phi3v.py

# Copyright 2024 The vLLM team.
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import re
from functools import cached_property, lru_cache
from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional,
                    Tuple, TypedDict, Union)

import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from transformers import CLIPVisionConfig, PretrainedConfig

from vllm.attention import AttentionMetadata
from vllm.config import (CacheConfig, ModelConfig, MultiModalConfig,
                         PoolerConfig)
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                         InputContext, token_inputs)
from vllm.logger import init_logger
from vllm.model_executor.layers.pooler import Pooler, PoolingType
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding)
from vllm.model_executor.models.clip import CLIPVisionModel
from vllm.model_executor.models.llama import LlamaForCausalLM
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.utils import cached_get_tokenizer, repeat_and_pad_token
from vllm.sequence import IntermediateTensors, PoolerOutput
from vllm.utils import is_list_of

from .clip import dummy_image_for_clip, dummy_seq_data_for_clip
from .interfaces import SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                    merge_multimodal_embeddings)

logger = init_logger(__name__)

# Cannot find the following 2 numbers from hf config.
_IMAGE_TOKEN_ID = 32044

# Result in the max possible feature size (h:w = 16:1)
MAX_IMAGE_FEATURE_SIZE_HEIGHT = 8000
MAX_IMAGE_FEATURE_SIZE_WIDTH = 50

CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(dropout=0.0,
                                                     hidden_act="quick_gelu",
                                                     hidden_size=1024,
                                                     image_size=336,
                                                     intermediate_size=4096,
                                                     num_attention_heads=16,
                                                     num_channels=3,
                                                     num_hidden_layers=24,
                                                     patch_size=14,
                                                     projection_dim=768)


def _init_img_processor(hf_config: PretrainedConfig,
                        quant_config: Optional[QuantizationConfig],
                        prefix: str = "") -> CLIPVisionModel:
    clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
    layer_idx = hf_config.img_processor.get('layer_idx', -2)

    # Initialize the CLIP only up to the required feature layer
    if layer_idx < 0:
        num_hidden_layers = clip_config.num_hidden_layers + \
            layer_idx + 1
    else:
        num_hidden_layers = layer_idx + 1

    img_processor = CLIPVisionModel(
        clip_config,
        quant_config,
        num_hidden_layers_override=num_hidden_layers,
        prefix=prefix,
    )

    return img_processor


class Phi3VImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    data: 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: torch.Tensor
    """
    Shape: `(batch_size * num_images, 2)`

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


class Phi3VImageEmbeddingInputs(TypedDict):
    type: Literal["image_embeds"]
    data: Union[torch.Tensor, List[torch.Tensor]]
    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`

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


Phi3VImageInputs = Union[Phi3VImagePixelInputs, Phi3VImageEmbeddingInputs]


class Phi3ImageEmbeddingBase(nn.Module):

    def __init__(self) -> None:
        super().__init__()
        self.layer_idx: int
        self.type_feature: str
        self.img_processor: CLIPVisionModel

    def get_img_features(self,
                         img_embeds: torch.FloatTensor) -> torch.FloatTensor:
        TYPE_FEATURE = self.type_feature

        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the img_processor
        img_feature = self.img_processor(img_embeds)

        if TYPE_FEATURE == "patch":
            patch_feature = img_feature[:, 1:]
            return patch_feature

        if TYPE_FEATURE == "cls_patch":
            return img_feature

        raise NotImplementedError


# adapted from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_embedding_phi3_v.py
class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase):
    """Phi3 Image embedding with HD transform."""

    def __init__(self,
                 config: PretrainedConfig,
                 quant_config: Optional[QuantizationConfig],
                 prefix: str = "") -> None:
        super().__init__()

        # n_embed or hidden_size
        hidden_size = config.n_embd if hasattr(
            config, 'n_embd') else config.hidden_size

        self.img_processor = _init_img_processor(
            config, quant_config, prefix=f"{prefix}.img_processor")

        image_dim_out = config.img_processor['image_dim_out']
        self.num_img_tokens = config.img_processor['num_img_tokens']

        self.image_dim_out = image_dim_out

        # global_gn and sub_gn for hd transform, serves as line separator
        self.use_hd_transform = config.embd_layer.get('use_hd_transform',
                                                      False)
        self.with_learnable_separator = config.embd_layer.get(
            'with_learnable_separator', False)
        self.hd_transform_order = config.embd_layer.get(
            'hd_transform_order', 'glb_sub')
        # with_hd_transform and with_learnable_separator should have same value
        assert self.use_hd_transform and self.with_learnable_separator

