# <img src="fairseq_logo.png" width="30"> 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:

- November 2019: [BART model and code released](examples/bart/README.md)
- November 2019: [XLM-R models and code released](examples/xlmr/README.md)
- September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md)
- August 2019: [WMT'19 models released](examples/wmt19/README.md)
- July 2019: fairseq relicensed under MIT license
- July 2019: [RoBERTa models and code released](examples/roberta/README.md)
- June 2019: [wav2vec models and code released](examples/wav2vec/README.md)

### 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)](examples/language_model/conv_lm/README.md)
  - [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)
  - [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)
  - [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)
  - [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)
- **LightConv and DynamicConv models**
  - [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)
- **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)](examples/scaling_nmt/README.md)
  - [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)
  - [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/transformer_lm/README.md)
  - [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)
  - [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)
  - [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)
  - [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md )
- **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)](examples/nonautoregressive_translation/README.md)


**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](https://arxiv.org/abs/1610.02424))
  - 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](https://developer.nvidia.com/tensor-cores))
- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide [pre-trained models](#pre-trained-models-and-examples) for several benchmark
translation and language modeling datasets.

![Model](fairseq.gif)

# Requirements and Installation

* [PyTorch](http://pytorch.org/) version >= 1.2.0
* Python version >= 3.5
* For training new models, you'll also need an NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl)
* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library with the `--cuda_ext` option

To install fairseq:
```bash
pip install fairseq
```

On MacOS:
```bash
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:
```bash
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable .
```

# Getting Started

The [full documentation](https://fairseq.readthedocs.io/) 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](examples/translation/README.md): convolutional and transformer models are available
- [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available
- [wav2vec](examples/wav2vec/README.md): wav2vec large model is available

We also have more detailed READMEs to reproduce results from specific papers:
- [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md )
- [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md)
- [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)
- [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)
- [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)
- [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)
- [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)
- [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)
- [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)
- [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md)
- [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)
- [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md)

# 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:

```bibtex
@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},
}
```
