vllm.model_executor.models.llava_next_video

LlavaNextMultiModalProjector

Bases: Module

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

class LlavaNextMultiModalProjector(nn.Module):

    def __init__(self, vision_hidden_size: int, text_hidden_size: int,
                 projector_hidden_act: str, multimodal_projector_bias: bool):
        super().__init__()

        self.linear_1 = nn.Linear(vision_hidden_size,
                                  text_hidden_size,
                                  bias=multimodal_projector_bias)
        self.act = get_act_fn(projector_hidden_act)
        self.linear_2 = nn.Linear(text_hidden_size,
                                  text_hidden_size,
                                  bias=multimodal_projector_bias)

    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
        hidden_states = self.linear_1(image_features)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states

act instance-attribute

act = get_act_fn(projector_hidden_act)

linear_1 instance-attribute

linear_1 = Linear(
    vision_hidden_size,
    text_hidden_size,
    bias=multimodal_projector_bias,
)

linear_2 instance-attribute

linear_2 = Linear(
    text_hidden_size,
    text_hidden_size,
    bias=multimodal_projector_bias,
)

__init__

__init__(
    vision_hidden_size: int,
    text_hidden_size: int,
    projector_hidden_act: str,
    multimodal_projector_bias: bool,
)

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

def __init__(self, vision_hidden_size: int, text_hidden_size: int,
             projector_hidden_act: str, multimodal_projector_bias: bool):
    super().__init__()

    self.linear_1 = nn.Linear(vision_hidden_size,
                              text_hidden_size,
                              bias=multimodal_projector_bias)
    self.act = get_act_fn(projector_hidden_act)
    self.linear_2 = nn.Linear(text_hidden_size,
                              text_hidden_size,
                              bias=multimodal_projector_bias)

forward

forward(image_features: Tensor) -> Tensor

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

def forward(self, image_features: torch.Tensor) -> torch.Tensor:
    hidden_states = self.linear_1(image_features)
    hidden_states = self.act(hidden_states)
    hidden_states = self.linear_2(hidden_states)
    return hidden_states

LlavaNextVideoDummyInputsBuilder

Bases: BaseDummyInputsBuilder[LlavaNextVideoProcessingInfo]

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

class LlavaNextVideoDummyInputsBuilder(
        BaseDummyInputsBuilder[LlavaNextVideoProcessingInfo]):

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

        processor = self.info.get_hf_processor()
        video_token = processor.video_token

        return video_token * num_videos

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

        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)

        return {
            "video":
            self._get_dummy_videos(
                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
            )
        }

get_dummy_mm_data

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

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

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

    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)

    return {
        "video":
        self._get_dummy_videos(
            width=target_width,
            height=target_height,
            num_frames=target_num_frames,
            num_videos=num_videos,
        )
    }

get_dummy_text

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

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

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

    processor = self.info.get_hf_processor()
    video_token = processor.video_token

    return video_token * num_videos

LlavaNextVideoForConditionalGeneration

Bases: Module, SupportsMultiModal, SupportsPP

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

@MULTIMODAL_REGISTRY.register_processor(
    LlavaNextVideoMultiModalProcessor,
    info=LlavaNextVideoProcessingInfo,
    dummy_inputs=LlavaNextVideoDummyInputsBuilder,
)
class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
                                             SupportsPP):

    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            # mapping for new names in checkpoint saved after transformers v4.52
            "model.language_model.": "language_model.model.",
            "model.vision_tower.": "vision_tower.",
            "model.multi_modal_projector.": "multi_modal_projector.",
            "model.image_newline": "image_newline",
            "lm_head.": "language_model.lm_head.",
        })

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<image>"
        if modality.startswith("video"):
            return "<video>"

        raise ValueError("Only image or video modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.multimodal_config = multimodal_config

        # Initialize the vision tower only up to the required feature layer
        self.vision_tower = init_vision_tower_for_llava(
            config,
            quant_config,
            require_post_norm=False,
            prefix=maybe_prefix(prefix, "vision_tower"))
        self.vision_resampler = LlavaNextVideoPooler(config)
        self.multi_modal_projector = LlavaNextMultiModalProjector(
            vision_hidden_size=config.vision_config.hidden_size,
            text_hidden_size=config.text_config.hidden_size,
            projector_hidden_act=config.projector_hidden_act,
            multimodal_projector_bias=config.multimodal_projector_bias)
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.model.make_empty_intermediate_tensors)

    def _validate_video_pixel_values(
        self, data: Union[torch.Tensor, list[torch.Tensor]]
    ) -> Union[torch.Tensor, list[torch.Tensor]]:

        h = w = self.config.vision_config.image_size
        expected_dims = (3, h, w)

