[sharktank] restore custom matmul kernel

sharktank/sharktank/examples/export_paged_llm_v1.py

@@ -83,6 +83,7 @@ def main():
         block_seq_stride=args.block_seq_stride,
         activation_dtype=args.activation_dtype,
         attention_dtype=args.attention_dtype,
+        kv_cache_dtype=args.kv_cache_dtype,
     )
     llama_config.fake_quant = args.fake_quant
 

sharktank/sharktank/examples/paged_llm_v1.py

@@ -288,8 +288,9 @@ def main():
         block_seq_stride=args.block_seq_stride,
         device=device,
         activation_dtype=args.activation_dtype,
-        attention_dtype=args.activation_dtype,
+        attention_dtype=args.attention_dtype,
         attention_kernel=args.attention_kernel,
+        kv_cache_dtype=args.kv_cache_dtype,
         use_hf=args.use_hf,
         tensor_parallelism_size=args.tensor_parallelism_size,
         fake_quant=args.fake_quant,

sharktank/sharktank/kernels/batch_matmul_transpose_b.py

@@ -7,16 +7,36 @@
 from sharktank.kernels.base import *
 
 import torch
+from typing import cast, Optional
 
-from iree.compiler.ir import IntegerType
+from iree.compiler.ir import IntegerType, Type
+from iree.turbine.support.conversions import (
+    TORCH_DTYPE_TO_IREE_TYPE_ASM,
+    IREE_TYPE_ASM_TO_TORCH_DTYPE,
+)
+from iree.turbine.runtime.op_reg import AttrArg
 
 __all__ = [
     "batch_matmul_transpose_b",
 ]
 
 
+def batch_matmul_transpose_b(
+    lhs: torch.Tensor,
+    rhs: torch.Tensor,
+    /,
+    *,
+    accum_dtype: Optional[torch.dtype] = None,
+) -> torch.Tensor:
+    if accum_dtype is None:
+        accum_dtype = lhs.dtype
+    return _batch_matmul_transpose_b(
+        lhs, rhs, accum_dtype=TORCH_DTYPE_TO_IREE_TYPE_ASM[accum_dtype]
+    )
+
+
 @CustomOp.register(library=LIBRARY)
-class batch_matmul_transpose_b(CustomOp):
+class _batch_matmul_transpose_b(CustomOp):
     """Generic block scaled matmul with transposed RHS.
 
     The LHS is expected to be a 3d tensor of shape [B, M, K]. RHS must be
@@ -25,11 +45,18 @@ class batch_matmul_transpose_b(CustomOp):
     The kernel will be specialized for all values of N, K and LHS dtype.
     """
 
-    signature = "batch_matmul_transpose_b(Tensor lhs, Tensor rhs) -> (Tensor)"
+    signature = (
+        "batch_matmul_transpose_b(Tensor lhs, Tensor rhs, str accum_dtype) -> (Tensor)"
+    )
+
+    def eager_execute(self, lhs: torch.Tensor, rhs: torch.Tensor, accum_dtype: str):
+        dtype = IREE_TYPE_ASM_TO_TORCH_DTYPE[accum_dtype]
+        return torch.matmul(lhs.to(dtype=dtype), rhs.transpose(-1, -2).to(dtype=dtype))
 
     def select(self, ksel: KernelSelection):
         lhs_desc = ksel.arg_tensor(0)  # Shape [B, M, K]
         rhs_desc = ksel.arg_tensor(1)  # Shape [B, N, K]
+        accum_type_attr = ksel.attr_str(2)
 
         # Rank check.
         torch._check(
@@ -60,7 +87,8 @@ def select(self, ksel: KernelSelection):
         )
         # Shape batch, m, n
         c_desc = ksel.return_new_tensor(
-            [lhs_batch, lhs_m, rhs_n], dtype=lhs_desc.t.dtype
+            [lhs_batch, lhs_m, rhs_n],
+            dtype=IREE_TYPE_ASM_TO_TORCH_DTYPE[accum_type_attr.v],
         )
         specialize_all_known_dims(lhs_desc)
         specialize_all_known_dims(rhs_desc)
@@ -74,12 +102,14 @@ def select(self, ksel: KernelSelection):
     def generate(self, ksel: KernelSelection, kb: KernelBuilder):
         lhs = kb.arg_value(0)
         rhs = kb.arg_value(1)
+        accum_type_str = cast(AttrArg, ksel.arg_descs[2]).v
         result_desc = ksel.result_descs[0]
 
