-------------------------------------------------------------------------------- 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: - April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md) - March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md) - February 2020: [mBART model and code released](examples/mbart/README.md) - February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/master/examples/backtranslation#training-your-own-model-wmt18-english-german) - December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0) - November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example) - November 2019: [CamemBERT model and code released](examples/camembert/README.md) - 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 ) - [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) - [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/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 for translation and language modeling](#pre-trained-models-and-examples) with a convenient `torch.hub` interface: ```python en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model') en2de.translate('Hello world', beam=5) # 'Hallo Welt' ``` See the PyTorch Hub tutorials for [translation](https://pytorch.org/hub/pytorch_fairseq_translation/) and [RoBERTa](https://pytorch.org/hub/pytorch_fairseq_roberta/) for more examples.  # Requirements and Installation * [PyTorch](http://pytorch.org/) version >= 1.4.0 * Python version >= 3.6 * 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: ```bash git clone https://github.com/NVIDIA/apex cd apex pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--deprecated_fused_adam" --global-option="--xentropy" --global-option="--fast_multihead_attn" ./ ``` 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: - [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) - [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}, } ```