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988 lines
40 KiB
Python
988 lines
40 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2024 The Qwen team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Inference-only Qwen2.5-Omni model (thinker part)."""
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from collections.abc import Iterable, Mapping, Sequence
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from copy import copy
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from functools import partial
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from typing import Annotated, Any, Callable, Literal, Optional, Union
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import torch
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import torch.nn as nn
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.models.qwen2_5_omni.configuration_qwen2_5_omni import (
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Qwen2_5OmniConfig, Qwen2_5OmniThinkerConfig)
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from transformers.models.qwen2_5_omni.modeling_qwen2_5_omni import (
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Qwen2_5OmniAudioEncoder)
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from transformers.models.qwen2_5_omni.processing_qwen2_5_omni import (
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Qwen2_5OmniProcessor)
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from transformers.models.whisper import WhisperFeatureExtractor
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.qwen2_5_vl import (
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Qwen2_5_VisionTransformer, Qwen2_5_VLImageEmbeddingInputs,
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Qwen2_5_VLImageInputs, Qwen2_5_VLImagePixelInputs,
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Qwen2_5_VLProcessingInfo, Qwen2_5_VLVideoEmbeddingInputs,
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Qwen2_5_VLVideoInputs, Qwen2_5_VLVideoPixelInputs)
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from vllm.model_executor.models.qwen2_audio import (
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Qwen2AudioProcessingInfo, _get_feat_extract_output_lengths)
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from vllm.model_executor.models.qwen2_vl import Qwen2VLMultiModalDataParser
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (ImageItem, ModalityData,
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MultiModalDataDict, MultiModalFieldConfig,
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MultiModalKwargsItems, NestedTensors)
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from vllm.multimodal.parse import (AudioProcessorItems, DictEmbeddingItems,
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ModalityDataItems, MultiModalDataItems,
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MultiModalDataParser)
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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MultiModalPromptUpdates,
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PlaceholderFeaturesInfo,
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PromptReplacement, PromptUpdate)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.tokenizer import decode_tokens, encode_tokens
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
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SupportsMultiModal, SupportsPP)
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from .utils import (AutoWeightsLoader, WeightsMapper,
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init_vllm_registered_model, maybe_prefix,
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merge_multimodal_embeddings)
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try:
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import flash_attn
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except (ImportError, ModuleNotFoundError):
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flash_attn = None
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logger = init_logger(__name__)
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class Qwen2_5OmniAudioFeatureInputs(TensorSchema):
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"""
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Dimensions:
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- na: Number of audios
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- nmb: Number of mel bins
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- msl: Maximum sequence length
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- tsl: Total sequence length
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"""
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type: Literal["audio_features"]
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input_features: Annotated[
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Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("nmb", "tsl"),
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]
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feature_attention_mask: Annotated[
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torch.Tensor,
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TensorShape("na", "msl"),
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]
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def create_qwen2_5_omni_thinker_field_factory(
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spatial_merge_size: int
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) -> Callable[[Mapping[str, torch.Tensor]], Mapping[str,
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MultiModalFieldConfig]]:
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def _qwen2_5_omni_thinker_field_config(hf_inputs: Mapping[str,
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torch.Tensor]):
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audio_feature_lengths = hf_inputs.get("audio_feature_lengths",
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torch.empty((0, )))
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image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
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image_pixel_grid_sizes = image_grid_thw.prod(-1)
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image_embed_grid_sizes = (image_pixel_grid_sizes //
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spatial_merge_size // spatial_merge_size)
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video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
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video_grid_sizes = video_grid_thw.prod(-1)
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video_embed_grid_sizes = (video_grid_sizes // spatial_merge_size //
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spatial_merge_size)
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num_videos = len(video_grid_sizes)
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return dict(
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input_audio_features=MultiModalFieldConfig.flat_from_sizes(
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"audio", audio_feature_lengths, dim=1),
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feature_attention_mask=MultiModalFieldConfig.batched("audio"),
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audio_feature_lengths=MultiModalFieldConfig.batched("audio"),
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pixel_values=MultiModalFieldConfig.flat_from_sizes(
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"image", image_pixel_grid_sizes),
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image_embeds=MultiModalFieldConfig.flat_from_sizes(
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"image", image_embed_grid_sizes),
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image_grid_thw=MultiModalFieldConfig.batched("image"),
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pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
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"video", video_grid_sizes),
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video_embeds=MultiModalFieldConfig.flat_from_sizes(
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"video", video_embed_grid_sizes),
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video_grid_thw=MultiModalFieldConfig.batched("video"),
