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upgrade vLLM to main (#4608)

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1. fix https://github.com/vllm-project/vllm/pull/28542
The model structure modifications we involved in are:
     - Qwen2.5-VL(still exist some patch)
     - Qwen2-VL
     - Qwen2
     - DeepSeek series
     - Qwen-moe series
2. fix https://github.com/vllm-project/vllm/pull/29121
   the output token now  type changed from np to `list[list[int]]`

3. fix https://github.com/vllm-project/vllm/pull/29262
    `xformers` backend for multimodal now has been deprecated
4. fix https://github.com/vllm-project/vllm/pull/29342

5. fix https://github.com/vllm-project/vllm/pull/28579
6. fix https://github.com/vllm-project/vllm/pull/28718
7. fix https://github.com/vllm-project/vllm/issues/28665
8. fix https://github.com/vllm-project/vllm/pull/26847
vllm introduced the `optimization-level`, some default config has been
changed, and the param `--enforce-eager` has been deprecated
9. fix http://github.com/vllm-project/vllm/pull/29223 it retuns tuple
for sampler.
10. fix https://github.com/vllm-project/vllm/pull/29471 we'll remove the
related patch to avoid this kind of error.

Co-authored-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: wangli <wangli858794774@gmail.com>


- vLLM version: v0.11.2

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
This commit is contained in:
wangxiyuan
2025-12-02 22:10:52 +08:00
committed by GitHub
parent 4588cdac02
commit 7f2673ea2d
60 changed files with 383 additions and 374 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
vllm_ascend/patch/worker/patch_qwen2_5_vl.py
View File

@@ -27,8 +27,7 @@ from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import \
from transformers.models.qwen2_vl.configuration_qwen2_vl import \
Qwen2VLVisionConfig
from vllm.attention.backends.registry import AttentionBackendEnum
from vllm.attention.layer import (check_upstream_fa_availability,
maybe_get_vit_flash_attn_backend)
from vllm.attention.layer import maybe_get_vit_flash_attn_backend
from vllm.model_executor.layers.activation import get_act_and_mul_fn
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -65,7 +64,6 @@ class AscendQwen2_5_VisionAttention(nn.Module):
rotary_pos_emb_cos: torch.Tensor,
rotary_pos_emb_sin: torch.Tensor,
max_seqlen: torch.Tensor,
seqlens: torch.Tensor = None,
) -> torch.Tensor:
# [s, b, c] --> [s, b, head * 3 * head_dim]
x, _ = self.qkv(x)
@@ -141,7 +139,6 @@ class AscendQwen2VisionBlock(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),
@@ -149,7 +146,6 @@ class AscendQwen2VisionBlock(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))
return x
@@ -198,7 +194,6 @@ class AscendQwen2VisionTransformer(nn.Module):
head_size=head_dim,
rotary_dim=head_dim // 2,
max_position=8192,
base=10000.0,
is_neox_style=True,
)
@@ -228,10 +223,6 @@ class AscendQwen2VisionTransformer(nn.Module):
attn_backend_override=attn_backend_override,
)
if (self.attn_backend != AttentionBackendEnum.FLASH_ATTN
and check_upstream_fa_availability(torch.get_default_dtype())):
self.attn_backend = AttentionBackendEnum.FLASH_ATTN
def rot_pos_emb(
self,
grid_thw: list[list[int]]) -> tuple[torch.Tensor, torch.Tensor]:
@@ -300,7 +291,7 @@ class AscendQwen2VisionTransformer(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)
for blk in self.blocks:
x = blk(
x,
@@ -308,7 +299,6 @@ class AscendQwen2VisionTransformer(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
@@ -326,7 +316,6 @@ class AscendQwen2_5_VisionBlock(nn.Module):
rotary_pos_emb_cos: torch.Tensor,
rotary_pos_emb_sin: torch.Tensor,
max_seqlen: torch.Tensor, # Only used for Flash Attention
seqlens: torch.Tensor, # Only used for xFormers
) -> torch.Tensor:
x_attn = self.attn(
self.norm1(x),
@@ -334,7 +323,6 @@ class AscendQwen2_5_VisionBlock(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_fused_norm, residual = self.norm2(x, residual=x_attn)
x = residual + self.mlp(x_fused_norm)
@@ -388,11 +376,9 @@ class AscendQwen2_5_VisionTransformer(nn.Module):
head_size=head_dim,
rotary_dim=head_dim // 2,
max_position=8192,
base=10000.0,
is_neox_style=True,
)
use_upstream_fa = False
self.attn_backend = get_vit_attn_backend(
head_size=head_dim,
dtype=torch.get_default_dtype(),
@@ -402,7 +388,6 @@ class AscendQwen2_5_VisionTransformer(nn.Module):
self.attn_backend, self.flash_attn_varlen_func = (
maybe_get_vit_flash_attn_backend(
self.attn_backend,
use_upstream_fa,
attn_backend_override=attn_backend_override,
))
@@ -418,7 +403,6 @@ class AscendQwen2_5_VisionTransformer(nn.Module):
prefix=f"{prefix}.blocks.{layer_idx}",
use_data_parallel=use_data_parallel,
attn_backend=self.attn_backend,
use_upstream_fa=use_upstream_fa,
attn_backend_override=attn_backend_override,
) for layer_idx in range(depth)
])
@@ -553,10 +537,8 @@ class AscendQwen2_5_VisionTransformer(nn.Module):
# transformers
# pre-compute seqlens for window/full attn to reduce cuMemcpy operations
max_seqlen_full, seqlens_full = self.compute_attn_mask_seqlen(
cu_seqlens)
max_seqlen_window, seqlens_window = self.compute_attn_mask_seqlen(
cu_window_seqlens)
max_seqlen_full = self.compute_attn_mask_seqlen(cu_seqlens)
max_seqlen_window = self.compute_attn_mask_seqlen(cu_window_seqlens)
cu_seqlens = cu_seqlens.to( # type: ignore[attr-defined]
device=self.device,
@@ -587,11 +569,9 @@ class AscendQwen2_5_VisionTransformer(nn.Module):
if layer_num in self.fullatt_block_indexes:
cu_seqlens_now = cu_seqlens
max_seqlen_now = max_seqlen_full
seqlens_now = seqlens_full
else:
cu_seqlens_now = cu_window_seqlens
max_seqlen_now = max_seqlen_window
seqlens_now = seqlens_window
hidden_states = blk(
hidden_states,
@@ -599,7 +579,6 @@ class AscendQwen2_5_VisionTransformer(nn.Module):
rotary_pos_emb_cos=rotary_pos_emb_cos,
rotary_pos_emb_sin=rotary_pos_emb_sin,
max_seqlen=max_seqlen_now,
seqlens=seqlens_now,
)
# For Qwen2.5-VL-3B, float16 will overflow at last block

