vllm.model_executor.models.llava_onevision

LlavaOnevisionImageInputs module-attribute

LlavaOnevisionImageInputs = Union[
    LlavaOnevisionImagePixelInputs,
    LlavaOnevisionImageEmbeddingInputs,
]

LlavaOnevisionMultiInputs module-attribute

LlavaOnevisionMultiInputs = Union[
    LlavaOnevisionImageInputs,
    LlavaOnevisionVideoPixelInputs,
]

_MAX_FRAMES_PER_VIDEO module-attribute

_MAX_FRAMES_PER_VIDEO = 16

LlavaOnevisionDummyInputsBuilder

Bases: LlavaDummyInputsBuilder[LlavaOnevisionProcessingInfo]

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

class LlavaOnevisionDummyInputsBuilder(
        LlavaDummyInputsBuilder[LlavaOnevisionProcessingInfo]):

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

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

        return image_token * num_images + video_token * num_videos

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

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_onevision.py

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

get_dummy_text

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

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

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

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

    return image_token * num_images + video_token * num_videos

LlavaOnevisionForConditionalGeneration

Bases: Module, SupportsMultiModal, SupportsPP

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

@MULTIMODAL_REGISTRY.register_processor(
    LlavaOnevisionMultiModalProcessor,
    info=LlavaOnevisionProcessingInfo,
    dummy_inputs=LlavaOnevisionDummyInputsBuilder)
class LlavaOnevisionForConditionalGeneration(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.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )
        self.image_newline = nn.Parameter(
            torch.empty(config.text_config.hidden_size))

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

    def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
        expected_dims = (2, )

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

            if actual_dims != expected_dims:
                expected_expr = str(expected_dims)
                raise ValueError(
                    f"The expected shape of image sizes per image per batch "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _validate_image_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[1:])

            if actual_dims != expected_dims:
                expected_expr = ("num_patches", *map(str, expected_dims))
                raise ValueError(
                    "The expected shape of pixel values per image per batch "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[LlavaOnevisionImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_sizes = kwargs.pop("image_sizes", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of pixel values. "
                                 f"Got type: {type(pixel_values)}")

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

            return LlavaOnevisionImagePixelInputs(
                type="pixel_values",
                pixel_values=self._validate_image_pixel_values(
                    flatten_bn(pixel_values)),
                image_sizes=self._validate_image_sizes(
                    flatten_bn(image_sizes, concat=True)),
            )

        if image_embeds is not None:
            if not isinstance(image_embeds, torch.Tensor):
                raise ValueError("Incorrect type of image embeds. "
                                 f"Got type: {type(image_embeds)}")

            return LlavaOnevisionImageEmbeddingInputs(
                type="image_embeds",
                data=flatten_bn(image_embeds),
            )

        raise AssertionError("This line should be unreachable.")

    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[LlavaOnevisionVideoPixelInputs]:
        """
        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 LlavaOnevisionVideoPixelInputs(
            type="pixel_values_videos",
            pixel_values_videos=flatten_bn(pixel_values_videos),
        )

    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)

        return mm_input_by_modality

    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 _image_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)
        return self._select_image_features(
            image_features,
            strategy=self.config.vision_feature_select_strategy,
        )

    # Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
    def _merge_image_patch_embeddings(self,
                                      image_size: torch.Tensor,
                                      patch_embeddings: torch.Tensor,
                                      *,
                                      image_newline=None,
                                      vision_aspect_ratio="anyres_max_9",
                                      strategy: str) -> torch.Tensor:
        if strategy == "flat":
            return patch_embeddings.flatten(0, 1)

        if strategy.startswith("spatial"):
            height = width = self.config.vision_config.image_size \
                // self.config.vision_config.patch_size

            base_patch_embeds = patch_embeddings[0]
            if height * width != base_patch_embeds.shape[0]:
                raise ValueError(
                    "The number of patches is not consistent with the "
                    "image size.")

            if patch_embeddings.shape[0] > 1:
                other_patch_embeds = patch_embeddings[1:]

                # Move to CPU to avoid floating-point errors
                orig_height, orig_width = image_size.tolist()

                # image_aspect_ratio == "anyres"
                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
                    (orig_height, orig_width),
                    self.config.image_grid_pinpoints,
                    self.config.vision_config.image_size,
                )
                num_patches = num_patch_height * num_patch_width

