llava_next.py

from functools import cached_property
from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                    TypedDict, Union)

import torch
import torch.nn as nn
from PIL import Image
from transformers import CLIPVisionConfig, LlavaNextConfig, SiglipVisionConfig
from transformers.models.llava_next.modeling_llava_next import (
    get_anyres_image_grid_shape, unpad_image)
from typing_extensions import NotRequired

from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, MultiModalConfig
from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext
from vllm.model_executor.layers.pooler import Pooler, PoolingType
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.sequence import IntermediateTensors, PoolerOutput
from vllm.utils import is_list_of

from .clip import (CLIPVisionModel, dummy_image_for_clip,
                   dummy_seq_data_for_clip, get_clip_image_feature_size,
                   get_clip_patch_grid_length, input_processor_for_clip)
from .interfaces import SupportsMultiModal, SupportsPP
from .llava import LlavaMultiModalProjector, init_vision_tower_for_llava
from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                     dummy_seq_data_for_siglip, get_siglip_image_feature_size,
                     get_siglip_patch_grid_length, input_processor_for_siglip)
from .utils import (AutoWeightsLoader, embed_multimodal, flatten_bn,
                    init_vllm_registered_model)


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

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


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

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


LlavaNextImageInputs = Union[LlavaNextImagePixelInputs,
                             LlavaNextImageEmbeddingInputs]


# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L79
def _get_llava_next_num_unpadded_features(
    original_height: int,
    original_width: int,
    npatches: int,
    num_patch_height: int,
    num_patch_width: int,
) -> Tuple[int, int]:
    current_height = npatches * num_patch_height
    current_width = npatches * num_patch_width

    original_aspect_ratio = original_width / original_height
    current_aspect_ratio = current_width / current_height

    if original_aspect_ratio > current_aspect_ratio:
        scale_factor = current_width / original_width
        new_height = int(original_height * scale_factor)
        padding = (current_height - new_height) // 2
        current_height -= 2 * padding
    else:
        scale_factor = current_height / original_height
        new_width = int(original_width * scale_factor)
        padding = (current_width - new_width) // 2
        current_width -= 2 * padding

    unpadded_features = current_height * current_width
    newline_features = current_height
    return (unpadded_features, newline_features)


# Based on: https://github.com/huggingface/text-generation-inference/blob/v2.2.0/server/text_generation_server/models/vlm_causal_lm.py#L106
def get_llava_next_image_feature_size(
    hf_config: LlavaNextConfig,
    *,
    input_height: int,
    input_width: int,
) -> int:
    vision_config = hf_config.vision_config

    if isinstance(vision_config, CLIPVisionConfig):
        num_patches = get_clip_patch_grid_length(
            image_size=vision_config.image_size,
            patch_size=vision_config.patch_size,
        )
        base_feature_size = get_clip_image_feature_size(vision_config)
    elif isinstance(vision_config, SiglipVisionConfig):
        num_patches = get_siglip_patch_grid_length(
            image_size=vision_config.image_size,
            patch_size=vision_config.patch_size,
        )
        base_feature_size = get_siglip_image_feature_size(vision_config)
    else:
        msg = f"Unsupported vision config: {type(vision_config)}"
        raise NotImplementedError(msg)

    strategy = hf_config.vision_feature_select_strategy
    if strategy == "default":
        base_feature_size -= 1
    elif strategy == "full":
        pass
    else:
        raise ValueError(f"Unexpected select feature strategy: {strategy}")

    num_patch_height, num_patch_width = get_anyres_image_grid_shape(
        image_size=(input_height, input_width),
        grid_pinpoints=hf_config.image_grid_pinpoints,
        patch_size=vision_config.image_size,
    )

    (
        unpadded_feature_size,
        newline_feature_size,
    ) = _get_llava_next_num_unpadded_features(input_height, input_width,
                                              num_patches, num_patch_height,
                                              num_patch_width)

    return unpadded_feature_size + newline_feature_size + base_feature_size


def get_max_llava_next_image_tokens(ctx: InputContext):
    """Compute the max feature size for all possible image grid pinpoints."""
    return _get_pinpoint_with_largest_features(ctx)[0]


