utils.py

# SPDX-License-Identifier: Apache-2.0

from itertools import groupby
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
from urllib.parse import ParseResult, urlparse

import numpy as np
import numpy.typing as npt
import torch
from PIL import Image

import vllm.envs as envs
from vllm.connections import HTTPConnection, global_http_connection
from vllm.distributed import (get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              tensor_model_parallel_all_gather)

from .audio import AudioMediaIO
from .base import MediaIO
from .image import ImageEmbeddingMediaIO, ImageMediaIO
from .inputs import PlaceholderRange
from .video import VideoMediaIO

_M = TypeVar("_M")

if TYPE_CHECKING:
    from .hasher import MultiModalHashDict
    from .inputs import MultiModalKwargs, MultiModalPlaceholderDict
else:
    MultiModalHashDict = Any
    MultiModalKwargs = Any
    MultiModalPlaceholderDict = Any


class MediaConnector:

    def __init__(
        self,
        connection: HTTPConnection = global_http_connection,
        *,
        allowed_local_media_path: str = "",
    ) -> None:
        super().__init__()

        self.connection = connection

        if allowed_local_media_path:
            allowed_local_media_path_ = Path(allowed_local_media_path)

            if not allowed_local_media_path_.exists():
                raise ValueError(
                    "Invalid `--allowed-local-media-path`: The path "
                    f"{allowed_local_media_path_} does not exist.")
            if not allowed_local_media_path_.is_dir():
                raise ValueError(
                    "Invalid `--allowed-local-media-path`: The path "
                    f"{allowed_local_media_path_} must be a directory.")
        else:
            allowed_local_media_path_ = None

        self.allowed_local_media_path = allowed_local_media_path_

    def _load_data_url(
        self,
        url_spec: ParseResult,
        media_io: MediaIO[_M],
    ) -> _M:
        data_spec, data = url_spec.path.split(",", 1)
        media_type, data_type = data_spec.split(";", 1)

        if data_type != "base64":
            msg = "Only base64 data URLs are supported for now."
            raise NotImplementedError(msg)

        return media_io.load_base64(media_type, data)

    def _load_file_url(
        self,
        url_spec: ParseResult,
        media_io: MediaIO[_M],
    ) -> _M:
        allowed_local_media_path = self.allowed_local_media_path
        if allowed_local_media_path is None:
            raise RuntimeError("Cannot load local files without "
                               "`--allowed-local-media-path`.")

        filepath = Path(url_spec.path)
        if allowed_local_media_path not in filepath.resolve().parents:
            raise ValueError(
                f"The file path {filepath} must be a subpath "
                f"of `--allowed-local-media-path` {allowed_local_media_path}.")

        return media_io.load_file(filepath)

    def load_from_url(
        self,
        url: str,
        media_io: MediaIO[_M],
        *,
        fetch_timeout: Optional[int] = None,
    ) -> _M:
        url_spec = urlparse(url)

        if url_spec.scheme.startswith("http"):
            connection = self.connection
            data = connection.get_bytes(url, timeout=fetch_timeout)

            return media_io.load_bytes(data)

        if url_spec.scheme == "data":
            return self._load_data_url(url_spec, media_io)

        if url_spec.scheme == "file":
            return self._load_file_url(url_spec, media_io)

        msg = "The URL must be either a HTTP, data or file URL."
        raise ValueError(msg)

    async def load_from_url_async(
        self,
        url: str,
        media_io: MediaIO[_M],
        *,
        fetch_timeout: Optional[int] = None,
    ) -> _M:
        url_spec = urlparse(url)

        if url_spec.scheme.startswith("http"):
            connection = self.connection
            data = await connection.async_get_bytes(url, timeout=fetch_timeout)

            return media_io.load_bytes(data)

        if url_spec.scheme == "data":
            return self._load_data_url(url_spec, media_io)

        if url_spec.scheme == "file":
            return self._load_file_url(url_spec, media_io)

        msg = "The URL must be either a HTTP, data or file URL."
        raise ValueError(msg)

    def fetch_audio(
        self,
        audio_url: str,
    ) -> tuple[np.ndarray, Union[int, float]]:
        """
        Load audio from a URL.
        """
        audio_io = AudioMediaIO()

        return self.load_from_url(
            audio_url,
            audio_io,
            fetch_timeout=envs.VLLM_AUDIO_FETCH_TIMEOUT,
        )

    async def fetch_audio_async(
        self,
        audio_url: str,
    ) -> tuple[np.ndarray, Union[int, float]]:
        """
        Asynchronously fetch audio from a URL.
        """
        audio_io = AudioMediaIO()

        return await self.load_from_url_async(
            audio_url,
            audio_io,
            fetch_timeout=envs.VLLM_AUDIO_FETCH_TIMEOUT,
        )

    def fetch_image(
        self,
        image_url: str,
        *,
        image_mode: str = "RGB",
    ) -> Image.Image:
        """
        Load a PIL image from a HTTP or base64 data URL.

        By default, the image is converted into RGB format.
        """
        image_io = ImageMediaIO(image_mode=image_mode)

        return self.load_from_url(
            image_url,
            image_io,
            fetch_timeout=envs.VLLM_IMAGE_FETCH_TIMEOUT,
        )

    async def fetch_image_async(
        self,
        image_url: str,
        *,
        image_mode: str = "RGB",
    ) -> Image.Image:
        """
        Asynchronously load a PIL image from a HTTP or base64 data URL.

        By default, the image is converted into RGB format.
        """
        image_io = ImageMediaIO(image_mode=image_mode)

        return await self.load_from_url_async(
            image_url,
            image_io,
            fetch_timeout=envs.VLLM_IMAGE_FETCH_TIMEOUT,
        )

    def fetch_video(
        self,
        video_url: str,
        *,
        image_mode: str = "RGB",
        num_frames: int = 32,
    ) -> npt.NDArray:
        """
        Load video from a HTTP or base64 data URL.
        """
        image_io = ImageMediaIO(image_mode=image_mode)
        video_io = VideoMediaIO(image_io, num_frames=num_frames)

        return self.load_from_url(
            video_url,
            video_io,
            fetch_timeout=envs.VLLM_VIDEO_FETCH_TIMEOUT,
        )

    async def fetch_video_async(
        self,
        video_url: str,
        *,
        image_mode: str = "RGB",
        num_frames: int = 32,
    ) -> npt.NDArray:
        """
        Asynchronously load video from a HTTP or base64 data URL.

        By default, the image is converted into RGB format.
        """
        image_io = ImageMediaIO(image_mode=image_mode)
        video_io = VideoMediaIO(image_io, num_frames=num_frames)

        return await self.load_from_url_async(
            video_url,
            video_io,
            fetch_timeout=envs.VLLM_VIDEO_FETCH_TIMEOUT,
        )

    def fetch_image_embedding(
        self,
        data: str,
    ) -> torch.Tensor:
        """
        Load image embedding from a URL.
        """
        image_embedding_io = ImageEmbeddingMediaIO()

        return image_embedding_io.load_base64("", data)


global_media_connector = MediaConnector()
"""The global [`MediaConnector`][vllm.multimodal.utils.MediaConnector]
instance used by vLLM."""

fetch_audio = global_media_connector.fetch_audio
fetch_image = global_media_connector.fetch_image
fetch_video = global_media_connector.fetch_video


def encode_audio_base64(
    audio: np.ndarray,
    sampling_rate: float,
) -> str:
    """Encode audio as base64."""
    audio_io = AudioMediaIO()
    return audio_io.encode_base64((audio, sampling_rate))


def encode_image_base64(
    image: Image.Image,
    *,
    image_mode: str = "RGB",
    format: str = "JPEG",
) -> str:
    """
    Encode a pillow image to base64 format.

    By default, the image is converted into RGB format before being encoded.
    """
    image_io = ImageMediaIO(image_mode=image_mode)
    return image_io.encode_base64(image, image_format=format)


def encode_video_base64(frames: npt.NDArray) -> str:
    image_io = ImageMediaIO()
    video_io = VideoMediaIO(image_io)
    return video_io.encode_base64(frames)


def merge_and_sort_multimodal_metadata(
    mm_positions: MultiModalPlaceholderDict,
    mm_hashes: Optional[MultiModalHashDict],
) -> tuple[list[str], list[PlaceholderRange], Optional[list[str]]]:
    """Given a MultiModalPlaceholderDict, merge all PlaceholderRange
    objects from all available modalities into a single list of 
    PlaceholderRange, sorted by their offset (starting index in the input
    sequence) in the ascending order.

