llava.py

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

import torch
import torch.nn as nn
from transformers import LlavaConfig

from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, VisionLanguageConfig
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.clip import CLIPVisionModel
from vllm.model_executor.models.llama import LlamaModel
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import get_dummy_image_data
from vllm.sequence import SamplerOutput

from .vlm_base import VisionLanguageModelBase

_KEYS_TO_MODIFY_MAPPING = {
    "language_model.lm_head": "lm_head",
    "language_model.model": "language_model",
}


# TODO(xwjiang): 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 merge_vision_embeddings(input_ids: torch.Tensor,
                            inputs_embeds: torch.Tensor,
                            vision_embeddings: torch.Tensor,
                            image_token_id: int) -> torch.Tensor:
    """In place merges in vision_embeddings with inputs_embeds."""
    mask = (input_ids == image_token_id)

    image_feature_size = vision_embeddings.shape[0] * vision_embeddings.shape[1]
    if mask.sum() != image_feature_size:
        raise ValueError(f"image_feature_size should be {image_feature_size}, "
                         f"but found: {mask.sum()}")

    inputs_embeds[mask] = vision_embeddings.view(image_feature_size,
                                                 vision_embeddings.shape[-1])

    return inputs_embeds


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


class LlavaImageFeatureInputs(TypedDict):
    type: Literal["image_features"]
    data: torch.Tensor
    """Shape: (batch_size, image_feature_size, hidden_size)"""


LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageFeatureInputs]


@MULTIMODAL_REGISTRY.register_image_feature_input()
@MULTIMODAL_REGISTRY.register_image_pixel_input()
@MULTIMODAL_REGISTRY.register_dummy_data(get_dummy_image_data)
class LlavaForConditionalGeneration(VisionLanguageModelBase):

    def __init__(self,
                 config: LlavaConfig,
                 vision_language_config: VisionLanguageConfig,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None) -> None:
        super().__init__(vision_language_config)

        self.config = config

        if self.vision_language_config.image_input_type == (
                VisionLanguageConfig.ImageInputType.PIXEL_VALUES):
            self.vision_tower = CLIPVisionModel(config.vision_config)
        else:
            self.vision_tower = None

        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.quant_config = quant_config
        self.language_model = LlamaModel(config.text_config, cache_config,
                                         quant_config)
        self.unpadded_vocab_size = config.text_config.vocab_size
        self.lm_head = ParallelLMHead(
            self.unpadded_vocab_size,
            config.text_config.hidden_size,
            org_num_embeddings=self.language_model.org_vocab_size)
        logit_scale = getattr(config, "logit_scale", 1.0)
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size, logit_scale)
        self.sampler = Sampler()

    def _validate_image_data(self, data: torch.Tensor) -> torch.Tensor:
        if list(data.shape[1:]) != list(
                self.vision_language_config.image_input_shape[1:]):
            raise ValueError(
                f"The expected image tensor shape is batch dimension plus "
                f"{self.vision_language_config.image_input_shape[1:]}. "
                f"You supplied {data.shape}. "
                f"If you are using vLLM's entrypoint, make sure your "
                f"supplied image input is consistent with "
                f"image_input_shape in engine args.")

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[LlavaImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_features = kwargs.pop("image_features", None)

        expected_input_type = self.vision_language_config.image_input_type
        ImageInputType = VisionLanguageConfig.ImageInputType

        if expected_input_type == ImageInputType.PIXEL_VALUES:
            if image_features is not None:
                raise ValueError(
                    "Expected pixel values but got image features")
            if pixel_values is None:
                return 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_image_data(pixel_values),
            )

        if expected_input_type == ImageInputType.IMAGE_FEATURES:
            if pixel_values is not None:
                raise ValueError(
                    "Expected image features but got pixel values")
            if image_features is None:
                return None

            if not isinstance(image_features, torch.Tensor):
                raise ValueError("Incorrect type of image features. "
                                 f"Got type: {type(image_features)}")

            return LlavaImageFeatureInputs(
                type="image_features",
                data=self._validate_image_data(image_features),
            )

        return None

    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: CLIPVisionModel,
                                  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.to(vision_tower.device),
                                      self.config.vision_feature_layer)

        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"] == "pixel_values":
            assert self.vision_tower is not None
            image_features = self._process_image_pixels(image_input)
        else:
            image_features = image_input["data"]

        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,
        **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:
        "<image>\nUSER: What's the content of the image?\nASSISTANT:".
        Tokenizer outputs:
        [1, 32000, 29871, 13, 11889, 29901, 1724, 29915, 29879, 278,
        2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901].
        The to-be-inserted image has a size of 576 (24 * 24) along the context
        length dimension.
        `input_ids` is thus [1, 32000, ..., 32000, 29871, 13, 11889, 29901,
        1724, 29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933,
        9047, 13566, 29901].
        There will be 576 `32000` in the `input_ids`.
        (32000 is the token id for `<image>`.)

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

        This model has two modes of image inputs:
        `PIXEL_VALUES` and `IMAGE_FEATURES`.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            pixel_values: The pixels in each input image.
                Expects a batch with shape `[1, 3, 336, 336]`.
                (Only applicable to `PIXEL_VALUES` mode)
            image_features: The image features for each input image outputted by
                the vision tower before passing to the multi-modal projector.
                Expects a batch with shape `[1, 576, 1024]`.
                (Only applicable to `IMAGE_FEATURES` mode)

        See also:
            Each input maps to huggingface implementation, as follows:

            - `pixel_values`: https://github.com/huggingface/transformers/blob/v4.41.1/src/transformers/models/llava/modeling_llava.py#L360
            - `image_features`: https://github.com/huggingface/transformers/blob/v4.41.1/src/transformers/models/llava/modeling_llava.py#L437
        """
        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.get_input_embeddings(input_ids)

            inputs_embeds = merge_vision_embeddings(
                input_ids, inputs_embeds, vision_embeddings,
                self.vision_language_config.image_token_id)

            input_ids = None
        else:
            inputs_embeds = None

        hidden_states = self.language_model(input_ids,
                                            positions,
                                            kv_caches,
                                            attn_metadata,
                                            inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head.weight, hidden_states,
                                       sampling_metadata)
        return logits

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

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        # only doing this for language model part for now.
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            # post_layernorm is not needed in CLIPVisionModel
            if "vision_model.post_layernorm" in name:
                continue
            for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
                if key_to_modify in name:
                    name = name.replace(key_to_modify, new_key)
            use_default_weight_loading = False
            if "vision" in name:
                if self.vision_tower is not None:
                    # We only do sharding for language model and
                    # not vision model for now.
                    use_default_weight_loading = True
            else:
                for (param_name, weight_name,
                     shard_id) in stacked_params_mapping:
                    if weight_name not in name:
                        continue
                    param = params_dict[name.replace(weight_name, param_name)]
                    weight_loader = param.weight_loader
                    weight_loader(param, loaded_weight, shard_id)
                    break
                else:
                    use_default_weight_loading = True
            if use_default_weight_loading:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)