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape[2:])

            if actual_dims != expected_dims:
                expected_expr = ("num_frames", *map(str, expected_dims))
                raise ValueError(
                    "The expected shape of pixel values in each video frame "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _parse_and_validate_video_input(
            self, **kwargs: object) -> Optional[LlavaNextVideoPixelInputs]:
        """
        A legal video input should have the following dimensions:
        {
            "pixel_values_videos" : 
                list[b, Tensor(nb_frames, nb_channels, height, width)]
        }
        """
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)

        if pixel_values_videos is None:
            return None

        if not isinstance(pixel_values_videos, (torch.Tensor, list)):
            raise ValueError("Incorrect type of pixel_values_videos. "
                             f"Got type: {type(pixel_values_videos)}")

        return LlavaNextVideoPixelInputs(
            type="pixel_values_videos",
            data=pixel_values_videos,
        )

    def _select_image_features(self, image_features: torch.Tensor, *,
                               strategy: str) -> torch.Tensor:
        if strategy == "default":
            return image_features[:, 1:]
        elif strategy == "full":
            return image_features

        raise ValueError(f"Unexpected select feature strategy: {strategy}")

    def _video_pixels_to_features(
        self,
        vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
        pixel_values: torch.Tensor,
    ) -> torch.Tensor:

        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the vision tower
        image_features = vision_tower(pixel_values)
        image_features = self._select_image_features(
            image_features,
            strategy=self.config.vision_feature_select_strategy,
        )
        image_features = self.vision_resampler(image_features)
        image_features = self.multi_modal_projector(image_features)
        return image_features

    def _process_video_pixels(self, inputs: LlavaNextVideoPixelInputs):
        assert self.vision_tower is not None

        video_pixels = inputs["data"]

        if isinstance(video_pixels, torch.Tensor):
            # TODO: support multiple videos per input
            b, num_videos, num_frames, c, h, w = video_pixels.shape
            assert (num_videos == 1)
            stacked_pixels = video_pixels.view(b * num_videos * num_frames, c,
                                               h, w)
            stacked_embeddings = self._video_pixels_to_features(
                self.vision_tower, stacked_pixels)
            embeds = stacked_embeddings.view(b, num_frames,
                                             *stacked_embeddings.shape[1:])

        elif is_list_of(video_pixels, torch.Tensor):
            frames_per_videos = [v.shape[0] for v in video_pixels]
            stacked_pixels = torch.cat(video_pixels, dim=0)
            stacked_embeddings = self._video_pixels_to_features(
                self.vision_tower, stacked_pixels)
            embeds = torch.split(stacked_embeddings, frames_per_videos, dim=0)
        else:
            raise ValueError(
                f"Unsupported type of video input {type(video_pixels)}")

        return [e.flatten(0, 1) for e in embeds]

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

    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
        video_input = self._parse_and_validate_video_input(**kwargs)
        if video_input is None:
            return []
        vision_embeddings = self._process_video_pixels(video_input)
        return vision_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:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings,
                self.config.video_token_index)
        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]:
        """Run forward pass for LlaVA-NeXT-Video.
        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            pixel_values_videos: Pixels in each frames for each input videos.
        """
        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:
            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_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,
            # This model doesn't support images for now
            ignore_unexpected_prefixes=["image_newline"],
        )
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

config instance-attribute

config = config

hf_to_vllm_mapper class-attribute instance-attribute

hf_to_vllm_mapper = WeightsMapper(
    orig_to_new_prefix={
        "model.language_model.": "language_model.model.",
        "model.vision_tower.": "vision_tower.",
        "model.multi_modal_projector.": "multi_modal_projector.",
        "model.image_newline": "image_newline",
        "lm_head.": "language_model.lm_head.",
    }
)

language_model instance-attribute

language_model = init_vllm_registered_model(
    vllm_config=vllm_config,
    hf_config=text_config,
    prefix=maybe_prefix(prefix, "language_model"),
)

make_empty_intermediate_tensors instance-attribute

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors
)

multi_modal_projector instance-attribute

multi_modal_projector = LlavaNextMultiModalProjector(
    vision_hidden_size=hidden_size,
    text_hidden_size=hidden_size,
    projector_hidden_act=projector_hidden_act,
    multimodal_projector_bias=multimodal_projector_bias,
)

multimodal_config instance-attribute

multimodal_config = multimodal_config

vision_resampler instance-attribute

vision_resampler = LlavaNextVideoPooler(config)

vision_tower instance-attribute

vision_tower = init_vision_tower_for_llava(
    config,
    quant_config,
    require_post_norm=False,
    prefix=maybe_prefix(prefix, "vision_tower"),
)

