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Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/580

Differential Revision: D14494390

Pulled By: myleott

fbshipit-source-id: 524cc16a106f2af630357e2ebdf7dde35fa7d494

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: Pull Request resolved: https://github.com/pytorch/fairseq/pull/542

Differential Revision: D14258895

Pulled By: myleott

fbshipit-source-id: 950a840e1d001a472be8d4737c9e4de5224137b3

Summary:
Pull Request resolved: https://github.com/pytorch/translate/pull/351

This makes it easier for tasks to plugin to generate.py/interactive.py
Pull Request resolved: https://github.com/pytorch/fairseq/pull/520

Differential Revision: D14183881

Pulled By: myleott

fbshipit-source-id: ede5e53ddc1215ed3b12b8f1eba048c946913c33

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

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/489

Differential Revision: D13956810

Pulled By: myleott

fbshipit-source-id: 61ace179d1d3790226c38b3f3e47f5452b5ec514

- 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

Remove custom ConvTBC code

This PR includes breaking API changes to modularize fairseq-py and adds support for distributed training across multiple nodes.

Changes:
- c7033ef: add support for distributed training! See updated README for usage.
- e016299: modularize fairseq-py, adding support for register_model, register_criterion, register_optimizer, etc.
- 154e440: update LSTM implementation to use PackedSequence objects in the encoder, better following best practices and improving perf
- 90c2973 and 1da6265: improve unit test coverage

Release notes:
- 5c7f4954: Added simple LSTM model with input feeding and attention
- 6e4b7e22: Refactored model definitions and incremental generation to be cleaner
- 7ae79c12: Split interactive generation out of generate.py and into a new binary: interactive.py
- 19a3865d: Subtle correctness fix in beam search decoder. Previously, for a beam size of k, we might emit a hypotheses
           if the <eos> was among the top 2*k candidates. Now we only emit hypotheses for which the <eos> is among the
           top-k candidates. This may subtly change generation results, and in the case of k=1 we will now produce
           strictly greedy outputs.
- 97d7fcb9: Fixed bug in padding direction, where previously we right-padded the source and left-padded the target. We
           now left-pad the source and right-pad the target. This should not effect existing trained models, but may
           change (usually improves) the quality of new models.
- f442f896: Add support for batching based on the number of sentences (`--max-sentences`) in addition to the number of
           tokens (`--max-tokens`). When batching by the number of sentences, one can optionally normalize the gradients
           by the number of sentences with `--sentence-avg` (the default is to normalize by the number of tokens).
- c6d6256b: Add `--log-format` option and JSON logger