + entry: mojo format mojo language: system - files: '^(mojo|examples/mojo).*\.(mojo|🔥|py)$' + files: '\.(mojo|🔥|py)$' stages: [commit] - id: check-docstrings name: check-docstrings @@ -17,7 +17,7 @@ repos: name: check-license entry: mojo mojo/stdlib/scripts/check_licenses.mojo language: system - files: '^(mojo|examples/mojo).*\.(mojo|🔥|py)$' + files: '\.(mojo|🔥|py)$' stages: [commit] - repo: https://github.com/igorshubovych/markdownlint-cli rev: v0.40.0 diff --git a/mojo/stdlib/src/memory/memory.mojo b/mojo/stdlib/src/memory/memory.mojo index eada6c0467..2d2d845f88 100644 --- a/mojo/stdlib/src/memory/memory.mojo +++ b/mojo/stdlib/src/memory/memory.mojo @@ -253,11 +253,21 @@ fn memcpy[ count: The number of elements to copy. """ var n = count * sizeof[dest.type]() - _memcpy_impl( - dest.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), - src.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), - n, - ) + + if __mlir_op.`kgen.is_compile_time`(): + # A fast version for the interpreter to evaluate + # this function during compile time. + llvm_intrinsic["llvm.memcpy", NoneType]( + dest.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), + src.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), + n, + ) + else: + _memcpy_impl( + dest.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), + src.bitcast[Byte]().origin_cast[origin=MutableAnyOrigin](), + n, + ) # ===-----------------------------------------------------------------------===# From ea31522342dde055693c7231935cfa6e426350f4 Mon Sep 17 00:00:00 2001 From: Patrick Dougherty Date: Thu, 27 Feb 2025 12:19:53 -0600 Subject: [PATCH 490/509] [CI] Add missed license MODULAR_ORIG_COMMIT_REV_ID: 0761f1c3c4629379ce11e037d05c0047b8104986 --- .pre-commit-config.yaml | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b04addfbb6..5e3e2ec6e2 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,3 +1,16 @@ +##===----------------------------------------------------------------------===## +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +##===----------------------------------------------------------------------===## + repos: - repo: local hooks: @@ -22,5 +35,5 @@ repos: - repo: https://github.com/igorshubovych/markdownlint-cli rev: v0.40.0 hooks: - - id: markdownlint - args: ['--config', 'mojo/stdlib/scripts/.markdownlint.yaml', '--fix'] + - id: markdownlint + args: ["--config", "mojo/stdlib/scripts/.markdownlint.yaml", "--fix"] From ee76f6c69062256f2f4b364aeee1f3c1c6b65a4a Mon Sep 17 00:00:00 2001 From: Katherine Wu <31663267+k-w-w@users.noreply.github.com> Date: Thu, 27 Feb 2025 11:41:11 -0800 Subject: [PATCH 491/509] [Graph] Add weight sharding strategy for Multi-GPU (#56810) The PR adds sharding strategy to `Weight`, and allow weights to be sharded with `Weight.shard(shard_idx, device)`. Also deletes `LayerV2.to`, we can add it back later but for now the API isn't fully thought out. This functionally adds sharding to the Weights, although some aspects may have unexpected behavior (like getting the `shape` of a sharded weight actually returns the `shape` of the original weight). Also, `LayerV2` needs to adapt to handling sharded weights. MODULAR_ORIG_COMMIT_REV_ID: 191c2f8dc605d2ea0765ac7a693e3f44cf968591 --- src/max/pipelines/nn/layer/layer.py | 37 ++--------------------------- 1 file changed, 2 insertions(+), 35 deletions(-) diff --git a/src/max/pipelines/nn/layer/layer.py b/src/max/pipelines/nn/layer/layer.py index 3b76c479b6..31443d97e1 100644 --- a/src/max/pipelines/nn/layer/layer.py +++ b/src/max/pipelines/nn/layer/layer.py @@ -17,7 +17,6 @@ import threading from abc import ABC, abstractmethod from collections import deque -from dataclasses import dataclass from functools import wraps from inspect import signature from typing import ( @@ -34,7 +33,7 @@ import numpy as np from max.driver import DLPackArray, Tensor from max.dtype import DType -from max.graph import DeviceRef, ShapeLike, TensorValue, Weight +from max.graph import ShapeLike, Weight from max.graph.quantization import QuantizationEncoding from max.graph.weights import WeightData @@ -43,14 +42,6 @@ DLPackCompatible = Union[DLPackArray, np.ndarray] -@dataclass -class ShardingStrategy: - """Defines how to load and shard a weight onto multiple devices.""" - - host_device: DeviceRef - shard_value: Callable[[Weight], tuple[TensorValue, ...]] - - class Layer: """Base Layer class. @@ -79,7 +70,7 @@ def __call__(self, *args, **kwargs): class LayerV2(Layer, ABC): - """(new) Base class for model layers with weight and device management. + """(new) Base class for model layers with weight management. This will be merged with the above class once all layers have been moved to V2. @@ -95,20 +86,10 @@ def __init__(self): self._weight_values: dict[str, DLPackCompatible] = {} self._shared_weights: dict[str, Weight] = {} - # Update device list and sharding strategy to a singular object - # similar to a partition spec. - self._devices: tuple[DeviceRef, ...] = () - self._sharding_strategy: ShardingStrategy | None = None - def __setattr__(self, name, value): try: if isinstance(value, LayerV2): self._sublayers[name] = value - if self._devices or self._sharding_strategy: - value.to( - *self._devices, - sharding_strategy=self._sharding_strategy, - ) elif isinstance(value, Weight): self._layer_weights[value.name] = value except AttributeError: @@ -122,20 +103,6 @@ def __repr__(self): # TODO: Make this pretty return f"{type(self).__name__}({len(self.sublayers)} layers, {len(self.layer_weights)} weights)" - def to( - self, - *devices: DeviceRef, - sharding_strategy: ShardingStrategy | None = None, - ) -> None: - if len(self._devices) > 1 and not sharding_strategy: - raise ValueError( - "Must provide a sharding strategy if multiple devices are provided." - ) - - for _, layer in recursive_named_layers(self): - layer._devices = devices - layer._sharding_strategy = sharding_strategy - @property def layer_weights(self) -> dict[str, Weight]: return self._layer_weights From 89d6288e989b70e56c76c730b60b57dfeec2d153 Mon Sep 17 00:00:00 2001 From: kcaverly Date: Thu, 27 Feb 2025 15:41:47 -0500 Subject: [PATCH 492/509] [Pipelines] Add draft_model to PipelineConfig for Speculative Decoding MODULAR_ORIG_COMMIT_REV_ID: d387534a36e5bc9a7299fef2650dee9184fab56e --- src/max/pipelines/config.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/src/max/pipelines/config.py b/src/max/pipelines/config.py index b71b604777..005915d889 100644 --- a/src/max/pipelines/config.py +++ b/src/max/pipelines/config.py @@ -688,6 +688,9 @@ class PipelineConfig: "USE_EXPERIMENTAL_KERNELS", "false" ) + draft_model: Optional[str] = None + """Draft model for use during Speculative Decoding.""" + def __post_init__(self) -> None: # Validate if a provided max_length is non-negative. if self.max_length is not None and self.max_length < 0: @@ -802,6 +805,13 @@ def __post_init__(self) -> None: ): raise ValueError("gpu_profiling must be a boolean or 'detailed'") + if self.draft_model: + if not _repo_exists_with_retry(self.draft_model): + raise ValueError( + "draft_model provided does not exist on HuggingFace." + "Only public HuggingFace draft models currently supported." + ) + def __getstate__(self) -> dict[str, Any]: """Override `__getstate__` to exclude the Hugging Face config.""" state = self.__dict__.copy() @@ -1058,6 +1068,7 @@ def help() -> dict[str, str]: "force_download": "Specify whether to forcefully download a file even if it already exists in local cache. Set this to true if you want to ensure you have the latest version.", "enable_echo": "Whether the model should be built with echo capabilities. This defaults to false.", "gpu_profiling": "Whether to enable GPU profiling of the model. This defaults to false.", + "draft_model": "Draft model for use in speculative decoding.", } def huggingface_weights_repo(self) -> HuggingFaceRepo: From 3794d427bc7bea34d7a0a4c9cf609e005620d7f9 Mon Sep 17 00:00:00 2001 From: kcaverly Date: Thu, 27 Feb 2025 15:43:35 -0500 Subject: [PATCH 493/509] [Pipelines] Load Scaffolded SpeculativeDecodingTextGenerationPipeline via PIPELINE_REGISTRY MODULAR_ORIG_COMMIT_REV_ID: a786273ce5c0890ba2ed15bc6717afee46199966 --- src/max/pipelines/__init__.py | 2 + src/max/pipelines/registry.py | 51 +++++++++++++++++++-- src/max/pipelines/speculative_decoding.py | 56 +++++++++++++++++++++++ 3 files changed, 104 insertions(+), 5 deletions(-) create mode 100644 src/max/pipelines/speculative_decoding.py diff --git a/src/max/pipelines/__init__.py b/src/max/pipelines/__init__.py index 33c02c405a..7e673e191f 100644 --- a/src/max/pipelines/__init__.py +++ b/src/max/pipelines/__init__.py @@ -50,6 +50,7 @@ upper_bounded_default, ) from .registry import PIPELINE_REGISTRY, SupportedArchitecture +from .speculative_decoding import SpeculativeDecodingTextGenerationPipeline from .tokenizer import ( IdentityPipelineTokenizer, PreTrainedPipelineTokenizer, @@ -96,5 +97,6 @@ "EmbeddingsGenerator", "EmbeddingsPipeline", "EmbeddingsResponse", + "SpeculativeDecodingTextGenerationPipeline", "upper_bounded_default", ] diff --git a/src/max/pipelines/registry.py b/src/max/pipelines/registry.py index 2e180772f2..1f8149e0fa 100644 --- a/src/max/pipelines/registry.py +++ b/src/max/pipelines/registry.py @@ -42,6 +42,7 @@ ) from .kv_cache import KVCacheStrategy from .pipeline import KVCacheMixin, PipelineModel, TextGenerationPipeline +from .speculative_decoding import SpeculativeDecodingTextGenerationPipeline from .tokenizer import TextAndVisionTokenizer, TextTokenizer logger = logging.getLogger("max.pipelines") @@ -54,10 +55,24 @@ SupportedEncoding.bfloat16: SupportedEncoding.float32, } -_PIPELINE_TASK_MAP = { - PipelineTask.TEXT_GENERATION: TextGenerationPipeline, - PipelineTask.EMBEDDINGS_GENERATION: EmbeddingsPipeline, -} + +def get_pipeline_for_task( + task: PipelineTask, pipeline_config: PipelineConfig +) -> ( + type[TextGenerationPipeline] + | type[EmbeddingsPipeline] + | type[SpeculativeDecodingTextGenerationPipeline] +): + if task == PipelineTask.TEXT_GENERATION: + if pipeline_config.draft_model is not None: + return SpeculativeDecodingTextGenerationPipeline + else: + return TextGenerationPipeline + elif task == PipelineTask.EMBEDDINGS_GENERATION: + return EmbeddingsPipeline + else: + msg = f"PipelineTask ({task}) does not have supported Pipeline" + raise ValueError(msg) _HF_PIPELINE_TASK_MAP: dict[ @@ -144,8 +159,34 @@ def architecture_details( else: return None + def _validate_pipeline_config_for_speculative_decoding( + self, pipeline_config: PipelineConfig + ) -> None: + # When `draft_model` is not provided, speculative decoding is disabled. + if not pipeline_config.draft_model: + return None + + # Assume `draft_model` is provided, and thus speculative decoding is enabled. + # We don't support running speculative decoding with the HuggingFace backend. + if pipeline_config.engine == PipelineEngine.HUGGINGFACE: + msg = ( + "Speculative Decoding not supported with the HuggingFace Engine" + ) + raise ValueError(msg) + def validate_pipeline_config( self, pipeline_config: PipelineConfig + ) -> PipelineConfig: + # Run Baseline Validation + pipeline_config = self._validate_pipeline_config(pipeline_config) + + # Run Additional Checks for Speculative Decoding + self._validate_pipeline_config_for_speculative_decoding(pipeline_config) + + return pipeline_config + + def _validate_pipeline_config( + self, pipeline_config: PipelineConfig ) -> PipelineConfig: """Update pipeline config with appropriate values if not provided. If invalid config is provided, error out with detailed reason.""" @@ -921,7 +962,7 @@ def retrieve_factory( # Keep MyPy happy. assert pipeline_config.architecture is not None - pipeline_class = _PIPELINE_TASK_MAP[task] + pipeline_class = get_pipeline_for_task(task, pipeline_config) # MAX pipeline arch = self.architectures[pipeline_config.architecture] diff --git a/src/max/pipelines/speculative_decoding.py b/src/max/pipelines/speculative_decoding.py new file mode 100644 index 0000000000..b72c1cf9f8 --- /dev/null +++ b/src/max/pipelines/speculative_decoding.py @@ -0,0 +1,56 @@ +# ===----------------------------------------------------------------------=== # +# Copyright (c) 2025, Modular Inc. All rights reserved. +# +# Licensed under the Apache License v2.0 with LLVM Exceptions: +# https://llvm.org/LICENSE.txt +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ===----------------------------------------------------------------------=== # +# mypy: disable-error-code="import-not-found" +"""Speculative Decoding Text Generation Pipeline""" + +from typing import Type, TypeVar + +from .config import PipelineConfig +from .context import InputContext +from .interfaces import TextGenerationResponse, TokenGenerator +from .pipeline import PipelineModel + +T = TypeVar("T", bound=InputContext) + + +class SpeculativeDecodingTextGenerationPipeline(TokenGenerator[T]): + """Generalized token generator pipeline with speculative decoding.""" + + def __init__( + self, + pipeline_config: PipelineConfig, + pipeline_model: Type[PipelineModel], + eos_token_id: int, + ) -> None: + raise NotImplementedError( + "init not yet implemented for SpeculativeDecodingTextGenerationPipeline" + ) + + def next_token( + self, batch: dict[str, T], num_steps: int + ) -> dict[str, TextGenerationResponse]: + """Provided a batch, execute both the draft model for num_steps and the target model for num_steps + 1 tokens, accepting final tokens via rejection sampling, returning the variable list of token integers.""" + raise NotImplementedError( + "next_token not yet implemented for SpeculativeDecodingTextGenerationPipeline" + ) + + def release(self, context: T) -> None: + """Releases resources associated with this context. + + Args: + context (TokenGeneratorContext): Finished context. + + """ + raise NotImplementedError( + "release not yet implemented for SpeculativeDecodingTextGenerationPipeline" + ) From fa342a77199fc6c61f1937615b779756f866308c Mon Sep 17 00:00:00 2001 From: Austin Doolittle Date: Thu, 27 Feb 2025 16:33:14 -0500 Subject: [PATCH 494/509] [KVCache] rework max_cache_length to max_context_length (#56592) We were previously passing areound the max number of encoded tokens in the cache before QKV projection. This, out, is less useful than the max context length, which is the sum of the number of encoded tokens in tthe KVCache plus the new tokens we're encoding in the active forward pass. This PR cleans this up, and is a