def _get_pinpoint_with_largest_features(
        ctx: InputContext) -> Tuple[int, Tuple[int, int]]:
    """Get the grid pinpoint with the largest features & its feature size."""
    hf_config = ctx.get_hf_config(LlavaNextConfig)
    largest_feature_size = 0
    largest_feature_pinpoint = None
    for (height, width) in hf_config.image_grid_pinpoints:
        feat_size = get_llava_next_image_feature_size(
            hf_config,
            input_height=height,
            input_width=width,
        )
        if feat_size > largest_feature_size:
            largest_feature_size = feat_size
            largest_feature_pinpoint = (height, width)
    if not largest_feature_size or largest_feature_pinpoint is None:
        raise ValueError("Cannot have a largest feature size of 0!")
    return largest_feature_size, largest_feature_pinpoint


def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
                              mm_counts: Mapping[str, int]):
    hf_config = ctx.get_hf_config(LlavaNextConfig)
    vision_config = hf_config.vision_config
    num_images = mm_counts["image"]

    image_feature_size, pinpoint = _get_pinpoint_with_largest_features(ctx)
    max_feat_height, max_feat_width = pinpoint

    if isinstance(vision_config, CLIPVisionConfig):
        seq_data = dummy_seq_data_for_clip(
            vision_config,
            seq_len,
            num_images,
            image_token_id=hf_config.image_token_index,
            image_feature_size_override=image_feature_size,
        )

        mm_data = dummy_image_for_clip(
            vision_config,
            num_images,
            image_width_override=max_feat_width,
            image_height_override=max_feat_height,
        )

        return seq_data, mm_data
    elif isinstance(vision_config, SiglipVisionConfig):
        seq_data = dummy_seq_data_for_siglip(
            vision_config,
            seq_len,
            num_images,
            image_token_id=hf_config.image_token_index,
            image_feature_size_override=image_feature_size,
        )

        mm_data = dummy_image_for_siglip(
            vision_config,
            num_images,
            image_width_override=max_feat_width,
            image_height_override=max_feat_height,
        )

        return seq_data, mm_data

    msg = f"Unsupported vision config: {type(vision_config)}"
    raise NotImplementedError(msg)


def input_processor_for_llava_next(ctx: InputContext,
                                   inputs: DecoderOnlyInputs):
    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_config(LlavaNextConfig)
    vision_config = hf_config.vision_config

    image_data = multi_modal_data["image"]
    if isinstance(image_data, Image.Image):
        width, height = image_data.size

        image_feature_size = get_llava_next_image_feature_size(
            hf_config,
            input_height=height,
            input_width=width,
        )
    elif is_list_of(image_data, Image.Image):
        image_feature_size = [
            get_llava_next_image_feature_size(hf_config,
                                              input_height=img.height,
                                              input_width=img.width)
            for img in image_data
        ]
    elif isinstance(image_data, torch.Tensor):
        num_images, image_feature_size, hidden_size = image_data.shape
    elif is_list_of(image_data, torch.Tensor):
        image_feature_size = [item.shape[1] for item in image_data]
    else:
        raise TypeError(f"Invalid image type: {type(image_data)}")

    vision_config = hf_config.vision_config

    if isinstance(vision_config, CLIPVisionConfig):
        return input_processor_for_clip(
            model_config,
            vision_config,
            inputs,
            image_token_id=hf_config.image_token_index,
            image_feature_size_override=image_feature_size,
        )
    elif isinstance(vision_config, SiglipVisionConfig):
        return input_processor_for_siglip(
            model_config,
            vision_config,
            inputs,
            image_token_id=hf_config.image_token_index,
            image_feature_size_override=image_feature_size,
        )

    msg = f"Unsupported vision config: {type(vision_config)}"
    raise NotImplementedError(msg)


@MULTIMODAL_REGISTRY.register_image_input_mapper()
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_next_image_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_next)
@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_next)
class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
                                        SupportsPP):

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

        self.config = config
        self.multimodal_config = multimodal_config

        # TODO: Optionally initializes this for supporting embeddings.
        self.vision_tower = init_vision_tower_for_llava(
            config, quant_config, require_post_norm=False)
        self.image_newline = nn.Parameter(
            torch.empty(config.text_config.hidden_size))
        self.multi_modal_projector = LlavaMultiModalProjector(
            vision_hidden_size=config.vision_config.hidden_size,
            text_hidden_size=config.text_config.hidden_size,
            projector_hidden_act=config.projector_hidden_act)

        self.language_model = init_vllm_registered_model(
            config.text_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(pooling_type=PoolingType.LAST, normalize=True)

        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 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 = self.config.vision_config.image_size
        expected_dims = (3, h, w)

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape[1:])

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

        for d in data:
            _validate_shape(d)

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[LlavaNextImageInputs]:
        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 LlavaNextImagePixelInputs(
                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 embeds. "
                                 f"Got type: {type(image_embeds)}")