        # 1024 * 4, merge spatial to channel dimension
        self.glb_GN = nn.Parameter(torch.empty([1, 1, self.image_dim_out * 4]))
        self.sub_GN = nn.Parameter(
            torch.empty([1, 1, 1, self.image_dim_out * 4]))

        dim_projection = hidden_size
        depth = 2
        layers = [nn.Linear(image_dim_out * 4, dim_projection)]
        for _ in range(1, depth):
            layers.extend(
                [nn.GELU(),
                 nn.Linear(dim_projection, dim_projection)])
        self.img_projection = nn.Sequential(*layers)

        self.type_feature = config.img_processor.get('type_feature', 'patch')

    def forward(self, pixel_values: torch.FloatTensor,
                image_sizes: torch.Tensor) -> torch.FloatTensor:
        """
        process image and return vision embeddings.

        pixel_values: (num_images, num_crops, c, h, w)
        output: (num_images, num_img_tokens, hidden_size)
        """
        num_images, num_crops, c, h, w = pixel_values.shape
        pixel_values = pixel_values.flatten(0, 1)
        img_features = self.get_img_features(pixel_values)
        img_features = img_features.reshape(num_images, num_crops, -1,
                                            self.image_dim_out)
        image_features_proj = self.hd_feature_transform(
            img_features, image_sizes)
        return image_features_proj

    def hd_feature_transform(self, image_features, image_sizes):
        """
        image_features: (num_images, num_crops+1, 24*24, 1024)
        """
        assert (
            self.hd_transform_order == 'sub_glb'
        ), f'hd_transform_order `{self.hd_transform_order}` not implemented'
        if isinstance(self.img_projection, nn.Sequential):
            target_device = self.img_projection[0].bias.device
            target_dtype = self.img_projection[0].bias.dtype
        else:  # It's a single nn.Linear layer
            target_device = self.img_projection.bias.device
            target_dtype = self.img_projection.bias.dtype

        global_image_features = image_features[:,
                                               0]  # (num_images, 24*24, 1024)
        # global feature can be viewed as a special HD case with num_crops 1x1
        global_image_features_hd = self.reshape_hd_patches_2x2merge(
            global_image_features, 1, 1)
        global_image_features_hd_newline = self.add_image_newline(
            global_image_features_hd)

        batch_image_features_proj = []
        # need a for loop to process each image because of different image sizes
        # (patch arrangement is different for each image)
        for i, img_size in enumerate(image_sizes):
            h, w = img_size
            h_crop = h // 336
            w_crop = w // 336
            num_crops = h_crop * w_crop

            # NOTE: real num_crops is padded
            # (num_crops, 24*24, 1024)
            sub_image_features = image_features[i, 1:1 + num_crops]
            sub_image_features_hd = self.reshape_hd_patches_2x2merge(
                sub_image_features, h_crop, w_crop)
            sub_image_features_hd_newline = self.add_image_newline(
                sub_image_features_hd)

            # [sub features, separator, global features]
            image_embeddings = torch.cat([
                sub_image_features_hd_newline.squeeze(
                    0),  # (h_crop*12*(w_crop*12+1), 4096)
                self.glb_GN.squeeze(0),
                global_image_features_hd_newline[i],
            ])
            img_proj = self.img_projection(
                image_embeddings.to(target_device, target_dtype))
            batch_image_features_proj.append(img_proj)

        return batch_image_features_proj

    def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop):
        """
        image_features: (num_images*num_crops, 24*24, 1024)
        output: (num_images, h_crop*12, w_crop*12, 4096)
        where h_crop*w_crop == num_crops
        """
        N, L, C = image_features.shape
        assert L == 576 and C == 1024 and N % (h_crop * w_crop) == 0
        num_images = N // (h_crop * w_crop)
        H = int(L**0.5)
        image_features_hd = (
            image_features.reshape(N, H, H, C)  # N, 24, 24, 1024
            .reshape(N, H // 2, 2, H // 2, 2, C)  # N, 12, 2, 12, 2, 1024
            .permute(0, 1, 3, 2, 4, 5)  # N, 12, 12, 2, 2, 1024
            .reshape(N, -1, 4 * C)  # N, 144, 4096
            .reshape(num_images, h_crop, w_crop, H // 2, H // 2,
                     -1)  # n_img, h_crop, w_crop, 12, 12, 4096
            .permute(0, 1, 3, 2, 4, 5)  # n_img, h_crop, 12, w_crop, 12, 4096
            .reshape(num_images, h_crop * H // 2, w_crop * H // 2,
                     4 * C)  # n_img, h_crop*12, w_crop*12, 4096
        )
        return image_features_hd

