vllm.model_executor.models.qwen2_5_omni_thinker

Inference-only Qwen2.5-Omni model (thinker part).

logger module-attribute

logger = init_logger(__name__)

Qwen2_5OmniConditionalGenerationMixin

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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):
            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[Qwen2AudioInputs]:
        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 Qwen2AudioInputs(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: Qwen2AudioInputs,
        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,
        )
        audio_features = audio_outputs.last_hidden_state
        return audio_features.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())

_parse_and_validate_audio_input

_parse_and_validate_audio_input(
    **kwargs: object,
) -> Optional[Qwen2AudioInputs]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _parse_and_validate_audio_input(
        self, **kwargs: object) -> Optional[Qwen2AudioInputs]:
    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 Qwen2AudioInputs(input_features=input_audio_features,
                            audio_feature_lengths=audio_feature_lengths,
                            feature_attention_mask=feature_attention_mask)

_parse_and_validate_image_input

_parse_and_validate_image_input(
    **kwargs: dict[str, Any],
) -> Optional[Qwen2_5_VLImageInputs]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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)

_parse_and_validate_video_input

_parse_and_validate_video_input(
    **kwargs: dict[str, Any],
) -> Optional[Qwen2_5_VLVideoInputs]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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)

_process_audio_input

_process_audio_input(
    audio_input: Qwen2AudioInputs,
    audio_hashes: list[str] = None,
    cached_audio_features: Tensor = None,
) -> Tensor

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _process_audio_input(
    self,
    audio_input: Qwen2AudioInputs,
    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,
    )
    audio_features = audio_outputs.last_hidden_state
    return audio_features.split(audio_output_lengths.tolist())

_process_image_input

_process_image_input(
    image_input: Qwen2_5_VLImageInputs,
) -> tuple[Tensor, ...]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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())

_process_video_input

_process_video_input(
    video_input: Qwen2_5_VLVideoInputs,
    video_hashes: list[str] = None,
    cached_video_embeds: Tensor = None,
) -> Tensor

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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())

_validate_and_reshape_mm_tensor

_validate_and_reshape_mm_tensor(
    mm_input: object, name: str, dim: int = 0
) -> Tensor

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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):
        return torch.concat(list(mm_input), dim=dim)
    else:
        return torch.concat(mm_input, dim=dim)

Qwen2_5OmniThinkerDummyInputsBuilder

Bases: BaseDummyInputsBuilder[Qwen2_5OmniThinkerProcessingInfo]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

class Qwen2_5OmniThinkerDummyInputsBuilder(
        BaseDummyInputsBuilder[Qwen2_5OmniThinkerProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_audios = mm_counts.get("audio", 0)
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        hf_processor = self.info.get_hf_processor()

        audio_token: str = hf_processor.audio_token
        image_token: str = hf_processor.image_token
        video_token: str = hf_processor.video_token

        return (audio_token * num_audios + image_token * num_images +
                video_token * num_videos)

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_audios = mm_counts.get("audio", 0)
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        feature_extractor = self.info.get_feature_extractor()

        target_audio_length = min(
            feature_extractor.chunk_length,
            30,
        ) * feature_extractor.sampling_rate
        target_width, target_height = \
            self.info.get_image_size_with_most_features()
        target_num_frames = \
            self.info.get_num_frames_with_most_features(seq_len, mm_counts)

        mm_data = {
            "audio":
            self._get_dummy_audios(length=target_audio_length,
                                   num_audios=num_audios),
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images),
            "video":
            self._get_dummy_videos(width=target_width,
                                   height=target_height,
                                   num_frames=target_num_frames,
                                   num_videos=num_videos),
        }

        return mm_data

get_dummy_mm_data

get_dummy_mm_data(
    seq_len: int, mm_counts: Mapping[str, int]
) -> MultiModalDataDict