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second_per_grid_ts=MultiModalFieldConfig.batched("video"),
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use_audio_in_video=MultiModalFieldConfig.shared(
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"video", num_videos),
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)
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return _qwen2_5_omni_thinker_field_config
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class Qwen2_5OmniThinkerMultiModalDataParser(Qwen2VLMultiModalDataParser):
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def __init__(self, spatial_merge_size: int, *args, **kwargs):
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self._spatial_merge_size = spatial_merge_size
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super().__init__(self._spatial_merge_size, *args, **kwargs)
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def _parse_audio_data(
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self,
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data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
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) -> ModalityDataItems[Any, Any]:
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if isinstance(data, dict):
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return DictEmbeddingItems(
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data,
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modality="audio",
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required_fields={
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"input_audio_features", "audio_feature_lengths"
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},
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fields_factory=create_qwen2_5_omni_thinker_field_factory(
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self._spatial_merge_size),
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)
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return super()._parse_audio_data(data)
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class Qwen2_5OmniThinkerProcessingInfo(Qwen2AudioProcessingInfo,
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Qwen2_5_VLProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(Qwen2_5OmniConfig).thinker_config
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def get_hf_processor(self, **kwargs: object) -> Qwen2_5OmniProcessor:
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return self.ctx.get_hf_processor(
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Qwen2_5OmniProcessor,
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use_fast=kwargs.pop("use_fast", True),
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**kwargs,
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)
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def get_feature_extractor(self, **kwargs: object):
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hf_processor = self.get_hf_processor(**kwargs)
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feature_extractor = hf_processor.feature_extractor # type: ignore
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assert isinstance(feature_extractor, WhisperFeatureExtractor)
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return feature_extractor
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"audio": None, "image": None, "video": None}
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class Qwen2_5OmniThinkerDummyInputsBuilder(
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BaseDummyInputsBuilder[Qwen2_5OmniThinkerProcessingInfo]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_audios = mm_counts.get("audio", 0)
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num_images = mm_counts.get("image", 0)
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num_videos = mm_counts.get("video", 0)
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hf_processor = self.info.get_hf_processor()
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audio_token: str = hf_processor.audio_token
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image_token: str = hf_processor.image_token
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video_token: str = hf_processor.video_token
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return (audio_token * num_audios + image_token * num_images +
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video_token * num_videos)
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> MultiModalDataDict:
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num_audios = mm_counts.get("audio", 0)
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num_images = mm_counts.get("image", 0)
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num_videos = mm_counts.get("video", 0)
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feature_extractor = self.info.get_feature_extractor()
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target_audio_length = min(
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feature_extractor.chunk_length,
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30,
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) * feature_extractor.sampling_rate
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target_width, target_height = \
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self.info.get_image_size_with_most_features()
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target_num_frames = \
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self.info.get_num_frames_with_most_features(seq_len, mm_counts)
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mm_data = {
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"audio":
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self._get_dummy_audios(length=target_audio_length,
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num_audios=num_audios),
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"image":
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self._get_dummy_images(width=target_width,
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height=target_height,
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num_images=num_images),
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"video":
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self._get_dummy_videos(width=target_width,
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height=target_height,
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num_frames=target_num_frames,
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num_videos=num_videos),
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}
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return mm_data
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class Qwen2_5OmniThinkerMultiModalProcessor(
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BaseMultiModalProcessor[Qwen2_5OmniThinkerProcessingInfo]):
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def _get_data_parser(self) -> MultiModalDataParser:
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feature_extractor = self.info.get_feature_extractor()
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return Qwen2_5OmniThinkerMultiModalDataParser(
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spatial_merge_size=self.info.get_hf_config(
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).vision_config.spatial_merge_size,
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target_sr=feature_extractor.sampling_rate)
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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tok_kwargs: Mapping[str, object],
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) -> BatchFeature:
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mm_data = dict(mm_data)
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audios = mm_data.pop("audios", [])
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# NOTE: WhisperFeatureExtractor cannot handle empty list of audios
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if audios:
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# NOTE: Qwen2.5-Omni processor accept "audio"
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mm_data["audio"] = audios
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mm_kwargs = dict(**mm_kwargs, )
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hf_inputs = super()._call_hf_processor(
