bb3a826e08
### What this PR does / why we need it?
Related to #4084. Before we add the patches temporarily for making
`set_forward_context` patched by `set_ascend_forward_context` in the
function `_process_image_input` and `_process_video_input` of
`Qwen2.5-VL` and `Qwen2.5-Omni` models. After removing these patches, I
met the `AttributeError` for `ForwardContext` missing
`prefetch_mlp_enabled`. So we need to add the defensive check for
`prefetch_mlp_enabled`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
```
vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
--max-model-len 30000 \
--max-num-batched-tokens 50000 \
--max-num-seqs 30 \
--no-enable-prefix-caching \
--trust-remote-code \
--dtype bfloat16
```
```
{"id":"chatcmpl-b66d8acb76905c49","object":"chat.completion","created":1765796863,"model":"/root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"The text in the illustration reads \"TONGYI Qwen.\"","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning":null,"reasoning_content":null},"logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":73,"total_tokens":88,"completion_tokens":15,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}
```
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import torch.nn as nn
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from vllm.model_executor.models.qwen2_5_omni_thinker import (
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Qwen2_5_VLImageInputs, Qwen2_5_VLVideoInputs)
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from vllm_ascend.ascend_forward_context import set_ascend_forward_context
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class AscendQwen2_5OmniThinkerForConditionalGeneration(nn.Module):
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def _process_image_input(
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self,
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image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
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if image_input["type"] == "image_embeds":
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return image_input["image_embeds"].type(self.visual.dtype)
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grid_thw = image_input["image_grid_thw"]
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assert grid_thw.ndim == 2
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pixel_values = image_input["pixel_values"].type(self.visual.dtype)
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with set_ascend_forward_context(None, self.vllm_config):
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image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
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# Split concatenated embeddings for each image item.
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merge_size = self.visual.spatial_merge_size
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sizes = grid_thw.prod(-1) // merge_size // merge_size
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return image_embeds.split(sizes.tolist())
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def _process_video_input(
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self,
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video_input: Qwen2_5_VLVideoInputs,
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video_hashes: list[str] | None = None,
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cached_video_embeds: torch.Tensor | None = None,
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) -> torch.Tensor:
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if video_input["type"] == "video_embeds":
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return video_input["video_embeds"].type(self.visual.dtype)
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grid_thw = video_input["video_grid_thw"]
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assert grid_thw.ndim == 2
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pixel_values_videos = video_input["pixel_values_videos"].type(
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self.visual.dtype)
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with set_ascend_forward_context(None, self.vllm_config):
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video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
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# Split concatenated embeddings for each video item.
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merge_size = self.visual.spatial_merge_size
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sizes = grid_thw.prod(-1) // merge_size // merge_size
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return video_embeds.split(sizes.tolist())
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