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facebookresearch/fairseq

2073

facebookresearch / fairseq

Lua

Facebook AI Research Sequence-to-Sequence Toolkit


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Introduction

This is fairseq, a sequence-to-sequence learning toolkit for Torch from Facebook AI Research tailored to Neural Machine Translation (NMT). It implements the convolutional NMT models proposed in Convolutional Sequence to Sequence Learning and A Convolutional Encoder Model for Neural Machine Translation as well as a standard LSTM-based model. It features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French, English to German and English to Romanian translation.

Note, there is now a PyTorch version fairseq-py of this toolkit and new development efforts will focus on it.

Model

Citation

If you use the code in your paper, then please cite it as:

@article{gehring2017convs2s,
  author          = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N},
  title           = "{Convolutional Sequence to Sequence Learning}",
  journal         = {ArXiv e-prints},
  archivePrefix   = "arXiv",
  eprinttype      = {arxiv},
  eprint          = {1705.03122},
  primaryClass    = "cs.CL",
  keywords        = {Computer Science - Computation and Language},
  year            = 2017,
  month           = May,
}

and

@article{gehring2016convenc,
  author          = {Gehring, Jonas, and Auli, Michael and Grangier, David and Dauphin, Yann N},
  title           = "{A Convolutional Encoder Model for Neural Machine Translation}",
  journal         = {ArXiv e-prints},
  archivePrefix   = "arXiv",
  eprinttype      = {arxiv},
  eprint          = {1611.02344},
  primaryClass    = "cs.CL",
  keywords        = {Computer Science - Computation and Language},
  year            = 2016,
  month           = Nov,
}

Requirements and Installation

  • A computer running macOS or Linux
  • For training new models, you'll also need a NVIDIA GPU and NCCL
  • A Torch installation. For maximum speed, we recommend using LuaJIT and Intel MKL.
  • A recent version nn. The minimum required version is from May 5th, 2017. A simple luarocks install nn is sufficient to update your locally installed version.

Install fairseq by cloning the GitHub repository and running

luarocks make rocks/fairseq-scm-1.rockspec

LuaRocks will fetch and build any additional dependencies that may be missing. In order to install the CPU-only version (which is only useful for translating new data with an existing model), do

luarocks make rocks/fairseq-cpu-scm-1.rockspec

The LuaRocks installation provides a command-line tool that includes the following functionality:

  • fairseq preprocess: Data pre-processing: build vocabularies and binarize training data
  • fairseq train: Train a new model on one or multiple GPUs
  • fairseq generate: Translate pre-processed data with a trained model
  • fairseq generate-lines: Translate raw text with a trained model
  • fairseq score: BLEU scoring of generated translations against reference translations
  • fairseq tofloat: Convert a trained model to a CPU model
  • fairseq optimize-fconv: Optimize a fully convolutional model for generation. This can also be achieved by passing the -fconvfast flag to the generation scripts.

Quick Start

Evaluating Pre-trained Models

First, download a pre-trained model along with its vocabularies:

$ curl https://s3.amazonaws.com/fairseq/models/wmt14.en-fr.fconv-cuda.tar.bz2 | tar xvjf -

This will unpack vocabulary files and a serialized model for English to French translation to wmt14.en-fr.fconv-cuda/.

Alternatively, use a CPU-based model:

$ curl https://s3.amazonaws.com/fairseq/models/wmt14.en-fr.fconv-float.tar.bz2 | tar xvjf -

Let's use fairseq generate-lines to translate some text. This model uses a Byte Pair Encoding (BPE) vocabulary, so we'll have to apply the encoding to the source text. This can be done with apply_bpe.py using the bpecodes file in within wmt14.en-fr.fconv-cuda/. @@ is used as a continuation marker and the original text can be easily recovered with e.g. sed s/@@ //g. Prior to BPE, input text needs to be tokenized using tokenizer.perl from mosesdecoder. Here, we use a beam size of 5:

$ fairseq generate-lines -path wmt14.en-fr.fconv-cuda/model.th7 -sourcedict wmt14.en-fr.fconv-cuda/dict.en.th7 \
    -targetdict wmt14.en-fr.fconv-cuda/dict.fr.th7 -beam 5
| [target] Dictionary: 44666 types
| [source] Dictionary: 44409 types
> Why is it rare to discover new marine mam@@ mal species ?
S	Why is it rare to discover new marine mam@@ mal species ?
O	Why is it rare to discover new marine mam@@ mal species ?
H	-0.068684287369251	Pourquoi est-il rare de découvrir de nouvelles espèces de mammifères marins ?
A	1 1 4 4 6 6 7 11 9 9 9 12 13

This generation script produces four types of output: a line prefixed with S shows the supplied source sentence after applying the vocabulary; O is a copy of the original source sentence; H is the hypothesis along with an average log-likelihood and A are attention maxima for each word in the hypothesis (including the end-of-sentence marker which is omitted from the text).

Check below for a full list of pre-trained models available.

Training a New Model

Data Pre-processing

The fairseq source distribution contains an example pre-processing script for the IWSLT14 German-English corpus. Pre-process and binarize the data as follows:

$ cd data/
$ bash prepare-iwslt14.sh
$ cd ..
$ TEXT=data/iwslt14.tokenized.de-en
$ fairseq preprocess -sourcelang de -targetlang en \
  -trainpref $TEXT/train -validpref $TEXT/valid -testpref $TEXT/test \
  -thresholdsrc 3 -thresholdtgt 3 -destdir data-bin/iwslt14.tokenized.de-en

This will write binarized data that can be used for model training to data-bin/iwslt14.tokenized.de-en.