__init__

__init__(
    *, vllm_config: VllmConfig, prefix: str = ""
) -> None

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

def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
    super().__init__()
    config = vllm_config.model_config.hf_config
    quant_config = vllm_config.quant_config
    multimodal_config = vllm_config.model_config.multimodal_config

    self.config = config
    self.multimodal_config = multimodal_config

    # Initialize the vision tower only up to the required feature layer
    self.vision_tower = init_vision_tower_for_llava(
        config,
        quant_config,
        require_post_norm=False,
        prefix=maybe_prefix(prefix, "vision_tower"))
    self.vision_resampler = LlavaNextVideoPooler(config)
    self.multi_modal_projector = LlavaNextMultiModalProjector(
        vision_hidden_size=config.vision_config.hidden_size,
        text_hidden_size=config.text_config.hidden_size,
        projector_hidden_act=config.projector_hidden_act,
        multimodal_projector_bias=config.multimodal_projector_bias)
    self.language_model = init_vllm_registered_model(
        vllm_config=vllm_config,
        hf_config=config.text_config,
        prefix=maybe_prefix(prefix, "language_model"),
    )

    self.make_empty_intermediate_tensors = (
        self.language_model.model.make_empty_intermediate_tensors)

_parse_and_validate_video_input

_parse_and_validate_video_input(
    **kwargs: object,
) -> Optional[LlavaNextVideoPixelInputs]

A legal video input should have the following dimensions: { "pixel_values_videos" : list[b, Tensor(nb_frames, nb_channels, height, width)] }

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

def _parse_and_validate_video_input(
        self, **kwargs: object) -> Optional[LlavaNextVideoPixelInputs]:
    """
    A legal video input should have the following dimensions:
    {
        "pixel_values_videos" : 
            list[b, Tensor(nb_frames, nb_channels, height, width)]
    }
    """
    pixel_values_videos = kwargs.pop("pixel_values_videos", None)

    if pixel_values_videos is None:
        return None

    if not isinstance(pixel_values_videos, (torch.Tensor, list)):
        raise ValueError("Incorrect type of pixel_values_videos. "
                         f"Got type: {type(pixel_values_videos)}")

    return LlavaNextVideoPixelInputs(
        type="pixel_values_videos",
        data=pixel_values_videos,
    )

_process_video_pixels

_process_video_pixels(inputs: LlavaNextVideoPixelInputs)

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

def _process_video_pixels(self, inputs: LlavaNextVideoPixelInputs):
    assert self.vision_tower is not None

    video_pixels = inputs["data"]

    if isinstance(video_pixels, torch.Tensor):
        # TODO: support multiple videos per input
        b, num_videos, num_frames, c, h, w = video_pixels.shape
        assert (num_videos == 1)
        stacked_pixels = video_pixels.view(b * num_videos * num_frames, c,
                                           h, w)
        stacked_embeddings = self._video_pixels_to_features(
            self.vision_tower, stacked_pixels)
        embeds = stacked_embeddings.view(b, num_frames,
                                         *stacked_embeddings.shape[1:])

    elif is_list_of(video_pixels, torch.Tensor):
        frames_per_videos = [v.shape[0] for v in video_pixels]
        stacked_pixels = torch.cat(video_pixels, dim=0)
        stacked_embeddings = self._video_pixels_to_features(
            self.vision_tower, stacked_pixels)
        embeds = torch.split(stacked_embeddings, frames_per_videos, dim=0)
    else:
        raise ValueError(
            f"Unsupported type of video input {type(video_pixels)}")

    return [e.flatten(0, 1) for e in embeds]

_select_image_features

_select_image_features(
    image_features: Tensor, *, strategy: str
) -> Tensor

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

def _select_image_features(self, image_features: torch.Tensor, *,
                           strategy: str) -> torch.Tensor:
    if strategy == "default":
        return image_features[:, 1:]
    elif strategy == "full":
        return image_features

    raise ValueError(f"Unexpected select feature strategy: {strategy}")

