Add TorchCodec as a video decoding backend

[NicolasHug]

Purpose

This PR adds TorchCodec as a video decoding backend, along with the existing opencv and pyav decoders. Like pyav, TorchCodec releases the GIL.

Things that TorchCodec can do

• Simple decoding APIs: just call VideoDecoder(bytes, ...).get_frames_at_indices(indices) and you're done. No need for your own decoding loop like for pyav or opencv
• Efficient on sparse sampling: both the opencv and pyav backends will just decode every single frame in the video, including all of those that aren't needed. This is very wasteful in sparse sampling scenarios (see benchmarks). In contrast, TorchCodec doesn't decode unnecessary frames. You could implement similar logic in pyav and opencv, at the cost of complexity.
• TorchCodec allows you to control num_ffmpeg_threads. Both the opencv and pyav backends are multi-threaded in their own specific ways, but none of them allow the user to control that (they could, it's just not exposed). This typically leads to overscription and thus very innefficient decoding when these backends are invoked on multiple processes / threads during serving.
• TorchCodec lets the user supply its own FFmpeg codecs. opencv and pyav ship the codecs in their Python wheels, which makes it impossible for users to choose which codec implementation they actually want to use.
• TorchCodec respects frame rotation metadata. Some videos have metadata indicating whether frames should be rotated before display (e.g. from phones). I'm not sure whether pyav and opencv natively support that?

Things that TorchCodec can also do, which are not included in this PR

• CUDA decoding - just pass device="cuda". I understand there's an open PR (Vram semaphore infra #44465) for including pynvvideocodec, which achieves similar goals.
• Audio decoding, with a fast wav decoder.
• video and audio encoding (cpu and CUDA for video)
• HDR video decoding
• Streaming support to only download the relevant parts of the video, instead of downloading all of it at once.

Benchmarks

Some results below. I had to patch the opencv and pyav backend to control their multi-threading, for an apple-to-apple comparison. Note that these benchmarks, like all benchmarks, are lying: all 3 backends actually use different codec implementations (which could be swapped in theory), and the perf highly depends on the actual sampling scenario. Generally though, a conservative conclusion is that TorchCodec will be faster than at least either pyav or opencv, or greatly outperform both in sparse sampling.

EDIT: updated benchmarks to also show resuts for pyav-new as proposed in #44598.

FFmpeg threads: 1 (forced, all codecs)
scenario                             |         opencv (1t) |           pyav (1t) |       pyav-new (1t) |    torchcodec (1t)
---------------------------------------------------------------------------------------------------------------------------
fps=2                                | 3153.8±11.8ms n=120 | 3420.0±72.5ms n=120 | 2912.1±52.5ms n=120 | 2682.7±8.6ms n=120
sparse (num_frames=18, ~every 100th) |   3034.5±6.2ms n=18 |  3077.7±23.8ms n=18 |   590.9±10.8ms n=18 |   566.6±6.4ms n=18


FFmpeg threads: 8 (forced, all codecs)
scenario                             |        opencv (8t) |           pyav (8t) |       pyav-new (8t) |    torchcodec (8t)
--------------------------------------------------------------------------------------------------------------------------
fps=2                                | 829.8±53.4ms n=120 | 1175.0±33.4ms n=120 | 1142.4±69.4ms n=120 | 874.2±24.3ms n=120
sparse (num_frames=18, ~every 100th) |  754.9±22.0ms n=18 |   965.1±22.4ms n=18 |    258.4±9.4ms n=18 |  244.2±16.5ms n=18


FFmpeg threads: 16 (forced, all codecs)
scenario                             |       opencv (16t) |           pyav (16t) |      pyav-new (16t) |   torchcodec (16t)
---------------------------------------------------------------------------------------------------------------------------
fps=2                                | 527.9±21.2ms n=120 | 1279.5±285.7ms n=120 | 1473.4±86.8ms n=120 | 571.3±24.3ms n=120
sparse (num_frames=18, ~every 100th) |   469.0±9.9ms n=18 |  1018.3±149.3ms n=18 |   329.9±35.8ms n=18 |  311.7±40.3ms n=18

Benchmark code:

Details

"""Benchmark the video-decoding codecs (opencv, pyav, torchcodec).