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def get_dummy_mm_data(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> MultiModalDataDict:
    num_audios = mm_counts.get("audio", 0)
    num_images = mm_counts.get("image", 0)
    num_videos = mm_counts.get("video", 0)

    feature_extractor = self.info.get_feature_extractor()

    target_audio_length = min(
        feature_extractor.chunk_length,
        30,
    ) * feature_extractor.sampling_rate
    target_width, target_height = \
        self.info.get_image_size_with_most_features()
    target_num_frames = \
        self.info.get_num_frames_with_most_features(seq_len, mm_counts)

    mm_data = {
        "audio":
        self._get_dummy_audios(length=target_audio_length,
                               num_audios=num_audios),
        "image":
        self._get_dummy_images(width=target_width,
                               height=target_height,
                               num_images=num_images),
        "video":
        self._get_dummy_videos(width=target_width,
                               height=target_height,
                               num_frames=target_num_frames,
                               num_videos=num_videos),
    }

    return mm_data

get_dummy_text

get_dummy_text(mm_counts: Mapping[str, int]) -> str

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
    num_audios = mm_counts.get("audio", 0)
    num_images = mm_counts.get("image", 0)
    num_videos = mm_counts.get("video", 0)

    hf_processor = self.info.get_hf_processor()

    audio_token: str = hf_processor.audio_token
    image_token: str = hf_processor.image_token
    video_token: str = hf_processor.video_token

    return (audio_token * num_audios + image_token * num_images +
            video_token * num_videos)

Qwen2_5OmniThinkerForConditionalGeneration

Bases: Module, SupportsMultiModal, SupportsPP, Qwen2_5OmniConditionalGenerationMixin

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

@MULTIMODAL_REGISTRY.register_processor(
    Qwen2_5OmniThinkerMultiModalProcessor,
    info=Qwen2_5OmniThinkerProcessingInfo,
    dummy_inputs=Qwen2_5OmniThinkerDummyInputsBuilder,
)
class Qwen2_5OmniThinkerForConditionalGeneration(
        nn.Module, SupportsMultiModal, SupportsPP,
        Qwen2_5OmniConditionalGenerationMixin):
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "thinker.lm_head.": "language_model.lm_head.",
            "thinker.model.": "language_model.model.",
            "thinker.": "",
        })

    @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.")

        self.audio_tower = Qwen2_5OmniAudioEncoder(thinker_config.audio_config)
        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"),
        )
        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 correspoending 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 modalitiy 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]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=["talker.", "token2wav."],
        )
        loaded_weights = loader.load_weights(weights,
                                             mapper=self.hf_to_vllm_mapper)

        return loaded_weights

audio_tower instance-attribute

audio_tower = Qwen2_5OmniAudioEncoder(audio_config)

config instance-attribute

config = thinker_config

hf_to_vllm_mapper class-attribute instance-attribute

hf_to_vllm_mapper = WeightsMapper(
    orig_to_new_prefix={
        "thinker.lm_head.": "language_model.lm_head.",
        "thinker.model.": "language_model.model.",
        "thinker.": "",
    }
)

language_model instance-attribute

language_model = init_vllm_registered_model(
    vllm_config=vllm_config,
    prefix=maybe_prefix(prefix, "language_model"),
    hf_config=text_config,
    architectures=["Qwen2ForCausalLM"],
)

make_empty_intermediate_tensors instance-attribute

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors
)

multimodal_config instance-attribute

multimodal_config = multimodal_config

quant_config instance-attribute

quant_config = quant_config

visual instance-attribute

visual = Qwen2_5_VisionTransformer(
    vision_config=vision_config,
    norm_eps=getattr(text_config, "rms_norm_eps", 1e-06),
    quant_config=quant_config,
    prefix=maybe_prefix(prefix, "visual"),
)

__init__

__init__(*, vllm_config: VllmConfig, prefix: str = '')

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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.")

    self.audio_tower = Qwen2_5OmniAudioEncoder(thinker_config.audio_config)
    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"),
    )
    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)

