Canlin GuoandHongsheng Liu
fe3d9a6a81
Signed-off-by: gcanlin <canlinguosdu@gmail.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
97 lines
4.7 KiB
Python
97 lines
4.7 KiB
Python
import sys
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from vllm.inputs.data import TokensPrompt as _OriginalTokensPrompt
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from vllm.model_executor.layers.rotary_embedding import (
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MRotaryEmbedding as _OriginalMRotaryEmbedding,
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)
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from vllm.v1.engine import EngineCoreOutput as _OriginalEngineCoreOutput
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from vllm.v1.engine import EngineCoreOutputs as _OriginalEngineCoreOutputs
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from vllm.v1.engine import EngineCoreRequest as _OriginalEngineCoreRequest
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from vllm.v1.request import Request as _OriginalRequest
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import vllm_omni.logger # noqa: F401
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from vllm_omni.engine import OmniEngineCoreOutput, OmniEngineCoreOutputs, OmniEngineCoreRequest
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from vllm_omni.inputs.data import OmniTokensPrompt
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from vllm_omni.model_executor.layers.mrope import MRotaryEmbedding
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from vllm_omni.request import OmniRequest
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from vllm_omni.utils import is_npu
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for module_name, module in sys.modules.items():
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# only do patch on module of vllm, pass others
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if "vllm" not in module_name:
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continue
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if hasattr(module, "EngineCoreOutput") and module.EngineCoreOutput == _OriginalEngineCoreOutput:
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module.EngineCoreOutput = OmniEngineCoreOutput
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if hasattr(module, "EngineCoreOutputs") and module.EngineCoreOutputs == _OriginalEngineCoreOutputs:
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module.EngineCoreOutputs = OmniEngineCoreOutputs
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if hasattr(module, "TokensPrompt") and module.TokensPrompt == _OriginalTokensPrompt:
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module.TokensPrompt = OmniTokensPrompt
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if hasattr(module, "MRotaryEmbedding") and module.MRotaryEmbedding == _OriginalMRotaryEmbedding:
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module.MRotaryEmbedding = MRotaryEmbedding
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if hasattr(module, "Request") and module.Request == _OriginalRequest:
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module.Request = OmniRequest
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if hasattr(module, "EngineCoreRequest") and module.EngineCoreRequest == _OriginalEngineCoreRequest:
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module.EngineCoreRequest = OmniEngineCoreRequest
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# Patch for vllm-ascend prefetch functions bug fix
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# Issue: The original functions access forward_context attributes like
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# prefetch_mlp_gate_up_proj, prefetch_mlp_down_proj, layer_idx without checking
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# if they exist, which causes AttributeError when prefetch_mlp_enabled is not set.
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# TODO: Remove this patch after upgrading to vllm-ascend v0.13.0 or later.
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# This issue has been fixed in https://github.com/vllm-project/vllm-ascend/pull/5035
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if is_npu():
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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 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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from vllm_omni.model_executor.models.qwen2_5_omni.qwen2_5_omni_thinker import (
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Qwen2_5OmniThinkerForConditionalGeneration,
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)
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class AscendQwen2_5OmniThinkerForConditionalGeneration(nn.Module):
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def _process_image_input(self, 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(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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Qwen2_5OmniThinkerForConditionalGeneration._process_image_input = (
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AscendQwen2_5OmniThinkerForConditionalGeneration._process_image_input
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)
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Qwen2_5OmniThinkerForConditionalGeneration._process_video_input = (
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AscendQwen2_5OmniThinkerForConditionalGeneration._process_video_input
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)
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