Compares the three decode codecs on two sampling scenarios:

- ``fps=2``: dense, fps-based sampling.
- ``sparse``: roughly one frame every 100 source frames (seek-dominated).
"""

import argparse
import importlib
import io
import statistics
import time

import av
import numpy as np

import vllm.multimodal.video as video_mod
from vllm.multimodal.video import VIDEO_LOADER_REGISTRY

CODECS = ["opencv", "pyav", "torchcodec"]


def print_codec_versions() -> None:
    """Print the installed version of each decode codec library."""
    for codec, module in (
        ("opencv", "cv2"),
        ("pyav", "av"),
        ("torchcodec", "torchcodec"),
    ):
        try:
            print(f"{codec}: {importlib.import_module(module).__version__}")
        except (ImportError, AttributeError):
            print(f"{codec}: not installed")


def make_synthetic_video(
    num_frames: int, width: int, height: int, fps: int, gop: int
) -> bytes:
    """Encode an H.264 clip with a moving gradient and a fixed GOP size.

    Moving content produces real inter-frame residuals (non-trivial decode),
    and ``gop`` controls the keyframe spacing that dominates seek cost.
    """
    buf = io.BytesIO()
    with av.open(buf, mode="w", format="mp4") as container:
        stream = container.add_stream("h264", rate=fps)
        stream.width = width
        stream.height = height
        stream.pix_fmt = "yuv420p"
        stream.codec_context.gop_size = gop
        xs = np.arange(width, dtype=np.uint16)
        ys = np.arange(height, dtype=np.uint16)[:, None]
        for i in range(num_frames):
            img = np.empty((height, width, 3), dtype=np.uint8)
            img[:, :, 0] = ((xs + i * 3) % 256).astype(np.uint8)
            img[:, :, 1] = ((ys + i * 2) % 256).astype(np.uint8)
            img[:, :, 2] = ((xs[None, :] + ys + i) % 256).astype(np.uint8)
            for packet in stream.encode(
                av.VideoFrame.from_ndarray(img, format="rgb24")
            ):
                container.mux(packet)
        for packet in stream.encode():
            container.mux(packet)
    return buf.getvalue()


def probe_total_frames(data: bytes) -> int:
    """Read the source frame count from the container header (no decode)."""
    with av.open(io.BytesIO(data)) as container:
        stream = container.streams.video[0]
        if stream.frames:
            return stream.frames
        if stream.duration and stream.average_rate:
            duration = float(stream.duration * stream.time_base)
            return int(duration * float(stream.average_rate))
    return 0


def force_threads(num_threads: int) -> None:
    """Force opencv and pyav to ``num_threads`` FFmpeg threads per decode.

    torchcodec takes the thread count directly through ``load_bytes`` (see
    ``bench``); only the other two need patching. ``num_threads == 0`` leaves
    every codec on its native default, matching the ``num_ffmpeg_threads``
    default in ``vllm.multimodal.video``.
    """
    if num_threads <= 0:
        return

    import cv2

    def open_with_threads(data: bytes):
        # CAP_PROP_N_THREADS must be passed at open time; calling cap.set()
        # after open is ignored by the FFMPEG stream backend (returns False),
        # leaving the decoder at its default min(cpu_count, 16) threads.
        backend = video_mod.OpenCVVideoBackendMixin.get_cv2_video_api()
        cap = cv2.VideoCapture(
            io.BytesIO(data), backend, [cv2.CAP_PROP_N_THREADS, num_threads]
        )
        if not cap.isOpened():
            raise ValueError("Could not open video stream")
        return cap

    video_mod.OpenCVVideoBackendMixin.open_video_capture = staticmethod(
        open_with_threads
    )