_parse_and_validate_multimodal_inputs

_parse_and_validate_multimodal_inputs(
    **kwargs: object,
) -> dict

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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

compute_logits

compute_logits(
    hidden_states: Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[Tensor]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def compute_logits(
    self,
    hidden_states: torch.Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
    return self.language_model.compute_logits(hidden_states,
                                              sampling_metadata)

forward

forward(
    input_ids: Tensor,
    positions: Tensor,
    intermediate_tensors: Optional[
        IntermediateTensors
    ] = None,
    inputs_embeds: Optional[Tensor] = None,
    **kwargs: object,
) -> Union[Tensor, IntermediateTensors]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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

get_input_embeddings

get_input_embeddings(
    input_ids: Tensor,
    multimodal_embeddings: Optional[
        MultiModalEmbeddings
    ] = None,
) -> Tensor

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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 modalitiy 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

get_input_embeddings_v0

get_input_embeddings_v0(
    input_ids: Tensor,
    multimodal_embeddings: Optional[NestedTensors] = None,
) -> Tensor

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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

get_language_model

get_language_model() -> Module

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def get_language_model(self) -> torch.nn.Module:
    return self.language_model

get_multimodal_embeddings

get_multimodal_embeddings(
    **kwargs: object,
) -> MultiModalEmbeddings

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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 correspoending 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

get_multimodal_embeddings_v0

get_multimodal_embeddings_v0(
    **kwargs: object,
) -> Optional[NestedTensors]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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

get_placeholder_str classmethod

get_placeholder_str(modality: str, i: int) -> Optional[str]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

@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")

load_weights

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def load_weights(self, weights: Iterable[tuple[str,
                                               torch.Tensor]]) -> set[str]:
    loader = AutoWeightsLoader(
        self,
        skip_prefixes=["talker.", "token2wav."],
    )
    loaded_weights = loader.load_weights(weights,
                                         mapper=self.hf_to_vllm_mapper)

    return loaded_weights

Qwen2_5OmniThinkerMultiModalDataParser

Bases: Qwen2VLMultiModalDataParser

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

class Qwen2_5OmniThinkerMultiModalDataParser(Qwen2VLMultiModalDataParser):

    def _parse_audio_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
    ) -> ModalityDataItems[Any, Any]:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="audio",
                required_fields={
                    "input_audio_features", "audio_feature_lengths"
                },
                fields_factory=_qwen2_5_omni_thinker_field_config,
            )

        return super()._parse_audio_data(data)

_parse_audio_data

_parse_audio_data(
    data: Union[dict[str, Tensor], ModalityData[ImageItem]],
) -> ModalityDataItems[Any, Any]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _parse_audio_data(
    self,
    data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
) -> ModalityDataItems[Any, Any]:
    if isinstance(data, dict):
        return DictEmbeddingItems(
            data,
            modality="audio",
            required_fields={
                "input_audio_features", "audio_feature_lengths"
            },
            fields_factory=_qwen2_5_omni_thinker_field_config,
        )

    return super()._parse_audio_data(data)

Qwen2_5OmniThinkerMultiModalProcessor

Bases: BaseMultiModalProcessor[Qwen2_5OmniThinkerProcessingInfo]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

class Qwen2_5OmniThinkerMultiModalProcessor(
        BaseMultiModalProcessor[Qwen2_5OmniThinkerProcessingInfo]):

    def _get_data_parser(self) -> MultiModalDataParser:
        feature_extractor = self.info.get_feature_extractor()
        return Qwen2_5OmniThinkerMultiModalDataParser(
            target_sr=feature_extractor.sampling_rate)

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        mm_data = dict(mm_data)
        audios = mm_data.pop("audios", [])

        # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
        if audios:
            # NOTE: Qwen2.5-Omni processor accept "audio"
            mm_data["audio"] = audios
            mm_kwargs = dict(**mm_kwargs, )

        hf_inputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        input_features = hf_inputs.pop('input_features', None)
        feature_attention_mask = hf_inputs.get('feature_attention_mask', None)
        if ('input_audio_features' not in hf_inputs
                and input_features is not None):
            if feature_attention_mask is not None:
                input_features = input_features.permute(
                    0, 2, 1)[feature_attention_mask.bool()].permute(1, 0)
            hf_inputs['input_audio_features'] = input_features
        if ('audio_feature_lengths' not in hf_inputs
                and feature_attention_mask is not None):
            hf_inputs['audio_feature_lengths'] = feature_attention_mask.sum(-1)
        return hf_inputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _qwen2_5_omni_thinker_field_config(hf_inputs)

    def _maybe_apply_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        prompt_ids: list[int],
        mm_kwargs: MultiModalKwargs,
        is_update_applied: bool,
    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
        """
        Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
        """
        unbound_prompt_updates = self._get_prompt_updates(
            mm_items,
            hf_processor_mm_kwargs,
            mm_kwargs,
        )
        mm_prompt_updates = self._bind_and_group_updates(
            unbound_prompt_updates)

        mm_item_counts = mm_items.get_all_counts()
        self._validate_mm_kwargs(mm_kwargs, mm_item_counts)

        use_audio_in_video = hf_processor_mm_kwargs.get(
            "use_audio_in_video", False)

        if is_update_applied:
            mm_placeholders = self._find_mm_placeholders(
                mm_prompt_updates,
                prompt_ids,
                mm_item_counts,
            )
            self._validate_mm_placeholders(
                mm_placeholders,
                mm_item_counts,
                use_audio_in_video=use_audio_in_video)

            tokenizer = self.info.get_tokenizer()
            prompt = decode_tokens(tokenizer, prompt_ids)
        else:
            (
                prompt_ids,
                prompt,
                mm_placeholders,
            ) = self._apply_prompt_updates(
                prompt_ids,
                mm_prompt_updates,
                mm_item_counts,
            )
            self._validate_mm_placeholders(
                mm_placeholders,
                mm_item_counts,
                use_audio_in_video=use_audio_in_video)

        tokenizer = self.info.get_tokenizer()
        prompt = decode_tokens(tokenizer, prompt_ids)

        if use_audio_in_video:
            mm_kwargs["use_audio_in_video"] = True

        return prompt_ids, prompt, mm_placeholders

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
        out_mm_kwargs: MultiModalKwargs,
    ) -> Sequence[PromptUpdate]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        tokenizer = self.info.get_tokenizer()
        image_processor = self.info.get_image_processor(
            **hf_processor_mm_kwargs)
        vocab = tokenizer.get_vocab()

        audio_token = processor.audio_token
        image_token = processor.image_token
        video_token = processor.video_token
        audio_token_id = vocab[audio_token]
        image_token_id = vocab[image_token]
        video_token_id = vocab[video_token]

        audio_feature_lengths = out_mm_kwargs.get("audio_feature_lengths")
        feature_attention_mask = out_mm_kwargs.get("feature_attention_mask")
        if audio_feature_lengths is None and feature_attention_mask is None:
            audio_output_lengths = []
        elif audio_feature_lengths is not None:
            _, audio_output_lens = _get_feat_extract_output_lengths(
                audio_feature_lengths)
            audio_output_lengths = audio_output_lens.tolist()
        elif feature_attention_mask is not None:
            assert isinstance(feature_attention_mask, torch.Tensor)
            _, audio_output_lens = _get_feat_extract_output_lengths(
                feature_attention_mask.sum(-1))
            audio_output_lengths = audio_output_lens.tolist()