Training

Use fairseq train to train a new model. Here a few example settings that work well for the IWSLT14 dataset:

# Standard bi-directional LSTM model
$ mkdir -p trainings/blstm
$ fairseq train -sourcelang de -targetlang en -datadir data-bin/iwslt14.tokenized.de-en \
  -model blstm -nhid 512 -dropout 0.2 -dropout_hid 0 -optim adam -lr 0.0003125 -savedir trainings/blstm

# Fully convolutional sequence-to-sequence model
$ mkdir -p trainings/fconv
$ fairseq train -sourcelang de -targetlang en -datadir data-bin/iwslt14.tokenized.de-en \
  -model fconv -nenclayer 4 -nlayer 3 -dropout 0.2 -optim nag -lr 0.25 -clip 0.1 \
  -momentum 0.99 -timeavg -bptt 0 -savedir trainings/fconv

# Convolutional encoder, LSTM decoder
$ mkdir -p trainings/convenc
$ fairseq train -sourcelang de -targetlang en -datadir data-bin/iwslt14.tokenized.de-en \
  -model conv -nenclayer 6 -dropout 0.2 -dropout_hid 0 -savedir trainings/convenc

By default, fairseq train will use all available GPUs on your machine. Use the CUDA_VISIBLE_DEVICES environment variable to select specific GPUs or -ngpus to change the number of GPU devices that will be used.

Generation

Once your model is trained, you can translate with it using fairseq generate (for binarized data) or fairseq generate-lines (for text). Here, we'll do it for a fully convolutional model:

# Optional: optimize for generation speed
$ fairseq optimize-fconv -input_model trainings/fconv/model_best.th7 -output_model trainings/fconv/model_best_opt.th7

# Translate some text
$ DATA=data-bin/iwslt14.tokenized.de-en
$ fairseq generate-lines -sourcedict $DATA/dict.de.th7 -targetdict $DATA/dict.en.th7 \
  -path trainings/fconv/model_best_opt.th7 -beam 10 -nbest 2
| [target] Dictionary: 24738 types
| [source] Dictionary: 35474 types
> eine sprache ist ausdruck des menschlichen geistes .
S	eine sprache ist ausdruck des menschlichen geistes .
O	eine sprache ist ausdruck des menschlichen geistes .
H	-0.23804219067097	a language is expression of human mind .
A	2 2 3 4 5 6 7 8 9
H	-0.23861141502857	a language is expression of the human mind .
A	2 2 3 4 5 7 6 7 9 9

CPU Generation

Use fairseq tofloat to convert a trained model to use CPU-only operations (this has to be done on a GPU machine):

# Optional: optimize for generation speed
$ fairseq optimize-fconv -input_model trainings/fconv/model_best.th7 -output_model trainings/fconv/model_best_opt.th7

# Convert to float
$ fairseq tofloat -input_model trainings/fconv/model_best_opt.th7 \
  -output_model trainings/fconv/model_best_opt-float.th7

# Translate some text
$ fairseq generate-lines -sourcedict $DATA/dict.de.th7 -targetdict $DATA/dict.en.th7 \
  -path trainings/fconv/model_best_opt-float.th7 -beam 10 -nbest 2
> eine sprache ist ausdruck des menschlichen geistes .
S	eine sprache ist ausdruck des menschlichen geistes .
O	eine sprache ist ausdruck des menschlichen geistes .
H	-0.2380430996418	a language is expression of human mind .
A	2 2 3 4 5 6 7 8 9
H	-0.23861189186573	a language is expression of the human mind .
A	2 2 3 4 5 7 6 7 9 9

Pre-trained Models

We provide the following pre-trained fully convolutional sequence-to-sequence models:

In addition, we provide pre-processed and binarized test sets for the models above:

Generation with the binarized test sets can be run in batch mode as follows, e.g. for English-French on a GTX-1080ti:

$ curl https://s3.amazonaws.com/fairseq/data/wmt14.en-fr.newstest2014.tar.bz2 | tar xvjf -

$ fairseq generate -sourcelang en -targetlang fr -datadir data-bin/wmt14.en-fr -dataset newstest2014 \
  -path wmt14.en-fr.fconv-cuda/model.th7 -beam 5 -batchsize 128 | tee /tmp/gen.out
...
| Translated 3003 sentences (95451 tokens) in 136.3s (700.49 tokens/s)
| Timings: setup 0.1s (0.1%), encoder 1.9s (1.4%), decoder 108.9s (79.9%), search_results 0.0s (0.0%), search_prune 12.5s (9.2%)
| BLEU4 = 43.43, 68.2/49.2/37.4/28.8 (BP=0.996, ratio=1.004, sys_len=92087, ref_len=92448)

# Word-level BLEU scoring:
$ grep ^H /tmp/gen.out | cut -f3- | sed 's/@@ //g' > /tmp/gen.out.sys
$ grep ^T /tmp/gen.out | cut -f2- | sed 's/@@ //g' > /tmp/gen.out.ref
$ fairseq score -sys /tmp/gen.out.sys -ref /tmp/gen.out.ref
BLEU4 = 40.55, 67.6/46.5/34.0/25.3 (BP=1.000, ratio=0.998, sys_len=81369, ref_len=81194)

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License

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