_validate_video_pixel_values

_validate_video_pixel_values(
    data: Union[Tensor, list[Tensor]],
) -> Union[Tensor, list[Tensor]]

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

def _validate_video_pixel_values(
    self, data: Union[torch.Tensor, list[torch.Tensor]]
) -> Union[torch.Tensor, list[torch.Tensor]]:

    h = w = self.config.vision_config.image_size
    expected_dims = (3, h, w)

    def _validate_shape(d: torch.Tensor):
        actual_dims = tuple(d.shape[2:])

        if actual_dims != expected_dims:
            expected_expr = ("num_frames", *map(str, expected_dims))
            raise ValueError(
                "The expected shape of pixel values in each video frame "
                f"is {expected_expr}. You supplied {tuple(d.shape)}.")

    for d in data:
        _validate_shape(d)

    return data

_video_pixels_to_features

_video_pixels_to_features(
    vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
    pixel_values: Tensor,
) -> Tensor

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

def _video_pixels_to_features(
    self,
    vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
    pixel_values: torch.Tensor,
) -> torch.Tensor:

    # NOTE: we skip the step to select the vision feature layer since
    # this is already done inside the vision tower
    image_features = vision_tower(pixel_values)
    image_features = self._select_image_features(
        image_features,
        strategy=self.config.vision_feature_select_strategy,
    )
    image_features = self.vision_resampler(image_features)
    image_features = self.multi_modal_projector(image_features)
    return image_features

compute_logits

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

Source code in vllm/model_executor/models/llava_next_video.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]

Run forward pass for LlaVA-NeXT-Video. Args: input_ids: Flattened (concatenated) input_ids corresponding to a batch. pixel_values_videos: Pixels in each frames for each input videos.

Source code in vllm/model_executor/models/llava_next_video.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]:
    """Run forward pass for LlaVA-NeXT-Video.
    Args:
        input_ids: Flattened (concatenated) input_ids corresponding to a
            batch.
        pixel_values_videos: Pixels in each frames for each input videos.
    """
    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:
        vision_embeddings = self.get_multimodal_embeddings(**kwargs)
        inputs_embeds = self.get_input_embeddings(input_ids,
                                                  vision_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/llava_next_video.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:
        inputs_embeds = merge_multimodal_embeddings(
            input_ids, inputs_embeds, multimodal_embeddings,
            self.config.video_token_index)
    return inputs_embeds

get_language_model

get_language_model() -> Module

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

def get_multimodal_embeddings(self,
                              **kwargs: object) -> MultiModalEmbeddings:
    video_input = self._parse_and_validate_video_input(**kwargs)
    if video_input is None:
        return []
    vision_embeddings = self._process_video_pixels(video_input)
    return vision_embeddings

get_placeholder_str classmethod

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

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

@classmethod
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
    if modality.startswith("image"):
        return "<image>"
    if modality.startswith("video"):
        return "<video>"

    raise ValueError("Only image or video modality is supported")

load_weights

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

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

def load_weights(self, weights: Iterable[tuple[str,
                                               torch.Tensor]]) -> set[str]:
    loader = AutoWeightsLoader(
        self,
        # This model doesn't support images for now
        ignore_unexpected_prefixes=["image_newline"],
    )
    return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

LlavaNextVideoMultiModalProcessor

Bases: BaseMultiModalProcessor[LlavaNextVideoProcessingInfo]

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

class LlavaNextVideoMultiModalProcessor(
        BaseMultiModalProcessor[LlavaNextVideoProcessingInfo]):

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(pixel_values_videos=MultiModalFieldConfig.batched("video"))

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> Sequence[PromptUpdate]:
        hf_config = self.info.get_hf_config()
        video_token_id = hf_config.video_token_index

        def get_replacement(item_idx: int):
            videos = mm_items.get_items(
                "video", (VideoEmbeddingItems, VideoProcessorItems))

            if isinstance(videos, VideoEmbeddingItems):
                num_video_tokens = videos.get_feature_size(item_idx)
            else:
                image_size = videos.get_frame_size(item_idx)
                num_video_tokens = self.info.get_num_video_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    num_frames=videos.get_num_frames(item_idx),
                )

            return [video_token_id] * num_video_tokens

        return [
            PromptReplacement(
                modality="video",
                target=[video_token_id],
                replacement=get_replacement,
            ),
        ]

