aphrodite-engine/aphrodite/modeling/models/llava.py

import itertools
from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union

import torch
import torch.nn as nn
from transformers import CLIPVisionConfig, LlavaConfig, SiglipVisionConfig

from aphrodite.attention import AttentionMetadata
from aphrodite.common.config import CacheConfig, MultiModalConfig
from aphrodite.common.sequence import IntermediateTensors, SamplerOutput
from aphrodite.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from aphrodite.modeling.layers.activation import get_act_fn
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.multimodal import MULTIMODAL_REGISTRY
from aphrodite.quantization.base_config import QuantizationConfig

from .clip import (CLIPVisionModel, dummy_image_for_clip,
                   dummy_seq_data_for_clip, get_max_clip_image_tokens,
                   input_processor_for_clip)
from .interfaces import SupportsMultiModal
from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                     dummy_seq_data_for_siglip, get_max_siglip_image_tokens,
                     input_processor_for_siglip)
from .utils import (filter_weights, init_aphrodite_registered_model,
                    merge_multimodal_embeddings)


class LlavaImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    data: torch.Tensor
    """Shape: `(batch_size, num_channels, height, width)`"""


class LlavaImageEmbeddingInputs(TypedDict):
    type: Literal["image_embeds"]
    data: torch.Tensor
    """Shape: `(batch_size, image_feature_size, hidden_size)`
    `hidden_size` must match the hidden size of language model backbone.
    """


LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageEmbeddingInputs]


# TODO: Run benchmark and decide if TP.
class LlavaMultiModalProjector(nn.Module):

    def __init__(self, vision_hidden_size: int, text_hidden_size: int,
                 projector_hidden_act: str):
        super().__init__()

        self.linear_1 = nn.Linear(vision_hidden_size,
                                  text_hidden_size,
                                  bias=True)
        self.act = get_act_fn(projector_hidden_act)
        self.linear_2 = nn.Linear(text_hidden_size,
                                  text_hidden_size,
                                  bias=True)

    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
        hidden_states = self.linear_1(image_features)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


def get_max_llava_image_tokens(ctx: InputContext):
    hf_config = ctx.get_hf_config(LlavaConfig)
    vision_config = hf_config.vision_config

    if isinstance(vision_config, CLIPVisionConfig):
        num_image_tokens = get_max_clip_image_tokens(vision_config)
    elif isinstance(vision_config, SiglipVisionConfig):
        num_image_tokens = get_max_siglip_image_tokens(vision_config)
    else:
        msg = f"Unsupported vision config: {type(vision_config)}"
        raise NotImplementedError(msg)

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


def dummy_data_for_llava(ctx: InputContext, seq_len: int):
    hf_config = ctx.get_hf_config(LlavaConfig)
    vision_config = hf_config.vision_config

    image_feature_size = get_max_llava_image_tokens(ctx)

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

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

        mm_data = dummy_image_for_siglip(vision_config)
        return seq_data, mm_data

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


def input_processor_for_llava(ctx: InputContext, llm_inputs: LLMInputs):
    multi_modal_data = llm_inputs.get("multi_modal_data")
    if multi_modal_data is None or "image" not in multi_modal_data:
        return llm_inputs

    model_config = ctx.model_config
    hf_config = ctx.get_hf_config(LlavaConfig)
    vision_config = hf_config.vision_config

    image_feature_size = get_max_llava_image_tokens(ctx)

    if isinstance(vision_config, CLIPVisionConfig):
        return input_processor_for_clip(
            model_config,
            vision_config,
            llm_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,
            llm_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)


def _init_vision_tower(hf_config: LlavaConfig):
    vision_config = hf_config.vision_config

    # Initialize the vision tower only up to the required feature layer
    vision_feature_layer = hf_config.vision_feature_layer
    if vision_feature_layer < 0:
        num_hidden_layers = hf_config.vision_config.num_hidden_layers \
            + vision_feature_layer + 1
    else:
        num_hidden_layers = vision_feature_layer + 1

    if isinstance(vision_config, CLIPVisionConfig):
        return CLIPVisionModel(
            vision_config,
            num_hidden_layers_override=num_hidden_layers,
        )
    elif isinstance(vision_config, SiglipVisionConfig):
        return SiglipVisionModel(
            vision_config,
            num_hidden_layers_override=num_hidden_layers,
        )

    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_image_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava)
@INPUT_REGISTRY.register_input_processor(input_processor_for_llava)
class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal):

    def __init__(self,
                 config: LlavaConfig,
                 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(config)
        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_aphrodite_registered_model(
            config.text_config, cache_config, quant_config)

    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
        h = w = self.config.vision_config.image_size
        expected_dims = (3, h, w)
        actual_dims = tuple(data.shape[1:])

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

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[LlavaImageInputs]:
        pixel_values = kwargs.pop("pixel_values", 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):
                raise ValueError("Incorrect type of pixel values. "
                                 f"Got type: {type(pixel_values)}")
            return LlavaImagePixelInputs(
                type="pixel_values",
                data=self._validate_pixel_values(pixel_values),
            )

        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 LlavaImageEmbeddingInputs(
                type="image_embeds",
                data=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,
        )

    def _process_image_pixels(self,
                              inputs: LlavaImagePixelInputs) -> torch.Tensor:
        assert self.vision_tower is not None

        pixel_values = inputs["data"]

        return self._image_pixels_to_features(self.vision_tower, pixel_values)

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

        if image_input["type"] == "image_embeds":
            return image_input["data"]

        assert self.vision_tower is not None
        image_features = self._process_image_pixels(image_input)
        return self.multi_modal_projector(image_features)

    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,
    ) -> SamplerOutput:
        """Run forward pass for LLaVA-1.5.

        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:
        `"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.

        Tokenizer outputs:
        `[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
        278, 2793, 310, 278, 1967, 29973, 13, 22933, 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, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
        29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
        29901]`.

        We insert 575 tokens so that including the original image token in the
        input, there are a total of 576 (24 * 24) image tokens, which
        corresponds to the number of image tokens inputted to the language
        model, i.e. the number of image tokens outputted by the visual encoder.

        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 input image.
        
        See also:
            :class:`LlavaImageInputs`
        """
        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.language_model.model.get_input_embeddings(
                input_ids)

            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, vision_embeddings,
                self.config.image_token_index)

            input_ids = None
        else:
            inputs_embeds = None

        hidden_states = self.language_model.model(input_ids,
                                                  positions,
                                                  kv_caches,
                                                  attn_metadata,
                                                  None,
                                                  inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        return self.language_model.sample(logits, sampling_metadata)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        # prepare weight iterators for components
        vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3)

        # load vision encoder
        vit_weights = filter_weights(vit_weights, "vision_tower")
        self.vision_tower.load_weights(vit_weights)

        # load mlp projector
        mlp_weights = filter_weights(mlp_weights, "multi_modal_projector")
        mlp_params_dict = dict(self.multi_modal_projector.named_parameters())
        for name, loaded_weight in mlp_weights:
            param = mlp_params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)

        # load llm backbone
        llm_weights = filter_weights(llm_weights, "language_model")
        self.language_model.load_weights(llm_weights)