                # Image patches might be padded for batch processing
                other_patch_embeds = other_patch_embeds[:num_patches] \
                    .view(num_patch_height, num_patch_width, height, width, -1)

                if "unpad" in strategy:
                    other_patch_embeds = other_patch_embeds \
                        .permute(4, 0, 2, 1, 3).contiguous() \
                        .flatten(1, 2).flatten(2, 3)
                    other_patch_embeds = unpad_image(other_patch_embeds,
                                                     (orig_height, orig_width))
                    max_num_patches = int(
                        vision_aspect_ratio.removeprefix("anyres_max_"))
                    channels, curr_height, curr_width = other_patch_embeds.shape
                    ratio = math.sqrt(curr_height * curr_width /
                                      (max_num_patches * height**2))
                    if ratio > 1.1:
                        other_patch_embeds = other_patch_embeds[None]
                        other_patch_embeds = nn.functional.interpolate(
                            other_patch_embeds, [
                                int(curr_height // ratio),
                                int(curr_width // ratio)
                            ],
                            mode="bilinear")[0]
                    if image_newline is not None:
                        other_patch_embeds = torch.cat(
                            (
                                other_patch_embeds,
                                image_newline[:, None, None] \
                                .expand(*other_patch_embeds.shape[:-1], 1) \
                                .to(other_patch_embeds.device),
                            ),
                        dim=-1)
                    other_patch_embeds = other_patch_embeds \
                        .flatten(1, 2).transpose(0, 1)
                else:
                    other_patch_embeds = other_patch_embeds \
                        .permute(0, 2, 1, 3, 4).contiguous() \
                        .flatten(0, 3)

                merged_patch_embeddings = torch.cat(
                    (base_patch_embeds, other_patch_embeds), dim=0)
            else:
                if "unpad" in strategy:
                    merged_patch_embeddings = torch.cat(
                        (base_patch_embeds,
                         self.image_newline[None] \
                            .to(base_patch_embeds.device)
                    ), dim=0)
                else:
                    merged_patch_embeddings = base_patch_embeds

            return merged_patch_embeddings

        raise ValueError(f"Unexpected patch merge strategy: {strategy}")

    def _process_image_pixels(
        self,
        inputs: LlavaOnevisionImagePixelInputs,
    ) -> Union[torch.Tensor, list[torch.Tensor]]:
        assert self.vision_tower is not None

        pixel_values = inputs["pixel_values"]

        if isinstance(pixel_values, torch.Tensor):
            b, num_patches, c, h, w = pixel_values.shape
            stacked_pixel_values = pixel_values.view(b * num_patches, c, h, w)
            stacked_image_features = self._image_pixels_to_features(
                self.vision_tower, stacked_pixel_values)
            stacked_patch_embeddings = self.multi_modal_projector(
                stacked_image_features)

            return stacked_patch_embeddings.view(
                b, num_patches, *stacked_patch_embeddings.shape[1:])

        num_patches_per_batch = [v.shape[0] for v in pixel_values]
        stacked_pixel_values = torch.cat(pixel_values)
        stacked_image_features = self._image_pixels_to_features(
            self.vision_tower, stacked_pixel_values)

        return [
            self.multi_modal_projector(image_features) for image_features in
            torch.split(stacked_image_features, num_patches_per_batch)
        ]

    def _process_image_input(
        self,
        image_input: LlavaOnevisionImageInputs,
    ) -> Union[torch.Tensor, list[torch.Tensor]]:
        if image_input["type"] == "image_embeds":
            return [image_input["data"]]

        patch_embeddings = self._process_image_pixels(image_input)

        image_sizes = image_input.get("image_sizes")
        if image_sizes is None:
            batch_size = len(image_input["pixel_values"])
            vision_config = self.config.vision_config
            default_height = default_width = vision_config.image_size
            image_sizes = torch.as_tensor([[default_height, default_width]
                                           for _ in range(batch_size)])

        return [
            self._merge_image_patch_embeddings(
                image_sizes[i],
                patch_features_batch,
                image_newline=self.image_newline,
                strategy="spatial_unpad")
            for i, patch_features_batch in enumerate(patch_embeddings)
        ]

    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
        video_features = vision_tower(pixel_values)
        video_features = self._select_image_features(
            video_features,
            strategy=self.config.vision_feature_select_strategy,
        )
        video_features = self.multi_modal_projector(video_features)
        video_features = self.apply_pooling(video_features)
        return video_features

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

        video_pixels = inputs["pixel_values_videos"]

        if isinstance(video_pixels, torch.Tensor):
            total_videos, frames, c, h, w = video_pixels.shape
            video_pixels_flat = video_pixels.view(total_videos * frames, c, h,
                                                  w)

            embeddings_flat = self._video_pixels_to_features(
                self.vision_tower, video_pixels_flat)

            embeddings_flat = embeddings_flat.reshape(
                total_videos, frames * embeddings_flat.shape[1], -1)

            image_newline = self.image_newline[None, None, :].expand(
                total_videos, -1, -1)
            return torch.cat((embeddings_flat, image_newline), dim=1)

        frames_per_video = [len(video) for video in video_pixels]
        video_pixels_flat = torch.cat(video_pixels)

        embeddings_flat = self._video_pixels_to_features(
            self.vision_tower, video_pixels_flat)

        image_newline = self.image_newline[None, None, :]

        return [
            torch.cat(
                (
                    embeds.reshape(1, num_frame * embeddings_flat.shape[1],
                                   -1),
                    image_newline,
                ),
                dim=1,
            ) for num_frame, embeds in zip(
                frames_per_video,
                torch.split(embeddings_flat, frames_per_video),
            )
        ]

    def apply_pooling(self, image_features: torch.Tensor, stride: int = 2):
        vision_config = self.config.vision_config
        height = width = vision_config.image_size // vision_config.patch_size
        batch_frames, _, dim = image_features.shape
        image_features = image_features.view(batch_frames, height, width, -1)
        image_features = image_features.permute(0, 3, 1, 2)

