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[Core] Deprecate xformers (#29262)

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Signed-off-by: Roger Wang <hey@rogerw.io>
This commit is contained in:
Roger Wang
2025-11-23 20:18:55 -08:00
committed by GitHub
parent 5253f4276f
commit 0ff70821c9
31 changed files with 77 additions and 963 deletions
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31
vllm/model_executor/models/qwen2_vl.py
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@@ -348,7 +348,6 @@ class Qwen2VisionAttention(nn.Module):
if self.attn_backend not in {
AttentionBackendEnum.FLASH_ATTN,
AttentionBackendEnum.TORCH_SDPA,
AttentionBackendEnum.XFORMERS,
AttentionBackendEnum.ROCM_AITER_FA,
}:
raise RuntimeError(
@@ -384,7 +383,6 @@ class Qwen2VisionAttention(nn.Module):
rotary_pos_emb_cos: torch.Tensor,
rotary_pos_emb_sin: torch.Tensor,
max_seqlen: int | None = None, # Only used for Flash Attention
seqlens: list[int] | None = None, # Only used for xFormers
) -> torch.Tensor:
# [s, b, c] --> [s, b, 3 * head * head_dim]
x, _ = self.qkv(x)
@@ -445,20 +443,6 @@ class Qwen2VisionAttention(nn.Module):
context_layer = rearrange(
context_layer, "b s h d -> s b (h d)"
).contiguous()
elif self.attn_backend == AttentionBackendEnum.XFORMERS:
from xformers import ops as xops
from xformers.ops.fmha.attn_bias import BlockDiagonalMask
attn_bias = BlockDiagonalMask.from_seqlens(
q_seqlen=seqlens, kv_seqlen=None, device=q.device
)
context_layer = xops.memory_efficient_attention_forward(
q, k, v, attn_bias=attn_bias, p=0, scale=None
)
context_layer = rearrange(
context_layer, "b s h d -> s b (h d)"
).contiguous()
output, _ = self.proj(context_layer)
return output
@@ -509,7 +493,6 @@ class Qwen2VisionBlock(nn.Module):
rotary_pos_emb_cos: torch.Tensor,
rotary_pos_emb_sin: torch.Tensor,
max_seqlen: int | None = None, # Only used for Flash Attention
seqlens: list[int] | None = None, # Only used for xFormers
) -> torch.Tensor:
x = x + self.attn(
self.norm1(x),
@@ -517,7 +500,6 @@ class Qwen2VisionBlock(nn.Module):
rotary_pos_emb_cos=rotary_pos_emb_cos,
rotary_pos_emb_sin=rotary_pos_emb_sin,
max_seqlen=max_seqlen,
seqlens=seqlens,
)
x = x + self.mlp(self.norm2(x))
@@ -728,18 +710,14 @@ class Qwen2VisionTransformer(nn.Module):
sin_combined = sin[pos_ids].flatten(1)
return cos_combined, sin_combined
def compute_attn_mask_seqlen(
self, cu_seqlens: torch.Tensor
) -> tuple[int | None, list[int] | None]:
max_seqlen, seqlens = None, None
def compute_attn_mask_seqlen(self, cu_seqlens: torch.Tensor) -> int | None:
max_seqlen = None
if self.attn_backend in {
AttentionBackendEnum.FLASH_ATTN,
AttentionBackendEnum.ROCM_AITER_FA,
}:
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
elif self.attn_backend == AttentionBackendEnum.XFORMERS:
seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
return max_seqlen, seqlens
return max_seqlen
def forward(
self,
@@ -771,7 +749,7 @@ class Qwen2VisionTransformer(nn.Module):
x = x.unsqueeze(1)
# pre-compute seqlens for attn mask to reduce cuMemcpy operations
max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
cu_seqlens = cu_seqlens.to(self.device, non_blocking=True)
for blk in self.blocks:
x = blk(
@@ -780,7 +758,6 @@ class Qwen2VisionTransformer(nn.Module):
rotary_pos_emb_cos=rotary_pos_emb_cos,
rotary_pos_emb_sin=rotary_pos_emb_sin,
max_seqlen=max_seqlen,
seqlens=seqlens,
)
# adapter