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vllm-omni/vllm_omni/diffusion/attention/backends/flash_attn.py
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Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import torch
from vllm.logger import init_logger
from vllm_omni.diffusion.attention.backends.abstract import (
AttentionBackend,
AttentionImpl,
AttentionMetadata,
)
logger = init_logger(__name__)
try:
# only tested with flash_attn v3
# from flash_attn_interface import flash_attn_func as flash_attn_3_func # not available in flash-attn 2.8.1
from flash_attn import flash_attn_func # can be FA2 or FA3
except ImportError:
logger.warning(
"FlashAttentionBackend is not available. You may install flash-attn "
"by running `uv pip install flash-attn==2.8.1 --no-build-isolation`"
" or install pre-built flash-attn from https://github.com/Dao-AILab/flash-attention/releases"
)
raise ImportError
class FlashAttentionBackend(AttentionBackend):
accept_output_buffer: bool = True
@staticmethod
def get_supported_head_sizes() -> list[int]:
return [64, 96, 128, 192, 256]
@staticmethod
def get_name() -> str:
return "FLASH_ATTN"
@staticmethod
def get_impl_cls() -> type["FlashAttentionImpl"]:
return FlashAttentionImpl
class FlashAttentionImpl(AttentionImpl):
def __init__(
self,
num_heads: int,
head_size: int,
softmax_scale: float,
causal: bool = False,
num_kv_heads: int | None = None,
prefix: str = "",
**extra_impl_args,
) -> None:
self.num_heads = num_heads
self.causal = causal
self.softmax_scale = softmax_scale
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_metadata: AttentionMetadata = None,
) -> torch.Tensor:
# TODO: flash_attn_func does not support attn_mask.
out: torch.Tensor = flash_attn_func(
query,
key,
value,
causal=self.causal,
softmax_scale=self.softmax_scale,
)
return out