[Misc] Allow using OpenCV as video IO fallback

requirements/common.txt

@@ -29,6 +29,7 @@ msgspec
 gguf == 0.10.0
 importlib_metadata
 mistral_common[opencv] >= 1.5.4
+opencv-python-headless >= 4.11.0    # required for video IO
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
 setuptools>=74.1.1; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12

requirements/test.in

@@ -9,7 +9,6 @@ pytest-shard
 # testing utils
 awscli
 backoff # required for phi4mm test
-decord # required for video tests
 einops # required for MPT, qwen-vl and Mamba
 httpx
 librosa # required for audio tests
@@ -28,6 +27,7 @@ torchvision==0.21.0
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
 mistral_common[opencv] >= 1.5.4 # required for pixtral test
+opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]==0.4.4 # required for model evaluation test
 transformers==4.48.2 

requirements/test.txt

@@ -92,8 +92,6 @@ datasets==3.0.2
     #   lm-eval
 decorator==5.1.1
     # via librosa
-decord==0.6.0
-    # via -r requirements/test.in
 dill==0.3.8
     # via
     #   datasets
@@ -271,7 +269,6 @@ numpy==1.26.4
     #   contourpy
     #   cupy-cuda12x
     #   datasets
-    #   decord
     #   einx
     #   encodec
     #   evaluate
@@ -332,8 +329,10 @@ nvidia-nvjitlink-cu12==12.4.127
     #   torch
 nvidia-nvtx-cu12==12.4.127
     # via torch
-opencv-python-headless==4.10.0.84
-    # via mistral-common
+opencv-python-headless==4.11.0.86
+    # via
+    #   -r requirements/test.in
+    #   mistral-common
 packaging==24.1
     # via
     #   accelerate

setup.py

@@ -690,7 +690,7 @@ def _read_requirements(filename: str) -> list[str]:
         "tensorizer": ["tensorizer>=2.9.0"],
         "runai": ["runai-model-streamer", "runai-model-streamer-s3", "boto3"],
         "audio": ["librosa", "soundfile"],  # Required for audio processing
-        "video": ["decord"]  # Required for video processing
+        "video": []  # Kept for backwards compatibility
     },
     cmdclass=cmdclass,
     package_data=package_data,

vllm/assets/video.py

@@ -10,8 +10,6 @@
 from huggingface_hub import hf_hub_download
 from PIL import Image
 
-from vllm.multimodal.video import sample_frames_from_video
-
 from .base import get_cache_dir
 
 
@@ -43,14 +41,19 @@ def video_to_ndarrays(path: str, num_frames: int = -1) -> npt.NDArray:
 
     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
     frames = []
-    for i in range(total_frames):
-        ret, frame = cap.read()
-        if ret:
-            frames.append(frame)
-    cap.release()
+
+    num_frames = num_frames if num_frames > 0 else total_frames
+    frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
+    for idx in range(total_frames):
+        ok = cap.grab()  # next img
+        if not ok:
+            break
+        if idx in frame_indices:  # only decompress needed
+            ret, frame = cap.retrieve()
+            if ret:
+                frames.append(frame)
 
     frames = np.stack(frames)
-    frames = sample_frames_from_video(frames, num_frames)
     if len(frames) < num_frames:
         raise ValueError(f"Could not read enough frames from video file {path}"
                          f" (expected {num_frames} frames, got {len(frames)})")

vllm/multimodal/video.py

@@ -13,7 +13,7 @@
 from vllm.inputs.registry import InputContext
 from vllm.logger import init_logger
 from vllm.transformers_utils.processor import cached_get_video_processor
-from vllm.utils import PlaceholderModule, is_list_of
+from vllm.utils import is_list_of
 
 from .base import MediaIO, ModalityData
 from .image import ImageMediaIO, ImagePlugin
@@ -22,11 +22,6 @@
 if TYPE_CHECKING:
     from vllm.config import ModelConfig
 
-try:
-    import decord
-except ImportError:
-    decord = PlaceholderModule("decord")  # type: ignore[assignment]
-
 logger = init_logger(__name__)
 
 
@@ -117,6 +112,69 @@ def sample_frames_from_video(frames: npt.NDArray,
     return sampled_frames
 
 
+class VideoLoader:
+
+    @classmethod
+    def load_bytes(self, data: bytes, num_frames: int = -1) -> npt.NDArray:
+        raise NotImplementedError
+
+
+class OpenCVVideoBackend(VideoLoader):
+
+    def get_cv2_video_api(self):
+        import cv2.videoio_registry as vr
+
+        api_pref = None
+        for backend in vr.getStreamBufferedBackends():
+            if not vr.hasBackend(backend):
+                continue
+            if not vr.isBackendBuiltIn(backend):
+                _, abi, api = vr.getStreamBufferedBackendPluginVersion(backend)
+                if (abi < 1 or (abi == 1 and api < 2)):
+                    continue
+            api_pref = backend
+            break
+        return api_pref
+
+    @classmethod
+    def load_bytes(cls, data: bytes, num_frames: int = -1) -> npt.NDArray:
+        import cv2
+
+        backend = cls().get_cv2_video_api()
+        cap = cv2.VideoCapture(BytesIO(data), backend, [])
+        if not cap.isOpened():
+            raise ValueError("Could not open video stream")
+
+        total_frames_num = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        full_read = num_frames == -1 or total_frames_num < num_frames
+        if full_read:
+            frame_idx = list(range(0, total_frames_num))
+        else:
+            uniform_sampled_frames = np.linspace(0,
+                                                 total_frames_num - 1,
+                                                 num_frames,
+                                                 dtype=int)
+            frame_idx = uniform_sampled_frames.tolist()
+
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        frames = np.empty((len(frame_idx), height, width, 3), dtype=np.uint8)
+
+        i = 0
+        for idx in range(total_frames_num):
+            ok = cap.grab()  # next img
+            if not ok:
+                break
+            if idx in frame_idx:  # only decompress needed
+                ret, frame = cap.retrieve()
+                if ret:
+                    frames[i] = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                    i += 1
+        # we expect all frames loaded
+        assert i == num_frames
+        return frames
+
+
 class VideoMediaIO(MediaIO[npt.NDArray]):
 
     def __init__(
@@ -129,22 +187,10 @@ def __init__(
 
         self.image_io = image_io
         self.num_frames = num_frames
+        self.video_loader = OpenCVVideoBackend
 
     def load_bytes(self, data: bytes) -> npt.NDArray:
-        vr = decord.VideoReader(BytesIO(data), num_threads=1)
-        total_frame_num = len(vr)
-
-        num_frames = self.num_frames
-        if total_frame_num > num_frames:
-            uniform_sampled_frames = np.linspace(0,
-                                                 total_frame_num - 1,
-                                                 num_frames,
-                                                 dtype=int)
-            frame_idx = uniform_sampled_frames.tolist()
-        else:
-            frame_idx = list(range(0, total_frame_num))
-
-        return vr.get_batch(frame_idx).asnumpy()
+        return self.video_loader.load_bytes(data, self.num_frames)
 
     def load_base64(self, media_type: str, data: str) -> npt.NDArray:
         if media_type.lower() == "video/jpeg":