[Multimodal] Add PyAV video backend for concurrent video decoding

[jaseelmohd2]

Purpose

Add a PyAV-based decode path for concurrent multimodal video serving. The codec is selectable at runtime via --media-io-kwargs '{"video": {"backend": "pyav" | "opencv"}}'; default is pyav.

The existing OpenCV decoder holds the Python GIL during grab() / retrieve(), serializing video decoding under concurrent load. The new PyAV path (av package) uses per-frame container.seek() + thread_type="SLICE", releasing the GIL between frames so concurrent requests can progress.

Single decode path — seek-only scales with frames sampled rather than video length, and benchmarks (see below) confirm it outperforms sequential scan on both short and long videos.

No new dependencies — av is already in setup.py.

Test Plan

.venv/bin/python -m pytest tests/multimodal/test_video.py -v

21 tests pass, including PyAV-specific tests covering frame loading, dynamic sampling, and frame count / fps interactions.

Pre-commit hooks all pass (ruff, mypy, typos, formatting).

Test Result

Benchmarked on Video-MME (100 videos: 20 short + 40 medium + 40 long, avg 21.5 min, range 0.5-59 min) and MMVU (100 short-form videos) with Qwen3-VL-8B-Instruct on 2x NVIDIA RTX PRO 6000 GPUs, num_frames=32, no MM cache:

# PyAV codec (new default)
vllm serve Qwen/Qwen3-VL-8B-Instruct \
    --tensor-parallel-size 2 \
    --max-model-len 32768 \
    --mm-processor-cache-gb 0 \
    --allowed-local-media-path / \
    --media-io-kwargs '{"video": {"backend": "pyav"}}' \
    --port 8000

# OpenCV codec (prior behavior)
vllm serve Qwen/Qwen3-VL-8B-Instruct \
    ... \
    --media-io-kwargs '{"video": {"backend": "opencv"}}'

Video-MME (long videos)

Concurrency	Backend	Req/s	Tok/s	Med TTFT	P99 TTFT
1	OpenCV	0.107	13.8	6057ms	32388ms
	PyAV	0.323	41.3	2275ms	2620ms
	Speedup	3.0x	3.0x	-62%	-92%
4	OpenCV	0.416	53.2	5426ms	31803ms
	PyAV	0.960	122.9	1937ms	3666ms
	Speedup	2.3x	2.3x	-64%	-88%
8	OpenCV	0.698	89.3	6028ms	33282ms
	PyAV	1.237	158.1	2944ms	5534ms
	Speedup	1.8x	1.8x	-51%	-83%
16	OpenCV	0.929	119.0	9523ms	36740ms
	PyAV	1.417	181.4	4826ms	10283ms
	Speedup	1.5x	1.5x	-49%	-72%

MMVU (short videos)

Concurrency	Backend	Req/s	Tok/s	Med TTFT	P99 TTFT
1	OpenCV	0.711	55.5	767ms	1173ms
	PyAV	0.776	61.5	695ms	801ms
	Speedup	1.09x	1.11x	-9%	-32%
4	OpenCV	2.362	189.7	652ms	1309ms
	PyAV	2.494	203.5	547ms	936ms
	Speedup	1.06x	1.07x	-16%	-29%
8	OpenCV	3.331	269.1	811ms	1613ms
	PyAV	3.521	289.1	693ms	1552ms
	Speedup	1.06x	1.07x	-15%	-4%
16	OpenCV	4.181	340.2	1284ms	3100ms
	PyAV	4.348	361.3	783ms	3012ms
	Speedup	1.04x	1.06x	-39%	-3%

This PR was developed with AI assistance (Claude). The submitting human has reviewed every changed line and run all tests.

---

Essential Elements of an Effective PR Description Checklist

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[gemini-code-assist]

Code Review

This pull request introduces ffmpeg and ffmpeg_dynamic video loading backends that utilize FFmpeg subprocesses to bypass the Python GIL, enabling better concurrency. The implementation includes a strategy to switch between sequential scanning and parallel timestamp-based seeking for long videos, supported by new environment variables and tests. Feedback highlights the need for safer file writing using os.fdopen instead of os.write to ensure complete data transfer and more robust parsing of ffprobe metadata to handle missing or non-numeric values.

[gemini-code-assist]

The os.write system call is not guaranteed to write the entire buffer in a single call and may return the number of bytes actually written. For large video files, this could result in a truncated temporary file, leading to decoding errors or corrupted frames. It is safer to use os.fdopen to create a file object and use its write method, which handles the writing loop internally and ensures all data is written.

Suggested change

	os.write(fd, data)
	os.close(fd)
	fd_closed = True
	with os.fdopen(fd, 'wb') as f:
	f.write(data)
	fd_closed = True

[gemini-code-assist]

Directly calling float() or int() on values from ffprobe output can raise ValueError or TypeError if the metadata is missing, null, or contains the string "N/A" (a common value used by ffprobe when it cannot determine a field for certain containers like MPEG-TS). This could cause vLLM to crash when processing valid video files. It is recommended to use a helper that safely parses these values with a default fallback (e.g., 0 or 0.0).

[DarkLight1337]

Can we simply use av package to achieve a similar result?

cc @Isotr0py

[Isotr0py]

I think so, av should have provided enough interface to control ffmpeg directly.

[Isotr0py]

I think we should use pyav to control its bundled ffmpeg instead of calling ffmpeg directly.

[Isotr0py]

I think these should be in --media-io-kwargs.

[Isotr0py]

I think so, av should have provided enough interface to control ffmpeg directly.

[jaseelmohd2]

Great suggestion @DarkLight1337 and @Isotr0py , I benchmarked a PyAV implementation and it matches the ffmpeg subprocess approach across all concurrency levels (within ~1-2% on throughput and TTFT). Since av is already a dependency and keeps everything in-process, I've updated the PR to use it instead.

Also moved seek_threshold to --media-io-kwargs per @Isotr0py's suggestion.

Thanks for the guidance!

[Isotr0py]

Overall LGTM, just leave some nits. PTAL :)

[Isotr0py]

This test is quite lightweight, so I think we can also parameterize backend=["opencv", "pyav"] for e2e tests here.

[jaseelmohd2]

Done 👍

[mergify]

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