add qwen3
This commit is contained in:
Chranos
501
vllm-v0.6.2/vllm/multimodal/utils.py
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501
vllm-v0.6.2/vllm/multimodal/utils.py
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import base64
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import os
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from functools import lru_cache
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from io import BytesIO
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from typing import Any, List, Optional, Tuple, TypeVar, Union
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import numpy as np
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import numpy.typing as npt
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from PIL import Image
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import vllm.envs as envs
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from vllm.connections import global_http_connection
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
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from .inputs import MultiModalDataDict, PlaceholderRange
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logger = init_logger(__name__)
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cached_get_tokenizer = lru_cache(get_tokenizer)
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def _load_image_from_bytes(b: bytes) -> Image.Image:
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image = Image.open(BytesIO(b))
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image.load()
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return image
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def _is_subpath(image_path: str, allowed_local_media_path: str) -> bool:
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# Get the common path
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common_path = os.path.commonpath([
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os.path.abspath(image_path),
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os.path.abspath(allowed_local_media_path)
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])
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# Check if the common path is the same as allowed_local_media_path
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return common_path == os.path.abspath(allowed_local_media_path)
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def _load_image_from_file(image_url: str,
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allowed_local_media_path: str) -> Image.Image:
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if not allowed_local_media_path:
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raise ValueError("Invalid 'image_url': Cannot load local files without"
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"'--allowed-local-media-path'.")
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if allowed_local_media_path:
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if not os.path.exists(allowed_local_media_path):
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raise ValueError(
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"Invalid '--allowed-local-media-path': "
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f"The path {allowed_local_media_path} does not exist.")
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if not os.path.isdir(allowed_local_media_path):
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raise ValueError(
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"Invalid '--allowed-local-media-path': "
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f"The path {allowed_local_media_path} must be a directory.")
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# Only split once and assume the second part is the image path
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_, image_path = image_url.split("file://", 1)
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if not _is_subpath(image_path, allowed_local_media_path):
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raise ValueError(
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f"Invalid 'image_url': The file path {image_path} must"
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" be a subpath of '--allowed-local-media-path'"
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f" '{allowed_local_media_path}'.")
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image = Image.open(image_path)
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image.load()
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return image
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def _load_image_from_data_url(image_url: str) -> Image.Image:
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# Only split once and assume the second part is the base64 encoded image
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_, image_base64 = image_url.split(",", 1)
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return load_image_from_base64(image_base64)
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def fetch_image(image_url: str,
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*,
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image_mode: str = "RGB",
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allowed_local_media_path: str = "") -> Image.Image:
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"""
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Load a PIL image from a HTTP or base64 data URL.
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By default, the image is converted into RGB format.
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"""
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if image_url.startswith('http'):
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image_raw = global_http_connection.get_bytes(
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image_url,
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timeout=envs.VLLM_IMAGE_FETCH_TIMEOUT,
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)
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image = _load_image_from_bytes(image_raw)
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elif image_url.startswith('data:image'):
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image = _load_image_from_data_url(image_url)
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elif image_url.startswith('file://'):
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image = _load_image_from_file(image_url, allowed_local_media_path)
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else:
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raise ValueError("Invalid 'image_url': A valid 'image_url' must start "
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"with either 'data:image', 'file://' or 'http'.")
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return image.convert(image_mode)
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async def async_fetch_image(image_url: str,
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*,
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image_mode: str = "RGB",
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allowed_local_media_path: str = "") -> Image.Image:
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"""
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Asynchronously load a PIL image from a HTTP or base64 data URL.
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By default, the image is converted into RGB format.
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"""
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if image_url.startswith('http'):
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image_raw = await global_http_connection.async_get_bytes(
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image_url,
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timeout=envs.VLLM_IMAGE_FETCH_TIMEOUT,
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)
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image = _load_image_from_bytes(image_raw)
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elif image_url.startswith('data:image'):
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image = _load_image_from_data_url(image_url)
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elif image_url.startswith('file://'):
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image = _load_image_from_file(image_url, allowed_local_media_path)
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else:
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raise ValueError("Invalid 'image_url': A valid 'image_url' must start "
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"with either 'data:image', 'file://' or 'http'.")
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return image.convert(image_mode)
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def _load_video_frames_from_bytes(b: bytes):
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frame = Image.open(BytesIO(b))
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return np.array(frame)
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def load_video_frames_from_base64(frame: Union[bytes, str]):
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"""Load frame from base64 format."""
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return _load_video_frames_from_bytes(base64.b64decode(frame))
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def _load_video_from_bytes(b: bytes, num_frames: int = 32):
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_, decord = try_import_video_packages()
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video_path = BytesIO(b)
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vr = decord.VideoReader(video_path, num_threads=1)
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total_frame_num = len(vr)
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if total_frame_num > num_frames:
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uniform_sampled_frames = np.linspace(0,
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total_frame_num - 1,
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num_frames,
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dtype=int)
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frame_idx = uniform_sampled_frames.tolist()
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else:
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frame_idx = [i for i in range(0, total_frame_num)]
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frames = vr.get_batch(frame_idx).asnumpy()
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return frames
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def _load_video_from_data_url(video_url: str):
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# Only split once and assume the second part is the base64 encoded image
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frames_base64 = video_url.split(",")[1:]
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return np.stack([
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load_video_frames_from_base64(frame_base64)
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for frame_base64 in frames_base64
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])
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def fetch_video(video_url: str, *, num_frames: int = 32) -> npt.NDArray:
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"""
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Load video from a HTTP or base64 data URL.
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"""
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if video_url.startswith('http') or video_url.startswith('https'):
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video_raw = global_http_connection.get_bytes(
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video_url,
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timeout=envs.VLLM_VIDEO_FETCH_TIMEOUT,
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)
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video = _load_video_from_bytes(video_raw, num_frames)
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elif video_url.startswith('data:video'):
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video = _load_video_from_data_url(video_url)
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else:
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raise ValueError("Invalid 'video_url': A valid 'video_url' must start "
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"with either 'data:video' or 'http'.")
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return video
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async def async_fetch_video(video_url: str,
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*,
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num_frames: int = 32) -> npt.NDArray:
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"""
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Asynchronously load video from a HTTP or base64 data URL.
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By default, the image is converted into RGB format.
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"""
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if video_url.startswith('http') or video_url.startswith('https'):
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video_raw = await global_http_connection.async_get_bytes(
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video_url,
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timeout=envs.VLLM_VIDEO_FETCH_TIMEOUT,
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)
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video = _load_video_from_bytes(video_raw, num_frames)
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elif video_url.startswith('data:video'):
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video = _load_video_from_data_url(video_url)
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else:
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raise ValueError("Invalid 'video_url': A valid 'video_url' must start "
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"with either 'data:video' or 'http'.")
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return video
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def try_import_audio_packages() -> Tuple[Any, Any]:
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try:
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import librosa
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import soundfile
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except ImportError as exc:
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raise ImportError(
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"Please install vllm[audio] for audio support.") from exc
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return librosa, soundfile
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def fetch_audio(audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:
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"""
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Load audio from a URL.
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"""
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librosa, _ = try_import_audio_packages()
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if audio_url.startswith("http"):
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audio_bytes = global_http_connection.get_bytes(
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audio_url,
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timeout=envs.VLLM_AUDIO_FETCH_TIMEOUT,
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)
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elif audio_url.startswith("data:audio"):
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_, audio_base64 = audio_url.split(",", 1)
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audio_bytes = base64.b64decode(audio_base64)
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else:
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raise ValueError("Invalid 'audio_url': A valid 'audio_url' must start "
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"with either 'data:audio' or 'http'.")
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return librosa.load(BytesIO(audio_bytes), sr=None)
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async def async_fetch_audio(
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audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:
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"""
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Asynchronously fetch audio from a URL.
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"""
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librosa, _ = try_import_audio_packages()
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if audio_url.startswith("http"):
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audio_bytes = await global_http_connection.async_get_bytes(
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audio_url,
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timeout=envs.VLLM_AUDIO_FETCH_TIMEOUT,
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)
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elif audio_url.startswith("data:audio"):
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_, audio_base64 = audio_url.split(",", 1)
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audio_bytes = base64.b64decode(audio_base64)
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else:
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raise ValueError("Invalid 'audio_url': A valid 'audio_url' must start "
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"with either 'data:audio' or 'http'.")
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return librosa.load(BytesIO(audio_bytes), sr=None)
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def get_and_parse_audio(audio_url: str) -> MultiModalDataDict:
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audio, sr = fetch_audio(audio_url)
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return {"audio": (audio, sr)}
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def get_and_parse_image(
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image_url: str,
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*,
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allowed_local_media_path: str = "") -> MultiModalDataDict:
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image = fetch_image(image_url,
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allowed_local_media_path=allowed_local_media_path)
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return {"image": image}
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def get_and_parse_video(video_url: str) -> MultiModalDataDict:
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video = fetch_video(video_url)
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return {"video": video}
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async def async_get_and_parse_audio(audio_url: str) -> MultiModalDataDict:
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audio, sr = await async_fetch_audio(audio_url)
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return {"audio": (audio, sr)}
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async def async_get_and_parse_image(
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image_url: str,
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*,
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allowed_local_media_path: str = "") -> MultiModalDataDict:
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image = await async_fetch_image(
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image_url, allowed_local_media_path=allowed_local_media_path)
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return {"image": image}
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async def async_get_and_parse_video(video_url: str) -> MultiModalDataDict:
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video = await async_fetch_video(video_url)
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return {"video": video}
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def encode_audio_base64(
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audio: np.ndarray,
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sampling_rate: int,
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) -> str:
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"""Encode audio as base64."""
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_, soundfile = try_import_audio_packages()
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buffered = BytesIO()
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soundfile.write(buffered, audio, sampling_rate, format="WAV")
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return base64.b64encode(buffered.getvalue()).decode('utf-8')
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def encode_image_base64(
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image: Image.Image,
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*,
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image_mode: str = "RGB",
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format: str = "JPEG",
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) -> str:
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"""
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Encode a pillow image to base64 format.
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By default, the image is converted into RGB format before being encoded.
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"""
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buffered = BytesIO()
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image = image.convert(image_mode)
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image.save(buffered, format)
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return base64.b64encode(buffered.getvalue()).decode('utf-8')
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def load_image_from_base64(image: Union[bytes, str]) -> Image.Image:
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"""Load image from base64 format."""
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return _load_image_from_bytes(base64.b64decode(image))
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def rescale_image_size(image: Image.Image,
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size_factor: float,
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transpose: int = -1) -> Image.Image:
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"""Rescale the dimensions of an image by a constant factor."""
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new_width = int(image.width * size_factor)
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new_height = int(image.height * size_factor)
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image = image.resize((new_width, new_height))
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if transpose >= 0:
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image = image.transpose(Image.Transpose(transpose))
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return image
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def try_import_video_packages() -> Any:
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try:
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import cv2
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import decord
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except ImportError as exc:
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raise ImportError(
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"Please install vllm[video] for video support.") from exc
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return cv2, decord
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def resize_video(frames: npt.NDArray, size: Tuple[int, int]) -> npt.NDArray:
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cv2, _ = try_import_video_packages()
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num_frames, _, _, channels = frames.shape
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new_height, new_width = size
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resized_frames = np.empty((num_frames, new_height, new_width, channels),
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dtype=frames.dtype)
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for i, frame in enumerate(frames):
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resized_frame = cv2.resize(frame, (new_width, new_height))
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resized_frames[i] = resized_frame
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return resized_frames
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def rescale_video_size(frames: npt.NDArray, size_factor: float) -> npt.NDArray:
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_, height, width, _ = frames.shape
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new_height = int(height * size_factor)
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new_width = int(width * size_factor)
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return resize_video(frames, (new_height, new_width))
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def sample_frames_from_video(frames: npt.NDArray,
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num_frames: int) -> npt.NDArray:
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total_frames = frames.shape[0]
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if num_frames == -1:
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return frames
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else:
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frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
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sampled_frames = frames[frame_indices, ...]
