vllm/vllm/model_executor/models/internvl.py

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

# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_internvl_chat.py
# --------------------------------------------------------
# InternVL
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from abc import abstractmethod
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Literal, TypeAlias, TypeVar

import torch
import torch.nn as nn
from transformers import BatchFeature, PretrainedConfig

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
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.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
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,
)
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.processors.internvl import (
    BaseInternVLProcessor,
    InternVLProcessor,
)
from vllm.utils.tensor_schema import TensorSchema, TensorShape

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


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

    type: Literal["pixel_values"]
    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
    num_patches: Annotated[torch.Tensor, TensorShape("bn")]


class InternVLImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - n: Number of images
        - f: Total image feature size
        - h: Hidden size (must match the hidden size of language model backbone)
    """

    type: Literal["image_embeds"]
    data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("n", "f", "h")]


InternVLImageInputs: TypeAlias = InternVLImagePixelInputs | InternVLImageEmbeddingInputs


class InternVLVideoPixelInputs(TensorSchema):
    """
    Dimensions:
        - bvf: Batch size * number of videos * num_frames
        - bn: Batch size * number of images
        - c: Number of channels (3)
        - h: Height of each video frame
        - w: Width of each video frame
    """

    type: Literal["pixel_values_videos"]
    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bvf", 3, "h", "w")]
    num_patches: Annotated[torch.Tensor, TensorShape("bn")]


class InternVLVideoEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - n: Number of videos
        - f: Total video feature size
        - h: Hidden size (must match the hidden size of language model backbone)
    """

    type: Literal["video_embeds"]
    data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("n", "f", "h")]


InternVLVideoInputs: TypeAlias = InternVLVideoPixelInputs | InternVLVideoEmbeddingInputs


class BaseInternVLProcessingInfo(BaseProcessingInfo):
    """Basic image-only ProcessingInfo for InternVL-style models."""

    @abstractmethod
    def get_hf_processor(self, **kwargs: object) -> BaseInternVLProcessor:
        raise NotImplementedError

    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: BaseInternVLProcessor,
    ) -> int:
        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

    def get_max_image_tokens(self) -> int:
        processor = self.get_hf_processor()
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_image_tokens(
            image_width=target_width,
            image_height=target_height,
            processor=processor,
        )


_I = TypeVar("_I", bound=BaseInternVLProcessingInfo)


class BaseInternVLDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
    """Basic image-only DummyInputsBuilder for InternVL-style models."""

    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],
    ) -> 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")

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


class BaseInternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
    """Basic image-only MultiModalProcessor for InternVL-style models."""

    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_internvl(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_internvl,
            )
        ]


class InternVLProcessingInfo(BaseInternVLProcessingInfo):
    """InternVL ProcessingInfo extended for video processing"""

    @property
    def supports_video(self):
        return self.get_hf_processor().supports_video

    def get_supported_mm_limits(self):
        video_limit = {"video": None} if self.supports_video else {}
        return {**super().get_supported_mm_limits(), **video_limit}

    def get_video_token(self) -> str | None:
        text_model_type = self.get_hf_config().get_text_config().model_type
        video_token_map = {
            "qwen2": "<|video_pad|>",
            "qwen3": "<|video_pad|>",
            "qwen3_moe": "<|video_pad|>",
            "gpt_oss": "<|reserved_200000|>",
        }
        return video_token_map.get(text_model_type)

    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)

        processor = self.get_hf_processor()

        max_image_tokens = self.get_max_image_tokens() * max_images
        max_total_frames = (seq_len - max_image_tokens) // processor.num_image_token
        max_frames_per_video = max_total_frames // max(max_videos, 1)

        return max(max_frames_per_video, 1)

    def get_hf_processor(self, **kwargs: object) -> InternVLProcessor:
        return self.ctx.init_processor(
            InternVLProcessor,
            config=self.get_hf_config(),
            tokenizer=self.get_tokenizer(),
            video_token=self.get_video_token(),
            **kwargs,
        )


class InternVLDummyInputsBuilder(
    BaseInternVLDummyInputsBuilder[InternVLProcessingInfo]
):
    """InternVL DummyInputsBuilder extended for video support"""

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

        return super().get_dummy_text(mm_counts) + "<video>" * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions],
    ) -> MultiModalDataDict:
        dummy_image = super().get_dummy_mm_data(seq_len, mm_counts, mm_options)
        if self.info.supports_video:
            config = self.info.get_hf_config()
            image_size: int = config.vision_config.image_size
            target_num_frames = self.info.get_num_frames_with_most_features(
                seq_len, mm_counts
            )
            num_videos = mm_counts.get("video", 0)
            video_overrides = mm_options.get("video")
            dummy_video = {
                "video": self._get_dummy_videos(
                    width=image_size,
                    height=image_size,
                    num_frames=target_num_frames,
                    num_videos=num_videos,
                    overrides=video_overrides,
                )
            }
        else:
            dummy_video = {}
        return {**dummy_image, **dummy_video}


class InternVLMultiModalProcessor(
    BaseInternVLMultiModalProcessor[InternVLProcessingInfo]
):
    """InternVL MultiModalProcessor extended for video support"""

