EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[MM][Model][Perf] Remove Qwen2.5-VL modeling files and add patch for VisionAttention (#4349)

Browse Source 
### What this PR does / why we need it?

- [x] Patch `Qwen2_5_VisionAttention` with
`AscendQwen2_5_VisionAttention`.
- [x] Replace `AscendQwen2_5_VisionTransformer` with
`Qwen2_5_VisionTransformer` in vllm.
- [x] Move padding logic (q/k/v and cos/sin) before FA to `forward()` of
`Qwen2_5_VisionAttention`.
- [x] Covert `cu_seqlens` in `Qwen2_5_VisionAttention` from cumulative
form to intervals and move it to cpu (compatible for npu FA).
- [x] Remove Qwen2.5-VL modeling files.
- [x] Remove Qwen2.5-VL (without padding) modeling files.
- [x] Remove related UT.
- [x] Make `set_forward_context` pluggable when getting MM embedding.
Find more details at https://github.com/vllm-project/vllm/pull/29388.
- [x] Simplify padding logic for FA.
- [x] Add patch for https://github.com/vllm-project/vllm/pull/28798.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

- [x] Functional test (eager mode)
- [x] Functional test (graph mode)
- [x] Benchmark


- vLLM version: v0.11.2

---------

Signed-off-by: shen-shanshan <467638484@qq.com>
This commit is contained in:
Shanshan Shen
2025-11-28 14:23:00 +08:00
committed by GitHub
parent bdc66972db
commit e52ebf8674
9 changed files with 802 additions and 2100 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
vllm_ascend/patch/worker/__init__.py
View File

@@ -27,3 +27,5 @@ import vllm_ascend.patch.worker.patch_roberta # noqa
import vllm_ascend.patch.worker.patch_weight_loader # noqa
import vllm_ascend.patch.worker.patch_multimodal_merge # noqa
import vllm_ascend.patch.worker.patch_minicpm # noqa
import vllm_ascend.patch.worker.patch_qwen2_5_vl # noqa
import vllm_ascend.patch.worker.patch_rope # noqa

