skyworkr1v.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/modeling_skywork_chat.py
# --------------------------------------------------------
# SkyworkR1V
# Copyright (c) 2025 Skywork
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Literal, TypeAlias

import torch
import torch.nn as nn
import torchvision.transforms as T
from PIL import Image
from transformers import BatchFeature, PretrainedConfig, TensorType

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.model_executor.layers.linear import ReplicatedLinear
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.awq import AWQConfig
from vllm.model_executor.models.intern_vit import (
    InternVisionModel,
    InternVisionPatchModel,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import convert_image_mode
from vllm.multimodal.inputs import (
    MultiModalDataDict,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
)
from vllm.multimodal.parse import (
    ImageEmbeddingItems,
    ImageProcessorItems,
    ImageSize,
    MultiModalDataItems,
)
from vllm.multimodal.processing import (
    BaseDummyInputsBuilder,
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
)
from vllm.sequence import IntermediateTensors
from vllm.tokenizers import TokenizerLike
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix

IMG_START = "<img>"
IMG_END = "</img>"
IMG_CONTEXT = "<IMG_CONTEXT>"

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


class SkyworkR1VImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bnp: Batch size * number of images * (1 + num_patches)
        - c: Number of channels (3)
        - h: Height
        - w: Width
        - bn: Batch size * number of images
    """

    type: Literal["pixel_values"] = "pixel_values"

    pixel_values_flat: Annotated[
        torch.Tensor,
        TensorShape("bnp", 3, "h", "w"),
    ]

    num_patches: Annotated[
        torch.Tensor,
        TensorShape("bn"),
    ]


class SkyworkR1VImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - ni: Number of images
        - ifs: Image feature size
        - hs: Hidden size (must match the hidden size of language model
          backbone)
    """

    type: Literal["image_embeds"] = "image_embeds"

    data: Annotated[
        torch.Tensor | list[torch.Tensor],
        TensorShape("ni", "ifs", "hs"),
    ]


SkyworkR1VImageInputs: TypeAlias = (
    SkyworkR1VImagePixelInputs | SkyworkR1VImageEmbeddingInputs
)


# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
def build_transform(input_size: int):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    return T.Compose(
        [
            T.Lambda(lambda img: convert_image_mode(img, "RGB")),
            T.Resize(
                (input_size, input_size), interpolation=T.InterpolationMode.BICUBIC
            ),
            T.ToTensor(),
            T.Normalize(mean=MEAN, std=STD),
        ]
    )


# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
def find_closest_aspect_ratio(
    aspect_ratio: float,
    target_ratios: list[tuple[int, int]],
    *,
    width: int,
    height: int,
    image_size: int,
) -> tuple[int, int]:
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def resolve_skyworkr1v_min_max_num(
    *,
    min_dynamic_patch: int,
    max_dynamic_patch: int,
    dynamic_image_size: bool,
    use_thumbnail: bool,
) -> tuple[int, int]:
    min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1

    if use_thumbnail and max_dynamic_patch != 1:
        max_dynamic_patch += 1

    return min_dynamic_patch, max_dynamic_patch


def get_skyworkr1v_target_ratios(
    min_num: int,
    max_num: int,
) -> list[tuple[int, int]]:
    target_ratios = {
        (i, j)
        for n in range(min_num, max_num + 1)
        for i in range(1, n + 1)
        for j in range(1, n + 1)
        if min_num <= i * j <= max_num
    }
    return sorted(target_ratios, key=lambda x: x[0] * x[1])


def calculate_skyworkr1v_targets(
    *,
    orig_width: int,
    orig_height: int,
    target_ratios: list[tuple[int, int]],
    image_size: int,
    use_thumbnail: bool,
) -> tuple[int, int, int]:
    aspect_ratio = orig_width / orig_height

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio,
        target_ratios,
        width=orig_width,
        height=orig_height,
        image_size=image_size,
    )

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # add thumbnail image if num_blocks != 1
    if use_thumbnail and blocks != 1:
        blocks += 1

    return blocks, target_width, target_height


def dynamic_preprocess_skyworkr1v(
    image: Image.Image,
    *,
    target_ratios: list[tuple[int, int]],
    image_size: int,
    use_thumbnail: bool,
) -> list[Image.Image]:
    orig_width, orig_height = image.size

    # calculate the number of blocks without thumbnail
    blocks, target_width, target_height = calculate_skyworkr1v_targets(
        orig_width=orig_width,
        orig_height=orig_height,
        target_ratios=target_ratios,
        image_size=image_size,
        use_thumbnail=False,
    )