        # TODO support other pooling types config
        height, width = image_features.shape[2:]
        scaled_shape = [math.ceil(height / stride), math.ceil(width / stride)]
        image_feature = nn.functional.interpolate(image_features,
                                                  size=scaled_shape,
                                                  mode='bilinear')
        image_feature = image_feature.permute(0, 2, 3, 1)
        image_feature = image_feature.view(batch_frames, -1, dim)
        return image_feature

    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 []
            return None

        # 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 += tuple(vision_embeddings)
            if modality == "video":
                video_embeddings = self._process_video_pixels(multimodal_input)
                multimodal_embeddings += tuple(video_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:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings,
                [self.config.image_token_index, self.config.video_token_index])
        return inputs_embeds

    def get_input_embeddings_v0(
        self,
        input_ids: torch.Tensor,
        image_input: Optional[LlavaOnevisionImagePixelInputs] = None,
        video_input: Optional[LlavaOnevisionVideoPixelInputs] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.get_input_embeddings(input_ids)
        if image_input is not None:
            image_embeds = self._process_image_input(image_input)
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                image_embeds,
                placeholder_token_id=self.config.image_token_index,
            )

        if video_input is not None:
            video_embeds = self._process_video_pixels(video_input)
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                video_embeds,
                placeholder_token_id=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-Onevision.
        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 from
        # `get_multimodal_embeddings` and `get_input_embeddings`, this
        # condition is only for v0 compatibility.
        elif inputs_embeds is None:
            image_input = self._parse_and_validate_image_input(**kwargs)
            video_input = self._parse_and_validate_video_input(**kwargs)

            if image_input is None and video_input is None:
                inputs_embeds = None
            else:
                inputs_embeds = self.get_input_embeddings_v0(
                    input_ids,
                    image_input=image_input,
                    video_input=video_input)
                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)
        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.",
    }
)

image_newline instance-attribute

image_newline = Parameter(empty(hidden_size))

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 = LlavaOnevisionMultiModalProjector(
    config
)

multimodal_config instance-attribute

multimodal_config = multimodal_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_onevision.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.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
    self.language_model = init_vllm_registered_model(
        vllm_config=vllm_config,
        hf_config=config.text_config,
        prefix=maybe_prefix(prefix, "language_model"),
    )
    self.image_newline = nn.Parameter(
        torch.empty(config.text_config.hidden_size))

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

_image_pixels_to_features

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

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

def _image_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)
    return self._select_image_features(
        image_features,
        strategy=self.config.vision_feature_select_strategy,
    )

_merge_image_patch_embeddings

_merge_image_patch_embeddings(
    image_size: Tensor,
    patch_embeddings: Tensor,
    *,
    image_newline=None,
    vision_aspect_ratio="anyres_max_9",
    strategy: str,
) -> Tensor

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

def _merge_image_patch_embeddings(self,
                                  image_size: torch.Tensor,
                                  patch_embeddings: torch.Tensor,
                                  *,
                                  image_newline=None,
                                  vision_aspect_ratio="anyres_max_9",
                                  strategy: str) -> torch.Tensor:
    if strategy == "flat":
        return patch_embeddings.flatten(0, 1)

    if strategy.startswith("spatial"):
        height = width = self.config.vision_config.image_size \
            // self.config.vision_config.patch_size

        base_patch_embeds = patch_embeddings[0]
        if height * width != base_patch_embeds.shape[0]:
            raise ValueError(
                "The number of patches is not consistent with the "
                "image size.")

        if patch_embeddings.shape[0] > 1:
            other_patch_embeds = patch_embeddings[1:]

            # Move to CPU to avoid floating-point errors
            orig_height, orig_width = image_size.tolist()

            # image_aspect_ratio == "anyres"
            num_patch_height, num_patch_width = get_anyres_image_grid_shape(
                (orig_height, orig_width),
                self.config.image_grid_pinpoints,
                self.config.vision_config.image_size,
            )
            num_patches = num_patch_height * num_patch_width

            # Image patches might be padded for batch processing
            other_patch_embeds = other_patch_embeds[:num_patches] \
                .view(num_patch_height, num_patch_width, height, width, -1)

            if "unpad" in strategy:
                other_patch_embeds = other_patch_embeds \
                    .permute(4, 0, 2, 1, 3).contiguous() \
                    .flatten(1, 2).flatten(2, 3)
                other_patch_embeds = unpad_image(other_patch_embeds,
                                                 (orig_height, orig_width))
                max_num_patches = int(
                    vision_aspect_ratio.removeprefix("anyres_max_"))
                channels, curr_height, curr_width = other_patch_embeds.shape
                ratio = math.sqrt(curr_height * curr_width /
                                  (max_num_patches * height**2))
                if ratio > 1.1:
                    other_patch_embeds = other_patch_embeds[None]
                    other_patch_embeds = nn.functional.interpolate(
                        other_patch_embeds, [
                            int(curr_height // ratio),
                            int(curr_width // ratio)
                        ],
                        mode="bilinear")[0]
                if image_newline is not None:
                    other_patch_embeds = torch.cat(
                        (
                            other_patch_embeds,
                            image_newline[:, None, None] \
                            .expand(*other_patch_embeds.shape[:-1], 1) \
                            .to(other_patch_embeds.device),
                        ),
                    dim=-1)
                other_patch_embeds = other_patch_embeds \
                    .flatten(1, 2).transpose(0, 1)
            else:
                other_patch_embeds = other_patch_embeds \
                    .permute(0, 2, 1, 3, 4).contiguous() \
                    .flatten(0, 3)

