GitLab

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

Don't suggest Miniconda (see #24)

Update PyTorch install instructions