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[Multimodal] Add selectable audio decoding backends, including torchcodec - #51354
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| if sr is not None and not math.isclose( | ||
| float(sr), float(native_sr), rel_tol=0.0, abs_tol=1e-6 | ||
| ): | ||
| # Resampling after the full decode, whereas load_audio_pyav resamples | ||
| # frame by frame during it, so the two do not agree sample-for-sample | ||
| # on this path. | ||
| audio = resample_audio_pyav(audio, orig_sr=native_sr, target_sr=sr) | ||
| return audio, sr | ||
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| return audio, native_sr |
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IMO, each audio backend should be decoupled during io and resample, especially TorchCodec. Because torchcodec is a common installation requirements while pyav and libsndfile not.
Ideally, I expect torchcodec can finish audio io and resample by itself, so that we can allow user enabling audio support by default without pip install vllm[audio]
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Good point — addressed. The torchcodec path is now self-contained for io, resampling, and imports: AudioDecoder is constructed with sample_rate=sr, so resampling happens in C++ via swresampler during decode and the resample_audio_pyav call is gone. av/soundfile are only bound via PlaceholderModule, so selecting torchcodec never needs the audio extras.
The sr=None path AudioMediaIO actually uses is unchanged — no resampling, and bit-exact with PyAV (test_torchcodec_bit_exact_at_native_rate).
"No PyAV" is enforced, not claimed:
test_torchcodec_backend_works_without_audio_extras — fresh interpreter with av/soundfile masked out of sys.modules, decoding end to end.
test_torchcodec_resamples_without_pyav — sr=8000 with resample_audio_pyav patched to raise.
test_torchcodec_duration_guard_uses_output_rate — the post-decode sample-count check now evaluates at the output rate.
Also on rebase: max_decode_bytes applies to torchcodec too. I kept the get_all_samples() / get_samples_played_in_range() branch after all — the bounded range request is load-bearing for the duration guard.
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…odec
Audio decoding has no backend mechanism, unlike video decoding which
already exposes several. `load_audio_pyav` drives FFmpeg through a
per-frame Python generator, so a multi-minute track crosses the Python/C
boundary tens of thousands of times per request. Under concurrency those
boundaries contend on the GIL and audio decoding becomes slower than
decoding the same inputs sequentially: decoding 8 tracks on 8 threads
measures 3x slower than decoding them one after another.
Add an `AUDIO_LOADER_REGISTRY` with four backends -- `auto` (soundfile
falling back to PyAV, the existing behaviour and still the default),
`soundfile`, `pyav`, and `torchcodec` -- selectable per request via
`--media-io-kwargs '{"audio": {"audio_backend": ...}}'` or globally via
`VLLM_AUDIO_LOADER_BACKEND`, the former taking precedence. This mirrors
`VIDEO_LOADER_REGISTRY` and reuses `ExtensionManager`.
`load_audio_torchcodec` keeps the frame loop inside C++ via a single
decode call that releases the GIL for its whole duration:
`get_samples_played_in_range()` when `max_duration_s` is set, which is
every request through `AudioMediaIO` since
`VLLM_MAX_AUDIO_DECODE_DURATION_S` defaults to 600, and
`get_all_samples()` when it is not. Both were measured to scale with
thread count (6.75x and 3.39x on 8 threads), and the two are
interchangeable in cost and bit-identical in output, so enforcing the
limit does not trade away the speed-up. torchcodec is already a
requirement on CUDA, CPU and XPU builds (requirements/cuda.txt and
friends), so this adds no new dependency there; ROCm and TPU builds do
not ship it and would need it installed to select this backend.
Measured on Qwen2.5-Omni-3B serving audio URLs at 8 concurrent requests
over 32 distinct inputs, with the decoding backend as the only variable:
mean TTFT drops from 7.785s to 0.704s (-91.0%) against an explicitly
pinned `pyav` baseline, faster on 32/32 paired inputs. Against the `auto`
default the drop is 8.753s to 0.704s, but ~0.97s of that is avoiding the
failed libsndfile attempt `auto` makes on video containers, so `pyav` is
the reference for the decoding claim.
`AudioMediaIO` always decodes at the native sample rate, and on that path
torchcodec is bit-exact with PyAV for both mono and multi-channel input,
on OGG/Vorbis and MP4/AAC alike, so model outputs do not change. Passing
an explicit `sr` resamples at a different stage in each backend, so the
backends are not bit-exact there; that path is unreachable through
`AudioMediaIO` today and is documented rather than asserted.