prerequisite to work included in E2EOPT-25t l MODULAR_ORIG_COMMIT_REV_ID: 2ba4178bd602dabfce4b9fb10181b98e42e241cb --- src/max/pipelines/kv_cache/_utils.py | 3 ++- src/max/pipelines/kv_cache/continuous_batching_cache.py | 8 +++++--- src/max/pipelines/kv_cache/paged_cache.py | 8 +++++--- 3 files changed, 12 insertions(+), 7 deletions(-) diff --git a/src/max/pipelines/kv_cache/_utils.py b/src/max/pipelines/kv_cache/_utils.py index 6942ad7823..1c8389142a 100644 --- a/src/max/pipelines/kv_cache/_utils.py +++ b/src/max/pipelines/kv_cache/_utils.py @@ -28,6 +28,7 @@ def build_max_lengths_tensor( for step in range(num_steps): max_lengths_np[step, 0] = step_max_seq_length max_lengths_np[step, 1] = step_max_cache_length - step_max_cache_length += step_max_seq_length step_max_seq_length = 1 + step_max_cache_length += 1 + return Tensor.from_numpy(max_lengths_np) diff --git a/src/max/pipelines/kv_cache/continuous_batching_cache.py b/src/max/pipelines/kv_cache/continuous_batching_cache.py index b6f75e9c8a..cb7936d97a 100644 --- a/src/max/pipelines/kv_cache/continuous_batching_cache.py +++ b/src/max/pipelines/kv_cache/continuous_batching_cache.py @@ -255,7 +255,7 @@ def _fetch( cache_lengths_np = np.zeros(active_batch_size, np.uint32) max_seq_length = 0 - max_cache_length = 0 + max_context_length = 0 for i, (seq_id, prompt) in enumerate(seq_ids_and_prompts.items()): if seq_id > self.max_batch_size: @@ -281,7 +281,9 @@ def _fetch( # Update the maximum lengths seen so far. max_seq_length = max(max_seq_length, len(prompt)) - max_cache_length = max(max_cache_length, cache_len) + max_context_length = max( + max_context_length, cache_len + len(prompt) + ) cache_lengths = [ Tensor.from_numpy(cache_lengths_np).to(d) for d in self.devices @@ -294,7 +296,7 @@ def _fetch( # Build a tensor of maximum lengths. Each step slices the first row to # advance to the values for the next row. max_lengths_host = build_max_lengths_tensor( - num_steps, max_seq_length, max_cache_length + num_steps, max_seq_length, max_context_length ) result = [ diff --git a/src/max/pipelines/kv_cache/paged_cache.py b/src/max/pipelines/kv_cache/paged_cache.py index ab33d3335e..aa2c392995 100644 --- a/src/max/pipelines/kv_cache/paged_cache.py +++ b/src/max/pipelines/kv_cache/paged_cache.py @@ -542,7 +542,7 @@ def _fetch( # Determine the number of pages required for each sequence. max_seq_length = 0 - max_cache_length = 0 + max_context_length = 0 total_sequence_length = 0 total_blocks_to_allocate = 0 blocks_to_allocate_by_seq = {} @@ -557,7 +557,9 @@ def _fetch( # Update the maximum lengths seen so far. max_seq_length = max(max_seq_length, len(prompt)) - max_cache_length = max(max_cache_length, cache_length) + max_context_length = max( + max_context_length, cache_length + len(prompt) + ) # Compute the total sequence length and the number of pages required to store it. total_sequence_length += data.seq_len @@ -604,7 +606,7 @@ def _fetch( # Build a tensor of maximum lengths. Each step slices the first row to # advance to the values for the next row. max_lengths_host = build_max_lengths_tensor( - num_steps, max_seq_length, max_cache_length + num_steps, max_seq_length, max_context_length ) lut_table_host = Tensor.from_numpy(lut_table_np) From 02b4fdb828f6299f4efdc0f0ec960770e46d3de1 Mon Sep 17 00:00:00 2001 From: Zac Bowling Date: Thu, 27 Feb 2025 13:41:51 -0800 Subject: [PATCH 495/509] [build] Don't use broken opentelemetry-exporter-prometheus (#56798) We only need to avoid the broken yanked version of this for conda (temporarily), not PyPI. MODULAR_ORIG_COMMIT_REV_ID: d0ca07b068ef783717aa2fef884e717b9479f7e1 --- src/max/pipelines/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/max/pipelines/requirements.txt b/src/max/pipelines/requirements.txt index dc050dddf6..708e101b5b 100644 --- a/src/max/pipelines/requirements.txt +++ b/src/max/pipelines/requirements.txt @@ -22,7 +22,7 @@ httpx>=0.28.1,<0.29 huggingface-hub>=0.24.0 opentelemetry-api>=1.29.0 opentelemetry-exporter-otlp-proto-http>=1.27.0 -opentelemetry-exporter-prometheus<1.0.0 +opentelemetry-exporter-prometheus>=0.48b0 opentelemetry-sdk>=1.29.0 pillow>=10.3.0 prometheus-async>=22.2.0 From aeedc3d65fb25615adbe5a750dffb54e8ce1b1bd Mon Sep 17 00:00:00 2001 From: Brian Zhang Date: Thu, 27 Feb 2025 14:13:18 -0800 Subject: [PATCH 496/509] [KVCache] Implement `query_fetch_stats` in `PagedKVCacheManager` (#56606) This new method will compute some statistics about running a fetch operation without actually running the fetch or mutating state. Such info includes - blocks reused from prefix cache - tokens to encode - new pages needed This will be needed by the scheduler when doing kvcache and prefix cache aware scheduling. To implement this, we split the prefix cache `fetch` and `fetch_cow` methods into additional smaller helper methods that also do not mutate state. To test this, I monkey patched each call to `paged_manager.fetch` to call `query_fetch_stats`. Then after running the original `fetch`, I compare the actual and predicted statistics. Towards E2EOPT-62 MODULAR_ORIG_COMMIT_REV_ID: 4f826941818a256c31455f51de548a2c78f1431e --- src/max/pipelines/kv_cache/paged_cache.py | 41 +++++ .../kv_cache/paged_cache_metadata.py | 17 ++ src/max/pipelines/kv_cache/prefix_cache.py | 158 ++++++++++++++---- 3 files changed, 186 insertions(+), 30 deletions(-) diff --git a/src/max/pipelines/kv_cache/paged_cache.py b/src/max/pipelines/kv_cache/paged_cache.py index aa2c392995..11ce2356cb 100644 --- a/src/max/pipelines/kv_cache/paged_cache.py +++ b/src/max/pipelines/kv_cache/paged_cache.py @@ -471,6 +471,47 @@ def get_num_cached_tokens(self, prompt: np.ndarray) -> int: return 0 return self.prefix_cache.get_num_cached_tokens(prompt) + def query_fetch_stats( + self, seq_id: int, prompt: np.ndarray, num_steps: int = 1 + ) -> tuple[set[int], int, int]: + """Query about the stats about running the fetch operation for a given + sequence. + + This method does not modify the state of the paged cache. + + Returns: + - prefix_blocks: Prefix cache blocks that would be reused for this seq. + - tokens_to_encode: Number of tokens in prompt we need to encode when running the fetch. + - new_pages_needed: Number of new pages we need to allocate when running the fetch. + """ + data = self.active_requests.get( + seq_id, PagedCacheMetadata(self.page_size, self.max_seq_len) + ) + data.fetch(prompt, num_steps) + prefix_blocks: set[int] = set() + cache_hit_tokens = 0 + if self.prefix_cache is not None: + # copy on write does not impact the number of new blocks needed + prefix_blocks, cache_hit_tokens = ( + self.prefix_cache.query_fetch_stats(seq_id, data) + ) + assert cache_hit_tokens >= 0 + + tokens_to_encode = len(data.prompt_tokens) - cache_hit_tokens + assert tokens_to_encode >= 1 + # recall that if we have any full cache hits, we will need to release our + # partial block as it is replaced with a full block + num_existing_pages = len(data.blocks) + num_existing_pages += len(prefix_blocks) + total_pages_needed = ceildiv(data.seq_len, self.page_size) + new_pages_needed = total_pages_needed - num_existing_pages + assert new_pages_needed >= 0 + + # reverse the fetch operation so that this method does not mutate state + data.undo_fetch(prompt, num_steps) + + return prefix_blocks, tokens_to_encode, new_pages_needed + def _fetch( self, seq_ids_and_prompts: dict[int, np.ndarray], num_steps: int = 1 ) -> List[KVCacheInputs]: diff --git a/src/max/pipelines/kv_cache/paged_cache_metadata.py b/src/max/pipelines/kv_cache/paged_cache_metadata.py index abeb8f7a40..0e1d8f6e62 100644 --- a/src/max/pipelines/kv_cache/paged_cache_metadata.py +++ b/src/max/pipelines/kv_cache/paged_cache_metadata.py @@ -169,3 +169,20 @@ def step(self, new_tokens: np.ndarray) -> None: "After step, all tokens should have a backing KV projection in the cache" ) self._validate_indices() + + def undo_fetch(self, prompt: np.ndarray, num_steps: int) -> None: + """Remove prompt from token array and release inflight tokens.""" + self._validate_indices() + assert len(self.prompt_tokens) > 0 + assert len(self.prompt_tokens) == len(prompt) + num_inflight_tokens = num_steps - 1 + assert len(self.inflight_tokens) == num_inflight_tokens + self.tokens[self.cached_idx : self.inflight_idx] = 0 + self.seq_len -= len(prompt) + num_inflight_tokens + self.inflight_idx -= len(prompt) + assert len(self.inflight_tokens) == 0 + assert len(self.prompt_tokens) == 0 + self._validate_indices() + + def __repr__(self) -> str: + return f"PagedCacheMetadata(committed_idx={self.committed_idx}, cached_idx={self.cached_idx}, inflight_idx={self.inflight_idx}, seq_len={self.seq_len}, blocks={self.blocks})" diff --git a/src/max/pipelines/kv_cache/prefix_cache.py b/src/max/pipelines/kv_cache/prefix_cache.py index 191f53ffd2..751abbd6f5 100644 --- a/src/max/pipelines/kv_cache/prefix_cache.py +++ b/src/max/pipelines/kv_cache/prefix_cache.py @@ -202,40 +202,67 @@ def _release_partial_block( data.cached_idx -= partial_tokens assert data.committed_idx == data.cached_idx - def fetch( + def _fetch_query_cache( self, seq_id: int, data: PagedCacheMetadata, - free_block_fn: Callable[[int], None], - alloc_block_fn: Callable[[], int], - ) -> list[int]: - """Extend the kv cache for given request with any cached prefixes. + ) -> tuple[TrieNode, list[int]]: + """A helper method used by `fetch` which queries the prefix cache for + full blocks which the request should reuse. - This will increment the committed_idx and cached_idx if there is a cache - hit. The prompt will be trimmed in the event that cached_idx is bumped. + This method does not modify the state of the prefix cache. + + Returns: + - node: the new position of the trie node for a given sequence once + it adds the prefix blocks to its assigned blocks + - prefix_blocks: blocks the sequence should reuse from the cache """ + node = self.active_requests.get(seq_id, self.radix_trie.root) + # If there is only one committable token, that means that the prompt # is one token. We cannot reduce the prompt length any further since # the model expects a prompt of length at least 1. committable_tokens = data.committable_tokens[:-1] if len(committable_tokens) == 0: - return [] + return node, [] # Query trie for all but last token. - node = self.active_requests[seq_id] node, prefix_blocks = self.radix_trie.match_prefix( committable_tokens, node=node ) - self.active_requests[seq_id] = node - # Mark the prefix blocks we retrieved from the radix trie cache as - # in use by this sequence so they don't get evicted prematurely. - self.radix_trie.mark_in_use_by(node, seq_id) + return node, prefix_blocks + + def fetch( + self, + seq_id: int, + data: PagedCacheMetadata, + free_block_fn: Callable[[int], None], + alloc_block_fn: Callable[[], int], + ) -> list[int]: + """Extend the kv cache for given request with any cached prefixes. + + This will increment the committed_idx and cached_idx if there is a cache + hit. The prompt will be trimmed in the event that cached_idx is bumped. + """ + node, prefix_blocks = self._fetch_query_cache(seq_id, data) # Update the cache hit rate metrics. num_cache_hit_tokens = len(prefix_blocks) * self.page_size self.cache_hit_tokens += num_cache_hit_tokens - self.all_tokens += len(committable_tokens) + self.all_tokens += len(data.committable_tokens) - 1 + + # Exit early if there are no cache hits. + if len(prefix_blocks) == 0: + if self.enable_cow: + self._fetch_cow(seq_id, data, free_block_fn, alloc_block_fn) + return [] + + self.active_requests[seq_id] = node + + # Mark the prefix blocks we retrieved from the radix trie cache as + # in use by this sequence so they don't get evicted prematurely. + self.radix_trie.mark_in_use_by(node, seq_id) # If there is a block with partially cached tokens, we should release it # if the cache hit blocks already contain these tokens and more @@ -253,6 +280,47 @@ def fetch( return prefix_blocks + def _fetch_cow_query_cache( + self, + node: TrieNode, + committable_tokens: np.ndarray, + partial_tokens: int, + ) -> tuple[Optional[int], int]: + """A helper method used by `_fetch_cow` which queries the prefix cache + for a block resident in prefix cache to copy tokens from for COW. + + This method does not modify the state of the prefix cache. + + Returns: + - partial_match_block: block to copy tokens from, this is None in + the event that performing COW is not beneficial + - num_cache_hit_tokens: tokens to copy from the block + """ + assert self.enable_cow + assert self.page_size > 1 + assert self.cow_strided_memcpy_graph is not None + assert len(committable_tokens) > 0 + assert 0 <= partial_tokens < self.page_size + + # Match page_size tokens in the radix trie + committable_tokens = committable_tokens[:-1] + if len(committable_tokens) == 0: + return None, 0 + committable_tokens_cropped = list(committable_tokens[: self.page_size]) + res = node.find_block_with_largest_common_prefix( + committable_tokens_cropped + ) + if res is None: + return None, 0 + partial_match_block, num_cache_hit_tokens = res + assert 0 < num_cache_hit_tokens < self.page_size + + # No point in performing COW if we have more cached but uncommitted tokens + # in the existing partial block than the matched prefix length. + if num_cache_hit_tokens <= partial_tokens: + return None, 0 + return partial_match_block, num_cache_hit_tokens + def _fetch_cow( self, seq_id: int, @@ -274,25 +342,16 @@ def _fetch_cow( return assert self.cow_strided_memcpy_graph is not None - # Match page_size tokens in the radix trie - committable_tokens = data.committable_tokens[:-1] - if len(committable_tokens) == 0: - return - committable_tokens_cropped = list(committable_tokens[: self.page_size]) + committable_tokens = data.committable_tokens node = self.active_requests[seq_id] - res = node.find_block_with_largest_common_prefix( - committable_tokens_cropped - ) - if res is None: - return - partial_match_block, num_cache_hit_tokens = res - assert 0 < num_cache_hit_tokens < self.page_size - - # No point in performing COW if we have more cached but uncommitted tokens - # in the existing partial block than the matched prefix length. partial_tokens = data.cached_idx - data.committed_idx - if num_cache_hit_tokens <= partial_tokens: + partial_match_block, num_cache_hit_tokens = self._fetch_cow_query_cache( + node, committable_tokens, partial_tokens + ) + if partial_match_block is None: return + assert num_cache_hit_tokens > 0 + assert num_cache_hit_tokens > partial_tokens # If we have a partially cached block, we need to release it before # appending additional blocks. @@ -314,6 +373,45 @@ def _fetch_cow( assert len(data.prompt_tokens) > 0 assert data.cached_idx < data.inflight_idx + def query_fetch_stats( + self, seq_id: int, data: PagedCacheMetadata + ) -> tuple[set[int], int]: + """Query the prefix trie for the cache hit stats for the given sequence. + + This method does not modify the state of the prefix cache. + + Returns: + - prefix_blocks: Prefix cache blocks that would be reused for this seq. + - num_cache_hit_tokens: Number of new cached tokens retrieved from prefix cache. + """ + node, prefix_blocks = self._fetch_query_cache(seq_id, data) + num_cache_hit_tokens = len(prefix_blocks) * self.page_size + + # If COW is enabled, we should consider the number of additional cache + # hits that would be incurred by performing a COW operation. + if self.enable_cow and self.page_size > 1: + committable_tokens = data.committable_tokens + committable_tokens = committable_tokens[num_cache_hit_tokens:] + partial_tokens = data.cached_idx - data.committed_idx + if len(prefix_blocks) > 0: + partial_tokens = 0 + partial_match_block, num_cow_cache_hit_tokens = ( + self._fetch_cow_query_cache( + node, committable_tokens, partial_tokens + ) + ) + if partial_match_block is not None: + num_cache_hit_tokens += num_cow_cache_hit_tokens + + # Determine the number of cache hit tokens, excluding any tokens that + # were already cached in a partial block. + new_cached_idx = max( + data.committed_idx + num_cache_hit_tokens, data.cached_idx + ) + num_cache_hit_tokens = new_cached_idx - data.cached_idx + assert num_cache_hit_tokens >= 0 + return set(prefix_blocks), num_cache_hit_tokens + def step( self, seq_id: int, From c3a576c31ee418487c2e4dc07cea3d837af67e41 Mon Sep 17 00:00:00 2001 From: Patrick Dougherty Date: Thu, 27 Feb 2025 16:37:02 -0600 Subject: [PATCH 497/509] [CI] Fix script paths MODULAR_ORIG_COMMIT_REV_ID: 88187ea34fe16c74adb8dac66d27c711c3d23b53 --- .github/workflows/test_pre_commit.yml | 5 ++--- .pre-commit-config.yaml | 6 +++--- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/.github/workflows/test_pre_commit.yml b/.github/workflows/test_pre_commit.yml index 99a2ddf73d..12ed62a50a 100644 --- a/.github/workflows/test_pre_commit.yml +++ b/.github/workflows/test_pre_commit.yml @@ -46,7 +46,6 @@ jobs: run: | pip install pre-commit pre-commit install - - - name: Run pre-commit - run: magic run pre-commit run --all-files + - name: Run pre-commit + run: magic run --manifest-path mojo pre-commit run --all-files diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5e3e2ec6e2..0e7d167ade 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,9 +16,9 @@ repos: hooks: - id: mojo-format name: mojo-format - entry: mojo format mojo + entry: mojo format language: system - files: '\.(mojo|🔥|py)$' + files: '^(mojo|examples/mojo).*\.(mojo|🔥|py)$' stages: [commit] - id: check-docstrings name: check-docstrings @@ -30,7 +30,7 @@ repos: name: check-license entry: mojo mojo/stdlib/scripts/check_licenses.mojo language: system - files: '\.(mojo|🔥|py)$' + files: '^(mojo|examples/mojo).*\.(mojo|🔥|py)$' stages: [commit] - repo: https://github.com/igorshubovych/markdownlint-cli rev: v0.40.0 From 3b88d3b4129ab4c5c1bd44cae836051549f9d9d9 Mon Sep 17 00:00:00 2001 From: Brian Zhang Date: Thu, 27 Feb 2025 14:47:19 -0800 Subject: [PATCH 498/509] [KVCache][Scheduler] Remove O(ce_batch_size^2 * seqlen) loop in batch creation (#56644) Fixes very inefficient kvcache / prefix cache aware scheduling. We were previously see-ing batch creation times of upwards of 1s. This fixes the above issue. ``` // Before ce_batch = [] loop: // O(ce_batch_size) req = q.pop() if not paged_manager.can_fetch(ce_batch + res): // O(ce_batch_size * seq_len) break ce_batch += res // After ce_batch = [] free_blocks = paged_manager.avail_blocks + stale_blocks tot_new_pages_needed = 0 loop: // O(ce_batch_size) req = q.pop() cache_hit_blocks, tokens_to_encode, new_pages_needed = paged_manager.query_fetch_stats(req) // O(seq_len) if free_blocks - cache_hit_blocks < tot_new_pages_needed + new_pages_needed: break ce_batch += req tot_new_pages_needed += new_pages_needed free_blocks -= cache_hit_blocks ``` Server: ``` br //SDK/lib/API/python/max/entrypoints:pipelines --test_output=streamed --test_arg=-svv -- serve --model-path=meta-llama/Meta-Llama-3.1-8B-Instruct --quantization-encoding bfloat16 --use-gpu --cache-strategy paged --kv-cache-page-size 128 --max-batch-size 512 --max-num-steps 10 --max-length 2048 --pad-to-multiple-of 2 --enable-prefix-caching ``` Client: ``` python3 $(realpath open-source/max/benchmark/benchmark_serving.py) --backend modular --model meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-name sonnet --dataset-path $(realpath utils/benchmarking/serving/sonnet_4x.txt) --endpoint /v1/completions --num-prompts 500 --sonnet-input-len 512 --sonnet-output-len 16 --sonnet-prefix 192 --request-rate inf --collect-gpu-stats --print-inputs-and-outputs ``` Results after warmup with different seed: ``` {'req_id': 498, 'output_len': 16, 'output': 'Here are the lines from the poem:\n\n1. FROM fairest creatures we desire'} {'req_id': 499, 'output_len': 16, 'output': 'Here are the lines from the poem:\n\n1. FROM fairest creatures we desire'} ============ Serving Benchmark Result ============ Successful requests: 500 Failed requests: 0 Benchmark duration (s): 12.11 Total input tokens: 232488 Total generated tokens: 8000 Request throughput (req/s): 41.28 Input token throughput (tok/s): 19192.00 Output token throughput (tok/s): 660.40 ---------------Time to First Token---------------- Mean TTFT (ms): 5806.47 Median TTFT (ms): 5896.86 P99 TTFT (ms): 10296.24 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 408.27 Median TPOT (ms): 402.32 P99 TPOT (ms): 725.17 ---------------Inter-token Latency---------------- Mean ITL (ms): 407.57 Median ITL (ms): 73.28 P99 ITL (ms): 8737.24 -------------------Token Stats-------------------- Max input tokens: 494 Max output tokens: 16 Max total tokens: 510 --------------------GPU Stats--------------------- GPU Utilization (%): 80.63 Peak GPU Memory Used (MiB): 76842.56 GPU Memory Available (MiB): 4313.19 ================================================== 00:39:24.134 INFO: benchmark_serving: finished benchmark run: Success. ``` Logs: ``` 3ms | KVCache usage: 0.0% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed CE batch with 1 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 14.1K tok/s, Generation Tput: 29.2 tok/s | Batch creation: 574.3us, Execution: 34.2ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed TG batch with 1 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 9.8 tok/s, Generation Tput: 97.9 tok/s | Batch creation: 90.4us, Execution: 102.2ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed TG batch with 1 reqs | Terminated: 1 reqs, Pending: 0 reqs | Prompt Tput: 9.8 tok/s, Generation Tput: 97.8 tok/s | Batch creation: 87.9us, Execution: 102.2ms | KVCache usage: 0.0% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed CE batch with 1 reqs | Terminated: 0 reqs, Pending: 20 reqs | Prompt Tput: 28.9K tok/s, Generation Tput: 59.9 tok/s | Batch creation: 463.2us, Execution: 16.7ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.2%, All Preemptions: 0 reqs Executed CE batch with 1 reqs | Terminated: 0 reqs, Pending: 69 reqs | Prompt Tput: 15.3K tok/s, Generation Tput: 33.2 tok/s | Batch creation: 312.9us, Execution: 30.1ms | KVCache usage: 0.2% of 3679 blocks, Cache hit rate: 20.2%, All Preemptions: 0 reqs Executed CE batch with 9 reqs | Terminated: 0 reqs, Pending: 489 reqs | Prompt Tput: 22.9K tok/s, Generation Tput: 49.3 tok/s | Batch creation: 1.7ms, Execution: 182.5ms | KVCache usage: 0.9% of 3679 blocks, Cache hit rate: 20.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 461 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.7 tok/s | Batch creation: 5.3ms, Execution: 520.9ms | KVCache usage: 3.3% of 3679 blocks, Cache hit rate: 20.6%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 451 reqs | Prompt Tput: 22.9K tok/s, Generation Tput: 52.6 tok/s | Batch creation: 1.8ms, Execution: 190.0ms | KVCache usage: 4.0% of 3679 blocks, Cache hit rate: 20.6%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 423 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.5 tok/s | Batch creation: 4.9ms, Execution: 522.4ms | KVCache usage: 6.3% of 3679 blocks, Cache hit rate: 20.9%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 413 reqs | Prompt Tput: 22.5K tok/s, Generation Tput: 50.9 tok/s | Batch creation: 1.8ms, Execution: 196.6ms | KVCache usage: 7.1% of 3679 blocks, Cache hit rate: 21.0%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 385 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 4.9ms, Execution: 523.2ms | KVCache usage: 9.5% of 3679 blocks, Cache hit rate: 21.2%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 375 reqs | Prompt Tput: 22.9K tok/s, Generation Tput: 52.9 tok/s | Batch creation: 1.9ms, Execution: 189.1ms | KVCache usage: 10.2% of 3679 blocks, Cache hit rate: 21.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 347 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 5.0ms, Execution: 523.3ms | KVCache usage: 12.6% of 3679 blocks, Cache hit rate: 21.5%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 337 reqs | Prompt Tput: 23.9K tok/s, Generation Tput: 54.3 tok/s | Batch creation: 1.8ms, Execution: 184.1ms | KVCache usage: 13.3% of 3679 blocks, Cache hit rate: 21.5%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 309 reqs | Prompt Tput: 25.7K tok/s, Generation Tput: 55.6 tok/s | Batch creation: 5.0ms, Execution: 521.7ms | KVCache usage: 15.7% of 3679 blocks, Cache hit rate: 21.7%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 299 reqs | Prompt Tput: 23.5K tok/s, Generation Tput: 55.5 tok/s | Batch creation: 1.7ms, Execution: 180.1ms | KVCache usage: 16.4% of 3679 blocks, Cache hit rate: 21.8%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 271 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.6 tok/s | Batch creation: 4.9ms, Execution: 521.8ms | KVCache usage: 18.7% of 3679 blocks, Cache hit rate: 22.0%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 261 reqs | Prompt Tput: 22.4K tok/s, Generation Tput: 50.1 tok/s | Batch creation: 1.9ms, Execution: 199.4ms | KVCache usage: 19.5% of 3679 blocks, Cache hit rate: 22.0%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 233 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.5 tok/s | Batch creation: 4.9ms, Execution: 522.8ms | KVCache usage: 21.9% of 3679 blocks, Cache hit rate: 22.2%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 223 reqs | Prompt Tput: 22.8K tok/s, Generation Tput: 54.1 tok/s | Batch creation: 1.8ms, Execution: 184.8ms | KVCache usage: 22.6% of 3679 blocks, Cache hit rate: 22.2%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 195 reqs | Prompt Tput: 25.4K tok/s, Generation Tput: 55.1 tok/s | Batch creation: 5.4ms, Execution: 526.8ms | KVCache usage: 25.0% of 3679 blocks, Cache hit rate: 22.4%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 185 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 55.7 tok/s | Batch creation: 1.9ms, Execution: 179.4ms | KVCache usage: 25.7% of 3679 blocks, Cache hit rate: 22.4%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 157 reqs | Prompt Tput: 25.6K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 5.0ms, Execution: 523.7ms | KVCache usage: 28.1% of 3679 blocks, Cache hit rate: 22.6%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 147 reqs | Prompt Tput: 22.9K tok/s, Generation Tput: 54.2 tok/s | Batch creation: 1.8ms, Execution: 184.4ms | KVCache usage: 28.8% of 3679 blocks, Cache hit rate: 22.6%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 119 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.3 tok/s | Batch creation: 5.0ms, Execution: 524.2ms | KVCache usage: 31.2% of 3679 blocks, Cache hit rate: 22.8%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 109 reqs | Prompt Tput: 23.7K tok/s, Generation Tput: 55.7 tok/s | Batch creation: 1.7ms, Execution: 179.6ms | KVCache usage: 31.9% of 3679 blocks, Cache hit rate: 22.8%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 81 reqs | Prompt Tput: 25.6K tok/s, Generation Tput: 55.3 tok/s | Batch creation: 4.9ms, Execution: 524.3ms | KVCache usage: 34.3% of 3679 blocks, Cache hit rate: 22.9%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 71 reqs | Prompt Tput: 23.7K tok/s, Generation Tput: 55.9 tok/s | Batch creation: 1.9ms, Execution: 179.0ms | KVCache usage: 35.0% of 3679 blocks, Cache hit rate: 23.0%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 43 reqs | Prompt Tput: 25.7K tok/s, Generation Tput: 55.6 tok/s | Batch creation: 152.1ms, Execution: 521.8ms | KVCache usage: 37.4% of 3679 blocks, Cache hit rate: 23.1%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 33 reqs | Prompt Tput: 22.8K tok/s, Generation Tput: 53.9 tok/s | Batch creation: 2.0ms, Execution: 185.7ms | KVCache usage: 38.1% of 3679 blocks, Cache hit rate: 23.1%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 5 reqs | Prompt Tput: 25.4K tok/s, Generation Tput: 55.0 tok/s | Batch creation: 5.2ms, Execution: 527.0ms | KVCache usage: 40.4% of 3679 blocks, Cache hit rate: 23.2%, All Preemptions: 0 reqs Executed CE batch with 5 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 22.7K tok/s, Generation Tput: 57.7 tok/s | Batch creation: 1.1ms, Execution: 86.6ms | KVCache usage: 40.8% of 3679 blocks, Cache hit rate: 23.2%, All Preemptions: 0 reqs Executed TG batch with 500 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 952.5 tok/s, Generation Tput: 9.5K tok/s | Batch creation: 10.1ms, Execution: 524.9ms | KVCache usage: 40.8% of 3679 blocks, Cache hit rate: 21.7%, All Preemptions: 0 reqs Executed TG batch with 500 reqs | Terminated: 500 reqs, Pending: 0 reqs | Prompt Tput: 950.1 tok/s, Generation Tput: 9.5K tok/s | Batch creation: 10.0ms, Execution: 526.2ms | KVCache usage: 0.0% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs ``` Completes E2EOPT-62 Completes E2EOPT-67 Towards E2EOPT-46 MODULAR_ORIG_COMMIT_REV_ID: 25ff131ef8dc9e2ee93d5c4c1b65e91995a06695 --- src/max/pipelines/kv_cache/paged_cache.py | 51 +++++----------------- src/max/pipelines/kv_cache/prefix_cache.py | 13 ------ 2 files changed, 12 