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prompt=prompt,
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mm_data=mm_data,
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mm_kwargs=mm_kwargs,
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tok_kwargs=tok_kwargs,
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)
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input_features = hf_inputs.pop('input_features', None)
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feature_attention_mask = hf_inputs.get('feature_attention_mask', None)
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if ('input_audio_features' not in hf_inputs
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and input_features is not None):
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if feature_attention_mask is not None:
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input_features = input_features.permute(
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0, 2, 1)[feature_attention_mask.bool()].permute(1, 0)
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hf_inputs['input_audio_features'] = input_features
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if ('audio_feature_lengths' not in hf_inputs
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and feature_attention_mask is not None):
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hf_inputs['audio_feature_lengths'] = feature_attention_mask.sum(-1)
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video_second_per_grid = hf_inputs.get("video_second_per_grid", None)
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if video_second_per_grid is not None:
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hf_inputs["second_per_grid_ts"] = video_second_per_grid
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use_audio_in_video = mm_kwargs.get("use_audio_in_video", False)
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hf_inputs["use_audio_in_video"] = torch.tensor(use_audio_in_video)
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return hf_inputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return create_qwen2_5_omni_thinker_field_factory(
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self.info.get_hf_config().vision_config.spatial_merge_size)(
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hf_inputs)
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def _maybe_apply_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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prompt_ids: list[int],
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mm_kwargs: MultiModalKwargsItems,
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mm_prompt_updates: MultiModalPromptUpdates,
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is_update_applied: bool,
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) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
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"""
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Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
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"""
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mm_item_counts = mm_items.get_all_counts()
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self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
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use_audio_in_video = (all(
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item["use_audio_in_video"].data
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for item in mm_kwargs["video"]) if "video" in mm_kwargs else False)
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if is_update_applied:
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mm_placeholders = self._find_mm_placeholders(
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prompt_ids,
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mm_prompt_updates,
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)
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self._validate_mm_placeholders(
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mm_placeholders,
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mm_item_counts,
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use_audio_in_video=use_audio_in_video)
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tokenizer = self.info.get_tokenizer()
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prompt = decode_tokens(tokenizer, prompt_ids)
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else:
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(
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prompt_ids,
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prompt,
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mm_placeholders,
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) = self._apply_prompt_updates(
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prompt_ids,
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mm_prompt_updates,
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)
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self._validate_mm_placeholders(
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mm_placeholders,
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mm_item_counts,
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use_audio_in_video=use_audio_in_video)
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tokenizer = self.info.get_tokenizer()
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prompt = decode_tokens(tokenizer, prompt_ids)
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return prompt_ids, prompt, mm_placeholders
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def _get_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, Any],
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out_mm_kwargs: MultiModalKwargsItems,
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) -> Sequence[PromptUpdate]:
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processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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tokenizer = self.info.get_tokenizer()
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image_processor = self.info.get_image_processor(
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**hf_processor_mm_kwargs)
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vocab = tokenizer.get_vocab()
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audio_token = processor.audio_token
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image_token = processor.image_token
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video_token = processor.video_token
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audio_token_id = vocab[audio_token]
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image_token_id = vocab[image_token]
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video_token_id = vocab[video_token]
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out_mm_data = out_mm_kwargs.get_data()
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audio_feature_lengths = out_mm_data.get("audio_feature_lengths")
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feature_attention_mask = out_mm_data.get("feature_attention_mask")
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if audio_feature_lengths is None and feature_attention_mask is None:
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audio_output_lengths = []
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elif audio_feature_lengths is not None:
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_, audio_output_lens = _get_feat_extract_output_lengths(
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audio_feature_lengths)
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audio_output_lengths = audio_output_lens.tolist()
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elif feature_attention_mask is not None:
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assert isinstance(feature_attention_mask, torch.Tensor)
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_, audio_output_lens = _get_feat_extract_output_lengths(
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feature_attention_mask.sum(-1))
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audio_output_lengths = audio_output_lens.tolist()
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# number of audios read from video.