    def add_image_newline(self, image_features_hd):
        """
        image_features_hd: (num_images, h_crop*12, w_crop*12, 4096)
        output: (num_images, (h_crop*12) * (w_crop*12+1), 4096)
        """
        num_images, h, w, hid_dim = image_features_hd.shape
        # add the newline token to the HD image feature patches
        newline_embeddings = self.sub_GN.expand(num_images, h, -1,
                                                -1)  # (n_img, h, 1, hid_dim)
        image_features_hd_newline = torch.cat(
            [image_features_hd, newline_embeddings],
            dim=2).reshape(num_images, -1, hid_dim)
        return image_features_hd_newline


# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L57
def _calc_padded_size(*, width: int, height: int, padding_unit: int = 336):
    target_height = int(np.ceil(height / padding_unit) * padding_unit)
    top_padding = int((target_height - height) / 2)
    bottom_padding = target_height - height - top_padding
    padded_width = width
    padded_height = height + top_padding + bottom_padding
    return padded_width, padded_height


# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L90
def _calc_hd_transform_size(*, width: int, height: int, hd_num: int):
    transposed = False
    if width < height:
        width, height = height, width
        transposed = True

    ratio = width / height
    scale = 1
    while scale * np.ceil(scale / ratio) <= hd_num:
        scale += 1
    scale -= 1

    new_width = int(scale * 336)
    new_height = int(new_width / ratio)

    padded_width, padded_height = _calc_padded_size(width=new_width,
                                                    height=new_height)

    if transposed:
        padded_width, padded_height = padded_height, padded_width

    return padded_width, padded_height


# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L181
def get_phi3v_image_feature_size(
    hf_config: Dict[str, Any],
    *,
    input_height: int,
    input_width: int,
    num_crops: int,
) -> int:
    if num_crops is None:
        num_crops = hf_config.get("num_crops", 16)
    new_width, new_height = _calc_hd_transform_size(width=input_width,
                                                    height=input_height,
                                                    hd_num=num_crops)

    return (new_height // 336 * new_width // 336 + 1) * 144 + 1 \
        + (new_height // 336 + 1) * 12


def get_max_phi3v_image_tokens(ctx: InputContext,
                               *,
                               num_crops: Optional[int] = None):

    return get_phi3v_image_feature_size(
        ctx.get_hf_image_processor_config(),
        input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
        input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
        num_crops=num_crops,
    )


def dummy_data_for_phi3v(ctx: InputContext,
                         seq_len: int,
                         mm_counts: Mapping[str, int],
                         *,
                         num_crops: Optional[int] = None):
    num_images = mm_counts["image"]

    image_feature_size = get_max_phi3v_image_tokens(ctx, num_crops=num_crops)

    seq_data, ranges = dummy_seq_data_for_clip(
        CLIP_VIT_LARGE_PATCH14_336_CONFIG,
        seq_len,
        num_images,
        image_token_id=_IMAGE_TOKEN_ID,
        image_feature_size_override=image_feature_size,
    )
    mm_data = dummy_image_for_clip(
        CLIP_VIT_LARGE_PATCH14_336_CONFIG,
        num_images,
        image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
        image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
    )

    return DummyData(seq_data, mm_data, ranges)


@lru_cache
def _get_image_placeholder_token_id_candidates(
    model_config: ModelConfig,
    idx: int,
) -> List[List[int]]:
    assert idx > 0

    tokenizer = cached_get_tokenizer(model_config.tokenizer)

    # This is used when the image token is at the start of the string
    start_candidate = tokenizer.encode(f"<|image_{idx}|>",
                                       add_special_tokens=False)

