* run all tests
* make torch a build-time dependency
* add 'dev' extra deps to install black, flake, pytest at once
* Build docs in CI
This should also help catch some import bugs, since sphinx inspect a lot of code
* CI should do the real install not "--editable"
* check installation succeeded
* add missing __init__.py file
* add check installation
* move check_installation.py to its own script
* fix pytest import mode, force recent numpy, torch
* run black before flake and tests
* torch >= 1.10.0
* use torch 1.10 for GPU tests
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/master/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?
## What does this PR do?
Fixes # (issue).
## 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/2297
Reviewed By: alexeib
Differential Revision: D30906090
Pulled By: dianaml0
fbshipit-source-id: 941d30db7f766c9077a1b5bb2a04680f57e2e070
Summary:
This will produce version strings like `1.0.0a0+3065963`, similar to PyTorch version strings.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/2774
Reviewed By: alexeib
Differential Revision: D24453517
Pulled By: myleott
fbshipit-source-id: 03a0c324ed6124bbc513ba7edc954abd71d63a0f
Summary:
No major API changes since the last release. Cutting a new release since we'll be merging significant (possibly breaking) changes to logging, data loading and the masked LM implementation soon.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/891
Differential Revision: D16377132
Pulled By: myleott
fbshipit-source-id: f1cb88e671ccd510e53334d0f449fe18585268c7
Summary:
Notable (possibly breaking) changes:
- d45db80: Remove checkpoint utility functions from utils.py into checkpoint_utils.py
- f2563c2: Move LM definitions into separate files
- dffb167: Updates to model API:
- `FairseqModel` -> `FairseqEncoderDecoderModel`
- add `FairseqDecoder.extract_features` and `FairseqDecoder.output_layer`
- `encoder_out_dict` -> `encoder_out`
- rm unused `remove_head` functions
- 34726d5: Move `distributed_init` into `DistributedFairseqModel`
- cf17068: Simplify distributed launch by automatically launching multiprocessing on each node for all visible GPUs (allows launching just one job per node instead of one per GPU)
- d45db80: Change default LR scheduler from `reduce_lr_on_plateau` to `fixed`
- 96ac28d: Rename `--sampling-temperature` -> `--temperature`
- fc1a19a: Deprecate dummy batches
- a1c997b: Add memory mapped datasets
- 0add50c: Allow cycling over multiple datasets, where each one becomes an "epoch"
Plus many additional features and bugfixes
Pull Request resolved: https://github.com/pytorch/fairseq/pull/817
Differential Revision: D15913844
Pulled By: myleott
fbshipit-source-id: d5b5d678efdd9dd3e4d7ca848ddcf1ec2b21bf6b
- no more FP16Trainer, we just have an FP16Optimizer wrapper
- most of the distributed code is moved to a new wrapper class called DistributedFairseqModel, which behaves like DistributedDataParallel and a FairseqModel at the same time
- Trainer now requires an extra dummy_batch argument at initialization, which we do fwd/bwd on when there's an uneven number of batches per worker. We hide the gradients from these dummy batches by multiplying the loss by 0
- Trainer.train_step now takes a list of samples, which will allow cleaner --update-freq
