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[Attention] Refactor CUDA attention backend selection logic (#24794)

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Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>
Signed-off-by: Matthew Bonanni <mbonanni001@gmail.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
This commit is contained in:
Matthew Bonanni
2025-11-11 06:40:44 -06:00
committed by GitHub
parent 2e78150d24
commit b30dfa03c5
61 changed files with 1338 additions and 1002 deletions
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42
vllm/model_executor/models/qwen2_5_vl.py
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@@ -42,7 +42,7 @@ from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import (
Qwen2_5_VLVisionConfig,
)
from vllm.attention.backends.registry import _Backend
from vllm.attention.backends.registry import AttentionBackendEnum
from vllm.attention.layer import maybe_get_vit_flash_attn_backend
from vllm.attention.ops.vit_attn_wrappers import (
vit_flash_attn_wrapper,
@@ -315,9 +315,9 @@ class Qwen2_5_VisionAttention(nn.Module):
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
attn_backend: _Backend = _Backend.TORCH_SDPA,
attn_backend: AttentionBackendEnum = AttentionBackendEnum.TORCH_SDPA,
use_upstream_fa: bool = False,
attn_backend_override: _Backend | None = None,
attn_backend_override: AttentionBackendEnum | None = None,
) -> None:
super().__init__()
# Per attention head and per partition values.
@@ -364,13 +364,16 @@ class Qwen2_5_VisionAttention(nn.Module):
# On ROCm with FLASH_ATTN backend, upstream flash_attn is used
from vllm.platforms import current_platform
if current_platform.is_rocm() and self.attn_backend == _Backend.FLASH_ATTN:
if (
current_platform.is_rocm()
and self.attn_backend == AttentionBackendEnum.FLASH_ATTN
):
self.use_upstream_fa = True
if current_platform.is_xpu():
self.use_upstream_fa = False
self.is_flash_attn_backend = self.attn_backend in {
_Backend.FLASH_ATTN,
_Backend.ROCM_AITER_FA,
AttentionBackendEnum.FLASH_ATTN,
AttentionBackendEnum.ROCM_AITER_FA,
}
def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
@@ -431,10 +434,10 @@ class Qwen2_5_VisionAttention(nn.Module):
cu_seqlens,
max_seqlen,
batch_size,
self.attn_backend == _Backend.ROCM_AITER_FA,
self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA,
self.use_upstream_fa,
)
elif self.attn_backend == _Backend.TORCH_SDPA:
elif self.attn_backend == AttentionBackendEnum.TORCH_SDPA:
# Execute attention entry by entry for speed & less VRAM.
from vllm.platforms import current_platform
@@ -450,7 +453,7 @@ class Qwen2_5_VisionAttention(nn.Module):
v,
cu_seqlens,
)
elif self.attn_backend == _Backend.XFORMERS:
elif self.attn_backend == AttentionBackendEnum.XFORMERS:
context_layer = vit_xformers_attn_wrapper(q, k, v, seqlens)
output, _ = self.proj(context_layer)
@@ -478,9 +481,9 @@ class Qwen2_5_VisionBlock(nn.Module):
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
attn_backend: _Backend = _Backend.TORCH_SDPA,
attn_backend: AttentionBackendEnum = AttentionBackendEnum.TORCH_SDPA,
use_upstream_fa: bool = False,
attn_backend_override: _Backend | None = None,
attn_backend_override: AttentionBackendEnum | None = None,
) -> None:
super().__init__()
if norm_layer is None:
@@ -656,7 +659,7 @@ class Qwen2_5_VisionTransformer(nn.Module):
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
attn_backend_override: _Backend | None = None,
attn_backend_override: AttentionBackendEnum | None = None,
) -> None:
super().__init__()
@@ -708,10 +711,10 @@ class Qwen2_5_VisionTransformer(nn.Module):
)
if self.attn_backend not in {
_Backend.FLASH_ATTN,
_Backend.TORCH_SDPA,
_Backend.XFORMERS,
_Backend.ROCM_AITER_FA,
AttentionBackendEnum.FLASH_ATTN,
AttentionBackendEnum.TORCH_SDPA,
AttentionBackendEnum.XFORMERS,
AttentionBackendEnum.ROCM_AITER_FA,
}:
raise RuntimeError(
f"Qwen2.5-VL does not support {self.attn_backend} backend now."
@@ -850,9 +853,12 @@ class Qwen2_5_VisionTransformer(nn.Module):
) -> tuple[torch.Tensor, torch.Tensor]:
max_seqlen = torch.zeros([], device=cu_seqlens.device)
seqlens = torch.zeros(1, device=cu_seqlens.device)
if self.attn_backend in {_Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA}:
if self.attn_backend in {
AttentionBackendEnum.FLASH_ATTN,
AttentionBackendEnum.ROCM_AITER_FA,
}:
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
elif self.attn_backend == _Backend.XFORMERS:
elif self.attn_backend == AttentionBackendEnum.XFORMERS:
seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
return max_seqlen, seqlens