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Neural Machine Translation

This README contains instructions for using pretrained translation models as well as training new models.

Pre-trained models

Model Description Dataset Download
conv.wmt14.en-fr Convolutional
(Gehring et al., 2017)		 WMT14 English-French model:
download (.tar.bz2)
newstest2014:
download (.tar.bz2)
newstest2012/2013:
download (.tar.bz2)
conv.wmt14.en-de Convolutional
(Gehring et al., 2017)		 WMT14 English-German model:
download (.tar.bz2)
newstest2014:
download (.tar.bz2)
conv.wmt17.en-de Convolutional
(Gehring et al., 2017)		 WMT17 English-German model:
download (.tar.bz2)
newstest2014:
download (.tar.bz2)
transformer.wmt14.en-fr Transformer
(Ott et al., 2018)		 WMT14 English-French model:
download (.tar.bz2)
newstest2014:
download (.tar.bz2)
transformer.wmt16.en-de Transformer
(Ott et al., 2018)		 WMT16 English-German model:
download (.tar.bz2)
newstest2014:
download (.tar.bz2)
transformer.wmt18.en-de Transformer
(Edunov et al., 2018)
WMT'18 winner		 WMT'18 English-German model:
download (.tar.gz)
See NOTE in the archive
transformer.wmt19.en-de Transformer
(Ng et al., 2019)
WMT'19 winner		 WMT'19 English-German model:
download (.tar.gz)
transformer.wmt19.de-en Transformer
(Ng et al., 2019)
WMT'19 winner		 WMT'19 German-English model:
download (.tar.gz)
transformer.wmt19.en-ru Transformer
(Ng et al., 2019)
WMT'19 winner		 WMT'19 English-Russian model:
download (.tar.gz)
transformer.wmt19.ru-en Transformer
(Ng et al., 2019)
WMT'19 winner		 WMT'19 Russian-English model:
download (.tar.gz)

Example usage (torch.hub)

We require a few additional Python dependencies for preprocessing:

pip install fastBPE sacremoses subword_nmt

Interactive translation via PyTorch Hub:

import torch

# List available models
torch.hub.list('pytorch/fairseq')  # [..., 'transformer.wmt16.en-de', ... ]

# Load a transformer trained on WMT'16 En-De
# Note: WMT'19 models use fastBPE instead of subword_nmt, see instructions below
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de',
                       tokenizer='moses', bpe='subword_nmt')
en2de.eval()  # disable dropout

# The underlying model is available under the *models* attribute
assert isinstance(en2de.models[0], fairseq.models.transformer.TransformerModel)

# Move model to GPU for faster translation
en2de.cuda()

# Translate a sentence
en2de.translate('Hello world!')
# 'Hallo Welt!'

# Batched translation
en2de.translate(['Hello world!', 'The cat sat on the mat.'])
# ['Hallo Welt!', 'Die Katze saß auf der Matte.']

Loading custom models:

from fairseq.models.transformer import TransformerModel
zh2en = TransformerModel.from_pretrained(
  '/path/to/checkpoints',
  checkpoint_file='checkpoint_best.pt',
  data_name_or_path='data-bin/wmt17_zh_en_full',
  bpe='subword_nmt',
  bpe_codes='data-bin/wmt17_zh_en_full/zh.code'
)
zh2en.translate('你好 世界')
# 'Hello World'

If you are using a transformer.wmt19 models, you will need to set the bpe argument to 'fastbpe' and (optionally) load the 4-model ensemble:

en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de',
                       checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
                       tokenizer='moses', bpe='fastbpe')
en2de.eval()  # disable dropout

Example usage (CLI tools)

Generation with the binarized test sets can be run in batch mode as follows, e.g. for WMT 2014 English-French on a GTX-1080ti:

mkdir -p data-bin
curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf - -C data-bin
curl https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2 | tar xvjf - -C data-bin
fairseq-generate data-bin/wmt14.en-fr.newstest2014  \
    --path data-bin/wmt14.en-fr.fconv-py/model.pt \
    --beam 5 --batch-size 128 --remove-bpe | tee /tmp/gen.out
# ...
# | Translated 3003 sentences (96311 tokens) in 166.0s (580.04 tokens/s)
# | Generate test with beam=5: BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787)

# Compute BLEU score
grep ^H /tmp/gen.out | cut -f3- > /tmp/gen.out.sys
grep ^T /tmp/gen.out | cut -f2- > /tmp/gen.out.ref
fairseq-score --sys /tmp/gen.out.sys --ref /tmp/gen.out.ref
# BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787)

Training a new model

IWSLT'14 German to English (Transformer)

The following instructions can be used to train a Transformer model on the IWSLT'14 German to English dataset.