    # This is used when the image token is in the middle of the string
    # We need to get the token for "<", not "▁<"
    # https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/raw/main/tokenizer.json
    a_token_id, = tokenizer.encode("a", add_special_tokens=False)
    a_token_id_, *middle_candidate = tokenizer.encode(f"a<|image_{idx}|>",
                                                      add_special_tokens=False)
    assert a_token_id == a_token_id_

    return [start_candidate, middle_candidate]


def input_processor_for_phi3v(ctx: InputContext,
                              inputs: DecoderOnlyInputs,
                              *,
                              num_crops: Optional[int] = None):
    multi_modal_data = inputs.get("multi_modal_data")
    if multi_modal_data is None or "image" not in multi_modal_data:
        return inputs

    model_config = ctx.model_config
    hf_config = ctx.get_hf_image_processor_config()

    image_data = multi_modal_data["image"]
    if isinstance(image_data, Image.Image):
        w, h = image_data.size
        image_feature_size = [
            get_phi3v_image_feature_size(hf_config,
                                         input_width=w,
                                         input_height=h,
                                         num_crops=num_crops)
        ]
        image_data = [image_data]
    elif is_list_of(image_data, Image.Image):
        image_feature_size = []
        for image in image_data:
            w, h = image.size
            image_feature_size.append(
                get_phi3v_image_feature_size(hf_config,
                                             input_width=w,
                                             input_height=h,
                                             num_crops=num_crops))
    elif isinstance(image_data, torch.Tensor):
        image_feature_size = [image_data.shape[0]]
        image_data = [image_data]
    elif is_list_of(image_data, torch.Tensor):
        image_feature_size = [item.shape[0] for item in image_data]
    else:
        raise TypeError(f"Invalid image type: {type(image_data)}")

    prompt = inputs.get("prompt")
    if prompt is None:
        # for async server request, we assume prompt and its token_ids is always
        # in correct format. And num_image_tags == len(image_data) always True.
        image_idx = range(1, len(image_data) + 1)
        new_prompt = None
    else:
        image_idx = sorted(map(int, re.findall(r"<\|image_(\d+)\|>+", prompt)))
        if prompt.count("<|image|>") > 0:
            logger.warning("Please follow the prompt format that is "
                           "documented on HuggingFace which does not involve "
                           "repeating <|image|> tokens.")
        elif (num_image_tags := len(image_idx)) > 1:
            assert num_image_tags == len(
                image_data), "The count of image_placeholder not match image's"
        new_prompt = prompt

    prompt_token_ids = inputs["prompt_token_ids"].copy()

    # masked placeholder with image token id
    for idx in image_idx:
        candidates = _get_image_placeholder_token_id_candidates(model_config,
                                                                idx=idx)

        for candidate in candidates:
            for i in range(len(prompt_token_ids) - len(candidate) + 1):
                if prompt_token_ids[i:i + len(candidate)] == candidate:
                    prompt_token_ids[i:i +
                                     len(candidate)] = ([_IMAGE_TOKEN_ID] *
                                                        len(candidate))
                    break

    # merge consecutive tag ids
    merged_token_ids: List[int] = []
    for is_placeholder, token_ids in itertools.groupby(
            prompt_token_ids, lambda x: x == _IMAGE_TOKEN_ID):
        if is_placeholder:
            merged_token_ids.append(_IMAGE_TOKEN_ID)
        else:
            merged_token_ids.extend(list(token_ids))

    # TODO: Move this to utils or integrate with clip.
    new_token_ids: List[int] = []
    placeholder_idx = 0
    while merged_token_ids:
        token_id = merged_token_ids.pop(0)
        if token_id == _IMAGE_TOKEN_ID:
            new_token_ids.extend(
                repeat_and_pad_token(
                    _IMAGE_TOKEN_ID,
                    repeat_count=image_feature_size[placeholder_idx],
                ))
            placeholder_idx += 1
        else:
            new_token_ids.append(token_id)

    # NOTE: Create a defensive copy of the original inputs
    return token_inputs(prompt_token_ids=new_token_ids,
                        prompt=new_prompt,
                        multi_modal_data=multi_modal_data)


