GitLab

Summary:
This is the first version of BART code / model release.

It still requires lot of clean up, instructions, making sure results are reproducible before we can release it.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/902

Differential Revision: D18389535

fbshipit-source-id: 77f16800307ce831bd29538fdd34800793210f46

Summary:
TODO:
1) Need to update bibtex entry
2) Need to upload models, spm_vocab and dict.txt to public s3 location.

For Future:

1) I will probably add instructions to finetune on XNLI and NER, POS etc. but currently no timeline for that.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/900

Reviewed By: myleott

Differential Revision: D18333076

Pulled By: myleott

fbshipit-source-id: 3f3d3716fcc41c78d2dd4525f60b519abbd0459c

Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/884

Differential Revision: D17774515

Pulled By: alexeib

fbshipit-source-id: d1ffe8ab723fa284c69b067bbd43d699eaa2f02f

Implementation of the paper "Jointly Learning to Align and Translate with Transformer Models" (#877)

Summary:
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/877

This PR implements guided alignment training described in  "Jointly Learning to Align and Translate with Transformer Models (https://arxiv.org/abs/1909.02074)".

In summary, it allows for training selected heads of the Transformer Model with external alignments computed by Statistical Alignment Toolkits. During inference, attention probabilities from the trained heads can be used to extract reliable alignments. In our work, we did not see any regressions in the translation performance because of guided alignment training.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/1095

Differential Revision: D17170337

Pulled By: myleott

fbshipit-source-id: daa418bef70324d7088dbb30aa2adf9f95774859

Summary:
Code for our NeurIPS paper [Levenshtein Transformer](https://arxiv.org/abs/1905.11006)
* Added Levenshtein Transformer model, task and criterion class
* Added iterative NAT Transformer, insertion Transformer and CMLM Transformer model class for baselines
* Add an option for prepending BOS to dictionary class and translation task class

Reviewed By: myleott

Differential Revision: D17297372

fbshipit-source-id: 54eca60831ae95dc721c2c34e882e1810ee575c7

Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/823

Differential Revision: D16804995

Pulled By: myleott

fbshipit-source-id: abac5dc0ed6b7bfe2309ba273456e54b37340b2c

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/1014

Differential Revision: D16784120

Pulled By: myleott

fbshipit-source-id: 946c0e33b594f8378e4ab6482ce49efcb36e1743

Summary:
To install on MacOS, `-stdlib=libc++` needs to be specified.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/1000

Differential Revision: D16733819

Pulled By: myleott

fbshipit-source-id: 7a1ed11e2b4e1071e61c64c379c84f72e02ad2b5

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:
Update README.md to include the recently implemented top-p/nucleus sampling.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/783

Differential Revision: D16543974

Pulled By: myleott

fbshipit-source-id: 27c502af10ee390d29607038118a99ff0067aec4

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/916

Differential Revision: D16537774

Pulled By: myleott

fbshipit-source-id: 86bb7b1913a428ee4a21674cc3fc7b39264067ec

Summary:
Merging wav2vec to master. Includes renames (Cpc -> wav2vec) and some light example files.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/654

Differential Revision: D15913409

Pulled By: alexeib

fbshipit-source-id: f723e6f211706cd9431c7d76dc12c4e80c9cfc80

Summary:
See #467. Ping myleott to review.

This is a work-related contribution. Ping lark to review.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/794

Differential Revision: D15756816

Pulled By: myleott

fbshipit-source-id: 6dce3ff3a713bf5f60e5782bc260b2ca9d2c0a9b

Summary:
Change the wording to avoid confusion. Mixed precision ensures both higher arithmetic throughput and numerical stability, not exactly synonymous to pure half-precision/FP16 training. Also add mentioning of tensor cores since older generation GPUs without tensor cores don't support true mixed precision training.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/766

Differential Revision: D15559565

Pulled By: myleott

fbshipit-source-id: c71e720772657bb3e8ad330b58bf69e23beb614e

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/676

Differential Revision: D15114128

Pulled By: myleott

fbshipit-source-id: b11dde77b2f2610d33649101aea03fb5a3eeb56a

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:
* Add FusedLayerNorm and FusedAdam
* Softmax and zero grad optimizations
Pull Request resolved: https://github.com/pytorch/fairseq/pull/531

Differential Revision: D14218457

Pulled By: myleott

fbshipit-source-id: 5656b2d0152cd85f77dc21ec0e1439ec04b9fa89

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/522

Differential Revision: D14194672

Pulled By: myleott

fbshipit-source-id: 4ff669826c4313de6f12076915cfb1bd15289ef0

Summary:
Code for the paper: [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](https://arxiv.org/abs/1902.07816).
Pull Request resolved: https://github.com/pytorch/fairseq/pull/521

Differential Revision: D14188021

Pulled By: myleott

fbshipit-source-id: ed5b1ed5ad9a582359bd5215fa2ea26dc76c673e

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:
Changelog:
- `e330f56`: Add code for the "Pay Less Attention with Lightweight and Dynamic Convolutions" paper
- `5e3b98c`: Add scripts for computing tokenized BLEU with compound splitting and sacrebleu
- update READMEs
- misc fixes
Pull Request resolved: https://github.com/pytorch/fairseq/pull/473

Differential Revision: D13819717

Pulled By: myleott

fbshipit-source-id: f2dc12ea89a436b950cafec3593ed1b04af808e9

Summary:
minor fixes:
1- adding fairseq logo
2- encoder padding for fconv self att
3- legacy ddp change
Pull Request resolved: https://github.com/pytorch/fairseq/pull/442

Differential Revision: D13651715

Pulled By: myleott

fbshipit-source-id: ac93c80f1dbffdfe03fbd4b8a8ea527aecb576a7

Summary:
Pull Request resolved: https://github.com/pytorch/translate/pull/283

Pull Request resolved: https://github.com/pytorch/fairseq/pull/428

Differential Revision: D13564190

Pulled By: myleott

fbshipit-source-id: 3b62282d7069c288f5bdd1dd2c120788cee4abb5

Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/300

Differential Revision: D10154711

Pulled By: edunov

fbshipit-source-id: 859d1ac59923b67c1547b6f7acb94f801b0c3318

Fix preprocessed test set download links

Add transformer models and replace list with table

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