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audio_in_video_item_idx = 0
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def get_replacement_qwen2_audio(item_idx: int):
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item_idx += audio_in_video_item_idx
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num_features = audio_output_lengths[item_idx]
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if num_features == 0:
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audios = mm_items.get_items("audio", AudioProcessorItems)
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audio = audios.get(item_idx)
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raise ValueError(
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f"The audio {audio} (len={len(audio)}) is too short "
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"to be represented inside the model")
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return [audio_token_id] * num_features
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def get_replacement_qwen2_vision(item_idx: int, modality: str):
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grid_thw = out_mm_data[f"{modality}_grid_thw"][item_idx]
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assert isinstance(grid_thw, torch.Tensor)
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merge_length = image_processor.merge_size**2
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token_id = image_token_id if modality == "image" else video_token_id
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return [token_id] * (int(grid_thw.prod()) // merge_length)
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use_audio_in_video = hf_processor_mm_kwargs.get(
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"use_audio_in_video", False)
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thinker_config = self.info.get_hf_config()
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def get_replacement_qwen2_use_audio_in_video(item_idx: int):
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nonlocal audio_in_video_item_idx
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audio_num_features = audio_output_lengths[audio_in_video_item_idx +
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item_idx]
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video_grid_thw = out_mm_data["video_grid_thw"][item_idx]
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audio_in_video_item_idx += 1
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second_per_grid_ts = hf_processor_mm_kwargs.get(
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"second_per_grid_ts", None)
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if second_per_grid_ts:
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video_second_per_grid_t = second_per_grid_ts[item_idx]
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else:
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video_second_per_grid_t = 1.0
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return MRotaryEmbedding.omni_get_updates_use_audio_in_video(
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thinker_config=thinker_config,
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audio_len=audio_num_features,
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video_grid_thw=video_grid_thw,
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video_second_per_grid_t=video_second_per_grid_t,
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)
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video_replacement_fn = (
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get_replacement_qwen2_use_audio_in_video if use_audio_in_video else
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|
partial(get_replacement_qwen2_vision, modality="video"))
|
|
|
|
return [
|
|
PromptReplacement(
|
|
modality="audio",
|
|
target=audio_token,
|
|
replacement=get_replacement_qwen2_audio,
|
|
),
|
|
PromptReplacement(
|
|
modality="image",
|
|
target=image_token,
|
|
replacement=partial(get_replacement_qwen2_vision,
|
|
modality="image"),
|
|
),
|
|
PromptReplacement(
|
|
modality="video",
|
|
target=video_token,
|
|
replacement=video_replacement_fn,
|
|
),
|
|
]
|
|
|
|
def _apply_hf_processor_main(
|
|
self,
|
|
prompt: Union[str, list[int]],