`max_duration_s` (the decompression-bomb guard) is enforced in two
stages, mirroring `load_audio_pyav`: the container header is checked
first, then the decode is bounded to just past the limit via
`get_samples_played_in_range()` and the decoded sample count is
re-checked. Container metadata is attacker-controlled, so it is only
trusted to reject an input, never to admit one -- a container that
under-reports its duration is still rejected, and the decode cannot
expand into unbounded PCM.
Signed-off-by: saltman155 <saltman155@outlook.com>
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It's indeed a great feature, but IMO the implementation is way too complicated |
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@JaredforReal Sounds good — please carry it forward in #51826. I've got other work coming up, so I'd rather not have two PRs competing for review time. Thanks for taking it on, and for the co-author credit. |
3 participants
Purpose
Audio decoding currently has no backend mechanism, unlike video decoding which
already exposes several through
VIDEO_LOADER_REGISTRY. The existing loaderload_audio_pyavdrives FFmpeg through a per-frame Python generator(
for frame in container.decode(stream)). AAC carries 1024 samples per frame,so a multi-minute track crosses the Python/C boundary once per 1024 samples —
a 295s 44.1kHz track measures 12701 frames here. Under concurrency those
crossings serialise on the GIL, and audio decoding ends up slower than
decoding the same inputs sequentially.
This is most visible when audio tracks are extracted from video, where several
requests decode long tracks at the same time.
This PR adds an
AUDIO_LOADER_REGISTRY(built on the existingExtensionManager) with four backends:auto(default)soundfilepyavtorchcodecSelectable per server via
--media-io-kwargs '{"audio": {"audio_backend": ...}}'or globally via
VLLM_AUDIO_LOADER_BACKEND, the former taking precedence.This mirrors
VLLM_VIDEO_LOADER_BACKEND.load_audio_torchcodeckeeps the frame loop inside C++ via a single decode callthat releases the GIL for its whole duration —
get_samples_played_in_range()when
max_duration_sis set, which is every request throughAudioMediaIO(
VLLM_MAX_AUDIO_DECODE_DURATION_Sdefaults to 600), andget_all_samples()when it is not. Both were measured to release the GIL; see Test Result. The
upstream video loader already relies on the same property. The signature matches
load_audio_pyav/load_audio_soundfileexactly, includingsr,monoandmax_duration_s.Notes on scope and compatibility:
autoremains the default, so this is a zerobehaviour change unless a backend is explicitly selected.
torchcodec>=0.14isalready a requirement for CUDA, CPU and XPU builds (
requirements/cuda.txt,cpu.txt,xpu.txt). ROCm and TPU builds do not ship it and would need itinstalled to select this backend; the docs say so. This follows up on the note
in Add TorchCodec as a video decoding backend #46609: "Beyond video decoding, TorchCodec supports audio decoding and
encoding (not included in this PR)."
AudioMediaIOalways decodes at thenative sample rate (
sr=Noneinload_bytes/load_file/load_base64),and there torchcodec is bit-exact with PyAV — see Test Result for the matrix.
ValueErrorlisting the valid values, and selectingtorchcodeccallscheck_torchcodec_available()at construction time rather than degrading perrequest — a silent fallback would look like "the backend had no effect".
(
autostill falls back by design; that is what the name means.)max_duration_s(
VLLM_MAX_AUDIO_DECODE_DURATION_S, default 600s) is enforced by checking thecontainer header, then bounding the decode to just past the limit via
get_samples_played_in_range()and re-checking the decoded sample count.Container metadata is attacker-controlled, so it is only trusted to reject an
input, never to admit one: a container that under-reports its duration is
still rejected, and the decode cannot expand into unbounded PCM. This mirrors
load_audio_pyav, which also checks the header and then re-checks duringincremental decode.
Docs:
docs/features/multimodal_inputs.mdgains an "Audio Decoding Backend"section with the backend table, both selection methods and their precedence,
the platform caveat, the fact that
soundfilecannot demux video containers,and guidance on when
torchcodecis worth choosing.Test Plan
Unit tests
17 new cases in
tests/multimodal/media/test_audio.pycovering: registrycontents; the default being
auto; unknown backend raising;load_bytesparameterised over all backends; each backend agreeing with the default;
torchcodec extracting audio from a video container; bit-exactness against PyAV
at the native rate for
mono=True/False; and the duration guard rejecting,admitting, not altering output, and rejecting an under-reported duration.
Following the convention in
tests/multimodal/media/test_video.py, torchcodeccases use
pytest.importorskip("torchcodec").Sample equivalence
Decoded the same inputs through each backend and compared waveform length,
sample rate and max absolute difference, across
sr∈ {native, 16000, 22050}and
mono∈ {True, False}, on both an OGG/Vorbis mono asset and an MP4/AACstereo video container.