501
vllm_ascend/patch/worker/patch_qwen2_5_vl.py Normal file
View File

@@ -0,0 +1,501 @@
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from functools import lru_cache, partial
import einops
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_npu
from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import \
Qwen2_5_VLVisionConfig
from vllm.attention.backends.registry import AttentionBackendEnum
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
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.models.qwen2_5_vl import (
Qwen2_5_VisionAttention, Qwen2_5_VisionBlock, Qwen2_5_VisionPatchEmbed,
Qwen2_5_VisionPatchMerger, Qwen2_5_VisionTransformer,
Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLImageInputs,
Qwen2_5_VLVideoInputs)
from vllm.model_executor.models.utils import cast_overflow_tensors
from vllm.model_executor.models.vision import (
get_vit_attn_backend, run_dp_sharded_mrope_vision_model)
import vllm_ascend.envs as envs_ascend
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
MIN_PAD_SIZE = 64 # min_size to pad weight
MAX_PAD_SIZE = 128 # max_size to pad weight
class AscendQwen2_5_VisionAttention(nn.Module):
def forward(
self,
x: torch.Tensor,
cu_seqlens: torch.Tensor,
rotary_pos_emb_cos: torch.Tensor,
rotary_pos_emb_sin: torch.Tensor,
max_seqlen: torch.Tensor,
seqlens: torch.Tensor,
) -> torch.Tensor:
# [s, b, c] --> [s, b, head * 3 * head_dim]
x, _ = self.qkv(x)
seq_len, batch_size, _ = x.shape
# Split q k v.
qkv = einops.rearrange(
x,
"s b (three head head_dim) -> b s three head head_dim",
three=3,
head=self.num_attention_heads_per_partition,
)
q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
origin_shape = q.shape[-1]
# Convert cumulative tensor to intervals and move it to cpu.
cu_seqlens = torch.diff(cu_seqlens).to("cpu")
cos = rotary_pos_emb_cos
sin = rotary_pos_emb_sin
cos = einops.rearrange(
torch.stack((cos, cos), dim=-1),
"... d two -> ...(d two)",
two=2,
)
sin = einops.rearrange(
torch.stack((sin, sin), dim=-1),
"... d two -> ...(d two)",
two=2,
)
cos = cos.reshape(1, -1, 1, self.hidden_size_per_attention_head)
sin = sin.reshape(1, -1, 1, self.hidden_size_per_attention_head)
q = torch_npu.npu_rotary_mul(q, cos, sin)
k = torch_npu.npu_rotary_mul(k, cos, sin)
q, k, v = [
einops.rearrange(x, "b s h d -> (b s) h d").contiguous()
for x in (q, k, v)
]
enable_pad = (envs_ascend.USE_OPTIMIZED_MODEL
and self.hidden_size_per_attention_head > MIN_PAD_SIZE
and self.hidden_size_per_attention_head < MAX_PAD_SIZE)
if enable_pad:
pad_len = MAX_PAD_SIZE - origin_shape
# q/k/v: [b * s, head, head_dim] -> [b * s, head, MAX_PAD_SIZE]
q = F.pad(q, (0, pad_len), mode="constant", value=0)
k = F.pad(k, (0, pad_len), mode="constant", value=0)
v = F.pad(v, (0, pad_len), mode="constant", value=0)
context_layer = torch.empty_like(q)
# operator requires pta version >= 2.5.1
torch_npu._npu_flash_attention_unpad(
query=q,
key=k,
value=v,
seq_len=cu_seqlens,
scale_value=self.hidden_size_per_attention_head**-0.5,
num_heads=self.num_attention_heads_per_partition,
num_kv_heads=self.num_attention_heads_per_partition,
out=context_layer,
)
if enable_pad:
context_layer = context_layer[..., :origin_shape]
context_layer = einops.rearrange(context_layer,
"(b s) h d -> s b (h d)",
b=batch_size).contiguous()
output, _ = self.proj(context_layer)
return output
class AscendQwen2_5_VisionBlock(nn.Module):
def forward(
self,
x: torch.Tensor,
cu_seqlens: torch.Tensor,
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),
cu_seqlens=cu_seqlens,
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)
return x
class AscendQwen2_5_VisionTransformer(nn.Module):
def __init__(
self,
vision_config: Qwen2_5_VLVisionConfig,
norm_eps: float = 1e-6,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
attn_backend_override: AttentionBackendEnum | None = None,
) -> None:
nn.Module.__init__(self)
patch_size = vision_config.patch_size
temporal_patch_size = vision_config.temporal_patch_size
in_channels = vision_config.in_channels
depth = vision_config.depth
self.hidden_size = vision_config.hidden_size
self.num_heads = vision_config.num_heads
self.use_data_parallel = use_data_parallel
self.out_hidden_size = vision_config.out_hidden_size
# args for get_window_index_thw
self.window_size = vision_config.window_size
self.patch_size = vision_config.patch_size
self.spatial_merge_size = vision_config.spatial_merge_size
self.fullatt_block_indexes = vision_config.fullatt_block_indexes
self.spatial_merge_unit = self.spatial_merge_size**2