|
|
mm_items: MultiModalDataItems,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
tokenization_kwargs: Mapping[str, object],
|
|
*,
|
|
enable_hf_prompt_update: bool,
|
|
) -> tuple[list[int], BatchFeature, bool]:
|
|
"""
|
|
Qwen2.5-Omni reimplements this function to handle text only.
|
|
"""
|
|
if isinstance(prompt, str):
|
|
if enable_hf_prompt_update:
|
|
return self._apply_hf_processor_text_mm(
|
|
prompt_text=prompt,
|
|
mm_items=mm_items,
|
|
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
)
|
|
tokenizer = self.info.get_tokenizer()
|
|
prompt_ids = encode_tokens(tokenizer, prompt)
|
|
else:
|
|
prompt_ids = self._apply_hf_processor_tokens_only(prompt)
|
|
|
|
mm_processed_data = self._apply_hf_processor_mm_only(
|
|
mm_items=mm_items,
|
|
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
)
|
|
|
|
return prompt_ids, mm_processed_data, False
|
|
|
|
def _apply_hf_processor_mm_only(
|
|
self,
|
|
mm_items: MultiModalDataItems,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
tokenization_kwargs: Mapping[str, object],
|
|
) -> BatchFeature:
|
|
"""
|
|
Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
|
|
"""
|
|
mm_counts = mm_items.get_all_counts()
|
|
|
|
use_audio_in_video = hf_processor_mm_kwargs.get(
|
|
"use_audio_in_video", False)
|
|
if use_audio_in_video and "video" in mm_counts:
|
|
assert "audio" in mm_counts
|
|
mm_counts["audio"] -= mm_counts["video"]
|
|
|
|
_, mm_processed_data, _ = self._apply_hf_processor_text_mm(
|
|
prompt_text=self.dummy_inputs.get_dummy_text(mm_counts),
|
|
mm_items=mm_items,
|
|
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
)
|
|
|
|
return mm_processed_data
|
|
|
|
def _validate_mm_placeholders(
|
|
self,
|
|
mm_placeholders: Mapping[str, list[PlaceholderFeaturesInfo]],
|
|
mm_item_counts: Mapping[str, int],
|
|
use_audio_in_video: bool = False,
|
|
) -> None:
|
|
if use_audio_in_video:
|
|
mm_item_counts = copy(mm_item_counts)
|
|
if "video" in mm_item_counts:
|
|
assert "audio" in mm_item_counts
|
|
mm_item_counts["audio"] -= mm_item_counts["video"]
|
|
super()._validate_mm_placeholders(mm_placeholders, mm_item_counts)
|
|
|
|
|
|
class Qwen2_5OmniConditionalGenerationMixin:
|
|
|
|
def _validate_and_reshape_mm_tensor(self,
|
|
mm_input: object,
|
|
name: str,
|
|
dim: int = 0) -> torch.Tensor:
|
|
if not isinstance(mm_input, (torch.Tensor, list)):
|
|
raise ValueError(f"Incorrect type of {name}. "
|
|
f"Got type: {type(mm_input)}")
|
|
if isinstance(mm_input, torch.Tensor):
|
|
if dim == 0:
|
|
return mm_input.reshape(-1, *mm_input.shape[2:])
|
|
return torch.concat(list(mm_input), dim=dim)
|
|
else:
|
|
return torch.concat(mm_input, dim=dim)
|
|
|
|
def _parse_and_validate_audio_input(
|
|
self, **kwargs: object) -> Optional[Qwen2_5OmniAudioFeatureInputs]:
|
|
input_audio_features = kwargs.pop('input_audio_features', None)
|
|
audio_feature_lengths = kwargs.pop('audio_feature_lengths', None)
|
|
feature_attention_mask = kwargs.pop('feature_attention_mask', None)
|
|
if input_audio_features is None:
|
|
return None
|
|
input_audio_features = self._validate_and_reshape_mm_tensor(
|
|
input_audio_features, 'input_audio_features', dim=1)
|
|
if feature_attention_mask is not None:
|
|
feature_attention_mask = self._validate_and_reshape_mm_tensor(
|
|
feature_attention_mask, 'feature_attention_mask')
|
|
if not isinstance(input_audio_features, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of audio input features. "
|
|
f"Got type: {type(input_audio_features)}")
|
|
return Qwen2_5OmniAudioFeatureInputs(
|
|
type="audio_features",
|
|
input_features=input_audio_features,
|
|
audio_feature_lengths=audio_feature_lengths,
|
|
feature_attention_mask=feature_attention_mask)
|
|
|
|
def _parse_and_validate_image_input(
|
|
self,
|
|
**kwargs: dict[str, Any],
|
|
) -> Optional[Qwen2_5_VLImageInputs]:
|
|
pixel_values = kwargs.pop("pixel_values", None)
|
|
image_embeds = kwargs.pop("image_embeds", None)
|
|
image_grid_thw = kwargs.pop("image_grid_thw", None)
|
|
|
|
if pixel_values is None and image_embeds is None:
|
|
return None
|
|
|
|
if pixel_values is not None:
|
|
pixel_values = self._validate_and_reshape_mm_tensor(
|
|
pixel_values, "image pixel values")
|
|
image_grid_thw = self._validate_and_reshape_mm_tensor(
|
|
image_grid_thw, "image grid_thw")
|
|
|
|