insertions(+), 52 deletions(-) diff --git a/src/max/pipelines/kv_cache/paged_cache.py b/src/max/pipelines/kv_cache/paged_cache.py index 11ce2356cb..8d3ad3663f 100644 --- a/src/max/pipelines/kv_cache/paged_cache.py +++ b/src/max/pipelines/kv_cache/paged_cache.py @@ -418,64 +418,37 @@ def can_fetch( ) -> bool: """Checks if there are sufficient KV pages to run `fetch` on given batch. - It is OK if some seq_id are not in the cache. We assume the cache lengths - are zero in those cases. + Sequences which have not been previously added to the cache can be handled + by this method. We assume the cache lengths are zero in those cases. """ - total_blocks_to_allocate = 0 + tot_new_pages_needed = 0 all_cache_hit_blocks: set[int] = set() for seq_id, prompt in seq_ids_and_prompts.items(): - data = self.active_requests.get( - seq_id, PagedCacheMetadata(self.page_size, self.max_seq_len) + prefix_blocks, _, new_pages_needed = self.query_fetch_stats( + seq_id, prompt, num_steps ) + tot_new_pages_needed += new_pages_needed + all_cache_hit_blocks.update(prefix_blocks) - # Extend the kv cache for given request with any cached prefixes. - cached_blocks: list[int] = [] - if self.prefix_cache is not None: - cached_blocks = self.prefix_cache.get_cached_blocks( - seq_id, prompt - ) - - # Compute the total sequence length and the number of pages required to store it. - total_sequence_length = ( - data.cached_idx + len(prompt) + num_steps - 1 - ) - num_pages_required = ceildiv(total_sequence_length, self.page_size) - - # Compute the number of *new* pages we need to allocate. - blocks_to_allocate = ( - num_pages_required - len(data.blocks) - len(cached_blocks) - ) - - total_blocks_to_allocate += blocks_to_allocate - all_cache_hit_blocks.update(cached_blocks) - - num_evictable_blocks = 0 + num_stale_blocks = 0 if self.prefix_cache is not None: # the blocks in the prefix cache that will be used by sequences in # this batch are no longer eligible for eviction / allocation. - num_evictable_blocks = len( + num_stale_blocks = len( self.prefix_cache.stale_blocks - all_cache_hit_blocks ) - num_free_blocks = len(self.available_blocks) + num_evictable_blocks - - return total_blocks_to_allocate <= num_free_blocks + num_free_blocks = len(self.available_blocks) + num_stale_blocks - def get_num_cached_tokens(self, prompt: np.ndarray) -> int: - """Returns the number of tokens in the CE prompt that are found in the - prefix cache. - """ - if self.prefix_cache is None: - return 0 - return self.prefix_cache.get_num_cached_tokens(prompt) + return tot_new_pages_needed <= num_free_blocks def query_fetch_stats( self, seq_id: int, prompt: np.ndarray, num_steps: int = 1 ) -> tuple[set[int], int, int]: """Query about the stats about running the fetch operation for a given - sequence. + sequence. It is OK if some seq_id are not in the cache. This method does not modify the state of the paged cache. diff --git a/src/max/pipelines/kv_cache/prefix_cache.py b/src/max/pipelines/kv_cache/prefix_cache.py index 751abbd6f5..07c2de5092 100644 --- a/src/max/pipelines/kv_cache/prefix_cache.py +++ b/src/max/pipelines/kv_cache/prefix_cache.py @@ -155,19 +155,6 @@ def validate_req_state_valid( assert (tokens == committed_tokens).all() assert blocks == committed_blocks - def get_cached_blocks(self, seq_id: int, prompt: np.ndarray) -> list[int]: - """Returns the blocks from the prefix cache that can be reused for the given prompt.""" - node = self.active_requests.get(seq_id, self.radix_trie.root) - # Attempt to match all but the last token in the prompt. This is - # because the model expects a prompt of length at least 1. - _, cached_blocks = self.radix_trie.match_prefix(prompt[:-1], node=node) - return cached_blocks - - def get_num_cached_tokens(self, prompt: np.ndarray) -> int: - """Returns the number of tokens in the CE prompt that are found in the prefix cache.""" - _, prefix_blocks = self.radix_trie.match_prefix(prompt[:-1]) - return len(prefix_blocks) * self.page_size - def evict_blocks(self, blocks_to_evict: Optional[int] = None) -> list[int]: """Evict a percentage of all blocks according to a LRU policy on the trie leaves.""" if blocks_to_evict is None: From acf5fa66a9fcf1a48170e9624f0e3288aaaaca60 Mon Sep 17 00:00:00 2001 From: Brian Zhang Date: Thu, 27 Feb 2025 15:21:48 -0800 Subject: [PATCH 499/509] [KVCache] More efficient impl of `_token_prefix_match` (#56706) The new impl of `_token_prefix_match` is faster: ``` For inputs: tokens0 = np.random.random((512,)) tokens1 = np.random.random((512,)) tokens0[:300] = tokens1[:300] Timing: old: ~20us new: ~10us ``` This gets called O(10) times per request when making a CE batch. For a CE batch with 50 requests, this can save 100-1000us for batch creation. This leads to a small (<1 RPS) boost in prefix caching benchmark. Also wow, literally trivial python operations (eg: numpy slicing or dict lookup) can take a hundred of ns to a few us. This is crazy. Server: ``` br //SDK/lib/API/python/max/entrypoints:pipelines --test_output=streamed --test_arg=-svv -- serve --model-path=meta-llama/Meta-Llama-3.1-8B-Instruct --quantization-encoding bfloat16 --use-gpu --cache-strategy paged --kv-cache-page-size 128 --max-batch-size 512 --max-num-steps 10 --max-length 2048 --pad-to-multiple-of 2 --enable-prefix-caching ``` Client: ``` python3 $(realpath open-source/max/benchmark/benchmark_serving.py) --backend modular --model meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-name sonnet --dataset-path $(realpath utils/benchmarking/serving/sonnet_4x.txt) --endpoint /v1/completions --num-prompts 500 --sonnet-input-len 512 --sonnet-output-len 16 --sonnet-prefix 192 --request-rate inf --collect-gpu-stats --print-inputs-and-outputs ``` After warmup with diff seed: ``` ... {'req_id': 498, 'output_len': 16, 'output': 'Here are the lines from the poem:\n\n1. FROM fairest creatures we desire'} {'req_id': 499, 'output_len': 16, 'output': 'Here are the lines from the poem:\n\n1. FROM fairest creatures we desire'} ============ Serving Benchmark Result ============ Successful requests: 500 Failed requests: 0 Benchmark duration (s): 12.05 Total input tokens: 232488 Total generated tokens: 8000 Request throughput (req/s): 41.50 Input token throughput (tok/s): 19294.81 Output token throughput (tok/s): 663.94 ---------------Time to First Token---------------- Mean TTFT (ms): 5749.38 Median TTFT (ms): 6021.95 P99 TTFT (ms): 10313.60 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 408.48 Median TPOT (ms): 390.12 P99 TPOT (ms): 725.80 ---------------Inter-token Latency---------------- Mean ITL (ms): 407.90 Median ITL (ms): 84.05 P99 ITL (ms): 8566.73 -------------------Token Stats-------------------- Max input tokens: 494 Max output tokens: 16 Max total tokens: 510 --------------------GPU Stats--------------------- GPU Utilization (%): 79.73 Peak GPU Memory Used (MiB): 76714.56 GPU Memory Available (MiB): 4441.19 ================================================== 00:28:48.863 INFO: benchmark_serving: finished benchmark run: Success. ``` Logs: ``` Executed CE batch with 1 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 14.5K tok/s, Generation Tput: 30.0 tok/s | Batch creation: 447.1us, Execution: 33.3ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed TG batch with 1 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 9.8 tok/s, Generation Tput: 97.6 tok/s | Batch creation: 83.0us, Execution: 102.4ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed TG batch with 1 reqs | Terminated: 1 reqs, Pending: 0 reqs | Prompt Tput: 9.8 tok/s, Generation Tput: 97.6 tok/s | Batch creation: 85.5us, Execution: 102.5ms | KVCache usage: 0.0% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed CE batch with 1 reqs | Terminated: 0 reqs, Pending: 7 reqs | Prompt Tput: 29.1K tok/s, Generation Tput: 60.2 tok/s | Batch creation: 388.0us, Execution: 16.6ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.2%, All Preemptions: 0 reqs Executed CE batch with 1 reqs | Terminated: 0 reqs, Pending: 35 reqs | Prompt Tput: 15.2K tok/s, Generation Tput: 33.1 tok/s | Batch creation: 314.0us, Execution: 30.2ms | KVCache usage: 0.2% of 3679 blocks, Cache hit rate: 20.2%, All Preemptions: 0 reqs Executed CE batch with 9 reqs | Terminated: 0 reqs, Pending: 489 reqs | Prompt Tput: 23.0K tok/s, Generation Tput: 49.7 tok/s | Batch creation: 1.7ms, Execution: 181.0ms | KVCache usage: 0.9% of 3679 blocks, Cache hit rate: 20.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 461 reqs | Prompt Tput: 25.6K tok/s, Generation Tput: 55.7 tok/s | Batch creation: 4.9ms, Execution: 520.5ms | KVCache usage: 3.3% of 3679 blocks, Cache hit rate: 20.6%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 451 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 54.3 tok/s | Batch creation: 1.8ms, Execution: 184.0ms | KVCache usage: 4.0% of 3679 blocks, Cache hit rate: 20.6%, All Preemptions: 0 reqs Executed CE batch with 9 reqs | Terminated: 0 reqs, Pending: 442 reqs | Prompt Tput: 22.8K tok/s, Generation Tput: 49.1 tok/s | Batch creation: 1.7ms, Execution: 183.1ms | KVCache usage: 4.8% of 3679 blocks, Cache hit rate: 20.7%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 414 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.7 tok/s | Batch creation: 4.8ms, Execution: 520.2ms | KVCache usage: 7.1% of 3679 blocks, Cache hit rate: 21.0%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 404 reqs | Prompt Tput: 22.6K tok/s, Generation Tput: 50.5 tok/s | Batch creation: 1.8ms, Execution: 197.9ms | KVCache usage: 7.9% of 3679 blocks, Cache hit rate: 21.0%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 376 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 4.9ms, Execution: 523.3ms | KVCache usage: 10.2% of 3679 blocks, Cache hit rate: 21.3%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 366 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 54.5 tok/s | Batch creation: 1.7ms, Execution: 183.4ms | KVCache usage: 11.0% of 3679 blocks, Cache hit rate: 21.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 338 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 4.9ms, Execution: 523.8ms | KVCache usage: 13.3% of 3679 blocks, Cache hit rate: 21.6%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 328 reqs | Prompt Tput: 22.4K tok/s, Generation Tput: 52.0 tok/s | Batch creation: 1.7ms, Execution: 192.4ms | KVCache usage: 14.1% of 3679 blocks, Cache hit rate: 21.6%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 300 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.5 tok/s | Batch creation: 4.8ms, Execution: 522.3ms | KVCache usage: 16.4% of 3679 blocks, Cache hit rate: 21.8%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 290 reqs | Prompt Tput: 22.7K tok/s, Generation Tput: 52.5 tok/s | Batch creation: 1.7ms, Execution: 190.4ms | KVCache usage: 17.2% of 3679 blocks, Cache hit rate: 21.9%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 262 reqs | Prompt Tput: 25.4K tok/s, Generation Tput: 55.1 tok/s | Batch creation: 4.8ms, Execution: 526.2ms | KVCache usage: 19.5% of 3679 blocks, Cache hit rate: 22.0%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 252 reqs | Prompt Tput: 22.3K tok/s, Generation Tput: 50.5 tok/s | Batch creation: 1.8ms, Execution: 198.0ms | KVCache usage: 20.3% of 3679 blocks, Cache hit rate: 22.1%, All Preemptions: 0 reqs Executed CE batch with 30 reqs | Terminated: 1 reqs, Pending: 223 reqs | Prompt Tput: 25.9K tok/s, Generation Tput: 57.2 tok/s | Batch creation: 5.1ms, Execution: 524.1ms | KVCache usage: 22.6% of 3679 blocks, Cache hit rate: 22.3%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 213 reqs | Prompt Tput: 23.4K tok/s, Generation Tput: 52.2 tok/s | Batch creation: 1.8ms, Execution: 191.5ms | KVCache usage: 23.4% of 3679 blocks, Cache hit rate: 22.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 185 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.2 tok/s | Batch creation: 4.8ms, Execution: 525.1ms | KVCache usage: 25.8% of 3679 blocks, Cache hit rate: 22.5%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 175 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 56.0 tok/s | Batch creation: 1.7ms, Execution: 178.5ms | KVCache usage: 26.5% of 3679 blocks, Cache hit rate: 22.5%, All Preemptions: 0 reqs Executed CE batch with 30 reqs | Terminated: 1 reqs, Pending: 146 reqs | Prompt Tput: 25.8K tok/s, Generation Tput: 57.1 tok/s | Batch creation: 4.9ms, Execution: 525.7ms | KVCache usage: 28.9% of 3679 blocks, Cache hit rate: 22.7%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 136 reqs | Prompt Tput: 22.5K tok/s, Generation Tput: 50.4 tok/s | Batch creation: 1.8ms, Execution: 198.3ms | KVCache usage: 29.7% of 3679 blocks, Cache hit rate: 22.7%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 108 reqs | Prompt Tput: 25.6K tok/s, Generation Tput: 55.3 tok/s | Batch creation: 4.8ms, Execution: 524.4ms | KVCache usage: 32.1% of 3679 blocks, Cache hit rate: 22.8%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 98 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 55.7 tok/s | Batch creation: 1.7ms, Execution: 179.5ms | KVCache usage: 32.8% of 3679 blocks, Cache hit rate: 22.9%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 70 reqs | Prompt Tput: 20.8K tok/s, Generation Tput: 45.2 tok/s | Batch creation: 4.7ms, Execution: 642.0ms | KVCache usage: 35.2% of 3679 blocks, Cache hit rate: 23.0%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 60 reqs | Prompt Tput: 23.0K tok/s, Generation Tput: 54.5 tok/s | Batch creation: 1.9ms, Execution: 183.3ms | KVCache usage: 35.9% of 3679 blocks, Cache hit rate: 23.0%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 32 reqs | Prompt Tput: 25.7K tok/s, Generation Tput: 55.5 tok/s | Batch creation: 5.0ms, Execution: 522.6ms | KVCache usage: 38.2% of 3679 blocks, Cache hit rate: 23.1%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 22 reqs | Prompt Tput: 23.7K tok/s, Generation Tput: 55.6 tok/s | Batch creation: 1.8ms, Execution: 180.0ms | KVCache usage: 39.0% of 3679 blocks, Cache hit rate: 23.2%, All Preemptions: 0 reqs Executed CE batch with 22 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 23.7K tok/s, Generation Tput: 50.7 tok/s | Batch creation: 3.9ms, Execution: 434.0ms | KVCache usage: 40.8% of 3679 blocks, Cache hit rate: 23.2%, All Preemptions: 0 reqs Executed TG batch with 500 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 949.0 tok/s, Generation Tput: 9.5K tok/s | Batch creation: 10.7ms, Execution: 526.9ms | KVCache usage: 40.8% of 3679 blocks, Cache hit rate: 21.7%, All Preemptions: 0 reqs Executed TG batch with 500 reqs | Terminated: 500 reqs, Pending: 0 reqs | Prompt Tput: 952.2 tok/s, Generation Tput: 9.5K tok/s | Batch creation: 9.6ms, Execution: 525.1ms | KVCache usage: 0.0% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs ``` Towards: E2EOPT-46 MODULAR_ORIG_COMMIT_REV_ID: 8424cff31060b25dc9e62a981762fb4d632290c5 --- src/max/pipelines/kv_cache/radix_trie.