            merged_patch_embeddings = torch.cat(
                (base_patch_embeds, other_patch_embeds), dim=0)
        else:
            if "unpad" in strategy:
                merged_patch_embeddings = torch.cat(
                    (base_patch_embeds,
                     self.image_newline[None] \
                        .to(base_patch_embeds.device)
                ), dim=0)
            else:
                merged_patch_embeddings = base_patch_embeds

        return merged_patch_embeddings

    raise ValueError(f"Unexpected patch merge strategy: {strategy}")

_parse_and_validate_image_input

_parse_and_validate_image_input(
    **kwargs: object,
) -> Optional[LlavaOnevisionImageInputs]

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

def _parse_and_validate_image_input(
        self, **kwargs: object) -> Optional[LlavaOnevisionImageInputs]:
    pixel_values = kwargs.pop("pixel_values", None)
    image_sizes = kwargs.pop("image_sizes", None)
    image_embeds = kwargs.pop("image_embeds", None)

    if pixel_values is None and image_embeds is None:
        return None

    if pixel_values is not None:
        if not isinstance(pixel_values, (torch.Tensor, list)):
            raise ValueError("Incorrect type of pixel values. "
                             f"Got type: {type(pixel_values)}")

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

        return LlavaOnevisionImagePixelInputs(
            type="pixel_values",
            pixel_values=self._validate_image_pixel_values(
                flatten_bn(pixel_values)),
            image_sizes=self._validate_image_sizes(
                flatten_bn(image_sizes, concat=True)),
        )

    if image_embeds is not None:
        if not isinstance(image_embeds, torch.Tensor):
            raise ValueError("Incorrect type of image embeds. "
                             f"Got type: {type(image_embeds)}")

        return LlavaOnevisionImageEmbeddingInputs(
            type="image_embeds",
            data=flatten_bn(image_embeds),
        )

    raise AssertionError("This line should be unreachable.")

_parse_and_validate_multimodal_inputs

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

Source code in vllm/model_executor/models/llava_onevision.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)

    return mm_input_by_modality

_parse_and_validate_video_input

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

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_onevision.py

def _parse_and_validate_video_input(
        self,
        **kwargs: object) -> Optional[LlavaOnevisionVideoPixelInputs]:
    """
    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 LlavaOnevisionVideoPixelInputs(
        type="pixel_values_videos",
        pixel_values_videos=flatten_bn(pixel_values_videos),
    )

_process_image_input

_process_image_input(
    image_input: LlavaOnevisionImageInputs,
) -> Union[Tensor, list[Tensor]]

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

def _process_image_input(
    self,
    image_input: LlavaOnevisionImageInputs,
) -> Union[torch.Tensor, list[torch.Tensor]]:
    if image_input["type"] == "image_embeds":
        return [image_input["data"]]

    patch_embeddings = self._process_image_pixels(image_input)

    image_sizes = image_input.get("image_sizes")
    if image_sizes is None:
        batch_size = len(image_input["pixel_values"])
        vision_config = self.config.vision_config
        default_height = default_width = vision_config.image_size
        image_sizes = torch.as_tensor([[default_height, default_width]
                                       for _ in range(batch_size)])

    return [
        self._merge_image_patch_embeddings(
            image_sizes[i],
            patch_features_batch,
            image_newline=self.image_newline,
            strategy="spatial_unpad")
        for i, patch_features_batch in enumerate(patch_embeddings)
    ]

_process_image_pixels

_process_image_pixels(
    inputs: LlavaOnevisionImagePixelInputs,
) -> Union[Tensor, list[Tensor]]

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

def _process_image_pixels(
    self,
    inputs: LlavaOnevisionImagePixelInputs,
) -> Union[torch.Tensor, list[torch.Tensor]]:
    assert self.vision_tower is not None

    pixel_values = inputs["pixel_values"]

    if isinstance(pixel_values, torch.Tensor):
        b, num_patches, c, h, w = pixel_values.shape
        stacked_pixel_values = pixel_values.view(b * num_patches, c, h, w)
        stacked_image_features = self._image_pixels_to_features(
            self.vision_tower, stacked_pixel_values)
        stacked_patch_embeddings = self.multi_modal_projector(
            stacked_image_features)

        return stacked_patch_embeddings.view(
            b, num_patches, *stacked_patch_embeddings.shape[1:])

    num_patches_per_batch = [v.shape[0] for v in pixel_values]
    stacked_pixel_values = torch.cat(pixel_values)
    stacked_image_features = self._image_pixels_to_features(
        self.vision_tower, stacked_pixel_values)

    return [
        self.multi_modal_projector(image_features) for image_features in
        torch.split(stacked_image_features, num_patches_per_batch)
    ]