        # number of audios read from video.
        audio_in_video_item_idx = 0

        def get_replacement_qwen2_audio(item_idx: int):
            item_idx += audio_in_video_item_idx

            num_features = audio_output_lengths[item_idx]
            if num_features == 0:
                audios = mm_items.get_items("audio", AudioProcessorItems)
                audio = audios.get(item_idx)
                raise ValueError(
                    f"The audio {audio} (len={len(audio)}) is too short "
                    "to be represented inside the model")

            return [audio_token_id] * num_features

        def get_replacement_qwen2_vision(item_idx: int, modality: str):
            grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
            assert isinstance(grid_thw, torch.Tensor)
            merge_length = image_processor.merge_size**2

            token_id = image_token_id if modality == "image" else video_token_id
            return [token_id] * (int(grid_thw.prod()) // merge_length)

        use_audio_in_video = hf_processor_mm_kwargs.get(
            "use_audio_in_video", False)
        thinker_config = self.info.get_hf_config()

        def get_replacement_qwen2_use_audio_in_video(item_idx: int):
            nonlocal audio_in_video_item_idx

            audio_num_features = audio_output_lengths[audio_in_video_item_idx +
                                                      item_idx]
            video_grid_thw = out_mm_kwargs["video_grid_thw"][item_idx]

            audio_in_video_item_idx += 1

            second_per_grid_ts = hf_processor_mm_kwargs.get(
                "second_per_grid_ts", None)
            if second_per_grid_ts:
                video_second_per_grid_t = second_per_grid_ts[item_idx]
            else:
                video_second_per_grid_t = 1.0

            return MRotaryEmbedding.omni_get_updates_use_audio_in_video(
                thinker_config=thinker_config,
                audio_len=audio_num_features,
                video_grid_thw=video_grid_thw,
                video_second_per_grid_t=video_second_per_grid_t,
            )

        video_replacement_fn = (
            get_replacement_qwen2_use_audio_in_video if use_audio_in_video else
            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], MultiModalKwargs, 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_kwargs = 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_kwargs, False

    def _apply_hf_processor_mm_only(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
    ) -> MultiModalKwargs:
        """
        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_kwargs, _ = 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_kwargs

    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)

_apply_hf_processor_main

_apply_hf_processor_main(
    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], MultiModalKwargs, bool]

Qwen2.5-Omni reimplements this function to handle text only.

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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], MultiModalKwargs, 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_kwargs = 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_kwargs, False

_apply_hf_processor_mm_only

_apply_hf_processor_mm_only(
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    tokenization_kwargs: Mapping[str, object],
) -> MultiModalKwargs

Qwen2.5-Omni reimplements this function to handle use_audio_in_video.

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _apply_hf_processor_mm_only(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    tokenization_kwargs: Mapping[str, object],
) -> MultiModalKwargs:
    """
    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_kwargs, _ = 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_kwargs

_call_hf_processor

_call_hf_processor(
    prompt: str,
    mm_data: Mapping[str, object],
    mm_kwargs: Mapping[str, object],
    tok_kwargs: Mapping[str, object],
) -> BatchFeature

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _call_hf_processor(
    self,
    prompt: str,
    mm_data: Mapping[str, object],
    mm_kwargs: Mapping[str, object],
    tok_kwargs: Mapping[str, object],
) -> BatchFeature:
    mm_data = dict(mm_data)
    audios = mm_data.pop("audios", [])

    # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
    if audios:
        # NOTE: Qwen2.5-Omni processor accept "audio"
        mm_data["audio"] = audios
        mm_kwargs = dict(**mm_kwargs, )

    hf_inputs = super()._call_hf_processor(
        prompt=prompt,
        mm_data=mm_data,
        mm_kwargs=mm_kwargs,
        tok_kwargs=tok_kwargs,
    )

    input_features = hf_inputs.pop('input_features', None)
    feature_attention_mask = hf_inputs.get('feature_attention_mask', None)
    if ('input_audio_features' not in hf_inputs
            and input_features is not None):
        if feature_attention_mask is not None:
            input_features = input_features.permute(
                0, 2, 1)[feature_attention_mask.bool()].permute(1, 0)
        hf_inputs['input_audio_features'] = input_features
    if ('audio_feature_lengths' not in hf_inputs
            and feature_attention_mask is not None):
        hf_inputs['audio_feature_lengths'] = feature_attention_mask.sum(-1)
    return hf_inputs

