3362be7f86
Update patch doc. After this PR is merged, all the new patch PR should
update this doc as well.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
73 lines
3.0 KiB
Python
73 lines
3.0 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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Qwen2_5OmniThinkerForConditionalGeneration)
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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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# NOTE: These will be removed after ascend_forward_context is refactored.
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Qwen2_5OmniThinkerForConditionalGeneration._process_image_input = AscendQwen2_5OmniThinkerForConditionalGeneration._process_image_input
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Qwen2_5OmniThinkerForConditionalGeneration._process_video_input = AscendQwen2_5OmniThinkerForConditionalGeneration._process_video_input
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