    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, mm_data, mm_kwargs, tok_kwargs
        )

        hf_processor = self.info.get_hf_processor(**mm_kwargs)
        if (
            self.info.supports_video
            and (video_token_id := hf_processor.video_token_id) is not None
        ):
            processed_outputs["video_token_id"] = torch.tensor(video_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_fields = super()._get_mm_fields_config(hf_inputs, hf_processor_mm_kwargs)
        if self.info.supports_video:
            video_num_patches = hf_inputs.get("video_num_patches", torch.empty(0))
            num_videos = len(video_num_patches)
            video_fields = dict(
                pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
                    "video", video_num_patches
                ),
                video_num_patches=MultiModalFieldConfig.batched("video"),
                video_token_id=MultiModalFieldConfig.shared("video", num_videos),
            )
        else:
            video_fields = {}

        return image_fields | video_fields

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

        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        out_mm_data = out_mm_kwargs.get_data()
        if "video_num_patches" in out_mm_data:
            video_num_patches = out_mm_data["video_num_patches"]
            assert isinstance(video_num_patches, torch.Tensor)
            video_num_patches = video_num_patches.tolist()
        else:
            video_num_patches = []

        def get_video_replacement_internvl(item_idx: int):
            feature_size = hf_processor.num_image_token
            num_patches = video_num_patches[item_idx]
            if num_patches is not None:
                assert isinstance(num_patches, int)

            return hf_processor.get_video_repl(
                feature_size, num_patches, video_context_token=hf_processor.video_token
            )

        if self.info.supports_video:
            prompt_repl = [
                *prompt_repl,
                PromptReplacement(
                    modality="video",
                    target="<video>",
                    replacement=get_video_replacement_internvl,
                ),
            ]

        return prompt_repl


@MULTIMODAL_REGISTRY.register_processor(
    InternVLMultiModalProcessor,
    info=InternVLProcessingInfo,
    dummy_inputs=InternVLDummyInputsBuilder,
)
class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
    supports_encoder_tp_data = True

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        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
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
        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.patch_tokens = (image_size // patch_size) ** 2
        self.num_image_token = int(self.patch_tokens * (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 == "InternLM2VEForCausalLM"

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            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)

        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.video_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) -> 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),
            nn.Linear(
                vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size
            ),
            nn.GELU(),
            nn.Linear(llm_hidden_size, llm_hidden_size),
        )

    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
    ) -> InternVLImageInputs | 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 InternVLImageEmbeddingInputs(
                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:
            expected_h = expected_w = self.config.vision_config.image_size
            resolve_bindings = {"h": expected_h, "w": expected_w}

            return InternVLImagePixelInputs(
                type="pixel_values",
                pixel_values_flat=pixel_values_flat,
                num_patches=image_num_patches,
                resolve_bindings=resolve_bindings,
            )

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

    def _parse_and_validate_video_input(
        self, **kwargs: object
    ) -> InternVLVideoPixelInputs | None:
        pixel_values_flat_video = kwargs.pop("pixel_values_flat_video", None)
        video_num_patches = kwargs.pop("video_num_patches", None)
        video_embeds = kwargs.pop("image_embeds", None)

        if pixel_values_flat_video is None and video_embeds is None:
            return None

        if video_embeds is not None:
            return InternVLVideoEmbeddingInputs(
                type="video_embeds",
                data=video_embeds,
            )

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

        assert isinstance(video_token_id, int)
        self.video_context_token_id = video_token_id

        if pixel_values_flat_video is not None:
            expected_h = expected_w = self.config.vision_config.image_size
            resolve_bindings = {"h": expected_h, "w": expected_w}

            return InternVLVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_flat=pixel_values_flat_video,
                num_patches=video_num_patches,
                resolve_bindings=resolve_bindings,
            )

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

    def _process_vision_input(
        self,
        image_input: InternVLImageInputs | InternVLVideoInputs,
    ) -> tuple[torch.Tensor, ...]:
        if (
            image_input["type"] == "image_embeds"
            or image_input["type"] == "video_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),)

        # 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 _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_flat", "image_embeds")
                and "images" not in modalities
            ):
                modalities["images"] = self._parse_and_validate_image_input(**kwargs)
            if input_key in ("pixel_values_flat_video",) and "videos" not in modalities:
                modalities["videos"] = self._parse_and_validate_video_input(**kwargs)

        return modalities

    def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
        if self.is_mono:
            assert self.img_context_token_id is not None
            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:
        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            return []

        # The result multimodal_embeddings is tuple of tensors, with each
        # tensor correspoending to a multimodal data item (image or video).
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
                image_embeddings = self._process_vision_input(image_input)
                multimodal_embeddings += tuple(image_embeddings)
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_vision_input(video_input)
                multimodal_embeddings += tuple(video_embeddings)

        return multimodal_embeddings

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: MultiModalEmbeddings | None = None,
        *,
        is_multimodal: torch.Tensor | None = None,
    ) -> 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,
        )

    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]:
        # unused modules appear in OpenGVLab/InternVideo2_5_Chat_8B
        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)

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector="mlp1",
            tower_model="vision_model",
        )

    def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
        if num_image_tokens <= 0 or self.num_image_token <= 0:
            return 0

        num_patches = num_image_tokens // self.num_image_token
        return num_patches * (self.patch_tokens + 1)

    def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
        if num_vision_tokens <= 0 or self.num_image_token <= 0:
            return 0

        num_patches = num_vision_tokens // (self.patch_tokens + 1)
        return num_patches * self.num_image_token