10
vllm_ascend/patch/worker/patch_qwen3_vl.py
View File

@@ -23,7 +23,6 @@ import torch.nn as nn
from transformers.models.qwen3_vl.configuration_qwen3_vl import \
Qwen3VLVisionConfig
from vllm.attention.backends.registry import AttentionBackendEnum
from vllm.attention.layer import check_upstream_fa_availability
from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
@@ -101,7 +100,6 @@ class AscendQwen3_VisionTransformer(nn.Module):
head_size=head_dim,
rotary_dim=head_dim // 2,
max_position=8192,
base=10000.0,
is_neox_style=True,
)
@@ -133,17 +131,10 @@ class AscendQwen3_VisionTransformer(nn.Module):
dtype=torch.get_default_dtype(),
attn_backend_override=attn_backend_override,
)
use_upstream_fa = False
if (self.attn_backend != AttentionBackendEnum.FLASH_ATTN
and self.attn_backend != AttentionBackendEnum.ROCM_AITER_FA
and check_upstream_fa_availability(torch.get_default_dtype())):
self.attn_backend = AttentionBackendEnum.FLASH_ATTN
use_upstream_fa = True
if self.attn_backend not in {
AttentionBackendEnum.FLASH_ATTN,
AttentionBackendEnum.TORCH_SDPA,
AttentionBackendEnum.XFORMERS,
AttentionBackendEnum.ROCM_AITER_FA,
}:
raise RuntimeError(
@@ -159,7 +150,6 @@ class AscendQwen3_VisionTransformer(nn.Module):
prefix=f"{prefix}.blocks.{layer_idx}",
use_data_parallel=use_data_parallel,
attn_backend=self.attn_backend,
use_upstream_fa=use_upstream_fa,
) for layer_idx in range(vision_config.depth)
])