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)

    assert len(processed_images) == blocks

    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)

    return processed_images


# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B
def image_to_pixel_values_skyworkr1v(
    image: Image.Image,
    *,
    input_size: int,
    min_num: int,
    max_num: int,
    use_thumbnail: bool,
) -> torch.Tensor:
    target_ratios = get_skyworkr1v_target_ratios(min_num, max_num)

    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess_skyworkr1v(
        image,
        target_ratios=target_ratios,
        image_size=input_size,
        use_thumbnail=use_thumbnail,
    )

    pixel_values = torch.stack([transform(image) for image in images])
    return pixel_values


class SkyworkR1VProcessor:
    """
    This model doesn't define its own HF processor,
    so we implement our own one here.

    The code to insert image tokens is based on:
    https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/modeling_skywork_chat.py#L252
    """

    def __init__(
        self,
        config: PretrainedConfig,
        tokenizer: TokenizerLike,
        *,
        min_dynamic_patch: int | None = None,
        max_dynamic_patch: int | None = None,
        dynamic_image_size: bool | None = None,
    ) -> None:
        super().__init__()

        self.config = config
        self.tokenizer = tokenizer

        image_size: int = config.vision_config.image_size
        patch_size: int = config.vision_config.patch_size

        if min_dynamic_patch is None:
            min_dynamic_patch = config.min_dynamic_patch
        assert isinstance(min_dynamic_patch, int)

        if max_dynamic_patch is None:
            max_dynamic_patch = config.max_dynamic_patch
        assert isinstance(max_dynamic_patch, int)

        if dynamic_image_size is None:
            dynamic_image_size = config.dynamic_image_size
        assert isinstance(dynamic_image_size, bool)

        self.num_image_token = int(
            (image_size // patch_size) ** 2 * (config.downsample_ratio**2)
        )
        self.image_size = image_size
        self.min_dynamic_patch = min_dynamic_patch
        self.max_dynamic_patch = max_dynamic_patch
        self.dynamic_image_size = dynamic_image_size
        self.use_thumbnail: bool = config.use_thumbnail

    @property
    def image_token_id(self) -> int:
        return self.tokenizer.get_vocab()[IMG_CONTEXT]

    def get_image_repl(
        self,
        feature_size: int,
        num_patches: int | None,
    ) -> PromptUpdateDetails[str]:
        repl_features = IMG_CONTEXT * feature_size
        repl_full = IMG_START + repl_features + IMG_END

        return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)

    def resolve_min_max_num(
        self,
        *,
        min_dynamic_patch: int | None = None,
        max_dynamic_patch: int | None = None,
        dynamic_image_size: bool | None = None,
        use_thumbnail: bool | None = None,
    ) -> tuple[int, int]:
        min_dynamic_patch = (
            self.min_dynamic_patch if min_dynamic_patch is None else min_dynamic_patch
        )
        max_dynamic_patch = (
            self.max_dynamic_patch if max_dynamic_patch is None else max_dynamic_patch
        )
        dynamic_image_size = (
            self.dynamic_image_size
            if dynamic_image_size is None
            else dynamic_image_size
        )
        use_thumbnail = self.use_thumbnail if use_thumbnail is None else use_thumbnail

        return resolve_skyworkr1v_min_max_num(
            min_dynamic_patch=min_dynamic_patch,
            max_dynamic_patch=max_dynamic_patch,
            dynamic_image_size=dynamic_image_size,
            use_thumbnail=use_thumbnail,
        )

    def resolve_target_ratios(
        self,
        *,
        min_dynamic_patch: int | None = None,
        max_dynamic_patch: int | None = None,
        dynamic_image_size: bool | None = None,
        use_thumbnail: bool | None = None,
    ) -> list[tuple[int, int]]:
        min_num, max_num = self.resolve_min_max_num(
            min_dynamic_patch=min_dynamic_patch,
            max_dynamic_patch=max_dynamic_patch,
            dynamic_image_size=dynamic_image_size,
            use_thumbnail=use_thumbnail,
        )

        return get_skyworkr1v_target_ratios(min_num, max_num)