         # Generate specialization signature and types.
-        a_asm_type, a_ident, accum_type = unpack_tensor_type(lhs.type)
+        a_asm_type, a_ident, _ = unpack_tensor_type(lhs.type)
         b_asm_type, b_ident, _ = unpack_tensor_type(rhs.type)
-        spec_sig = f"L{a_ident}_R{b_ident}"
+        accum_type = Type.parse(accum_type_str)
+        spec_sig = f"L{a_ident}_R{b_ident}_{accum_type_str}"
         template_file = "batch_matmul_transpose_b.mlir"
         target_function_name = f"sharktank_batch_matmul_transpose_b_{spec_sig}"
         cst_zero = "0" if IntegerType.isinstance(accum_type) else "0."

sharktank/sharktank/layers/configs/llm_configs.py

@@ -158,6 +158,9 @@ class LlamaModelConfig:
     # Either "paged" or "direct".
     kv_cache_type: str = "paged"
 
+    # If None will use attention_dtype.
+    kv_cache_dtype: Optional[torch.dtype] = None
+
     # The device on which to place intermediate state.
     device: Optional[torch.device] = None
 

sharktank/sharktank/layers/kv_cache.py

@@ -275,7 +275,14 @@ def write_timestep(
             partitions = partitions.repeat(bs, 1)
 
             indices = (page_id, transformer_block, partitions, page_offset)
-            page_table.index_put_(indices=indices, values=cache_partition)
+            values = ops.to(cache_partition, dtype=page_table.dtype)
+            if page_table.dtype == torch.float8_e4m3fnuz:
+                # Workaround for Torch not supporting torch.Tensor.index_copy_ for f8.
+                page_table_as_int8 = page_table.view(dtype=torch.int8)
+                values_int8 = values.view(dtype=torch.int8)
+                page_table_as_int8.index_put_(indices=indices, values=values_int8)
+            else:
+                page_table.index_put_(indices=indices, values=values)
 
         return
 
@@ -320,4 +327,11 @@ def write(
                 (base_subblock_ids + index) if index > 0 else base_subblock_ids
             ).flatten(0, 1)
 
-            subblock_table.index_copy_(0, subblock_ids, part_block_view)
+            part_block = ops.to(part_block_view, dtype=subblock_table.dtype)
+            if subblock_table.dtype == torch.float8_e4m3fnuz:
+                # Workaround for Torch not supporting torch.Tensor.index_copy_ for f8.
+                subblock_table_as_int8 = subblock_table.view(dtype=torch.int8)
+                part_block_as_int8 = part_block.view(dtype=torch.int8)
+                subblock_table_as_int8.index_copy_(0, subblock_ids, part_block_as_int8)
+            else:
+                subblock_table.index_copy_(0, subblock_ids, part_block)

sharktank/sharktank/layers/linear.py

@@ -85,10 +85,6 @@ def forward(self, x):
         # We can truncate to fp16 in iree, so we do a cast here
         # to account for this in the IR. This is may not be the right
         # level to do this, but for now its here.
-        if not isinstance(y, QuantizedTensor):
-            if y.dtype == torch.float8_e4m3fnuz:
-                y = ops.to(y, torch.bfloat16)
-                return y
         if qdq_output is not None:
             y = qdq_output.quantize(y).unpack().dequant()
         return y

