Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?
## What does this PR do?
- [x] applies flake8 fixes to main branch (https://github.com/fairinternal/fairseq-py/issues/2546) - still more to be fixed
Fix GPU tests:
- [x] when torch.ao.quantization import doesn't work use torch.quantization
- [x] build apex from earlier commit in circleci so that its compatible with pytorch 1.8 and 1.9
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2570
Reviewed By: Mortimerp9
Differential Revision: D32955312
Pulled By: dianaml0
fbshipit-source-id: e163cbd4998f171f819e31b0682c1c0f1986f9e1
Summary:
The previous BSD+PATENTS license was controversial. We have been
approved to relicense fairseq under the MIT license.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/786
Differential Revision: D16560654
Pulled By: myleott
fbshipit-source-id: f78b1beb4f2895dd7b9bfc79f5f952a2bfb94034
Summary:
Pull Request resolved: https://github.com/pytorch/fairseq/pull/639
Add argument sampling_func in the constructor to enable custom sampling over a list of dataset keys. The default strategy is to sample uniformly as it did previously.
Reviewed By: liezl200
Differential Revision: D14965774
fbshipit-source-id: f3285688a9ae3729c0ba12c22254c1144d0eea9e