First download and preprocess the data:

# Download and prepare the data
cd examples/translation/
bash prepare-iwslt14.sh
cd ../..

# Preprocess/binarize the data
TEXT=examples/translation/iwslt14.tokenized.de-en
fairseq-preprocess --source-lang de --target-lang en \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir data-bin/iwslt14.tokenized.de-en \
    --workers 20

Next we'll train a Transformer translation model over this data:

CUDA_VISIBLE_DEVICES=0 fairseq-train \
    data-bin/iwslt14.tokenized.de-en \
    --arch transformer_iwslt_de_en --share-decoder-input-output-embed \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
    --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
    --dropout 0.3 --weight-decay 0.0001 \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --max-tokens 4096 \
    --eval-bleu \
    --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \
    --eval-bleu-detok moses \
    --eval-bleu-remove-bpe \
    --eval-bleu-print-samples \
    --best-checkpoint-metric bleu --maximize-best-checkpoint-metric

Finally we can evaluate our trained model:

fairseq-generate data-bin/iwslt14.tokenized.de-en \
    --path checkpoints/checkpoint_best.pt \
    --batch-size 128 --beam 5 --remove-bpe

WMT'14 English to German (Convolutional)

The following instructions can be used to train a Convolutional translation model on the WMT English to German dataset. See the Scaling NMT README for instructions to train a Transformer translation model on this data.

The WMT English to German dataset can be preprocessed using the prepare-wmt14en2de.sh script. By default it will produce a dataset that was modeled after Attention Is All You Need (Vaswani et al., 2017), but with additional news-commentary-v12 data from WMT'17.

To use only data available in WMT'14 or to replicate results obtained in the original Convolutional Sequence to Sequence Learning (Gehring et al., 2017) paper, please use the --icml17 option.

# Download and prepare the data
cd examples/translation/
# WMT'17 data:
bash prepare-wmt14en2de.sh
# or to use WMT'14 data:
# bash prepare-wmt14en2de.sh --icml17
cd ../..

# Binarize the dataset
TEXT=examples/translation/wmt17_en_de
fairseq-preprocess \
    --source-lang en --target-lang de \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir data-bin/wmt17_en_de --thresholdtgt 0 --thresholdsrc 0 \
    --workers 20

# Train the model
mkdir -p checkpoints/fconv_wmt_en_de
fairseq-train \
    data-bin/wmt17_en_de \
    --arch fconv_wmt_en_de \
    --dropout 0.2 \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --optimizer nag --clip-norm 0.1 \
    --lr 0.5 --lr-scheduler fixed --force-anneal 50 \
    --max-tokens 4000 \
    --save-dir checkpoints/fconv_wmt_en_de

# Evaluate
fairseq-generate data-bin/wmt17_en_de \
    --path checkpoints/fconv_wmt_en_de/checkpoint_best.pt \
    --beam 5 --remove-bpe

WMT'14 English to French

# Download and prepare the data
cd examples/translation/
bash prepare-wmt14en2fr.sh
cd ../..

# Binarize the dataset
TEXT=examples/translation/wmt14_en_fr
fairseq-preprocess \
    --source-lang en --target-lang fr \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir data-bin/wmt14_en_fr --thresholdtgt 0 --thresholdsrc 0 \
    --workers 60

# Train the model
mkdir -p checkpoints/fconv_wmt_en_fr
fairseq-train \
    data-bin/wmt14_en_fr \
    --arch fconv_wmt_en_fr \
    --dropout 0.1 \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --optimizer nag --clip-norm 0.1 \
    --lr 0.5 --lr-scheduler fixed --force-anneal 50 \
    --max-tokens 3000 \
    --save-dir checkpoints/fconv_wmt_en_fr

# Evaluate
fairseq-generate \
    data-bin/fconv_wmt_en_fr \
    --path checkpoints/fconv_wmt_en_fr/checkpoint_best.pt \
    --beam 5 --remove-bpe

Multilingual Translation

We also support training multilingual translation models. In this example we'll train a multilingual {de,fr}-en translation model using the IWSLT'17 datasets.