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/src/max/pipelines/kv_cache/radix_trie.py b/src/max/pipelines/kv_cache/radix_trie.py index fe848cbe9e..ac1be1b26b 100644 --- a/src/max/pipelines/kv_cache/radix_trie.py +++ b/src/max/pipelines/kv_cache/radix_trie.py @@ -25,6 +25,10 @@ from collections import OrderedDict +def align_down(x: int, y: int) -> int: + return (x // y) * y + + def _token_prefix_match_len( tokens0: np.ndarray, tokens1: np.ndarray, page_size: int ) -> int: @@ -38,10 +42,11 @@ def _token_prefix_match_len( assert len(tokens0) % page_size == 0 assert len(tokens1) % page_size == 0 shorter_len = min(len(tokens0), len(tokens1)) - for i in range(0, shorter_len, page_size): - if (tokens0[i : i + page_size] != tokens1[i : i + page_size]).any(): - return i - return shorter_len + diff = tokens0[:shorter_len] != tokens1[:shorter_len] + idx = np.nonzero(diff)[0] + if len(idx) == 0: + return shorter_len + return align_down(idx[0], page_size) def _token_to_key(tokens: np.ndarray, page_size: int) -> tuple[TokenId, ...]: @@ -106,7 +111,6 @@ def find_block_with_largest_common_prefix( if prefix_len == 0: return None assert prefix_len <= len(target) - assert target[:prefix_len] == key[:prefix_len] return self.children[tuple(key)].blocks[0], prefix_len def get_prefix_tokens_and_blocks(self) -> Tuple[np.ndarray, List[BlockId]]: @@ -316,7 +320,7 @@ def match_prefix_helper( if len(tokens) == 0: return node, [] - tokens = tokens[: len(tokens) // self.page_size * self.page_size] + tokens = tokens[: align_down(len(tokens), self.page_size)] curr = match_prefix_helper(node, tokens, blocks) return curr, blocks From b9433d746091848368bea5437dafc7b2b0797110 Mon Sep 17 00:00:00 2001 From: Ivan Posva Date: Thu, 27 Feb 2025 15:24:52 -0800 Subject: [PATCH 500/509] [SDK] Rename Mojo Device creation APIs to follow Python convention Update Mojo `Device` creation to follow Python MAX Driver APIs by dropping the `_device` suffix from `accelerator_device()` and `cpu_device()`. MODULAR_ORIG_COMMIT_REV_ID: 7d55784535e97994ac02e6a9a0eaadc37525870d --- examples/custom_ops/benchmarks.mojo | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/custom_ops/benchmarks.mojo b/examples/custom_ops/benchmarks.mojo index f02bb8b731..b2f7962c05 100644 --- a/examples/custom_ops/benchmarks.mojo +++ b/examples/custom_ops/benchmarks.mojo @@ -15,7 +15,7 @@ from kernels.top_k import TopK from kernels.matrix_multiplication import MatrixMultiplication from gpu.host import DeviceContext from utils import IndexList -from max.driver.device import cpu_device +from max.driver import cpu from max.tensor import ( ManagedTensorSlice, InputTensor, @@ -67,7 +67,7 @@ def top_k(): rand(in_vals, els) - var cpu_ctx_ptr = cpu_device().unsafe_ptr() + var cpu_ctx_ptr = cpu().unsafe_ptr() @parameter @always_inline @@ -189,7 +189,7 @@ def matmul(): rand(a_ptr, a_els) rand(b_ptr, b_els) - var cpu_ctx_ptr = cpu_device().unsafe_ptr() + var cpu_ctx_ptr = cpu().unsafe_ptr() var bench = Bench() var flops = ThroughputMeasure(BenchMetric.flops, FLOPS) var elements = ThroughputMeasure(BenchMetric.elements, M * N) From 2c6a976e6a9ba1a012a23da1c97f157b24146c0b Mon Sep 17 00:00:00 2001 From: Brian Zhang Date: Thu, 27 Feb 2025 15:56:14 -0800 Subject: [PATCH 501/509] [KVCache] Reduce python memory allocations by reusing PagedCacheMetadata (#56707) Stacked PRs: * #56844 * __->__#56707 --- --- --- ### [KVCache] Reduce python memory allocations by reusing PagedCacheMetadata Server: ``` br //SDK/lib/API/python/max/entrypoints:pipelines --test_output=streamed --test_arg=-svv -- serve --model-path=meta-llama/Meta-Llama-3.1-8B-Instruct --quantization-encoding bfloat16 --use-gpu --cache-strategy paged --kv-cache-page-size 128 --max-batch-size 512 --max-num-steps 10 --max-length 2048 --pad-to-multiple-of 2 --enable-prefix-caching ``` Client: ``` python3 $(realpath open-source/max/benchmark/benchmark_serving.py) --backend modular --model meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-name sonnet --dataset-path $(realpath utils/benchmarking/serving/sonnet_4x.txt) --endpoint /v1/completions --num-prompts 500 --sonnet-input-len 512 --sonnet-output-len 16 --sonnet-prefix 192 --request-rate inf --collect-gpu-stats --print-inputs-and-outputs ``` After warmup with diff seed: ``` ============ Serving Benchmark Result ============ Successful requests: 500 Failed requests: 0 Benchmark duration (s): 12.08 Total input tokens: 232488 Total generated tokens: 8000 Request throughput (req/s): 41.38 Input token throughput (tok/s): 19241.35 Output token throughput (tok/s): 662.10 ---------------Time to First Token---------------- Mean TTFT (ms): 5743.03 Median TTFT (ms): 5965.71 P99 TTFT (ms): 10296.76 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 410.71 Median TPOT (ms): 395.74 P99 TPOT (ms): 718.80 ---------------Inter-token Latency---------------- Mean ITL (ms): 410.06 Median ITL (ms): 78.55 P99 ITL (ms): 8588.68 -------------------Token Stats-------------------- Max input tokens: 494 Max output tokens: 16 Max total tokens: 510 --------------------GPU Stats--------------------- GPU Utilization (%): 79.88 Peak GPU Memory Used (MiB): 76682.56 GPU Memory Available (MiB): 4473.19 ================================================== 00:24:04.695 INFO: benchmark_serving: finished benchmark run: Success. ``` Logs: ``` Executed CE batch with 1 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 14.4K tok/s, Generation Tput: 29.7 tok/s | Batch creation: 532.3us, Execution: 33.6ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed TG batch with 1 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 9.8 tok/s, Generation Tput: 97.5 tok/s | Batch creation: 70.3us, Execution: 102.5ms | KVCache usage: 0.1% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed TG batch with 1 reqs | Terminated: 1 reqs, Pending: 0 reqs | Prompt Tput: 9.7 tok/s, Generation Tput: 97.5 tok/s | Batch creation: 75.2us, Execution: 102.6ms | KVCache usage: 0.0% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs Executed CE batch with 3 reqs | Terminated: 0 reqs, Pending: 62 reqs | Prompt Tput: 26.9K tok/s, Generation Tput: 57.6 tok/s | Batch creation: 738.5us, Execution: 52.1ms | KVCache usage: 0.3% of 3679 blocks, Cache hit rate: 20.2%, All Preemptions: 0 reqs Executed CE batch with 14 reqs | Terminated: 0 reqs, Pending: 483 reqs | Prompt Tput: 23.6K tok/s, Generation Tput: 50.9 tok/s | Batch creation: 2.3ms, Execution: 274.8ms | KVCache usage: 1.4% of 3679 blocks, Cache hit rate: 20.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 455 reqs | Prompt Tput: 25.6K tok/s, Generation Tput: 55.6 tok/s | Batch creation: 4.5ms, Execution: 521.2ms | KVCache usage: 3.8% of 3679 blocks, Cache hit rate: 20.6%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 445 reqs | Prompt Tput: 22.4K tok/s, Generation Tput: 51.5 tok/s | Batch creation: 1.6ms, Execution: 194.2ms | KVCache usage: 4.5% of 3679 blocks, Cache hit rate: 20.7%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 417 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.6 tok/s | Batch creation: 4.4ms, Execution: 521.6ms | KVCache usage: 6.8% of 3679 blocks, Cache hit rate: 21.0%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 407 reqs | Prompt Tput: 22.4K tok/s, Generation Tput: 50.2 tok/s | Batch creation: 1.7ms, Execution: 199.0ms | KVCache usage: 7.6% of 3679 blocks, Cache hit rate: 21.0%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 379 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.3 tok/s | Batch creation: 4.4ms, Execution: 524.6ms | KVCache usage: 9.9% of 3679 blocks, Cache hit rate: 21.3%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 369 reqs | Prompt Tput: 22.7K tok/s, Generation Tput: 52.4 tok/s | Batch creation: 1.5ms, Execution: 190.9ms | KVCache usage: 10.7% of 3679 blocks, Cache hit rate: 21.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 341 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.3 tok/s | Batch creation: 4.4ms, Execution: 524.2ms | KVCache usage: 13.0% of 3679 blocks, Cache hit rate: 21.5%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 331 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 54.3 tok/s | Batch creation: 1.6ms, Execution: 184.1ms | KVCache usage: 13.8% of 3679 blocks, Cache hit rate: 21.6%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 303 reqs | Prompt Tput: 25.6K tok/s, Generation Tput: 55.5 tok/s | Batch creation: 4.5ms, Execution: 522.8ms | KVCache usage: 16.1% of 3679 blocks, Cache hit rate: 21.8%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 293 reqs | Prompt Tput: 23.9K tok/s, Generation Tput: 55.9 tok/s | Batch creation: 1.5ms, Execution: 178.9ms | KVCache usage: 16.9% of 3679 blocks, Cache hit rate: 21.8%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 265 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 4.4ms, Execution: 523.1ms | KVCache usage: 19.2% of 3679 blocks, Cache hit rate: 22.0%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 255 reqs | Prompt Tput: 22.3K tok/s, Generation Tput: 50.6 tok/s | Batch creation: 1.5ms, Execution: 197.6ms | KVCache usage: 20.0% of 3679 blocks, Cache hit rate: 22.1%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 227 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 4.5ms, Execution: 523.7ms | KVCache usage: 22.4% of 3679 blocks, Cache hit rate: 22.2%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 217 reqs | Prompt Tput: 22.5K tok/s, Generation Tput: 54.0 tok/s | Batch creation: 1.6ms, Execution: 185.1ms | KVCache usage: 23.1% of 3679 blocks, Cache hit rate: 22.3%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 189 reqs | Prompt Tput: 25.7K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 4.3ms, Execution: 523.3ms | KVCache usage: 25.4% of 3679 blocks, Cache hit rate: 22.4%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 179 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 55.5 tok/s | Batch creation: 1.6ms, Execution: 180.1ms | KVCache usage: 26.2% of 3679 blocks, Cache hit rate: 22.5%, All Preemptions: 0 reqs Executed CE batch with 9 reqs | Terminated: 0 reqs, Pending: 170 reqs | Prompt Tput: 24.8K tok/s, Generation Tput: 53.9 tok/s | Batch creation: 1.6ms, Execution: 166.9ms | KVCache usage: 26.9% of 3679 blocks, Cache hit rate: 22.5%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 142 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.2 tok/s | Batch creation: 4.5ms, Execution: 525.2ms | KVCache usage: 29.3% of 3679 blocks, Cache hit rate: 22.7%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 132 reqs | Prompt Tput: 23.8K tok/s, Generation Tput: 55.8 tok/s | Batch creation: 1.5ms, Execution: 179.1ms | KVCache usage: 30.0% of 3679 blocks, Cache hit rate: 22.7%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 104 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.4 tok/s | Batch creation: 4.4ms, Execution: 523.9ms | KVCache usage: 32.4% of 3679 blocks, Cache hit rate: 22.8%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 94 reqs | Prompt Tput: 22.8K tok/s, Generation Tput: 52.5 tok/s | Batch creation: 1.6ms, Execution: 190.5ms | KVCache usage: 33.1% of 3679 blocks, Cache hit rate: 22.9%, All Preemptions: 0 reqs Executed CE batch with 9 reqs | Terminated: 0 reqs, Pending: 85 reqs | Prompt Tput: 25.0K tok/s, Generation Tput: 53.9 tok/s | Batch creation: 1.5ms, Execution: 166.9ms | KVCache usage: 33.9% of 3679 blocks, Cache hit rate: 22.9%, All Preemptions: 0 reqs Executed CE batch with 9 reqs | Terminated: 0 reqs, Pending: 76 reqs | Prompt Tput: 25.0K tok/s, Generation Tput: 54.1 tok/s | Batch creation: 1.5ms, Execution: 166.5ms | KVCache usage: 34.6% of 3679 blocks, Cache hit rate: 22.9%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 48 reqs | Prompt Tput: 25.5K tok/s, Generation Tput: 55.2 tok/s | Batch creation: 4.5ms, Execution: 524.9ms | KVCache usage: 36.9% of 3679 blocks, Cache hit rate: 23.1%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 38 reqs | Prompt Tput: 23.7K tok/s, Generation Tput: 55.6 tok/s | Batch creation: 1.7ms, Execution: 179.9ms | KVCache usage: 37.7% of 3679 blocks, Cache hit rate: 23.1%, All Preemptions: 0 reqs Executed CE batch with 29 reqs | Terminated: 1 reqs, Pending: 10 reqs | Prompt Tput: 20.8K tok/s, Generation Tput: 45.1 tok/s | Batch creation: 4.5ms, Execution: 642.9ms | KVCache usage: 40.0% of 3679 blocks, Cache hit rate: 23.2%, All Preemptions: 0 reqs Executed CE batch with 10 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 24.1K tok/s, Generation Tput: 56.5 tok/s | Batch creation: 1.8ms, Execution: 177.0ms | KVCache usage: 40.8% of 3679 blocks, Cache hit rate: 23.2%, All Preemptions: 0 reqs Executed TG batch with 500 reqs | Terminated: 0 reqs, Pending: 0 reqs | Prompt Tput: 948.4 tok/s, Generation Tput: 9.5K tok/s | Batch creation: 7.4ms, Execution: 527.2ms | KVCache usage: 40.8% of 3679 blocks, Cache hit rate: 21.7%, All Preemptions: 0 reqs Executed TG batch with 500 reqs | Terminated: 500 reqs, Pending: 0 reqs | Prompt Tput: 948.6 tok/s, Generation Tput: 9.5K tok/s | Batch creation: 7.8ms, Execution: 527.1ms | KVCache usage: 0.0% of 3679 blocks, Cache hit rate: 20.1%, All Preemptions: 0 reqs ``` MODULAR_ORIG_COMMIT_REV_ID: a065e25c9c956299d6451a012665a1267b9dd08c --- src/max/pipelines/kv_cache/paged_cache.py | 17 ++++++++++++++--- .../pipelines/kv_cache/paged_cache_metadata.py | 12 +++++++++--- 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/src/max/pipelines/kv_cache/paged_cache.py b/src/max/pipelines/kv_cache/paged_cache.py index 8d3ad3663f..6e8ba7d7ee 100644 --- a/src/max/pipelines/kv_cache/paged_cache.py +++ b/src/max/pipelines/kv_cache/paged_cache.py @@ -260,6 +260,10 @@ def __init__( tensors=self.blocks, ) + # Preallocate a PagedCacheMetadata to use for sequences not in the cache. + # This is to reduce the number of allocations. This is NOT thread safe. + self.tmp_data = PagedCacheMetadata(self.page_size, self.max_seq_len) + # Whether to enable runtime correctness checks. These correctness checks # are expensive and should only be used in tests. self.enable_runtime_checks = enable_runtime_checks @@ -457,9 +461,13 @@ def query_fetch_stats( - tokens_to_encode: Number of tokens in prompt we need to encode when running the fetch. - new_pages_needed: Number of new pages we need to allocate when running the fetch. """ - data = self.active_requests.get( - seq_id, PagedCacheMetadata(self.page_size, self.max_seq_len) - ) + reusing_tmp_data = False + if seq_id in self.active_requests: + data = self.active_requests[seq_id] + else: + reusing_tmp_data = True + data = self.tmp_data + data.fetch(prompt, num_steps) prefix_blocks: set[int] = set() cache_hit_tokens = 0 @@ -483,6 +491,9 @@ def query_fetch_stats( # reverse the fetch operation so that this method does not mutate state data.undo_fetch(prompt, num_steps) + if reusing_tmp_data: + self.tmp_data.clear() + return prefix_blocks, tokens_to_encode, new_pages_needed def _fetch( diff --git a/src/max/pipelines/kv_cache/paged_cache_metadata.py b/src/max/pipelines/kv_cache/paged_cache_metadata.py index 0e1d8f6e62..28accfe316 100644 --- a/src/max/pipelines/kv_cache/paged_cache_metadata.py +++ b/src/max/pipelines/kv_cache/paged_cache_metadata.py @@ -36,7 +36,7 @@ class PagedCacheMetadata: - Committable tokens are tokens that are not yet committed but have a known value (i.e. not inflight). We can query the prefix cache for such tokens. - Inflight tokens are slots allocated for tokens that have not been generated - yet. Such tokens have a filler value of 0. After `fetch`, there should be + yet. Such tokens have a undefined values. After `fetch`, there should be `num_steps - 1` inflight tokens. They will be replaced with actual tokens in `step`. @@ -60,7 +60,7 @@ def __init__(self, page_size: int, max_seq_len: int) -> None: self.inflight_idx: int = 0 self.seq_len: int = 0 self.blocks: list[int] = [] - self.tokens: np.ndarray = np.full((max_seq_len,), 0, dtype=np.int64) + self.tokens: np.ndarray = np.empty((max_seq_len,), dtype=np.int64) @property def committed_blocks(self) -> list[int]: @@ -177,12 +177,18 @@ def undo_fetch(self, prompt: np.ndarray, num_steps: int) -> None: assert len(self.prompt_tokens) == len(prompt) num_inflight_tokens = num_steps - 1 assert len(self.inflight_tokens) == num_inflight_tokens - self.tokens[self.cached_idx : self.inflight_idx] = 0 self.seq_len -= len(prompt) + num_inflight_tokens self.inflight_idx -= len(prompt) assert len(self.inflight_tokens) == 0 assert len(self.prompt_tokens) == 0 self._validate_indices() + def clear(self) -> None: + assert len(self.blocks) == 0 + self.committed_idx = 0 + self.cached_idx = 0 + self.inflight_idx = 0 + self.seq_len = 0 + def __repr__(self) -> str: return f"PagedCacheMetadata(committed_idx={self.committed_idx}, cached_idx={self.cached_idx}, inflight_idx={self.inflight_idx}, seq_len={self.seq_len}, blocks={self.blocks})" From d8b8beb3bfcc1e6f9883979bd285b2c15e70ec47 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Thu, 27 Feb 2025 16:13:07 -0800 Subject: [PATCH 502/509] Add README links to the corresponding tutorials MODULAR_ORIG_COMMIT_REV_ID: 52fe2e414e3612a4b5e42e25b292effc735ff99c --- tutorials/hello-magic/README.md | 4 ++++ tutorials/helm/max-openai-api/README.md | 4 ++++ tutorials/max-graph-python/README.md | 6 +++++- tutorials/max-serve-cloud-configs/README.md | 4 ++++ tutorials/run-embeddings-with-max-serve/README.md | 4 ++++ 5 files changed, 21 insertions(+), 1 deletion(-) create mode 100644 tutorials/hello-magic/README.md create mode 100644 tutorials/max-serve-cloud-configs/README.md diff --git a/tutorials/hello-magic/README.md b/tutorials/hello-magic/README.md new file mode 100644 index 0000000000..57d5b941e8 --- /dev/null +++ b/tutorials/hello-magic/README.md @@ -0,0 +1,4 @@ +# Hello Magic + +This code corresponds to the tutorial, +[A step-by-step guide to Magic](https://docs.modular.com/max/tutorials/magic). diff --git a/tutorials/helm/max-openai-api/README.md b/tutorials/helm/max-openai-api/README.md index 5af9a9380c..c67353afcc 100644 --- a/tutorials/helm/max-openai-api/README.md +++ b/tutorials/helm/max-openai-api/README.md @@ -7,6 +7,10 @@ NOTE: This file is generated by helm-docs: https://github.com/norwoodj/helm-docs The MAX platform unifies the leading AI development frameworks (TensorFlow, PyTorch, ONNX) and hardware backends in order to simplify deployment for AI production teams and accelerate innovation for AI developers. +For more information about using this Helm chart, see the tutorial to [Deploy +Llama 3 on GPU-powered Kubernetes +clusters](https://docs.modular.com/max/tutorials/deploy-max-serve-on-kubernetes/) + **Homepage:** ## Source Code diff --git a/tutorials/max-graph-python/README.md b/tutorials/max-graph-python/README.md index 4972b568c6..c1556efdad 100644 --- a/tutorials/max-graph-python/README.md +++ b/tutorials/max-graph-python/README.md @@ -3,6 +3,10 @@ This is an example of building a model with the MAX Graph API in Python and execute it with MAX Engine. +For more explanation about this code, see the tutorial to [Get started with MAX +Graph in +Python](https://docs.modular.com/max/tutorials/get-started-with-max-graph-in-python). + ## Usage First, install [Magic](https://docs.modular.com/magic/). @@ -37,7 +41,7 @@ You should see the following output: platform darwin -- Python 3.12.7, pytest-8.3.3, pluggy-1.5.0 rootdir: /max-repo configfile: pyproject.toml -collected 6 items +collected 6 items tests/test_addition.py ...... [100%] diff --git a/tutorials/max-serve-cloud-configs/README.md b/tutorials/max-serve-cloud-configs/README.md new file mode 100644 index 0000000000..698896c488 --- /dev/null +++ b/tutorials/max-serve-cloud-configs/README.md @@ -0,0 +1,4 @@ +# MAX Serve cloud configurations + +For usage instructions, see the tutorial to [Deploy Llama 3 on GPU with MAX +Serve](https://docs.modular.com/max/tutorials/max-serve-local-to-cloud). diff --git a/tutorials/run-embeddings-with-max-serve/README.md b/tutorials/run-embeddings-with-max-serve/README.md index 4fc60548b3..2afdea295e 100644 --- a/tutorials/run-embeddings-with-max-serve/README.md +++ b/tutorials/run-embeddings-with-max-serve/README.md @@ -14,3 +14,7 @@ search through documents semantically. ```bash python -m embeddings.kb_system ``` + +For a complete walkthrough, see the tutorial to [Deploy a text embedding model +with an +endpoint](https://docs.modular.com/max/tutorials/run-embeddings-with-max-serve) From e31b0fd5774033f43de1c1e3c78fa493599b7aca Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 27 Feb 2025 16:18:11 -0800 Subject: [PATCH 503/509] [Stdlib] Add string_literal.base64.encode/decode ops and tests. This allows one to perform base64 encode and decode StringLiteral via the compiler at compile time. MODULAR_ORIG_COMMIT_REV_ID: 90429a816e301f242830654b35a048d00b66c73a --- mojo/stdlib/src/builtin/string_literal.mojo | 18 ++++++++++++++++++ .../test/builtin/test_string_literal.mojo | 11 +++++++++++ 2 files changed, 29 insertions(+) diff --git a/mojo/stdlib/src/builtin/string_literal.mojo b/mojo/stdlib/src/builtin/string_literal.mojo index da60f9515c..6365aba14d 100644 --- a/mojo/stdlib/src/builtin/string_literal.mojo +++ b/mojo/stdlib/src/builtin/string_literal.mojo @@ -955,3 +955,21 @@ struct StringLiteral( A copy of the string with no leading whitespaces. """ return String(String(self).lstrip()) + + +fn _base64_encode[str: StringLiteral]() -> StringLiteral: + """Encode the string literal using Base64 encoding. + + Returns: + A new string literal with the Base64 encoded string. + """ + return __mlir_op.`pop.string.base64.encode`(str.value) + + +fn _base64_decode[str: StringLiteral]() -> StringLiteral: + """Decode the string literal using Base64 encoding. + + Returns: + A new string literal with the Base64 decoded string. + """ + return __mlir_op.`pop.string.base64.decode`(str.value) diff --git a/mojo/stdlib/test/builtin/test_string_literal.mojo b/mojo/stdlib/test/builtin/test_string_literal.mojo index 00ef2fec3f..de10a42d19 100644 --- a/mojo/stdlib/test/builtin/test_string_literal.mojo +++ b/mojo/stdlib/test/builtin/test_string_literal.mojo @@ -22,6 +22,7 @@ from testing import ( assert_raises, assert_true, ) +from builtin.string_literal import _base64_encode, _base64_decode def test_add(): @@ -525,6 +526,15 @@ def test_string_literal_from_stringable(): ) +def test_base64_encode_decode(): + assert_equal(_base64_encode["hello"](), "aGVsbG8=") + assert_equal(_base64_decode["aGVsbG8="](), "hello") + + alias encoded = _base64_encode["I'm a mojo string"]() + alias decoded = _base64_decode[encoded]() + assert_equal(decoded, "I'm a mojo string") + + def main(): test_add() test_iadd() @@ -558,3 +568,4 @@ def main(): test_splitlines() test_float_conversion() test_string_literal_from_stringable() + test_base64_encode_decode() From 3e034d828b1ff62edc4d30cae822f61647a29263 Mon Sep 17 00:00:00 2001 From: Brad Larson Date: Thu, 27 Feb 2025 18:33:31 -0600 Subject: [PATCH 504/509] [Examples] [Driver API] Provide an easy-to-read warning when building GPU function examples for CPUs When attempting to build the GPU function examples on a system without a MAX-supported GPU, they could provide a cryptic compilation error. This firmly gates compilation behind the presence or absence of a supported GPU and gives an easy-to-read warning if one is missing. MODULAR_ORIG_COMMIT_REV_ID: c361d392ad2f446c40d1950f539dc00b402f55df --- examples/gpu_functions/grayscale.mojo | 122 ++++++++-------- examples/gpu_functions/mandelbrot.mojo | 110 ++++++++------- .../naive_matrix_multiplication.mojo | 130 ++++++++++-------- examples/gpu_functions/vector_addition.mojo | 108 ++++++++------- 4 files changed, 252 insertions(+), 218 deletions(-) diff --git a/examples/gpu_functions/grayscale.mojo b/examples/gpu_functions/grayscale.mojo index f0bcc52566..ec8657fbba 100755 --- a/examples/gpu_functions/grayscale.mojo +++ b/examples/gpu_functions/grayscale.mojo @@ -23,6 +23,7 @@ from max.driver import ( cpu_device, ) from max.driver.accelerator import compile +from sys import has_nvidia_gpu_accelerator alias channel_dtype = DType.uint8 alias internal_float_dtype = DType.float32 @@ -65,60 +66,67 @@ fn color_to_grayscale_conversion( def main(): - # Attempt to connect to a compatible GPU. If one is not found, this will - # error out and exit. - gpu_device = accelerator_device() - host_device = cpu_device() - - alias IMAGE_WIDTH = 5 - alias IMAGE_HEIGHT = 10 - alias NUM_CHANNELS = 3 - - # Allocate the input image tensor on the host. - rgb_tensor = Tensor[channel_dtype, tensor_rank]( - (IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS), host_device - ) - - # Fill the image with initial colors. - for row in range(IMAGE_HEIGHT): - for col in range(IMAGE_WIDTH): - rgb_tensor[row, col, 0] = row + col - rgb_tensor[row, col, 1] = row + col + 20 - rgb_tensor[row, col, 2] = row + col + 40 - - # Move the image tensor to the accelerator. - rgb_tensor = rgb_tensor.move_to(gpu_device) - - # Allocate a tensor on the accelerator to host the grayscale image. - gray_tensor = Tensor[channel_dtype, tensor_rank]( - (IMAGE_HEIGHT, IMAGE_WIDTH, 1), gpu_device - ) - - # Compile the function to run across a grid on the GPU. - gpu_function = compile[color_to_grayscale_conversion](gpu_device) - - # The grid is divided up into blocks, making sure there's an extra - # full block for any remainder. This hasn't been tuned for any specific - # GPU. - alias BLOCK_SIZE = 16 - num_col_blocks = ceildiv(IMAGE_WIDTH, BLOCK_SIZE) - num_row_blocks = ceildiv(IMAGE_HEIGHT, BLOCK_SIZE) - - # Launch the compiled function on the GPU. The target device is specified - # first, followed by all function arguments. The last two named parameters - # are the dimensions of the grid in blocks, and the block dimensions. - gpu_function( - gpu_device, - IMAGE_WIDTH, - IMAGE_HEIGHT, - rgb_tensor.unsafe_slice(), - gray_tensor.unsafe_slice(), - grid_dim=Dim(num_col_blocks, num_row_blocks), - block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), - ) - - # Move the output tensor back onto the CPU so that we can read the results. - gray_tensor = gray_tensor.move_to(host_device) - - print("Resulting grayscale image:") - print_image[IMAGE_HEIGHT, IMAGE_WIDTH](gray_tensor) + @parameter + if has_nvidia_gpu_accelerator(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + alias IMAGE_WIDTH = 5 + alias IMAGE_HEIGHT = 10 + alias NUM_CHANNELS = 3 + + # Allocate the input image tensor on the host. + rgb_tensor = Tensor[channel_dtype, tensor_rank]( + (IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS), host_device + ) + + # Fill the image with initial colors. + for row in range(IMAGE_HEIGHT): + for col in range(IMAGE_WIDTH): + rgb_tensor[row, col, 0] = row + col + rgb_tensor[row, col, 1] = row + col + 20 + rgb_tensor[row, col, 2] = row + col + 40 + + # Move the image tensor to the accelerator. + rgb_tensor = rgb_tensor.move_to(gpu_device) + + # Allocate a tensor on the accelerator to host the grayscale image. + gray_tensor = Tensor[channel_dtype, tensor_rank]( + (IMAGE_HEIGHT, IMAGE_WIDTH, 1), gpu_device + ) + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[color_to_grayscale_conversion](gpu_device) + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + num_col_blocks = ceildiv(IMAGE_WIDTH, BLOCK_SIZE) + num_row_blocks = ceildiv(IMAGE_HEIGHT, BLOCK_SIZE) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + IMAGE_WIDTH, + IMAGE_HEIGHT, + rgb_tensor.unsafe_slice(), + gray_tensor.unsafe_slice(), + grid_dim=Dim(num_col_blocks, num_row_blocks), + block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + gray_tensor = gray_tensor.move_to(host_device) + + print("Resulting grayscale image:") + print_image[IMAGE_HEIGHT, IMAGE_WIDTH](gray_tensor) + else: + print( + "These examples require a MAX-compatible NVIDIA GPU and none was" + " detected." + ) diff --git a/examples/gpu_functions/mandelbrot.mojo b/examples/gpu_functions/mandelbrot.mojo index 4356e624b1..608eeea706 100755 --- a/examples/gpu_functions/mandelbrot.mojo +++ b/examples/gpu_functions/mandelbrot.mojo @@ -18,6 +18,7 @@ from gpu.id import thread_idx, block_dim, block_idx from math import ceildiv from max.driver import DynamicTensor, Tensor, accelerator_device, cpu_device from max.driver.accelerator import compile +from sys import has_nvidia_gpu_accelerator alias float_dtype = DType.float32 alias int_dtype = DType.int32 @@ -76,54 +77,61 @@ fn mandelbrot( def main(): - # Attempt to connect to a compatible GPU. If one is not found, this will - # error out and exit. - gpu_device = accelerator_device() - host_device = cpu_device() - - # Compile the function to run across a grid on the GPU. - gpu_function = compile[mandelbrot](gpu_device) - - # Set the resolution of the Mandelbrot set grid that will be calculated. - alias GRID_WIDTH = 60 - alias GRID_HEIGHT = 25 - - # The grid is divided up into blocks, making sure there's an extra - # full block for any remainder. This hasn't been tuned for any specific - # GPU. - alias BLOCK_SIZE = 16 - num_col_blocks = ceildiv(GRID_WIDTH, BLOCK_SIZE) - num_row_blocks = ceildiv(GRID_HEIGHT, BLOCK_SIZE) - - # Set the parameters for the area of the Mandelbrot set we'll be examining. - alias MIN_X: Scalar[float_dtype] = -2.0 - alias MAX_X: Scalar[float_dtype] = 0.7 - alias MIN_Y: Scalar[float_dtype] = -1.12 - alias MAX_Y: Scalar[float_dtype] = 1.12 - alias SCALE_X = (MAX_X - MIN_X) / GRID_WIDTH - alias SCALE_Y = (MAX_Y - MIN_Y) / GRID_HEIGHT - alias MAX_ITERATIONS = 100 - - # Allocate a tensor on the target device to hold the resulting set. - out_tensor = Tensor[int_dtype, 2]((GRID_HEIGHT, GRID_WIDTH), gpu_device) - - # Launch the compiled function on the GPU. The target device is specified - # first, followed by all function arguments. The last two named parameters - # are the dimensions of the grid in blocks, and the block dimensions. - gpu_function( - gpu_device, - MIN_X, - MIN_Y, - SCALE_X, - SCALE_Y, - MAX_ITERATIONS, - out_tensor.unsafe_slice(), - grid_dim=Dim(num_col_blocks, num_row_blocks), - block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), - ) - - # Move the output tensor back onto the CPU so that we can read the results. - out_tensor = out_tensor.move_to(host_device) - - # Draw the final Mandelbrot set. - draw_mandelbrot[GRID_HEIGHT, GRID_WIDTH](out_tensor, max=MAX_ITERATIONS) + @parameter + if has_nvidia_gpu_accelerator(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[mandelbrot](gpu_device) + + # Set the resolution of the Mandelbrot set grid that will be calculated. + alias GRID_WIDTH = 60 + alias GRID_HEIGHT = 25 + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + num_col_blocks = ceildiv(GRID_WIDTH, BLOCK_SIZE) + num_row_blocks = ceildiv(GRID_HEIGHT, BLOCK_SIZE) + + # Set the parameters for the area of the Mandelbrot set we'll be examining. + alias MIN_X: Scalar[float_dtype] = -2.0 + alias MAX_X: Scalar[float_dtype] = 0.7 + alias MIN_Y: Scalar[float_dtype] = -1.12 + alias MAX_Y: Scalar[float_dtype] = 1.12 + alias SCALE_X = (MAX_X - MIN_X) / GRID_WIDTH + alias SCALE_Y = (MAX_Y - MIN_Y) / GRID_HEIGHT + alias MAX_ITERATIONS = 100 + + # Allocate a tensor on the target device to hold the resulting set. + out_tensor = Tensor[int_dtype, 2]((GRID_HEIGHT, GRID_WIDTH), gpu_device) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + MIN_X, + MIN_Y, + SCALE_X, + SCALE_Y, + MAX_ITERATIONS, + out_tensor.unsafe_slice(), + grid_dim=Dim(num_col_blocks, num_row_blocks), + block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + out_tensor = out_tensor.move_to(host_device) + + # Draw the final Mandelbrot set. + draw_mandelbrot[GRID_HEIGHT, GRID_WIDTH](out_tensor, max=MAX_ITERATIONS) + else: + print( + "These examples require a MAX-compatible NVIDIA GPU and none was" + " detected." + ) diff --git a/examples/gpu_functions/naive_matrix_multiplication.mojo b/examples/gpu_functions/naive_matrix_multiplication.mojo index 515f87e2b5..1e0d6e0dd4 100755 --- a/examples/gpu_functions/naive_matrix_multiplication.mojo +++ b/examples/gpu_functions/naive_matrix_multiplication.mojo @@ -23,6 +23,7 @@ from max.driver import ( cpu_device, ) from max.driver.accelerator import compile +from sys import has_nvidia_gpu_accelerator alias float_dtype = DType.float32 alias tensor_rank = 2 @@ -47,64 +48,71 @@ fn naive_matrix_multiplication( def main(): - # Attempt to connect to a compatible GPU. If one is not found, this will - # error out and exit. - gpu_device = accelerator_device() - host_device = cpu_device() - - alias I = 5 - alias J = 4 - alias K = 6 - - # Allocate the two input matrices on the host. - m_tensor = Tensor[float_dtype, tensor_rank]((I, J), host_device) - n_tensor = Tensor[float_dtype, tensor_rank]((J, K), host_device) - - # Fill them with initial values. - for m_row in range(I): - for m_col in range(J): - m_tensor[m_row, m_col] = m_row - m_col - - for n_row in range(J): - for n_col in range(K): - n_tensor[n_row, n_col] = n_row + n_col - - print("M matrix:", m_tensor) - print("N matrix:", n_tensor) - - # Move the input matrices to the accelerator. - m_tensor = m_tensor.move_to(gpu_device) - n_tensor = n_tensor.move_to(gpu_device) - - # Allocate a tensor on the accelerator to host the calculation results. - p_tensor = Tensor[float_dtype, tensor_rank]((I, K), gpu_device) - - # Compile the function to run across a grid on the GPU. - gpu_function = compile[naive_matrix_multiplication](gpu_device) - - # The grid is divided up into blocks, making sure there's an extra - # full block for any remainder. This hasn't been tuned for any specific - # GPU. - alias BLOCK_SIZE = 16 - num_col_blocks = ceildiv(I, BLOCK_SIZE) - num_row_blocks = ceildiv(J, BLOCK_SIZE) - - # Launch the compiled function on the GPU. The target device is specified - # first, followed by all function arguments. The last two named parameters - # are the dimensions of the grid in blocks, and the block dimensions. - gpu_function( - gpu_device, - I, - J, - K, - m_tensor.unsafe_slice(), - n_tensor.unsafe_slice(), - p_tensor.unsafe_slice(), - grid_dim=Dim(num_col_blocks, num_row_blocks), - block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), - ) - - # Move the output tensor back onto the CPU so that we can read the results. - p_tensor = p_tensor.move_to(host_device) - - print("Resulting matrix:", p_tensor) + @parameter + if has_nvidia_gpu_accelerator(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + alias I = 5 + alias J = 4 + alias K = 6 + + # Allocate the two input matrices on the host. + m_tensor = Tensor[float_dtype, tensor_rank]((I, J), host_device) + n_tensor = Tensor[float_dtype, tensor_rank]((J, K), host_device) + + # Fill them with initial values. + for m_row in range(I): + for m_col in range(J): + m_tensor[m_row, m_col] = m_row - m_col + + for n_row in range(J): + for n_col in range(K): + n_tensor[n_row, n_col] = n_row + n_col + + print("M matrix:", m_tensor) + print("N matrix:", n_tensor) + + # Move the input matrices to the accelerator. + m_tensor = m_tensor.move_to(gpu_device) + n_tensor = n_tensor.move_to(gpu_device) + + # Allocate a tensor on the accelerator to host the calculation results. + p_tensor = Tensor[float_dtype, tensor_rank]((I, K), gpu_device) + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[naive_matrix_multiplication](gpu_device) + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + num_col_blocks = ceildiv(I, BLOCK_SIZE) + num_row_blocks = ceildiv(J, BLOCK_SIZE) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + I, + J, + K, + m_tensor.unsafe_slice(), + n_tensor.unsafe_slice(), + p_tensor.unsafe_slice(), + grid_dim=Dim(num_col_blocks, num_row_blocks), + block_dim=Dim(BLOCK_SIZE, BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + p_tensor = p_tensor.move_to(host_device) + + print("Resulting matrix:", p_tensor) + else: + print( + "These examples require a MAX-compatible NVIDIA GPU and none was" + " detected." + ) diff --git a/examples/gpu_functions/vector_addition.mojo b/examples/gpu_functions/vector_addition.mojo index dd4aaa4361..951eb3ed50 100755 --- a/examples/gpu_functions/vector_addition.mojo +++ b/examples/gpu_functions/vector_addition.mojo @@ -22,6 +22,7 @@ from max.driver import ( cpu_device, ) from max.driver.accelerator import compile +from sys import has_nvidia_gpu_accelerator alias float_dtype = DType.float32 alias tensor_rank = 1 @@ -42,52 +43,61 @@ fn vector_addition( def main(): - # Attempt to connect to a compatible GPU. If one is not found, this will - # error out and exit. - gpu_device = accelerator_device() - host_device = cpu_device() - - alias VECTOR_WIDTH = 10 - - # Allocate the two input tensors on the host. - lhs_tensor = Tensor[float_dtype, 1]((VECTOR_WIDTH), host_device) - rhs_tensor = Tensor[float_dtype, 1]((VECTOR_WIDTH), host_device) - - # Fill them with initial values. - for i in range(VECTOR_WIDTH): - lhs_tensor[i] = 1.25 - rhs_tensor[i] = 2.5 - - # Move the input tensors to the accelerator. - lhs_tensor = lhs_tensor.move_to(gpu_device) - rhs_tensor = rhs_tensor.move_to(gpu_device) - - # Allocate a tensor on the accelerator to host the calculation results. - out_tensor = Tensor[float_dtype, tensor_rank]((VECTOR_WIDTH), gpu_device) - - # Compile the function to run across a grid on the GPU. - gpu_function = compile[vector_addition](gpu_device) - - # The grid is divided up into blocks, making sure there's an extra - # full block for any remainder. This hasn't been tuned for any specific - # GPU. - alias BLOCK_SIZE = 16 - var num_blocks = ceildiv(VECTOR_WIDTH, BLOCK_SIZE) - - # Launch the compiled function on the GPU. The target device is specified - # first, followed by all function arguments. The last two named parameters - # are the dimensions of the grid in blocks, and the block dimensions. - gpu_function( - gpu_device, - VECTOR_WIDTH, - lhs_tensor.unsafe_slice(), - rhs_tensor.unsafe_slice(), - out_tensor.unsafe_slice(), - grid_dim=Dim(num_blocks), - block_dim=Dim(BLOCK_SIZE), - ) - - # Move the output tensor back onto the CPU so that we can read the results. - out_tensor = out_tensor.move_to(host_device) - - print("Resulting vector:", out_tensor) + @parameter + if has_nvidia_gpu_accelerator(): + # Attempt to connect to a compatible GPU. If one is not found, this will + # error out and exit. + gpu_device = accelerator_device() + host_device = cpu_device() + + alias VECTOR_WIDTH = 10 + + # Allocate the two input tensors on the host. + lhs_tensor = Tensor[float_dtype, 1]((VECTOR_WIDTH), host_device) + rhs_tensor = Tensor[float_dtype, 1]((VECTOR_WIDTH), host_device) + + # Fill them with initial values. + for i in range(VECTOR_WIDTH): + lhs_tensor[i] = 1.25 + rhs_tensor[i] = 2.5 + + # Move the input tensors to the accelerator. + lhs_tensor = lhs_tensor.move_to(gpu_device) + rhs_tensor = rhs_tensor.move_to(gpu_device) + + # Allocate a tensor on the accelerator to host the calculation results. + out_tensor = Tensor[float_dtype, tensor_rank]( + (VECTOR_WIDTH), gpu_device + ) + + # Compile the function to run across a grid on the GPU. + gpu_function = compile[vector_addition](gpu_device) + + # The grid is divided up into blocks, making sure there's an extra + # full block for any remainder. This hasn't been tuned for any specific + # GPU. + alias BLOCK_SIZE = 16 + var num_blocks = ceildiv(VECTOR_WIDTH, BLOCK_SIZE) + + # Launch the compiled function on the GPU. The target device is specified + # first, followed by all function arguments. The last two named parameters + # are the dimensions of the grid in blocks, and the block dimensions. + gpu_function( + gpu_device, + VECTOR_WIDTH, + lhs_tensor.unsafe_slice(), + rhs_tensor.unsafe_slice(), + out_tensor.unsafe_slice(), + grid_dim=Dim(num_blocks), + block_dim=Dim(BLOCK_SIZE), + ) + + # Move the output tensor back onto the CPU so that we can read the results. + out_tensor = out_tensor.move_to(host_device) + + print("Resulting vector:", out_tensor) + else: + print( + "These examples require a MAX-compatible NVIDIA GPU and none was" + " detected." + ) From 32883bcd85999e1f1ee2589cf366b76fa3848fff Mon Sep 17 00:00:00 2001 From: Jack Clayton Date: Thu, 27 Feb 2025 19:33:49 -0500 Subject: [PATCH 505/509] [Kernels] Improve ergonomics for custom ops benchmarking Allow constructing a `ManagedTensorSlice` with random / sequential numbers using the `StaticTensorSpec` to determine the number and type of elements to initialize. This improves ergonomics when testing and benchmarking custom ops. MODULAR_ORIG_COMMIT_REV_ID: 06122a82d16097ffdf1510ba50389691b2d82e10 --- examples/custom_ops/benchmarks.mojo | 127 +++++++++++++--------------- 1 file changed, 60 insertions(+), 67 deletions(-) diff --git a/examples/custom_ops/benchmarks.mojo b/examples/custom_ops/benchmarks.mojo index b2f7962c05..67490d0c16 100644 --- a/examples/custom_ops/benchmarks.mojo +++ b/examples/custom_ops/benchmarks.mojo @@ -61,13 +61,11 @@ def top_k(): out_lambda=None, ) - var in_vals = UnsafePointer[Scalar[val_dtype]].alloc(els) - var out_vals = UnsafePointer[Scalar[val_dtype]].alloc(els) - var out_idxs = UnsafePointer[Scalar[idx_dtype]].alloc(els) + var in_vals = InputTensor[static_spec=val_spec].rand() + var out_vals = OutputTensor[static_spec=val_spec].rand() + var out_idxs = OutputTensor[static_spec=idx_spec].rand() - rand(in_vals, els) - - var cpu_ctx_ptr = cpu().unsafe_ptr() + var cpu_ctx = DeviceContext(api="cpu") @parameter @always_inline @@ -76,10 +74,7 @@ def top_k(): @always_inline fn run_bench() raises: TopK.execute[K=K, target="cpu"]( - out_vals=OutputTensor[static_spec=val_spec](out_vals, shape), - out_idxs=OutputTensor[static_spec=idx_spec](out_idxs, shape), - in_vals=InputTensor[static_spec=val_spec](in_vals, shape), - ctx=cpu_ctx_ptr, + out_vals, out_idxs, in_vals, cpu_ctx ) b.iter[run_bench]() @@ -93,44 +88,48 @@ def top_k(): @parameter if has_nvidia_gpu_accelerator() or has_amd_gpu_accelerator(): var gpu_ctx = DeviceContext() - var dev_in_vals = gpu_ctx.enqueue_create_buffer[val_dtype](els) - var dev_out_vals = gpu_ctx.enqueue_create_buffer[val_dtype](els) - var dev_out_idxs = gpu_ctx.enqueue_create_buffer[idx_dtype](els) - gpu_ctx.copy(dev_in_vals, in_vals) + + var in_vals_dev_buff = gpu_ctx.enqueue_create_buffer[val_dtype](els) + var out_vals_dev_buff = gpu_ctx.enqueue_create_buffer[val_dtype](els) + var out_idxs_dev_buff = gpu_ctx.enqueue_create_buffer[idx_dtype](els) + + gpu_ctx.enqueue_copy(in_vals_dev_buff, in_vals.unsafe_ptr()) + + var out_vals_dev = OutputTensor[static_spec=val_spec]( + out_vals_dev_buff.unsafe_ptr(), shape + ) + var out_idxs_dev = OutputTensor[static_spec=idx_spec]( + out_idxs_dev_buff.unsafe_ptr(), shape + ) + var in_vals_dev = InputTensor[static_spec=val_spec]( + in_vals_dev_buff.unsafe_ptr(), shape + ) @parameter @always_inline fn bench_gpu(mut b: Bencher) raises: @parameter @always_inline - fn