aya_vision.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://github.com/huggingface/transformers/tree/main/src/transformers/models/aya_vision
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Literal

import torch
from torch import nn
from transformers import BatchFeature, GotOcr2ImageProcessor
from transformers.activations import ACT2FN
from transformers.image_processing_utils import get_size_dict
from transformers.models.aya_vision import AyaVisionConfig
from transformers.models.aya_vision.processing_aya_vision import AyaVisionProcessor
from transformers.models.got_ocr2.image_processing_got_ocr2 import (
    get_optimal_tiled_canvas,
)

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalDataDict, MultiModalKwargsItems
from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
from vllm.multimodal.processing import (
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    MultiModalFieldConfig,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .siglip import SiglipVisionModel
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    init_vllm_registered_model,
    maybe_prefix,
)


class AyaVisionImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - np: The total number of patches over each image over each prompt in
              the batch
        - c: Number of channels
        - h: Height of each image patch
        - w: Width of each image patch
        - bn: Batch size * number of images
    """

    type: Literal["pixel_values"]

    pixel_values: Annotated[
        torch.Tensor,
        TensorShape("np", 3, "h", "w"),
    ]

    num_patches: Annotated[
        torch.Tensor,
        TensorShape("bn"),
    ]


class AyaVisionMultiModalProjector(nn.Module):
    def __init__(self, config: AyaVisionConfig):
        super().__init__()
        self.config = config
        self.downsample_factor = config.downsample_factor
        self.alignment_intermediate_size = getattr(
            config, "alignment_intermediate_size", config.text_config.hidden_size
        )
        self.layernorm = nn.LayerNorm(
            config.vision_config.hidden_size * (config.downsample_factor**2),
            eps=config.adapter_layer_norm_eps,
        )

        self.linear_1 = nn.Linear(
            config.vision_config.hidden_size * (config.downsample_factor**2),
            self.alignment_intermediate_size,
            bias=True,
        )

        self.act = ACT2FN["silu"]  # SwiGLU uses SiLU activation
        # For SwiGLU, project down to half size since we split intermediate dim
        self.linear_2 = nn.Linear(
            self.alignment_intermediate_size // 2,
            config.text_config.hidden_size,
            bias=True,
        )

    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
        image_features = self.pixel_shuffle(image_features)
        image_features = self.layernorm(image_features)
        hidden_states = self.linear_1(image_features)

        # Split along last dimension and apply SwiGLU
        x, gate = hidden_states.chunk(2, dim=-1)
        hidden_states = self.act(gate) * x

        hidden_states = self.linear_2(hidden_states)
        return hidden_states

    def pixel_shuffle(self, image_features: torch.Tensor) -> torch.Tensor:  # B, S, D
        batch_size, seq_length, _ = image_features.shape
        height = width = int(seq_length**0.5)
        image_features = image_features.reshape(
            image_features.shape[0], width, height, -1
        )
        channels = image_features.shape[-1]
        image_features = image_features.reshape(
            batch_size,
            width,
            int(height / self.downsample_factor),
            int(channels * self.downsample_factor),
        )
        image_features = image_features.permute(0, 2, 1, 3)
        image_features = image_features.reshape(
            batch_size,
            int(height / self.downsample_factor),
            int(width / self.downsample_factor),
            -1,
        )
        image_features = image_features.permute(0, 2, 1, 3)
        return image_features


class AyaVisionProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self) -> AyaVisionConfig:
        return self.ctx.get_hf_config(AyaVisionConfig)

    def get_hf_processor(self, **kwargs: object) -> AyaVisionProcessor:
        return self.ctx.get_hf_processor(AyaVisionProcessor, **kwargs)

    def get_image_processor(self, **kwargs: object) -> GotOcr2ImageProcessor:
        return self.get_hf_processor(**kwargs).image_processor

    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
        return {"image": None}

    def get_image_size_with_most_features(self) -> ImageSize:
        image_processor = self.get_image_processor()
        height = image_processor.size["height"]
        width = image_processor.size["width"]
        max_patches = image_processor.max_patches
        return ImageSize(height=height * max_patches, width=width * max_patches)

    def get_num_patches(
        self,
        *,
        image_width: int,
        image_height: int,
        size: dict,
        min_patches: int,
        max_patches: int,
    ) -> int:
        """
        Calculate the number of patches needed for a given image based on size
        constraints.  This method replicates and adjusts the logic from:
        transformers/models/got_ocr2/image_processing_got_ocr2
        """
        size = get_size_dict(size, default_to_square=False)
        num_columns, num_rows = get_optimal_tiled_canvas(
            (image_height, image_width),
            (size["height"], size["width"]),
            min_patches,
            max_patches,
        )
        num_blocks = num_columns * num_rows
        return num_blocks if num_blocks == 1 else num_blocks + 1


class AyaVisionDummyInputsBuilder(BaseDummyInputsBuilder[AyaVisionProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)

        processor = self.info.get_hf_processor()
        image_token = processor.image_token

        return image_token * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions] | None = None,
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        image_size = self.info.get_image_size_with_most_features()

        image_overrides = mm_options.get("image") if mm_options else None

        return {
            "image": self._get_dummy_images(
                width=image_size.width,
                height=image_size.height,
                num_images=num_images,
                overrides=image_overrides,
            )
        }


class AyaVisionMultiModalProcessor(BaseMultiModalProcessor[AyaVisionProcessingInfo]):
    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        processed_outputs = super()._call_hf_processor(
            prompt,
            mm_data,
            mm_kwargs,
            tok_kwargs,
        )
        hf_processor = self.info.get_hf_processor(**mm_kwargs)
        image_processor = hf_processor.image_processor

