Introduction

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

What's New:

• September 2019: Nonautoregressive translation code released
• August 2019: WMT'19 models released
• July 2019: fairseq relicensed under MIT license
• July 2019: RoBERTa models and code released
• June 2019: wav2vec models and code released

Features:

Fairseq provides reference implementations of various sequence-to-sequence models, including:

• Convolutional Neural Networks (CNN)
  • Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
  • Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
  • Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
  • Hierarchical Neural Story Generation (Fan et al., 2018)
  • wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
• LightConv and DynamicConv models
  • Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
• Long Short-Term Memory (LSTM) networks
  • Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)
• Transformer (self-attention) networks
  • Attention Is All You Need (Vaswani et al., 2017)
  • Scaling Neural Machine Translation (Ott et al., 2018)
  • Understanding Back-Translation at Scale (Edunov et al., 2018)
  • Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)
  • Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
  • RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
  • Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
• Non-autoregressive Transformers
  • Non-Autoregressive Neural Machine Translation (Gu et al., 2017)
  • Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)
  • Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)
  • Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)
  • Levenshtein Transformer (Gu et al., 2019)

Additionally:

• multi-GPU (distributed) training on one machine or across multiple machines
• fast generation on both CPU and GPU with multiple search algorithms implemented:
  • beam search
  • Diverse Beam Search (Vijayakumar et al., 2016)
  • sampling (unconstrained, top-k and top-p/nucleus)
• large mini-batch training even on a single GPU via delayed updates
• mixed precision training (trains faster with less GPU memory on NVIDIA tensor cores)
• extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide pre-trained models for several benchmark translation and language modeling datasets.

Requirements and Installation

• PyTorch version >= 1.2.0
• Python version >= 3.5
• For training new models, you'll also need an NVIDIA GPU and NCCL
• For faster training install NVIDIA's apex library with the --cuda_ext option

To install fairseq:

pip install fairseq

On MacOS:

CFLAGS="-stdlib=libc++" pip install fairseq

If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run.

Installing from source

To install fairseq from source and develop locally:

git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable .

Getting Started

The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.

Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, as well as example training and evaluation commands.

• Translation: convolutional and transformer models are available
• Language Modeling: convolutional and transformer models are available

We also have more detailed READMEs to reproduce results from specific papers:

• Levenshtein Transformer (Gu et al., 2019)
• Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
• RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
• wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
• Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
• Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
• Understanding Back-Translation at Scale (Edunov et al., 2018)
• Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
• Hierarchical Neural Story Generation (Fan et al., 2018)
• Scaling Neural Machine Translation (Ott et al., 2018)
• Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
• Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)

Join the fairseq community

• Facebook page: https://www.facebook.com/groups/fairseq.users
• Google group: https://groups.google.com/forum/#!forum/fairseq-users

License

fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well.

Citation

Please cite as:

@inproceedings{ott2019fairseq,
  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
  year = {2019},
}