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

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

import torch
from loguru import logger
from torch import nn
from transformers import PaliGemmaConfig

from aphrodite.attention import AttentionMetadata
from aphrodite.common.config import CacheConfig, MultiModalConfig
from aphrodite.common.sequence import IntermediateTensors
from aphrodite.inputs import INPUT_REGISTRY, InputContext, LLMInputs
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.sampler import Sampler, SamplerOutput
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.models.gemma import GemmaForCausalLM
from aphrodite.modeling.models.gemma2 import Gemma2ForCausalLM
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.multimodal import MULTIMODAL_REGISTRY
from aphrodite.multimodal.utils import cached_get_tokenizer
from aphrodite.quantization.base_config import QuantizationConfig

from .interfaces import SupportsMultiModal
from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                     dummy_seq_data_for_siglip, get_max_siglip_image_tokens)
from .utils import filter_weights, merge_multimodal_embeddings


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


class PaliGemmaImageEmbeddingInputs(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.
    """


PaliGemmaImageInputs = Union[PaliGemmaImagePixelInputs,
                             PaliGemmaImageEmbeddingInputs]


def get_max_paligemma_image_tokens(ctx: InputContext):
    hf_config = ctx.get_hf_config(PaliGemmaConfig)
    vision_config = hf_config.vision_config

    return get_max_siglip_image_tokens(vision_config)


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

    seq_data = dummy_seq_data_for_siglip(
        vision_config,
        seq_len,
        num_images,
        image_token_id=hf_config.image_token_index,
    )

    mm_data = dummy_image_for_siglip(vision_config, num_images)
    return seq_data, mm_data


def input_processor_for_paligemma(ctx: InputContext, llm_inputs: LLMInputs):

    """
    The correct prompt format needs to be:
    '<image>' * image_feature_size + '<bos>' + prompt + '\n'
    See https://github.com/huggingface/transformers/blob/25245ec26dc29bcf6102e1b4ddd0dfd02e720cf5/src/transformers/models/paligemma/processing_paligemma.py#L55
    """ # noqa

    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(PaliGemmaConfig)

    tokenizer = cached_get_tokenizer(model_config.tokenizer)
    image_feature_size = hf_config.text_config.num_image_tokens
    image_token_str = tokenizer.decode(hf_config.image_token_index)
    bos_token = tokenizer.decode(hf_config.bos_token_id)
    image_token_str_pad = image_token_str * image_feature_size
    image_token_ids_pad = [hf_config.image_token_index] * image_feature_size

    orig_prompt = llm_inputs.get("prompt")
    orig_prompt_ids = llm_inputs.get("prompt_token_ids")

    if orig_prompt is not None and image_token_str in orig_prompt:
        logger.warning(
            f"The image token '{image_token_str}' was detected in the prompt "
            "and will be removed. Please follow the proper prompt format"
            " documented on HuggingFace.")
        orig_prompt = orig_prompt.replace(image_token_str, "")
        orig_prompt_ids.remove(hf_config.image_token_index)

    new_prompt = f"{image_token_str_pad}{bos_token}{orig_prompt}\n"
    # The PaliGemma 2 tokenizer does not include a starting BOS token
    if orig_prompt_ids[0] != hf_config.bos_token_id:
        orig_prompt_ids = [hf_config.bos_token_id] + orig_prompt_ids
    new_token_ids = image_token_ids_pad + orig_prompt_ids + [108]  #newline

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


class PaliGemmaMultiModalProjector(nn.Module):

    def __init__(self, vision_hidden_size: int, projection_dim: int):
        super().__init__()

        self.linear = nn.Linear(vision_hidden_size, projection_dim, bias=True)

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


@MULTIMODAL_REGISTRY.register_image_input_mapper()
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_paligemma_image_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_paligemma)
@INPUT_REGISTRY.register_input_processor(input_processor_for_paligemma)
class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal):

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

        self.config = config
        self.multimodal_config = multimodal_config

        self.vision_tower = SiglipVisionModel(config.vision_config)
        self.multi_modal_projector = PaliGemmaMultiModalProjector(
            vision_hidden_size=config.vision_config.hidden_size,
            projection_dim=config.vision_config.projection_dim)

        self.quant_config = quant_config
        if config.text_config.model_type == "gemma":
            self.language_model = GemmaForCausalLM(config.text_config,
                                                cache_config, quant_config)
        else:
            self.language_model = Gemma2ForCausalLM(config.text_config,
                                                    cache_config,
                                                    quant_config)
        self.unpadded_vocab_size = config.text_config.vocab_size
        logit_scale = getattr(config, "logit_scale", 1.0)
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.text_config.vocab_size,
                                                logit_scale)
        self.sampler = Sampler()

    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[PaliGemmaImageInputs]:
        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)}")
            # Remove the N dimension until multiple images are supported.
            pixel_values = pixel_values.squeeze(1)
            return PaliGemmaImagePixelInputs(
                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)}")
            # Remove the N dimension until multiple images are supported.
            image_embeds = image_embeds.squeeze(1)
            return PaliGemmaImageEmbeddingInputs(
                type="image_embeds",
                data=image_embeds,
            )

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

    def _image_pixels_to_features(
        self,
        vision_tower: SiglipVisionModel,
        pixel_values: torch.Tensor,
    ) -> torch.Tensor:

        target_dtype = vision_tower.get_input_embeddings().weight.dtype
        image_features = vision_tower(pixel_values.to(dtype=target_dtype))

        return image_features

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

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

        assert self.vision_tower is not None
        pixel_values = image_input["data"]
        image_features = self._image_pixels_to_features(
            self.vision_tower,
            pixel_values,
        )

        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:

        parsed_image_input = self._parse_and_validate_image_input(**kwargs)

        if parsed_image_input is not None:
            vision_embeddings = self._process_image_input(parsed_image_input)
            # https://github.com/huggingface/transformers/blob/main/src/transformers/models/paligemma/modeling_paligemma.py#L294 # noqa
            vision_embeddings = vision_embeddings * (self.config.hidden_size**
                                                     -0.5)

            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 tower
        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)