_process_video_pixels

_process_video_pixels(
    inputs: LlavaOnevisionVideoPixelInputs,
)

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

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

    video_pixels = inputs["pixel_values_videos"]

    if isinstance(video_pixels, torch.Tensor):
        total_videos, frames, c, h, w = video_pixels.shape
        video_pixels_flat = video_pixels.view(total_videos * frames, c, h,
                                              w)

        embeddings_flat = self._video_pixels_to_features(
            self.vision_tower, video_pixels_flat)

        embeddings_flat = embeddings_flat.reshape(
            total_videos, frames * embeddings_flat.shape[1], -1)

        image_newline = self.image_newline[None, None, :].expand(
            total_videos, -1, -1)
        return torch.cat((embeddings_flat, image_newline), dim=1)

    frames_per_video = [len(video) for video in video_pixels]
    video_pixels_flat = torch.cat(video_pixels)

    embeddings_flat = self._video_pixels_to_features(
        self.vision_tower, video_pixels_flat)

    image_newline = self.image_newline[None, None, :]

    return [
        torch.cat(
            (
                embeds.reshape(1, num_frame * embeddings_flat.shape[1],
                               -1),
                image_newline,
            ),
            dim=1,
        ) for num_frame, embeds in zip(
            frames_per_video,
            torch.split(embeddings_flat, frames_per_video),
        )
    ]

_select_image_features

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

Source code in vllm/model_executor/models/llava_onevision.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_image_pixel_values

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

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

def _validate_image_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[1:])

        if actual_dims != expected_dims:
            expected_expr = ("num_patches", *map(str, expected_dims))
            raise ValueError(
                "The expected shape of pixel values per image per batch "
                f"is {expected_expr}. You supplied {tuple(d.shape)}.")

    for d in data:
        _validate_shape(d)

    return data

_validate_image_sizes

_validate_image_sizes(data: Tensor) -> Tensor

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

def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
    expected_dims = (2, )

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

        if actual_dims != expected_dims:
            expected_expr = str(expected_dims)
            raise ValueError(
                f"The expected shape of image sizes per image per batch "
                f"is {expected_expr}. You supplied {tuple(d.shape)}.")

    for d in data:
        _validate_shape(d)

    return data

_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_onevision.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_onevision.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
    video_features = vision_tower(pixel_values)
    video_features = self._select_image_features(
        video_features,
        strategy=self.config.vision_feature_select_strategy,
    )
    video_features = self.multi_modal_projector(video_features)
    video_features = self.apply_pooling(video_features)
    return video_features

apply_pooling

apply_pooling(image_features: Tensor, stride: int = 2)

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

def apply_pooling(self, image_features: torch.Tensor, stride: int = 2):
    vision_config = self.config.vision_config
    height = width = vision_config.image_size // vision_config.patch_size
    batch_frames, _, dim = image_features.shape
    image_features = image_features.view(batch_frames, height, width, -1)
    image_features = image_features.permute(0, 3, 1, 2)

    # TODO support other pooling types config
    height, width = image_features.shape[2:]
    scaled_shape = [math.ceil(height / stride), math.ceil(width / stride)]
    image_feature = nn.functional.interpolate(image_features,
                                              size=scaled_shape,
                                              mode='bilinear')
    image_feature = image_feature.permute(0, 2, 3, 1)
    image_feature = image_feature.view(batch_frames, -1, dim)
    return image_feature

compute_logits

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

Source code in vllm/model_executor/models/llava_onevision.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-Onevision. 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_onevision.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-Onevision.
    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 from
    # `get_multimodal_embeddings` and `get_input_embeddings`, this
    # condition is only for v0 compatibility.
    elif inputs_embeds is None:
        image_input = self._parse_and_validate_image_input(**kwargs)
        video_input = self._parse_and_validate_video_input(**kwargs)

        if image_input is None and video_input is None:
            inputs_embeds = None
        else:
            inputs_embeds = self.get_input_embeddings_v0(
                input_ids,
                image_input=image_input,
                video_input=video_input)
            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_onevision.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.image_token_index, self.config.video_token_index])
    return inputs_embeds

get_input_embeddings_v0

get_input_embeddings_v0(
    input_ids: Tensor,
    image_input: Optional[
        LlavaOnevisionImagePixelInputs
    ] = None,
    video_input: Optional[
        LlavaOnevisionVideoPixelInputs
    ] = None,
) -> Tensor