sharktank/sharktank/layers/paged_llama_attention_block.py

@@ -37,6 +37,7 @@ def __init__(
         head_dim: int,
         head_count_kv: int,
         rms_epsilon: float,
+        attention_dtype: Optional[torch.dtype] = None,
         attention_kernel: str = "decomposed",
         attention_scale: Optional[float] = None,
         softcap: Optional[float] = None,
@@ -49,6 +50,7 @@ def __init__(
         self.head_count = head_count
         self.head_dim = head_dim
         self.head_count_kv = head_count_kv
+        self.attention_dtype = attention_dtype
         self.attention_kernel = attention_kernel
         self.attention_scale = attention_scale
         self.softcap = softcap
@@ -161,13 +163,13 @@ def repeat_kv(x: torch.Tensor) -> torch.Tensor:
         # Fake quant is already dequantized when stored in the cache.
         if self.cache_quantizer and not self.fake_quant:
             xk = self.cache_quantizer.dequantize_raw_tensor(
-                xk, torch.bfloat16, name="xk_deq"
+                xk, self.attention_dtype, name="xk_deq"
             )
             xv = self.cache_quantizer.dequantize_raw_tensor(
-                xv, torch.bfloat16, name="xv_deq"
+                xv, self.attention_dtype, name="xv_deq"
             )
             if attention_mask is not None:
-                attention_mask = attention_mask.to(torch.bfloat16)
+                attention_mask = attention_mask.to(self.attention_dtype)
 
         # Transpose into [bs, heads, sl, dim]
         xq = xq.transpose(1, 2)

sharktank/sharktank/models/llama/llama.py

@@ -117,6 +117,7 @@ def __init__(self, theta: Theta, config: LlamaModelConfig):
                     head_dim=hp.attn_head_dim,
                     head_count_kv=hp.attention_head_count_kv,
                     rms_epsilon=hp.attention_layer_norm_rms_epsilon,
+                    attention_dtype=config.attention_dtype,
                     attention_kernel=self.attention_kernel,
                     fake_quant=self.fake_quant,
                 )
@@ -241,6 +242,7 @@ def __init__(
         head_dim: int,
         head_count_kv: int,
         rms_epsilon: float,
+        attention_dtype: Optional[torch.dtype] = None,
         attention_kernel: str = "decomposed",
         fake_quant: bool = True,
     ):
@@ -255,6 +257,7 @@ def __init__(
                 head_dim=head_dim,
                 head_count_kv=head_count_kv,
                 rms_epsilon=rms_epsilon,
+                attention_dtype=attention_dtype,
                 attention_kernel=attention_kernel,
                 fake_quant=fake_quant,
             ),

sharktank/sharktank/ops/qlinear_impls.py

@@ -44,16 +44,16 @@ def qlinear_tensor_scaled(
     # Now we know that both the x/weight are TensorScaledLayout. There are still
     # degrees of freedom:
     #  * Either/both can be per-tensor or per-axis scaled (d is 0D or d is nd>0).
-    #  * Either/both can have offsets of not (m is not None).
+    #  * Either/both can have offsets or not (m is not None).
     x_layout: TensorScaledLayout = x.unpack()
     weight_layout: TensorScaledLayout = weight.unpack()
 
     # Handle only integer and fp8 quantizations.
     if x_layout.qs.dtype.is_floating_point or weight_layout.qs.dtype.is_floating_point:
-        if x_layout.qs.dtype == torch.float8_e4m3fnuz:
-            # assume quark
-            return matmul(x_layout.qs, weight_layout.qs, transpose_rhs=True)
-        else:
+        if (
+            x_layout.qs.dtype != torch.float8_e4m3fnuz
+            or weight_layout.qs.dtype != torch.float8_e4m3fnuz
+        ):
             return NotImplemented
 
     # Bias.
@@ -160,6 +160,8 @@ def linear_quantized_weight(
     *,
     accum_dtype: Optional[torch.dtype],
 ) -> AnyTensor:
+    if accum_dtype is not None:
+        raise NotImplementedError("TODO: implement when is passed accum_dtype")
     res = matmul(x, weight, transpose_rhs=True)
     if bias is not None:
         res = res + bias
@@ -170,7 +172,13 @@ def linear_quantized_weight(
 linear.override(Tensor, QuantizedTensor, AnyTensor)(linear_quantized_weight)
 
