e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
247 lines
9.0 KiB
Python
247 lines
9.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from itertools import accumulate, product
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from typing import Dict, List, Optional
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import pytest
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import torch
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.platforms import current_platform
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from .allclose_default import get_default_atol, get_default_rtol
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IS_NEOX_STYLE = [True, False]
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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HEAD_SIZES = [64, 80, 112, 120, 256]
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ROTARY_DIMS = [None, 32] # None means rotary dim == head size
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NUM_HEADS = [17] # Arbitrary values for testing
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BATCH_SIZES = [5] # Arbitrary values for testing
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SEQ_LENS = [11, 8192] # Arbitrary values for testing
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SEEDS = [0]
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CUDA_DEVICES = [
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f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
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]
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@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
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@pytest.mark.parametrize("batch_size", BATCH_SIZES)
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@pytest.mark.parametrize("seq_len", SEQ_LENS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@torch.inference_mode()
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def test_rotary_embedding(
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is_neox_style: bool,
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batch_size: int,
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seq_len: int,
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num_heads: int,
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head_size: int,
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rotary_dim: Optional[int],
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dtype: torch.dtype,
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seed: int,
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device: str,
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max_position: int = 8192,
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base: int = 10000,
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) -> None:
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if rotary_dim is None:
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rotary_dim = head_size
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current_platform.seed_everything(seed)
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torch.set_default_device(device)
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if rotary_dim is None:
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rotary_dim = head_size
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rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style)
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rope = rope.to(dtype=dtype)
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positions = torch.randint(0, max_position, (batch_size, seq_len))
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query = torch.randn(batch_size,
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seq_len,
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num_heads * head_size,
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dtype=dtype)
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key = torch.randn_like(query)
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# NOTE(woosuk): The reference implementation should be executed first
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# because the custom kernel is in-place.
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ref_query, ref_key = rope.forward_native(positions, query, key)
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out_query, out_key = rope.forward(positions, query, key)
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# Compare the results.
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torch.testing.assert_close(out_query,
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ref_query,
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atol=get_default_atol(out_query),
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rtol=get_default_rtol(out_query))
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torch.testing.assert_close(out_key,
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ref_key,
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atol=get_default_atol(out_key),
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rtol=get_default_rtol(out_key))
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@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
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@pytest.mark.parametrize("batch_size", BATCH_SIZES)
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@pytest.mark.parametrize("seq_len", SEQ_LENS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@torch.inference_mode()
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def test_batched_rotary_embedding(
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is_neox_style: bool,
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batch_size: int,
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seq_len: int,
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num_heads: int,
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head_size: int,
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rotary_dim: Optional[int],
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dtype: torch.dtype,
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seed: int,
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device: str,
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max_position: int = 8192,
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base: int = 10000,
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) -> None:
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current_platform.seed_everything(seed)
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torch.set_default_device(device)
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if rotary_dim is None:
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rotary_dim = head_size
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rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style, {
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"rope_type": "linear",
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"factor": (1, )
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})
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rope = rope.to(dtype=dtype)
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positions = torch.randint(0, max_position, (batch_size, seq_len))
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query = torch.randn(batch_size,
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seq_len,
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num_heads * head_size,
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dtype=dtype)
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key = torch.randn_like(query)
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# NOTE(woosuk): The reference implementation should be executed first
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# because the custom kernel is in-place.
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ref_query, ref_key = rope.forward_native(positions, query, key)
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out_query, out_key = rope.forward(positions,
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query,
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key,
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offsets=torch.zeros(batch_size * seq_len,
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dtype=torch.long,
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device=device))
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# Compare the results.
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torch.testing.assert_close(out_query,
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ref_query,
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atol=get_default_atol(out_query),
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rtol=get_default_rtol(out_query))
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torch.testing.assert_close(out_key,
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ref_key,
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atol=get_default_atol(out_key),
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rtol=get_default_rtol(out_key))
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@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
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@pytest.mark.parametrize("batch_size", BATCH_SIZES)
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@pytest.mark.parametrize("seq_len", SEQ_LENS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@torch.inference_mode()
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def test_batched_rotary_embedding_multi_lora(
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is_neox_style: bool,
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batch_size: int,
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seq_len: int,
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num_heads: int,
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head_size: int,
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rotary_dim: Optional[int],
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dtype: torch.dtype,
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seed: int,
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device: str,
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max_position: int = 8192,
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base: int = 10000,
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) -> None:
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current_platform.seed_everything(seed)
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torch.set_default_device(device)
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if rotary_dim is None:
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rotary_dim = head_size
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scaling_factors: List[int] = [1, 2, 4]
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rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style, {
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"rope_type": "linear",
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"factor": tuple(scaling_factors)
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})
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rope = rope.to(dtype=dtype)
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positions = torch.randint(0, max_position, (batch_size, seq_len))
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query = torch.randn(batch_size,
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seq_len,
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num_heads * head_size,
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dtype=dtype)
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key = torch.randn_like(query)
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offset_map = torch.tensor(
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list(
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accumulate([0] + [
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max_position * scaling_factor * 2
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for scaling_factor in scaling_factors[:-1]
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])))
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query_types = torch.randint(0,
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len(scaling_factors), (batch_size, seq_len),
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device=device)
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query_offsets = offset_map[query_types]
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# NOTE(woosuk): The reference implementation should be executed first
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# because the custom kernel is in-place.
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ref_query, ref_key = rope.forward_native(positions, query, key,
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query_offsets)
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out_query, out_key = rope.forward(positions, query, key,
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query_offsets.flatten())
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# Compare the results.
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torch.testing.assert_close(out_query,
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ref_query,
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atol=get_default_atol(out_query),
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rtol=get_default_rtol(out_query))
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torch.testing.assert_close(out_key,
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ref_key,
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atol=get_default_atol(out_key),
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rtol=get_default_rtol(out_key))
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@torch.inference_mode()
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def test_rope_module_cache():
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MAX_POSITIONS = [123, 1234]
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BASES = [10000, 1000000]
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ROPE_SCALINGS = (None, {
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"rope_type": "linear",
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"factor": (1, )
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}, {
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"rope_type": "dynamic",
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"factor": 1
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})
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settings = (HEAD_SIZES, ROTARY_DIMS, MAX_POSITIONS, BASES, IS_NEOX_STYLE,
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ROPE_SCALINGS, DTYPES)
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rope_setting_id_map: Dict[str, int] = {}
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for setting in product(*settings):
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head_size, rotary_dim, max_position, base, \
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is_neox_stype, rope_scaling, dtype = setting
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if rotary_dim is None:
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rotary_dim = head_size
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rope = get_rope(head_size, rotary_dim, max_position, base,
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is_neox_stype, rope_scaling, dtype)
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# different settings cannot share the same rope module
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assert id(rope) not in rope_setting_id_map.values()
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assert all(x.dtype == dtype for x in rope.buffers())
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assert all(x.dtype == dtype for x in rope.parameters())
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rope_setting_id_map[str(setting)] = id(rope)
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for setting in product(*settings):
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head_size, rotary_dim, max_position, base, \
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is_neox_stype, rope_scaling, dtype = setting
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if rotary_dim is None:
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rotary_dim = head_size
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rope = get_rope(head_size, rotary_dim, max_position, base,
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is_neox_stype, rope_scaling, dtype)
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# check if cache take effect
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assert id(rope) == rope_setting_id_map[str(setting)]
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