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

def get_input_embeddings_v0(
    self,
    input_ids: torch.Tensor,
    image_input: Optional[LlavaOnevisionImagePixelInputs] = None,
    video_input: Optional[LlavaOnevisionVideoPixelInputs] = None,
) -> torch.Tensor:
    inputs_embeds = self.get_input_embeddings(input_ids)
    if image_input is not None:
        image_embeds = self._process_image_input(image_input)
        inputs_embeds = merge_multimodal_embeddings(
            input_ids,
            inputs_embeds,
            image_embeds,
            placeholder_token_id=self.config.image_token_index,
        )

    if video_input is not None:
        video_embeds = self._process_video_pixels(video_input)
        inputs_embeds = merge_multimodal_embeddings(
            input_ids,
            inputs_embeds,
            video_embeds,
            placeholder_token_id=self.config.video_token_index,
        )

    return inputs_embeds

get_language_model

get_language_model() -> Module

Source code in vllm/model_executor/models/llava_onevision.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_onevision.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 []
        return None

    # 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 += tuple(vision_embeddings)
        if modality == "video":
            video_embeddings = self._process_video_pixels(multimodal_input)
            multimodal_embeddings += tuple(video_embeddings)

    return multimodal_embeddings

get_placeholder_str classmethod

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

Source code in vllm/model_executor/models/llava_onevision.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_onevision.py

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

LlavaOnevisionImageEmbeddingInputs

Bases: TypedDict

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

class LlavaOnevisionImageEmbeddingInputs(TypedDict):
    type: Literal["image_embeds"]
    data: torch.Tensor
    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`

    `hidden_size` must match the hidden size of language model backbone.
    """

data instance-attribute

data: Tensor

Shape: (batch_size * num_images, image_feature_size, hidden_size)

hidden_size must match the hidden size of language model backbone.

type instance-attribute

type: Literal['image_embeds']

LlavaOnevisionImagePixelInputs

Bases: TypedDict

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

class LlavaOnevisionImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    pixel_values: Union[torch.Tensor, list[torch.Tensor]]
    """
    Shape:
    `(batch_size * num_images, 1 + num_patches, num_channels, height, width)`

    Note that `num_patches` may be different per batch and image,
    in which case the data is passed as a list instead of a batched tensor.
    """

    image_sizes: NotRequired[torch.Tensor]
    """
    Shape: `(batch_size * num_images, 2)`

    This should be in `(height, width)` format.
    """

image_sizes instance-attribute

image_sizes: NotRequired[Tensor]

Shape: (batch_size * num_images, 2)

This should be in (height, width) format.

pixel_values instance-attribute

pixel_values: Union[Tensor, list[Tensor]]

Shape: (batch_size * num_images, 1 + num_patches, num_channels, height, width)

Note that num_patches may be different per batch and image, in which case the data is passed as a list instead of a batched tensor.

type instance-attribute

type: Literal['pixel_values']

LlavaOnevisionLikeConfig

Bases: LlavaNextLikeConfig, Protocol

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

class LlavaOnevisionLikeConfig(LlavaNextLikeConfig, Protocol):
    video_token_index: Final[int]

video_token_index instance-attribute

video_token_index: Final[int]

LlavaOnevisionMultiModalProcessor

Bases: BaseLlavaNextMultiModalProcessor[LlavaOnevisionProcessingInfo]

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

class LlavaOnevisionMultiModalProcessor(
        BaseLlavaNextMultiModalProcessor[LlavaOnevisionProcessingInfo]):

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

    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)
        videos = mm_data.pop("videos", [])
        assert isinstance(videos, list)

        if not videos:
            return super()._call_hf_processor(
                prompt=prompt,
                mm_data=mm_data,
                mm_kwargs=mm_kwargs,
                tok_kwargs=tok_kwargs,
            )

        # LLaVA-OneVision processor doesn't support multiple videos
        # with different sizes when converting back to tensors
        # So, we process each component separately
        # NOTE: No prompt replacement is applied in this case
        processor = self.info.get_hf_processor()
        image_token = processor.image_token
        video_token = processor.video_token

        text_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data={},
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        images = mm_data.pop("images", [])
        assert isinstance(images, list)
        if images:
            processor_outputs = super()._call_hf_processor(
                prompt=image_token * len(images),
                mm_data={"images": images},
                mm_kwargs=mm_kwargs,
                tok_kwargs=tok_kwargs,
            )
            image_outputs = {
                k: v
                for k, v in processor_outputs.items()
                if k in ("pixel_values", "image_sizes")
            }
        else:
            image_outputs = {}

        pixel_values_videos = []
        for video in videos:
            item_outputs = super()._call_hf_processor(
                prompt=video_token,
                mm_data={"videos": video},
                mm_kwargs=mm_kwargs,
                tok_kwargs=tok_kwargs,
            )

            pixel_values_videos.append(item_outputs["pixel_values_videos"][0])

        video_outputs = {"pixel_values_videos": pixel_values_videos}

        combined_outputs = dict(
            text_outputs,
            **image_outputs,
            **video_outputs,
        )
        return BatchFeature(combined_outputs)

    def _hf_processor_applies_updates(
        self,
        prompt_text: str,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
    ) -> bool:
        base_result = super()._hf_processor_applies_updates(
            prompt_text=prompt_text,
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            tokenization_kwargs=tokenization_kwargs,
        )

        return base_result and mm_items.get_count("video", strict=False) == 0

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> Sequence[PromptUpdate]:
        image_repls = super()._get_prompt_updates(
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            out_mm_kwargs=out_mm_kwargs,
        )

        hf_config = self.info.get_hf_config()
        video_token_id = hf_config.video_token_index

        def get_video_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 [
            *image_repls,
            PromptReplacement(
                modality="video",
                target=[video_token_id],
                replacement=get_video_replacement,
            ),
        ]