if not isinstance(pixel_values, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of image pixel values. "
|
|
f"Got type: {type(pixel_values)}")
|
|
|
|
return Qwen2_5_VLImagePixelInputs(type="pixel_values",
|
|
pixel_values=pixel_values,
|
|
image_grid_thw=image_grid_thw)
|
|
|
|
if image_embeds is not None:
|
|
image_embeds = self._validate_and_reshape_mm_tensor(
|
|
image_embeds, "image embeds")
|
|
image_grid_thw = self._validate_and_reshape_mm_tensor(
|
|
image_grid_thw, "image grid_thw")
|
|
|
|
if not isinstance(image_embeds, torch.Tensor):
|
|
raise ValueError("Incorrect type of image embeddings. "
|
|
f"Got type: {type(image_embeds)}")
|
|
return Qwen2_5_VLImageEmbeddingInputs(
|
|
type="image_embeds",
|
|
image_embeds=image_embeds,
|
|
image_grid_thw=image_grid_thw)
|
|
|
|
def _parse_and_validate_video_input(
|
|
self,
|
|
**kwargs: dict[str, Any],
|
|
) -> Optional[Qwen2_5_VLVideoInputs]:
|
|
pixel_values_videos = kwargs.pop("pixel_values_videos", None)
|
|
video_embeds = kwargs.pop("video_embeds", None)
|
|
video_grid_thw = kwargs.pop("video_grid_thw", None)
|
|
|
|
if pixel_values_videos is None and video_embeds is None:
|
|
return None
|
|
|
|
if pixel_values_videos is not None:
|
|
pixel_values_videos = self._validate_and_reshape_mm_tensor(
|
|
pixel_values_videos, "video pixel values")
|
|
video_grid_thw = self._validate_and_reshape_mm_tensor(
|
|
video_grid_thw, "video grid_thw")
|
|
|
|
return Qwen2_5_VLVideoPixelInputs(
|
|
type="pixel_values_videos",
|
|
pixel_values_videos=pixel_values_videos,
|
|
video_grid_thw=video_grid_thw,
|
|
)
|
|
|
|
if video_embeds is not None:
|
|
video_embeds = self._validate_and_reshape_mm_tensor(
|
|
video_embeds, "video embeds")
|
|
video_grid_thw = self._validate_and_reshape_mm_tensor(
|
|
video_grid_thw, "video grid_thw")
|
|
|
|
if not isinstance(video_embeds, torch.Tensor):
|
|
raise ValueError("Incorrect type of video embeddings. "
|
|
f"Got type: {type(video_embeds)}")
|
|
return Qwen2_5_VLVideoEmbeddingInputs(
|
|
type="video_embeds",
|
|
video_embeds=video_embeds,
|
|
video_grid_thw=video_grid_thw)
|
|
|
|
def _process_audio_input(
|
|
self,
|
|
audio_input: Qwen2_5OmniAudioFeatureInputs,
|
|
audio_hashes: list[str] = None,
|
|
cached_audio_features: torch.Tensor = None,
|
|
) -> torch.Tensor:
|
|
|
|
input_features = audio_input["input_features"]
|
|
audio_feature_lengths = audio_input["audio_feature_lengths"]
|
|
if input_features.ndim == 3:
|
|
assert input_features.shape[0] == 1
|
|
input_features = input_features.squeeze(0)
|
|
if audio_feature_lengths.ndim == 2:
|
|
assert audio_feature_lengths.shape[
|
|
0] == 1 or audio_feature_lengths.shape[1] == 1
|
|
if audio_feature_lengths.shape[0] == 1:
|
|
audio_feature_lengths = audio_feature_lengths.squeeze(0)
|
|
else:
|
|
audio_feature_lengths = audio_feature_lengths.squeeze(1)
|
|
|
|
audio_feat_lengths, audio_output_lengths = (
|
|
self.audio_tower._get_feat_extract_output_lengths(
|
|
audio_feature_lengths))
|
|
|
|
audio_outputs = self.audio_tower(
|
|
input_features.to(self.audio_tower.dtype),
|
|
feature_lens=audio_feature_lengths,
|
|
aftercnn_lens=audio_feat_lengths,
|
|
)
|
|
return audio_outputs.last_hidden_state.split(
|
|
audio_output_lengths.tolist())
|
|
|
|
def _process_image_input(
|
|
self,
|
|
image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
|
|
if image_input["type"] == "image_embeds":
|
|
return image_input["image_embeds"].type(self.visual.dtype)
|
|
|
|
grid_thw = image_input["image_grid_thw"]
|
|
assert grid_thw.ndim == 2
|
|
|
|
pixel_values = image_input["pixel_values"].type(self.visual.dtype)
|
|
image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
|
|
# Split concatenated embeddings for each image item.
|
|
merge_size = self.visual.spatial_merge_size
|
|
sizes = grid_thw.prod(-1) // merge_size // merge_size
|
|
|
|
return image_embeds.split(sizes.tolist())
|
|
|
|
def _process_video_input(
|
|
self,
|
|
video_input: Qwen2_5_VLVideoInputs,
|
|
video_hashes: list[str] = None,
|
|
cached_video_embeds: torch.Tensor = None) -> torch.Tensor:
|
|
if video_input["type"] == "video_embeds":
|
|
return video_input["video_embeds"].type(self.visual.dtype)
|
|
|
|
grid_thw = video_input["video_grid_thw"]
|
|
assert grid_thw.ndim == 2
|
|
|
|
pixel_values_videos = video_input["pixel_values_videos"].type(
|
|
self.visual.dtype)