@MULTIMODAL_REGISTRY.register_image_input_mapper()
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_phi3v_image_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_phi3v)
@INPUT_REGISTRY.register_input_processor(input_processor_for_phi3v)
class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):

    def __init__(self,
                 config: PretrainedConfig,
                 multimodal_config: MultiModalConfig,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 pooler_config: Optional[PoolerConfig] = None) -> None:
        super().__init__()

        self.config = config
        self.multimodal_config = multimodal_config
        self.image_token_id = _IMAGE_TOKEN_ID

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            quant_config=quant_config,
            prefix="model.embed_tokens",
        )

        # TODO: Optionally initializes this for supporting input embeddings.
        self.vision_embed_tokens = Phi3HDImageEmbedding(
            config, quant_config, prefix="model.vision_embed_tokens")

        # The prefix is empty intentionally because default prefix of
        # LlamaForCausalLM is "model"
        self.language_model = LlamaForCausalLM(config, cache_config,
                                               quant_config)

        # The same model class supports both language generation and embedding
        # because the architecture name is the same
        self._pooler = Pooler.from_config_with_defaults(
            pooler_config,
            pooling_type=PoolingType.LAST,
            normalize=True,
            softmax=False)
        self.make_empty_intermediate_tensors = (
            self.language_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_pixel_values(
        self, data: Union[torch.Tensor, List[torch.Tensor]]
    ) -> Union[torch.Tensor, List[torch.Tensor]]:

        h = w = CLIP_VIT_LARGE_PATCH14_336_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[Phi3VImageInputs]:
        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 Phi3VImagePixelInputs(
                type="pixel_values",
                data=self._validate_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 embeddings. "
                                 f"Got type: {type(image_embeds)}")

            return Phi3VImageEmbeddingInputs(
                type="image_embeds",
                data=flatten_bn(image_embeds),
            )

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

    def _process_image_input(
        self,
        image_input: Phi3VImageInputs,
    ) -> torch.Tensor:

        if image_input["type"] == "image_embeds":
            image_data = image_input["data"]
            if is_list_of(image_data, torch.Tensor):
                # it's already a list of tensors
                return image_data
            if len(image_data.shape) == 3:
                # 3D tensor
                return list(torch.unbind(image_data, dim=0))
            raise ValueError(
                "We expect batched 2D tensors;"
                "this can be either a list of 2D tensors or a single 3D tensor."
            )

        assert self.vision_embed_tokens is not None
        image_embeds = self.vision_embed_tokens(image_input["data"],
                                                image_input["image_sizes"])

        return image_embeds

    def forward(self,
                input_ids: torch.Tensor,
                positions: torch.Tensor,
                kv_caches: List[torch.Tensor],
                attn_metadata: AttentionMetadata,
                intermediate_tensors: Optional[IntermediateTensors] = None,
                **kwargs: object):
        if intermediate_tensors is not None:
            inputs_embeds = None
        else:
            image_input = self._parse_and_validate_image_input(**kwargs)

            if image_input is not None:
                vision_embeddings = self._process_image_input(image_input)
                inputs_embeds = self.embed_tokens(input_ids)
                inputs_embeds = merge_multimodal_embeddings(
                    input_ids, inputs_embeds, vision_embeddings,
                    self.image_token_id)
            else:
                inputs_embeds = self.language_model.model.embed_tokens(
                    input_ids)

        # always pass the input via `inputs_embeds`
        # to make sure the computation graph is consistent
        # for `torch.compile` integration
        input_ids = None

        hidden_states = self.language_model.model(input_ids,
                                                  positions,
                                                  kv_caches,
                                                  attn_metadata,
                                                  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 pooler(
        self,
        hidden_states: torch.Tensor,
        pooling_metadata: PoolingMetadata,
    ) -> Optional[PoolerOutput]:
        return self._pooler(hidden_states, pooling_metadata)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        hf_to_vllm_mapper = WeightsMapper(
            orig_to_new_prefix={
                "model.vision_embed_tokens.wte": "embed_tokens",
                "model.vision_embed_tokens.": "vision_embed_tokens.",
                "lm_head.": "language_model.lm_head.",
                "model.": "language_model.model.",
            })

        loader = AutoWeightsLoader(self)
        autoloaded_weights = loader.load_weights(weights,
                                                 mapper=hf_to_vllm_mapper)

        # The HF config doesn't specify whether these are tied,
        # so we detect it this way
        if "embed_tokens" not in autoloaded_weights:
            self.embed_tokens = self.language_model.model.embed_tokens