_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/llava_onevision.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)
    videos = mm_data.pop("videos", [])
    assert isinstance(videos, list)

    if not videos:
        return super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

    # LLaVA-OneVision processor doesn't support multiple videos
    # with different sizes when converting back to tensors
    # So, we process each component separately
    # NOTE: No prompt replacement is applied in this case
    processor = self.info.get_hf_processor()
    image_token = processor.image_token
    video_token = processor.video_token

    text_outputs = super()._call_hf_processor(
        prompt=prompt,
        mm_data={},
        mm_kwargs=mm_kwargs,
        tok_kwargs=tok_kwargs,
    )

    images = mm_data.pop("images", [])
    assert isinstance(images, list)
    if images:
        processor_outputs = super()._call_hf_processor(
            prompt=image_token * len(images),
            mm_data={"images": images},
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )
        image_outputs = {
            k: v
            for k, v in processor_outputs.items()
            if k in ("pixel_values", "image_sizes")
        }
    else:
        image_outputs = {}

    pixel_values_videos = []
    for video in videos:
        item_outputs = super()._call_hf_processor(
            prompt=video_token,
            mm_data={"videos": video},
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        pixel_values_videos.append(item_outputs["pixel_values_videos"][0])

    video_outputs = {"pixel_values_videos": pixel_values_videos}

    combined_outputs = dict(
        text_outputs,
        **image_outputs,
        **video_outputs,
    )
    return BatchFeature(combined_outputs)

_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_onevision.py

def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return dict(
        pixel_values=MultiModalFieldConfig.batched("image"),
        image_sizes=MultiModalFieldConfig.batched("image"),
        image_embeds=MultiModalFieldConfig.batched("image"),
        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_onevision.py

def _get_prompt_updates(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> Sequence[PromptUpdate]:
    image_repls = super()._get_prompt_updates(
        mm_items=mm_items,
        hf_processor_mm_kwargs=hf_processor_mm_kwargs,
        out_mm_kwargs=out_mm_kwargs,
    )

    hf_config = self.info.get_hf_config()
    video_token_id = hf_config.video_token_index

    def get_video_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 [
        *image_repls,
        PromptReplacement(
            modality="video",
            target=[video_token_id],
            replacement=get_video_replacement,
        ),
    ]

_hf_processor_applies_updates

_hf_processor_applies_updates(
    prompt_text: str,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    tokenization_kwargs: Mapping[str, object],
) -> bool

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

def _hf_processor_applies_updates(
    self,
    prompt_text: str,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    tokenization_kwargs: Mapping[str, object],
) -> bool:
    base_result = super()._hf_processor_applies_updates(
        prompt_text=prompt_text,
        mm_items=mm_items,
        hf_processor_mm_kwargs=hf_processor_mm_kwargs,
        tokenization_kwargs=tokenization_kwargs,
    )

    return base_result and mm_items.get_count("video", strict=False) == 0

LlavaOnevisionMultiModalProjector

Bases: Module

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

class LlavaOnevisionMultiModalProjector(nn.Module):

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

        self.linear_1 = nn.Linear(config.vision_config.hidden_size,
                                  config.text_config.hidden_size,
                                  bias=config.multimodal_projector_bias)
        self.act = get_act_fn(config.projector_hidden_act)
        self.linear_2 = nn.Linear(config.text_config.hidden_size,
                                  config.text_config.hidden_size,
                                  bias=config.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(
    hidden_size, hidden_size, bias=multimodal_projector_bias
)

linear_2 instance-attribute

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

__init__

__init__(config: LlavaOnevisionConfig)

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

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

    self.linear_1 = nn.Linear(config.vision_config.hidden_size,
                              config.text_config.hidden_size,
                              bias=config.multimodal_projector_bias)
    self.act = get_act_fn(config.projector_hidden_act)
    self.linear_2 = nn.Linear(config.text_config.hidden_size,
                              config.text_config.hidden_size,
                              bias=config.multimodal_projector_bias)

forward

forward(image_features: Tensor) -> Tensor

Source code in vllm/model_executor/models/llava_onevision.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

LlavaOnevisionProcessingInfo

Bases: LlavaNextProcessingInfo

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

class LlavaOnevisionProcessingInfo(LlavaNextProcessingInfo):

    def get_hf_config(self) -> LlavaOnevisionLikeConfig:
        return self.ctx.get_hf_config(LlavaOnevisionConfig)