_get_data_parser

_get_data_parser() -> MultiModalDataParser

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _get_data_parser(self) -> MultiModalDataParser:
    feature_extractor = self.info.get_feature_extractor()
    return Qwen2_5OmniThinkerMultiModalDataParser(
        target_sr=feature_extractor.sampling_rate)

_get_mm_fields_config

_get_mm_fields_config(
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return _qwen2_5_omni_thinker_field_config(hf_inputs)

_get_prompt_updates

_get_prompt_updates(
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, Any],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, Any],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
    tokenizer = self.info.get_tokenizer()
    image_processor = self.info.get_image_processor(
        **hf_processor_mm_kwargs)
    vocab = tokenizer.get_vocab()

    audio_token = processor.audio_token
    image_token = processor.image_token
    video_token = processor.video_token
    audio_token_id = vocab[audio_token]
    image_token_id = vocab[image_token]
    video_token_id = vocab[video_token]

    audio_feature_lengths = out_mm_kwargs.get("audio_feature_lengths")
    feature_attention_mask = out_mm_kwargs.get("feature_attention_mask")
    if audio_feature_lengths is None and feature_attention_mask is None:
        audio_output_lengths = []
    elif audio_feature_lengths is not None:
        _, audio_output_lens = _get_feat_extract_output_lengths(
            audio_feature_lengths)
        audio_output_lengths = audio_output_lens.tolist()
    elif feature_attention_mask is not None:
        assert isinstance(feature_attention_mask, torch.Tensor)
        _, audio_output_lens = _get_feat_extract_output_lengths(
            feature_attention_mask.sum(-1))
        audio_output_lengths = audio_output_lens.tolist()

    # number of audios read from video.
    audio_in_video_item_idx = 0

    def get_replacement_qwen2_audio(item_idx: int):
        item_idx += audio_in_video_item_idx

        num_features = audio_output_lengths[item_idx]
        if num_features == 0:
            audios = mm_items.get_items("audio", AudioProcessorItems)
            audio = audios.get(item_idx)
            raise ValueError(
                f"The audio {audio} (len={len(audio)}) is too short "
                "to be represented inside the model")

        return [audio_token_id] * num_features

    def get_replacement_qwen2_vision(item_idx: int, modality: str):
        grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
        assert isinstance(grid_thw, torch.Tensor)
        merge_length = image_processor.merge_size**2

        token_id = image_token_id if modality == "image" else video_token_id
        return [token_id] * (int(grid_thw.prod()) // merge_length)

    use_audio_in_video = hf_processor_mm_kwargs.get(
        "use_audio_in_video", False)
    thinker_config = self.info.get_hf_config()

    def get_replacement_qwen2_use_audio_in_video(item_idx: int):
        nonlocal audio_in_video_item_idx

        audio_num_features = audio_output_lengths[audio_in_video_item_idx +
                                                  item_idx]
        video_grid_thw = out_mm_kwargs["video_grid_thw"][item_idx]

        audio_in_video_item_idx += 1

        second_per_grid_ts = hf_processor_mm_kwargs.get(
            "second_per_grid_ts", None)
        if second_per_grid_ts:
            video_second_per_grid_t = second_per_grid_ts[item_idx]
        else:
            video_second_per_grid_t = 1.0

        return MRotaryEmbedding.omni_get_updates_use_audio_in_video(
            thinker_config=thinker_config,
            audio_len=audio_num_features,
            video_grid_thw=video_grid_thw,
            video_second_per_grid_t=video_second_per_grid_t,
        )

    video_replacement_fn = (
        get_replacement_qwen2_use_audio_in_video if use_audio_in_video else
        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,
        ),
    ]

_maybe_apply_prompt_updates

_maybe_apply_prompt_updates(
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    prompt_ids: list[int],
    mm_kwargs: MultiModalKwargs,
    is_update_applied: bool,
) -> tuple[
    list[int],
    str,
    Mapping[str, list[PlaceholderFeaturesInfo]],
]

Qwen2.5-Omni reimplements this function to handle use_audio_in_video.