|
|
video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
|
|
# Split concatenated embeddings for each video item.
|
|
merge_size = self.visual.spatial_merge_size
|
|
sizes = grid_thw.prod(-1) // merge_size // merge_size
|
|
|
|
return video_embeds.split(sizes.tolist())
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(
|
|
Qwen2_5OmniThinkerMultiModalProcessor,
|
|
info=Qwen2_5OmniThinkerProcessingInfo,
|
|
dummy_inputs=Qwen2_5OmniThinkerDummyInputsBuilder,
|
|
)
|
|
class Qwen2_5OmniThinkerForConditionalGeneration(
|
|
nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
|
|
Qwen2_5OmniConditionalGenerationMixin):
|
|
hf_to_vllm_mapper = WeightsMapper(
|
|
orig_to_new_prefix={
|
|
"thinker.lm_head.": "language_model.lm_head.",
|
|
"thinker.model.": "language_model.model.",
|
|
"thinker.": "",
|
|
})
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"attn.qkv": [
|
|
"attn.q",
|
|
"attn.k",
|
|
"attn.v",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
|
|
@classmethod
|
|
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
|
|
if modality.startswith("image"):
|
|
return "<|vision_start|><|IMAGE|><|vision_end|>"
|
|
if modality.startswith("video"):
|
|
return "<|vision_start|><|VIDEO|><|vision_end|>"
|
|
if modality.startswith("audio"):
|
|
return f"Audio {i}: <|audio_bos|><|AUDIO|><|audio_eos|>"
|
|
|
|
raise ValueError("Only image, video or audio modality is supported")
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
thinker_config: Qwen2_5OmniThinkerConfig = (
|
|
vllm_config.model_config.hf_config.thinker_config)
|
|
quant_config = vllm_config.quant_config
|
|
multimodal_config = vllm_config.model_config.multimodal_config
|
|
self.config = thinker_config
|
|
self.multimodal_config = multimodal_config
|
|
|
|
# force "use_flash_attention_2=True" to audio tower to align
|
|
# the results.
|
|
if flash_attn is not None:
|
|
audio_config = thinker_config.audio_config
|
|
audio_config._attn_implementation_autoset = True
|
|
audio_config._attn_implementation = "flash_attention_2"
|
|
else:
|
|
logger.warning(
|
|
"flash_attn is not available, the model may not yield the "
|
|
"exactly same result as the transformers implementation "
|
|
"in the audio tower part.")
|
|
|
|
if multimodal_config.get_limit_per_prompt("audio"):
|
|
self.audio_tower = Qwen2_5OmniAudioEncoder(
|
|
thinker_config.audio_config)
|
|
else:
|
|
self.audio_tower = None
|
|
|
|
if multimodal_config.get_limit_per_prompt(
|
|
"image") or multimodal_config.get_limit_per_prompt("video"):
|
|
self.visual = Qwen2_5_VisionTransformer(
|
|
vision_config=thinker_config.vision_config,
|
|
norm_eps=getattr(thinker_config.text_config, "rms_norm_eps",
|
|
1e-6),
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "visual"),
|
|
)
|
|
else:
|
|
self.visual = None
|
|
|
|
self.quant_config = quant_config
|
|
self.language_model = init_vllm_registered_model(
|
|
vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "language_model"),
|
|
hf_config=thinker_config.text_config,
|
|
architectures=["Qwen2ForCausalLM"],
|
|
)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.language_model.make_empty_intermediate_tensors)
|
|
|
|
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
|
|
mm_input_by_modality = {}
|
|
|
|
# Preserve the order of modalities if there are multiple of them
|
|
# from the order of kwargs.
|
|
for input_key in kwargs:
|
|
if input_key in ("pixel_values", "image_embeds"
|
|
) and "image" not in mm_input_by_modality:
|
|
mm_input_by_modality[
|
|
"image"] = self._parse_and_validate_image_input(**kwargs)
|
|
if input_key in ("pixel_values_videos", "video_embeds"
|
|
) and "video" not in mm_input_by_modality:
|
|
mm_input_by_modality[
|
|
"video"] = self._parse_and_validate_video_input(**kwargs)
|
|
if input_key in ("input_audio_features"
|
|
) and "audio" not in mm_input_by_modality:
|
|
mm_input_by_modality[
|
|
"audio"] = self._parse_and_validate_audio_input(**kwargs)
|
|
return mm_input_by_modality
|
|
|
|
def get_language_model(self) -> torch.nn.Module:
|
|
return self.language_model
|
|
|
|
def get_multimodal_embeddings(self,
|
|
**kwargs: object) -> MultiModalEmbeddings:
|
|
|
|
mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
|
|
**kwargs)
|
|
if not mm_input_by_modality:
|
|
return []
