GitLab

Changing the test Docker image for GPU tests to use Nvidia Docker `devel` image. This is necessary for #1187. 

TorchScript BC test checks that if dumped object from the previous version is compatible with the latest master. However, PyTorch does not guarantee backward compatibility of serialized TorchScript object across versions. Domain libraries do not guarantee this either.

* Support file-like object in save func

* Disable CircleCI cache for TP artifacts for cleaner build

There's an issue with the CUDA 11.0 package for conda with
conda-package-handling that relates to https://github.com/conda/conda-package-handling/issues/71



This should fix issues with the conda smoke testing we encountered previously.
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* Import torchaudio #996 758f6c2a

Reviewed By: cpuhrsch

Differential Revision: D24606263

fbshipit-source-id: 4301b1df84d20c671783ec34c52d5b257374abf1

* Import torchaudio #1004 5e54c770

Summary: Import torchaudio up to #1004 5e54c770

Reviewed By: vincentqb, cpuhrsch

Differential Revision: D24841498

fbshipit-source-id: 3829130636f36779d84f01ff0d0120b80b2396d7

Upstream is on 9.4.1 and we were experiencing issues when conda-build
was attempting to check for overlinking and failed out due to some files
not existing in xcode 9.0
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

When running smoke test, proper way to install conda package is to
install from workspace, instead of installing from archive.
Otherwise the dependencies are not properly installed

https://github.com/conda/conda/issues/1884

For this, we pass the whole `conda-bld` directory from
conda package build job to upload job then smoke test job.

`torchaudio` does not have a mandatory third-party python package dependency,
so this was not an issue.

See also https://github.com/pytorch/text/pull/803

This CC adds CI test to check the backward compatibility of Torchscript functions/object dumps.

The job first dumps Torchscript objects into file from torchaudio 0.6.0 release environment (x Python 3.6, 3.7, 3,8), then load & run the function in master build (in Python 3.6, 3.7, 3.8).

If there is a BC-breaking change in master build, (registration schema change), then the test should fail.

At this moment, `info` function does not work due to suspected bug in torch side, so the test is disabled for `info` function. See https://github.com/pytorch/pytorch/issues/42258 for the detail. Once https://github.com/pytorch/pytorch/issues/42258 is resolved we can enable it.

* Use cmake for third party

* Apply patch to libmad

* Update gitignore

* Update docker test image

* packaging: Add test channels to pytorch dependency resolution
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* .circleci: Add test channel to smoke tests
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* .circleci: Put pytorch-test into a higher priority

pytorch-nightly was getting prioritized over pytorch-nightly which
shouldn't be the case
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

This PR refactors cache generation mechanism by introducing dedicated command
and bust cache on daily basis.

At this moment, Windows unittest job for 3.6 and 3.7 are broken because of
broken scipy but the environment is cached this persists until the next week.

As we have nightly build, we do not need to keep cache for one week.

* Add caching to binary build for more reliable build

* Apply same logic to unittest workflow

* Separate style check from unit test

* TEST flake8 fail

* TEST clang-format fail

* Revert "TEST flake8 fail"

This reverts commit 88d90185cefe294b9a662129a05c2e26a9844fe4.

* Revert "TEST clang-format fail"

This reverts commit 93517d1ccba650a6ccd813268fd73a4fd63b252c.

* Clean up extension build mechanism and extension location

* Add back the switch to depend on external sox

* Remove print

* Fix

1. Docker image used for smoke test is old (No Python 3.8). Updating this to the latest one. build [here](https://app.circleci.com/pipelines/github/pytorch/audio/1763/workflows/56c846a5-acaa-41a7-92f5-46ec66186c61/jobs/44484)
2. Add `latest` tag to the newly built docker so that later we can always use `latest` version and we do not have to update docker image tag manually.

* Add windows binary jobs

* Make wheel package platform-specific and python-version-specific

* Enable CUDA tests for Windows

* Add back branch filters

* Add GPU test

* Run only on master

* Turn on tests when building through conda-build

* skip one failling test on windows.

* Switch to unittest

* Fix conda env activation

* add linux only condition

* Fix librosa installation

* conda-forge channel as a fallback

* Revert "conda-forge channel as a fallback"

This reverts commit 8c15f6638e34792640a3de3e057d9c2995425180.

* Try the pytorch way

* Use conda-forge

* Disable conda-forge

* Install future at setup env

* Move linux unittest CI job tools to dedicated directory

* Restore Linux scripts and move the updated souces to a new subdir

* Add back future

* Remove comments
Co-authored-by: Vincent Quenneville-Belair <vincentqb@gmail.com>
Co-authored-by: moto <855818+mthrok@users.noreply.github.com>