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _maybe_apply_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    prompt_ids: list[int],
    mm_kwargs: MultiModalKwargs,
    is_update_applied: bool,
) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
    """
    Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
    """
    unbound_prompt_updates = self._get_prompt_updates(
        mm_items,
        hf_processor_mm_kwargs,
        mm_kwargs,
    )
    mm_prompt_updates = self._bind_and_group_updates(
        unbound_prompt_updates)

    mm_item_counts = mm_items.get_all_counts()
    self._validate_mm_kwargs(mm_kwargs, mm_item_counts)

    use_audio_in_video = hf_processor_mm_kwargs.get(
        "use_audio_in_video", False)

    if is_update_applied:
        mm_placeholders = self._find_mm_placeholders(
            mm_prompt_updates,
            prompt_ids,
            mm_item_counts,
        )
        self._validate_mm_placeholders(
            mm_placeholders,
            mm_item_counts,
            use_audio_in_video=use_audio_in_video)

        tokenizer = self.info.get_tokenizer()
        prompt = decode_tokens(tokenizer, prompt_ids)
    else:
        (
            prompt_ids,
            prompt,
            mm_placeholders,
        ) = self._apply_prompt_updates(
            prompt_ids,
            mm_prompt_updates,
            mm_item_counts,
        )
        self._validate_mm_placeholders(
            mm_placeholders,
            mm_item_counts,
            use_audio_in_video=use_audio_in_video)

    tokenizer = self.info.get_tokenizer()
    prompt = decode_tokens(tokenizer, prompt_ids)

    if use_audio_in_video:
        mm_kwargs["use_audio_in_video"] = True

    return prompt_ids, prompt, mm_placeholders

_validate_mm_placeholders

_validate_mm_placeholders(
    mm_placeholders: Mapping[
        str, list[PlaceholderFeaturesInfo]
    ],
    mm_item_counts: Mapping[str, int],
    use_audio_in_video: bool = False,
) -> None

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

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)

Qwen2_5OmniThinkerProcessingInfo

Bases: Qwen2AudioProcessingInfo, Qwen2_5_VLProcessingInfo

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

class Qwen2_5OmniThinkerProcessingInfo(Qwen2AudioProcessingInfo,
                                       Qwen2_5_VLProcessingInfo):

    def get_hf_config(self):
        return self.ctx.get_hf_config(Qwen2_5OmniConfig).thinker_config

    def get_hf_processor(
        self,
        *,
        sampling_rate: Optional[int] = None,
        min_pixels: Optional[int] = None,
        max_pixels: Optional[int] = None,
        size: Optional[dict[str, int]] = None,
        fps: Optional[Union[float, list[float]]] = None,
        **kwargs: object,
    ) -> Qwen2_5OmniProcessor:
        if fps is not None:
            kwargs["fps"] = fps
        processor = self.ctx.get_hf_processor(
            Qwen2_5OmniProcessor,
            image_processor=self.get_image_processor(min_pixels=min_pixels,
                                                     max_pixels=max_pixels,
                                                     size=size,
                                                     use_fast=kwargs.get(
                                                         "use_fast", True)),
            **kwargs,
        )
        if not hasattr(processor, "audio_token"):
            processor.audio_token = "<|AUDIO|>"
        if not hasattr(processor, "image_token"):
            processor.image_token = "<|IMAGE|>"
        if not hasattr(processor, "video_token"):
            processor.video_token = "<|VIDEO|>"
        return processor

    def get_feature_extractor(
        self,
        *,
        sampling_rate: Optional[int] = None,
        **kwargs: object,
    ):
        hf_processor = self.get_hf_processor(sampling_rate=sampling_rate)
        feature_extractor = hf_processor.feature_extractor  # type: ignore
        assert isinstance(feature_extractor, WhisperFeatureExtractor)
        return feature_extractor

