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>
257 lines
9.3 KiB
Python
257 lines
9.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import List, Optional, Tuple
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import pytest
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import torch
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from vllm.platforms import current_platform
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from vllm.vllm_flash_attn import (fa_version_unsupported_reason,
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flash_attn_varlen_func,
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flash_attn_with_kvcache,
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is_fa_version_supported)
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NUM_HEADS = [(4, 4), (8, 2), (16, 2)]
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HEAD_SIZES = [128, 256]
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BLOCK_SIZES = [16, 32]
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DTYPES = [torch.float16, torch.bfloat16]
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# one value large enough to test overflow in index calculation.
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# one value small enough to test the schema op check
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NUM_BLOCKS = [32768, 2048]
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def ref_paged_attn(
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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query_lens: List[int],
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kv_lens: List[int],
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block_tables: torch.Tensor,
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scale: float,
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sliding_window: Optional[int] = None,
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soft_cap: Optional[float] = None,
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) -> torch.Tensor:
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num_seqs = len(query_lens)
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block_tables = block_tables.cpu().numpy()
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_, block_size, num_kv_heads, head_size = key_cache.shape
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outputs: List[torch.Tensor] = []
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start_idx = 0
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for i in range(num_seqs):
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query_len = query_lens[i]
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kv_len = kv_lens[i]
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q = query[start_idx:start_idx + query_len]
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q *= scale
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num_kv_blocks = (kv_len + block_size - 1) // block_size
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block_indices = block_tables[i, :num_kv_blocks]
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k = key_cache[block_indices].view(-1, num_kv_heads, head_size)
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k = k[:kv_len]
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v = value_cache[block_indices].view(-1, num_kv_heads, head_size)
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v = v[:kv_len]
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if q.shape[1] != k.shape[1]:
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k = torch.repeat_interleave(k, q.shape[1] // k.shape[1], dim=1)
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v = torch.repeat_interleave(v, q.shape[1] // v.shape[1], dim=1)
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attn = torch.einsum("qhd,khd->hqk", q, k).float()
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empty_mask = torch.ones(query_len, kv_len)
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mask = torch.triu(empty_mask, diagonal=kv_len - query_len + 1).bool()
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if sliding_window is not None:
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sliding_window_mask = torch.triu(empty_mask,
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diagonal=kv_len -
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(query_len + sliding_window) +
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1).bool().logical_not()
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mask |= sliding_window_mask
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if soft_cap is not None:
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attn = soft_cap * torch.tanh(attn / soft_cap)
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attn.masked_fill_(mask, float("-inf"))
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attn = torch.softmax(attn, dim=-1).to(v.dtype)
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out = torch.einsum("hqk,khd->qhd", attn, v)
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outputs.append(out)
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start_idx += query_len
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return torch.cat(outputs, dim=0)
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@pytest.mark.parametrize("use_out", [True, False])
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@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
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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("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0])
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("sliding_window", [None, 256])
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@pytest.mark.parametrize("fa_version", [2, 3])
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@torch.inference_mode()
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def test_flash_attn_with_paged_kv(
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use_out: bool,
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kv_lens: List[int],
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num_heads: Tuple[int, int],
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head_size: int,
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dtype: torch.dtype,
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block_size: int,
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soft_cap: Optional[float],
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num_blocks: int,
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sliding_window: Optional[int],
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fa_version: int,
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) -> None:
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torch.set_default_device("cuda")
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if not is_fa_version_supported(fa_version):
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pytest.skip(f"Flash attention version {fa_version} not supported due "
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f"to: \"{fa_version_unsupported_reason(fa_version)}\"")
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current_platform.seed_everything(0)
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num_seqs = len(kv_lens)
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num_query_heads = num_heads[0]
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num_kv_heads = num_heads[1]
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assert num_query_heads % num_kv_heads == 0
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max_kv_len = max(kv_lens)
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scale = head_size**-0.5
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window_size = ((sliding_window - 1, 0) if sliding_window is not None else
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(-1, -1))
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query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)
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key_cache = torch.randn(num_blocks,
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block_size,
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num_kv_heads,
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head_size,
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dtype=dtype)
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value_cache = torch.randn_like(key_cache)
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kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int32)
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max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
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block_tables = torch.randint(0,
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num_blocks,
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(num_seqs, max_num_blocks_per_seq),
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dtype=torch.int32)
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q = query.unsqueeze(1)
