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Upgrade to vllm 0.17.0 corex v4.1 overlay

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LZiBee
2026-04-29 19:38:22 +08:00
parent 8fac6062e4
commit 938d0854a5
430 changed files with 35969 additions and 14511 deletions
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27
vllm/model_executor/models/pixtral.py
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@@ -83,15 +83,16 @@ from .vision import (
resolve_visual_encoder_outputs,
)
import ixformer.inference.functions as ixf
try:
# Note: vLLM does not install xformers by default.
from xformers import ops as xops
if current_platform.is_cuda() and current_platform.has_device_capability(100):
if current_platform.is_cuda():
# Xformers FA is not compatible with B200
USE_XFORMERS_OPS = False
else:
USE_XFORMERS_OPS = True
else:
USE_XFORMERS_OPS = False
except ImportError:
USE_XFORMERS_OPS = False
@@ -698,23 +699,21 @@ class Attention(nn.Module):
q, k, v = self.wq(x), self.wk(x), self.wv(x)
q = q.reshape(batch, patches, self.n_heads, self.head_dim)
k = k.reshape(batch, patches, self.n_heads, self.head_dim)
v = v.reshape(batch, patches, self.n_heads, self.head_dim)
q, k = apply_rotary_emb_vit(q, k, freqs_cis=freqs_cis)
if USE_XFORMERS_OPS:
out = xops.memory_efficient_attention(q, k, v, attn_bias=mask)
v = v.reshape(batch * patches, self.n_heads, self.head_dim)
q, k = apply_rotary_emb_vit(q, k, freqs_cis=freqs_cis)
q = q.view(batch * patches, self.n_heads, self.head_dim)
k = k.view(batch * patches, self.n_heads, self.head_dim)
out = ixf.ixinfer_flash_attn_unpad(q,k,v, mask.q_seqinfo.seqstart.to(q.device), mask.k_seqinfo.seqstart.to(q.device), mask.q_seqinfo.max_seqlen, mask.k_seqinfo.max_seqlen)
# out = memory_efficient_attention(q, k, v, attn_bias=mask)
else:
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
out = nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
out = out.transpose(1, 2)
assert False, "xformers failed !"
out = out.reshape(batch, patches, self.n_heads * self.head_dim)
return self.wo(out)
class TransformerBlock(nn.Module):
def __init__(self, args: VisionEncoderArgs):
super().__init__()