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

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

    # Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L86
    # with additional logic afterwards taken from LlavaOnevisionProcessor
    def _get_num_unpadded_features(
        self,
        *,
        original_height: int,
        original_width: int,
        npatches: int,
        num_patch_height: int,
        num_patch_width: int,
    ) -> tuple[int, int]:
        current_height = npatches * num_patch_height
        current_width = npatches * num_patch_width

        aspect_ratio = original_width / original_height
        current_aspect_ratio = current_width / current_height

        if aspect_ratio > current_aspect_ratio:
            new_height = int(
                round(original_height * (current_width / original_width), 7))
            padding = (current_height - new_height) // 2
            current_height = current_height - (2 * padding)
        else:
            new_width = int(
                round(original_width * (current_height / original_height), 7))
            padding = (current_width - new_width) // 2
            current_width = current_width - (2 * padding)

        unpadded_features = current_height * current_width
        newline_features = current_height

        ratio = math.sqrt(current_height * current_width / (9 * npatches**2))
        if ratio > 1.1:
            height_factor = int(current_height // ratio)
            width_factor = int(current_width // ratio)
            unpadded_features = height_factor * width_factor
            newline_features = height_factor

        return (unpadded_features, newline_features)

    def get_image_size_with_most_features(self) -> ImageSize:
        # NOTE: This hardcoded value is found via processor tests
        return ImageSize(width=1153, height=944)

    def _get_num_frame_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        hf_config = self.get_hf_config()
        spatial_pool_stride = getattr(hf_config, "spatial_pool_stride", 2)

        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 + 1  # Newline token

    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_images = mm_counts.get("image", 0)
        max_videos = mm_counts.get("video", 0)

        max_image_tokens = self.get_max_image_tokens() * max_images
        max_total_frames = self._get_max_video_frames(seq_len -
                                                      max_image_tokens)
        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
                                   _MAX_FRAMES_PER_VIDEO)

        return max(max_frames_per_video, 1)

    def get_max_video_tokens(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_video_tokens(
            image_width=target_width,
            image_height=target_height,
            num_frames=self.get_num_frames_with_most_features(
                seq_len, mm_counts),
        )

_get_max_video_frames

_get_max_video_frames(max_tokens: int) -> int

Source code in vllm/model_executor/models/llava_onevision.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_onevision.py

def _get_num_frame_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    hf_config = self.get_hf_config()
    spatial_pool_stride = getattr(hf_config, "spatial_pool_stride", 2)

    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_num_unpadded_features

_get_num_unpadded_features(
    *,
    original_height: int,
    original_width: int,
    npatches: int,
    num_patch_height: int,
    num_patch_width: int,
) -> tuple[int, int]

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

def _get_num_unpadded_features(
    self,
    *,
    original_height: int,
    original_width: int,
    npatches: int,
    num_patch_height: int,
    num_patch_width: int,
) -> tuple[int, int]:
    current_height = npatches * num_patch_height
    current_width = npatches * num_patch_width

    aspect_ratio = original_width / original_height
    current_aspect_ratio = current_width / current_height

    if aspect_ratio > current_aspect_ratio:
        new_height = int(
            round(original_height * (current_width / original_width), 7))
        padding = (current_height - new_height) // 2
        current_height = current_height - (2 * padding)
    else:
        new_width = int(
            round(original_width * (current_height / original_height), 7))
        padding = (current_width - new_width) // 2
        current_width = current_width - (2 * padding)

    unpadded_features = current_height * current_width
    newline_features = current_height

    ratio = math.sqrt(current_height * current_width / (9 * npatches**2))
    if ratio > 1.1:
        height_factor = int(current_height // ratio)
        width_factor = int(current_width // ratio)
        unpadded_features = height_factor * width_factor
        newline_features = height_factor

    return (unpadded_features, newline_features)

get_hf_config

get_hf_config() -> LlavaOnevisionLikeConfig

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

def get_hf_config(self) -> LlavaOnevisionLikeConfig:
    return self.ctx.get_hf_config(LlavaOnevisionConfig)

get_hf_processor

get_hf_processor(**kwargs: object)

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

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

get_image_size_with_most_features

get_image_size_with_most_features() -> ImageSize

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

def get_image_size_with_most_features(self) -> ImageSize:
    # NOTE: This hardcoded value is found via processor tests
    return ImageSize(width=1153, height=944)

get_max_video_tokens

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

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

def get_max_video_tokens(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> int:
    target_width, target_height = self.get_image_size_with_most_features()

    return self.get_num_video_tokens(
        image_width=target_width,
        image_height=target_height,
        num_frames=self.get_num_frames_with_most_features(
            seq_len, mm_counts),
    )

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_onevision.py

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

    max_image_tokens = self.get_max_image_tokens() * max_images
    max_total_frames = self._get_max_video_frames(seq_len -
                                                  max_image_tokens)
    max_frames_per_video = min(max_total_frames // max(max_videos, 1),
                               _MAX_FRAMES_PER_VIDEO)

    return max(max_frames_per_video, 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_onevision.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 + 1  # Newline token

get_supported_mm_limits

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

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

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

LlavaOnevisionVideoPixelInputs

Bases: TypedDict

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

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

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

pixel_values_videos instance-attribute

pixel_values_videos: Union[Tensor, list[Tensor]]

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

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

type instance-attribute

type: Literal['pixel_values_videos']