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out = torch.empty_like(q) if use_out else None
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output = flash_attn_with_kvcache(
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q=q,
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k_cache=key_cache,
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v_cache=value_cache,
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out=out,
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softmax_scale=scale,
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causal=True,
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block_table=block_tables,
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cache_seqlens=kv_lens_tensor,
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softcap=soft_cap if soft_cap is not None else 0,
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window_size=window_size,
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fa_version=fa_version,
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)
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output = output if not use_out else out
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output = output.squeeze(1)
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ref_output = ref_paged_attn(query=query,
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key_cache=key_cache,
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value_cache=value_cache,
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query_lens=[1] * num_seqs,
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kv_lens=kv_lens,
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block_tables=block_tables,
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scale=scale,
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soft_cap=soft_cap,
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sliding_window=sliding_window)
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torch.testing.assert_close(output, ref_output, atol=2e-2, rtol=1e-2), \
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f"{torch.max(torch.abs(output - ref_output))}"
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@pytest.mark.parametrize("use_out", [True, False])
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@pytest.mark.parametrize("seq_lens",
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[[(1, 1328), (5, 18),
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(129, 463)], [(1, 523), (1, 37), (1, 2011)]])
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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("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("sliding_window", [None, 256])
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0])
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("fa_version", [2, 3])
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@torch.inference_mode()
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def test_varlen_with_paged_kv(
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use_out: bool,
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seq_lens: List[Tuple[int, int]],
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num_heads: Tuple[int, int],
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head_size: int,
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sliding_window: Optional[int],
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dtype: torch.dtype,
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block_size: int,
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soft_cap: Optional[float],
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num_blocks: int,
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fa_version: int,
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) -> None:
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torch.set_default_device("cuda")
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if not is_fa_version_supported(fa_version):
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pytest.skip(f"Flash attention version {fa_version} not supported due "
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f"to: \"{fa_version_unsupported_reason(fa_version)}\"")
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current_platform.seed_everything(0)
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num_seqs = len(seq_lens)
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query_lens = [x[0] for x in seq_lens]
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kv_lens = [x[1] for x in seq_lens]
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num_query_heads = num_heads[0]
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num_kv_heads = num_heads[1]
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assert num_query_heads % num_kv_heads == 0
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max_query_len = max(query_lens)
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max_kv_len = max(kv_lens)
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window_size = ((sliding_window - 1, 0) if sliding_window is not None else
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(-1, -1))
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scale = head_size**-0.5
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query = torch.randn(sum(query_lens),
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num_query_heads,
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head_size,
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dtype=dtype)
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key_cache = torch.randn(num_blocks,
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block_size,
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num_kv_heads,
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head_size,
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dtype=dtype)
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value_cache = torch.randn_like(key_cache)
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cu_query_lens = torch.tensor([0] + query_lens,
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dtype=torch.int32).cumsum(dim=0,
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dtype=torch.int32)
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kv_lens = torch.tensor(kv_lens, dtype=torch.int32)
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max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
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block_tables = torch.randint(0,
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num_blocks,
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(num_seqs, max_num_blocks_per_seq),
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dtype=torch.int32)
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out = torch.empty_like(query) if use_out else None
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output = flash_attn_varlen_func(
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q=query,
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k=key_cache,
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v=value_cache,
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out=out,
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cu_seqlens_q=cu_query_lens,
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seqused_k=kv_lens,
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max_seqlen_q=max_query_len,
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max_seqlen_k=max_kv_len,
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softmax_scale=scale,
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causal=True,
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window_size=window_size,
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block_table=block_tables,
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softcap=soft_cap if soft_cap is not None else 0,
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fa_version=fa_version,
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)
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output = output if not use_out else out
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ref_output = ref_paged_attn(
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query=query,
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key_cache=key_cache,
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value_cache=value_cache,
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query_lens=query_lens,
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kv_lens=kv_lens,
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block_tables=block_tables,
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scale=scale,
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sliding_window=sliding_window,
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soft_cap=soft_cap,
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)
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torch.testing.assert_close(output, ref_output, atol=2e-2, rtol=1e-2), \
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f"{torch.max(torch.abs(output - ref_output))}"
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