    Optionally if a `MultiModalHashDict` is given, same operation will be
    applied to the object and the sorted list of hashes will be returned.
    
    Returns:
        list[str]: List of item modalities in order of their positions in the
        input sequence.
        list[PlaceholderRange]: Sorted list of all PlaceholdeRanges from
        mm_positions.
        Optional[list[str]]: Sorted list of all hashes from mm_hashes if given,
        None otherwise.
    """

    modalities = list(mm_positions.keys())

    assert len(modalities) > 0, "No modalities found in the mm_positions."

    # For single modality, placeholder ranges and hashes are already sorted
    # so we can return the list directly.
    if len(modalities) == 1:
        modality = modalities[0]
        placeholder_list = list(mm_positions[modality])

        return [modality] * len(
            placeholder_list
        ), placeholder_list, None if not mm_hashes else mm_hashes[modality]

    # Create a list of (modality, placeholder, hash) tuples for all placeholders
    all_items = []
    for modality in modalities:
        placeholder_list = list(mm_positions[modality])
        hash_list: list[Optional[str]] = list(
            mm_hashes[modality]) if mm_hashes and modality in mm_hashes else [
                None
            ] * len(placeholder_list)

        for placeholder, hash_value in zip(placeholder_list, hash_list):
            all_items.append((modality, placeholder, hash_value))

    # Sort all items by offset
    all_items.sort(key=lambda x: x[1].offset)

    # Split into separate lists
    sorted_modalities = [item[0] for item in all_items]
    merged_placeholders = [item[1] for item in all_items]
    merged_hashes = [str(item[2])
                     for item in all_items] if mm_hashes is not None else None

    return sorted_modalities, merged_placeholders, merged_hashes


def group_mm_inputs_by_modality(
        mm_inputs: list[MultiModalKwargs]) -> list[list[MultiModalKwargs]]:
    """Group consecutive MultiModalKwargs from mm_inputs with the same modality
    together into the same list for batching purpose. For MultiModalKwargs with
    multiple modalities, put them into their own list.

    Args:
        mm_inputs: List of MultiModalKwargs.

    Returns:
        list[list[vllm.multimodal.MultiModalKwargs]]: List of list of
        `MultiModalKwargs`, each inner list contains consecutive
        `MultiModalKwargs` with same modality.
    """
    if not mm_inputs:
        return []

    def modality_group_func(mm_input: MultiModalKwargs) -> Union[str, int]:
        # If the input has multiple modalities, return a id as the unique key
        # for the mm_input input.
        if len(mm_input.modalities) > 1:
            return id(mm_input)

        elif len(mm_input.modalities) == 1:
            return list(mm_input.modalities)[0]

        # FIXME(Isotr0py): Modality of mm_input from legacy pipeline is empty,
        # this is used to make InternVL with legacy pipeline still work with v1.
        else:
            return ""

    return [
        list(group) for _, group in groupby(mm_inputs, key=modality_group_func)
    ]


def run_dp_sharded_vision_model(image_input: torch.Tensor,
                                vision_model: torch.nn.Module) -> torch.Tensor:
    """Run a vision model with data parallelism (DP) sharding. The function 
    will shard the input image tensor on the first dimension and run the vision
    model

    Args:
        image_input (torch.Tensor): Image input tensor.
        vision_model (torch.nn.Module): Vision model.

    Returns:
        torch.Tensor: Output image embeddings
    """

    num_chunks = image_input.shape[0]
    mp_world_size = get_tensor_model_parallel_world_size()
    num_chunks_per_rank = (num_chunks + mp_world_size - 1) // mp_world_size
    num_padded_chunks = num_chunks_per_rank * mp_world_size - num_chunks
    pad = (0, ) * (2 * (image_input.dim() - 1)) + (0, num_padded_chunks)
    image_input_padded = torch.nn.functional.pad(image_input, pad)
    rank = get_tensor_model_parallel_rank()
    image_input_per_rank = image_input_padded[rank *
                                              num_chunks_per_rank:(rank + 1) *
                                              num_chunks_per_rank, ...]

    vision_embeddings = vision_model(image_input_per_rank)
    vision_embeddings = tensor_model_parallel_all_gather(vision_embeddings,
                                                         dim=0)
    vision_embeddings = vision_embeddings[:num_chunks, ...]
    return vision_embeddings