# TODO[@lucaskabela]: Investigate fixing this usage
# see https://github.com/vllm-project/vllm/issues/27044
# DO NOT MOVE THIS IMPORT
from vllm.compilation.backends import set_model_tag
with set_model_tag("Qwen2_5_VisionPatchEmbed"):
self.patch_embed = Qwen2_5_VisionPatchEmbed(
patch_size=patch_size,
temporal_patch_size=temporal_patch_size,
in_channels=in_channels,
hidden_size=self.hidden_size,
)
norm_layer = partial(RMSNorm, eps=norm_eps)
head_dim = self.hidden_size // self.num_heads
self.rotary_pos_emb = get_rope(
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(),
attn_backend_override=attn_backend_override,
)
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,
))
with set_model_tag("Qwen2_5_VisionBlock"):
self.blocks = nn.ModuleList([
Qwen2_5_VisionBlock(
dim=self.hidden_size,
num_heads=self.num_heads,
mlp_hidden_dim=vision_config.intermediate_size,
act_fn=get_act_and_mul_fn(vision_config.hidden_act),
norm_layer=norm_layer,
quant_config=quant_config,
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)
])
with set_model_tag("Qwen2_5_VisionPatchMerger"):
self.merger = Qwen2_5_VisionPatchMerger(
d_model=vision_config.out_hidden_size,
context_dim=self.hidden_size,
norm_layer=norm_layer,
spatial_merge_size=self.spatial_merge_size,
quant_config=quant_config,
prefix=f"{prefix}.merger",
use_data_parallel=use_data_parallel,
)
def rotary_pos_emb_thw(self, t, h, w):
hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
hpos_ids = (hpos_ids.reshape(
h // self.spatial_merge_size,
self.spatial_merge_size,
w // self.spatial_merge_size,
self.spatial_merge_size,
).permute(0, 2, 1, 3).flatten())
wpos_ids = (wpos_ids.reshape(
h // self.spatial_merge_size,
self.spatial_merge_size,
w // self.spatial_merge_size,
self.spatial_merge_size,
).permute(0, 2, 1, 3).flatten())
pos_ids = torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)
max_size = max(h, w)
# Use pre-computed cos_sin_cache from RotaryEmbedding
cos, sin = self.rotary_pos_emb.get_cos_sin(max_size)
cos_h = cos[pos_ids[:, 0]] # (num_tokens, rotary_dim // 2)
cos_w = cos[pos_ids[:, 1]]
sin_h = sin[pos_ids[:, 0]]
sin_w = sin[pos_ids[:, 1]]
cos_combined = torch.cat([cos_h, cos_w], dim=-1)
sin_combined = torch.cat([sin_h, sin_w], dim=-1)
cos_combined = cos_combined.reshape(
cos_combined.shape[0] // self.spatial_merge_unit,
self.spatial_merge_unit,
-1,
)
sin_combined = sin_combined.reshape(
sin_combined.shape[0] // self.spatial_merge_unit,
self.spatial_merge_unit,
-1,
)
return cos_combined, sin_combined
@lru_cache(maxsize=1024) # noqa: B019
def get_rope_by_thw(self, t, h, w):
window_index_thw, cu_seqlens_window_thw = self.get_window_index_thw(
t, h, w)
cos_thw, sin_thw = self.rotary_pos_emb_thw(t, h, w)
cos_thw = cos_thw[window_index_thw, :, :]
cos_thw = cos_thw.flatten(start_dim=0, end_dim=1)
sin_thw = sin_thw[window_index_thw, :, :]
sin_thw = sin_thw.flatten(start_dim=0, end_dim=1)
cu_seqlens_thw = torch.repeat_interleave(
torch.tensor([h * w], dtype=torch.int32), t)
return (
cos_thw,
sin_thw,
window_index_thw,
cu_seqlens_window_thw,
cu_seqlens_thw,
)
def forward(
self,
x: torch.Tensor,
grid_thw: list[list[int]],
) -> torch.Tensor:
# patchify
seq_len, _ = x.size()
rotary_pos_emb_cos: list = []
rotary_pos_emb_sin: list = []
window_index: list = []
cu_window_seqlens: list = [torch.tensor([0], dtype=torch.int32)]
cu_seqlens: list = []
hidden_states = x.to(device=self.device, dtype=self.dtype)
hidden_states = self.patch_embed(hidden_states)
window_index_id = 0
cu_window_seqlens_last = 0
for t, h, w in grid_thw:
t, h, w = int(t), int(h), int(w)
llm_h = h // self.spatial_merge_size
llm_w = w // self.spatial_merge_size
(
cos_thw,
sin_thw,
window_index_thw,
cu_seqlens_window_thw,
cu_seqlens_thw,
) = self.get_rope_by_thw(t, h, w)
window_index.append(window_index_thw + window_index_id)
window_index_id += t * llm_h * llm_w
cu_seqlens_window_thw = cu_seqlens_window_thw + cu_window_seqlens_last
cu_window_seqlens_last = cu_seqlens_window_thw[-1]
cu_window_seqlens.append(cu_seqlens_window_thw)
rotary_pos_emb_cos.append(cos_thw)
rotary_pos_emb_sin.append(sin_thw)
cu_seqlens.append(cu_seqlens_thw)
rotary_pos_emb_cos = torch.cat(rotary_pos_emb_cos)
rotary_pos_emb_sin = torch.cat(rotary_pos_emb_sin)
window_index = torch.cat(window_index)
# compute reverse indices
reverse_indices = self.invert_permutation(window_index)
cu_window_seqlens = torch.cat(cu_window_seqlens)
cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
cu_seqlens = torch.cat(cu_seqlens)
cu_seqlens = torch.cumsum(cu_seqlens, dim=0, dtype=torch.int32)
cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
# 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)
cu_seqlens = cu_seqlens.to( # type: ignore[attr-defined]