_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/llava_next_video.py

def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return dict(pixel_values_videos=MultiModalFieldConfig.batched("video"))

_get_prompt_updates

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

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

def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    hf_config = self.info.get_hf_config()
    video_token_id = hf_config.video_token_index

    def get_replacement(item_idx: int):
        videos = mm_items.get_items(
            "video", (VideoEmbeddingItems, VideoProcessorItems))

        if isinstance(videos, VideoEmbeddingItems):
            num_video_tokens = videos.get_feature_size(item_idx)
        else:
            image_size = videos.get_frame_size(item_idx)
            num_video_tokens = self.info.get_num_video_tokens(
                image_width=image_size.width,
                image_height=image_size.height,
                num_frames=videos.get_num_frames(item_idx),
            )

        return [video_token_id] * num_video_tokens

    return [
        PromptReplacement(
            modality="video",
            target=[video_token_id],
            replacement=get_replacement,
        ),
    ]

LlavaNextVideoPixelInputs

Bases: TypedDict

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

class LlavaNextVideoPixelInputs(TypedDict):
    type: Literal["pixel_values_videos"]
    data: Union[torch.Tensor, list[torch.Tensor]]
    """
    Shape: `(batch_size, num_frames, num_channels, height, width)`

    Note that `num_frames` may be different for each batch, in which case
    the data is passed as a list instead of a batched tensor.

    Note that it only supports one video input for one batch.
    """

data instance-attribute

data: Union[Tensor, list[Tensor]]

Shape: (batch_size, num_frames, num_channels, height, width)

Note that num_frames may be different for each batch, in which case the data is passed as a list instead of a batched tensor.

Note that it only supports one video input for one batch.

type instance-attribute

type: Literal['pixel_values_videos']

LlavaNextVideoPooler

Bases: Module

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

class LlavaNextVideoPooler(nn.Module):

    def __init__(self, config: LlavaNextVideoConfig):
        super().__init__()

        mode = config.spatial_pool_mode
        stride = config.spatial_pool_stride
        image_size = config.vision_config.image_size
        patch_size = config.vision_config.patch_size
        self.image_size = image_size // patch_size**2

        if mode == "average":
            self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
        elif mode == "max":
            self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
        else:
            # TODO: Support Conv2d pooling layer, need to load weights
            raise ValueError(
                f"Unknown pooling mode: {mode}. Expected [`average`, `max`]")

    def forward(self, image_features: torch.Tensor):
        ori_width = int(
            math.sqrt(image_features.shape[1] * self.image_size //
                      self.image_size))
        ori_height = int(ori_width * self.image_size // self.image_size)

        batch_size, _, dim = image_features.shape
        image_features_spatial = image_features \
            .view(batch_size, ori_height, ori_height, dim) \
            .permute(0, 3, 1, 2)
        image_features_spatial = self.pool(image_features_spatial)

        return image_features_spatial.flatten(2).transpose(1, 2).contiguous()

image_size instance-attribute

image_size = image_size // patch_size ** 2

pool instance-attribute

pool = AvgPool2d(kernel_size=stride, stride=stride)

__init__

__init__(config: LlavaNextVideoConfig)

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

def __init__(self, config: LlavaNextVideoConfig):
    super().__init__()

    mode = config.spatial_pool_mode
    stride = config.spatial_pool_stride
    image_size = config.vision_config.image_size
    patch_size = config.vision_config.patch_size
    self.image_size = image_size // patch_size**2

    if mode == "average":
        self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
    elif mode == "max":
        self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
    else:
        # TODO: Support Conv2d pooling layer, need to load weights
        raise ValueError(
            f"Unknown pooling mode: {mode}. Expected [`average`, `max`]")

forward

forward(image_features: Tensor)

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

def forward(self, image_features: torch.Tensor):
    ori_width = int(
        math.sqrt(image_features.shape[1] * self.image_size //
                  self.image_size))
    ori_height = int(ori_width * self.image_size // self.image_size)

    batch_size, _, dim = image_features.shape
    image_features_spatial = image_features \
        .view(batch_size, ori_height, ori_height, dim) \
        .permute(0, 3, 1, 2)
    image_features_spatial = self.pool(image_features_spatial)

    return image_features_spatial.flatten(2).transpose(1, 2).contiguous()

