llava_onevision.py

# SPDX-License-Identifier: Apache-2.0

import math
from collections.abc import Iterable, Mapping, Sequence
from functools import cached_property
from typing import (Final, List, Literal, Optional, Protocol, Set, Tuple,
                    TypedDict, Union)

import torch
import torch.nn as nn
from transformers import (BatchFeature, LlavaOnevisionConfig,
                          LlavaOnevisionProcessor)
from transformers.models.llava_onevision.modeling_llava_onevision import (
    get_anyres_image_grid_shape, unpad_image)
from typing_extensions import NotRequired

from vllm.config import VllmConfig
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
from vllm.multimodal.parse import (ImageSize, MultiModalDataItems,
                                   VideoEmbeddingItems, VideoProcessorItems)
from vllm.multimodal.processing import PromptReplacement, PromptUpdate
from vllm.multimodal.profiling import ProcessorInputs
from vllm.sequence import IntermediateTensors

from .clip import CLIPVisionModel
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .llava import LlavaDummyInputsBuilder, init_vision_tower_for_llava
from .llava_next import (BaseLlavaNextMultiModalProcessor, LlavaNextLikeConfig,
                         LlavaNextProcessingInfo)
from .siglip import SiglipVisionModel
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                    maybe_prefix, merge_multimodal_embeddings)

# For profile run
_MAX_FRAMES_PER_VIDEO = 16


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.
    """


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.
    """


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.
    """


LlavaOnevisionImageInputs = Union[LlavaOnevisionImagePixelInputs,
                                  LlavaOnevisionImageEmbeddingInputs]

LlavaOnevisionMultiInputs = Union[LlavaOnevisionImageInputs,
                                  LlavaOnevisionVideoPixelInputs]


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


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}

    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Mapping[str, int]:
        return {
            "image": self.get_max_image_tokens(),
            "video": self.get_max_video_tokens(seq_len, mm_counts),
        }

    # 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 = (original_height * current_width) // original_width
            padding = (current_height - new_height) // 2
            current_height = current_height - (2 * padding)
        else:
            new_width = (original_width * current_height) // original_height
            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),
        )


class LlavaOnevisionDummyInputsBuilder(
        LlavaDummyInputsBuilder[LlavaOnevisionProcessingInfo]):

    def get_dummy_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> ProcessorInputs:
        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

        target_width, target_height = \
            self.info.get_image_size_with_most_features()
        target_num_frames = \
            self.info.get_num_frames_with_most_features(seq_len,
                                                        mm_counts)

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

        return ProcessorInputs(
            prompt_text=image_token * num_images + video_token * num_videos,
            mm_data=mm_data,
        )


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],
    ) -> 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,
            )

        # 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,
        )

        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,
            )
            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,
            )

            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],
    ) -> bool:
        base_result = super()._hf_processor_applies_updates(
            prompt_text=prompt_text,
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_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,
            ),
        ]


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


@MULTIMODAL_REGISTRY.register_processor(
    LlavaOnevisionMultiModalProcessor,
    info=LlavaOnevisionProcessingInfo,
    dummy_inputs=LlavaOnevisionDummyInputsBuilder)
class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
                                             SupportsPP):

    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)

    @cached_property
    def sampler(self):
        if hasattr(self.language_model, "sampler"):
            return self.language_model.sampler

        return get_sampler()

    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:
        modalities = {}

        # 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 "images" not in modalities:
                modalities["images"] = self._parse_and_validate_image_input(
                    **kwargs)
            if input_key in ("pixel_values_videos",
                             "video_embeds") and "videos" not in modalities:
                modalities["videos"] = self._parse_and_validate_video_input(
                    **kwargs)

        return modalities

    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_multimodal_embeddings(
            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            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 modalities:
            if modality == "images":
                image_input = modalities["images"]
                vision_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += tuple(vision_embeddings)
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_video_pixels(video_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:
            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 sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        return self.language_model.sample(logits, sampling_metadata)

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)