vllm/vllm/model_executor/models/vision.py

# SPDX-License-Identifier: Apache-2.0

from abc import ABC, abstractmethod
from typing import Final, Generic, Optional, Protocol, TypeVar, Union, cast

import torch
from transformers import PretrainedConfig

import vllm.envs as envs
from vllm.attention.selector import (backend_name_to_enum,
                                     get_global_forced_attn_backend)
from vllm.jsontree import JSONTree, json_map_leaves
from vllm.logger import init_logger
from vllm.platforms import _Backend, current_platform

from .interfaces import MultiModalEmbeddings

logger = init_logger(__name__)

_C = TypeVar("_C", bound=PretrainedConfig)


class VisionEncoderInfo(ABC, Generic[_C]):

    def __init__(self, vision_config: _C) -> None:
        super().__init__()

        self.vision_config = vision_config

    @abstractmethod
    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        raise NotImplementedError

    @abstractmethod
    def get_max_image_tokens(self) -> int:
        raise NotImplementedError

    @abstractmethod
    def get_image_size(self) -> int:
        raise NotImplementedError

    @abstractmethod
    def get_patch_size(self) -> int:
        raise NotImplementedError

    @abstractmethod
    def get_patch_grid_length(self) -> int:
        raise NotImplementedError


class VisionLanguageConfig(Protocol):
    vision_config: Final[PretrainedConfig]


def get_vision_encoder_info(
        hf_config: VisionLanguageConfig) -> VisionEncoderInfo:
    # Avoid circular imports
    from .clip import CLIPEncoderInfo, CLIPVisionConfig
    from .pixtral import PixtralHFEncoderInfo, PixtralVisionConfig
    from .siglip import SiglipEncoderInfo, SiglipVisionConfig

    vision_config = hf_config.vision_config
    if isinstance(vision_config, CLIPVisionConfig):
        return CLIPEncoderInfo(vision_config)
    if isinstance(vision_config, PixtralVisionConfig):
        return PixtralHFEncoderInfo(vision_config)
    if isinstance(vision_config, SiglipVisionConfig):
        return SiglipEncoderInfo(vision_config)

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


def get_vit_attn_backend(support_fa: bool = False) -> _Backend:
    """
    Get the available attention backend for Vision Transformer.
    """
    # TODO(Isotr0py): Remove `support_fa` after support FA for all ViTs attn.
    selected_backend: Optional[_Backend] = get_global_forced_attn_backend()
    if selected_backend is None:
        backend_by_env_var: Optional[str] = envs.VLLM_ATTENTION_BACKEND
        if backend_by_env_var is not None:
            selected_backend = backend_name_to_enum(backend_by_env_var)
    if selected_backend is None:
        if current_platform.is_cuda():
            device_available = current_platform.has_device_capability(80)
            if device_available and support_fa:
                from transformers.utils import is_flash_attn_2_available
                if is_flash_attn_2_available():
                    selected_backend = _Backend.FLASH_ATTN
                else:
                    logger.warning_once(
                        "Current `vllm-flash-attn` has a bug inside vision "
                        "module, so we use xformers backend instead. You can "
                        "run `pip install flash-attn` to use flash-attention "
                        "backend.")
                    selected_backend = _Backend.XFORMERS
            else:
                # For Volta and Turing GPUs, use xformers instead.
                selected_backend = _Backend.XFORMERS
        else:
            # Default to torch SDPA for other non-GPU platforms.
            selected_backend = _Backend.TORCH_SDPA
    return selected_backend


def resolve_visual_encoder_outputs(
    encoder_outputs: Union[torch.Tensor, list[torch.Tensor]],
    feature_sample_layers: Optional[list[int]],
    post_layer_norm: Optional[torch.nn.LayerNorm],
    max_possible_layers: int,
) -> torch.Tensor:
    """Given the outputs a visual encoder module that may correspond to the
    output of the last layer, or a list of hidden states to be stacked,
    handle post normalization and resolve it into a single output tensor.

    Args:
        encoder_outputs: Output of encoder's last layer or all hidden states.
        feature_sample_layers: Optional layer indices to grab from the encoder
            outputs; if provided, encoder outputs must be a list.
        post_layer_norm: Post norm to apply to the output of the encoder.
        max_possible_layers: Total layers in the fully loaded visual encoder.

    """
    if feature_sample_layers is None:
        if post_layer_norm is not None:
            return post_layer_norm(encoder_outputs)
        return encoder_outputs

    # Get the hidden states corresponding to the layer indices.
    # Negative values are relative to the full visual encoder,
    # so offset them depending on how many layers were loaded.
    # NOTE: this assumes that encoder_outputs is a list containing
    # the inputs to the visual encoder, followed by the hidden states
    # of each layer.
    num_loaded_layers = len(encoder_outputs) - 1
    offset = max_possible_layers - num_loaded_layers
    hs_pool = [
        encoder_outputs[layer_idx]
        if layer_idx >= 0 else encoder_outputs[layer_idx + offset]
        for layer_idx in feature_sample_layers
    ]

    # Apply post-norm on the final hidden state if we are using it
    uses_last_layer = feature_sample_layers[-1] in (len(hs_pool) - 1, -1)
    if post_layer_norm is not None and uses_last_layer:
        hs_pool[-1] = post_layer_norm(encoder_outputs)
    return torch.cat(hs_pool, dim=-1)


def scatter_patch_features(
    features: torch.Tensor,
    num_embeds: torch.Tensor,
    embed_is_patch: torch.Tensor,
) -> tuple[torch.Tensor, ...]:
    """
    Scatter the patch features into a contiguous tensor that corresponds
    to the embedding tokens defined by the multimodal processor.
    
    The rest of the values in the tensor are set to NaN so that they
    can be filtered out by :func`select_patch_features`.

    Args:
        features: The patch features, concatenated across each image.
          Shape: `(num_patch, feature_depth)`
        num_embeds: The number of image embeddings for each image.
          Shape: `(num_images,)`
        embed_is_patch: A boolean mask indicating which image embeddings
          correspond to patch tokens for each image.
          Shape: `(num_images, num_embeds)`
    """
    num_embeds_per_image: list[int] = num_embeds.tolist()

    embeds_flat = features.new_full(
        (sum(num_embeds_per_image), features.shape[-1]),
        fill_value=torch.nan,
    )
    embeds_flat[embed_is_patch.view(-1)] = features.flatten(0, -2)

    return embeds_flat.split(num_embeds_per_image)


def select_patch_features(
        multimodal_embeddings: MultiModalEmbeddings) -> MultiModalEmbeddings:
    """
    Given the outputs of :func:`scatter_patch_features`, return only
    the values that correspond to patch features.
    """
    selected_features = json_map_leaves(
        lambda x: x[~x.isnan()].view(-1, *x.shape[1:]),
        cast(JSONTree[torch.Tensor], multimodal_embeddings),
    )
    return cast(MultiModalEmbeddings, selected_features)