kernel_launch() raises: + fn kernel_launch(gpu_ctx: DeviceContext) raises: TopK.execute[K=K, target="gpu"]( - out_vals=OutputTensor[static_spec=val_spec]( - dev_out_vals.unsafe_ptr(), shape - ), - out_idxs=OutputTensor[static_spec=idx_spec]( - dev_out_idxs.unsafe_ptr(), shape - ), - in_vals=InputTensor[static_spec=val_spec]( - dev_in_vals.unsafe_ptr(), shape - ), - ctx=gpu_ctx, + out_vals_dev, out_idxs_dev, in_vals_dev, gpu_ctx ) - b.iter[kernel_launch]() + b.iter_custom[kernel_launch](gpu_ctx) b.bench_function[bench_gpu]( BenchId("top_k_custom", "gpu"), flops, elements ) - _ = gpu_ctx + _ = in_vals_dev_buff + _ = out_vals_dev_buff + _ = out_idxs_dev_buff b.config.verbose_metric_names = False print(b) - _ = cpu_ctx_ptr - in_vals.free() - out_vals.free() - out_idxs.free() + _ = in_vals + _ = out_vals + _ = out_idxs def matmul(): @@ -178,18 +177,11 @@ def matmul(): out_lambda=None, ) - var a_ptr = UnsafePointer[Scalar[dtype]].alloc(a_els) - var b_ptr = UnsafePointer[Scalar[dtype]].alloc(b_els) - var c_ptr = UnsafePointer[Scalar[dtype]].alloc(c_els) + var a = InputTensor[static_spec=a_spec].rand() + var b = InputTensor[static_spec=b_spec].rand() + var c = OutputTensor[static_spec=c_spec].rand() - var a = InputTensor[static_spec=a_spec](a_ptr, a_shape) - var b = InputTensor[static_spec=b_spec](b_ptr, b_shape) - var c = OutputTensor[static_spec=c_spec](c_ptr, c_shape) - - rand(a_ptr, a_els) - rand(b_ptr, b_els) - - var cpu_ctx_ptr = cpu().unsafe_ptr() + var cpu_ctx = DeviceContext(api="cpu") var bench = Bench() var flops = ThroughputMeasure(BenchMetric.flops, FLOPS) var elements = ThroughputMeasure(BenchMetric.elements, M * N) @@ -201,10 +193,7 @@ def matmul(): @always_inline fn run_bench() raises: MatrixMultiplication["naive"].execute[target="cpu"]( - c, - a, - b, - cpu_ctx_ptr, + c, a, b, cpu_ctx ) bencher.iter[run_bench]() @@ -214,14 +203,22 @@ def matmul(): @parameter if has_nvidia_gpu_accelerator() or has_amd_gpu_accelerator(): var gpu_ctx = DeviceContext() - var a_dev = gpu_ctx.enqueue_create_buffer[dtype](a_els) - var b_dev = gpu_ctx.enqueue_create_buffer[dtype](b_els) - var c_dev = gpu_ctx.enqueue_create_buffer[dtype](c_els) - var c = InputTensor[static_spec=c_spec](c_dev.unsafe_ptr(), c_shape) - var a = OutputTensor[static_spec=a_spec](a_dev.unsafe_ptr(), a_shape) - var b = OutputTensor[static_spec=b_spec](b_dev.unsafe_ptr(), b_shape) - gpu_ctx.copy(a_dev, a_ptr) - gpu_ctx.copy(b_dev, b_ptr) + var a_dev_buf = gpu_ctx.enqueue_create_buffer[dtype](a_els) + var b_dev_buf = gpu_ctx.enqueue_create_buffer[dtype](b_els) + var c_dev_buf = gpu_ctx.enqueue_create_buffer[dtype](c_els) + + gpu_ctx.enqueue_copy(a_dev_buf, a.unsafe_ptr()) + gpu_ctx.enqueue_copy(b_dev_buf, b.unsafe_ptr()) + + var c_dev = InputTensor[static_spec=c_spec]( + c_dev_buf.unsafe_ptr(), c_shape + ) + var a_dev = OutputTensor[static_spec=a_spec]( + a_dev_buf.unsafe_ptr(), a_shape + ) + var b_dev = OutputTensor[static_spec=b_spec]( + b_dev_buf.unsafe_ptr(), b_shape + ) @parameter def bench_matmul_kernel[impl: StringLiteral](): @@ -232,15 +229,10 @@ def matmul(): @always_inline fn kernel_launch(gpu_ctx: DeviceContext) raises: MatrixMultiplication[impl].execute[target="gpu"]( - c, - a, - b, - gpu_ctx, + c_dev, a_dev, b_dev, gpu_ctx ) - var gpu_ctx = DeviceContext() bench.iter_custom[kernel_launch](gpu_ctx) - _ = gpu_ctx bench.bench_function[bench_gpu]( BenchId("gpu", impl), flops, elements @@ -252,17 +244,18 @@ def matmul(): bench_matmul_kernel["tiled_register"]() bench_matmul_kernel["block_tiled"]() bench_matmul_kernel["block_tiled_vectorized"]() - _ = gpu_ctx - _ = a_dev - _ = b_dev - _ = c_dev + # TODO add origin to `ManagedTensorSlice` to avoid this + _ = a_dev_buf + _ = b_dev_buf + _ = c_dev_buf bench.config.verbose_metric_names = False print(bench) - a_ptr.free() - b_ptr.free() - c_ptr.free() + # TODO add origin to `ManagedTensorSlice` to avoid this + _ = a + _ = b + _ = c # TODO: arg parsing to select benchmarks From 6470b771e16d1f7147f25537dcc65ab253731568 Mon Sep 17 00:00:00 2001 From: Laszlo Kindrat Date: Thu, 27 Feb 2025 21:15:57 -0500 Subject: [PATCH 506/509] Revert "[KVCache] rework max_cache_length to max_context_length (#56592)" (#56872) This reverts commit 2ba4178bd602dabfce4b9fb10181b98e42e241cb because it breaks the MI300 CI tests, as well as the torch compile llama3 on ROCm. See [Internal link] MODULAR_ORIG_COMMIT_REV_ID: bc34143cebe46055fbadcb3da5094f735ee82d61 --- src/max/pipelines/kv_cache/_utils.py | 3 +-- src/max/pipelines/kv_cache/continuous_batching_cache.py | 8 +++----- src/max/pipelines/kv_cache/paged_cache.py | 8 +++----- 3 files changed, 7 insertions(+), 12 deletions(-) diff --git a/src/max/pipelines/kv_cache/_utils.py b/src/max/pipelines/kv_cache/_utils.py index 1c8389142a..6942ad7823 100644 --- a/src/max/pipelines/kv_cache/_utils.py +++ b/src/max/pipelines/kv_cache/_utils.py @@ -28,7 +28,6 @@ def build_max_lengths_tensor( for step in range(num_steps): max_lengths_np[step, 0] = step_max_seq_length max_lengths_np[step, 1] = step_max_cache_length + step_max_cache_length += step_max_seq_length step_max_seq_length = 1 - step_max_cache_length += 1 - return Tensor.from_numpy(max_lengths_np) diff --git a/src/max/pipelines/kv_cache/continuous_batching_cache.py b/src/max/pipelines/kv_cache/continuous_batching_cache.py index cb7936d97a..b6f75e9c8a 100644 --- a/src/max/pipelines/kv_cache/continuous_batching_cache.py +++ b/src/max/pipelines/kv_cache/continuous_batching_cache.py @@ -255,7 +255,7 @@ def _fetch( cache_lengths_np = np.zeros(active_batch_size, np.uint32) max_seq_length = 0 - max_context_length = 0 + max_cache_length = 0 for i, (seq_id, prompt) in enumerate(seq_ids_and_prompts.items()): if seq_id > self.max_batch_size: @@ -281,9 +281,7 @@ def _fetch( # Update the maximum lengths seen so far. max_seq_length = max(max_seq_length, len(prompt)) - max_context_length = max( - max_context_length, cache_len + len(prompt) - ) + max_cache_length = max(max_cache_length, cache_len) cache_lengths = [ Tensor.from_numpy(cache_lengths_np).to(d) for d in self.devices @@ -296,7 +294,7 @@ def _fetch( # Build a tensor of maximum lengths. Each step slices the first row to # advance to the values for the next row. max_lengths_host = build_max_lengths_tensor( - num_steps, max_seq_length, max_context_length + num_steps, max_seq_length, max_cache_length ) result = [ diff --git a/src/max/pipelines/kv_cache/paged_cache.py b/src/max/pipelines/kv_cache/paged_cache.py index 6e8ba7d7ee..cc36c1bf3d 100644 --- a/src/max/pipelines/kv_cache/paged_cache.py +++ b/src/max/pipelines/kv_cache/paged_cache.py @@ -567,7 +567,7 @@ def _fetch( # Determine the number of pages required for each sequence. max_seq_length = 0 - max_context_length = 0 + max_cache_length = 0 total_sequence_length = 0 total_blocks_to_allocate = 0 blocks_to_allocate_by_seq = {} @@ -582,9 +582,7 @@ def _fetch( # Update the maximum lengths seen so far. max_seq_length = max(max_seq_length, len(prompt)) - max_context_length = max( - max_context_length, cache_length + len(prompt) - ) + max_cache_length = max(max_cache_length, cache_length) # Compute the total sequence length and the number of pages required to store it. total_sequence_length += data.seq_len @@ -631,7 +629,7 @@ def _fetch( # Build a tensor of maximum lengths. Each step slices the first row to # advance to the values for the next row. max_lengths_host = build_max_lengths_tensor( - num_steps, max_seq_length, max_context_length + num_steps, max_seq_length, max_cache_length ) lut_table_host = Tensor.from_numpy(lut_table_np) From 9116c0c43e894d0652fb662c69137f58ffa3d686 Mon Sep 17 00:00:00 2001 From: Scott Main Date: Thu, 27 Feb 2025 20:47:26 -0800 Subject: [PATCH 507/509] Update README diagram MODULAR_ORIG_COMMIT_REV_ID: c9f17deec51aaf559b4b7546100228d046a4fe6c --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 24507ed98d..dec744d018 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ commonly fragmented AI deployment workflows. MAX accelerates time to market for the latest innovations by giving AI developers a single toolchain that unlocks full programmability, unparalleled performance, and seamless hardware portability. -![Modular Architecture Diagram](https://modular-assets.s3.amazonaws.com/images/modular_architecture_diagram_bg.png) +![](https://docs.modular.com/images/github/max-stack.png) [See here to get started with MAX](https://docs.modular.com/max/get-started) and when you want to report issues or request features, From b3dfb49827627801d2b8119ed1994edcc3fde772 Mon Sep 17 00:00:00 2001 From: abdul dakkak Date: Thu, 27 Feb 2025 20:59:45 -0800 Subject: [PATCH 508/509] [Stdlib] Add StringLiteral compress/decompress private functions This adds helper functions on StringLiteral to be able to do compile time compression/decompression. MODULAR_ORIG_COMMIT_REV_ID: a5e8b20e84e797dbab07bbb4c9b5384da63873bd --- mojo/stdlib/src/builtin/string_literal.mojo | 18 ++++++++++++++++++ .../test/builtin/test_string_literal.mojo | 17 ++++++++++++++++- 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/mojo/stdlib/src/builtin/string_literal.mojo b/mojo/stdlib/src/builtin/string_literal.mojo index 6365aba14d..f72938df4b 100644 --- a/mojo/stdlib/src/builtin/string_literal.mojo +++ b/mojo/stdlib/src/builtin/string_literal.mojo @@ -973,3 +973,21 @@ fn _base64_decode[str: StringLiteral]() -> StringLiteral: A new string literal with the Base64 decoded string. """ return __mlir_op.`pop.string.base64.decode`(str.value) + + +fn _compress[str: StringLiteral]() -> StringLiteral: + """Compress the string literal using zlib. + + Returns: + A new string literal with the compressed string. + """ + return __mlir_op.`pop.string.compress`(str.value) + + +fn _decompress[str: StringLiteral]() -> StringLiteral: + """Decompress the string literal using zlib. + + Returns: + A new string literal with the decompressed string. + """ + return __mlir_op.`pop.string.decompress`(str.value) diff --git a/mojo/stdlib/test/builtin/test_string_literal.mojo b/mojo/stdlib/test/builtin/test_string_literal.mojo index de10a42d19..a8da563eb3 100644 --- a/mojo/stdlib/test/builtin/test_string_literal.mojo +++ b/mojo/stdlib/test/builtin/test_string_literal.mojo @@ -22,7 +22,12 @@ from testing import ( assert_raises, assert_true, ) -from builtin.string_literal import _base64_encode, _base64_decode +from builtin.string_literal import ( + _base64_encode, + _base64_decode, + _compress, + _decompress, +) def test_add(): @@ -535,6 +540,15 @@ def test_base64_encode_decode(): assert_equal(decoded, "I'm a mojo string") +def test_compress_decompress(): + alias compressed = _compress["hello"]() + alias decompressed = _decompress[compressed]() + alias compressed_base64 = _base64_encode[compressed]() + assert_equal(compressed_base64, "eNrLSM3JyQcABiwCFQ==") + assert_equal(len(compressed), 13) + assert_equal(decompressed, "hello") + + def main(): test_add() test_iadd() @@ -569,3 +583,4 @@ def main(): test_float_conversion() test_string_literal_from_stringable() test_base64_encode_decode() + test_compress_decompress() From b29a1f737e33d656bfee64e856069d3c68acce90 Mon Sep 17 00:00:00 2001 From: modularbot Date: Fri, 28 Feb 2025 07:09:14 +0000 Subject: [PATCH 509/509] Update lockfiles to point to latest nightly version: 25.2.0.dev2025022805 --- examples/custom_ops/magic.lock | 146 +++++------ examples/gpu_functions/magic.lock | 146 +++++------ examples/graph-api/magic.lock | 146 +++++------ .../inference/bert-c-torchscript/magic.lock | 146 +++++------ .../bert-mojo-torchscript/magic.lock | 146 +++++------ .../bert-python-torchscript/magic.lock | 146 +++++------ .../resnet50-python-torchscript/magic.lock | 146 +++++------ .../stable-diffusion-mojo-onnx/magic.lock | 146 +++++------ .../stable-diffusion-python-onnx/magic.lock | 146 +++++------ .../inference/yolo-python-onnx/magic.lock | 146 +++++------ examples/mojo/life/magic.lock | 146 +++++------ examples/mojo/magic.lock | 146 +++++------ examples/mojo/operators/magic.lock | 146 +++++------ examples/mojo/testing/magic.lock | 146 +++++------ examples/notebooks/magic.lock | 146 +++++------ examples/offline-inference/magic.lock | 227 +++++++++--------- mojo/magic.lock | 146 +++++------ src/max/magic.lock | 227 +++++++++--------- tutorials/hello-magic/local/magic.lock | 110 ++++----- tutorials/hello-magic/magic.lock | 130 +++++----- tutorials/max-graph-api/magic.lock | 110 ++++----- 21 files changed, 1569 insertions(+), 1571 deletions(-) diff --git a/examples/custom_ops/magic.lock b/examples/custom_ops/magic.lock index 9473097321..6014677668 100644 --- a/examples/custom_ops/magic.lock +++ b/examples/custom_ops/magic.lock @@ -42,11 +42,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022718-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-core-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/linux-64/max-python-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022805-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda @@ -107,11 +107,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.38.1-hb4cce97_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h86ecc28_2.conda - - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-core-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022718-release.conda - - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022718-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/max-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-core-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/linux-aarch64/max-python-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mblack-25.2.0.dev2025022805-release.conda + - conda: https://conda.modular.com/max-nightly/noarch/mojo-jupyter-25.2.0.dev2025022805-release.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.0.0-pyha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-ha32ae93_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.26.4-py312h470d778_0.conda @@ -163,11 +163,11 @@ environments: - 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    • \n", - "Note: Mojo Playground is designed only for testing the Mojo language.\n", - "The cloud environment is not always stable and performance varies, so it is not\n", - "an appropriate environment for performance benchmarking. However, we believe it\n", - "can still demonstrate the magnitude of performance gains provided by Mojo. For\n", - "more information about the compute power in the Mojo Playground, see the Mojo FAQ.\n", - "