[Kernel] Add test_pos_encoding.py

Signed-off-by: Yizhou Liu <liuyizhou5@h-partners.com>

tests/kernels/test_pos_encoding.py

@@ -0,0 +1,259 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from itertools import accumulate, product
+from typing import Callable, Dict, List, Optional
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.platforms import current_platform
+
+from .allclose_default import get_default_atol, get_default_rtol
+
+IS_NEOX_STYLE = [True, False]
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+HEAD_SIZES = [64, 80, 112, 120, 256]
+ROTARY_DIMS = [None, 32]  # None means rotary dim == head size
+NUM_HEADS = [17]  # Arbitrary values for testing
+BATCH_SIZES = [5]  # Arbitrary values for testing
+SEQ_LENS = [11, 8192]  # Arbitrary values for testing
+SEEDS = [0]
+CUDA_DEVICES = [
+    f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
+]
+
+
+def _get_flat_tensor_shape(batch_size: int, seq_len: int, num_heads: int,
+                           head_size: int) -> tuple[int, ...]:
+    return (batch_size, seq_len, num_heads * head_size)
+
+
+def _get_batch_tensor_shape(batch_size: int, seq_len: int, num_heads: int,
+                            head_size: int) -> tuple[int, ...]:
+    return (batch_size, seq_len, num_heads, head_size)
+
+
+TENSORS_SHAPES_FN = [_get_batch_tensor_shape, _get_flat_tensor_shape]
+
+
+@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
+@pytest.mark.parametrize("tensor_shape_fn", TENSORS_SHAPES_FN)
+@pytest.mark.parametrize("batch_size", BATCH_SIZES)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@torch.inference_mode()
+def test_rotary_embedding(
+    is_neox_style: bool,
+    tensor_shape_fn: Callable[[int, int, int, int], tuple[int]],
+    batch_size: int,
+    seq_len: int,
+    num_heads: int,
+    head_size: int,
+    rotary_dim: Optional[int],
+    dtype: torch.dtype,
+    seed: int,
+    device: str,
+    max_position: int = 8192,
+    base: int = 10000,
+) -> None:
+    if rotary_dim is None:
+        rotary_dim = head_size
+
+    current_platform.seed_everything(seed)
+    torch.set_default_device(device)
+    if rotary_dim is None:
+        rotary_dim = head_size
+    rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style)
+    rope = rope.to(dtype=dtype)
+
+    positions = torch.randint(0, max_position, (batch_size, seq_len))
+    query_shape = tensor_shape_fn(batch_size, seq_len, num_heads, head_size)
+    query = torch.randn(query_shape, dtype=dtype)
+    key = torch.randn_like(query)
+
+    # NOTE(woosuk): The reference implementation should be executed first
+    # because the custom kernel is in-place.
+    ref_query, ref_key = rope.forward_native(positions, query, key)
+    out_query, out_key = rope.forward(positions, query, key)
+    # Compare the results.
+    torch.testing.assert_close(out_query,
+                               ref_query,
+                               atol=get_default_atol(out_query),
+                               rtol=get_default_rtol(out_query))
+    torch.testing.assert_close(out_key,
+                               ref_key,
+                               atol=get_default_atol(out_key),
+                               rtol=get_default_rtol(out_key))
+
+
+@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
+@pytest.mark.parametrize("tensor_shape_fn", TENSORS_SHAPES_FN)
+@pytest.mark.parametrize("batch_size", BATCH_SIZES)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@torch.inference_mode()
+def test_batched_rotary_embedding(
+    is_neox_style: bool,
+    tensor_shape_fn: Callable[[int, int, int, int], tuple[int]],
+    batch_size: int,
+    seq_len: int,
+    num_heads: int,
+    head_size: int,
+    rotary_dim: Optional[int],
+    dtype: torch.dtype,
+    seed: int,
+    device: str,
+    max_position: int = 8192,
+    base: int = 10000,
+) -> None:
+    current_platform.seed_everything(seed)
+    torch.set_default_device(device)
+    if rotary_dim is None:
+        rotary_dim = head_size
+    rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style, {
+        "rope_type": "linear",
+        "factor": (1, )
+    })
+    rope = rope.to(dtype=dtype)
+
+    positions = torch.randint(0, max_position, (batch_size, seq_len))
+    query_shape = tensor_shape_fn(batch_size, seq_len, num_heads, head_size)
+    query = torch.randn(query_shape, dtype=dtype)
+    key = torch.randn_like(query)
+
+    # NOTE(woosuk): The reference implementation should be executed first
+    # because the custom kernel is in-place.
+    ref_query, ref_key = rope.forward_native(positions, query, key)
+    out_query, out_key = rope.forward(positions,
+                                      query,
