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

• Convolutional Neural Networks (CNN)
  • Dauphin et al. (2017): Language Modeling with Gated Convolutional Networks
  • Gehring et al. (2017): Convolutional Sequence to Sequence Learning
  • Edunov et al. (2018): Classical Structured Prediction Losses for Sequence to Sequence Learning
  • Fan et al. (2018): Hierarchical Neural Story Generation
• LightConv and DynamicConv models
  • New Wu et al. (2019): Pay Less Attention with Lightweight and Dynamic Convolutions
• Long Short-Term Memory (LSTM) networks
  • Luong et al. (2015): Effective Approaches to Attention-based Neural Machine Translation
  • Wiseman and Rush (2016): Sequence-to-Sequence Learning as Beam-Search Optimization
• Transformer (self-attention) networks
  • Vaswani et al. (2017): Attention Is All You Need
  • Ott et al. (2018): Scaling Neural Machine Translation
  • Edunov et al. (2018): Understanding Back-Translation at Scale
  • New Shen et al. (2019) Mixture Models for Diverse Machine Translation: Tricks of the Trade

Fairseq features:

• 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 and top-k)
• large mini-batch training even on a single GPU via delayed updates
• fast half-precision floating point (FP16) training
• 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

• A PyTorch installation
• For training new models, you'll also need an NVIDIA GPU and NCCL
• Python version 3.6

Currently fairseq requires PyTorch version >= 1.0.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.

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.

After PyTorch is installed, you can install fairseq with pip:

pip install fairseq

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 models are available

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

• Shen et al. (2019) Mixture Models for Diverse Machine Translation: Tricks of the Trade
• Wu et al. (2019): Pay Less Attention with Lightweight and Dynamic Convolutions
• Edunov et al. (2018): Understanding Back-Translation at Scale
• Edunov et al. (2018): Classical Structured Prediction Losses for Sequence to Sequence Learning
• Fan et al. (2018): Hierarchical Neural Story Generation
• Ott et al. (2018): Scaling Neural Machine Translation
• Gehring et al. (2017): Convolutional Sequence to Sequence Learning
• Dauphin et al. (2017): Language Modeling with Gated Convolutional Networks

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 BSD-licensed. The license applies to the pre-trained models as well. We also provide an additional patent grant.

Credits

This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross.