87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
from functools import lru_cache
|
|
from typing import Any, Dict, List, Optional, Union
|
|
|
|
import numpy as np
|
|
|
|
from vllm.config import ModelConfig
|
|
from vllm.inputs.registry import InputContext
|
|
from vllm.logger import init_logger
|
|
from vllm.transformers_utils.processor import get_video_processor
|
|
from vllm.transformers_utils.tokenizer import get_tokenizer
|
|
from vllm.utils import is_list_of
|
|
|
|
from .base import MultiModalData, MultiModalInputs
|
|
from .image import ImagePlugin
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
cached_get_video_processor = lru_cache(get_video_processor)
|
|
cached_get_tokenizer = lru_cache(get_tokenizer)
|
|
|
|
VideoInput = Union[
|
|
"np.ndarray", # single video input
|
|
List["np.ndarray"],
|
|
# TODO: support more types
|
|
# List[Image.Image], List[List[Image.Image]],
|
|
# "torch.Tensor",
|
|
# List["torch.Tensor"],
|
|
# List[List["np.ndarrray"]],
|
|
# List[List["torch.Tensor"]],
|
|
]
|
|
|
|
|
|
class VideoPlugin(ImagePlugin):
|
|
"""Plugin for video data."""
|
|
|
|
def get_data_key(self) -> str:
|
|
return "video"
|
|
|
|
def _get_hf_video_processor(
|
|
self,
|
|
model_config: ModelConfig,
|
|
mm_processor_kwargs: Optional[Dict[str, Any]] = None,
|
|
):
|
|
if mm_processor_kwargs is None:
|
|
mm_processor_kwargs = {}
|
|
return cached_get_video_processor(
|
|
model_config.model,
|
|
trust_remote_code=model_config.trust_remote_code,
|
|
**mm_processor_kwargs)
|
|
|
|
def _default_input_mapper(
|
|
self,
|
|
ctx: InputContext,
|
|
data: MultiModalData[object],
|
|
**mm_processor_kwargs,
|
|
) -> MultiModalInputs:
|
|
model_config = ctx.model_config
|
|
|
|
# single video input as np.ndarray
|
|
if isinstance(data, np.ndarray):
|
|
video_processor = self._get_hf_video_processor(
|
|
model_config,
|
|
mm_processor_kwargs,
|
|
)
|
|
if video_processor is None:
|
|
raise RuntimeError("No HuggingFace processor is available "
|
|
"to process the image object")
|
|
try:
|
|
# NOTE: Similar to image; it may be a good idea to filter and
|
|
# pass mm_processor_kwargs here too, but for now we don't to
|
|
# avoid extra complexity if the initializer and preprocess
|
|
# signatures of the processor don't align
|
|
batch_data = video_processor(data, return_tensors="pt").data
|
|
except Exception:
|
|
logger.error("Failed to process image (%s)", data)
|
|
raise
|
|
|
|
return MultiModalInputs(batch_data)
|
|
elif is_list_of(data, np.ndarray):
|
|
raise NotImplementedError(
|
|
"Multi video for a prompt is not supported yet")
|
|
|
|
raise TypeError(f"Invalid video type: {type(data)}")
|
|
|
|
def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
|
|
return 4096
|