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return sampled_frames
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def encode_video_base64(frames: npt.NDArray):
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||||
base64_frames = []
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||||
frames_list = [frames[i] for i in range(frames.shape[0])]
|
||||
for frame in frames_list:
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img_base64 = encode_image_base64(Image.fromarray(frame))
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base64_frames.append(img_base64)
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return ",".join(base64_frames)
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|
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|
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# Utilities for input processors
|
||||
_T = TypeVar("_T", str, int)
|
||||
|
||||
|
||||
def repeat_and_pad_token(
|
||||
token: _T,
|
||||
*,
|
||||
repeat_count: int = 1,
|
||||
pad_token_left: Optional[_T] = None,
|
||||
pad_token_right: Optional[_T] = None,
|
||||
) -> List[_T]:
|
||||
replacement = [token] * repeat_count
|
||||
if pad_token_left is not None:
|
||||
replacement = [pad_token_left] + replacement
|
||||
if pad_token_right is not None:
|
||||
replacement = replacement + [pad_token_right]
|
||||
|
||||
return replacement
|
||||
|
||||
|
||||
def repeat_and_pad_placeholder_tokens(
|
||||
tokenizer: AnyTokenizer,
|
||||
prompt: Optional[str],
|
||||
prompt_token_ids: List[int],
|
||||
*,
|
||||
placeholder_token_id: int,
|
||||
repeat_count: Union[int, List[int]],
|
||||
pad_token_left: Optional[int] = None,
|
||||
pad_token_right: Optional[int] = None,
|
||||
) -> Tuple[Optional[str], List[int], List[PlaceholderRange]]:
|
||||
if isinstance(repeat_count, int):
|
||||
repeat_count = [repeat_count]
|
||||
|
||||
if prompt is None:
|
||||
new_prompt = None
|
||||
else:
|
||||
placeholder_token_str = tokenizer.decode(placeholder_token_id)
|
||||
pad_token_str_left = (None if pad_token_left is None else
|
||||
tokenizer.decode(pad_token_left))
|
||||
pad_token_str_right = (None if pad_token_right is None else
|
||||
tokenizer.decode(pad_token_right))
|
||||
|
||||
placeholder_token_count = prompt.count(placeholder_token_str)
|
||||
# This is an arbitrary number to distinguish between the two cases
|
||||
if placeholder_token_count > 16:
|
||||
logger.warning(
|
||||
"Please follow the prompt format that is "
|
||||
"documented on HuggingFace which does not involve "
|
||||
"repeating %s tokens.", placeholder_token_str)
|
||||
if placeholder_token_count < len(repeat_count):
|
||||
logger.warning(
|
||||
"The number of multi-modal placeholder tokens in the prompt "
|
||||
"is less than the number of multi-modal inputs. Extra "
|
||||
"placeholder tokens will be treated as plain text")
|
||||
repeat_count = repeat_count[:placeholder_token_count]
|
||||
|
||||
prompt_parts = prompt.split(placeholder_token_str,
|
||||
maxsplit=len(repeat_count))
|
||||
new_prompt = ""
|
||||
for i, repeat_count_item in enumerate(repeat_count):
|
||||
replacement_str = "".join(
|
||||
repeat_and_pad_token(
|
||||
placeholder_token_str,
|
||||
repeat_count=repeat_count_item,
|
||||
pad_token_left=pad_token_str_left,
|
||||
pad_token_right=pad_token_str_right,
|
||||
))
|
||||
# The image tokens are removed to be consistent with HuggingFace
|
||||
new_prompt += prompt_parts[i] + replacement_str
|
||||
new_prompt += prompt_parts[-1]
|
||||
|
||||
new_token_ids: List[int] = []
|
||||
placeholder_ranges: List[PlaceholderRange] = []
|
||||
placeholder_token_idx = 0
|
||||
for i, token in enumerate(prompt_token_ids):
|
||||
if token == placeholder_token_id:
|
||||
replacement_ids = repeat_and_pad_token(
|
||||
placeholder_token_id,
|
||||
repeat_count=repeat_count[placeholder_token_idx],
|
||||
pad_token_left=pad_token_left,
|
||||
pad_token_right=pad_token_right,
|
||||
)
|
||||
placeholder_ranges.append({
|
||||
"offset": len(new_token_ids),
|
||||
"length": len(replacement_ids)
|
||||
})
|
||||
new_token_ids.extend(replacement_ids)
|
||||
placeholder_token_idx += 1
|
||||
|
||||
# No need to further scan the list since we replaced all tokens
|
||||
if placeholder_token_idx >= len(repeat_count):
|
||||
new_token_ids.extend(prompt_token_ids[i + 1:])
|
||||
break
|
||||
else:
|
||||
new_token_ids.append(token)
|
||||
|
||||
return new_prompt, new_token_ids, placeholder_ranges
|
||||
|
||||
|
||||
def consecutive_placeholder_ranges(num_items: int,
|
||||
item_size: int) -> List[PlaceholderRange]:
|
||||
"""Returns a list of consecutive PlaceholderRanges of a fixed size"""
|
||||
|
||||
return [
|
||||
PlaceholderRange(offset=i * item_size, length=item_size)
|
||||
for i in range(num_items)
|
||||
]
|
||||
Reference in New Issue
Block a user