    orig_decode = video_mod.PyAVVideoBackendMixin.decode_frames

    def decode_with_threads(container, frame_indices, fps, duration):
        container.streams.video[0].codec_context.thread_count = num_threads
        return orig_decode(container, frame_indices, fps, duration)

    video_mod.PyAVVideoBackendMixin.decode_frames = staticmethod(decode_with_threads)


def codec_label(codec: str, num_threads: int) -> str:
    """Column label; annotate the thread count only when it is set (> 0)."""
    return f"{codec} ({num_threads}t)" if num_threads > 0 else codec


def bench(
    data: bytes, codec: str, kwargs: dict, repeats: int, num_threads: int
) -> tuple[float, float, int]:
    """Time ``load_bytes`` latency; returns (mean_ms, std_ms, frames_returned)."""
    loader = VIDEO_LOADER_REGISTRY.load("opencv")  # base uniform-sampling backend
    codec_kwargs = dict(kwargs)
    if codec == "torchcodec":
        # 0 means "let FFmpeg pick", matching the load_bytes default.
        codec_kwargs["num_ffmpeg_threads"] = num_threads

    def one() -> int:
        frames, _meta = loader.load_bytes(data, backend=codec, **codec_kwargs)
        return frames.shape[0]

    one()  # warm up codec / FFmpeg state outside the timed region
    timings: list[float] = []
    num_returned = 0
    for _ in range(repeats):
        start = time.perf_counter()
        num_returned = one()
        timings.append(time.perf_counter() - start)
    mean_ms = statistics.mean(timings) * 1e3
    std_ms = statistics.stdev(timings) * 1e3 if len(timings) > 1 else 0.0
    return mean_ms, std_ms, num_returned


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--video", type=str, default=None, help="Path to a video file to decode."
    )
    parser.add_argument("--repeats", type=int, default=10)
    parser.add_argument(
        "--num-ffmpeg-threads",
        type=int,
        default=1,
        help="FFmpeg threads per decode for ALL codecs (0 = native defaults).",
    )
    parser.add_argument(
        "--num-source-frames",
        type=int,
        default=1800,
        help="Frames in the synthetic clip (ignored when --video is set).",
    )
    parser.add_argument("--width", type=int, default=1280)
    parser.add_argument("--height", type=int, default=720)
    parser.add_argument("--fps", type=int, default=30)
    parser.add_argument(
        "--gop",
        type=int,
        default=30,
        help="Keyframe interval of the synthetic clip (realistic H.264: 30-250).",
    )
    args = parser.parse_args()

    print_codec_versions()

    if args.video is not None:
        with open(args.video, "rb") as f:
            data = f.read()
        print(f"Decoding {args.video} ({len(data) / 1e6:.1f} MB)")
    else:
        data = make_synthetic_video(
            args.num_source_frames, args.width, args.height, args.fps, args.gop
        )
        print(
            f"Synthetic H.264 clip: {args.num_source_frames} frames @ "
            f"{args.width}x{args.height}, {args.fps} fps, GOP={args.gop} "
            f"({len(data) / 1e6:.1f} MB)"
        )

    force_threads(args.num_ffmpeg_threads)

    total_frames = probe_total_frames(data)
    sparse_num_frames = max(1, total_frames // 100)
    scenarios: list[tuple[str, dict]] = [
        ("fps=2", {"fps": 2}),
        (
            f"sparse (num_frames={sparse_num_frames}, ~every 100th)",
            {"num_frames": sparse_num_frames},
        ),
    ]

    threads_desc = (
        f"{args.num_ffmpeg_threads} (forced, all codecs)"
        if args.num_ffmpeg_threads > 0
        else "unset (native defaults)"
    )
    print(f"Timing: mean wall-clock per decode over {args.repeats} repeats")
    print(f"FFmpeg threads: {threads_desc}\n")