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        target_ratios = self.resolve_target_ratios(
            use_thumbnail=False,  # Applied in calculate_targets
        )

        num_patches, _, _ = calculate_skyworkr1v_targets(
            orig_width=image_width,
            orig_height=image_height,
            image_size=self.image_size,
            target_ratios=target_ratios,
            use_thumbnail=self.use_thumbnail,
        )

        return num_patches * self.num_image_token

    def _images_to_pixel_values_lst(
        self,
        images: list[Image.Image],
        min_dynamic_patch: int | None = None,
        max_dynamic_patch: int | None = None,
        dynamic_image_size: bool | None = None,
    ) -> list[torch.Tensor]:
        min_num, max_num = self.resolve_min_max_num(
            min_dynamic_patch=min_dynamic_patch,
            max_dynamic_patch=max_dynamic_patch,
            dynamic_image_size=dynamic_image_size,
            use_thumbnail=False,  # Applied in image_to_pixel_values
        )

        return [
            image_to_pixel_values_skyworkr1v(
                image,
                input_size=self.image_size,
                min_num=min_num,
                max_num=max_num,
                use_thumbnail=self.use_thumbnail,
            )
            for image in images
        ]

    def __call__(
        self,
        text: str | list[str] | None = None,
        images: Image.Image | list[Image.Image] | None = None,
        min_dynamic_patch: int | None = None,
        max_dynamic_patch: int | None = None,
        dynamic_image_size: bool | None = None,
        return_tensors: str | TensorType | None = None,
    ) -> BatchFeature:
        if text is None:
            text = []
        if not isinstance(text, list):
            text = [text]
        if images is None:
            images = []
        if not isinstance(images, list):
            images = [images]

        if len(images) == 0:
            image_inputs = {}
        else:
            pixel_values_lst = self._images_to_pixel_values_lst(
                images,
                min_dynamic_patch=min_dynamic_patch,
                max_dynamic_patch=max_dynamic_patch,
                dynamic_image_size=dynamic_image_size,
            )
            image_inputs = {
                "pixel_values_flat": torch.cat(pixel_values_lst),
                "image_num_patches": torch.tensor(
                    [len(item) for item in pixel_values_lst]
                ),
            }

            for pixel_values in pixel_values_lst:
                num_patches = pixel_values.shape[0]
                feature_size = num_patches * self.num_image_token

                image_repl = self.get_image_repl(feature_size, num_patches)

                text = [t.replace("<image>", image_repl.full, 1) for t in text]

        text_inputs = self.tokenizer(text)

        combined_outputs = {**text_inputs, **image_inputs}

        return BatchFeature(combined_outputs, tensor_type=return_tensors)


class SkyworkR1VProcessingInfo(BaseProcessingInfo):
    def get_hf_processor(self, **kwargs: object) -> SkyworkR1VProcessor:
        return self.ctx.init_processor(
            SkyworkR1VProcessor,
            config=self.get_hf_config(),
            tokenizer=self.get_tokenizer(),
            **kwargs,
        )

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

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: SkyworkR1VProcessor | None,
    ) -> int:
        if processor is None:
            processor = self.get_hf_processor()

        return processor.get_num_image_tokens(
            image_width=image_width,
            image_height=image_height,
        )

    def get_image_size_with_most_features(self) -> ImageSize:
        processor = self.get_hf_processor()

        base_size = processor.image_size
        target_ratios = processor.resolve_target_ratios()

        largest_feature_size, largest_feature_pinpoint = 0, None
        for wr, hr in target_ratios:
            width, height = base_size * wr, base_size * hr

            feat_size = self.get_num_image_tokens(
                image_width=width,
                image_height=height,
                processor=processor,
            )
            if feat_size > largest_feature_size:
                largest_feature_size = feat_size
                largest_feature_pinpoint = ImageSize(width=width, height=height)

        if largest_feature_size == 0 or largest_feature_pinpoint is None:
            raise ValueError("Cannot have a largest feature size of 0!")

        return largest_feature_pinpoint


class SkyworkR1VDummyInputsBuilder(BaseDummyInputsBuilder[SkyworkR1VProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)

        return "<image>" * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions] | None = None,
        mm_processor_kwargs: Mapping[str, object] | None = None,
    ) -> MultiModalDataDict:
        target_width, target_height = self.info.get_image_size_with_most_features()
        num_images = mm_counts.get("image", 0)

        image_overrides = mm_options.get("image") if mm_options else None

        return {
            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            )
        }


class SkyworkR1VMultiModalProcessor(BaseMultiModalProcessor[SkyworkR1VProcessingInfo]):
    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        hf_processor = self.info.get_hf_processor(**mm_kwargs)
        image_token_id = hf_processor.image_token_id

