GitLab

Summary:
Changelog:
- Relicensed under MIT license
- Add RoBERTa
- Add wav2vec
- Add WMT'19 models
- Add initial ASR code
- Changed torch.hub interface (`generate` renamed to `translate`)
- Add `--tokenizer` and `--bpe`
- f812e529: Renamed data.transforms -> data.encoders
- 654affc0: New Dataset API (optional)
- `47fd9852`: Deprecate old Masked LM components
- `5f78106a`: Set mmap as default dataset format and infer format automatically
- Misc fixes for sampling
- Misc fixes to support PyTorch 1.2
Pull Request resolved: https://github.com/pytorch/fairseq/pull/1017

Differential Revision: D16799880

Pulled By: myleott

fbshipit-source-id: 45ad8bc531724a53063cbc24ca1c93f715cdc5a7

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: Pull Request resolved: https://github.com/pytorch/fairseq/pull/818

Differential Revision: D15916265

Pulled By: myleott

fbshipit-source-id: c66c0bd988d3472c4150226952f34ee8d4c3db86

Summary:
Notable (possibly breaking) changes:
- d45db804: Remove checkpoint utility functions from utils.py into checkpoint_utils.py
- f2563c21: Move LM definitions into separate files
- dffb1674: Updates to model API:
  - `FairseqModel` -> `FairseqEncoderDecoderModel`
  - add `FairseqDecoder.extract_features` and `FairseqDecoder.output_layer`
  - `encoder_out_dict` -> `encoder_out`
  - rm unused `remove_head` functions
- 34726d56: Move `distributed_init` into `DistributedFairseqModel`
- cf17068a: 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)
- d45db804: Change default LR scheduler from `reduce_lr_on_plateau` to `fixed`
- 96ac28d3: Rename `--sampling-temperature` -> `--temperature`
- fc1a19a3: Deprecate dummy batches
- a1c997bd: Add memory mapped datasets
- 0add50c2: 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

Summary:
Changelog:
- 998ba4f: Add language models from Baevski & Auli (2018)
- 4294c4f6: Add mixture of experts code from Shen et al. (2019)
- 00493490: Add example for multilingual training
- 48d9afbe: Speed improvements, including fused operators from apex
- 44d27e64: Add Tensorboard support
- d17fa851: Add Adadelta optimizer
- 9e1c880f: Add `FairseqEncoderModel`
- b65c579b: Add `FairseqTask.inference_step` to modularize generate.py
- 2ad1178e: Add back `--curriculum`
- Misc bug fixes and other features

Pull Request resolved: https://github.com/pytorch/fairseq/pull/577

Differential Revision: D14481233

Pulled By: myleott

fbshipit-source-id: 4ff8625ef1c0b24273fc65df7c5658e3c932e8b7

Summary:
- fairseq can now be installed via pip: `pip install fairseq`
- command-line tools are globally accessible: `fairseq-preprocess`, `fairseq-train`, `fairseq-generate`, etc.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/495

Differential Revision: D14017761

Pulled By: myleott

fbshipit-source-id: 10c9f6634a3056074eac2f33324b4f1f404d4235

- 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