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

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

    def _select_image_features(self, image_features: torch.Tensor, *,
                               strategy: str) -> torch.Tensor:
        # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421  # noqa
        if strategy == "default":
            return image_features[:, 1:]
        elif strategy == "full":
            return image_features

        raise ValueError(f"Unexpected select feature strategy: {strategy}")

    def _image_pixels_to_features(
        self,
        vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
        pixel_values: torch.Tensor,
    ) -> torch.Tensor:

        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the vision tower
        image_features = vision_tower(pixel_values)

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

    # Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
    def _merge_image_patch_embeddings(self, image_size: torch.Tensor,
                                      patch_embeddings: torch.Tensor, *,
                                      strategy: str) -> torch.Tensor:
        if strategy == "flat":
            return patch_embeddings.flatten(0, 1)

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

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

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

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

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

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

                if "unpad" in strategy:
                    other_patch_embeds = other_patch_embeds \
                        .permute(4, 0, 2, 1, 3).contiguous() \
                        .flatten(1, 2).flatten(2, 3)
                    other_patch_embeds = unpad_image(other_patch_embeds,
                                                     (orig_height, orig_width))
                    other_patch_embeds = torch.cat((
                        other_patch_embeds,
                        self.image_newline[:, None, None] \
                            .expand(*other_patch_embeds.shape[:-1], 1) \
                            .to(other_patch_embeds.device),
                    ), dim=-1)
                    other_patch_embeds = other_patch_embeds \
                        .flatten(1, 2).transpose(0, 1)
                else:
                    other_patch_embeds = other_patch_embeds \
                        .permute(0, 2, 1, 3, 4).contiguous() \
                        .flatten(0, 3)

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

            return merged_patch_embeddings

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

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

        pixel_values = inputs["data"]

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

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

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

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

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

        patch_embeddings = self._process_image_pixels(image_input)

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

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

    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,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        """Run forward pass for LlaVA-NeXT.

        One key thing to understand is the `input_ids` already accounts for the
        positions of the to-be-inserted image embeddings.

        Concretely, consider a text prompt:
        `"A chat between a curious human and an artificial intelligence
        assistant. The assistant gives helpful, detailed, and polite answers to
        the human's questions.
        USER: <image>\\nWhat is shown in this image? ASSISTANT:"`.

        Tokenizer outputs:
        `[1, 319, 13563, 1546, 263, 12758, 5199, 322, 385, 23116, 21082, 20255,
        29889, 450, 20255, 4076, 8444, 29892, 13173, 29892, 322, 1248, 568,
        6089, 304, 278, 5199, 29915, 29879, 5155, 29889, 3148, 1001, 29901,
        29871, 32000, 13, 5618, 338, 4318, 297, 445, 1967, 29973, 319, 1799,
        9047, 13566, 29901]`.

        To reserve space in KV cache, we have to insert placeholder tokens
        before they are inputted to the model, so the input processor prepends
        additional image tokens (denoted as `32000`), resulting in:
        `[1, 319, 13563, 1546, 263, 12758, 5199, 322, 385, 23116, 21082, 20255,
        29889, 450, 20255, 4076, 8444, 29892, 13173, 29892, 322, 1248, 568,
        6089, 304, 278, 5199, 29915, 29879, 5155, 29889, 3148, 1001, 29901,
        29871, 32000, ..., 32000, 13, 5618, 338, 4318, 297, 445, 1967, 29973,
        319, 1799, 9047, 13566, 29901]`.

        Unlike in LLaVA-1.5, the number of image tokens inputted to the language
        model depends on the original size of the input image. Including the
        original image token in the input, the required number of image tokens
        is given by :func:`get_llava_next_image_feature_size`.

        This way, the `positions` and `attn_metadata` are consistent
        with the `input_ids`.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            pixel_values: The pixels in each grid patch for each input image.
            image_sizes: The original `(height, width)` for each input image.

        See also:
            :class:`LlavaNextImageInputs`
        """
        if intermediate_tensors is not None:
            input_ids = None
            inputs_embeds = None
        else:
            image_input = self._parse_and_validate_image_input(**kwargs)

            if image_input is not None:
                inputs_embeds = embed_multimodal(
                    input_ids,
                    self.config.image_token_index,
                    self.language_model.model.get_input_embeddings,
                    lambda _: self._process_image_input(image_input),
                )
                input_ids = None
            else:
                inputs_embeds = 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]]):
        loader = AutoWeightsLoader(self)
        loader.load_weights(weights)