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.
We provide reference implementations of various sequence modeling papers:
List of implemented papers
- Convolutional Neural Networks (CNN)
- LightConv and DynamicConv models
- 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)
- Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)
- Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (Dai et al., 2019)
- Adaptive Attention Span in Transformers (Sukhbaatar et al., 2019)
- 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)
- Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
- Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)
- Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)
- wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)
- Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)
- Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)
- Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)
- Deep Transformers with Latent Depth (Li et al., 2020)
- Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)
- Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020)
- Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)
- Unsupervised Speech Recognition (Baevski, et al., 2021)
- Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021)
- VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (Xu et. al., 2021)
- VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding (Xu et. al., 2021)
- NormFormer: Improved Transformer Pretraining with Extra Normalization (Shleifer et. al, 2021)
- 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)
- Finetuning
What's New:
Previous updates
Features:
We also provide pre-trained models for translation and language modeling
with a convenient torch.hub interface:
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
en2de.translate('Hello world', beam=5)
See the PyTorch Hub tutorials for translation
and RoBERTa for more examples.
Requirements and Installation
- PyTorch version >= 1.5.0
- Python version >= 3.6
- For training new models, you'll also need an NVIDIA GPU and NCCL
- To install fairseq and develop locally:
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./
- For faster training install NVIDIA's apex library:
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" ./
- For large datasets install PyArrow:
pip install pyarrow - 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 .
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.
We also have more detailed READMEs to reproduce results from specific papers:
- XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale (Babu et al., 2021)
- Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)
- wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)
- Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)
- Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
- Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)
- Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)
- Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
- 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)
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},
}
