vllm.multimodal.utils

_M module-attribute

_M = TypeVar('_M')

fetch_audio module-attribute

fetch_audio = fetch_audio

fetch_image module-attribute

fetch_image = fetch_image

fetch_video module-attribute

fetch_video = fetch_video

global_media_connector module-attribute

global_media_connector = MediaConnector()

The global MediaConnector instance used by vLLM.

MediaConnector

Source code in vllm/multimodal/utils.py

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)

allowed_local_media_path instance-attribute

allowed_local_media_path = allowed_local_media_path_

connection instance-attribute

connection = connection

__init__

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

Source code in vllm/multimodal/utils.py

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_

_load_data_url

_load_data_url(
    url_spec: ParseResult, media_io: MediaIO[_M]
) -> _M

Source code in vllm/multimodal/utils.py

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)

_load_file_url

_load_file_url(
    url_spec: ParseResult, media_io: MediaIO[_M]
) -> _M

Source code in vllm/multimodal/utils.py

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)

fetch_audio

fetch_audio(
    audio_url: str,
) -> tuple[ndarray, Union[int, float]]

Load audio from a URL.

Source code in vllm/multimodal/utils.py

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,
    )

fetch_audio_async async

fetch_audio_async(
    audio_url: str,
) -> tuple[ndarray, Union[int, float]]

Asynchronously fetch audio from a URL.

Source code in vllm/multimodal/utils.py

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,
    )

fetch_image

fetch_image(
    image_url: str, *, image_mode: str = "RGB"
) -> Image

Load a PIL image from a HTTP or base64 data URL.

By default, the image is converted into RGB format.

Source code in vllm/multimodal/utils.py

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,
    )

fetch_image_async async

fetch_image_async(
    image_url: str, *, image_mode: str = "RGB"
) -> Image

Asynchronously load a PIL image from a HTTP or base64 data URL.

By default, the image is converted into RGB format.

Source code in vllm/multimodal/utils.py

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,
    )

fetch_image_embedding

fetch_image_embedding(data: str) -> Tensor

Load image embedding from a URL.

Source code in vllm/multimodal/utils.py

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)

fetch_video

fetch_video(
    video_url: str,
    *,
    image_mode: str = "RGB",
    num_frames: int = 32,
) -> NDArray

Load video from a HTTP or base64 data URL.

Source code in vllm/multimodal/utils.py

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,
    )

fetch_video_async async

fetch_video_async(
    video_url: str,
    *,
    image_mode: str = "RGB",
    num_frames: int = 32,
) -> NDArray

Asynchronously load video from a HTTP or base64 data URL.

By default, the image is converted into RGB format.

Source code in vllm/multimodal/utils.py

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,
    )

load_from_url

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

Source code in vllm/multimodal/utils.py

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)

load_from_url_async async

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

Source code in vllm/multimodal/utils.py

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)

encode_audio_base64

encode_audio_base64(
    audio: ndarray, sampling_rate: float
) -> str

Encode audio as base64.

Source code in vllm/multimodal/utils.py

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))

encode_image_base64

encode_image_base64(
    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.

Source code in vllm/multimodal/utils.py

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)

encode_video_base64

encode_video_base64(frames: NDArray) -> str

Source code in vllm/multimodal/utils.py

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

group_mm_inputs_by_modality

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.

Parameters:

Name	Type	Description	Default
mm_inputs	list[MultiModalKwargs]	List of MultiModalKwargs.	required

Returns:

Type	Description
list[list[MultiModalKwargs]]	list[list[vllm.multimodal.MultiModalKwargs]]: List of list of
list[list[MultiModalKwargs]]	MultiModalKwargs, each inner list contains consecutive
list[list[MultiModalKwargs]]	MultiModalKwargs with same modality.

Source code in vllm/multimodal/utils.py

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)
    ]

merge_and_sort_multimodal_metadata

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:

Type	Description
list[str]	list[str]: List of item modalities in order of their positions in the
list[PlaceholderRange]	input sequence.
Optional[list[str]]	list[PlaceholderRange]: Sorted list of all PlaceholdeRanges from
tuple[list[str], list[PlaceholderRange], Optional[list[str]]]	mm_positions.
tuple[list[str], list[PlaceholderRange], Optional[list[str]]]	Optional[list[str]]: Sorted list of all hashes from mm_hashes if given,
tuple[list[str], list[PlaceholderRange], Optional[list[str]]]	None otherwise.

Source code in vllm/multimodal/utils.py

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