vllm.multimodal.utils ¶

def argsort_mm_positions(
    mm_positions: MultiModalPlaceholders,
) -> list[tuple[str, int]]:
    """
    Given a `MultiModalPlaceholders`, output a sequence of keys to
    sort the dictionary by `offset` (starting index in the input sequence)
    in ascending order.

    Returns:
        A list of `(modality, idx)`, which can be used to access an item
        by `mm_positions[modality][idx]`.
    """
    flat_items = (
        (modality, idx, item)
        for modality, items in mm_positions.items()
        for idx, item in enumerate(items)
    )

    sorted_flat_items = sorted(flat_items, key=lambda x: x[2].offset)

    return [(modality, idx) for modality, idx, _ in sorted_flat_items]

def encode_audio_base64(
    audio: np.ndarray,
    sampling_rate: int,
    *,
    format: str = "WAV",
) -> str:
    """Encode audio as base64."""
    audio_io = AudioMediaIO()
    return audio_io.encode_base64((audio, sampling_rate), audio_format=format)

def encode_audio_url(
    audio: np.ndarray,
    sampling_rate: int,
    *,
    format: str = "WAV",
) -> str:
    """Encode audio as a data URL."""
    audio_b64 = encode_audio_base64(audio, sampling_rate, format=format)
    mimetype = mimetypes.types_map.get("." + format.lower(), "audio")
    return f"data:{mimetype};base64,{audio_b64}"

def encode_image_base64(
    image: Image.Image,
    *,
    image_mode: str = "RGB",
    format: str = "PNG",
) -> 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_image_url(
    image: Image.Image,
    *,
    image_mode: str = "RGB",
    format: str = "PNG",
) -> str:
    """
    Encode a pillow image as a data URL.

    By default, the image is converted into RGB format before being encoded.
    """
    image_b64 = encode_image_base64(image, image_mode=image_mode, format=format)
    mimetype = mimetypes.types_map.get("." + format.lower(), "image")
    return f"data:{mimetype};base64,{image_b64}"

def fetch_audio(
    audio_url: str,
    audio_io_kwargs: dict[str, Any] | None = None,
) -> tuple[np.ndarray, int | float]:
    """
    Args:
        audio_url: URL of the audio file to fetch.
        audio_io_kwargs: Additional kwargs passed to handle audio IO.

    Warning:
        This method has direct access to local files and is only intended
        to be called by user code. Never call this from the online server!
    """
    media_io_kwargs = None if not audio_io_kwargs else {"audio": audio_io_kwargs}
    media_connector = MediaConnector(
        media_io_kwargs=media_io_kwargs,
        allowed_local_media_path="/",
    )
    return media_connector.fetch_audio(audio_url)

def fetch_image(
    image_url: str,
    image_io_kwargs: dict[str, Any] | None = None,
) -> Image.Image:
    """
    Args:
        image_url: URL of the image file to fetch.
        image_io_kwargs: Additional kwargs passed to handle image IO.

    Warning:
        This method has direct access to local files and is only intended
        to be called by user code. Never call this from the online server!
    """
    media_io_kwargs = None if not image_io_kwargs else {"image": image_io_kwargs}
    media_connector = MediaConnector(
        media_io_kwargs=media_io_kwargs,
        allowed_local_media_path="/",
    )
    return media_connector.fetch_image(image_url)

def fetch_video(
    video_url: str,
    video_io_kwargs: dict[str, Any] | None = None,
) -> tuple[npt.NDArray, dict[str, Any]]:
    """
    Args:
        video_url: URL of the video file to fetch.
        video_io_kwargs: Additional kwargs passed to handle video IO.

    Warning:
        This method has direct access to local files and is only intended
        to be called by user code. Never call this from the online server!
    """
    media_io_kwargs = None if not video_io_kwargs else {"video": video_io_kwargs}
    media_connector = MediaConnector(
        media_io_kwargs=media_io_kwargs,
        allowed_local_media_path="/",
    )
    return media_connector.fetch_video(video_url)

def get_mm_features_in_window(
    mm_features: list[MultiModalFeatureSpec],
    start: int,
    end: int,
) -> tuple[int, int]:
    """Return (lo, hi) indices for features overlapping [start, end).

    Assumes mm_features are sorted by offset and non-overlapping, so
    offset + length is also sorted.
    """
    lo = bisect.bisect_left(
        mm_features,
        start + 1,
        key=lambda f: f.mm_position.offset + f.mm_position.length,
    )
    hi = bisect.bisect_left(
        mm_features,
        end,
        key=lambda f: f.mm_position.offset,
    )
    return lo, hi

def group_and_batch_mm_items(
    items: Sequence[MultiModalKwargsItem],
    *,
    device: torch.types.Device = None,
    pin_memory: bool = False,
) -> Generator[tuple[int, BatchedTensorInputs]]:
    """
    Group consecutive items (possibly from different requests) into batches.

    Items must be split across groups if any of the following occurs,
    as the batch would otherwise be invalid:
    - They have different fields (e.g. mixed image and embedding inputs).
    - They have different values in `MultiModalSharedField`.

    Args:
        items: List of `MultiModalKwargsItem`.
        device: The device to place the grouped tensors on.
        pin_memory: Whether to pin memory for faster host-to-device transfer.

    Yields:
        A tuple `(num_items, grouped_kwargs)`, where:
        - `kwargs` is a dictionary of keyword arguments to pass to the model;
        - `num_items` is the corresponding number of items.
    """
    group_ids = [
        tuple(
            (key, _get_group_hash(elem))
            for key, elem in sorted(item.items(), key=lambda kv: kv[0])
        )
        for item in items
    ]
    group_sizes = [sum(1 for _ in group) for _, group in groupby(group_ids)]

    start_idx = 0
    for group_size in group_sizes:
        group_data = _batch_mm_items(
            items[start_idx : start_idx + group_size],
            device=device,
            pin_memory=pin_memory,
        )

        yield group_size, group_data

        start_idx += group_size

    assert start_idx == len(items)

def group_and_batch_mm_kwargs(
    mm_kwargs: list[tuple[str, MultiModalKwargsItem]],
    *,
    device: torch.types.Device = None,
    pin_memory: bool = False,
) -> Generator[tuple[str, int, BatchedTensorInputs], None, None]:
    """
    Group consecutive items (possibly from different requests) into batches.

    Items must be split across groups if any of the following occurs,
    as the batch would otherwise be invalid:
    - They have different fields (e.g. mixed image and embedding inputs).
    - They have different values in `MultiModalSharedField`.

    To simplify the implementation of `embed_multimodal`, we add another
    restriction that the items in a batch must belong to the same modality.

    Args:
        mm_kwargs: List of `(modality, item)`.
        device: The device to place the grouped tensors on.
        pin_memory: Whether to pin memory for faster host-to-device transfer.

    Yields:
        A tuple `(modality, num_items, grouped_kwargs)`, where:
        - `modality` is the modality of the batch;
        - `kwargs` is a dictionary of keyword arguments to pass to the model;
        - `num_items` is the corresponding number of items.
    """
    for modality, group in groupby(mm_kwargs, key=lambda x: x[0]):
        items_lst = [item for _, item in group]

        for num_items, mm_kwargs_batch in group_and_batch_mm_items(
            items_lst,
            device=device,
            pin_memory=pin_memory,
        ):
            yield modality, num_items, mm_kwargs_batch