First commit

vllm/multimodal/video.py

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+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