|
|
|
|
# The result multimodal_embeddings is tuple of tensors, with each
|
|
# tensor corresponding to a multimodal data item (image or video).
|
|
multimodal_embeddings: tuple[torch.Tensor, ...] = ()
|
|
|
|
# NOTE: It is important to iterate over the keys in this dictionary
|
|
# to preserve the order of the modalities.
|
|
for modality in mm_input_by_modality:
|
|
multimodal_input = mm_input_by_modality[modality]
|
|
if modality == "image":
|
|
vision_embeddings = self._process_image_input(multimodal_input)
|
|
multimodal_embeddings += vision_embeddings
|
|
if modality == "video":
|
|
video_embeddings = self._process_video_input(multimodal_input)
|
|
multimodal_embeddings += video_embeddings
|
|
if modality == "audio":
|
|
audio_embeddings = self._process_audio_input(multimodal_input)
|
|
multimodal_embeddings += audio_embeddings
|
|
return multimodal_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
if multimodal_embeddings is not None \
|
|
and len(multimodal_embeddings) != 0:
|
|
|
|
# TODO (ywang96): support overlapping modality embeddings so that
|
|
# `use_audio_in_video` will work on V1.
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, multimodal_embeddings, [
|
|
self.config.image_token_index,
|
|
self.config.video_token_index,
|
|
self.config.audio_token_index
|
|
])
|
|
return inputs_embeds
|
|
|
|
def get_multimodal_embeddings_v0(
|
|
self, **kwargs: object) -> Optional[NestedTensors]:
|
|
audio_input = self._parse_and_validate_audio_input(**kwargs)
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
video_input = self._parse_and_validate_video_input(**kwargs)
|
|
|
|
if audio_input is None and image_input is None and video_input is None:
|
|
return None
|
|
|
|
multimodal_embeddings: list[tuple[NestedTensors, str]] = []
|
|
|
|
if audio_input is not None:
|
|
audio_embeds = self._process_audio_input(audio_input)
|
|
multimodal_embeddings.append((audio_embeds, "audio"))
|
|
if image_input is not None:
|
|
image_embeds = self._process_image_input(image_input)
|
|
multimodal_embeddings.append((image_embeds, "image"))
|
|
if video_input is not None:
|
|
video_embeds = self._process_video_input(video_input)
|
|
multimodal_embeddings.append((video_embeds, "video"))
|
|
return multimodal_embeddings
|
|
|
|
def get_input_embeddings_v0(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[NestedTensors] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
|
|
return inputs_embeds
|
|
|
|
for embeddings, modality in multimodal_embeddings:
|
|
if modality == "audio":
|
|
placeholder_token_id = self.config.audio_token_index
|
|
if modality == "image":
|
|
placeholder_token_id = self.config.image_token_index
|
|
if modality == "video":
|
|
placeholder_token_id = self.config.video_token_index
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, embeddings, placeholder_token_id)
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
|
# condition is for v0 compatibility.
|
|
elif inputs_embeds is None:
|
|
multimodal_embeddings = self.get_multimodal_embeddings_v0(**kwargs)
|
|
inputs_embeds = self.get_input_embeddings_v0(
|
|
input_ids, multimodal_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
skip_prefixes = ["talker.", "token2wav."]
|
|
if self.audio_tower is None:
|
|
skip_prefixes.extend(["audio_tower."])
|
|
if self.visual is None:
|
|
skip_prefixes.extend(["visual."])
|
|
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=skip_prefixes,
|
|
)
|
|
loaded_weights = loader.load_weights(weights,
|
|
mapper=self.hf_to_vllm_mapper)
|
|
|
|
return loaded_weights
|
|
|
|
def get_mm_mapping(self) -> MultiModelKeys:
|
|
"""
|
|
Get the module prefix in multimodal models
|
|
"""
|
|
return MultiModelKeys.from_string_field(
|
|
language_model="language_model",
|
|
connector="merger.",
|
|
tower_model=["visual.", "audio_tower."])
|