LlavaNextVideoProcessingInfo

Bases: BaseProcessingInfo

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

class LlavaNextVideoProcessingInfo(BaseProcessingInfo):

    def get_hf_config(self):
        return self.ctx.get_hf_config(LlavaNextVideoConfig)

    def get_vision_encoder_info(self):
        return get_vision_encoder_info(self.get_hf_config())

    def get_hf_processor(self, **kwargs: object):
        return self.ctx.get_hf_processor(LlavaNextVideoProcessor, **kwargs)

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"video": 1}

    def get_image_size_with_most_features(self) -> ImageSize:
        vision_encoder_info = self.get_vision_encoder_info()
        width = height = vision_encoder_info.get_image_size()
        return ImageSize(width=width, height=height)

    def _get_num_frame_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        hf_config = self.get_hf_config()
        spatial_pool_stride = hf_config.spatial_pool_stride

        vision_encoder_info = self.get_vision_encoder_info()
        patch_grid_length = vision_encoder_info.get_patch_grid_length()
        pooled_grid_length = math.ceil(patch_grid_length / spatial_pool_stride)

        return pooled_grid_length * pooled_grid_length

    def get_num_video_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
    ) -> int:
        num_frame_tokens = self._get_num_frame_tokens(
            image_width=image_width,
            image_height=image_height,
        )

        return num_frame_tokens * num_frames

    def _get_max_video_frames(self, max_tokens: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        num_frames = 0

        while True:
            next_num_frames = num_frames + 1
            next_max_tokens = self.get_num_video_tokens(
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
            )

            if next_max_tokens > max_tokens:
                break

            num_frames = next_num_frames

        return num_frames

    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        max_videos = mm_counts.get("video", 0)

        max_total_frames = self._get_max_video_frames(seq_len)

        return max(max_total_frames // max(max_videos, 1), 1)

_get_max_video_frames

_get_max_video_frames(max_tokens: int) -> int

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

def _get_max_video_frames(self, max_tokens: int) -> int:
    target_width, target_height = self.get_image_size_with_most_features()

    num_frames = 0

    while True:
        next_num_frames = num_frames + 1
        next_max_tokens = self.get_num_video_tokens(
            image_width=target_width,
            image_height=target_height,
            num_frames=next_num_frames,
        )

        if next_max_tokens > max_tokens:
            break

        num_frames = next_num_frames

    return num_frames

_get_num_frame_tokens

_get_num_frame_tokens(
    *, image_width: int, image_height: int
) -> int

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

def _get_num_frame_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    hf_config = self.get_hf_config()
    spatial_pool_stride = hf_config.spatial_pool_stride

    vision_encoder_info = self.get_vision_encoder_info()
    patch_grid_length = vision_encoder_info.get_patch_grid_length()
    pooled_grid_length = math.ceil(patch_grid_length / spatial_pool_stride)

    return pooled_grid_length * pooled_grid_length

get_hf_config

get_hf_config()

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

def get_hf_config(self):
    return self.ctx.get_hf_config(LlavaNextVideoConfig)

get_hf_processor

get_hf_processor(**kwargs: object)

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

def get_hf_processor(self, **kwargs: object):
    return self.ctx.get_hf_processor(LlavaNextVideoProcessor, **kwargs)

get_image_size_with_most_features

get_image_size_with_most_features() -> ImageSize

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

def get_image_size_with_most_features(self) -> ImageSize:
    vision_encoder_info = self.get_vision_encoder_info()
    width = height = vision_encoder_info.get_image_size()
    return ImageSize(width=width, height=height)

get_num_frames_with_most_features

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

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

def get_num_frames_with_most_features(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> int:
    max_videos = mm_counts.get("video", 0)

    max_total_frames = self._get_max_video_frames(seq_len)

    return max(max_total_frames // max(max_videos, 1), 1)

get_num_video_tokens

get_num_video_tokens(
    *, image_width: int, image_height: int, num_frames: int
) -> int

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

def get_num_video_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
    num_frames: int,
) -> int:
    num_frame_tokens = self._get_num_frame_tokens(
        image_width=image_width,
        image_height=image_height,
    )

    return num_frame_tokens * num_frames

get_supported_mm_limits

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

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

def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
    return {"video": 1}

get_vision_encoder_info

get_vision_encoder_info()

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

def get_vision_encoder_info(self):
    return get_vision_encoder_info(self.get_hf_config())