        # Since there may be extra tokens in the feature placeholders,
        # we need to pass the image token ID to the model to select the
        # tokens to merge from the vision encoder outputs
        processed_outputs["image_token_id"] = torch.tensor(image_token_id)

        return processed_outputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
        num_images = len(image_num_patches)

        return dict(
            pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
                "image", image_num_patches
            ),
            image_num_patches=MultiModalFieldConfig.batched("image"),
            image_embeds=MultiModalFieldConfig.batched("image"),
            image_token_id=MultiModalFieldConfig.shared("image", num_images),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        out_mm_data = out_mm_kwargs.get_data()
        if "image_num_patches" in out_mm_data:
            image_num_patches = out_mm_data["image_num_patches"]
            assert isinstance(image_num_patches, torch.Tensor)
            image_num_patches = image_num_patches.tolist()
        elif "image_embeds" in out_mm_data:
            # TODO: Use image size information in dictionary embedding inputs
            # to compute num_patches (similar to Qwen2-VL)
            image_num_patches = [None] * len(out_mm_data["image_embeds"])
        else:
            image_num_patches = []

        def get_replacement_skyworkr1v(item_idx: int):
            images = mm_items.get_items(
                "image", (ImageEmbeddingItems, ImageProcessorItems)
            )

            if isinstance(images, ImageEmbeddingItems):
                feature_size = images.get_feature_size(item_idx)
            else:
                image_size = images.get_image_size(item_idx)
                feature_size = self.info.get_num_image_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    processor=hf_processor,
                )

            num_patches = image_num_patches[item_idx]
            if num_patches is not None:
                assert isinstance(num_patches, int)

            return hf_processor.get_image_repl(feature_size, num_patches)

        return [
            PromptReplacement(
                modality="image",
                target="<image>",
                replacement=get_replacement_skyworkr1v,
            )
        ]


@MULTIMODAL_REGISTRY.register_processor(
    SkyworkR1VMultiModalProcessor,
    info=SkyworkR1VProcessingInfo,
    dummy_inputs=SkyworkR1VDummyInputsBuilder,
)
class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return "<image>"

        raise ValueError("Only image 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
        self._patch_quant_config(config, quant_config)

        image_size = config.force_image_size or config.vision_config.image_size
        patch_size = config.vision_config.patch_size
        self.patch_size = patch_size
        self.num_image_token = int(
            (image_size // patch_size) ** 2 * (config.downsample_ratio**2)
        )
        self.downsample_ratio = config.downsample_ratio
        self.ps_version = config.ps_version

        llm_arch_name = config.text_config.architectures[0]
        self.is_mono = llm_arch_name == "SkyworkLM2VEForCausalLM"

        with self._mark_tower_model(vllm_config, "image"):
            self.vision_model = self._init_vision_model(
                config,
                quant_config=quant_config,
                is_mono=self.is_mono,
                prefix=maybe_prefix(prefix, "vision_model"),
            )
            self.mlp1 = self._init_mlp1(
                config, quant_config, prefix=maybe_prefix(prefix, "mlp1")
            )

        with self._mark_language_model(vllm_config):
            self.language_model = init_vllm_registered_model(
                vllm_config=vllm_config,
                hf_config=config.text_config,
                prefix=maybe_prefix(prefix, "language_model"),
            )

        self.img_context_token_id = None
        self.visual_token_mask = None
        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def _patch_quant_config(
        self, config: PretrainedConfig, quant_config: QuantizationConfig
    ):
        # the awq models from OpenGVLab missing `modules_to_not_convert`
        # patch the quant_config to add `modules_to_not_convert` back
        if isinstance(quant_config, AWQConfig):
            text_config = config.text_config
            llm_quant_config = getattr(text_config, "quantization_config", None)
            if (not quant_config.modules_to_not_convert) and (
                llm_quant_config is not None
            ):
                quant_config.modules_to_not_convert.append("vision_model")

    def _init_vision_model(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig | None,
        *,
        is_mono: bool,
        prefix: str,
    ):
        if not is_mono:
            vision_feature_layer = config.select_layer
            if vision_feature_layer < 0:
                num_hidden_layers = (
                    config.vision_config.num_hidden_layers + vision_feature_layer + 1
                )
            else:
                num_hidden_layers = vision_feature_layer + 1

            return InternVisionModel(
                config.vision_config,
                quant_config=quant_config,
                num_hidden_layers_override=num_hidden_layers,
                prefix=prefix,
            )
        else:
            return InternVisionPatchModel(config.vision_config)

    def _init_mlp1(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig,
        prefix: str = "",
    ) -> nn.Module:
        vit_hidden_size = config.vision_config.hidden_size
        llm_hidden_size = config.text_config.hidden_size

        return nn.Sequential(
            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
            ReplicatedLinear(
                vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
                llm_hidden_size,
                return_bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.1",
            ),
            nn.GELU(),
            ReplicatedLinear(
                llm_hidden_size,
                llm_hidden_size,
                return_bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.3",
            ),
        )

