Closed yoyolicoris closed 2 months ago
Note: Links to docs will display an error until the docs builds have been completed.
As of commit 28575a77b065e3bc206027172ab21bf0deb86db7 with merge base 97ed7b36b7a741253d4e41e4da3c901d83294503 ():
* [Integration Test / build (3.8)](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722292) ([gh](https://github.com/pytorch/audio/actions/runs/10861157218/job/30142722292)) * [Unit-tests on Linux GPU / tests (3.10, 11.8) / linux-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722438) ([gh](https://github.com/pytorch/audio/actions/runs/10861157225/job/30142722438)) `RuntimeError: Command docker exec -t 273ccb60ecc5704c8cc9801f73771505967f02bfa36fb7d13c0088b1622322d2 /exec failed with exit code 2`
* [Unit-tests on Linux CPU / tests (3.10) / linux-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722518) ([gh](https://github.com/pytorch/audio/actions/runs/10861157222/job/30142722518)) `##[error]The operation was canceled.` * [Unit-tests on Linux CPU / tests (3.8) / linux-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722609) ([gh](https://github.com/pytorch/audio/actions/runs/10861157222/job/30142722609)) `##[error]The operation was canceled.` * [Unit-tests on Linux CPU / tests (3.9) / linux-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722732) ([gh](https://github.com/pytorch/audio/actions/runs/10861157222/job/30142722732)) `##[error]The operation was canceled.`
* [Build Linux Wheels / pytorch/audio / upload / manywheel-py3_9-cpu](https://hud.pytorch.org/pr/pytorch/audio/3831#30142988214) ([gh](https://github.com/pytorch/audio/actions/runs/10861157251/job/30142988214)) (detected as infra flaky with no log or failing log classifier) * [Unittests on Windows CPU / unittests-windows-cpu / windows-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722333) ([gh](https://github.com/pytorch/audio/actions/runs/10861157226/job/30142722333)) (detected as infra flaky with no log or failing log classifier)
👉 Rebase onto the `viable/strict` branch to avoid these failures
* [Unit-tests on Linux GPU / tests (3.8, 11.8) / linux-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722579) ([gh](https://github.com/pytorch/audio/actions/runs/10861157225/job/30142722579)) ([trunk failure](https://hud.pytorch.org/pytorch/audio/commit/97ed7b36b7a741253d4e41e4da3c901d83294503#30015022717)) `##[error]The operation was canceled.` * [Unit-tests on Linux GPU / tests (3.9, 11.8) / linux-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722686) ([gh](https://github.com/pytorch/audio/actions/runs/10861157225/job/30142722686)) ([trunk failure](https://hud.pytorch.org/pytorch/audio/commit/97ed7b36b7a741253d4e41e4da3c901d83294503#30015023211)) `torchaudio_unittest/models/hdemucs/hdemucs_gpu_test.py::HDemucsFloat32GPUTest::test_import_recreate_low_model_3` * [Unit-tests on Macos CPU / tests / macos-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722360) ([gh](https://github.com/pytorch/audio/actions/runs/10861157230/job/30142722360)) ([trunk failure](https://hud.pytorch.org/pytorch/audio/commit/97ed7b36b7a741253d4e41e4da3c901d83294503#30015021251)) `torchaudio_unittest/prototype/functional/pyroomacoustics_compatibility_test.py::CompatibilityTest::test_simulate_rir_ism_single_band_1` * [Unittests on Windows GPU / unittests-windows-gpu / windows-job](https://hud.pytorch.org/pr/pytorch/audio/3831#30142722466) ([gh](https://github.com/pytorch/audio/actions/runs/10861157240/job/30142722466)) ([trunk failure](https://hud.pytorch.org/pytorch/audio/commit/97ed7b36b7a741253d4e41e4da3c901d83294503#30015021592)) `torchaudio_unittest\prototype\functional\pyroomacoustics_compatibility_test.py::CompatibilityTest::test_simulate_rir_ism_single_band_1`
This comment was automatically generated by Dr. CI and updates every 15 minutes.
Thanks, looks good. Do you want to update the docstring to mention the new algorithm / paper?
Oh, sure, I totally forgot. Will update the docstring later this week.
The code passes related unit tests on my computer.
This PR update the backpropagation computation of
DifferentiableIIR
. The update is based on my recent work (https://arxiv.org/abs/2406.05128), which uses just oneDifferentiableIIR::apply
instead of two to compute the gradients for both input anda_coeffs
. The algorithm has been tested in torchlpc.Below is the benchmark to version 2.4.1
lfilter
. The backward computation runs slightly faster especially when using just one thread.v2.4.1
This version