Files
xc-llm-ascend/vllm_ascend/patch/worker/patch_rope.py
Shanshan Shen e52ebf8674 [MM][Model][Perf] Remove Qwen2.5-VL modeling files and add patch for VisionAttention (#4349)
### 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>
2025-11-28 14:23:00 +08:00

34 lines
1.2 KiB
Python

#
# 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