 
-def _invoke_mmt_kernel(lhs, rhs, *, accum_dtype):
+def _is_dtype_unsigned_integer(dtype: torch.dtype):
+    return not dtype.is_complex and not dtype.is_floating_point and not dtype.is_signed
+
+
+def _invoke_mmt_kernel(
+    lhs: torch.Tensor, rhs: torch.Tensor, *, accum_dtype: torch.dtype
+):
     if debugging.flags.use_custom_iree_kernels:
         # The custom kernel requires that the lhs and rhs be the same
         # rank. Broadcast the rhs to match.
@@ -187,13 +195,17 @@ def _invoke_mmt_kernel(lhs, rhs, *, accum_dtype):
             rhs_size = [lhs.shape[0]] + list(rhs.shape)
             rhs = rhs.unsqueeze(0).expand(rhs_size)
             rhs_rank = len(rhs.shape)
-        y_qs = kernels.batch_matmul_transpose_b(
-            lhs.to(accum_dtype), rhs.to(accum_dtype)
-        )
-        # Squeeze the batch dimension to maintain shape parity with other
-        # layers.
-        if len(y_qs.shape) > 2:
-            y_qs = y_qs.squeeze(0)
+        if (
+            _is_dtype_unsigned_integer(lhs.dtype)
+            or _is_dtype_unsigned_integer(rhs.dtype)
+            or _is_dtype_unsigned_integer(accum_dtype)
+        ):
+            # TODO: make the kernel work with unsigned types.
+            y_qs = kernels.batch_matmul_transpose_b(
+                lhs.to(dtype=accum_dtype), rhs.to(dtype=accum_dtype)
+            )
+        else:
+            y_qs = kernels.batch_matmul_transpose_b(lhs, rhs, accum_dtype=accum_dtype)
     else:
         # FP emulation.
         y_qs = torch.matmul(

sharktank/sharktank/utils/cli.py

@@ -32,10 +32,12 @@ def parse(parser: argparse.ArgumentParser, *, args: Sequence[str] | None = None)
     """Parses arguments and does any prescribed global process setup."""
     parsed_args = parser.parse_args(args)
     # Set torch dtypes
-    for attr in ["activation_dtype", "attention_dtype"]:
+    for attr in ["activation_dtype", "attention_dtype", "kv_cache_dtype"]:
         if hasattr(parsed_args, attr):
-            dtype = getattr(torch, getattr(parsed_args, attr))
-            assert isinstance(dtype, torch.dtype)
+            dtype = getattr(parsed_args, attr)
+            if dtype is not None:
+                dtype = getattr(torch, dtype)
+                assert isinstance(dtype, torch.dtype)
             setattr(parsed_args, attr, dtype)
     return parsed_args
 
@@ -100,6 +102,11 @@ def add_model_options(parser: argparse.ArgumentParser):
         help="DType to use for activations in the model",
         default="float16",
     )
+    parser.add_argument(
+        "--kv-cache-dtype",
+        help="DType to use for the KV cache. If not given will be attention dtype",
+        default=None,
+    )
     parser.add_argument("--device", help="Torch device (or default)")
 
     parser.add_argument(

sharktank/sharktank/utils/create_cache.py

@@ -12,13 +12,14 @@ def create_paged_kv_cache(config: LlamaModelConfig) -> PagedKVCache:
         raise ValueError("Model does not use paged kv cache, cannot create kv cache")
 
     hp = config.hp
+    dtype = config.kv_cache_dtype or config.attention_dtype
     return PagedKVCache(
         transformer_block_count=hp.block_count,
         attn_head_count=hp.attention_head_count_kv,
         attn_head_dim=hp.attn_head_dim,
         cache_partition_count=2,  # One for each of K/V.
         block_seq_stride=config.block_seq_stride,
         device=config.device,
-        dtype=config.attention_dtype,
+        dtype=dtype,
         shard_count=config.tensor_parallelism_size,
     )

sharktank/sharktank/utils/hf_datasets.py

@@ -93,23 +93,6 @@ def alias_dataset(from_name: str, to_name: str):
 # Dataset definitions
 ################################################################################
 
-Dataset(
-    "SanctumAI/Meta-Llama-3.1-8B-Instruct-GGUF",
-    (
-        RemoteFile(
-            "gguf",
-            "SanctumAI/Meta-Llama-3.1-8B-Instruct-GGUF",
-            "meta-llama-3.1-8b-instruct.f16.gguf",
-        ),
-        RemoteFile(
-            "tokenizer_config.json",
-            "NousResearch/Meta-Llama-3-8B-Instruct",
-            "tokenizer_config.json",
-            extra_filenames=["tokenizer.json"],
-        ),
-    ),
-).alias_to("llama3_8B_fp16")
-
 Dataset(
     "QuantFactory/Llama-3-8B_q4_1_gguf",
     (