    col_labels = [codec_label(c, args.num_ffmpeg_threads) for c in CODECS]
    rows: list[tuple[str, list[str]]] = []
    for name, kwargs in scenarios:
        cells = []
        for codec in CODECS:
            try:
                mean_ms, std_ms, n = bench(
                    data, codec, kwargs, args.repeats, args.num_ffmpeg_threads
                )
                cells.append(f"{mean_ms:.1f}±{std_ms:.1f}ms n={n}")
            except Exception as e:  # noqa: BLE001
                cells.append(type(e).__name__)
        rows.append((name, cells))

    label_w = max(len("scenario"), *(len(name) for name, _ in rows))
    col_w = [
        max(len(col_labels[i]), *(len(cells[i]) for _, cells in rows))
        for i in range(len(CODECS))
    ]

    def fmt_row(label: str, cells: list[str]) -> str:
        body = " | ".join(f"{c:>{col_w[i]}}" for i, c in enumerate(cells))
        return f"{label:<{label_w}} | {body}"

    header = fmt_row("scenario", col_labels)
    print(header)
    print("-" * len(header))
    for name, cells in rows:
        print(fmt_row(name, cells))


if __name__ == "__main__":
    main()

Test Plan

Added tests mirroring the existing pyav tests

Test Result

Passing.

---

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[claude]

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[NicolasHug]

Note to reviewers and maintainers: I think the details above matter a lot for performance, and you might want to look at this (if not already done!) regardless of whether this PR gets merged. I'm happy to help with anything here.

Basically, both the opencv and pyav backends of vllm are currently multi-threaded, but:

• they are multi-threaded in different ways and with different num_threads values
• they don't allow the user to tune the number of threads (they could, there are options for that in both opencv and pyav, it's just not wired in vllm).
• multi-threaded decoding can absolutely lead to thread over-subscription and poor decoding performance if multiple decoders are spawned at the same time. This of course, depends on the inference patterns.

I set the default of num_ffmpeg_threads for TorchCodec here to be 0, which means "use most CPUs". I did that so that the torchcodec backend is (mostly) consistent with the other backends, but I'm not sure it's a good default in general. In TorchCodec's VideoDecoder, the default for that parameter is 1, which deactivates ffmpeg multi-threading - we thought it was the only safe default that would prevent over-subscription.

[Isotr0py]

BTW, I have a draft PR to optimize PyAV's peformance in sparse frames sampling case: #44598. I think it's worthwhile to benchmark against it as well :)

[Isotr0py]

Maybe update requirements/cuda.txt and requirements/cpu.txt etc?

[NicolasHug]

BTW, I have a draft PR to optimize PyAV's peformance in sparse frames sampling case: #44598. I think it's worthwhile to benchmark against it as well :)

I updated the benchmarks above with the results against #44598. The TL;DR is that TorchCodec is still faster in both sparse and dense sampling, and I left a comment (#44598 (comment)) in the PR to explain why.

My (obviously partial !) opinion is that video decoding is hard, and you might prefer letting TorchCodec handle all that complexity. If you want to cover a wide variety of use-cases (e.g. video streaming) while still being fast in all scenarios, you'd end up re-implementing what already exists in TorchCodec - but don't take my word for it, hopefully #44598 (comment) provides some reasons why.

[Isotr0py]

Overall LGTM! Just have a nit.

[mergify]

Hi @NicolasHug, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

[WindChimeRan]

Hi @NicolasHug

I saw your comment on #44598, and seek_mode="approximate" is exactly what we need for our large-scale video classification workload — it would also supersede my draft in #45203.

Would you be open to exposing seek_mode as a parameter here? Since num_ffmpeg_threads is already threaded through, it should follow the same pattern, and users could opt in via:
--media-io-kwargs '{"video": {"backend": "torchcodec", "seek_mode": "approximate"}}'

And could you please add some doc on the new torchcodec backend?

---

cc @Isotr0py

[mergify]

Documentation preview: https://vllm--46609.org.readthedocs.build/en/46609/

[ywang96]

Thank you for the great work! @NicolasHug