+                                      key,
+                                      offsets=torch.zeros(batch_size * seq_len,
+                                                          dtype=torch.long,
+                                                          device=device))
+    # Compare the results.
+    torch.testing.assert_close(out_query,
+                               ref_query,
+                               atol=get_default_atol(out_query),
+                               rtol=get_default_rtol(out_query))
+    torch.testing.assert_close(out_key,
+                               ref_key,
+                               atol=get_default_atol(out_key),
+                               rtol=get_default_rtol(out_key))
+
+
+@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
+@pytest.mark.parametrize("batch_size", BATCH_SIZES)
+@pytest.mark.parametrize("seq_len", SEQ_LENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@torch.inference_mode()
+def test_batched_rotary_embedding_multi_lora(
+    is_neox_style: bool,
+    batch_size: int,
+    seq_len: int,
+    num_heads: int,
+    head_size: int,
+    rotary_dim: Optional[int],
+    dtype: torch.dtype,
+    seed: int,
+    device: str,
+    max_position: int = 8192,
+    base: int = 10000,
+) -> None:
+    current_platform.seed_everything(seed)
+    torch.set_default_device(device)
+    if rotary_dim is None:
+        rotary_dim = head_size
+    scaling_factors: List[int] = [1, 2, 4]
+    rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style, {
+        "rope_type": "linear",
+        "factor": tuple(scaling_factors)
+    })
+    rope = rope.to(dtype=dtype)
+
+    positions = torch.randint(0, max_position, (batch_size, seq_len))
+    query = torch.randn(batch_size,
+                        seq_len,
+                        num_heads * head_size,
+                        dtype=dtype)
+    key = torch.randn_like(query)
+
+    offset_map = torch.tensor(
+        list(
+            accumulate([0] + [
+                max_position * scaling_factor * 2
+                for scaling_factor in scaling_factors[:-1]
+            ])))
+    query_types = torch.randint(0,
+                                len(scaling_factors), (batch_size, seq_len),
+                                device=device)
+    query_offsets = offset_map[query_types]
+
+    # NOTE(woosuk): The reference implementation should be executed first
+    # because the custom kernel is in-place.
+    ref_query, ref_key = rope.forward_native(positions, query, key,
+                                             query_offsets)
+    out_query, out_key = rope.forward(positions, query, key,
+                                      query_offsets.flatten())
+    # Compare the results.
+    torch.testing.assert_close(out_query,
+                               ref_query,
+                               atol=get_default_atol(out_query),
+                               rtol=get_default_rtol(out_query))
+    torch.testing.assert_close(out_key,
+                               ref_key,
+                               atol=get_default_atol(out_key),
+                               rtol=get_default_rtol(out_key))
+
+
+@torch.inference_mode()
+def test_rope_module_cache():
+    MAX_POSITIONS = [123, 1234]
+    BASES = [10000, 1000000]
+    ROPE_SCALINGS = (None, {
+        "rope_type": "linear",
+        "factor": (1, )
+    }, {
+        "rope_type": "dynamic",
+        "factor": 1
+    })
+    settings = (HEAD_SIZES, ROTARY_DIMS, MAX_POSITIONS, BASES, IS_NEOX_STYLE,
+                ROPE_SCALINGS, DTYPES)
+    rope_setting_id_map: Dict[str, int] = {}
+    for setting in product(*settings):
+        head_size, rotary_dim, max_position, base, \
+            is_neox_stype, rope_scaling, dtype = setting
+        if rotary_dim is None:
+            rotary_dim = head_size
+        rope = get_rope(head_size, rotary_dim, max_position, base,
+                        is_neox_stype, rope_scaling, dtype)
+        # different settings cannot share the same rope module
+        assert id(rope) not in rope_setting_id_map.values()
+        assert all(x.dtype == dtype for x in rope.buffers())
+        assert all(x.dtype == dtype for x in rope.parameters())
+        rope_setting_id_map[str(setting)] = id(rope)
+
+    for setting in product(*settings):
+        head_size, rotary_dim, max_position, base, \
+            is_neox_stype, rope_scaling, dtype = setting
+        if rotary_dim is None:
+            rotary_dim = head_size
+        rope = get_rope(head_size, rotary_dim, max_position, base,
+                        is_neox_stype, rope_scaling, dtype)
+        # check if cache take effect
+        assert id(rope) == rope_setting_id_map[str(setting)]