    def pixel_shuffle(self, x, scale_factor=0.5):
        n, w, h, c = x.size()
        # N, W, H, C --> N, W, H * scale, C // scale
        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
        x = x.permute(0, 2, 1, 3).contiguous()
        x = x.view(
            n,
            int(h * scale_factor),
            int(w * scale_factor),
            int(c / (scale_factor * scale_factor)),
        )
        if self.ps_version == "v1":
            pass
        else:
            x = x.permute(0, 2, 1, 3).contiguous()
        return x

    def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
        vit_embeds = self.vision_model(pixel_values=pixel_values)
        vit_embeds = vit_embeds[:, 1:, :]

        h = w = int(vit_embeds.shape[1] ** 0.5)
        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
        vit_embeds = self.mlp1(vit_embeds)
        return vit_embeds

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> SkyworkR1VImageInputs | None:
        pixel_values_flat = kwargs.pop("pixel_values_flat", None)
        image_num_patches = kwargs.pop("image_num_patches", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values_flat is None and image_embeds is None:
            return None

        if image_embeds is not None:
            return SkyworkR1VImageEmbeddingInputs(
                type="image_embeds",
                data=image_embeds,
            )

        image_token_id = kwargs["image_token_id"]
        if isinstance(image_token_id, torch.Tensor):
            image_token_id = image_token_id.flatten().unique().item()

        assert isinstance(image_token_id, int)
        self.img_context_token_id = image_token_id

        if pixel_values_flat is not None:
            return SkyworkR1VImagePixelInputs(
                type="pixel_values",
                pixel_values_flat=pixel_values_flat,
                num_patches=image_num_patches,
                resolve_bindings={
                    "h": self.config.vision_config.image_size,
                    "w": self.config.vision_config.image_size,
                },
            )

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

    def _process_image_input(
        self,
        image_input: SkyworkR1VImageInputs,
    ) -> torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor, ...]:
        if image_input["type"] == "image_embeds":
            return image_input["data"]

        image_embeds = self.extract_feature(image_input["pixel_values_flat"])

        num_patches = image_input["num_patches"]

        # Only one image in the current batch
        if len(num_patches) == 1:
            return image_embeds.view(-1, self.config.text_config.hidden_size).unsqueeze(
                0
            )

        # NOTE: Image embeddings are split into separate tensors for each image
        # by the size of each embedding.
        feature_size = image_embeds.shape[1]
        image_embeds = image_embeds.view(-1, self.config.text_config.hidden_size)
        image_feature_sizes = [
            num_patches * feature_size for num_patches in num_patches
        ]
        return image_embeds.split(image_feature_sizes)

    def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
        if self.is_mono:
            self.visual_token_mask = (input_ids == self.img_context_token_id).reshape(
                -1, 1
            )
        else:
            self.visual_token_mask = None

    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input)

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: MultiModalEmbeddings | None = None,
        *,
        is_multimodal: torch.Tensor | None = None,
        handle_oov_mm_token: bool = False,
    ) -> torch.Tensor:
        if multimodal_embeddings is not None and len(multimodal_embeddings) > 0:
            self._set_visual_token_mask(input_ids)

        # This is to satisfy the type checker for each overload
        if multimodal_embeddings is None or is_multimodal is None:
            return super().embed_input_ids(input_ids)

        return super().embed_input_ids(
            input_ids,
            multimodal_embeddings=multimodal_embeddings,
            is_multimodal=is_multimodal,
            handle_oov_mm_token=handle_oov_mm_token,
        )

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> IntermediateTensors:
        if intermediate_tensors is not None:
            inputs_embeds = None

        forward_kwargs = {
            "input_ids": input_ids,
            "positions": positions,
            "intermediate_tensors": intermediate_tensors,
            "inputs_embeds": inputs_embeds,
        }

        # Only required if the model is mono-architecture
        if self.visual_token_mask is not None:
            forward_kwargs.update({"visual_token_mask": self.visual_token_mask})
            self.visual_token_mask = None

        hidden_states = self.language_model.model(**forward_kwargs)
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.language_model.compute_logits(hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        skip_prefixes = [
            "action_embed",
            "temporal_embed",
            "track_embed",
            "track_embed_decoder",
            "box_token",
            "cg_criterion",
            "cg_model",
            "loc_encoder",
            "loc_decoder",
            "sam",
            "temporal_token",
            "track_token",
        ]
        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
        return loader.load_weights(weights)