        # HF processor pops the `num_patches` kwarg, which is needed by vLLM
        if (images := mm_data.get("images")) is not None:
            parsed_images = (
                self._get_data_parser()
                .parse_mm_data({"image": images})
                .get_items("image", ImageProcessorItems)
            )
            image_sizes = [
                parsed_images.get_image_size(i) for i in range(len(parsed_images))
            ]

            num_patches = [
                self.info.get_num_patches(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    size=image_processor.size,
                    min_patches=image_processor.min_patches,
                    max_patches=image_processor.max_patches,
                )
                for image_size in image_sizes
            ]
            processed_outputs["num_patches"] = torch.tensor(num_patches)

        return processed_outputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        num_patches = hf_inputs.get("num_patches", torch.empty(0))
        return dict(
            pixel_values=MultiModalFieldConfig.flat_from_sizes("image", num_patches),
            num_patches=MultiModalFieldConfig.batched("image"),
            image_embeds=MultiModalFieldConfig.batched("image"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        image_token = hf_processor.image_token
        img_patch_token = hf_processor.img_patch_token
        image_processor = hf_processor.image_processor

        def get_replacement(item_idx: int):
            images = mm_items.get_items("image", ImageProcessorItems)
            image_size: ImageSize = images.get_image_size(item_idx)
            num_patches = self.info.get_num_patches(
                image_width=image_size.width,
                image_height=image_size.height,
                size=image_processor.size,
                min_patches=image_processor.min_patches,
                max_patches=image_processor.max_patches,
            )
            repl = hf_processor._prompt_split_image(num_patches=num_patches)

            return PromptUpdateDetails.select_text(repl, img_patch_token)

        return [
            PromptReplacement(
                modality="image",
                target=image_token,
                replacement=get_replacement,
            )
        ]


def _get_num_hidden_layers(hf_config: AyaVisionConfig) -> int:
    feature_layers = hf_config.vision_feature_layer
    num_hidden_layers = hf_config.vision_config.num_hidden_layers
    # If we have one feature layer, initialize up to that layer
    if isinstance(feature_layers, int):
        return _get_layer_index(feature_layers, num_hidden_layers)
    # If we have multiple feature layers, initialize up to the deepest m
    elif isinstance(feature_layers, (list, tuple)):
        return max(_get_layer_index(idx, num_hidden_layers) for idx in feature_layers)
    raise TypeError(
        f"vision_layer_feature type: {type(feature_layers)} is not supported"
    )


def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
    if feature_layer_index < 0:
        return num_hidden_layers + feature_layer_index + 1
    return feature_layer_index


@MULTIMODAL_REGISTRY.register_processor(
    AyaVisionMultiModalProcessor,
    info=AyaVisionProcessingInfo,
    dummy_inputs=AyaVisionDummyInputsBuilder,
)
class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            # mapping for new names in checkpoint saved after transformers v4.52
            "model.language_model.": "language_model.model.",
            "model.vision_tower.": "vision_tower.",
            "model.multi_modal_projector.": "multi_modal_projector.",
            "lm_head.": "language_model.lm_head.",
        }
    )

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return "<image>"

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config: AyaVisionConfig = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        num_hidden_layers = _get_num_hidden_layers(config)
        self.config = config
        self.quant_config = quant_config
        self.multimodal_config = multimodal_config

        self.vision_tower = SiglipVisionModel(
            config.vision_config,
            quant_config,
            num_hidden_layers_override=num_hidden_layers,
            prefix=maybe_prefix(prefix, "vision_model"),
        )
        self.vocab_size = config.text_config.vocab_size
        self.multi_modal_projector = AyaVisionMultiModalProjector(config)
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "model"),
            # Cohere2ForCausalLM and CohereForCausalLM are the same on vllm
            architectures=["Cohere2ForCausalLM"],
        )

    @property
    def dtype(self):
        return next(self.parameters()).dtype

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

    def _image_pixels_to_features(
        self,
        vision_tower: SiglipVisionModel,
        pixel_values: torch.Tensor,
    ) -> torch.Tensor | tuple[torch.Tensor, ...]:
        return vision_tower(
            pixel_values.to(dtype=vision_tower.dtype),
            feature_select_strategy=self.config.vision_feature_select_strategy,
        )

    def _process_image_input(
        self, image_input: AyaVisionImagePixelInputs, **kwargs
    ) -> list[torch.Tensor]:
        assert self.vision_tower is not None
        pixel_values = image_input["pixel_values"]
        num_patches = image_input["num_patches"]
        image_features = self._image_pixels_to_features(
            self.vision_tower, pixel_values=pixel_values
        )
        image_embeds = self.multi_modal_projector(image_features)
        return [e.flatten(0, 2) for e in image_embeds.split(num_patches.tolist())]

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> AyaVisionImagePixelInputs | None:
        pixel_values = kwargs.pop("pixel_values", None)
        num_patches = kwargs.pop("num_patches", None)
        image_embeds = kwargs.pop("image_embeds", None)
        assert image_embeds is None, "Aya Vision does not support image_embeds."

        if pixel_values is None:
            return None

        return AyaVisionImagePixelInputs(
            type="pixel_values",
            pixel_values=pixel_values,
            num_patches=num_patches,
            resolve_bindings={
                "h": self.config.vision_config.image_size,
                "w": self.config.vision_config.image_size,
            },
        )

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input, **kwargs)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> torch.Tensor | IntermediateTensors:
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.language_model.compute_logits(hidden_states)