GIL release
Since the performance argument rests on the decode call releasing the GIL, both
torchcodec entry points were measured rather than assumed: decode the same
5-minute AAC track 8 times, once through a 1-thread pool and once through an
8-thread pool, and compare wall clock. A call that holds the GIL cannot scale.
End-to-end benchmark
Qwen/Qwen2.5-Omni-3Bserved withvllm serve, driven through the OpenAI ChatCompletions API with
audio_urlinputs (real audio tracks from Video-MMEvideos, ~5 minutes each). 8 concurrent requests × 4 rounds = 32 requests. The
only variable between arms is the audio decoding backend; same model, same
server flags, same inputs, same request order, warm page cache on all arms.
Three arms, so the win can be attributed rather than just observed:
auto— the current default. On MP4 this is soundfile attempts and fails,then falls back to PyAV, so it carries the cost of a failed attempt.
pyav— PyAV pinned explicitly. This is the reference for the main claim,since PyAV is the loader torchcodec replaces.
torchcodec— the new backend.Two properties of the harness worth stating, since both would otherwise be
plausible confounders:
[k*8, (k+1)*8)from a 40-video whitelist, so all 32 requests are distinctinputs (verified: 32 unique video IDs, zero repeats). No multimodal
preprocessor cache hits inflate the later rounds.
paired with the same video's TTFT in the other.
Test Result
Unit tests — 123 passed, no regressions
tests/multimodal/media/test_audio.py: 24 passed (17 new, 7 pre-existing)tests/multimodal/media/+tests/multimodal/test_audio.py: 123 passedwith
--deselect tests/multimodal/media/test_connector.py(those 82 casesneed network access from the test host and are unrelated to this change)
ruff checkandruff formatcleanSample equivalence — bit-exact at the native sample rate
PyAV is the reference here, since that is the loader torchcodec replaces.
(
autoresolves to a different backend per container — soundfile for OGG,PyAV for MP4 — so it is not a stable baseline.)
srmonoNative rate is the only case
AudioMediaIOcan produce, so this covers thewhole reachable surface. Bit-exactness is expected rather than lucky: torchcodec
and PyAV bind the same FFmpeg in-process, and mono downmixing reuses
np.mean(axis=0)to matchload_audio_pyav.For completeness, passing an explicit
sr— which noAudioMediaIOentry pointdoes — is not bit-exact, because each backend resamples at a different stage
(PyAV per frame during the decode, torchcodec after it), giving a 16–22 sample
filter-delay difference. On that path
load_audio_pyavadditionally forceslayout="mono"regardless of itsmonoargument, so it returns 1 channel wheretorchcodec returns 2. That is pre-existing upstream behaviour, out of scope here,
and left untouched; the tests deliberately do not encode it as expected.
GIL release — both torchcodec entry points scale, PyAV does not
Decoding one 5-minute AAC track 8 times, 1-thread pool vs 8-thread pool:
get_samples_played_in_range()(used when a limit is set)get_all_samples()(used when no limit is set)load_audio_pyav()(per-frame, for contrast)Both torchcodec paths scale with threads, so the GIL argument holds for the call
the production path actually makes, not only for
get_all_samples(). PyAV goesslower with more threads — decoding 8 tracks concurrently takes 3× longer than
doing them one after another — which is the contention this PR addresses.
The two torchcodec paths are also interchangeable in cost and output: at 8
concurrent decodes the bounded path takes 0.229s and the unbounded one 0.231s
(best of 3), and they return bit-identical samples. So enforcing the duration
limit does not trade away the speed-up.
End-to-end — mean TTFT 7.785s → 0.704s (−91.0%) against PyAV
auto(current default)pyav(reference)torchcodecPaired per input:
torchcodecvspyav(main claim)torchcodecvsautopyavvsautoPaired-difference stdev is 0.805s for the main comparison, far below the effect
size. The worst-case input improves most (9.66s → 1.03s), which is why p99 gains
more than the mean.
The third row isolates what the failed soundfile attempt costs: pinning PyAV is
0.97s faster than letting
autotry libsndfile first and fall back. So ofthe 8.05s that
torchcodecsaves against the default, ~0.97s is avoiding thatwasted attempt and ~7.08s is the decoding change itself. Quoting −91.0%
against
pyavrather than −92.0% againstautokeeps the claim about decoding.Server-side stages are unchanged across arms, which localises the win to
decoding rather than to anything on the GPU:
autopyavtorchcodecWith prefill at ~44ms in every arm, essentially all of the baseline's TTFT is
fetch + decode, and that is what shrinks.
As a sanity check on direction, the same comparison from a cold page cache gives
10.247s → 3.638s for
auto→torchcodec(−64.5%, 32/32 faster) — a smallerimprovement than the warm case, which is what should happen, since cold runs
spend a larger share of time on download I/O that this change does not touch.