device=self.device,
non_blocking=True)
cu_window_seqlens = cu_window_seqlens.to( # type: ignore[attr-defined]
device=self.device,
non_blocking=True)
rotary_pos_emb_cos = rotary_pos_emb_cos.to( # type: ignore[attr-defined]
device=self.device,
non_blocking=True)
rotary_pos_emb_sin = rotary_pos_emb_sin.to( # type: ignore[attr-defined]
device=self.device,
non_blocking=True)
window_index = window_index.to( # type: ignore[attr-defined]
device=hidden_states.device,
non_blocking=True)
reverse_indices = reverse_indices.to(device=hidden_states.device,
non_blocking=True)
hidden_states = hidden_states.reshape(
seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
hidden_states = hidden_states[window_index, :, :]
hidden_states = hidden_states.reshape(seq_len, -1)
hidden_states = hidden_states.unsqueeze(1)
for layer_num, blk in enumerate(self.blocks):
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,
cu_seqlens=cu_seqlens_now,
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
# for long visual tokens sequences.
if hidden_states.dtype == torch.float16:
hidden_states = cast_overflow_tensors(hidden_states)
# adapter
hidden_states = self.merger(hidden_states)
hidden_states = hidden_states[reverse_indices, :]
return hidden_states
class AscendQwen2_5_VLForConditionalGeneration(nn.Module):
def _process_image_input(
self,
image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
grid_thw = image_input["image_grid_thw"]
assert grid_thw.ndim == 2
grid_thw_list = grid_thw.tolist()
if image_input["type"] == "image_embeds":
image_embeds = image_input["image_embeds"].type(self.visual.dtype)
else:
pixel_values = image_input["pixel_values"]
with set_ascend_forward_context(None, self.vllm_config):
if self.use_data_parallel:
return run_dp_sharded_mrope_vision_model(
self.visual,
pixel_values,
grid_thw_list,
rope_type="rope_3d")
else:
image_embeds = self.visual(pixel_values,
grid_thw=grid_thw_list)
# Split concatenated embeddings for each image item.
merge_size = self.visual.spatial_merge_size
sizes = (grid_thw.prod(-1) // merge_size // merge_size).tolist()
return image_embeds.split(sizes)
def _process_video_input(
self,
video_input: Qwen2_5_VLVideoInputs) -> tuple[torch.Tensor, ...]:
grid_thw = video_input["video_grid_thw"]
assert grid_thw.ndim == 2
grid_thw_list = grid_thw.tolist()
if video_input["type"] == "video_embeds":
video_embeds = video_input["video_embeds"].type(self.visual.dtype)
else:
pixel_values_videos = video_input["pixel_values_videos"]
with set_ascend_forward_context(None, self.vllm_config):
if self.use_data_parallel:
return run_dp_sharded_mrope_vision_model(
self.visual,
pixel_values_videos,
grid_thw_list,
rope_type="rope_3d",
)
else:
video_embeds = self.visual(pixel_values_videos,
grid_thw=grid_thw_list)
# Split concatenated embeddings for each video item.
merge_size = self.visual.spatial_merge_size
sizes = (grid_thw.prod(-1) // merge_size // merge_size).tolist()
return video_embeds.split(sizes)
# NOTE: This will be removed after MMEncoderAttention has been extract as a CustomOp in vllm.
Qwen2_5_VisionAttention.forward = AscendQwen2_5_VisionAttention.forward
# NOTE: These will be removed after https://github.com/vllm-project/vllm/pull/29388 is merged.
Qwen2_5_VLForConditionalGeneration._process_image_input = AscendQwen2_5_VLForConditionalGeneration._process_image_input
Qwen2_5_VLForConditionalGeneration._process_video_input = AscendQwen2_5_VLForConditionalGeneration._process_video_input
# NOTE: These will be removed after vllm-ascend is aligned with vllm latest main.
Qwen2_5_VisionBlock.forward = AscendQwen2_5_VisionBlock.forward
Qwen2_5_VisionTransformer.__init__ = AscendQwen2_5_VisionTransformer.__init__
Qwen2_5_VisionTransformer.rotary_pos_emb_thw = AscendQwen2_5_VisionTransformer.rotary_pos_emb_thw
Qwen2_5_VisionTransformer.get_rope_by_thw = AscendQwen2_5_VisionTransformer.get_rope_by_thw
Qwen2_5_VisionTransformer.forward = AscendQwen2_5_VisionTransformer.forward

33
vllm_ascend/patch/worker/patch_rope.py Normal file
View File

@@ -0,0 +1,33 @@
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import torch
import torch.nn as nn
from vllm.model_executor.layers.rotary_embedding.base import \
RotaryEmbeddingBase
class AscendRotaryEmbeddingBase(nn.Module):
def get_cos_sin(self, seqlen: int) -> tuple[torch.Tensor, torch.Tensor]:
cos_sin = self.cos_sin_cache[:seqlen]
cos, sin = cos_sin.chunk(2, dim=-1)
return cos, sin
# NOTE: These will be removed after vllm-ascend is aligned with vllm latest main.
RotaryEmbeddingBase.get_cos_sin = AscendRotaryEmbeddingBase.get_cos_sin