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"audio": None, "image": None, "video": None}

get_feature_extractor

get_feature_extractor(
    *, sampling_rate: Optional[int] = None, **kwargs: object
)

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def get_feature_extractor(
    self,
    *,
    sampling_rate: Optional[int] = None,
    **kwargs: object,
):
    hf_processor = self.get_hf_processor(sampling_rate=sampling_rate)
    feature_extractor = hf_processor.feature_extractor  # type: ignore
    assert isinstance(feature_extractor, WhisperFeatureExtractor)
    return feature_extractor

get_hf_config

get_hf_config()

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def get_hf_config(self):
    return self.ctx.get_hf_config(Qwen2_5OmniConfig).thinker_config

get_hf_processor

get_hf_processor(
    *,
    sampling_rate: Optional[int] = None,
    min_pixels: Optional[int] = None,
    max_pixels: Optional[int] = None,
    size: Optional[dict[str, int]] = None,
    fps: Optional[Union[float, list[float]]] = None,
    **kwargs: object,
) -> Qwen2_5OmniProcessor

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def get_hf_processor(
    self,
    *,
    sampling_rate: Optional[int] = None,
    min_pixels: Optional[int] = None,
    max_pixels: Optional[int] = None,
    size: Optional[dict[str, int]] = None,
    fps: Optional[Union[float, list[float]]] = None,
    **kwargs: object,
) -> Qwen2_5OmniProcessor:
    if fps is not None:
        kwargs["fps"] = fps
    processor = self.ctx.get_hf_processor(
        Qwen2_5OmniProcessor,
        image_processor=self.get_image_processor(min_pixels=min_pixels,
                                                 max_pixels=max_pixels,
                                                 size=size,
                                                 use_fast=kwargs.get(
                                                     "use_fast", True)),
        **kwargs,
    )
    if not hasattr(processor, "audio_token"):
        processor.audio_token = "<|AUDIO|>"
    if not hasattr(processor, "image_token"):
        processor.image_token = "<|IMAGE|>"
    if not hasattr(processor, "video_token"):
        processor.video_token = "<|VIDEO|>"
    return processor

get_supported_mm_limits

get_supported_mm_limits() -> Mapping[str, Optional[int]]

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
    return {"audio": None, "image": None, "video": None}

_qwen2_5_omni_thinker_field_config

_qwen2_5_omni_thinker_field_config(
    hf_inputs: Mapping[str, Tensor],
)

Source code in vllm/model_executor/models/qwen2_5_omni_thinker.py

def _qwen2_5_omni_thinker_field_config(hf_inputs: Mapping[str, torch.Tensor]):
    audio_feature_lengths = hf_inputs.get("audio_feature_lengths",
                                          torch.empty((0, )))

    image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
    image_grid_sizes = image_grid_thw.prod(-1)

    video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
    video_grid_sizes = video_grid_thw.prod(-1)

    return dict(
        input_audio_features=MultiModalFieldConfig.flat_from_sizes(
            "audio", audio_feature_lengths, dim=1),
        feature_attention_mask=MultiModalFieldConfig.batched("audio"),
        audio_feature_lengths=MultiModalFieldConfig.batched("audio"),
        pixel_values=MultiModalFieldConfig.flat_from_sizes(
            "image", image_grid_sizes),
        image_embeds=MultiModalFieldConfig.flat_from_sizes(
            "image", image_grid_sizes),
        image_grid_thw=MultiModalFieldConfig.batched("image"),
        pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
            "video", video_grid_sizes),
        video_embeds=MultiModalFieldConfig.flat_from_sizes(
            "video", video_grid_sizes),
        video_grid_thw=MultiModalFieldConfig.batched("video"),
        second_per_grid_ts=MultiModalFieldConfig.batched("video"),
    )