Note that we use slightly different preprocessing here than for the IWSLT'14 En-De data above. In particular we learn a joint BPE code for all three languages and use fairseq-interactive and sacrebleu for scoring the test set.

# First install sacrebleu and sentencepiece
pip install sacrebleu sentencepiece

# Then download and preprocess the data
cd examples/translation/
bash prepare-iwslt17-multilingual.sh
cd ../..

# Binarize the de-en dataset
TEXT=examples/translation/iwslt17.de_fr.en.bpe16k
fairseq-preprocess --source-lang de --target-lang en \
    --trainpref $TEXT/train.bpe.de-en \
    --validpref $TEXT/valid0.bpe.de-en,$TEXT/valid1.bpe.de-en,$TEXT/valid2.bpe.de-en,$TEXT/valid3.bpe.de-en,$TEXT/valid4.bpe.de-en,$TEXT/valid5.bpe.de-en \
    --destdir data-bin/iwslt17.de_fr.en.bpe16k \
    --workers 10

# Binarize the fr-en dataset
# NOTE: it's important to reuse the en dictionary from the previous step
fairseq-preprocess --source-lang fr --target-lang en \
    --trainpref $TEXT/train.bpe.fr-en \
    --validpref $TEXT/valid0.bpe.fr-en,$TEXT/valid1.bpe.fr-en,$TEXT/valid2.bpe.fr-en,$TEXT/valid3.bpe.fr-en,$TEXT/valid4.bpe.fr-en,$TEXT/valid5.bpe.fr-en \
    --tgtdict data-bin/iwslt17.de_fr.en.bpe16k/dict.en.txt \
    --destdir data-bin/iwslt17.de_fr.en.bpe16k \
    --workers 10

# Train a multilingual transformer model
# NOTE: the command below assumes 1 GPU, but accumulates gradients from
#       8 fwd/bwd passes to simulate training on 8 GPUs
mkdir -p checkpoints/multilingual_transformer
CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt17.de_fr.en.bpe16k/ \
    --max-epoch 50 \
    --ddp-backend=legacy_ddp \
    --task multilingual_translation --lang-pairs de-en,fr-en \
    --arch multilingual_transformer_iwslt_de_en \
    --share-decoders --share-decoder-input-output-embed \
    --optimizer adam --adam-betas '(0.9, 0.98)' \
    --lr 0.0005 --lr-scheduler inverse_sqrt \
    --warmup-updates 4000 --warmup-init-lr '1e-07' \
    --label-smoothing 0.1 --criterion label_smoothed_cross_entropy \
    --dropout 0.3 --weight-decay 0.0001 \
    --save-dir checkpoints/multilingual_transformer \
    --max-tokens 4000 \
    --update-freq 8

# Generate and score the test set with sacrebleu
SRC=de
sacrebleu --test-set iwslt17 --language-pair ${SRC}-en --echo src \
    | python scripts/spm_encode.py --model examples/translation/iwslt17.de_fr.en.bpe16k/sentencepiece.bpe.model \
    > iwslt17.test.${SRC}-en.${SRC}.bpe
cat iwslt17.test.${SRC}-en.${SRC}.bpe \
    | fairseq-interactive data-bin/iwslt17.de_fr.en.bpe16k/ \
      --task multilingual_translation --lang-pairs de-en,fr-en \
      --source-lang ${SRC} --target-lang en \
      --path checkpoints/multilingual_transformer/checkpoint_best.pt \
      --buffer-size 2000 --batch-size 128 \
      --beam 5 --remove-bpe=sentencepiece \
    > iwslt17.test.${SRC}-en.en.sys
grep ^H iwslt17.test.${SRC}-en.en.sys | cut -f3 \
    | sacrebleu --test-set iwslt17 --language-pair ${SRC}-en
Argument format during inference

During inference it is required to specify a single --source-lang and --target-lang, which indicates the inference langauge direction. --lang-pairs, --encoder-langtok, --decoder-langtok have to be set to the same value as training.